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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0072016263488PerspectivesEditorialGuest Editorial: Nuclear Power and Public Health Clapp Richard W. Department of Environmental, Health Boston University School of Public Health Boston, Massachusetts E-mail:
[email protected] Clapp is professor of public health at Boston University School of Public Health. His primary research focuses on environmental causes of cancer. He previously served as the director of the Massachusetts Cancer Registry and examined the pattern of leukemia near the Pilgrim nuclear power station in Massachusetts.
The author declares he has no competing financial interests.
11 2005 113 11 A720 A721 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Because of concern about the health and environmental effects of burning fossil fuels such as coal and oil to produce electrical energy in recent years, there has been a resurgence of interest in nuclear power stations as a “carbon-free” method of generating electricity. For example, an interdisciplinary study titled The Future of Nuclear Energy, released by the Massachusetts Institute of Technology (MIT 2003) suggests that there are four options for reducing carbon dioxide emissions from electricity: increasing efficiency, expanding renewable energy sources, capturing carbon dioxide and sequestering the carbon, and increasing use of nuclear power. This study and one of its authors have gotten considerable public attention in the past couple of years for putting the nuclear power option back on the table for discussion in the United States (Baue 2005).
Furthermore, evolving nuclear power plant technologies, including one design that has been described as inherently safe (Uranium Information Centre 2005), have been making their way through the review and approval processes in other countries. The so-called pebble bed modular reactor (PBMR) is now being proposed for construction in Cape Town, South Africa, and there is a lively debate in that country about whether such a design is verifiably safe, whether the country truly needs such a power plant, where the waste would be sent and whether those affected would have a political voice in the debate, and ultimately how the effort to approve and construct a PBMR in South Africa would impact this industry in the rest of the world (Groenewald 2005; Nuclear Engineering Department, MIT 2001). This debate is still under way, and it is being watched closely by interested parties. Updated plans for the South African project will be submitted to the utility regulatory body in 2006. The PBMR design would not be acceptable under current U.S. regulations because it does not require an expensive containment dome; this means that it is less expensive to build but is more vulnerable from a security standpoint.
A further question about the security of existing U.S. nuclear power plants arose in the aftermath of the 11 September 2001 attacks on the World Trade Center and the Pentagon. Nuclear power plants appear to have been a potential target of the organizers of these attacks; therefore, the exposure of spent fuel in aboveground storage tanks on the property of many commercial plants is a concern. New security arrangements and proposals for more secure dry-storage casks have been made, but long-term secure storage of high-level reactor waste remains an unresolved problem.
As the discussion of the nuclear power option moves forward, it is critically important to consider what is now known about the health and environmental risks of the nuclear fuel cycle, based on the lessons of the past 60 years (Cardis et al. 2005; Wing et al. 1997). There is now a large body of knowledge about the impact of uranium mining and milling, transportation of partially enriched ore, fabrication of fuel-grade material, power reactor operations, and waste disposal and decommissioning of commercial reactors. We have learned from disasters such as Chernobyl [United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) 2000], as well as the less obvious but long-term problems of disposal of mine wastes and mill tailings and the ecologic impacts of this technology (Makhijani et al. 1996). We have also learned about the human health effects of low-level radiation exposure on workers exposed in the nuclear industry, most recently summarized in the BEIR (Biological Effects of Ionizing Radiation) VII report [National Academy of Sciences (NAS) 2005]. The conclusions of this recent review, although couched in careful scientific language, indicate that carcinogenic effects of exposure increase proportionately with dose, especially regarding leukemia mortality, and that for some types of exposure the current regulatory controls in the United States may be insufficient. A proliferation of nuclear power plants inevitably means more nuclear workers and more residents exposed to low-level ionizing radiation, with increased health risks attendant to this exposure.
The Future of Nuclear Energy (MIT 2003) suggests that nuclear power generation of electricity is currently not cost-effective compared with other technologies. The report notes that “carbon emission credits, if enacted by government, can give nuclear power a cost advantage” (MIT 2003). In fact, there are several other carbon-free or low-carbon options that are currently more cost-effective than nuclear power; these include wind power, combined-cycle gas power plants, and end-use efficiency measures. According to a recent analysis, “nuclear power saves as little as half as much carbon per dollar as windpower and cogeneration, and from several-fold to at least tenfold less carbon per dollar as end-use efficiency” (Lovins 2005). Lovins (2005) also succinctly added that
No other energy technology spreads do-it-yourself kits and innocent disguises for making weapons of mass destruction, nor creates terrorist targets or potential for mishaps that can devastate a region, nor creates wastes so hazardous, nor is unable to restart for days after an unexpected shutdown.
The accumulated experience of the past six decades provides ample evidence of adverse health effects in workers in the nuclear fuel cycle, the potential for disastrous accidents that lead to widespread environmental contamination, the unresolved problems of permanent and secure storage of high-level radioactive wastes, and the extraordinarily high costs of building additional nuclear power generation facilities. Some of these problems are ignored in the current public discourse, perhaps because of the immediacy of the need to solve the problems of carbon-based fuel. Given the availability of alternative carbon-free and low-carbon options and the potential to develop more efficient renewable technologies, it seems evident that public health would be better served in the long term by these alternatives than by increasing the number of nuclear power plants in the United States and the rest of the world.
I thank E. Cairncross, W. Dougherty, P. Epstein, and W. Kakos for discussions that aided in the preparation of this editorial.
==== Refs
References
Baue W 2005. Nuclear Power: Still an Environmental Scourge or Now a Climate Change Mitigator? Available: http://www.socialfunds.com/news/article.cgi/article1729.html [accessed 4 October 2005].
Cardis E Vrijheid M Blettner M Gilbert E Hakama M Hill C 2005 Risk of cancer after low doses of ionizing radiation: retrospective cohort study in 15 countries BMJ 331 77 10.1136/bmj.38499.599861.E0.15987704
Groenewald Y 2005. No final home for nuke waste. Mail and Guardian (13 May). Available: http://www.mg.co.za/articledirect.aspx?articleid=238248&area=%2Finsight%2Finsight__national%2F [accessed 4 October 2005].
Lovins AB 2005. Nuclear Power: Economics and Climate Protection Potential. Snowmass, CO:Rocky Mountain Institute. Available: http://www.rmi.org/images/other/Energy/E05-08_NukePwrEcon.pdf [accessed 4 October 2005].
Makhijani A Hu H Yih K eds. 1995. Nuclear Wastelands. Cambridge, MA:MIT Press.
MIT (Massachusetts Institute of Technology) 2003. The Future of Nuclear Energy: An Interdisciplinary MIT Study. Available: http://web.mit.edu/nuclearpower/ [accessed 23 September 2005].
NAS (National Academy of Sciences) 2005. Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII Phase 2. Washington, DC:National Academy Press.
Nuclear Engineering Department, MIT 2001. Modular Pebble Bed Reactor. Cambridge, MA:Massachusetts Institute of Technology. Available: http://web.mit.edu/pebble-bed/ [accessed 4 October 2005].
UNSCEAR (United Nations Scientific Committee on the Effects of Atomic Radiation) 2000. Sources and Effects of Ionizing Radiation, Vol 2. New York:United Nations. Available: http://www.unscear.org/reports/2000_2.html [accessed 4 October 2005].
Uranium Information Centre 2005. Advanced Nuclear Power Reactors. Nuclear Issues Briefing Paper 16. Available: http://www.uic.com.au/nip16.htm [accessed 5 October 2005].
Wing S Richardson D Armstrong D Crawford-Brown D 1997 A reevaluation of cancer incidence near the Three Mile Island nuclear plant: the collision of evidence and assumptions Environ Health Perspect 105 52 57 9074881
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0072216263489PerspectivesDirector's PerspectiveThe NIEHS Responds to Hurricane Katrina Schwartz David A. MDDirector, NIEHS and NTP,
[email protected] 2005 113 11 A722 A722 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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I am very proud of the role that the NIEHS has played in the Hurricane Katrina relief efforts. Extramural and intramural scientists have jointly developed and are implementing both short-term and long-term responses to this natural disaster. Many individuals have contributed to our response, but I especially want to acknowledge the leadership of Sam Wilson, Rich Freed, Bill Suk, Allen Dearry, Mary Wolfe, and Angie Sanders. These six people have played a critical role in responding to the devastation and hardship that was caused by Hurricane Katrina. While these people deserve special recognition, I am overwhelmed by the outpouring of support, encouragement, and volunteerism from many other members of our institute.
Here at home, Bill Suk continues to lead an effort to develop an NIEHS website that will utilize geographic information system (GIS) mapping to help identify the environmental hazards from Katrina that may affect the health of those living in or returning to the Gulf region. This website will be continuously updated through a collaboration established between the NIEHS and the University of California, San Diego (UCSD), super-computing center. Mark Ellisman at UCSD and Marie Lynn Miranda at Duke University have played key roles in developing this website. We anticipate that the website will serve as a national resource to track environmental hazards and focus various medical and environmental responses in areas that are in greatest need. Included on this website is up-to-date information on safety and hazardous waste cleanup training for the thousands of workers involved in the cleanup and recovery activities in the region, as well as a compendium of environmental health resources for medical responders.
Allen Dearry has established a consultative service to address environmental hazards that may have an impact on human health. This consultative service, available through the Katrina website, is linked to experts in our intramural and extramural research programs who have provided informed responses to specific environmental health concerns of residents, healthcare providers, and workers involved in the rescue and recovery efforts.
We have established a cooperative relationship with the Centers for Disease Control and Prevention (CDC) to assist them in the deployment of public health teams to areas that are in need of food, shelter, and a safe water supply. Mary Wolfe has taken a leadership role in this effort and is working directly with Tom Sinks of the CDC to provide expertise from the NIEHS to support the CDC in more effectively responding to the emerging public health needs.
The NIH deployed a team to the Gulf region to provide direct medical care to the victims of Hurricane Katrina. The NIEHS played a major role in this effort, and established a North Carolina contingent that consisted of physicians, nurses, and other health care providers from both the NIEHS and Duke University. Sam Wilson and Rich Freed coordinated activities between North Carolina and the central headquarters at the National Institutes of Health (NIH). I was fortunate to be one of the members of the team that traveled to Meridian, Mississippi, and established a 500-bed medical facility there including a triage area, ambulatory care area, acute care component, and an area for hospitalized patients.
Unfortunately, many of those affected by Katrina were reluctant to leave their homes for medical care despite the extensive amount of local destruction. Consequently, individuals in our group made several trips to the Gulfport coastal region, an area most severely affected by the hurricane, to assess the extent of medical need and to determine whether we could safely establish a medical facility in that region. As all of you know, the extent of destruction was overwhelming, with cars upturned, tractor trailers scattered like matchsticks, homes completely leveled, buildings destroyed, and rotting food scattered throughout the area; the Gulf Coast was nothing short of what one would expect in a war zone.
An accelerated environmental health research plan is vital to protecting vulnerable populations during present and future catastrophes.
Despite this, we found that the medical infrastructure was relatively intact. Patients were being seen effectively in clinics, and hospitals were open with sufficient bed capacity. We visited at least six different communities in the Gulfport region, and were sufficiently convinced that our team of health care providers was simply not needed. Thus, we decided to suspend the NIH medical mission, although we remain on call if other needs are identified. I was impressed with the cooperative and constructive relationship that was established between the NIH, Duke University, and the Public Health Service. Moreover, other universities, including the University of North Carolina at Chapel Hill, are providing additional support to the region.
Hurricane Katrina has made it abundantly clear that there is an unmet need for a national coordinated response plan to assess environmental and biological exposures to hazardous agents, to understand the relationship of exposures to adverse health outcomes through appropriate surveillance, and to develop early prevention and intervention strategies designed to identify at-risk individuals and reduce morbidity and mortality. To address this need, the NIEHS is working with the CDC and the EPA to develop a proposal to prevent adverse health consequences related to environmental conditions and exposures in the wake of Hurricane Katrina, and to establish the scientific expertise and infrastructure in environmental health needed to address both immediate and future preventive health care needs and the response capacity following man-made and natural disasters. We believe that an accelerated environmental health research plan is vital to protecting vulnerable populations during present and future catastrophes.
In tough times, the strengths and weaknesses of an institute become apparent. I’m proud to say that in the time since Hurricane Katrina struck, the NIEHS has proven to be an exceedingly strong institution with vitality, resolve, and a deep sense of dedication to relieving human suffering. I’m sincerely proud to be a member of the NIEHS, and I am confident that our team is fully capable of addressing the unforeseen challenges and opportunities that lie ahead.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0072616263490PerspectivesCorrespondenceOccupational Carcinogens: ELF MFs Mild Kjell Hanson National Institute for Working Life, Umeå, SwedenMattsso Mats-Olof Hardell Lennart Örebro University, Örebro, SwedenBowman Joseph D. National Institute for Occupational, Safety and Health, Cincinnati, Ohio, E-mail:
[email protected] Michael Medical University of Vienna, Vienna, AustriaThe authors declare they have no competing financial interests.
K.H.M. was a member of IARC’s 2001 group of experts. M.O.M. and J.D.B. were members of the NIEHS working group.
11 2005 113 11 A726 A727 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Siemiatycki et al. (2004) published a list of occupational carcinogens based largely on the evaluations published by the International Agency for Research on Cancer (IARC), augmented with additional information on the extent of workplace exposure. They considered 28 agents as definite human occupational carcinogens (IARC group 1), 27 agents as probable occupational carcinogens (group 2A), and 113 agents as possible occupational carcinogens (group 2B). However, missing from their list of occupational carcinogens is magnetic fields (MFs) at extremely low frequencies (ELF; 3–3000-Hz), which were classified as group 2B by IARC (2002).
IARC’s final conclusion (IARC 2002) is as follows:
Overall, extremely low frequency magnetic fields were evaluated as possibly carcinogenic to humans (IIB), based on the statistical association of higher level residential ELF magnetic fields and increased risk for childhood leukaemia.
Thus, although the evaluation is based on epidemiologic studies of childhood leukemia, the classification applies to all human exposure to ELF MFs, and thus also to occupational exposure. This interpretation has been discussed and confirmed with an IARC representative on their ELF MF panel (Cardis E, personal communication). Because enough workers are exposed to ELF MFs to clearly meet the criteria for occupational exposures set by Siemiatycki et al. (2004), we are surprised that they did not include it in their list of possible occupational carcinogens.
Other groups and agencies have applied IARC’s criteria to the evaluation of ELF MF carcinogenicity. The National Institute of Environmental Health Sciences working group (NIEHS 1998) evaluated the research in that era and classified ELF EMFs (electric and magnetic fields) as possibly carcinogenic (group 2B); this classification was based on the occurrence of chronic lymphocytic leukemia (CLL) associated with occupational exposure. The California Department of Health Services also evaluated the cancer risks of EMF in 2002, and their reviewers classified it as at least group 2B, including childhood leukemia and adult brain cancer (Neutra et al. 2002).
Since the IARC evaluation, several relevant studies have been published—both in vitro and in vivo work, as well as epidemiologic studies, including the following examples. Tynes et al. (2003) reported an association between exposure to calculated residential MFs and cutaneous malignant melanoma. In a cohort including all female workers, Weiderpass et al. (2003) found an association between exposure to electromagnetic fields and stomach and pancreatic cancer; Villeneuve et al. (2002) found that occupational MF exposure increased the risk of glioblastoma multiforme; Håkansson et al. (2002) investigated cancer incidence in resistance welding workers exposed to high levels of MF and found that men in the very high exposure group showed an increased incidence of tumors of the kidney, pituitary gland, biliary passages, and liver; an exposure–response relationship was indicated for these cancer sites. Women in the very high exposure group showed an increased incidence of astrocytoma I–IV, with a clear exposure–response pattern.
Ivancsits et al. (2002, 2003a, 2003b) have shown that human lymphocytes exposed to ELF MFs can generate DNA single and double strand breaks from a flux density as low as 35 μT and with a strong correlation between both the intensity and duration of the MF exposure.
The IARC evaluation (IARC 2002) ruled out a probable carcinogen classification (group 2A) because the expert panel found the animal studies were “inadequate evidence of carcinogenicity.” This judgment was due to many conflicting results in the repetition of long-term animal experiments. In particular, Löscher and Mevissen (1995) reported that MF exposure to Sprague-Dawley (SD) rats after 7,12-dimethylbenz[a]anthracene (DMBA) initiation increased breast tumors in the exposed animals at 50 μT compared with the control group (see also Thun-Battersby et al. 1999). However, in a similar study Anderson et al. (1999) found no evidence for a cocarcinogenic or tumor-promoting effect of MF exposure, but the study used different substrains of SD rats than used in the original study. Anderson et al. (2000) stated that “the U.S. rats were more susceptible to DMBA than the European rats”; diet and DMBA were from different sources, and there were differences in environmental conditions and in MF exposure metrics. Fedrowitz et al. (2004) compared two sub-strains of SD outbred rats; MF exposure significantly increased mammary tumor development and growth in one of the strains of rats but not in the other. These data suggest that genetic background may play a pivotal role in effects of MF exposure; this which might explain the difficulties in replicating the original animal studies of breast tumor promotion.
According to the criteria used by Siemiatycki et al. (2004), a complete list of occupational agents classified as possible human carcinogens would include ELF MFs.
==== Refs
References
Anderson LE Boorman GA Morris JE Sasser LB Mann PC Grumbein SL 1999 Effect of 13 week magnetic field exposures on DMBA-initiated mammary gland carcinomas in female Sprague-Dawley rats Carcinogenesis 20 8 1615 1620 10426815
Anderson LE Morris JE Sasser LB Löscher W 2000 Effects of 50- or 60-hertz, 100 μT magnetic field exposure in the DMBA mammary cancer model in Sprague-Dawley rats: possible explanations for different results from two laboratories Environ Health Perspect 108 797 802 11017883
Fedrowitz M Kamino K Löscher W 2004 Significant differences in the effects of magnetic field exposure on 7,12-dimethylbenz(a )anthracene-induced mammary carcinogenesis in two substrains of Sprague-Dawley rats Cancer Res 64 1 243 251 14729631
Håkansson N Floderus B Gustavsson P Johansen C Olsen JH 2002 Cancer incidence and magnetic field exposure in industries using resistance welding in Sweden Occup Environ Med 59 7 481 486 12107298
IARC 2002 Non-ionizing Radiation, Part 1: Static and Extremely Low-Frequency Electric and Magnetic Fields IARC Monogr Eval Carcinog Risk Hum 80
Ivancsits S Diem E Jahn O Rudiger HW 2003a Age-related effects on induction of DNA strand breaks by intermittent exposure to electromagnetic fields Mech Ageing Dev 124 7 847 850 12875748
Ivancsits S Diem E Jahn O Rudiger HW 2003b Intermittent extremely low frequency electromagnetic fields cause DNA damage in a dose-dependent way Int Arch Occup Environ Health 76 6 431 436 12802592
Ivancsits S Diem E Pilger A Rudiger HW Jahn O 2002 Induction of DNA strand breaks by intermittent exposure to extremely-low-frequency electromagnetic fields in human diploid fibroblasts Mutat Res 519 1–2 1 13 12160887
Löscher W Mevissen M 1995 Linear relationship between flux density and tumor co-promoting effect of prolonged magnetic field exposure in a breast cancer model Cancer Lett 96 2 175 180 7585454
Neutra RR Del Pizzo V Lee GM 2002. An Evaluation of the Possible Risks from Electric and Magnetic Fields (EMFs) from Power Lines, Internal Wiring, Electrical Occupations and Appliances. Oakland, CA:California EMF Program. Available: http://www.dhs.ca.gov/ehib/emf/RiskEvaluation/riskeval.html [accessed 11 October 2005].
NIEHS 1998. Assessment of Health Effects from Exposure to Power-Line Frequency Electric and Magnetic Fields. Working Group Report (Portier C, Wolfe M, eds). NIH publcation no. 98-3981. Research Triangle Park, NC:National Institute of Environmental Health Sciences. Available: http://www.niehs.nih.gov/emfrapid/html/WGReport/WorkingGroup.html [accessed 6 October 2005].
Siemiatycki J Richardson L Straif K Latreille B Lakhani R Campbell S 2004 Listing occupational carcinogens Environ Health Perspect 112 1447 1459 15531427
Thun-Battersby S Mevissen M Löscher W 1999 Exposure of Sprague-Dawley rats to a 50-Hertz, 100-μTesla magnetic field for 27 weeks facilitates mammary tumorigenesis in the 7,12-dimethylbenz[a ]-anthracene model of breast cancer Cancer Res 59 15 3627 3633 10446973
Tynes T Klaeboe L Haldorsen T 2003 Residential and occupational exposure to 50 Hz magnetic fields and malignant melanoma: a population based study Occup Environ Med 60 5 343 347 12709519
Villeneuve PJ Agnew DA Johnson KC Mao Y Canadian Cancer Registries Epidemiology Research Group 2002 Brain cancer and occupational exposure to magnetic fields among men: results from a Canadian population-based case-control study Int J Epidemiol 31 1 210 217 11914323
Weiderpass E Vainio H Kauppinen T Vasama-Neuvonen K Partanen T Pukkala E 2003 Occupational exposures and gastrointestinal cancers among Finnish women J Occup Environ Med 45 3 305 315 12661188
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0072816276624PerspectivesCorrespondenceThe NAS Perchlorate Review: Adverse Effects? Johnston Richard B. JrUniversity of Colorado School of Medicine, Denver, ColoradoCorley Richard Pacific Northwest National Laboratory, Richland, WashingtonCowan Linda University of Oklahoma Health, Sciences Center, Oklahoma City, OklahomaUtiger Robert D. Harvard Medical School, Boston, Massachusetts, E-mail:
[email protected] authors declare they have no competing financial interests.
11 2005 113 11 A728 A729 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Ginsberg and Rice (2005) argued that the reference dose for perchlorate of 0.0007 mg/kg per day recommended by the National Academy of Sciences (NAS) Committee to Assess the Health Implications of Perchlorate Ingestion is not adequately protective. As members of the committee, we disagree.
Ginsberg and Rice (2005) based their conclusion on three points. The first involves the designation of the point of departure as a NOEL (no observed effect level) versus a LOAEL (lowest observed adverse effect level). The committee chose as its point of departure a dose of perchlorate (0.007 mg/kg per day) that, when given for 14 days to seven normal subjects, did not cause a statistically significant decrease in the group mean thyroid iodide uptake (Greer et al. 2002). Accordingly, the committee considered it a NOEL. Ginsberg and Rice (2005) focused on the fact that only seven subjects were given that dose; they seem to say that attention should be paid only to the results in those subjects in whom there was a decrease in thyroid iodide uptake and that the results in those in whom there was no decrease or an increase should be ignored. They considered the dose to be a LOAEL because of the decrease in uptake in those few subjects. It is important to note that a statistically significant decrease of, for example, 5% or even 10% would not be biologically important and, more important, would not be sustained. For example, in another study (Braverman et al. 2004), administration of 0.04 mg/kg per day to normal subjects for 6 months had no effect on thyroid iodide uptake when measured at 3 and 6 months, and no effect on serum thyroid hormone or thyrotropin concentrations measured monthly. [Inspection of Figure 5A in Greer et al. (2002) suggests that this dose would inhibit thyroid iodide uptake by about 25% if measured at 2 weeks.]
The second issue involves database uncertainty. In clinical studies, perchlorate has been administered prospectively to 68 normal subjects for 2 weeks to 6 months. In one study (Brabant et al. 1992), a dose of 9.2 mg/kg per day for 4 weeks had no effect on thyroid function. In occupational studies, doses as high as 0.5 mg/kg per day had no effect on thyroid hormone or thyrotropin production in workers. In epidemiologic studies, there were no abnormalities in growth or thyroid function in children exposed life-long to 100–120 μg perchlorate per liter of drinking water, or in pregnant women and newborn infants similarly exposed. Given the choice of a nonadverse effect (inhibition of iodide uptake by the thyroid) as the point of departure and the multiple studies in which doses of perchlorate much higher than 0.007 mg/kg per day had no effect on any aspect of thyroid function, the committee did not apply a database uncertainty factor.
Finally, Ginsberg and Rice (2005) argued that inhibition of thyroid iodide uptake is adverse. That conclusion assumes that any acute inhibition would be sustained, so thyroid hormone production would decrease. That is not the case. There is remarkable compensation for even substantial reductions in thyroid iodide uptake—and thyroid hormone production. As noted above, subjects given 0.04 mg/kg per day for 6 months and 9.2 mg/kg per day for 4 weeks—doses that certainly would inhibit thyroid iodide uptake for a few weeks—had no decrease in serum thyroid hormone or increase in serum thyrotropin concentrations (the hallmark of even mild hypothyroidism). Short-term inhibition of thyroid iodide uptake is not an adverse effect; it has no adverse consequences because there is rapid compensation mediated by several independent processes. One of these processes is up-regulation of the thyroid sodium-iodide transport system, as a result of intrathyroidal iodide deficiency. The second, should there be even a very small decrease in thyroid hormone production, is an increase in thyrotropin secretion, resulting in overall stimulation of the thyroid gland. Analyses of the effects of any substance on thyroid function must take these compensatory processes into account, particularly the fact that the effect of any substance that inhibits thyroid function will diminish with time. Only if all of these mechanisms fail will there be hypothyroidism, the first adverse effect in the continuum of effects resulting from perchlorate ingestion. If there is no inhibition of iodide uptake to begin with, there will be no other changes in thyroid function at any time.
We believe that the committee’s recommended reference dose of 0.0007 mg/kg per day provides a wide margin of safety for all subjects of all ages.
==== Refs
References
Brabant G Bergmann P Kirsch CM Kohrle J Hesch RD von zur Muhlen A 1992 Early adaptation of thyrotropin and thyroglobulin secretion to experimentally decreased iodide supply in man Metabolism 41 1093 1096 1328820
Braverman LE He X Pino S Magnani B Firek A 2004 The effect of low dose perchlorate on thyroid function in normal volunteers [Abstract] Thyroid 14 691
Ginsberg G Rice D 2005 The NAS perchlorate review: questions remain about the perchlorate RfD Environ Health Perspect 113 1117 1119 10.1289/ehp.8254 16140613
Greer MA Goodman G Pleus RC Greer SE 2002 Health effects assessment for environmental perchlorate contamination: the dose response for inhibition of thyroidal radioiodine uptake in humans. Environ Health Perspect 110 927 937
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0072916276625PerspectivesCorrespondenceThe NAS Perchlorate Review: Is the RfD Acceptable? Strawson Joan Dourson Michael L. Zhao Qiyu (Jay) Toxicology Excellence for Risk Assessment, Cincinnati, Ohio, E-mail:
[email protected] Excellence for Risk Assessment (TERA) is a nonprofit organization dedicated to the best use of toxicity data for risk values.
The authors have collectively studied the toxicity of perchlorate since 1991 on behalf of U.S. EPA and the nonprofit corporation Toxicology Excellence for Risk Assessment at the request of the Perchlorate Study Group. Opinions expressed in this commentary, however, reflect solely those of the authors and not of any organization or other individual. Resources to support this technical commentary were provided by the developmental reserve fund of TERA.
11 2005 113 11 A729 A730 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Risk assessors should always carefully evaluate whether a given reference dose (RfD) is the most appropriate choice for assessing risk. In the case of perchlorate, Ginsberg and Rice (2005) suggested that the RfD proposed by the National Research Council (NRC) is inappropriate because the NRC did not thoroughly evaluate the underlying science. However, we suggest that the NRC RfD is inappropriate because of the NRC’s “unconventional” approach.
In contrast to Ginsberg and Rice (2005), we applaud the insightful and conclusive discussion of the science of perchlorate and the thyroid by the NRC (2005). The NRC concluded that human studies are the most relevant for risk assessment, and that the thyroid has a remarkable ability to compensate for iodine deficiency, so that hypothyroidism would be the first observed adverse effect. By definition, this is perchlorate’s critical effect (Faustman and Omenn 2001), although U.S. Environmental Protection Agency (EPA) methods allow for the use of a known and immediate precursor (the choice of immediate precursor is based on practice of using the highest no observed adverse effect level (NOAEL) of the critical effect and is codified in several places (e.g., Barnes and Dourson 1988, p. 473). The NRC also concluded that in healthy adults the perchlorate dose required to cause hypothyroidism would be > 0.4 mg/kg-day.
In risk assessment parlance, this dose would be a NOAEL of the critical effect. The practice of risk assessment allows us to draw conclusions about public health in the absence of observable data and in the presence of scientific uncertainty. The traditional practice of developing RfD, a dose–response part of risk assessment (Barnes and Dourson 1988), would suggest two possible approaches to developing an RfD from the perchlorate data. The first would be to use the NOAEL of the critical effect from an adult population and apply uncertainty factors to account for sensitive populations and for lack of precision in defining a NOAEL. The second approach would be to use the NOAEL of an immediate precursor effect in a sensitive population and apply appropriate uncertainty factors.
Using the first approach with the NRC NOAEL, the RfD would lie in the range of 0.04–0.004 mg/kg-day depending on the choice of uncertainty factor. Using the second approach, a NOAEL of 0.005 mg/kg-day (Gibbs et al. 2004) can be identified from thyroid hormone and goiter data in a sensitive population. The RfD based on this approach would lie near the value of 0.002 mg/kg-day proposed by Strawson et al. (2004).
In contrast, the approach the NRC actually used was a nonstandard approach for developing an RfD based on the inhibition of iodine uptake, a distant precursor to the critical effect. This nonstandard approach yields a safe dose, but it is not an RfD, by definition, because, according to the NRC’s own scheme, it is not based on the critical effect or its known and immediate precursor.
We continue to advocate that the best risk assessment approach for perchlorate is to use data collected from sensitive populations such as children and, in particular, the published and ongoing work in Chile. This is consistent with the NRC’s conclusion that the data from Chile could be considered in the evaluation of the U.S. experience with perchlorate in drinking water (NRC 2005). Specifically, the Chilean experience (Crump et al. 2000; Tellez et al. 2005) can be used to help frame the public debate in the United States, which suggests perchlorate water standards as low as 1 ppb. In Chile, perchlorate water concentrations of 100–120 ppb do not result in an exposure that would inhibit iodine uptake inhibition in adults. In fact, these concentrations have not caused any adverse effects in pregnant women, neonates, or older children exposed chronically. Following traditional RfD methods and using data from a sensitive human population results in an RfD that can be used with high confidence in the United States.
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References
Barnes DG Dourson ML 1988 Reference dose (RfD): description and use in health risk assessments Regul Toxicol Pharmacol 8 471 486 3222488
Crump C Michaud P Tellez R Reyes C Gonzalez G Montgomery EL nction in newborns or school-age children? J Occup Environ Med 42 603 612 10874653
Faustman EM Omenn GS 2001. Risk Assessment. In: Casarett and Doull’s Toxicology: The Basic Science of Poisons (Klaassen CD, ed.). 6th ed. New York:McGraw-Hill, 92.
Gibbs JP Narayanan L Mattie DR 2004 Crump et al. study among school children in Chile: subsequent urine and serum perchlorate levels are consistent with perchlorate in water in Taltal J Occup Environ Med 46 6 516 517 15213511
Ginsberg G Rice D 2005 The NAS perchlorate review: questions remain about the perchlorate RfD Environ Health Perspect Environ Health Perspect 113 1117 1119 10.1289/ehp.8254
NRC (National Research Council) 2005. Health Implications of Perchlorate Ingestion. Washington, DC:National Academies Press.
Strawson J Zhao Q Dourson M 2004 Reference dose for perchlorate based on thyroid hormone change in pregnant women as the critical effect Regul Toxicol Pharmacol 39 44 65 14746779
Tellez RT Michaud P Reyes C Blount BC Van Landingham CB Crump KS 2005 Long-term environmental exposure to perchlorate through drinking water and thyroid function during pregnancy and the neonatal period Thyroid 15 9 963 975 16187904
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00730PerspectivesCorrespondenceThe NAS Perchlorate Review: Ginsberg et al. Respond Ginsberg Gary Connecticut Department of Public Health, Hartford, Connecticut, E-mail:
[email protected] Deborah Maine Bureau of Health, Augusta, MaineThe authors declare they have no competing financial interests.
11 2005 113 11 A730 A732 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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We would like to respond to the comments from several members of the NAS perchlorate panel (Johnston et al.) and from two other groups (Gibbs et al., Strawson et al.). These letters were in response to our commentary published in EHP (Ginsberg and Rice 2005). The letters take an opposing viewpoint but do not invalidate our main assertions that a) the low dose reported in the Greer et al. study (Greer et al. 2002) does in fact demonstrate a majority of subjects with the perchlorate-induced effect; b) there is the potential for greater perchlorate vulnerability in pregnant women and newborns than in the general population; and c) inhibition of iodide uptake is a key step in the perchlorate toxicodynamic pathway, with moderate levels of uptake inhibition potentially sufficient to produce adverse effects in sensitive subgroups.
The low dose reported by Greer et al. (2002) was termed a no observable effect level (NOEL) by the National Research Council (NRC 2005). We disagreed with this view in our commentary because four of seven individuals at this dose showed the characteristic perchlorate-induced suppression of iodine uptake. Johnston et al. claim that we ignored the nonresponders when we described the low dose in the Greer study (Greer et al. 2002) as an effect level. We did not disregard these subjects, but we pointed out that they segregate out as a subgroup who appear to be less sensitive to the perchlorate effect and have low baseline values. We further pointed out that, because of the small sample size (n = 7), there is very little statistical power to detect an effect at Greer et al.’s low dose (0.007 mg/kg/day) given the variability in response. Rather than simply relying on a weak test of significance, our closer inspection of the data indicated that the majority of the low-dose subjects were responders. When the results are organized categorically into responders and nonresponders, it is evident that the low dose is part of the dose–response continuum with no evidence of a threshold: 0.5 mg/kg/day, 9 responders out of 9 subjects; 0.1 mg/kg/day, 10 responders out of 10 subjects; 0.02 mg/kg/day, 6 responders out of 10 subjects; and 0.007 mg/kg/day, 4 responders out of 7 subjects. The lack of statistical significance should not be used as grounds for disqualifying what appears to be a biologically significant response.
Hydrogen sulfide provides a good example for illustrating biologic versus statistical significance. In a key study, Jappinen et al. (1990) found that an inhaled dose of hydrogen sulfide did not cause a statistically significant effect on airway parameters in a group of 10 subjects with asthma. However, when these data were used by the Agency for Toxic Substances and Disease Registry (ATSDR) to set a public health benchmark (the acute minimum risk level), the fact that 2 of the 10 asthmatics were responders was sufficient for this dose to be considered a critical effect level (ATSDR 1999). The perchlorate low-dose responders should not be ignored, just as the hydrogen sulfide low-dose responders were not ignored.
Although the NRC considered Greer et al.’s (2002) low dose a NOEL, like us, the U.S. Environmental Protection Agency (EPA) draft assessment (U.S. EPA 2002) and a risk assessment by the Massachusetts Department of Environmental Protection (Mass DEP 2004) considered this dose to be a LOAEL (lowest observed adverse effect level). The California EPA conducted a benchmark dose analysis on the data published by Greer et al. (2002), finding 0.0037 mg/kg/day (approximately 2-fold below Greer et al.’s low dose) the critical point of departure for standard setting (California EPA 2004).
Strawson et al. make the argument that the critical adverse effect of perchlorate is hypothyroidism. It is important to understand that clinical hypothyroidism is not the critical end point for derivation of the perchlorate RfD. Subclinical hypothyroidism in pregnant women can result in adverse nervous system effects in offspring (Zoeller et al. 2002), including decreased IQ (Haddow et al. 1999). Perchlorate’s inhibition of iodine uptake increases the risk for hypothyroidism, which even if subclinical, may still be associated with neurodevelopmental effects.
The rebuttal letters (Gibbs et al., Johnston et al., and Strawson et al.) consider inhibition of iodine uptake a nonadverse effect because it is only temporary and because compensatory homeostatic mechanisms would not allow actual declines in thyroid hormone to occur. They cite an abstract by Braverman et al. (2004) to demonstrate that the perchlorate effects seen by Greer et al. (2002) disappear upon longer-term (6 month) exposure. As we pointed out in our commentary (Ginsberg and Rice 2005), the study by Braverman et al. (2004) has not been published or peer reviewed and involves small numbers of subjects. It is unclear whether there was sufficient statistical power to see the perchlorate effect. Since the publication of our commentary we became aware of a different study by this same group (Braverman et al. 2005). Gibbs et al. also mentioned this study. In contrast to their abstract (Braverman et al. 2004), Braverman et al. (2005) show iodine uptake inhibition in relatively young male Caucasian workers who had a median perchlorate exposure period of 5.9 years. The dose response for these long-term perchlorate workers was similar to that shown for subjects exposed to perchlorate for 2 weeks (Greer et al. 2002). This suggests that, contrary to the NRC report (NRC 2005) and Braverman et al. (2004), perchlorate does not lose its potency to inhibit iodide uptake under conditions of long-term exposure.
The fact that the workers in the study by Braverman et al. (2005) did not have indications of thyroid deficiency suggests that healthy workers can compensate for this type of biochemical impairment. This is likely due to several factors, including sufficient iodide and hormone reserves in these workers. However, it is uncertain that perchlorate-induced impairment of iodine uptake would be compensated for in all members of the population. In particular, a substantial percentage of the general public has low iodine intake [Centers for Disease Control and Prevention (CDC) 2000; Hollowell et al. 1998], pregnant women can be at greater risk for iodine deficiency (Azizi et al. 2003), and the neonate appears to have minimal stores of thyroid hormone (Delange 1998; van den Hove et al. 1999). In addition, the data of Braverman et al. (2005) suggest up-regulation of the iodide symporter in these workers, a protective mechanism that may not exist in the fetus or neonate. Infants have added susceptibility because perchlorate is excreted into breast milk and appears to inhibit iodine secretion into breast milk (Kirk et al. 2005).
On this last point, the letter by Gibbs et al. casts doubt on the relationship between perchlorate and iodine levels in breast milk by quoting from Kirk et al. (2005): “If we take all the available data, there is no meaningful correlation between the perchlorate and iodide levels in breast milk.” This is a case of selective quoting, as the very next sentence states, “On the other hand, for breast milk that contained ≥10 μg/L perchlorate, the iodide concentration expressed in milk is linearly related to the reciprocal of perchlorate concentration.” Although we would agree that the findings of Kirk et al. (2005) need to be further explored, Gibbs et al.’s dismissal of these findings—on the basis of an out-of-context quote—is misleading.
Strawson et al. claim in their letter that the NRC used a nonstandard approach in deriving the perchlorate RfD. Citing an article by Barnes and Dourson (1988), they state that there are two possible approaches to developing an RfD: the use of a NOAEL of a critical effect from an adult population, or the use of the NOAEL of a precursor effect in a sensitive population. Barnes and Dourson (1988) did not discuss such a dichotomy of approaches, nor did more recent U.S. EPA guidance (e.g., U.S. EPA 1991, 2002). In fact, IRIS (the Intergrated Risk Information System; IRIS 2005) defines “critical effect” as “[T]he first adverse effect, or its known precursor, that occurs to the most sensitive species.” There is no distinction in any of these documents made for critical end point being chosen based on sensitive population, nor is there discussion of “immediate precursor” versus other precursors, a distinction made by Strawson et al. Therefore, the assertion that the NRC used a nonstandard approach in using a precursor event in a nonsensitive population (adults) is not supported in U.S. EPA guidance (U.S. EPA 1991, 2002).
Also at issue are the uncertainty factors that need to be applied to the data of Greer et al. (2002) to derive a health-protective RfD. The NRC risk assessment included a total 10-fold uncertainty factor (NRC 2005). This factor is expected to cover a lot of ground: variability in toxicokinetics and toxicodynamics among healthy adults, variability caused by low iodine uptake, pregnancy, neonatal vulnerabilities described above, and the data gaps and temporal uncertainties described in our commentary (Ginsberg and Rice 2005). Because of these factors, our scientific judgment is that a 10-fold uncertainty factor is insufficient, which is the same judgment arrived at in the U.S. EPA draft assessment (U.S. EPA 2002) and in the Massachusetts risk assessment (Mass DEP 2004).
The letters of Gibbs et al. and Strawson et al. allude to the Chilean data set (Crump et al. 2000; Tellez et al. 2005) as documenting that early life stages are not especially affected by relatively high exposure to perchlorate in drinking water. If this were the case, it would decrease the level of uncertainty contained in the risk assessment. However, in our commentary (Ginsberg and Rice 2005), we pointed out the limitations of the Chilean data. It requires extrapolation from an iodine-enriched population in Chile to the United States, which has considerably less iodine intake. Further, nearly 5% of school-age children and 15% of women of childbearing age in the United States have low iodine intake (CDC 2000; Hollowell et al. 1998) these individuals are likely not well represented by the Chilean data set. Crump et al. (2000) show an association between high perchlorate in drinking water and family history of thyroid disease. The fact that this association did not extend to altered thyroid status in the children studied raises the possibility that iodine supplementation efforts in recent decades in Chile prevented the perchlorate effect in current-day children (Crump et al. 2000). This leaves open the question of perchlorate-induced effects in children in the United States whose iodine intake is suboptimal. A follow-up study by Tellez et al. (2005) reproduces some of the earlier Chilean findings but shows that in spite of very recent reductions in the iodide content of salt in Chile, iodine levels are still approximately 2-fold higher there than in the United States. The Chilean studies do not remove the uncertainties present in the perchlorate database.
Our disagreement with the NAS perchlorate document (NRC 2005) and with these letters centers around how a no effect level is defined and how vulnerable life stages are factored into a risk assessment. These authors recommend stretching the definition of NOEL to include a dose level in which the majority of the subjects demonstrate the perchlorate effect. Gibbs et al., Johnston et al., and Strawson et al. also recommend using studies of healthy adults and a poorly matched Chilean population to dismiss the adverse nature of perchlorate-induced iodide uptake inhibition for vulnerable subgroups. As state risk assessors, we strive to keep methods and judgment consistent across all chemicals. Applying that to perchlorate leads us to a different analysis than what was presented by the NAS and what is promoted in the letters responding to our commentary (Ginsberg and Rice 2005).
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References
ATSDR (Agency for Toxic Substances and Disease Registry) 1999. Toxicological Profile for Hydrogen Sulfide.
Azizi F Aminorroya A Hedayati M Rezvanian H Amini M Mirmiran P 2003 Urinary iodine excretion in pregnant women residing in areas with adequate iodine intake Public Health Nutr 6 95 98 12581471
Barnes DG Dourson ML 1988 Reference dose (RfD): description and use in health risk assessments Regul Toxicol Pharmacol 8 471 486 3222488
Braverman LE He X Pino S Cross M Magnani B Lamm SH 2005 The effect of perchlorate, thiocyanate, and nitrate on thyroid function in workers exposed to perchlorate long-term J Clin Endocrinol Metab 90 2 700 706 15572417
Braverman LE He X Pino S Magnani B Firek A 2004 The effect of low dose perchlorate on thyroid function in normal volunteers [Abstract] Thyroid 14 691
California EPA (Environmental Protection Agency) 2004. Public Health Goals for Perchlorate in Drinking Water. Available: http://www.oehha.org/water/phg/pdf/finalperchlorate31204.pdf [accessed 6 October 2005]
CDC (Centers for Disease Control), National Center for Health Statistics 2000. Iodine level, United States. Available: http://www.cdc.gov/nchs/products/pubs/pubd/hestats/iodine.htm [accessed 1 August 2005].
Crump C Michaud P Tellez R Reyes C Gonzalez G Montgomery EL 2000 Does perchlorate in drinking water affect thyroid function in newborns or school-age children? J Occup Environ Med 42 603 612 10874653
Delange F 1998 Screening for congenital hypothyroidism used as an indicator of the degree of iodine deficiency and of its control Thyroid 8 12 1185 1192 9920376
Ginsberg G Rice D 2005 The NAS perchlorate review: questions remain about the perchlorate RfD Environ Health Perspect 113 1117 1119 10.1289/ehp.8254.16140613
Greer MA Goodman G Pleus RC Greer SE 2002 Health effects assessment for environmental perchlorate contamination: the dose response for inhibition of thyroidal radioiodine uptake in humans Environ Health Perspect 110 927 937 12204829
Haddow JE Palomaki GE Allan WC Williams JR Knight GJ Gagnon J 1999 Maternal thyroid deficiency during pregnancy and subsequent neuropsychological development of the child N Engl J Med 341 549 555 10451459
Hollowell JG Staehling NW Hannon WH Flanders DW Gunter EW Maberly GF 1998 Iodine nutrition in the United States. Trends and public health implications: iodine excretion data from National Health and Nutrition Examination Surveys I and III (1971–1974 and 1988–1994) J Clin Endocrinol Metab 83 3401 3408 9768638
IRIS (Integrated Risk Information System 2005. Glossary of IRIS Terms. Available: http://www.epa.gov/iris/gloss8.htm [accessed 7 October 2005].
Jappinen P Vikka V Marttila O Haahtela T 1990 Exposure to hydrogen sulfide and respiratory function Br J Ind Med 47 824 828 2271389
Kirk AB Martinelango PK Tian K Dutta A Smith EE Dasgupta PK 2005 Perchlorate and iodide in dairy and breast milk Environ Sci Technol 39 2011 2017 10.1021/es048118t S0013-936X(04)08118-0 [Online 22 February 2005].15871231
Mass DEP (Massachusetts Department of Environmental Protection) 2004. Final Draft. Perchlorate Toxicological Profile and Health Assessment. Available: http://www.mass.gov/dep/ors/files/perchlor.pdf [accessed 6 October 2005].
NRC (National Research Council) 2005. Health Implicatons of Perchlorate Ingestion. Washington, DC:National Academies Press.
Van den Hove MF Beckers C Devlieger H de Zegher F De Nayer P 1999 Hormone synthesis and storage in the thyroid of human preterm and term newborns: effect of thyroxine treatment Biochimie 81 563 570 10403191
Tellez RT Michaud P Reyes C Blount BC Van Landingham CB Crump KS 2005 Long-term environmental exposure to perchlorate through drinking water and thyroid function during pregnancy and the neonatal period Thyroid 15 9 963 975 16187904
U.S. EPA (Environmental Protection Agency) 1991. Guidelines for Developmental Toxicity Risk Assessment. EPA/600/FR-91/001. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/ncea/raf/pdfs/devtox.pdf [accessed 7 October 2005].
U.S. EPA (Environmental Protection Agency) 2002. A Review of the Reference Dose and Reference Concentration Processes. EPA/630/P-02/002F. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/iris/RFD_FINAL%5B1%5D.pdf [accessed 7 October 2005].
Zoeller RT Dowling ALS Herzig CTA Iannacone EA Gauger KJ Bansai R 2002 Thyroid hormone, brain development, and the environment Environ Health Perspect 110 355 361 12060829
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00732AnnouncementsErratumErratum 11 2005 113 11 A732 A732 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In Figure 2D of Greer et al. [
Environ Health Perspect 110:927–937 (2002)], there should have been seven subjects in the 0.007 mg/kg-day group, but EHP erroneously included an extra line (without symbols), indicating a nonexistent eighth subject. This error was reproduced in the commentary of Ginsberg and Rice [
Environ Health Perspect 113:1117–1119 (2005)] and was included in their argument that there was an inhibitory effect overall in that dose group.
EHP regrets the error.
Figure 2D The 24-hr thyroid radioiodine uptake (RAIU) at the baseline visit (BV) and on exposure day 14 (E14) and postexposure day 15 (P15) for each subject in the 0.007-mg/kg-day dose group.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0073416263492EnvironewsForumInfectious Disease: In Disaster’s Wake: Tsunami Lung Potera Carol 11 2005 113 11 A734 A734 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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When the Asian tsumani struck on 26 December 2004, health authorities braced for an onslaught of waterborne illnesses including malaria and cholera, which often follow such disasters. But saltwater flooded the freshwater breeding grounds of the mosquitoes that spread malaria, and relief agencies quickly distributed bottled water, thwarting a cholera epidemic. Instead, a type of aspiration pneumonia named “tsunami lung” emerged and afflicted some survivors.
Tsunami lung occurs when people being swept by tsunami waves inhale salt-water contaminated with mud and bacteria. The resulting pneumonia-like infections normally are treated with antibiotics. However, the 2004 tsunami “wiped out the medical infrastructure, and antibiotics were not available to treat infections in the early stages,” says David Systrom, a pulmonologist at Massachusetts General Hospital in Boston. Consequently, victims’ lung infections festered, entered the bloodstream, and spread to the brain, producing abscesses and neurological problems such as paralysis.
Systrom and colleagues volunteered to work on a medical disaster team with Project HOPE (Health Opportunities for People Everywhere) aboard the hospital ship U.S. Naval Ship Mercy off the coast of Banda Aceh, Sumatra. When they arrived three weeks after the tsunami hit, “we saw infections not seen in the United States since before the development of antibiotics,” says Systrom. Among them were about 25 cases of tsunami lung. “No one expected the number of tsunami lung cases we saw,” says Systrom. “It was not on the radar screen.”
The diagnosis of tsunami lung requires a chest radiograph and computed tomography scan of the brain to confirm abscesses. This sophisticated equipment was available on the hospital ship. “Only the most severe cases with central nervous system involvement made it to the ship,” says Systrom. The team suspects that hundreds of milder cases went unreported.
In the 23 June 2005 issue of the New England Journal of Medicine, the team describes the case of a 17-year-old girl who aspirated water and mud while engulfed by a wave and carried about half a mile. She developed pneumonia two weeks later and was treated at a local clinic with unknown medicines. A week later, the right side of her face drooped, her right arm and leg became paralyzed, and she stopped talking.
A chest radiograph revealed air and pus outside the lining of the lung (a condition known as hydropneumothorax), and a brain scan showed four abscesses. After the doctors treated her with a combination of intravenous antibiotics (imipenem until the stock of that drug ran out, then vancomycin, cef-tazadime, and metronidazole), her speech and facial movement recovered first. When she moved her right leg and arm for the first time, she “burst into peals of laughter,” according to the report. She was transferred to an International Committee of the Red Cross–Crescent field hospital. “I suspect she’ll fully recover,” says Sydney Cash, a neurologist at Massachusetts General Hospital and member of the team, who has since received pictures of her walking.
A combination of microbes likely contributes to tsunami lung, but no lab facility was available to culture and identify those found in the Indonesian patients before the Mercy arrived. However, in a letter published in the 4 April 2005 issue of The Medical Journal of Australia, Anthony Allworth, director of infectious diseases at Royal Brisbane and Women’s Hospital, describes culturing Burkholderia pseudomallei from two tsunami lung patients in a land-based hospital and Nocardia species from a third.
B. pseudomallei lives in the Asian soil and water. Mark Pasternack, an infectious disease specialist at Massachusetts General Hospital who also served on the Mercy, says, “You do not have to directly aspirate Burkholderia to produce pneumonia. . . . After the tsunami, people had soft tissue injuries from being forced into objects, so they could have gotten Burkholderia from wounds or aspiration.”
Cash echoes this thought: “Natural disasters produce odd combinations of pathogens and unexpected ways for the body to be damaged that lead to unexpected clinical circumstances. [Medical disaster physicians need to] keep an open mind and expect the unexpected.”
Could an infection like tsunami lung emerge in victims of Hurricane Katrina? Probably not, speculates Pasternack. Although the water sweeping the Gulf Coast area may have been contaminated, “it was not forced down peoples’ lungs by high-speed waves,” he says. Therefore, aspiration pneumonia and its complications are unlikely to appear commonly during the Gulf Coast relief efforts.
New concerns in devastation’s wake.
Some survivors of the tsunami that struck South Asia on 26 December 2004 are experiencing a new peril—mud and bacteria they inhaled as they were swept along with the waves has led to a type of aspiration pneumonia called “tsunami lung.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00737EnvironewsForumEHPnet: Greener Education Materials for Chemists Dooley Erin E. 11 2005 113 11 A737 A737 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Green chemistry aims in part to help clean up chemical processing by reducing or eliminating toxic elements from production and use. One university at the forefront of the movement is the University of Oregon, which has developed a website, Greener Education Materials for Chemists (GEMS), to educate teachers on introducing green chemistry concepts to their students. Although the site, located at http://greenchem.uoregon.edu/gems.html, currently contains only materials for university-level education, the developers hope to eventually include content for K–12 teachers.
The site consists of a database of print resources, which visitors can search using free text or by selecting search terms from seven categories, including chemistry concepts, laboratory techniques, green chemistry principles, and chemistry sub-disciplines. Each item in the database has an overview that summarizes its content and its connection to green chemistry as well as contact information for the person that contributed the material to the database.
The different types of material that are currently available on the site, which is partially funded by the National Science Foundation, include laboratory exercises, lecture materials, course syllabi, and multimedia content. To aid educators in determining which materials best suit their needs, threaded discussions will soon be included for each item. Here educators will be able to discuss how they integrated materials into their lesson plans and relate their success in using them.
The site, unveiled in June 2005 at an American Chemical Society meeting, was developed by a partnership between the university’s Green Chemistry Group and Center for Educational Technologies. Students and high school teachers were involved in the design of the site, as were more than 100 college instructors who attended national green chemistry education workshops at the university. The site’s developers have provided information on the site advising people how to contribute material to the database. They are also looking for educators to evaluate materials, test laboratory procedures, and adapt content for varying age groups. The developers want the web-site to be as inclusive as possible so it can serve as many grade levels and subject areas as possible.
A link to information about the university’s Green Chemistry Center is sited in the toolbar at the top of the homepage. Here visitors can find an overview of the program’s work in developing undergraduate green chemistry curricula, the history of the program, and media coverage. A description of Green Organic Chemistry: Tools, Strategies and Laboratory Experiments, a textbook/laboratory manual released in 2004 for the undergraduate organic chemistry laboratory, is available from this page as well.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0073816263493EnvironewsNIEHS NewsGlobal Earth Observations for Health Schmidt Charles W. 11 2005 113 11 A738 A739 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Every day, Earth-observing satellites outfitted with remote-sensing technology generate vast data streams that scientists use to study the biosphere—the part of the Earth and its atmosphere that can support life. These orbiting systems are rapidly advancing studies of climate change, weather, and other global phenomena. Now experts are looking for ways to put them to work in the field of environmental health research.
Recently, the NIEHS and the U.S. Environmental Protection Agency (EPA) united health and Earth scientists in a workshop charged with two key objectives. The first was to determine if observations of air quality and climate from space could be used as public health tools for research, policy decisions, and environmental and health planning. The second was to engage the NIEHS extramural research community in dialogue with remote-sensing data producers and organizers including the National Oceanic and Atmospheric Administration, the National Aeronautics and Space Administration, and the EPA. Together these experts explored ways to use Earth observation data in studies of air pollution and health.
The NIEHS and Earth Observations
The workshop, titled “Global Earth Observations: Application to Air Quality and Health,” was held at the NIEHS campus on 1–2 August 2005, and was attended by several dozen academic and government scientists. “Health researchers already use ground-based measurements of air pollution [to assess human exposures], and the workshop provided a mechanism for them to consider if addition of remote-sensed data would improve their exposure assessment and analysis of disease outcomes,” says Sally Tinkle, a program administrator with the Cellular, Organs, and Systems Pathobiology Branch of the NIEHS Division of Extramural Research and Training. Tinkle, together with NIEHS program analysts Mary Gant and Mike Humble and EPA representatives Gary Foley, Valerie Garcia, and Andy Bond, organized the event and provided NIEHS scientific support.
The NIEHS plays a growing role in the use of this technology, in part through its membership in the U.S. Group on Earth Observations (USGEO), a standing committee that reports to the National Science and Technology Council’s Committee on Environment and Natural Resources. The USGEO recently drafted a 10-year strategic plan for applying Earth observations to health and environmental research, which was released by the White House on 6 April 2005. Tinkle is the NIEHS’s USGEO representative, and Gant leads the USGEO’s User Interface Working Group.
At the August workshop, speakers covered issues ranging from the strength and adequacy of remote-sensing data to new directions in satellite research, coverage with land-based monitoring networks, and the challenges of using spatial data to address air quality and health outcomes. Participants also split into working groups to identify potential demonstration projects for remote sensing in three areas of health research: respiratory disease, cardiovascular disease, and developmental biology. Outcomes in all three areas have been linked to air pollution.
An Emphasis on Feasibility
Despite an initial focus on user needs in the area of remote-sensing data architecture—the way data are organized, stored, and made available to users—the workshop dialogue shifted frequently to feasibility issues. While the health scientists present found the technology intriguing, they raised questions about its potential for human exposure assessment.
Resolution limits were of particular concern. Remote sensing’s spatial resolution, for instance, is rarely less than a square kilometer, although there is increasing evidence that air pollution levels vary at much finer scales of resolution (for instance, city blocks). Temporal resolution can also be problematic, especially for polar-orbiting satellites, whose positions remain fixed as the Earth rotates beneath them (this is less of a problem for geostationary satellites, which orbit in sync with a particular location and thus image that area all the time).
Discussions also addressed methods for averaging pollution concentrations measured from space. Remote sensors measure pollution in atmospheric columns that extend to the outer edge of the stratosphere. Humans, however, are exposed to pollutants close to the Earth’s surface.
Finally, participants discussed limits on remote particulate measurements, which don’t extend below the 10-micron level and cannot distinguish between chemical species on particle surfaces. “All these factors contribute to the uncertainty of linking remote-sensing data to human effects,” says workshop participant Raymond Hoff, a professor of physics at The University of Maryland, Baltimore County.
According to Tinkle, feasibility discussions exposed the need for demonstration studies that layer remote-sensing data over existing ground-based pollution data sets. “This would permit us to determine if the addition of remote-sensing data improves the correlation of air pollution with adverse health events—such as asthma exacerbation and myocardial arrhythmias, for instance—in the area of respiratory and cardiovascular disease,” she says.
Working Group Conclusions
Peggy Reynolds, an investigator with the Environmental Health Investigations Branch of the California Department of Health Services, moderated the working group on respiratory disease. During breakout sessions, participants identified key data needs in this area. They included improved measures for data quality assurance and control, validated correlations with health outcomes, and confirmation that remote-sensing data accurately represent exposures on the ground. Participants speculated that remote sensing could help fill gaps in existing exposure data and suggested a demonstration project that correlates asthma prevalence with remote-sensed measures of airborne particulates and bioallergens.
Diane Gold, an associate professor at the Harvard University School of Public Health, moderated the cardiovascular disease working group. Participants in this group identified “applications,” or health outcomes, that might be served by remote-sensing data. Among them were myocardial infarction, arrhythmia, heart failure, hypertension, and stroke, in addition to a number of subclinical outcomes such as blood pressure changes and heart rate variability. Population-level application areas were also identified; they included hospital admissions and emergency room visits. Participants concluded that resolution limits might not pose problems for chronic applications, but that acute events like myocardial infarction and stroke would be better served by higher-resolution technology.
The developmental biology working group, moderated by Beate Ritz, an associate professor of epidemiology at the University of California, Los Angeles, identified several uses of remote-sensing data to assess developmental outcomes; they included critical windows of vulnerability that occur before, during, and following parturition; acute versus chronic pollutant exposure dynamics; and the interaction of maternal and fetal genetic susceptibilities. Participants also identified data needs such as adequate temporal and spatial resolution in pollution measures, and improved identification and quantification of chemical species in air pollution.
The workshop prompted Earth and health scientists to begin a dialogue to develop web-based pilot studies that integrate existing remote-sensing data with ground-based analyses as a preliminary step toward this broader validation. The workshop generated significant enthusiasm for collaboration between NIEHS extramural researchers and scientists at the participating agencies and for the possible use of remote-sensing air quality and climate data to improve public health. Ideally, space-based measures will produce new views of air pollution and the extent of human exposure, possibly leading to better opportunities to protect public health.
Eyes in the sky.
Earth-observing satellites such as Aqua (above) are being used to monitor problems including air pollution, weather, and climate change. A recent meeting at the NIEHS brought together scientists from a broad range of disciplines to discuss how satellite data might be brought to bear on addressing issues of human health.
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Environ Health Perspect. 2005 Nov; 113(11):A738-A739
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00739EnvironewsNIEHS NewsBeyond the Bench: Online and On Track with Veggie-Mon Tillett Tanya 11 2005 113 11 A739 A740 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Too much computer time may not be good for kids, but sometimes surfing the Internet can be a wholesome activity, especially when it involves websites that help children learn how to make informed choices about their own health. One such site is the Veggie-Mon website at http://www.veggie-mon.org/. Created in 2000 by the Community Outreach and Education Program (COEP) of The Center for Research on Environmental Disease, a joint NIEHS center of The University of Texas M.D. Anderson Cancer Center and The University of Texas at Austin, the Veggie-Mon website has informed thousands of kids about the choices they can make to lead a healthy life.
The Veggie-Mon site introduces concepts of environmental risk factors and disease prevention to elementary- and middle-school students in a compelling and comprehensible way. “The goal of the site is to inform students, even young ones, that they can have an important and long-term impact on their own health by reducing their exposure to environmental risk factors and improving their diet,” says COEP director Robin Fuchs-Young.
The homepage offers three portals, one for students in grades 4–6, one for students in grades 7–8, and one for teachers. Both student portals present information on three main topics: nutrition, sun and ultraviolet (UV) exposure, and tobacco use. According to Fuchs-Young, these are among the most important environmental risks faced by school-age children, and are also some of the risks that are most easily mitigated.
Each visitor is accompanied through the different sections by Veggie-Mon himself, a character reminiscent of a walking, talking artichoke who offers site navigation tips and provides extra details on the information presented. Each of the three sections has information that is both informative and fun. Along the way, Veggie-Mon encounters different acquaintances who help him explain the subject matter.
In the Nutrition section, students meet Strawberry Girl, an advocate of healthy eating habits. Here students can learn how to make healthy food choices through an illustrated food pyramid, and can also find recipes for delicious, wholesome snacks like a strawberry banana blast or a peanut butter and honey sandwich.
The Sun and UV section features Sunspot, a character who discusses some of the dangers of too much sunlight. In this section, students learn how fish research is helping scientists study the connection between sun exposure and skin cancer, and they can also take Sunspot’s quiz to gauge how much they’ve learned.
In the Tobacco Road section, students meet Igna-Ray-Mouse, a misinformed rodent who has decided to smoke. Here they can take a virtual journey down Tobacco Road with Igna-Ray-Mouse and learn how advertising messages and peer pressure may be used to try to convince them to smoke. At each fork in the road, evidence is presented to prove that choosing to smoke is a bad idea.
Other tools on the site include a submission form to send questions to real scientists, a glossary, and a “laboratory” with instructions for simple experiments that students can conduct themselves. Each section also includes age-appropriate games and puzzles.
Teachers have their own features on the site. In a password-protected area, they can access lesson plans and provide feedback on how the website has helped them with classroom activities. Educators also contribute directly to the development of the website. During a 4- to 6-week educator fellowship held each summer at The Center for Research on Environmental Disease, teachers from grades K–12 help the COEP staff translate center research findings into age-appropriate content.
The COEP regularly revises the Veggie-Mon website to improve its usefulness for both students and teachers. Next up for the site is an exercise unit for the Nutrition section that will offer suggestions for fun and safe activities as well as information on healthy weight maintenance.
A virtual journey to real health.
The Veggie-Mon website uses a cartoon character (inset) to introduce students to concepts of good diet, nutrition, and healthy lifestyle choices. In one activity, students take a virtual journey along Tobacco Road and read billboards with messages about smoking.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00740EnvironewsNIEHS NewsHeadliners: Uterine Leiomyoma: Genetic Reprogramming and Benign Uterine Tumors Phelps Jerry 11 2005 113 11 A740 A740 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Cook JD, Davis BJ, Cai SL, Barrett JC, Conti CJ, Walker CL. 2005. Interaction between genetic susceptibility and early-life environmental exposure determines tumor-suppressor-gene penetrance. Proc Natl Acad Sci USA 102:8644–8649.
Uterine leiomyomas (fibroids) are common benign tumors in the muscle tissue of the uterus. Previous research has suggested a link between environmental exposures and uterine fibroids. NIEHS grantee Cheryl Lyn Walker and colleagues at The University of Texas M.D. Anderson Cancer Center were interested in how such exposures contribute to uterine fibroids. They propose that early-life exposure to xenoestrogens may alter genetic programming during development, setting the stage for an adverse response to later natural estrogen stimulation.
Uterine fibroids occur in up to 77% of women, can cause severe menstrual bleeding and pelvic discomfort, and result in more than 200,000 hysterectomies each year in the United States alone; although “benign,” they are far from harmless. Lesions causing symptoms range in size from 1 to 20 centimeters. Data indicate that 25% of white women have problematic lesions. Black women have about a threefold higher risk of developing fibroids and, in general, their clinical symptoms are worse.
Diethylstilbestrol (DES), a xenoestrogen, is one environmental exposure that has been posited as contributing to uterine fibroids. To determine the actions of this chemical, Walker and colleagues studied rats with a genetic predisposition to developing uterine fibroids, exposing some of them to DES during their first week of life. By age 16 months, the DES-exposed animals had almost a 95% incidence of tumor formation, while the unexposed animals had a 64% incidence. There were more tumors in each affected DES-exposed animal, and the tumors were larger in size and more invasive, compared to controls.
The researchers determined that DES did not cause a mutation in estrogen-responsive genes, but rather caused them to become “reprogrammed” so that they responded differently to natural estrogen stimulation later in life. These findings indicate that reprogramming of genes during the developmental period as a consequence of xenoestrogenic exposure can interact with a preexisting genetic condition to increase the formation and severity of uterine fibroids. If additional research confirms these results, this study’s findings could have implications for other hormonally mediated cancers such as those of the breast and prostate.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0074216263494EnvironewsFocusPower Surge: Renewed Interest in Nuclear Energy Holton W. Conard 11 2005 113 11 A742 A749 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Just past its 50th birthday, commercial nuclear energy is experiencing a tentative rejuvenation that could result in a greater role as a global source of electricity. Skeptics still harbor many of the objections that have slowed or stopped the construction of new nuclear power plants, but rising concerns about the cost and security of energy supplies and global climate change have reframed the debate in terms more favorable for nuclear power advocates.
As a result, the question of whether governments should encourage the construction of new nuclear power plants is no longer off the table in developed countries such as Australia, the United Kingdom, and the United States. For other developed countries such as France and Japan, and for countries with fast-growing economies such as China and India, nuclear energy has remained a central component of energy policy. For example, to achieve its goal of generating 4% of electricity from nuclear power, China plans to add more than 30 new nuclear plants by 2020 to the 11 currently in operation or under construction. India’s goal is to supply 25% of its electricity from nuclear power by 2050.
Worldwide there are now 440 nuclear power reactors operating in 31 countries and producing a combined capacity of 367 gigawatts electric, or about 16% of the world’s supply of electricity. The Vienna-based International Atomic Energy Agency (IAEA)—the agency of the United Nations chartered to promote cooperation on nuclear issues—estimates that at least 60 new nuclear plants will be constructed in the next 15 years. Given the world’s growing demand for electricity, however, this added capacity will still account for only 17% of global electricity use.
Environmental Conundrum
One central issue facing policy makers and electric utilities is the question of how to meet the rapidly growing worldwide demand for electricity while not increasing global greenhouse gas emissions. The U.S. Department of Energy’s Energy Information Administration tracks world energy trends and projects a 75% increase in global electricity use between 2000 and 2020. By 2050 a tripling of use is probable. Electricity production currently is responsible for an estimated one-third of all greenhouse gas emissions.
In terms of human welfare, this growth in electricity usage is desirable as reflected in the strong correlation between electricity consumption per capita and the United Nations’ human development index, which combines indicators of health, education, and economic prosperity. Overall energy consumption per capita in the developing world is less than one-fifth that in the developed world, and as developing countries industrialize, they will tend to seek the least expensive supply to meet their electricity needs. In most cases this means coal-fired plants, which produce significantly more greenhouse gases—primarily carbon dioxide—than other carbon-based sources such as natural gas–fired generators. Nuclear and noncarbon-based renewable sources such as wind and solar power do not directly create greenhouse gases.
Global climate change and the 2005 entry into force of the Kyoto Protocol to the United Nations Framework Convention on Climate Change have spurred new thinking about the potential value of nuclear energy by both environmental groups and the nuclear energy industry. Recently, several prominent environmentalists have publicly supported nuclear energy, including former Anglican bishop Hugh Montefiore, a long-time trustee of Friends of the Earth, and Patrick Moore, cofounder of Greenpeace.
Their support has alienated them from many in their former organizations, but indicates a more nuanced challenge to nuclear energy by some environmental activists, who are perhaps more willing to consider the nuclear option but still do not think it’s the wisest choice. Organizations such as the Natural Resources Defense Council and the Union of Concerned Scientists now talk in terms of the proper role of government in energy policy and ensuring the safe operation of nuclear plants, rather than whether nuclear power should even be considered.
Minds Differ
The potential for building new nuclear power plants is quite different in different countries. For example, the role of nuclear power is unlikely to change substantially in countries with a flat demand for electricity, such as Japan, which now relies on nuclear power for 30% of its electric capacity and expects to see a population decline, or France, with a stable population and a power industry that is 80% nuclear. On the other hand, the United States, which currently operates 103 nuclear power plants and relies on nuclear energy for 20% of its electricity, expects to see a rising population and consequent greater demand. Developing countries offer the potential for considerably more use of nuclear power, especially as much of their populations will be urban, providing a concentrated market for large electric-generating plants.
So in answer to the question of whether nuclear power makes economic sense, it simply depends—“in some countries it does, in others it does not,” says Alan McDonald, a staff expert in planning and economic studies at the IAEA. “In countries like China and India, you need [every source of power] you can get. Asia has major pollution problems and energy needs. Sometimes it seems to be a matter of national preferences. In countries like Austria and Denmark, nuclear power is anathema; in others like Germany, opinions may be changing. In the United States, Wall Street is very skeptical and will watch developments closely.”
Relative costs of nuclear energy vary depending on what options and factors are being considered, but in general, McDonald says, the up-front costs of nuclear energy are very high while the cost of operation is relatively low. Thus, countries with government-owned electric utilities have an advantage in new power plant construction because they can fund investments more easily than investor-owned utilities, which are subject to the capital markets and the demand for rapid returns on investments.
“Until the Kyoto Protocol, the environmental value of nuclear energy could not be translated into financial terms,” says McDonald. “But now, obtaining greenhouse gas emission permits for a new coal-fired plant in Europe can cost more than the coal itself. Although the United States is not bound by Kyoto, U.S. investors may see the writing on the wall. If the treaty is changed and nuclear power becomes part of the international market mechanism that allows credit for clean energy sources and the trading of carbon emission credits, that would be a big incentive.”
But more nuclear power doesn’t come without potential security threats of another sort. “If the world sees a big increase in nuclear energy, there will be an increased risk of [nuclear arms] proliferation—all things being equal,” McDonald notes. Indeed, the director general of the IAEA, Mohamed ElBaradei, says that recent revelations about undeclared uranium enrichment activities and reprocessing of spent fuel, along with the discovery of an international illicit market in nuclear technologies, underlines the need for improved controls. On 7 October 2005 ElBaradei and the IAEA were awarded the 2005 Nobel Peace Prize for their efforts to stop the spread of nuclear weapons and prevent North Korea and Iran from acquiring nuclear arms.
In response to the threat of proliferation, the IAEA has developed a model Additional Protocol that signatories can add to their IAEA Safeguards Agreements, which address questions of traceability and verification of nuclear materials. The Additional Protocol strengthens safeguards, protects nuclear materials and facilities, and bolsters the systems of nuclear export controls. So far more than 100 countries have added the protocol to their agreements. The IAEA further proposes that future reactor technologies be designed to be more resistant to proliferation, and that the international enrichment and reprocessing of nuclear fuel be centralized in a few countries under a structure that guarantees supply to member nations.
An Industry with a Storied Past
The question of whether nuclear energy should play a significant role in future electric power generation cannot be separated from its history, the role played by governments, or the nuclear fuel cycle itself. The cycle has always been a focus of concern, from the potential hazards of uranium mining operations, through the processing of uranium into fuel, to the controlled fission process in the reactor core, and finally to the disposal or reprocessing of the fuel and related waste products.
The civilian nuclear power industry was created through U.S. government–electric utility industry cooperation that officially began with the Atomic Energy Act of 1954. Until that point, all U.S. atomic energy resources had been devoted to military activities. President Dwight Eisenhower’s “Atoms for Peace” speech to the United Nations in December 1953 led to the U.S. government’s financial and technical support of commercial nuclear energy. The government also enacted the Price-Anderson Act of 1957, requiring nuclear power operators to carry the maximum insurance offered by private insurance companies but also limiting their liability—a stipulation demanded by the utility companies before they would invest in building nuclear power plants.
The U.S. Navy first developed the now widely used pressurized-water reactor for propulsion in submarines. This design became the basis for the first commercial nuclear plant at Shippingport, Pennsylvania, which began operation in 1957. In the Soviet Union, reactors designed for producing plutonium for weapons were modified and new ones developed to generate heat and electricity. The first such reactor began producing electricity for the city of Obninsk in 1954.
The fostering of nuclear energy was woven into many U.S. foreign policy initiatives during the early days of the Cold War. The United States sponsored the creation of the IAEA as the global manager of nuclear technology and materials, it supported international research reactors and isotopes for nuclear medicine and agriculture, and it helped create a nuclear energy industry in Europe, where coal production was declining and other sources of electric power were limited.
The U.S. commercial nuclear power industry flourished from the mid-1960s through the early 1970s, although the power plants operating then were not economical compared to other sources at the time. Nuclear energy advocates argued that, with moderate and selective government assistance, the technology could cross the economic threshold into widespread acceptance by the utility industry. The U.S. Atomic Energy Commission—which then combined the functions of today’s Nuclear Regulatory Commission (NRC) and Department of Energy—estimated that the United States would exhaust its oil and coal supplies within 100 years and that nuclear energy was the best replacement for fossil fuels in electricity production. The commission optimistically estimated that by 2000 as much as two-thirds of the nation’s electric power could come from nuclear energy.
The peak year for achieving this scenario in the United States was 1973, when 50 orders were placed for new nuclear plants, although in the following years leading up to 1979, cancellations began to exceed new orders. Then, in March 1979, a series of operator errors and miscommunications led to the partial core meltdown in the pressurized-water reactor at Three Mile Island Unit 2. The accident did not result in major damage outside of the core and primary cooling system, and according to all official estimates, the radiation released during the accident was minimal, well below levels that have been associated with health effects from radiation exposure. However, a panicked evacuation of nearby residents took place, followed by extensive investigations and a government-subsidized 10-year cleanup effort. The notoriety of the accident, combined with the high cost of construction, slow regulatory processes, and political opposition, essentially halted the growth of the U.S. nuclear industry. Although numerous nuclear power plants that had been under construction at the time eventually came online, no new U.S. plants were ordered.
The devastating accident at Chernobyl Unit 4 in April 1986 could have been the death knell of the industry worldwide. The steam explosion, fire, and nuclear fuel melting at the site were the result of a flawed reactor design operated by inadequately trained personnel who violated safety procedures. The reactor design widely used for nuclear power in the Soviet Union did not include the containment system used with most Western reactors, and so substantial quantities of radioactive material, dust, and gases escaped into the atmosphere.
The Chernobyl site is now entombed in a concrete structure known as the Sarcophagus, but it is not stable for the long term and is not air-or watertight (a major new Sarcophagus is planned, but funding is slow to materialize). The accident was a deeply traumatic experience for the 350,000 people who relocated from the area. A 30-square-kilometer area around the site remains closed because of high levels of contamination. About 50 people were killed in the initial accident and emergency response. A September 2005 IAEA report, Chernobyl’s Legacy: Health, Environmental, and Socio-Economic Impacts and Recommendations to the Governments of Belarus, the Russian Federation, and Ukraine, estimates that around 4,000 people have died or will die as the result of exposure related to the accident. The report observes that “mental health is the largest public health problem created by the accident,” referring to affected residents’ subsequent poverty, substance abuse problems, and “paralyzing fatalism,” manifested as negative self-assessments of health, belief in a shortened life expectancy, lack of initiative, and dependency on assistance from the state.
Even with the resulting public outcry against nuclear power, the world did not halt new construction of nuclear power plants. However, some European countries such as Belgium, Germany, and Sweden began to reconsider their plans for nuclear energy, and eventually developed policies to phase out existing plants. Now some of these countries are under the gun to find replacement energy sources. Sweden, for example, aims to be nuclear-free by 2010, having taken a second reactor offline in June 2005 (the first was closed in 1999). But the remaining 10 plants still supply about half of Sweden’s domestic energy production, according to the World Nuclear Association.
New/Old Thinking
An influential 2003 report out of the Massachusetts Institute of Technology (MIT), The Future of Nuclear Power: An Interdisciplinary MIT Study, spelled out the major areas of concern surrounding nuclear energy and proposed a plan that the authors hoped would allow the United States to resume development of nuclear power in order to reduce greenhouse gas emissions. The study identified the four critical problems that must be overcome for nuclear power to succeed—cost, safety, waste, and proliferation. It also offered policy recommendations for making the nuclear energy option commercially viable, including steps to lower cost and a limited production tax credit to “first movers,” private sector investors who build and then operate new nuclear plants.
“Our recommendations are basically holding up,” says study cochair Ernest Moniz, who is codirector of MIT’s Laboratory for Energy and the Environment and former undersecretary for energy during the Clinton administration. “On the positive side, new regulatory approaches are being developed, the industry’s intent is to build a new reactor, there are more open discussions with environmental groups, and the Energy Policy Act became law,” he says. “On the negative side, the situation with spent fuel management is worse—Yucca Mountain casts a shadow over any decision. And the non-proliferation situation in Iran is a real problem.”
The fate of Nevada’s Yucca Mountain nuclear burial site is unclear. In the face of sustained resistance from the state and citizens groups, the federal government has slowed in its effort to build a long-term geological repository for commercial spent fuel and high-level radioactive waste. Opposition to the Yucca Mountain project is based on a long history of Nevada being a nuclear weapons testing grounds, resentment at becoming a repository for toxic waste generated elsewhere in the country, and concerns that the site is not geologically stable enough to guarantee that the radioactivity will remain confined over the required 10,000-year span. But several more such sites will be needed in future decades if a significant number of new nuclear power plants are built.
Moniz says the MIT study endorses a robust research and development program and tax credits for the nuclear industry. This is because, in the past, there has been considerable regulatory uncertainty, causing prohibitively high financial risk for utility investors. In addition, the true cost of burning carbon-based fuels has not been internalized, meaning that if the health and environmental costs of pollution and greenhouse gases could be factored in, nuclear energy would be very competitive. As a result, public subsidy of noncarbon-based energy sources is justified.
The comprehensive Energy Policy Act of 2005 that Moniz cites provides loan guarantees to develop energy technologies, including nuclear power, that avoid, reduce, or sequester greenhouse gases. It also provides a tax credit of 1.8¢ per kilowatt hour for 6,000 megawatts of capacity at new nuclear power plants (equivalent to the output of about six new plants). Important to the industry, the act provides investment protection against delays in licensing and startup that are beyond the control of industry, including litigation.
The act also provides several billion dollars for nuclear energy research and development, which translates into work on a more cost-efficient and inherently safer generation of reactors known as Generation IV. These reactors achieve greater safety through passive technologies that automatically shut down the reactor in an emergency, bypassing the risk of operator error (humans still control the normal operation and shutdown of these reactors). They are also more efficient and relatively more cost-effective than their Generation III predecessors. In another bow to the environment, the act funds construction of a cogeneration reactor that will produce both electricity and hydrogen, which advocates hope will be a new, carbon-free fuel for automobiles—the single largest source of greenhouse gas emissions.
Finally, the act funds a central nuclear energy program of the Bush administration: Nuclear Power 2010. The program was unveiled in 2002 as a government–industry cost-sharing plan to identify three sites for new nuclear power plants, develop Generation III reactors, and develop a single-license process with the NRC for approval of both plant construction and operation, thereby removing much of the delay and uncertainty for investors.
In response, three consortia of electric utility companies, reactor suppliers, and construction firms have made proposals. None are yet committed to building a new nuclear plant. The consortia are led by Dominion Resources, Exelon and Entergy (via the NuStart Energy Development consortium), and the Tennessee Valley Authority. These consortia represent operators of 67 of the nation’s nuclear plants, and their proposals have all focused on building a new plant on sites where plants already operate—in much the same way that a consortium of 10 electric utilities built the Yankee Rowe plant, one of the first commercial nuclear plants, in the 1950s.
The consortia embrace a number of different reactor vendors and designs, some of which have already been certified by the NRC. The final decision on building a nuclear power plant will depend on factors as they stand later this decade, including the power market, the status of permanent spent fuel storage, and the ability of the participants to obtain financing without adversely affecting their credit ratings.
Concerned Parties
“The industry’s interest is very real,” says Russ Bell, a senior project manager for new plant development at the Nuclear Energy Institute, a utility trade association. “The utilities are [participating in consortia and spending money on preliminary designs and siting plans] because the economics are turning in favor of nuclear, especially over the long term. [The Kyoto Protocol] is not driving us, but it makes sense and there is increasing concern about pollution in the United States and more stringent environmental regulations.”
Bell says the industry is getting what it needs from the Energy Policy Act and is looking to government to do no more than jumpstart new builds after so much time has passed. He acknowledges the long time horizon for building new plants in the United States. Assuming that any of the consortia meet the 2010 goal of being licensed to build and operate a plant, another four to five years will pass before construction is complete and electricity flows. Meanwhile, the electric utility industry will continue to improve operating performance of existing nuclear power plants and apply for license extensions.
Originally licensed for 40 years, the first operating license issued by the NRC will expire in 2006, approximately 10% will expire by the end of 2010, and more than 40% will expire by 2015. The decision to seek license renewal is strictly voluntary, and nuclear power plant owners must decide whether they are likely to satisfy NRC requirements and whether license renewal is more cost-effective than shutting down and pursuing other sources of energy. The NRC has now granted 35 plants the right to operate for another 20 years. Three-quarters of the nation’s plants have received, have applied for, or are expected to apply for an extension.
The question of plant life extension can bring the relationship between nuclear energy and greenhouse gases into sharp focus. For example, the governors of nine Northeast states have proposed an agreement to cap greenhouse gas emissions from all power plants in their states. Two nuclear power plants in the region, one in Vermont and one in New Jersey, are up for life extension, yet if these plants are shut down, the result would be increased reliance on carbon-based fuels. This could potentially triple greenhouse gas emissions in Vermont and double them in New Jersey, according to the 14 September 2005 edition of The New York Times.
“We are not fundamentally opposed to nuclear power,” says David Lochbaum, a nuclear safety engineer at the Union of Concerned Scientists, “but there are better choices. In addition, we now have spent nuclear fuel in storage places where it is not meant to be. It’s not a health threat yet, but it could be.”
Lochbaum is also concerned about the oversight role played by the NRC. “The NRC budget has been cut for a decade,” he notes. “It is understaffed to support a nuclear resurgence. And the industry still has operational troubles at some plants.”
These concerns are echoed by Thomas Cochran, director of the nuclear program at the Natural Resources Defense Council and an advisory committee member on the MIT study. “The Energy Policy Act was the result of successful lobbying by the nuclear industry,” he says. “They will probably build a few plants and then the issue is, are you back to where you are today?” Cochran does not believe that the subsidy or the economics will work for nuclear power. “It’s not helpful to just say you are for or against nuclear,” he says. “Ultimately you must make a decision on real policy to address global warming, and a carbon tax is the best way.”
The objective of a carbon tax would be to internalize the environmental costs and hope for an open competitive market for energy. “To balance the energy market, you either tax a pollutant or regulate it,” says Cochran. “If public policy was made correctly, it would help the nuclear industry.”
Is there a real, economically justified “nuclear resurgence,” or simply a steady growth in some regions to meet rising demand for electricity? Nothing happens quickly in the world of power plant construction. Yet major investments by government and industry can change the bases of electricity supplies in the time frame of a decade or two. France closed its last coal mine in 2004, and its transition from 15% to 80% nuclear-based electricity was accomplished in 20 years. A sense of optimism and urgency now surrounds the question of whether to pursue nuclear power. How this translates into results should unfold at a brisk, measurable pace.
The core of the matter.
The view looking down into a research reactor core in Chile shows the fuel elements and control rods hanging in a water pool.
Full steam ahead.
Construction is well under way on China’s first experimental fast breeder reactor, located in Tuoli.
Terror target?
Some critics’ reservations about nuclear energy revolve around the fear that reactors and their contents may pose an attractive target for terrorists.
Locations of Nuclear Power Plants Worldwide
Building on the past.
Construction of new nuclear plants continues worldwide. Although stalled in the United States, renewed interest and the need for energy may bring this power source back online.
Nuclear fallout.
A researcher buys food samples from a local farmer for radionuclide analysis during the International Chernobyl Assessment Project. A recent IAEA report states, though, that the greatest long-term health impact from the accident is psychological trauma.
Iran moves ahead.
(above) Construction of the Bushehr nuclear power plant, being built under an agreement with Russia, is under way in Iran. (right) Journalists examine a scale model of the Bushehr plant during a visit to the construction site.
A yucky situation.
Yucca Mountain, in the Mojave Desert of Nevada, is the site Congress designated as a geologic repository for the nation’s spent nuclear fuel and high-level radioactive waste. However, the project has been fraught with technical problems and public opposition.
Vive la nuclear!
France has embraced nuclear energy and now obtains 80% of its electricity from nuclear power.
Nuclear microcosm.
Many nations have an ambivalent relationship with nuclear energy. (above) A family walks along the beach in Kenting, China, with National Nuclear Power Station No. 3 behind them. Until recently, the waste from this power station was shipped to a controversial storage facility on nearby Orchid (Lanyu) Island. (below) Masked student protestors voice their opposition at an antinuclear rally.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0075016263495EnvironewsSpheres of InfluenceHarvesting the Potential of BIOMASS Tenenbaum David J. 11 2005 113 11 A750 A753 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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As fossil fuel prices and consumption both continue rising, the search is on for alternative fuels. Fuel for vehicles is taking center stage, now that 67% of U.S. petroleum consumption goes toward fueling vehicles, according to the U.S. Department of Energy (DOE). Could biomass energy derived from plant matter supply a significant percentage of future transportation fuel? The answer is yes, according to Biomass as Feedstock for a Bioenergy and Bioproducts Industry: the Technical Feasibility of a Billion-Ton Annual Supply, a report funded by the DOE and the U.S. Department of Agriculture (USDA) and issued by Oak Ridge National Laboratory in April 2005.
Biomass Today
The report defines biomass as all plant-derived molecules, including grain, starch, sugar, oil, and waste products, as well as the plant structural components cellulose, hemicellulose, and lignin. Fossil fuels and animal matter are excluded. While the diversity of resources is a strength of biomass, it also raises a problem: different facilities are needed to convert the array of molecules from biomass into the hydrocarbons needed for transportation fuel. Furthermore, large changes in infrastructure would be needed to harvest the various potential sources.
The report gives an overview of the biomass situation today in the United States. About 190 million dry tons of biomass feedstock are consumed annually. Biomass accounts for about 3% of total U.S. energy supplies, and has recently surpassed hydropower as the largest renewable energy source. A great deal of biomass is waste material that is both produced and consumed by industry. For example, forest-products firms (including paper companies) use 96 million dry tons of biomass, largely to power their factories. Currently, 3.4 billion gallons of ethanol are blended into gasoline each year; that amount could soar to 80 billion gallons by 2030.
The report states that by 2030, American acreage could produce enough biomass to displace at least 30% of the country’s current consumption of petroleum fuels with some changes in land use and agricultural and forestry practices, and up to 50% with advanced conversion technologies. This calculates to up to 1.366 billion tons of biomass produced annually.
Lynn Wright, who formerly worked in Oak Ridge National Laboratory’s biomass program and now consults to it, says the report was a response to a common question from the energy industry. “We were hearing from people with connections to oil companies that they hardly considered it worth their while to think about biomass unless we could show that it could supply as much as a billion dry tons per year,” she says.
The Billion-Ton report does not attempt to assess the economic viability of large-scale biomass energy, and Wright explains that it’s difficult at best to predict the relative price of fossil fuels and the various sources of biomass in 25 years. Today, she says, when prices are measured by energy content (British thermal units), energy from switchgrass or corn stover, the residue that remains in the field after a grain crop is harvested, is cheaper than energy from oil but more expensive than energy from coal.
Boosting Production
According to the report, wood could grow to supply 368 million dry tons of biomass by 2030. The supply could expand with enhanced collection of urban tree trimmings and construction waste, and greater efforts to prevent forest fires by clearing deadwood from forests.
But transportation and processing costs may keep wood expensive, cautions report coauthor Bryce Stokes, program leader of vegetation management and protection research at the USDA Forest Service. He says, “We still have to overcome some economic and conversion efficiency barriers . . . to make wood competitive” in transportation fuels. Woody biomass will seem more competitive, he adds, if the benefits of improving forest heath, reducing fire risk, and recycling carbon from the atmosphere are held in view.
Farms could potentially contribute a far larger quantity of biomass (998 million dry tons), and much of that may come from corn stover and perennial crops managed with no-till production techniques and collected with advanced harvesting equipment. However, Wally Wilhelm, a plant physiologist with the USDA Agricultural Research Service, says it is unlikely that all land will ever be switched to no tillage. “Use of no-tillage methods and producing crops without tillage is far more complex than simply not passing over the field with a plow or disk,” he explains. “It takes time and skill, and trial and error, to become proficient at no-till farming. Not all farmers are willing, nor have the flexibility—the money in the bank—to pursue the knowledge and skill.”
Corn grain is currently the source of most of the ethanol used as motor fuel in the United States today. Corn production has been growing by 1.7 bushels per acre per year for 30 to 40 years, says Achim Dobermann, a professor of soil science and nutrient management at the University of Nebraska. Dobermann says irrigated cornfields in Nebraska could produce 250 to 350 bushels per acre.
These yields require intensive inputs, especially in the form of nitrogen fertilizer, which is usually derived from natural gas. Farmers already manage nitrogen closely, due to its price and potential for polluting groundwater, but ever-higher yields will force them to work even harder to carefully manage nitrogen. “It requires a more fine-tuned type of management,” says Dobermann. “You can’t just go in and apply anhydrous ammonia [a common nitrogen fertilizer] in the fall and take off for vacation.” Instead, he suggests multiple nitrogen applications, timed and placed when and where the crop needs it.
More Mass, Sustainably
If biomass harvesting is to be sustainable, it must not diminish soil’s fertility (its ability to supply nutrients for plant growth) or other properties influencing productivity. A market for stover creates an incentive for farmers to remove more after the harvest. But crop residue left in the fields reduces soil erosion; it also improves soil fertility and structure through the addition of organic carbon, which fuels microbial activity that drives the cycling of nutrients and structures in productive lands.
Estimates of how much stover must remain on the fields if erosion is to be controlled rely on the concept of a tolerable amount of soil loss, as defined for particular soils by the USDA Natural Resource Conservation Service. But this amounts to “an educated guess,” says Wilhelm. “The assumption is that if we keep losses below the tolerable level, we should not notice a significant impact on productivity.” Erosion is affected by farming practices, soil types, and weather, and Wilhelm says it’s “a very good question” whether it’s possible to predict what level of stover removal will hold soil organic carbon loss below tolerable levels.
Because crop residue is converted into organic matter that maintains soil structure, Wilhelm says levels of organic matter may be a good metric of soil health and the amount of stover that must be retained on or in the soil to sustain productivity. Wilhelm, who is leading a project to develop guidelines for sustainable removal of corn stover, says extensive biomass extraction raises the danger that soil organic carbon will be “mined” rather than be treated as the irreplaceable resource that it is. He adds that stakeholders must work together to develop systems that enhance the use of renewable sources of energy and produce renewable energy in a sustainable manner.
A Question of Impact
One of the key arguments over biomass energy concerns the net energy contribution of biomass—how much energy is gained from the crop. For example, David Pimentel, a professor of ecology and agriculture at Cornell University, and Tad Patzek, a professor of civil and environmental engineering at the University of California, Berkeley, published calculations in the March 2005 issue of Natural Resources Research showing that ethanol derived from corn contains only 71% of the energy used to grow, harvest, and convert the grain into ethanol. At the other end of the spectrum, calculations by federal researchers Hosein Shapouri, James A. Duffield, and Michael Wang in the July 2002 Agricultural Economic Report Number 814 showed a net energy gain of up to 130–140%.
Pimentel and Patzek based their calculations on average U.S. corn production output for 2003—140 bushels per acre. As to the assertions put forth in the Billion-Ton report, Pimentel contends that providing enough biomass to cover 30% of current U.S. gasoline and diesel use would require a land area greater than that of the United States. He believes the actual U.S. biomass capacity is about half the 1.366 billion tons cited in the report.
But calculating net energy efficiency is difficult, says Robert Anex, an associate professor of agricultural and biosystems engineering at Iowa State University who studies life-cycle assessments of biomass resources. “One must account for all of the resources that are used, all of the product created, and also those resources that are saved via substitution of the biomass product for some other probably petroleum-based product,” he says. “This involves many assumptions about how crops are grown, harvested, and converted, but also what resource use is avoided.” This, he says, is why these sorts of measures are often contentious.
Biomass advocates such as Thomas Foust, biomass program technology manager at the DOE National Renewable Energy Laboratory, say more biomass should become available if agricultural productivity continues its steady rise and improvements in conversion technologies are made—for example, ethanol production per bushel of corn has grown by about 25% in the past 25 years. Furthermore, Foust says that net energy balances for ethanol are not that useful, and the real metric should be imported oil displacement, which can be as high as 6 to 1 for ethanol.
The environmental health impact of gathering 1 billion tons of biomass through whatever means—a plan that could affect hundreds of millions of acres—must also be investigated thoroughly. For example, the impact of harvesting biomass from millions of acres of farmland now set aside under the USDA Conservation Reserve Program remains to be studied. One higher-production scenario in the Billion-Ton report assumes that 60 million acres would be shifted from a combination of Conservation Reserve Program land, pasture land, and commodity crop production in order to produce woody and grass crops as a source of biomass. The land used to produce wood and grass crops would provide bird and mammal habitat similar to the Conservation Reserve Program but would be harvested more frequently.
Donald Waller, a professor of botany and environmental studies at the University of Wisconsin–Madison, raises other questions about the impact of boosted biomass energy production on forest health. While noting that the report does not call for building roads in roadless forests or removing biomass from wilderness areas, he warns of broad ecological consequences from removing massive amounts of tree biomass and thus essential nutrients. “In most forests, the old growth is dominated by decomposers in terms of species number and complexity,” he says. “Deadwood is there in far greater quantity than live wood.”
Waller emphasizes the importance of leaving behind “biological legacies”—standing dead trees, live trees, and tree material on the ground. “You can’t take it all away without seriously diminishing the ecosystem functions and the plant and animals that live there,” he says.
Achieving Critical Mass
In the face of tight fossil fuel supplies, the federal government is moving ahead with plans to expand biomass output. The National Renewable Energy Laboratory, for example, has a considerable effort working to improve biomass conversion into liquid fuel. Wright suggests putting more effort into pilot projects that use large amounts of biomass. “I think it would help a great deal to get some demonstrations in place on the part of farmers and power producers,” she says.
Government subsidies similar to the tax credits already offered to build wind power towers and install solar energy panels may be another way to enhance the appeal of biomass. In the 17 October 2005 issue of Newsweek, Frances Beinecke, executive director and incoming president of the Natural Resources Defense Council, says, “We think subsidies or assistance from the federal government should go to the new technologies that need to come to the market. . . . Biofuels are definitely part of the renewables portfolio. There’s growing interest in the agricultural sector, because that way we could have home-grown fuels.”
The energy business may be at a turning point. After years of concern about funding levels for alternative energy research, the prices of oil and natural gas have changed the equation, says Wright: “If prices stay high, I don’t think the government will have to do very much [to jumpstart the biomass bandwagon].”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0075416263496EnvironewsInnovationsMicrobe Power! Holzman David C. 11 2005 113 11 A754 A757 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Increasingly, problems of rising energy demands, dwindling resources, and pollution concerns are being mitigated by turning waste into usable products. Now some researchers are eyeing organic wastes from homes, food processing, and other sources as an energy feedstock—bacteria including Rhodoferax and Geobacter are being harnessed in devices called microbial fuel cells (MFCs) to break down organic waste products, converting the energy of their chemical bonds into electricity and hydrogen.
Significant Energy Resource
In the United States, 46 trillion liters of household wastewater are treated annually, according to an article by Bruce Logan, director of the Hydrogen Energy Center at The Pennsylvania State University, in the 1 May 2004 issue of Environmental Science & Technology. This costs $25 billion, and the electricity required—mostly for aeration—constitutes 1.5% of the electricity used in the nation, says Lars Angenent, an assistant professor in the Department of Chemical Engineering at Washington University in St. Louis. According to Angenent, most of that energy could be saved by treating wastewater using MFCs. He says one of these devices could produce enough extra energy to power 900 homes by treating the wastes from a single large food processing plant. According to Logan, MFCs would cut the cost of aerating activated sludge in wastewater by as much as 50% of the electricity usage, and should generate 50–90% less solids to be disposed of.
Logan put this potential in context in his 1 May 2004 article when he wrote that the United States consumed 97 quads (short for “quadrillion British thermal units”) of total energy in 2002; of this, 13 quads were generated electricity. Should hydrogen become the transportation fuel of choice, as many believe it will—with most hydrogen produced ultimately from fossil fuels—another 12 quads would be required to make hydrogen from water, he wrote.
According to Logan, all the U.S. household wastewater produced in one year contains 0.11 quad organic matter, livestock production waste-water contains 0.3 quad, and food processing wastewater possibly 0.1 quad. Though small, these amounts are potentially significant, says Scott Sklar, the former executive director of the Solar Energy Industries Association and current president of The Stella Group, an energy generation marketing and policy analysis firm. There will be no one-size-fits-all solution to the nation’s energy problems, he says. Instead, energy will come from many sources, many of them small sources, and power will be created through a patchwork of technologies tailored to local circumstances and needs.
MFCs could also become important energy sources in the lesser developed parts of the world, says Logan. These fuel cells used locally produced fuel, and their power output can be managed locally. “Microbial fuel cells [appear] destined, at least at this moment, to utilize some energy resources that are not otherwise available on an industrial scale, like sea bottom sediments, or some biomass from waste,” says Plamen Atanassov, an assistant professor of chemical engineering at the University of New Mexico. One candidate bacterium for MFCs, Rhodoferax ferrireducens, was first isolated from sediments collected in Oyster Bay, Virginia; Geobacter metallireducens was first isolated from sediments from the Potomac River.
Breakthroughs Boost Prospects
MFCs go back to the early 1900s, says Angenent. It was at a 1996 American Chemical Society meeting titled “Emerging Technologies in Hazardous Waste Management” that Korean scientists Byung Hong Kim and Doo-Hong Park first described the use of a “mediator-less biofuel cell” to treat wastewater. Breakthroughs in the last five years have suggested fresh promise for this technology.
One breakthrough was the discovery, reported in the 18 January 2002 issue of Science by Derek Lovley, a professor in the Department of Microbiology at the University of Massachusetts Amherst, that Geobacter produces electricity. That followed the discovery by German and Australian researchers, published in Bacteriology in July 1998 (issue 14), that in certain iron-reducing bacteria, the cytochromes—specialized enzymes known to transfer electrons to other proteins—span the outer cell membrane, enabling direct transfer of electrons to external metals and the creation of a circuit. This is the ultimate source of electricity in MFCs. These discoveries opened up the possibility of engineering both the bacteria and the electrodes in the MFC to improve electron transfer.
In the 23 June 2005 issue of Nature, Lovley announced the discovery of “nanowires,” literally tiny wires produced by Geobacter, which the bacterium presumably uses to transfer electrons. This discovery opened up further possibilities for electron transfer. He also published a study in the Octobe 2003 issue of Nature Biotechnology showing that Rhodoferax provides a constant flow of electrons while oxidizing glucose at 80% electron efficiency—a boon for drawing power from carbohydrates.
Still another breakthrough was the discovery, published by Park and University of Michigan molecular biologist J. Greg Zeikus in the June 2002 issue of Applied Microbiology and Biotechnology, that one could increase power output in MFCs by about sixfold by using mixed microbial communities rather than pure cultures. This is a big advantage for harvesting energy from waste-water, which is microbially diverse, says Angenent. The question of exactly why this is so is an area Angenent plans to address in future research.
The technology has also seen the benefit of engineering advances. A year ago, in unpublished research, Angenent combined the “upflow” system used in methane digesters with the MFC technology to eliminate the need for mechanical pumping and mixing. In the upflow system, wastewater is piped from above the fuel cell, down, around, and then upwards into the bottom of the anode powered by gravity—the opposite of a syphon. Thus, pumping and mixing become unnecessary.
The first microbial fuel cells produced between 1 and 40 milliwatts per square meter (mW/m2) of anode electrode surface area, says Logan. In just the past year, he says, his laboratory has generated power in the range of up to 500 mW/m2 using domestic waste-water and 1,500 mW/m2 with glucose and air. He adds that researchers in Belgium recently achieved 3,600 mW/m2 using glucose, although they needed a nonrenewable chemical instead of air for their process.
Electric versus Hydrogen
MFCs generate electricity, but can be modified to produce hydrogen instead. In both systems, the source of electricity is the chemical energy contained in the bonds of organic compounds. Bacteria, living in biofilms on the anode, break down the organics, separating electrons from protons. These electrons and protons then travel to the cathode, the former via an external wire, the latter by diffusing through the electrolyte, a substance that does not conduct electricity.
In the electricity-generating MFCs, the protons and electrons combine at the cathode with oxygen to form water. This “uses up” the electrons, allowing more to keep flowing from the anode to the cathode.
In the MFC modified to produce hydrogen, the cathode is kept free of oxygen. But in order to make hydrogen, a thermodynamic barrier must be breached. To overcome this barrier, Logan uses a power source to add voltage into the circuit.
The hydrogen MFC appears to be twice as efficient as the electricity-producing cells, says Logan, because in the latter some oxygen leaks back into the anode. However, adding the voltage in the hydrogen-producing system requires about one-sixth of the energy that is produced as hydrogen. Further losses occur if the hydrogen is converted into other forms of energy. Bottom line: in terms of efficiency for electricity as a final product, neither electricity nor hydrogen production possesses a clear advantage.
The main benefit of hydrogen-producing MFCs is that they would provide additional options to fit production to energy needs, says Logan. For example, hydrogen could be stored to make off-peak electricity or for use as a transportation fuel. “But if you just want to use electricity locally, you are probably better off making electricity to start with,” he says.
Many Technological Challenges
MFC technology is still strictly at the laboratory scale. “[It] doesn’t have its own design principals, and borrows from neighboring technologies,” says Atanassov. “It is absolutely premature to even address [questions of design].”
The cathode oxygen in electricity-producing devices creates a big challenge for MFCs. A “proton exchange membrane” separates anode from cathode, allowing protons to pass, but blocking the larger oxygen molecules from diffusing to the anode. However, some oxygen manages to cross the proton exchange membrane into the anode, where it takes electrons that would otherwise flow in the circuit, reducing the power, says Lovley.
The low power density of MFCs is also a major problem. Researchers working on MFCs measure power density in W/m2, while those working on conventional fuel cells measure power density in W/cm2, a highly illustrative disparity, says Atanassov. That low power density of MFCs means electrodes—which aren’t cheap—must be exceptionally bulky.
Power density is a function of the interface between the microbes and the electrodes, says Harold Bright, a program manager in the Office of Naval Research, which is funding studies on MFCs. “We have fairly slow electron transfer from the bacteria into the electrode.”
Scale-up for commercial uses adds to the challenges. The current laboratory-scale prototypes use materials that aren’t sturdy enough to be used in a commercial system, such as carbon paper and carbon cloth electrodes. Further, experimental MFCs, now smaller than a beer mug, would need to be as big as a mansion (in large part to compensate for the low power density), undoubtedly greatly increasing the distance between anode and cathode. That, in turn, would slow diffusion of hydrogen from the former to the latter, damping efficiency.
To be competitive with methane digester technology, MFCs’ practical predecessor, the power density must more than double the maximum achieved so far, to 8,500 mW/m2, says Angenent. And for this, he says, “another breakthrough is required.”
Advances in microbiology and electrode technology leading to higher rates of electron transfer could improve power density; bacteria could be engineered for better electron transfer. Lovley has been systematically deleting genes for outer membrane cytochromes in order to discern which cytochrome was essential for electricity production. “Now we can determine if engineering Geobacter to produce more of this cytochrome and/or modifying the electrode to better interact with the cytochrome will result in more power production,” he says.
There is ample room for improvement. “If Geobacter could transfer electrons to electrodes as fast as it can to its natural electron acceptor, ferric iron, the rate of electron flow—that is, the current—could possibly be ten thousand times higher,” says Lovley.
The use of wastes as cost-free substrates will further improve economics, says Logan. Wastes are ideal since their disposal, he says, “is already an economic burden.”
Currently, there is virtually no government funding for MFCs except for use in applications such as remote sensors, which are funded by the Navy, the Department of Energy, and the Defense Advanced Research Projects Agency. “The current laboratory systems that we build cost way too much money for the amount of electricity we get back,” Logan admits. “[But] the same was true of solar energy fifty years ago.” Now solar has become an important—if still small—contributor to the nation’s energy supply, and Logan predicts that MFCs will follow suit.
Skimming the surface.
Bruce Logan and colleagues at Penn State have begun demonstrating that MFCs can produce electricity directly from wastewater, potentially cutting both power costs and solid wastes.
Big plans for small microbes.
Jason He (left) and Lars Angenent inspect their MFC. In Derek Lovley’s lab (right), a model SUV is powered by marine geobatteries.
Microbial Fuel Cells: The Basics
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Suggested Reading
Holmes DE Nicoll JS Bond DR Lovley DR 2004 Potential role of a novel psychrotolerant member of the family Geobacteraceae , Geopsychrobacter electrodiphilus gen. nov., sp. nov., in electricity production by a marine sediment fuel cell Appl Environ Microbiol 70 6023 6030 15466546
Liu H Grot S Logan BE 2005 Electrochemically assisted microbial production of hydrogen from acetate Environ Sci Technol 39 4317 4320 15984815
Logan BE 2004 Extracting hydrogen and electricity from renewable resources [review] Environ Sci Technol 38 160A 167A
Reguera G McCarthy KD Mehta T Nicoll JS Tuominen MT Lovley DR 2005 Extracellular electron transfer via microbial nanowires Nature 435 1098 1101 15973408
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00759EnvironewsScience SelectionsCadmium and Kidneys: Low-Level Exposure and Effects in Women Barrett Julia R. 11 2005 113 11 A759 A760 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Widespread exposure to the heavy metal cadmium occurs through both natural and industry-related sources. The general population is likely to encounter low-level chronic exposure through smoking and from dietary sources, particularly shellfish, grains, and vegetables. In 1999 an ongoing population-based Swedish study, Women’s Health in the Lund Area, was expanded to include low-level cadmium exposure. Analysis of the data collected now reveals a small but significant kidney response to low-level cadmium exposure [EHP 113:1627–1631]. This suggests that low-level cadmium exposure may pose a significant public health risk.
Owing to extremely slow excretion, cadmium accumulates in the body, especially in the kidneys. Kidney damage is the primary consequence, but most toxicity data are from exposures in occupational settings or severely polluted areas. The effects of low-level exposure are less certain.
A primary function of the kidney is to filter excess water and metabolic by-products from the blood for urinary excretion. This filtration occurs in more than 1 million nephrons, each of which contains a blood capillary (the glomerulus) intertwined with a urine-collecting tubule. In the current study, researchers assessed glomerular and tubular fitness by measuring kidney function markers in blood and urine, respectively. Blood testing also revealed ongoing cadmium exposure, and urinalysis indicated cadmium body burden.
The team analyzed data, including blood and urine samples, collected from 820 women aged 54–63 years. Blood levels of creatinine and cystatin C were measured in 742 participants to calculate glomerular function. Urinary concentrations of calcium, human complex-forming protein, and N-acetyl-β-d-glucosaminidase—all markers of tubule function—were available for 813 women. The researchers additionally collected data on medications taken, smoking history, lead exposure, and incidence of diabetes and hypertension to control for potential confounding factors.
Cadmium concentrations were similar or slightly higher compared with previous data from Sweden and much lower than concentrations reported for populations in highly polluted areas in Europe and Japan. Current or former smokers had cadmium concentrations that were 90% higher in blood and 40% higher in urine than concentrations measured in participants who never smoked. Consequently, multivariate analyses were conducted on data from all participants as one group and from those who had never smoked as another group.
Cadmium concentrations were positively associated with the tubular function markers, indicating some damage to the tubules. Increased cadmium was also associated with decreased creatinine clearance, reflecting a reduced glomerular filtration rate. The lowest-observed-effect level for increased tubular markers was a mean urinary cadmium concentration of 0.6 microgram per liter, which is lower than previously reported. A reduction in glomerular filtration rate was associated with a minimum mean urinary cadmium concentration of 0.86 microgram per liter.
The researchers speculate that effect levels might be even lower for people with diabetes, a disease carrying high risk of kidney damage similar to that caused by cadmium exposure. Although the effects of low-level cadmium exposure are clinically minor, they should be viewed as early indicators of potential severe health effects, according to the researchers. Given the size of the exposed population, there may be a significant public health risk, and efforts beyond smoking cessation programs are needed to reduce exposure.
Cadmium connection.
A new study shows that kidneys respond to even low-level chronic cadmium exposure such as that obtained from smoking and eating grains.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00760EnvironewsScience SelectionsIndoor Air Complaints: VOCs May Not Be Cause of Acute Effects Weinhold Bob 11 2005 113 11 A760 A760 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Over the past few decades, researchers have been trying to pin down the specific chemical culprits behind increasing complaints of poor air quality inside offices and other buildings. Among the many chemicals suspected so far have been volatile organic compounds (VOCs) and ozone, prominent pollutants in indoor environments. But VOCs alone, or in combination with ozone, may not be the prime source of acute health problems, says a team of New Jersey investigators [EHP 113:1542–1548]. Instead, they found that psychological stress was a more salient factor, but they acknowledge that a number of limitations in their study preclude applying this finding to all indoor air complaints.
The study investigated the short-term acute health effects of exposure to ozone, a mixture of 23 VOCs, and stress. The research was conducted in a controlled chamber into which either a relatively high level of the VOC mixture (26 milligrams per cubic meter), the VOCs plus moderate concentrations of ozone (40 parts per billion), or clean air with a low one-minute spike of VOCs (about 2.5 milligrams per cubic meter) was introduced. In the middle of each three-hour test session half of the volunteer subjects had to make a four-minute speech on a controversial subject as a stress test, while the other half performed simple arithmetic problems. The test sessions were held one week apart.
The researchers evaluated stress by measuring cortisol secretions in saliva. To assess health effects, they evaluated selected performance measures, as well as 33 observed and self-reported physical and behavioral indicators, such as headache, nausea, eye irritation, nervousness, and leg cramps.
They found the challenge of public speaking induced a significant increase in the subjects’ measures of stress. However, even with that increase in stress, no significant increase in health symptoms or reduction in neurobehavioral performance was linked to the exposures to VOCs either alone or combined with ozone, despite sharp increases in many secondary pollutants resulting when ozone was added to the VOC mixture.
The 130 female volunteers exposed to each air mixture constituted the largest group evaluated in a study of this kind, and the researchers determined the numbers were of sufficient power to produce significant findings. However, all the subjects were healthy, young (mean age 27.2 years), and well educated (mean education of 15.2 years), demographically limiting the applicability of the findings.
In addition, the team acknowledges that its testing, while extensive, didn’t represent many aspects of a typical office building. For instance, the test chamber did not include carpet, many office furniture materials, and other normal interior accoutrements that might interact with VOCs and ozone. The mix of VOCs, although extensive, likely didn’t represent the mix in many buildings. Further, the ventilation rate in the test chamber was substantially higher than in many buildings at which complaints have been lodged.
Further, the public speaking challenge, although successful at inducing stress, wasn’t representative of the multiple complex stressors experienced in a typical work day. And the testing period was very short, providing no information on the potential chronic effects that may be induced by longer-term exposures to chemicals and stress. Nonetheless, the findings are helpful in pinning down the relative contribution, or lack thereof, of certain mixtures and concentrations of VOCs and ozone to poor indoor air quality.
Maybe it’s nerves.
A study of indoor air pollutants and stress (induced by public speaking) shows that stress, not VOCs, may be a larger culprit behind sick building complaints.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00761AnnouncementsNIEHS Extramural UpdateDevelopment of Medical Countermeasures to Chemical Terrorism—The NIEHS’s Involvement in a Government-Wide Research Effort 11 2005 113 11 A761 A761 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The attacks of September 11, 2001, using airplanes, followed closely by the biologic attacks using anthrax spores placed in letters, have illustrated how vulnerable our society is to such acts of terrorism. Since these events, the NIH has led the national research effort focused on the development of medical countermeasures to treat civilian mass casualties. The NIH has received more than $1.5 billion annually, starting in FY 2002, to fund research focused primarily on development of products to diagnose, treat, or prevent bacterial and viral infectious diseases and toxemias that could result from bioterrorist attacks. These initial efforts, focused on infectious agents, have been led by the National Institute of Allergy and Infectious Diseases. In FY 2005, the NIH was given an additional $47.1 million to establish a radiation and nuclear medical countermeasures program to identify products to treat civilians injured by a dirty bomb or nuclear attack. Similar plans are under way to initiate research in FY 2006 to develop products to diagnose and treat victims of a chemical attack.
As part of the planning for an expanded effort related to chemical agents, the NIEHS and the National Institute on Neurological Diseases and Stroke co-sponsored, in FY 2005, an administrative supplement program to existing grants. The intent of these supplements is to develop improved detection, diagnosis, and treatment strategies for likely chemical threat agents. As a result of this program, NIEHS has funded six supplements for a total of $450,000. The NIEHS supplements went to:
» Paul Bishop and Joseph Caruso, University of Cincinnati, to work on increasing the sensitivity of methods they have developed for detecting hydrolysis products of nerve agents and to extend their analysis to spiked food and water samples
» Brian Day, National Jewish Medical and Research Center, to test the ability of lipoic acid, dihydrolipoic acid, and thioredoxin to reverse or prevent pulmonary injury caused by sulfur mustard
» Richard DiGiulio and Ted Slotkin, Duke University, to study the developmental neurotoxicity of nerve agents and to assess some possible protective treatment strategies
» Clem Furlong, University of Washington, to attempt to increase the activity of human PON1 enzyme to levels sufficient to protect against paraoxon exposure in a mouse model. If successful, he will test the modified enzyme’s ability to protect against exposure to sarin, soman, and VX agents in the same mouse model.
» Bruce Hammock and Ian Kennedy, University of California, Davis, to work on the development of miniaturized sensors for use in detecting botulinum toxin, ricin, and abrin
» Cary Pope, Oklahoma State University, to study the differential toxicity of the organophosphorus pesticides chlorpyrifos and parathion, as well as the threat agents sarin and soman, and their inhibition of acetyl-cholinesterase and the accumulation of acetylcholine in a cannulated rat brain model
This administrative supplement program for selected grant mechanisms funded by NIEHS has just been reannounced for FY 2006 in the NIH Guide (http://grants.nih.gov/grants/guide/notice-files/NOT-ES-06-001.html).
Contact
Dennis Lang, PhD | [email protected]
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00762AnnouncementsFellowships, Grants, & AwardsFellowships, Grants, & Awards 11 2005 113 11 A762 A763 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The Environmental Fellows Program at Harvard University
The University Center for the Environment has created the Environmental Fellows program to enable recent doctorate recipients to use and expand Harvard’s extraordinary resources to tackle complex environmental problems. The Environmental Fellows will work for two years with Harvard faculty members in any school or department to create new knowledge while also strengthening connections across the university’s academic disciplines.
The fellowships will be awarded on a competitive basis. Candidates will propose a research program and secure a commitment from one or more Harvard faculty members to host the candidate’s work. Candidates should have received their terminal degree between May 2001 and September 2006. (Fellows must have completed all requirements of their degree before starting work in September 2006.) Candidates with a doctorate or equivalent in any field are eligible, and they may propose research projects in any discipline. Candidates who received terminal degrees from Harvard and postdocs currently working at Harvard are eligible for the fellowship, provided their research and host arrangements take them in new directions and forge new connections within the University.
The fellowship will provide an annual stipend of $50,000 plus health insurance, other benefits, and a $5,000 allowance for travel and professional expenses.
The Harvard University Center for the Environment expects to award up to eight fellowships in 2006 and an average of six per year thereafter. The center will organize a co-curricular program to ensure that the fellows get to know each other and each other’s work. All fellows are required to attend biweekly dinners with other fellows, faculty members, and guests.
The center encourages research and education about the environment and its many interactions with human society. The center draws its strength from faculty members and students across the university who make up a remarkable intellectual community of scholars, researchers, teachers, and practitioners of diverse fields. The center’s mission is to strengthen and expand that community by supporting research, encouraging faculty and students to apply their particular expertise to environmental topics, and providing a convivial space for collaboration. The center is located in the University’s Geological Museum at 24 Oxford Street, Cambridge.
Selection criteria: 1) Applicant’s prior success and potential contribution to scholarship or practice. 2) Project significance: the potential impact of the research project on scholarship at Harvard and on environmental problems. 3) Diversity: The committee will select a group of fellows from a range of academic disciplines whose work will focus on a variety of topics. Recipients and hosts may include people with degrees in the sciences, economics, law, government, public policy, public health, medicine, design, and the full array of humanities. Their research topics will be equally varied. Interdisciplinary research projects are encouraged, although this is not a requirement for the fellowship. Candidates with interests in a single discipline are encouraged to apply. 4) Host’s commitment: the host faculty member’s enthusiasm for the proposed project, his or her ability to mentor the fellow, and his or her ability to provide office space and a productive work environment.
Potential candidates should start early to identify and establish a relationship with a Harvard faculty member to host his or her research. The host will be a mentor to the fellow and will provide office space and basic administrative support. The host may not be the candidate’s thesis adviser. The host must, however, submit a letter of support (maximum of two pages) to the selection committee describing in detail the level of commitment to the research and the candidate.
Applicants unfamiliar with Harvard faculty members will find many of them listed on the center’s web pages organized both by academic areas (economics, engineering) and by research topics (climate, human health). Most faculty members have their own web pages which will provide much more detailed information about publications and interests. Applicants are encouraged to use the center’s faculty lists as a starting point. Any faculty member from any discipline can serve as a host, regardless of whether the host has had prior experience with environmental research.
Applicants, referees, and hosts may e-mail all portions of the application to the center, attaching all documents to the e-mail as PDFs or Word files. Referees and hosts should e-mail or mail their letters directly to the center.
A complete application includes 1) a cover sheet (see below); 2) a detailed research proposal (a maximum of 5 pages, including illustrations; 12-point type); 3) a letter of support from the applicant’s host committing to serve as a mentor and explaining his or her commitment to the proposed research, including the provision of office space and any financial commitments; 4) curriculum vitae including list of publications; 5) letters of reference from at least three professional colleagues, including the applicant’s dissertation adviser.
Submit applications and letters of reference by e-mail to the Harvard University Center for the Environment by January 15, 2006. The center will announce its selections by March 1, 2006. Fellows must start work the following September. For information about application requirements contact Richard A. Minard, Jr., Harvard University Center for the Environment, 24 Oxford Street, 3rd Floor, Cambridge, MA 02138 USA, 617-495-0368, e-mail:
[email protected].
Established Investigator Award in Cancer Prevention & Control
The objective of the NCI Established Investigator Award in Cancer Prevention and Control (K05) is to provide qualified researchers with protected time to devote to research and mentoring. The award is designed for established scientists who have already demonstrated a sustained, high level of research and mentoring productivity and who need K05 support to continue these activities. The award provides partial salary support for up to 5 years and for up to 50 percent effort. It is renewable for one additional 5-year period. Examples of cancer prevention and control research and mentoring activities supported by this funding opportunity include, but are not limited to, the following areas: 1) the identification of modifiable risk factors for cancer, such as nutrient intake, exercise, exposure to carcinogens, or behavioral lifestyle factors; 2) molecular epidemiology, to identify allelic variants in genes in relation to cancer incidence or course; 3) studies of interactions of genetic and endogenous factors (e.g., hormonal milieu) with exogenous risk factors as related to cancer incidence and course; 4) the identification of community structural and social variables (e.g., location of health care facilities, access to health care, culturally conditioned attitudes affecting health behaviors) that are barriers to or facilitators of cancer prevention and control efforts; 5) studies of the above factors in relation to health disparities in cancer incidence and outcomes; 6) identification of biomarkers and clinical/screening studies of their utility as predictors of cancer risk and outcome; 7) behavioral research to identify cognitive or motivational attributes that affect the individual's acceptance of screening guidelines or treatments and propensity to engage in health-promoting or cancer risk-reducing behaviors; 8) the development of preventive interventions to decrease cancer risk behaviors and/or increase health-promoting behaviors; 9) chemoprevention from studies of the identification and early-phase characterization of candidate agents to preventive intervention trials; 10) studies of nutritional supplements or complementary/alternative interventions in relation to cancer prevention and control; 11) health services research, including patient outcome studies, practice research, and medical decision analyses related to cancer prevention or to cancer care and patient outcomes; 12) palliative care studies, including interventions to improve quality of life; 13) survivorship research, including studies of outcomes and quality of life as related to cancer course, treatments, and treatment side effects; and 14) studies of the effectiveness of cancer health communications in reducing high risk behaviors or in increasing participation in screening activities.
This funding opportunity will use the K05 award mechanism. The Established Investigator Award in Cancer Prevention & Control is a special NCI modification of the NIH Senior Scientist Award or K05 grant mechanism. In addition, the institution must demonstrate a commitment to the candidate and the candidate's goals for research, career development, and mentoring.
This funding opportunity uses the just-in-time budget concepts. It also uses the nonmodular budget format described in the PHS 398 application instructions (see http://grants.nih.gov/grants/funding/phs398/phs398.html). The applicant should follow the PHS 398 instructions for budget information, providing only the total direct costs requested for each year and for the entire proposed period of support, and provide budget justification information. For further assistance contact GrantsInfo, 301-435-0714, (telecommunications for the hearing impaired: TTY 301-451-0088) or by e-mail:
[email protected].
Applications must be prepared using the most current PHS 398 research grant application instructions and forms. Applications must have a Dun & Bradstreet (D&B) Data Universal Numbering System number as the universal identifier when applying for Federal grants or cooperative agreements. The D&B number can be obtained by calling 866-705-5711 or through the web site at http://www.dnb.com/us/. The D&B number should be entered on line 11 of the face page of the PHS 398 form.
The deadline for receipt of applications is July 2, 2008. The full PA is available at http://grants.nih.gov/grants/guide/pa-files/PAR-05-145.html
Contact: Mary C. Blehar, Cancer Training Branch, National Cancer Institute, 6116 Executive Boulevard, Suite 7019, MSC 8346, Bethesda, MD 20892-8346 USA, 301-496-8580, fax: 301-402-4472, e-mail:
[email protected].
Reference PAR-03-149
Fall 2006 Greater Research Opportunities (GRO) Undergraduate Student Fellowships
The U.S. Environmental Protection Agency (EPA), as part of its Greater Research Opportunities (GRO) program, is offering Undergraduate Fellowships for bachelor level students in environmentally related fields of study. The deadline for receipt of preapplications is November 3, 2005. Subject to availability of funding, the agency plans to award approximately 15 new fellowships by July 21, 2006. Eligible students will receive support for their junior and senior years of undergraduate study and for an internship at an EPA facility during the summer between their junior and senior years. The fellowship provides up to $17,000 per year of academic support and up to $7,500 of internship support for a three-month summer period.
The GRO Undergraduate Fellowship program, like its predecessor (the Minority Academic Institution or MAI program), is intended to strengthen the environmental research capacity of institutions of higher education that receive limited funding to build such capacity, including in particular institutions with substantial minority enrollment. The program supports quality environmental education for undergraduate students, thereby encouraging them to pursue careers in environmentally related fields and to continue their education beyond the baccalaureate level. This goal is consistent with the mission of EPA, which is to provide leadership in the nation’s environmental science, research, education, assessment, restoration, and preservation efforts. This program will benefit both the public and private sectors which will need a steady stream of well-trained and diverse environmental specialists if our society is to meet the environmental challenges of the future.
It is anticipated that a total of approximately $622,500 will be awarded under this announcement, depending on the availability of funds. The EPA anticipates funding approximately 15 fellowships under this RFA. The projected award per fellowship is $17,000 per year total costs, for up to 2 years. The EPA reserves the right to reject all preapplications and make no awards under this RFA. The EPA reserves the right to make additional awards under this RFA if additional funding becomes available. Any additional selections for awards will be made no later than 4 months after the original selection.
You may submit either a paper or an electronic preapplication (but not both) for this announcement. For paper preapplications, forms can be found on the NCER web site: http://es.epa.gov/ncer/rfa/forms/. For electronic preapplications, use the preapplication package available at https://apply.grants.gov/forms_ apps_idx.html (see "Submission Instructions for Electronic Pre-Applications").
Contact: Georgette Boddie, 202-343-9741, e-mail:
[email protected].
The Obese and Diabetic Intrauterine Environment: Long-Term Metabolic or Cardiovascular Consequences in the Offspring
It has long been recognized that the intrauterine environment can have profound effects on the development and health of the fetus. Alterations in maternal nutritional status resulting from caloric or protein restrictions as well as specific micronutrient deficiencies have been shown to impact fetal development. Maternal undernutrition can lead to intrauterine growth retardation and has been shown to increase the risk of metabolic disorders such as diabetes, hypertension, and cardiovascular disease in the offspring.
Emerging evidence suggests that maternal over-nutrition may have similar long-term metabolic consequences in the offspring as those seen with undernutrition. Recent animal studies suggest that the pups of obese dams develop obesity and gain more weight than offspring of normal-weight rats. In rodents, maternal high-fat or cholesterol over-feeding during pregnancy results in offspring with elevated risk factors for cardiovascular disease such as increased blood pressure, abnormal lipid profiles and abnormal glucose homeostasis. Fat-rich diets have also been shown to produce endothelial dysfunction in the offspring. In humans, the reported increases in prepregnancy body mass index and increased weight gain during pregnancy, particularly in obese women, are associated with increases in neonatal weight and body adiposity. Furthermore, several studies have shown that fetuses and children born of hypercholesterolemic mothers have an increased incidence of fatty streaks in the aorta. Recent studies in the Pima Indian Population indicate that intrauterine exposure to diabetes significantly increases systolic blood pressure (SBP) and hemoglobin A1c (HbA1c) during childhood. Evidence also exists that increased birth weight is positively correlated with subsequent risk of cancer. As infant and childhood obesity can predict obesity in the adult, these data suggest that maternal obesity may exacerbate an already alarming incidence of obesity and potentially Type II diabetes in the general population. Understanding the mechanisms by which the obese and diabetic maternal intrauterine environment elicits permanent metabolic and cardiovascular disease in the fetus will provide a basis for future interventional studies in humans.
Maternal insulin resistance as observed in obese women and women with gestational diabetes has been associated with increased fetal fat mass. Leptin, elevated in obesity, has been shown to alter placental gene transcription and cell proliferation and it is possible that other cytokines, also increased in obesity, may play a role as well. The adipose tissue also secretes factors that are implicated in inflammatory processes, blood pressure, coagulation and fibrinolysis that can influence the development of cardiovascular disease. In addition, islet cell or adipocyte development and morphology may be altered. However, the exact mechanisms by which maternal obesity increases the risk of metabolic and cardiovascular disease in the offspring have not been established. Potentially permanent changes may be occurring at the genetic, cellular, or tissue level, either peripherally or at the level of the central nervous system.
Neural pathways regulating food intake, body weight, and the cardiovascular system may be particularly vulnerable to the metabolic status of the obese or diabetic mother. Leptin, through direct central effects, can affect the sympathetic nervous system and lead to hypertension and/or heart disorders. Recent studies demonstrate that leptin exerts a trophic effect on hypothalamic neurons and that the pathways involved in feeding regulation exhibit extensive neuroplasticity in response to metabolic perturbations, even during the postnatal period. However, the role of postnatal nutrition in the development of metabolic, cardiovascular disease, or cancer in the offspring of obese mothers is not clear. Maternal under-nutrition in both animal and population-based studies indicate that the detrimental effects are primarily manifested when postnatal nutrition is excessive relative to the intrauterine environment but therapeutic windows with respect to the consequences of maternal obesity have not been identified or defined.
The objective of this RFA is to support mechanistic research investigating the effects of maternal obesity, gestational diabetes, or diabetes on the development of obesity, and other metabolic and cardiovascular diseases, or cancer in the offspring. This is a new and burgeoning area with important clinical implications. Thus, the applications solicited by this RFA should not only elucidate factors involved in the etiology of obesity in the offspring of obese mothers but in addition, should provide the scientific basis whereby future prevention and intervention studies in humans can be developed. Proposed studies in murine, rat, and large animal models, like sheep and nonhuman primates, should be focused on identifying the potential mechanisms mediating the proposed long-term consequences of maternal obesity or diabetes on the offspring. Outcome variables should not be limited solely to the weight of the offspring but should also include specific measurement of biological endpoints in appropriate tissues such as the brain, adipocyte, heart, and vasculature, placenta, endothelium, mammary gland or pancreatic islet. Pilot studies demonstrating the feasibility of conducting ethical and appropriate studies in humans will also be considered. Studies conducted in nonhuman primates and where possible, in humans, are encouraged.
Potential research topics include, but are not limited to: 1) Defining critical periods of plasticity and/or susceptibility to metabolic perturbations in the maternal environment for neural pathways involved in the regulation of food intake, motivation, body adiposity, and the cardiovascular system in the rodent and non-human primates. 2) Development of appropriate animal models to facilitate determination of the relative roles of the genetic, maternal in utero environment, and postnatal environment. 3) Brain imaging studies to study development of neural pathways involved in regulation of food intake and motivational pathways associated with food intake in humans and nonhuman primates. 4) Imaging of the adipocyte, heart and vasculature, placenta, pancreatic b-cell, body adiposity, or body composition of the offspring to determine the effects of maternal obesity and diabetes. 5) Mechanistic investigations of the role of stress at a cellular or systemic level as a mediator of the long-term consequences of maternal obesity and diabetes. 6) Mechanistic investigations on the effect of maternal obesity and diabetes on factors that influence the development of cardiovascular disease in the offspring. Examples include lipid metabolism, inflammation, vascular reactivity, hypertension, endothelial dysfunction, endocrine systems, and hormones. 7) Mechanistic investigations on the effect of maternal obesity and diabetes on factors that influence the development of cancer in the offspring. 8) Investigation of the role of epigenetics as a mechanism mediating the effects of maternal obesity and diabetes on development of metabolic, cancer, or cardiovascular disease in the fetus.
This funding opportunity will use the R01 and R21 award mechanisms. This funding opportunity uses just-in-time concepts. It also uses the modular as well as the nonmodular budget formats (see http://grants.nih.gov/grants/funding/modular/modular.htm). Specifically, if you are submitting an application with direct costs in each year of $250,000 or less, use the modular budget format described in the PHS 398 application instructions, available at http://grants.nih.gov/grants/funding/phs398/phs398.html. Otherwise, follow the instructions for nonmodular research grant applications. For further assistance contact GrantsInfo, 301-435-0714 (telecommunications for the hearing impaired: TTY 301-451-0088) or by e-mail:
[email protected].
Applications must be prepared using the most current PHS 398 research grant application instructions and forms. Applications must have a D&B Data Universal Numbering System (DUNS) number as the universal identifier when applying for federal grants or cooperative agreements. The D&B number can be obtained by calling 866-705-5711 or through the web site at http://www.dnb.com/us/. The D&B number should be entered on line 11 of the face page of the PHS 398 form.
The deadline for receipt of letters of intent is February 16, 2006, with March 16, 2006 the deadline for receipt of applications. The complete version of this RFA is available at http://grants.nih.gov/grants/guide/rfa-files/RFA-DK-05-014.html.
Contact: Karen Teff, Division of Diabetes, Endocrinology and Metabolic Diseases, National Institute of Diabetes, Digestive and Kidney Disease Democracy II, Room 734, 6707 Democracy Boulevard, Bethesda, MD 20892 USA, 301-451-7335, fax: 301-480-0475, e-mail:
[email protected]; Cristina Rabadan-Diehl, Division of Heart and Vascular Diseases, National Heart, Lung, and Blood Institute, Rockledge II, Room 10186, 6701 Rockledge Drive, Bethesda, MD 20892 USA, 301-435-0550, fax: 301-480-2858, e-mail:
[email protected]; Cindy Davis, Division of Cancer Prevention, National Cancer Institute, 6130 Executive Boulevard, EPN Room 3159, Bethesda, MD 20892 USA, Rockville, MD 20852 USA (express/courier service), 301-594-9692, fax: 301-480-3925, e-mail:
[email protected]. Reference RFA-DK-05-014.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0727a16263491PerspectivesCorrespondenceELF MFs: Straif et al. Respond Straif Kurt Cardis Elisabeth Boffetta Paolo International Agency for Research on Cancer, Lyon, FranceRousseau Marie-Claude INRS-Institut Armand-Frappier, Université du Québec, Laval, Québec, CanadaSiemiatycki Jack Université de Montréal, Montréal, Québec, Canada, E-mail:
[email protected] authors declare they have no competing financial interests.
11 2005 113 11 A727 A727 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Mild et al. suggest that we should have included magnetic fields at extremely low frequencies (ELF MFs) in our listing of occupational carcinogens (Siemiatycki et al. 2004). We acknowledge that ELF MFs have been classified as “possibly carcinogenic to humans” (Group 2B) by the Monographs Programme of the International Agency for Research on Cancer (IARC 2002) and that there is significant occupational exposure, thereby meeting our operational criterion for inclusion as a possible occupational carcinogen. However, the nature of the evidence that led to the IARC classification complicates the designation of ELF MFs as an occupational carcinogen.
For our article (Siemiatycki et al. 2004), we drew on the evaluations of the IARC Monographs Programme. Each evaluation was based on data that were available at the time of the deliberations of the working group. We supplemented the evaluation by adding information on major occupational exposure circumstances and on the cancer sites affected. For some carcinogens, notably those evaluated recently, such information was explicitly mentioned in the published monograph, but for others it was based on our expert judgment.
For ELF MFs, the IARC evaluation of “possibly carcinogenic” was founded on a determination that there was limited evidence of carcinogenicity in humans based on its effects on childhood leukemia and “inadequate evidence” in experimental animals (IARC 2002). In contrast with an earlier evaluation [National Institute of Environmental Health Sciences (NIEHS) 1998], the IARC Working Group considered that studies conducted among adults, at work or elsewhere, did not provide consistent enough and strong enough evidence to support an evaluation of carcinogenicity. There is no clear-cut way to classify an exposure that has only been demonstrated to be carcinogenic (albeit group 2B) in children, but also occurs among workers. Although we decided not to include ELF MFs in our tables of occupational carcinogens (Siemiatycki et al. 2004), we could have done so with a footnote to explain that the evidence supporting that evaluation was based on children.
Mild et al. also discuss the evidence on the carcinogenic effects of ELF MFs that has arisen since 2002. Although we agree that some of these studies may substantially contribute to an evaluation of the carcinogenic effects of ELF MFs, it was not in the scope of our work to evaluate new information and update the evaluations on all of the agents reviewed. The World Health Organization (WHO) will be holding a meeting of an Environmental Health Criteria Task Group in October 2005; this task group will evaluate the health effects of ELF MFs (including cancer and noncancer outcomes). We anticipate that they will review the recent evidence in conjunction with the evaluation of the 2002 IARC Monograph. The WHO Environmental Health Criteria document on ELF MFs should be published shortly after this meeting.
==== Refs
References
IARC 2002 Non-ionizing Radiation, Part 1: Static and Extremely Low-Frequency Electric and Magnetic Fields IARC Monogr Eval Carcinog Risk Hum 80
NIEHS 1998. Assessment of Health Effects from Exposure to Power-Line Frequency Electric and Magnetic Fields. Working Group Report (Portier C, Wolfe M, eds). NIH publication no. 98-3981. Research Triangle Park, NC:National Institute of Environmental Health Sciences. Available: http://www.niehs.nih.gov/emfrapid/html/WGReport/WorkingGroup.html [accessed 6 October 2005].
Siemiatycki J Richardson L Straif K Latreille B Lakhani R Campbell S 2004 Listing occupational carcinogens Environ Health Perspect 112 1447 1459 15531427
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0727b16263491PerspectivesCorrespondenceThe NAS Perchlorate Review: Second-Guessing the Experts Gibbs John P. Kerr-McGee Shared Services LLC, Oklahoma City, Oklahoma, E-mail:
[email protected] Arnold Lamm Steven H. Consultants in Epidemiology & Occupational Health, LLC, Washington, DCKerr-McGee Chemical LLC previously manufactured ammonium perchlorate at Henderson, Nevada, and currently is remediating perchlorate at that site. J.G. has co-authored several perchlorate studies, as has S.H.L. S.H.L. is a consultant to companies that manufacture and/or use perchlorates. A.E. works with S.H.L. at Consultants in Epidemiology & Occupational Health, LLC.
11 2005 113 11 A727 A728 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
The Committee to Assess the Health Implications of Perchlorate Ingestion [National Academy of Sciences (NAS)] released its final report [National Research Council (NRC) 2005] in January 2005, recommending a reference dose (RfD) for perchlorate of 0.0007 mg/kg-day. In a commentary published online on 25 May 2005, Ginsberg and Rice (2005) criticized the adequacy of the NAS committee’s scientific deliberations, mischaracterizing the studies reviewed by the committee and second-guessing its conclusions. Ginsberg and Rice (2005) implied that the U.S. Environmental Protections Agency’s (EPA’s) previous draft RfD of 0.00003 mg/kg-day (U. S. EPA 2002)—and by inference the Massachusetts perchlorate risk assessment [Massachusetts Department of Environmental Protection (Mass DEP) 2004] that mirrored the U.S. EPA’s approach and which Ginsberg and Rice peer reviewed—is more scientifically defensible.
The NAS committee was composed of 15 leading physicians and scientists with combined range of expertise to evaluate every scientific aspect of the perchlorate database and of the U.S. EPA’s assessment of that database. The makeup of this committee and its credentials are available on the NAS website (NAS 2004). The NAS committee studied and deliberated for more than 15 months before issuing its report. Those deliberations included three public meetings during which it accepted verbal and/or written comments from the U.S. EPA, other government agencies, industry, states, environmental groups, and attorneys. After careful study and consideration of the scientific studies that formed the basis for the U.S. EPA’s 2002 draft RfD as well as the 2004 Massachusetts risk assessment (Mass DEP 2004), the NAS committee considered
several of the animal studies … to be flawed in their design and execution. Conclusions based on those studies, particularly the neurodevelopmental studies, were not supported by the results of the studies.
Although Ginsberg and Rice (2005) implied that the NAS committee should have considered the threshold for measurable iodine uptake inhibition “adverse” and that the NAS inadvertently left out the “A” in NOAEL (no observed adverse effect level), the committee decisively stated that “inhibition of iodide uptake by the thyroid clearly is not an adverse effect.” The committee carefully considered the issue of a NOEL (no observed effect level) and a NOAEL. Based on a clinical study of patients receiving perchlorate long term, the NAS established the NOAEL as 0.4 mg/kg-day (57 times higher than its identified NOEL).
Ginsberg and Rice (2005) further expressed concerns regarding perchlorate in breast milk and the subsequent possibility of decreased breast milk iodine, citing Kirk et al. (2005) and Gibbs (2004). Kirk et al. (2005) reported perchlorate and iodide levels in breast milk samples and noted that “if we take all the available data, there is no meaningful correlation between the perchlorate and iodide levels in breast milk.” The study from Chile that Ginsberg and Rice refer to as Gibbs (2004) is now published as Tellez et al. (2005). The study found that iodine nutrition of pregnant women in Chile is very similar to that in the United States. Tellez et al. (2005) found no maternal or neonatal perchlorate-related thyroid effects or decreases in breast milk iodine with perchlorate doses spanning the 0.0007–0.007 mg/kg-day range.
Ginsberg and Rice (2005) argued that perchlorate database deficiencies require an additional uncertainty factor of 3–10 because of key data gaps, citing breast milk concerns and the extrapolation from a 14-day exposure study to chronic exposure. The NAS committee (NRC 2005) considered this and concluded that
if inhibition of iodide uptake by the thyroid is duration-dependent, the effect should decrease rather than increase with time, because compensation would increase the activity of the sodium-iodide symporter and therefore increase iodide transport into the thyroid.
Evidence has subsequently shown this to be the case (Braverman et al. 2005).
The California EPA perchlorate risk assessment (California EPA 2004) relied on the same studies as the NRC report (NRC 2005). The “point of departure” was based on iodine uptake inhibition by Greer et al. (2002), and a total uncertainty factor of 10 was applied to account for interindividual variability. After reviewing the NRC report (NRC 2005), the California EPA elected not to change its risk assessment or public health goal (California EPA 2005).
In summary, the concerns presented by Ginsberg and Rice (2005) have already been addressed thoroughly by experts on perchlorate and thyroid toxicology and were found to be unsubstantiated. The NAS committee and other experts came to this conclusion based on a comprehensive review of the science in the field, not based entirely on an individual study, which has been mischaracterized by Ginsberg and Rice.
==== Refs
References
Braverman LE He X Pino S Cross M Magnani B Lamm SH 2005 The effect of perchlorate, thiocyanate, and nitrate on thyroid function in workers exposed to perchlorate long-term J Clin Endocrinol Metab 90 2 700 706 15572417
California EPA (Environmental Protection Agency) 2004. Public Health Goals for Perchlorate in Drinking Water. Available: http://www.oehha.org/water/phg/pdf/finalperchlorate31204.pdf [accessed 21 June 2005].
California EPA (Environmental Protection Agency) 2005. State’s Drinking Water Goal For Perchlorate Consistent With Findings of Major Federal Study. Available: http://www.oehha.ca.gov/public_info/press/perchloratepress-rel040105.pdf [accessed 21 June 2005].
Gibbs JP 2004. Chronic Environmental Exposure to Perchlorate in Drinking Water and Thyroid Function during Pregnancy and the Neonatal Period. 8 August 2004 Update. Letter to Richard Johnston, Chair NAS Perchlorate Committee, from John P. Gibbs, Kerr-McGee Corp.
Ginsberg G Rice D 2005 The NAS perchlorate review: questions remain about the perchlorate RfD Environ Health Perspect 113 1117 1119 10.1289/ehp.8254 16140613
Greer MA Goodman G Pleus RC Greer SE 2002 Health effects assessment for environmental perchlorate contamination: the dose response for inhibition of thyroidal radioiodine uptake in humans Environ Health Perspect 110 927 937 12204829
Kirk AB Martinelango PK Tian K Dutt A Smith EE Dasgupta PK 2005 Perchlorate and iodide in dairy and breast milk Environ Sci Technol 39 2011 2017 15871231
Mass DEP (Massachusetts Department of Environmental Protection) 2004 Final Draft. Perchlorate Toxicological Profile and Health Assessment. Available: http://www.mass.gov/dep/ors/files/perchlor.pdf [accessed 22 June 2005].
NAS (National Academy of Sciences) 2004. Committee to Assess the Health Implications of Perchlorate Ingestion. Available: http://www4.nas.edu/cp.nsf/Projects%20_by%20_PIN/BEST-K-03-05-A?OpenDocument [accessed 21 June 2005].
NRC (National Research Council) 2005. Health Implications of Perchlorate Ingestion. Washington, DC:National Academies Press.
Tellez RT Michaud P Reyes C Blount BC Van Landingham CB Crump KS 2005 Long-term environmental exposure to perchlorate through drinking water and thyroid function during pregnancy and the neonatal period Thyroid 15 9 963 975 16187904
U.S. EPA 2002. Perchlorate Environmental Contamination: Toxicological Review and Risk Characterization. External Review Draft. NCEA-1-0503. Washington, DC:National Center for Environmental Assessment, Office of Research and Development.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0735a16276647EnvironewsForumRadiation: Any Dose Is Too High Davidson Sarah Todd 11 2005 113 11 A735 A735 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Any exposure to radiation may cause cell damage that could lead to cancer, according to a June 2005 report from the National Research Council. The risk noted by the report, though small, is a third higher than the risk of 8.46 cancers per 10,000 people exposed to 1 rem (or 10 millisieverts [mSv]) currently used by U.S. regulators. The report contradicts critics who believe there is a threshold below which radiation is harmless; it also fails to support those who say low doses of radiation cause greater health damage per unit dose than high levels.
The seventh Biological Effects of Ionizing Radiation (BEIR) report, sponsored by several federal agencies, assessed and updated the health risks from low linear energy transfer (low-LET) radiation, which deposits little energy in a cell and thus tends to cause little damage. The last BEIR report that addressed these health risks was published in 1990.
Richard Monson, a professor of epidemiology at the Harvard School of Public Health and chair of the group that conducted the study, says, “We judged that the most reasonable shape is a line through the origin.” Simply put, this means any low-LET ionizing radiation may increase the risk of a cell becoming cancerous—there is no threshold below which there is no risk—and as exposure increases, so does the health risk. Researchers refer to this straight line as the linear-no-threshold model.
Less than 20% of people’s low-level radiation exposure comes from anthropogenic sources. The Earth and cosmic sources emit the remainder. Nearly 80% of human-induced exposure comes from medical procedures, about 15% from products like tobacco and building materials, and around 5% from exposure at work.
For the purpose of the BEIR VII report, the authoring committee defined low-LET radiation as levels up to about 100 mSv. For comparison, a chest X ray averages around 0.1 mSv. The committee concluded it’s likely that about 1 out of 100 people would develop a tumor or leukemia from exposure to 100 mSv above background. Of that same 100 people, experts would expect 42 to develop cancers for other reasons, but at the press conference marking the release of the report, the committee said it did not fully exclude the possibility of some radiation exposure being a factor in those cases.
The BEIR VII report employed statistical data to draw its conclusions and reviewed studies of people exposed at work and in medical settings. It also relied heavily on data from the Japanese atomic bomb survivors.
As these survivors age, more is revealed about the relationship between radiation exposure and eventual health outcomes. Investigators have also improved their estimate of the levels of exposure this population received. But critics question the heavy reliance on the Japanese survivors because of the “healthy survivor” effect—those who survived the bombing might have been hardier than those who died early on, potentially skewing the results.
Many researchers say the latest report helps reaffirm the general accuracy of federal standards in place for limiting health risks from low-level radiation. “We believe the data are more convincing than fifteen years ago and show that the radiation protection standards we use are reasonable,” says Monson.
Mike Boyd, a health physicist who works on setting and updating those standards for the Environmental Protection Agency, concurs. “I don’t think we’ll be changing any federal standards,” he says. “I’m not willing to say there will be no impact. This report will go into our estimation of risk and could lead to refinements, but generally standards should stay the same.”
Although most scientists agree the report incorporated the majority of pertinent data up through 2003, information about low-LET radiation continues to emerge. One hypothesis under investigation, says biologist Andrew Wyrobek of Lawrence Livermore National Laboratory, is the possible adaptive response cells developed over eons of natural exposure. Other hypotheses include genetic instability (the idea that some cells already have genetic mutations and are thus more prone to becoming cancerous, given the incentive) and the “bystander effect” (in which cells respond adversely to nearby irradiation although they themselves weren’t hit directly). These concepts were among those reviewed for the BEIR VII report but were not incorporated into the risk estimates.
Most experts agree that the BEIR VII report won’t be the last in the series. “Right now there is just a lot we don’t know about how cells react to very low doses of radiation,” says Wyrobek. “But with multiple exposures from more and more people undergoing medical diagnostics in the low-dose range, and increased amounts of radioactive waste, it’s important to understand these ranges better.” Says Boyd, “I will be excited to see some future academy report after we find out more about how radiation affects cells at very low doses.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0735b16276647EnvironewsForumThe Beat Dooley Erin E. 11 2005 113 11 A735 A737 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Smoke-Free Beijing Olympics
WHO officials announced in April 2005 that the 2008 Beijing Olympic Games will be smoke-free, a ban the city’s mayor has personally endorsed. Experts say the move signals an official effort by China to heighten awareness of the dangers of smoking among its population, of whom 360 million smoke. The 2000 Sydney Olympics were smoke-free, but smoking was permitted at some venues of the 2004 Athens games.
China is a signatory of the WHO Framework Convention on Tobacco Control, yet as the world’s largest tobacco producer it faces challenges in reducing smoking among its population. Sharing cigarettes is a form of social courtesy, and 67% of Chinese males smoke. The WHO estimates at least 1.3 million Chinese die each year from smoking-related illnesses.
Nuclear Cleanup Slowdown
In 2002, the U.S. DOE began an accelerated cleanup program for nuclear waste aimed at reducing cleanup costs by $50 billion and shortening the timeline by 35 years. In July 2005, the GAO released a review of this program which found that progress is varied among the 16 cleanup activities measured. The DOE is ahead of schedule on packaging nuclear materials for disposal, disposing of low-level waste, and removing buildings, but lags on the tougher and costlier tasks of disposing of transuranic and radioactive tank wastes and closing tanks that contained radioactive waste. Because of these factors the DOE is not likely to achieve its full estimated cost and time reduction. The GAO advised the agency to revise its performance reporting and better highlight critical activities that will help it meet its goals.
Flush with Progress
The homeless population of Vancouver, British Columbia, has doubled in recent years. Now high populations of homeless persons and drug abusers have created an unsanitary problem for the city—streets, alleys, and parking lots around the downtown are habitually used as outdoor toilets. The city is now purchasing several new high-tech, self-cleaning bathroom booths—at up to $300,000 apiece—to be installed in critical areas, with an urban anthropologist to pinpoint major problem spots. The city is looking at a stainless steel model that cleans and dries every surface of its interior after each use.
Heavy Metals in Ayurvedic Meds
Health Canada has issued a warning to consumers following a 15 December 2004 JAMA report that 1 in 5 Ayurvedic medicinal products made in South Asia and sold in the Boston area contained potentially harmful levels of lead, mercury, or arsenic. Ayurveda (Sanskrit for “science of life”) often employs heavy metals because of their purported therapeutic properties.
Although none of the products tested are authorized for sale in Canada, the agency suspects some are sold there nonetheless. The agency tested one product, sold as a blood purifier for skin diseases and digestive problems, and found more than 40 times the allowable concentration of arsenic. Health Canada is reviewing the JAMA findings and assessing availability of the products in Canada, with results posted on the agency’s website.
Ecolabeling for Fisheries
As concern over the fate of wild marine fish stocks grows, the UN Food and Agriculture Organization took action in March 2005 by adopting a set of voluntary guidelines for the ecolabeling of fish products. These guidelines advise governments and organizations that oversee or plan to implement labels for fish and fishery products from well-managed marine capture fisheries. Included are minimum requirements and criteria for determining whether a fishery should be certified to use the ecolabel, based on the agency’s Code of Conduct for Responsible Fisheries. The guidelines, acknowledging the financial and technical challenges faced by developing nations in managing their fisheries, call for support in these areas to help these countries implement and benefit from the program.
Wildfire Pollution Widespread
Research by the U.S. National Center for Atmospheric Research in the 14 June 2005 issue of Geophysical Research Letters shows that particularly intense wildfires in Alaska and Canada during the summer of 2004 emitted as much carbon monoxide as human activities in the continental United States during the same period. The fires also boosted ground-level ozone across the northern continental United States, even increasing levels of this pollutant by 10% as far away as Europe. The researchers used a novel combination of satellite-based observing instruments, computer models, and numerical techniques to help them distinguish between fire-generated carbon monoxide and that from other sources.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0736a16276648EnvironewsForumInformation Technology: This Is YourAir Calling Schmidt Charles W. 11 2005 113 11 A736 A736 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
Consider the following scenario: You’re making last-minute preparations the night before a planned day of outdoor activities. Suddenly your cell phone rings. It’s not a friend or relative, but a text message warning you that air pollution levels will spike near your destination the next day. If you suffered from asthma or heart disease, this would be crucial information—high levels of air pollution can trigger life-threatening reactions in vulnerable people. With prior knowledge of the risk, you might take steps to limit your exposure and protect your health.
Thanks to a pilot project called YourAir, subscribers in some areas of London, England, are getting just this type of service. Coordinated by the European Space Agency (ESA) and Cambridge Environmental Research Consultants (CERC), YourAir calls subscribers with text message alerts on evenings before high levels of ozone, nitrogen dioxide, and particulates are predicted in their locations.
YourAir currently serves Central London and the boroughs of Croydon, Camden, and Wakefield. Iarla Kilbane-Dawe, a senior scientist with CERC, predicts the service will cover all of London and its population of 7 million by next year. The effort was developed as a demonstration service of ESA’s PROMOTE project, which uses real-time atmospheric data to improve quality of life and public decision making.
Subscribers to the free service are recruited through newspaper ads. They provide CERC with a street address or postcode, and are alerted only when pollution levels in that area are expected to rise. According to Kilbane-Dawe, YourAir integrates measurements of transboundary pollution movements generated by an ESA satellite with weather forecasts and knowledge of local traffic patterns. Through this approach, citizens get high-resolution air quality predictions at the street-by-street level.
YourAir also has a web-based interface, located at http://www.cerc.co.uk/YourAir/index.asp, that provides air quality predictions for all of Central London. With upcoming improvements to the site, Kilbane-Dawe says “you’ll be able to zoom in, pan, and scroll the air quality map and even look at air quality in the vicinity of individual houses.”
A key goal of the first-of-its-kind service is to enhance the medical community’s predictive capacity. For instance, pharmacies are more likely to run out of inhalers when pollution levels rise, and better air quality predictions might alert them to stock up in advance. “Air pollution alerts are a growth area,” Kilbane-Dawe says. “We think we’ll have air pollution issues in London for another twenty years at least.”
Get the message?
A pilot project in the United Kingdom sends text messages to people at risk for complications from severe air pollution, warning of days when it might be safer to stay inside.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0736b16276649EnvironewsForumAllergies: The Radical Theory of Sneezing Adler Tina 11 2005 113 11 A736 A736 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Anyone with common seasonal allergies knows perfectly well what’s causing their misery: pollen! And allergists know why pollen makes people sneeze: the body’s immune system is releasing a lot of inflammatory cells, including neutrophils and eosinophils, in response to the invading pollen proteins. However, new research reveals that it’s more than just pollen’s proteins wreaking havoc on human airways.
Earlier work had shown that the inflammatory cells the body spews out in response to pollen harbor enzymes called NADPH oxidases. Now researchers report in the August 2005 Journal of Clinical Investigation that even before the immune system cranks up, NADPH oxidases in pollen itself generate a type of free radical known as reactive oxygen species (ROS), which interfere with cell signaling pathways and cause the immune system to overreact.
“We demonstrate for the first time to our knowledge that pollen extracts from weeds, trees, and grasses have intrinsic NADPH oxidase activity that induces ROS in airway epithelium within minutes,” the team writes. ROS are formed when NADPH oxidases interact with cells lining the airways. The result is oxidative stress, which health experts suspect exacerbates asthma and allergies. Pollen’s double whammy causes the often quick, intense allergic reaction seen in sensitized patients, explains lead author Istvan Boldogh, a molecular biologist at the University of Texas Medical Branch at Galveston.
The surprising new findings reveal that “pollen is more active than we thought,” says J. David Lambeth, a molecular biologist at Emory University School of Medicine, who wrote a commentary on the study for the same journal. “We knew that pollen can make the body make free radicals, but this study shows that pollen takes an active role in making free radicals itself,” he says.
Plant cells were known to contain NADPH oxidases similar to those found in white blood cells in humans and other mammals. Among other important functions, the oxidases protect the plant against pathogens. However, researchers had not tested pollen for NADPH oxidases, says Boldogh. He and his colleagues uncovered pollen’s double-barreled effect on lungs by exposing sensitized mice to different forms of pollen, some with excess NADPH oxidases added, others that were NADPH-free. When they eliminated the NADPH oxidase activity, the mice had little or no inflammation in their airways and produced few of the cells that indicate an allergic response. When the researchers tested the effects of pollen extracts on cells taken from the lining of the lung, they found that adding NADPH oxidase increased the intracellular levels of free radicals.
Patients may someday use an inhaler containing antioxidants to counter ROS and minimize the effects of pollen, says Boldogh. The team’s recent studies show that a combination of the antioxidants ascorbic acid and N-acetyl-l-cysteine prevents airway inflammation in pollen-exposed mice.
But antioxidants available now clear from the lungs too quickly to be effective in people, so companies are looking into developing longer-lasting products, Boldogh says. However, the group warns against developing treatments for patients based on its single study, noting that the results are circumstantial and need to be established in patients, work the team is now attempting.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0758aEnvironewsScience SelectionsParticles in Practice: How Ultrafines Disseminate in the Body Weinhold Bob 11 2005 113 11 A758 A758 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Ultrafine particles (UFPs), those less than 100 nanometers in diameter, have existed for millennia in natural settings. But with the significant increase in UFPs resulting from human activities in the past few centuries (largely through combustion processes) and the potential for a deluge of nanoparticles as that industry gears up, are ancient human bodily defenses up to the substantial new hazards they now face? Findings by a team of Swiss, German, and Canadian researchers suggest that animals may be largely defenseless against the rapid dissemination of UFPs into cells throughout the body [EHP 113:1555–1560]. Their findings, which include the first evidence of how individual particles are distributed within the lung, raise some concerns, especially since UFPs often end up in locations within cells where the tiny particles can impair many cellular functions.
General knowledge about the rapid penetration of UFPs into various body organs has surfaced in the past few years, but the specific distribution and mechanisms remain largely unknown. To explore the distribution, the research team performed two parallel sets of experiments.
In the first set of experiments, they investigated the spread of titanium dioxide UFPs in rats after a 1-hour inhalation of an aerosol containing the material. The team then evaluated lung tissue taken from the rats either 1 or 24 hours after inhalation.
They found that on average, 24% of the inhaled titanium dioxide they detected had penetrated cells throughout the lung and the bloodstream just 1 hour after inhalation. Within cells in different lung compartments, there was no difference in the 1-hour and 24-hour samples, suggesting that UFPs can easily move between compartments. The team continues to investigate what happens with the remaining 76% of the particles and with those that enter the bloodstream. There is evidence the particles spread throughout the body.
Of the particles they did find, 79.3% lodged in cells on the inner surface of airways and alveoli, 11.3% were within capillaries, 4.8% were within connective tissue, and 4.6% were within epithelial or endothelial cells. The researchers were surprised to find that most of the particles in the cellular cytoplasm were not attached to the membrane, as would have been expected if the particles had been encapsulated through endocytosis or phagocytosis. Floating in the cytoplasm, the particles can access many of the structures within the cell, such as the nucleus and mitochondria, increasing the potential toxicity of the particles.
In the second set of experiments, the researchers explored the movement of three sizes of fluorescent polystyrene UFPs and of gold UFPs after the particles were introduced to cultures of swine macrophages and human red blood cells. They found all three particle sizes (1.0, 0.2, and 0.078 micrometer) penetrated the swine macrophages, though in perplexingly different proportions—only 21% of the macrophages contained the medium size, while 77% contained the smallest and 56% contained the largest. In human red blood cells, they found the smallest and medium sizes, but not the largest.
The experiments did not offer evidence about exactly how the tiny, insoluble particles disseminate so extensively and rapidly into so many different cells, but the researchers note that other experiments have demonstrated a number of possible mechanisms. The researchers also note their findings are specific to just the few substances they studied, and differ in some ways from those for iridium, one of the few other materials evaluated in some detail.
Ultrafine infusion.
A series of recent experiments demonstrates that ultrafine particles are widely disseminated in a variety of cells. A micrograph of one such experiment shows 0.2-micrometer fluorescent polystyrene particles (green) taken up by a macrophage (red).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0758bEnvironewsScience SelectionsTesting the Additivity Assumption: Chemical Mixtures and Thyroid Function Brown Valerie J. 11 2005 113 11 A758 A759 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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It is well established that many environmental contaminants can disrupt thyroid hormone (TH) homeostasis, which is vital during fetal development and for a variety of physiological processes in adults. Among known TH disruptors are polychlorinated biphenyls (PCBs), dioxins, and dibenzofurans, all members of the polyhalogenated aromatic hydrocarbon (PHAH) chemical family. Little is known, however, about how mixtures of such chemicals at typical environmental exposure levels may disrupt TH functions. Nor is it clear whether effects are additive, synergistic, or antagonistic—that is, whether there is interaction between constituent chemicals, whether their cumulative influence is more than the sum of its parts, or whether they cancel each other out. With respect to risk assessment, the U.S. Environmental Protection Agency’s default assumption is that the effects of chemicals in mixtures are additive. Now a team of researchers has tested the additivity assumption and found that it is relatively robust at exposure levels typical for humans [EHP 113:1549–1554].
Over a four-day period the team exposed young female rats to six different doses of a combination of 18 PHAHs comprising 2 dioxins, 4 dibenzofurans, and 12 PCBs. The team determined dose–response information for each constituent chemical before the mixture was tested. The concentration of each chemical in the mixture reflected typical concentrations measured in breast milk and in fish and other foods. The mixture was also formulated so that even at the highest mixture doses, the rats’ exposure to each constituent chemical was at or below the known no-observed-effect level for that chemical.
The mixture reduced the rats’ serum thyroxine (T4; the most common form of circulating TH) in a dose-dependent manner. At lower doses the effects were additive. At higher doses T4 declined by as much as 50%, and the effects were mildly synergistic—about twice what was predicted by additivity—so that even in the upper range the effects as predicted by the additivity hypothesis came close to actual results.
Significantly, the study also showed that the mixture exerted an effect on T4 even though concentrations of its constituent chemicals were at least an order of magnitude below their known effective doses. This indicates that considering individual chemicals in isolation may not predict their effects in mixtures because, even though chemicals may not be potent enough by themselves to cause effects, the cumulative effects of low doses of many chemicals may be enough to do so.
The multiple functions of TH, such as its role in fetal development and its regulation of metabolism and heart rate, make it vulnerable at many points. The team estimates that there could be as many as five distinct mechanisms by which chemicals exert antithyroid effects for which a reduction in circulating T4 is the common end point.
Several factors temper the study results. One is that this study was a series of short-term exposures that did not encompass all the chemicals’ varied half-lives. The results therefore cannot be directly extrapolated to the effects of chronic exposures and may be subject to confounding by pharmacokinetic differences. Another is that thyroid disruption mechanisms in rats may not be identical to those in humans. The team is now working on testing how a more complex chemical mixture may interact with dietary iodine insufficiency to produce thyrotoxic effects.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0780a16263497AnnouncementsBook ReviewEssentials of Medical Geology: Impacts of the Natural Environment on Public Health Pokras Mark Mark Pokras trained in ecology at Cornell University and earned his DVM at Tufts University’s Cummings School of Veterinary Medicine, where he currently teaches wildlife medicine and serves as Director of Tufts’ Center for Conservation Medicine.11 2005 113 11 A780 A780 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Edited by Olle Selinus, Brian Alloway, José A. Centeno, Robert B. Finkleman, Ron Fuge, Ulf Lindh, and Pauline Smedley
Burlington, MA:Elsevier Academic Press, 2005. 812 pp. ISBN: 0-1263-6341-2, $99.95 cloth
Emerging disease, pesticides, antibiotic resistance, heavy metals—every time we turn around it seems we face frightening new threats to the health of every living organism on our planet. In response, we have seen a dramatic increase in the development of new, transdisciplinary approaches including environmental medicine, conservation medicine, health social science, and One World, One Health—but medical geology?
Readers will not have to get far into this book to become convinced that geologic expertise has much to contribute to our understanding of and response to global health issues. Medical geology, which examines the impacts of geologic materials and processes on human and ecosystem health including both natural and anthropogenic sources of potential health problems, includes animal and plant diseases. The editors set ambitious goals for this book, noting in the preface that this volume could be used as both a reference and a general textbook for a diverse audience including students, geoscientists, medics, decision makers, and the general public.
The first section, “Environmental Biology,” builds from individual inorganic reactions to cells, organisms, and ecosystems, laying a sound foundation for the concepts to follow. For those with medical backgrounds, one of the most useful aspects is the very different view that geologists bring to health issues. This section builds a firm foundation for the subsequent chapters, intertwining geologic and biologic chapters.
The second section, “Pathways and Exposures,” focuses primarily on “natural sources” of pollutants and their transport through air, water, and food chains and demonstrates the importance of unifying and integrating themes for understanding long-term, large-scale processes in ecosystem dynamics. Ecologic concepts are integrated with the epidemiologic and illustrated by real-world examples and experiments. Most chapters are fundamentally strong, but biologists may wish for deeper discussions of topics such as biologic magnification and effects on predatory species.
The third section, “Environmental Toxicology, Pathways and Medical Geology,” includes a significant focus on epidemiology. This section is strong but repeats many basic principles (e.g., the metabolic handling of exogenous chemicals) and specific examples (e.g., discussions of arsenic, mercury, and lead) discussed in early chapters. The presence of contrasting explanations, opinions, and viewpoints can serve important didactic functions. Given that medically oriented authors wrote most of these chapters, this section may hold the most exciting ideas for the geologic readership.
The last section, “Techniques and Tools,” is an excellent reminder of the breadth of applications included in medical geology. The discussions span imaging techniques from cellular to global and analytic methodologies from the molecular to tissue, watershed, and continental scales. Necessarily, many abstruse or cutting-edge techniques have not been included, but the medical audience will find relevance in those that are focused upon.
This wide-ranging and challenging introduction is filled with wonderful and frightening examples from around the world. The amalgam of theoretical, ecologic, and clinical cases with discussions of policy is one of the book’s strongest points. The focus is primarily on human health, and although some examples involve domestic animals or plants, almost none address nonmammalian (especially nonvertebrate) species. Some chapters assume a fair degree of quantitative sophistication, so instructors should ensure that the text matches student abilities. Much of the material will excite students and involve them in analysis and discussion. Most chapters include a useful summary or conclusions, a list of related topics in other chapters, and a list of further reading, and many chapters emphasize a significant problem faced by researchers and policy makers working on the natural world.
This tour de force has several great strengths, including the marvelously international contributors. More than a theoretical approach, this volume is packed with real-world examples, cases, and information from research, clinical, and policy perspectives. Excellent illustrations, graphs, and photographs also complement many chapters and add markedly to the value of the book. Unfortunately, repetition of key examples throughout the text has made the book a bit less useful than some readers might wish; more diversity might be desirable in later editions.
For health specialists, graduate students, and the technically inclined, this book will be an invaluable resource. But it is a bit too large, technical, and imposing to be called “Essentials.” The editors might consider preparing an abbreviated introductory text to attract a wider audience. The book is a forceful reminder that we need more geologic input incorporated into health assessments, environmental toxicology studies, and planning and policy initiatives—as recent events on the Gulf Coast of the United States so strongly demonstrate.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0780b16263497AnnouncementsNew BooksNew Books 11 2005 113 11 A780 A780 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Comprehensive Toxicology: General Principles
J.A. Bond
Burlington, MA:Elsevier, 2005. 354 pp. ISBN: 0-08-042966-1, $146.95
Comprehensive Toxicology: Renal Toxicology
R.S. Goldstein
Burlington, MA:Elsevier, 2005. 716 pp. ISBN: 0-08-042972-6, $196.95
Deliberative Environmental Politics: Democracy and Ecological Rationality
Walter F. Baber, Robert V. Bartlett
Cambridge, MA:MIT Press, 2005. 288 pp. ISBN: 0-262-02587-6, $60
Environmental Chemistry: Chemistry of Major Environmental Cycles
Teh Fu Yen
Hackensack, NJ:World Scientific Publishing, 2005. 640 pp. ISBN: 1-86094-474-4, $76
Environmental Citizenship
Andrew Dobson, Derek Bell
Cambridge, MA:MIT Press, 2005. 312 pp. ISBN: 0-262-02590-6, $60
Global Change and the Earth System: A Planet Under Pressure
W. Steffen, S. Sanderson, P.D. Tyson, J. Jäger, P.A. Matson, B. Moore III, et al.
New York:Springer-Verlag, 2005. 332 pp. ISBN: 3-540-26594-5, $139
Governing Water: Contentious Transnational Politics and Global Institution Building
Ken Conca
Cambridge, MA:MIT Press, 2005. 456 pp. ISBN: 0-262-03339-9, $70
Hot Spot Pollutants: Pharmaceuticals in the Environment
Daniel Dietrich, Simon Webb, Thomas Petry
Burlington, MA:Elsevier, 2005. 352 pp. ISBN: 0-12-032953-0, $190
Industrial Transformation: Environmental Policy Innovation in the United States and Europe
Theo de Bruijn, Vicki Norberg-Bohm, eds.
Cambridge, MA:MIT Press, 2005. 376 pp. ISBN: 0-262-54181-5, $27
Inventing for the Environment
Arthur Molella, Joyce Bedi, eds.
Cambridge, MA:MIT Press, 2005. 424 pp. ISBN: 0-262-63328-0, $17.95
Seeing the Forest and the Trees: Human-Environment Interactions in Forest Ecosystems
Emilio F. Moran, Elinor Ostrom, eds.
Cambridge, MA:MIT Press, 2005. 504 pp. ISBN: 0-262-13453-5, $83
Small-Scale Freshwater Toxicity Investigations: Vol. 1—Toxicity Test Methods
Christian Blaise, Jean-François Férard, eds.
New York:Springer-Verlag, 2005. 551 pp. ISBN: 1-4020-3119-X, $129
Small-Scale Freshwater Toxicity Investigations: Vol. 2—Hazard Assessment Schemes
Christian Blaise, Jean-François Férard, eds.
New York:Springer-Verlag, 2005. 551 pp. ISBN: 1-4020-3543-8, $129
Sustainability and Cities: Concept and Assessment
Ooi Giok Ling
Hackensack, NJ:World Scientific Publishing, 2005. 252 pp. ISBN: 981-256-163-3, $48
The Law and Ethics of the Pharmaceutical Industry
M.N.G. Dukes
Burlington, MA:Elsevier, 2005. 450 pp. ISBN: 0-444-51868-1, $129.95
Worldviews, Science and Us: Redemarcating Knowledge and Its Social and Ethical Implications
Diederik Aerts, Bart D’Hooghe, Nicole Note, eds.
Hackensack, NJ:World Scientific Publishing, 2005. 232 pp. ISBN: 981-256-190-0, $58
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PLoS PathogPLoS PathogppatplpaplospathPLoS Pathogens1553-73661553-7374Public Library of Science San Francisco, USA 1634486210.1371/journal.ppat.001004205-PLPA-RA-0113R4plpa-01-04-03Research ArticleImmunologyInfectious DiseasesLungInnateFungalInflammationThe Beta-Glucan Receptor Dectin-1 Recognizes Specific Morphologies of Aspergillus fumigatus
Inflammatory Responses to
A. fumigatusSteele Chad 1*Rapaka Rekha R 1Metz Allison 1Pop Shannon M 1Williams David L 2Gordon Siamon 3Kolls Jay K 1Brown Gordon D 4
1 Department of Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
2 Department of Surgery, James H. Quillen College of Medicine, East Tennessee State University, Johnson City, Tennessee, United States of America
3 Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom
4 Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
Filler Scott G EditorUniversity of California at Los Angeles, United States of America* To whom correspondence should be addressed. E-mail: [email protected] 2005 9 12 2005 1 4 e4228 7 2005 3 11 2005 Copyright: © 2005 Steele et al.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.Alveolar macrophages represent a first-line innate host defense mechanism for clearing inhaled Aspergillus fumigatus from the lungs, yet contradictory data exist as to which alveolar macrophage recognition receptor is critical for innate immunity to A. fumigatus. Acknowledging that the A. fumigatus cell wall contains a high beta-1,3–glucan content, we questioned whether the beta-glucan receptor dectin-1 played a role in this recognition process. Monoclonal antibody, soluble receptor, and competitive carbohydrate blockage indicated that the alveolar macrophage inflammatory response, specifically the production of tumor necrosis factor-α (TNF-α), interleukin-1α (IL-1α), IL-1β, IL-6, CXCL2/macrophage inflammatory protein-2 (MIP-2), CCL3/macrophage inflammatory protein-1α (MIP-1α), granulocyte-colony stimulating factor (G-CSF), and granulocyte monocyte–CSF (GM-CSF), to live A. fumigatus was dependent on recognition via the beta-glucan receptor dectin-1. The inflammatory response was triggered at the highest level by A. fumigatus swollen conidia and early germlings and correlated to the levels of surface-exposed beta glucans, indicating that dectin-1 preferentially recognizes specific morphological forms of A. fumigatus. Intratracheal administration of A. fumigatus conidia to mice in the presence of a soluble dectin-Fc fusion protein reduced both lung proinflammatory cytokine/chemokine levels and cellular recruitment while modestly increasing the A. fumigatus fungal burden, illustrating the importance of beta-glucan–initiated dectin-1 signaling in defense against this pathogen. Collectively, these data show that dectin-1 is centrally required for the generation of alveolar macrophage proinflammatory responses to A. fumigatus and to our knowledge provides the first in vivo evidence for the role of dectin-1 in fungal innate defense.
Synopsis
Individuals with defective immune systems are highly susceptible to infection by parasites, bacteria, viruses, and fungi. Infection by the opportunistic fungal organism Aspergillus fumigatus can be particularly severe in this population. Because many pathogenic microorganisms, including A. fumigatus, enter the body through the lung, it is important to understand the function of its immune system. The alveolar macrophage is one of the first cell types to come in contact with inhaled pathogens. An intense area of research is how lung immune cells—i.e., alveolar macrophages—recognize inhaled pathogens and respond to them. Steele et al. recently discovered that alveolar macrophages express a receptor on their surface, dectin-1, that is essential in recognizing and responding to inhaled fungal pathogens. They now have investigated the interaction between dectin-1 and A. fumigatus to determine how the dectin-1 receptor orchestrates the alveolar macrophage response. They found that alveolar macrophages respond poorly to A. fumigatus when the dectin-1 receptor is blocked. Also, in animal experiments, blocking dectin-1 renders the animals more susceptible to infection with A. fumigatus. This study may lay the foundation for developing new and novel strategies to combat infections caused by A. fumigatus.
Citation:Steele C, Rapaka RR, Metz A, Pop SM, Williams DL, et al. (2005) The beta-glucan receptor dectin-1 recognizes specific morphologies of Aspergillus fumigatus. PLoS Pathog 1(4): e42.
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Introduction
Individuals with compromised immune systems are at high risk for acquired invasive fungal infections. Aspergillus fumigatus, the etiological agent of invasive pulmonary aspergillosis (IPA), is a ubiquitous mold that causes severe, invasive, life-threatening disease in patients who are severely immunocompromised. Disease acquisition includes such risk factors as neutropenia and impaired neutrophil function and myeloablative-immunosuppressive therapies associated with hematopoietic stem-cell transplantation [1]. Despite available anti-fungal therapy, the prognosis of IPA remains poor, and mortality ranges from 30% to 90% [2,3]. This is thought to be due in part to the relatively small arsenal of effective anti-fungal drugs, some of which cause severe nephrotoxicity—specifically, amphotericin B, which is associated with response rates of between 10% and 40% [4]. IPA has risen dramatically over the past several decades due to the consistent increase in immunosuppressed patients, and by the early 1990s 60% of invasive fungal infections diagnosed at autopsy were IPA [5]. It must also be stated that IPA is not only associated with stem-cell transplantation, but also presents in whole-organ transplantation, primarily lung and heart, with mortality rates of 68% to 78% [6]. A. fumigatus is also the etiological agent of allergic bronchopulmonary aspergillosis, an allergic airway disease characterized by persistent bronchial inflammation and bronchiectasis [7].
Upon inhalation of A. fumigatus conidia from the environment, alveolar macrophages rapidly ingest and attempt to clear the invading pathogen. Conidia that escape the fungicidal activities of alveolar macrophages begin to germinate, leading to the rapid recruitment of neutrophils, which subsequently promote anti-hyphal defenses [8,9]. A major focus in innate immunity and host-pathogen interactions in the past decade has been elucidation of the receptors involved in the recognition and response to pathogens, the most characterized of which are the toll-like receptors (TLRs). However, in the context of macrophage–A. fumigatus interactions, there is no clear role for the TLRs in recognition and responsiveness. TLR2 and TLR4 are the most studied. However, the data on TLR2 are conflicting in that several reports have shown roles for and against its importance in host defense against this pathogen. For example, macrophages from TLR2−/− mice produce less tumor necrosis factor-α (TNF-α) [10] and CXCL2/MIP-2 [11] in response to A. fumigatus, whereas antibody-mediated blockage of TLR2 had no effect on TNF-α production [12] and TLR2−/− mice challenged with A. fumigatus survived better than wild-type control mice and had higher lung levels of TNF-α [13]. A role for TLR4 in the inflammatory response to A. fumigatus conidia, but not hyphae, has also been demonstrated [14,15], suggesting that TLR4 is critical for recognition of different A. fumigatus morphologies. TLR4−/− mice challenged with A. fumigatus have increased susceptibility compared with control mice [13], although this is not associated with defects in TNF-α production, as it was unaffected by TLR4−/− deficiency [13]. Other studies show that TLR4 signaling is essential for the anti-fungal effector activity of neutrophils [16], but not Kupffer cells [17], against both conidia and hyphae of A. fumigatus. In addition, many of these studies investigating the role of TLRs and A. fumigatus recognition have not been performed with alveolar macrophages, and thus it is uncertain if the mechanisms described are representative of these cells, which have a unique phenotype.
Non-TLRs are also important in innate recognition of A. fumigatus. The cell wall of A. fumigatus is known to contain galactomannan moieties that are thought to be covalently linked to the non-reducing ends of beta-1,3–glucan side chains [18]. Accordingly, several studies have described mannose- or mannan-specific receptors in the uptake of A. fumigatus conidia by phagocytic cells [19]. Studies have identified the C-type lectin DC-SIGN (dendritic-cell-specific, ICAM-3-grabbing nonintegrin) as being involved in the binding of A. fumigatus conidia to human macrophages and dendritic cells [20].
The A. fumigatus cell wall is also rich in beta-1,3–glucan moieties, and although receptors for these carbohydrates, including CR3, have been implicated, the role of these receptors in innate immune response to this organism is unclear [21]. We have previously shown that recognition of cell-wall beta glucan plays an important role in the induction of inflammatory mediators by macrophage populations in response to Pneumocystis carinii and Candida albicans [22,23]. Dectin-1 is a 43-kDa, type II transmembrane receptor containing a single cytoplasmic immunoreceptor tyrosine activation motif and is the predominant macrophage receptor for beta-1,3 glucans [23–25]. As dectin-1 is highly expressed on resident alveolar macrophages, we examined the role of this receptor in response to A. fumigatus, and show here that dectin-1 is centrally involved in generating inflammatory responses to specific morphological forms of this organism both in vitro and in vivo.
Results/Discussion
Dectin-1 Is Involved in the Macrophage Cytokine and Chemokine Response to A. fumigatus
Extensive reports have shown that zymosan, a beta-glucan–rich, yeast-derived particle, and the beta-glucan–containing fungal organisms C. albicans and P. carinii [22,25] bind to the dectin-1 beta-glucan receptor leading to phagocytosis and proinflammatory cytokine production [25,26]. A. fumigatus similarly possesses a cell wall significantly made up of beta glucans [18]; thus, we questioned whether macrophage interactions with A. fumigatus involved dectin-1. The results in Figure 1A show that RAW 264.7 cells, a macrophage cell line that was established from a tumor induced by Abelson murine leukemia virus [27], can produce a number of cytokines and chemokines in response to live A. fumigatus after 24 h of co-culture, and that this response is greatly enhanced in RAW 264.7 macrophages transduced to over-express dectin-1. In both cell types, inhibition of dectin-1 function with the monoclonal antibody 2A11 [26] significantly blocked these responses. Control experiments stimulated RAW 264.7 cells with the TLR ligands LPS and Pam(3)Cys in the presence or absence of 2A11. Results showed that dectin-1–blockage did not impair the TNF-α and MIP-2 response to these stimulants (unpublished data). Thus, dectin-1 can recognize and respond to live A. fumigatus.
Figure 1 Dectin-1 Dependent Cytokine and Chemokine Production in Response to A. fumigatus
(A) Native RAW 264.7 macrophages or RAW 264.7 over-expressing murine dectin-1 were pretreated with isotype or 2A11 and co-cultured for 24 h with live A. fumigatus. Supernatant cytokine and chemokine levels were determined by Bio-Plex or ELISA. (A) illustrates cumulative results from five separate experiments. Asterisks, double asterisks, and the hash sign represent significant differences between RAW and RAW + 2A11, RAW and RAW-Dect, and RAW-Dect and RAW-Dect + 2A11, respectively (p < 0.05). Data are expressed as mean pg/ml + SEM.
(B–D) Identical experimental design with alveolar macrophages. (B–D) illustrate cumulative results from five separate experiments. Asterisks represent significant differences between isotype and 2A11 (p < 0.05). Data are expressed as mean pg/ml + SEM.
(E) Alveolar macrophages were pre-treated with 10-μM cytochalasin D and co-cultured with live A. fumigatus for 16 h. Supernatant cytokine and chemokine levels were determined by Bio-Plex or ELISA. (E) illustrates cumulative results from three separate experiments. Data are expressed as mean pg/ml + SEM.
Innate immune cells of the lung, particularly alveolar macrophages, are critical for recognizing and reacting to A. fumigatus [8,9]. Since we have previously shown that dectin-1 is expressed at high levels on alveolar macrophages [22,28], we assessed their response to live A. fumigatus. The results in Figure 1B and 1C show that co-culture of live A. fumigatus with alveolar macrophages for 24 h led to production of TNF-α, CCL3/MIP-1α, CXCL2/MIP-2, IL-1β, IL-1α, IL-6, G-CSF, and GM-CSF, all of which were significantly attenuated by blocking dectin-1 with the monoclonal antibody 2A11 [26]. We observed little spontaneous production of cytokines and chemokines by unstimulated alveolar macrophages (e.g., spontaneous production of TNF-α, CCL3/MIP-1α, and CXCL2/MIP-2 was 54.7 ± 20, 186 ± 45, and 122 ± 25 pg/ml, respectively). T helper type-1 cell-mediated immunity is essential for optimal pulmonary host defense against fungal infections [29]. Innate cells, such as alveolar macrophages, play a central role in aiding the development of T helper type-1 responses [29]. In our studies, we found that alveolar macrophages stimulated with A. fumigatus had dectin-1–dependent induction of IFN-γ (Figure 1D), suggesting that dectin-1–ligation by A. fumigatus may also promote the generation of T helper type-1 immunity. IL-12 was also induced by A. fumigatus, but not found to be dectin-1–dependent (233 ± 83 pg/ml and 71 ± 22 pg/ml for isotype and 2A11, respectively, p = 0.0902). We also observed dectin-1–dependent induction of IL-10, a critical cytokine for regulating the pulmonary inflammatory response [30].
Previous studies indicated that cytochalasin D–treated macrophages stimulated with the fungal particle zymosan had enhanced TNF-α production, indicating that internalization was not required for dectin-1–mediated cytokine and chemokine production in RAW macrophages [23]. Although zymosan is employed as a representative fungal particle, it is not clear whether its use predicts the subsequent events associated with dectin-1 ligation by a live, intact fungal organism such as A. fumigatus. Experimental studies have shown that unstimulated alveolar macrophages are quite efficient at internalizing A. fumigatus conidia, a process that requires actin polymerization [31,32]. We questioned whether blocking actin polymerization would affect the ability of alveolar macrophages to produce inflammatory mediators in response to A. fumigatus. We found that alveolar macrophages pretreated with cytochalasin D retained the inflammatory response to live A. fumigatus (Figure 1E), and that the production of TNF-α, MIP-1α, and MIP-2 was exacerbated. Fluorescent deconvolution microscopy performed to assess the internalization of A. fumigatus in vehicle versus cytochalasin D-treated alveolar macrophages indicated efficient uptake of fluorescein isothiocyanate–conjugated conidia in vehicle-treated, but not cytochalasin D–treated, alveolar macrophages [20,31,32] (Figure S1). We did not observe non-specific induction of cytokines/chemokines by unstimulated alveolar macrophages in the presence of cytochalasin D. Moreover, lactate dehydrogenase (LDH) analysis of co-culture supernatants indicated that cytochalasin D concentration employed in these studies was not cytotoxic to alveolar macrophages (unpublished data). The heightened response in cytochalasin D-treated cultures is likely to be a result of prolonged stimulation at the cell surface, as observed previously with zymosan [33]. These results therefore suggest that alveolar macrophage cytokine and chemokine production in response to live A. fumigatus is mediated by dectin-1 and does not require organism uptake.
Role of TLR2 in Dectin-1–Dependent Alveolar Macrophage Responses to A. fumigatus
Dectin-1–mediated inflammatory responses to the fungal particle zymosan have been shown to be dependent on TLR2 [23,34]. However, the role of TLR2 in the inflammatory response to A. fumigatus is less clear, with reports both supporting and arguing against its role in the inflammatory response [10–13]. To address the role of dectin-1 in TLR2-mediated responses to A. fumigatus, we co-cultured alveolar macrophages isolated from wild-type C57BL/6 and TLR2−/− mice with A. fumigatus for 24 h, followed by analysis of cytokine and chemokine levels in co-culture supernatants. Data presented in Table 1 indicate that only TNF-α production was significantly affected (p < 0.05) by TLR2 deficiency in 24-h alveolar macrophage–A. fumigatus co-cultures. However, reductions were observed for all cytokines examined. Although reduced, TLR2−/− alveolar macrophages could still produce nanogram concentrations of TNF-α in response to this fungal pathogen, suggesting that TLR2 was not absolutely required for the production of TNF-α, but did appear to be required for optimal dectin-1–mediated TNF-α production. Inhibition of dectin-1, which is expressed equally on these cells (unpublished data), resulted in more than 80% reduction in most cytokines (the exception being IL-1α and GM-CSF) including TNF-α, in both C57BL/6 and TLR2−/− alveolar macrophage–A. fumigatus co-cultures. We have also made similar observations with the dectin-1–dependent fungal organisms P. carinii (unpublished data). Although previous studies with zymosan have suggested an accessory role of TLR2 in dectin-1–mediated responses [23,34], acknowledging our A. fumigatus and P. carinii data, we hypothesize that zymosan may contain a ligand with high specificity for TLR2, one that is present at lower amounts in the cell wall of A. fumigatus and P. carinii. Our data support this hypothesis, since all cytokines examined with TLR2–/– alveolar macrophages were moderately, though not always significantly, reduced in response to these two organisms. Together, these results suggest that TLR2 plays an accessory role with dectin-1 in mediating the alveolar macrophage inflammatory response to live A. fumigatus.
Table 1 Cytokine and Chemokine Responses of Naïve Alveolar Macrophages from TLR2-Deficient Mice to A. fumigatus
Macrophage Cytokine and Chemokine Production Is Dependent on Recognition of Exposed Beta Glucan in A. fumigatus Swollen Conidia and Early Germlings
Disease caused by A. fumigatus is initiated when conidia, termed “resting conidia” (RC), are inhaled into the lung. Upon entering the lung, RC go through phenotypic changes that lead to swelling, now termed “swollen conidia” (SC), followed by germination [35]. As alveolar macrophages are thought to be the most critical in early stages after inhalation [8,9], the alveolar macrophage inflammatory response to various morphological stages of A. fumigatus was investigated. Examining the kinetics of cytokine production after exposure to live A. fumigatus RC, we observed that while TNF-α, CCL3/MIP-1α, or CXCL2/MIP-2 were readily detectable after 8 h of co-culture, very low concentrations (<30 pg/ml) of these mediators were detected at 4 h (Figure 2A). Furthermore, we did not observe an effect of dectin-1 blockage in cytokine levels at 4 h; however, the levels at 8 h were significantly blunted (e.g., TNF-α levels at 4 h and 8 h were reduced from 16 pg/ml and 373 pg/ml to 7 pg/ml and 2 pg/ml in the presence of 2A11, respectively). As this suggested that the A. fumigatus RC did not have beta glucans exposed within the first 4 h of culture, we examined the response to live A. fumigatus SC or hyphae, using TNF-α, CCL3/MIP-1α, or CXCL2/MIP-2 as an indicator of dectin-1–mediated recognition. In contrast to A. fumigatus RC, a 4-h incubation of alveolar macrophages with A. fumigatus SC (RC incubated for 6 h at 37 °C prior to the addition of macrophages) efficiently induced CXCL2/MIP-2 production, which could be abrogated by blocking dectin-1 (Figure 2B). It must be stated that these co-cultures were initiated with the A. fumigatus SC morphology, but this morphology did convert to early germlings over the course of the 4-h incubation period. Alveolar macrophages added to live A. fumigatus hyphae (RC allowed to germinate for 24 h prior to the addition of macrophages), however, induced only low levels of TNF-α, CCL3/MIP-1α, and CXCL2/MIP-2, even after prolonged stimulation (Figure 2C). Although previous studies have shown higher cytokine responses to A. fumigatus hyphae using peritoneal macrophages and non-viable hyphae [10–12,14,15], our studies are nevertheless in agreement with live, mature hyphae inducing TNF-α production. We do not think that the lower amounts of cytokines induced by live, mature hyphae in our study was a result of macrophage death, as we did not observe increases in LDH or caspase-3 levels (measured in cell lysates) compared to unstimulated supernatants (unpublished data). Possible explanations for the lower levels of cytokine induction in our studies may be differences in macrophage-to-hyphae ratios, our usage of live versus heat-killed/fixed hyphae, or the tissue source of the macrophages (peritoneal versus alveolar). As we had observed significantly higher cytokine levels at 24 h of co-culture (see Figure 1B) than at 8 h of co-culture (Figure 2A), we performed a time-course analysis of cytokine levels produced by alveolar macrophages in response to A. fumigatus. We observed that more than 75% of the TNF-α, CCL3/MIP-1α, and CXCL2/MIP-2 concentrations observed at 24 h were elicited between 9 h and 12 h (Figure 2D and 2E), during conidial swelling and germination. These data suggest that less than 25% of the total cytokine/chemokine response in a 24-h culture is produced after 12 h, further suggesting that dectin-1 recognizes beta-glucan moieties of A. fumigatus morphologies that are present well before 12 h.
Figure 2 Cytokine and Chemokine Production Is Dependent on Beta-Glucan Recognition in A. fumigatus SC and Pre-Competent Hyphae
Alveolar macrophages were added to (A) live A. fumigatus RC and incubated for 4 h and 8 h, (B) live RC or SC and incubated for 4 h in the presence of isotype or 2A11, or (C) live A. fumigatus hyphae and incubated for 4 h, 8 h, and 24 h. (D and E) Alveolar macrophages were added to A. fumigatus RC and incubated for 24 h. Samples for cytokine analysis were taken between 9 h and 12 h, and at 24 h, as indicated. Also shown in (D) is a representative 4-h response to pre-generated SC, and inhibition of this response by 2A11. Thereafter, cytokine and chemokine levels were determined in supernatants by Bio-Plex or ELISA. Percentage data expressed in (E) were calculated by dividing the pg/ml cytokine/chemokine level in 12-h samples by that in 24-h samples. Asterisks represent significant differences between isotype (rat IgG) and 2A11 (p < 0.05). (A–C,E, and F) are cumulative results from four experiments.
To further determine A. fumigatus morphological induction of the inflammatory response mediated by dectin-1, we cultured A. fumigatus for 3 h, 6 h, and 9 h, subjected them to heat-inactivation, and thereafter added the heat-killed conidia to alveolar macrophages for 6 h. Results presented in Figure 3 show that 6-h, heat-killed organisms elicited TNF-α (Figure 3A) and CXCL2/MIP-2 (Figure 3B) from alveolar macrophages in a dectin-1–dependent manner. Additional studies employing 6-h, ethanol-killed organisms showed similar dectin-1–dependent induction (for TNF-α, 1,208 pg/ml versus 65 pg/ml in 2A11-treated cells; for CXCL2/MIP-2, 1,817 pg/ml versus 722 pg/ml for 2A11-treated cells). Moreover, 9-h, heat-killed A. fumigatus, or early germlings, were also efficient in eliciting TNF-α and CXCL2/MIP-2 from alveolar macrophages in a dectin-1–dependent manner. Previous reports have also suggested that A. fumigatus conidia cultured in 20% serum swell within 3 h [31]. We therefore examined the ability of A. fumigatus conidia cultured for 3 h in 10% and 20% serum followed by heat-inactivation to induce dectin-1–dependent TNF-α and CXCL2/MIP-2 production. Although 3-h, heat-killed A. fumigatus originally cultured in 20% serum elicited more TNF-α than the 10% serum A. fumigatus and in a dectin-1–dependent manner, this induction was less than 15% of that induced by either 6-h, heat-killed organisms or 9-h, heat-killed early germlings (Figure 3A). There was no difference in the level of CXCL2/MIP-2 induction between the 3 h in 10% and 20% serum morphologies. Taken collectively, these results indicate that alveolar macrophages recognize beta glucans via dectin-1 when A. fumigatus conidia begin to swell and vigorously respond to SC and early germlings.
Figure 3 Cytokine and Chemokine Induction in Response to Different Heat-Killed Morphologies of A. fumigatus
A. fumigatus was grown for 3 h (in 10% or 20% serum), 6 h, or 9 h and thereafter subjected to heat-killing. Alveolar macrophages were added and incubated in the presence or absence of isotype or 2A11 for 6 h. Thereafter, cytokine and chemokine levels were determined in supernatants by Bio-Plex or ELISA. Asterisks represent significant differences between isotype (rat IgG) and 2A11 (p < 0.05). Figure 3 shows cumulative results from three experiments. Data are expressed as mean pg/ml + SEM.
The cell wall of A. fumigatus is known to contain galactomannan moieties [36], and several studies have implicated mannose- or mannan-specific receptors, including DC-SIGN (dendritic-cell-specific, ICAM-3-grabbing nonintegrin) and the long pentraxin PTX3, in the recognition of A. fumigatus [19,20]. To address the possible role of a mannose- or mannan-specific receptor in the induction of inflammatory response by SC, we pretreated alveolar macrophages with Saccharomyces cerevisiae–derived mannan [20] prior to their addition to SC and observed no effect, whereas addition of a soluble beta glucan, glucan phosphate [23,26] significantly inhibited TNF-α production (Figure 4A).
Figure 4 Competitive Carbohydrate Blockage Indicates No Role of Mannan Receptors in the Inflammatory Response
Alveolar macrophages (A) or RAW 264.7 macrophages over-expressing dectin-1 (B) were pretreated with 250 μg/ml S. cerevisiae-derived mannan or 100 μg/ml glucan phosphate and then added to RC or SC. Thereafter, cytokine and chemokine levels were determined in supernatants by Bio-Plex or ELISA. Each graph illustrates cumulative results from four separate experiments. Asterisks represent significant differences between untreated and glucan-phosphate treated (p < 0.05).
To confirm these findings, we examined the response of RAW 264.7 macrophages over-expressing dectin-1 to A. fumigatus SC. Similar to what we had observed in the alveolar macrophages, there was little to no production of TNF-α in response to RC, whereas SC resulted in significant induction of TNF-α (Figure 4B). The production of TNF-α could be significantly inhibited by pretreatment with 2A11 or the dectin-1 antagonist glucan phosphate, but not by mannan. Taken collectively, these results indicate that cytokine and chemokine production by alveolar macrophages in response to A. fumigatus is mediated by dectin-1, but that this occurs only during the swelling and germination of conidia.
Specific Binding of Dectin-1 to A. fumigatus SC
To further verify the interaction between dectin-1 and beta-glucan moieties of A. fumigatus, we constructed a soluble fusion protein consisting of the extracellular portion of dectin-1 containing the carbohydrate recognition domain [25] fused with the Fc portion of murine IgG1 (termed s-dectin-mFc). Using s-dectin-mFc as a probe to examine the levels of exposed beta glucan on the surface of the different morphologies, we observed by fluorescent deconvolution microscopy that RC (2 h) showed very low beta-glucan staining with s-dectin-mFc (Figure 5A), whereas SC (6 h) (Figure 5B) and early germlings (10 h) (Figure 5C) stained brightly. Beta-glucan moieties exposed on mature hyphae (24 h) (Figure 5D) were present and recognized by s-dectin-mFc, but at a much lower intensity than SC and early germlings. The beta-glucan staining pattern of SC and early germlings–pre-competent hyphae appeared more uniform with beta-glucan moieties exposed over the surface of the conidia and along the early hyphal extension. In contrast, RC beta-glucan staining was qualitatively less and more irregular and punctuated. Beta-glucan staining on live, mature hyphae appeared qualitatively lower than early germlings, but was present nonetheless. The staining pattern of beta-glucan exposure on live, mature hyphae that we observed is in general agreement with a report showing beta-glucan staining with the monoclonal 2G8 [37]. Thus, these data support the concept that beta-glucan moieties are recognized by the carbohydrate recognition domain of dectin-1 initially in A. fumigatus SC and continually recognized through the conversion to early germlings. After live, mature hyphae have completely formed, the dectin-1 ligands are still present and recognized by dectin-1, but seemingly at lower levels and therefore do not stimulate as vigorous a response. The amount of beta-glucan exposure of these four morphological forms correlated to the level of cytokine induced by RC (see Figures 2A and 3), SC/early germlings (see Figures 2B and 3), and live, mature hyphae (see Figure 2C).
Figure 5 Heightened Binding of Dectin-1 to A. fumigatus SC
A soluble fusion protein consisting of the extracellular carbohydrate recognition domain of dectin-1 fused with the Fc portion of murine IgG1 (s-dectin-mFc) was constructed and incubated with live A. fumigatus RC and SC. Binding of s-dectin-mFc was detected by Cy3-conjugated, goat anti-mouse IgG antibody followed by imaging with a Zeiss Axioplan 2 upright fluorescent deconvolution microscope (Zeiss), and images were captured using 3i Slidebook Version 4.0 software (Optical Analysis). Representative micrographs show s-dectin-mFc binding to A. fumigatus grown for 2 h (A), 6 h (B), 10 h (C), and 24 h (D). Left lane images are differential interference contrast (DIC) images, and right lane images are Cy3 staining. Magnification is 630 × oil emersion for all frames.
In Vivo Administration of a s-Dectin-1-Fc Fusion Protein Abrogates Inflammation in Response to A. fumigatus
To examine the role of dectin-1 in vivo, we constructed a second s-dectin-1 fusion protein, containing mutated human IgG1 Fc (s-dectin-hFc) [38]. Preliminary studies confirmed that s-dectin-hFc could abrogate TNF-α and CCL3/MIP-1α production by alveolar macrophages in vitro (Figure 6A). A. fumigatus conidia were intratracheally administered to mice in the presence or absence of s-dectin-hFc, and a bronchoalveolar lavage (BAL) was performed after 24 h. By 24 h, detectable levels of CXCL1/KC, TNF-α, CCL3/MIP-1α, IL-6, and GM-CSF (Figure 6B) were found in BAL fluid. In contrast, mice that received A. fumigatus in the presence of s-dectin-hFc had significantly lower concentrations of each cytokine and chemokine (p < 0.05). Control experiments examining the specificity of s-dectin-hFc indicated that mice administered LPS in the presence or absence of s-dectin-hFc for 24 h had no differences in BAL-fluid TNF-α levels (unpublished data). As CXCL1/KC and CCL3/MIP-1α have been shown to be essential for the recruitment of innate immune cells such as neutrophils during IPA [39,40], we hypothesized that their reduction may lead to a difference in cellular recruitment to the infected lungs. Indeed, mice that had received A. fumigatus in the presence of s-dectin-hFc had fewer total recruited cells in the BAL fluid as determined by differential counting as well as fewer absolute neutrophil numbers when compared with mice challenged with A. fumigatus in the absence of s-dectin-hFc (Figure 6C). Quantitative plate count analysis indicated that mice receiving s-dectin-hFc at the time of inoculation had modestly higher (approximately 35%) A. fumigatus organisms levels 24 h after challenge (Figure 6D; p = 0.0502 by two-tailed t test, p = 0.0281 by the two-tailed Mann-Whitney test). Thus, these data indicate that administration of a s-dectin-1 fusion protein in vivo inhibits the inflammatory response to A. fumigatus, leading to reductions in innate cell recruitment to the lung and subsequent impairment of lung clearance of A. fumigatus.
Figure 6 Blockage of Dectin-1-Mediated Inflammation In Vivo after A. fumigatus Lung Challenge
C57BL/6 mice were intratracheally administered live A. fumigatus conidia in the presence or absence of s-dectin-hFc. Mice were sacrificed 24 h later, and a BAL was performed.
(A) Alveolar macrophages were co-cultured for 24 h with live A. fumigatus in the presence or absence of s-dectin-hFc (10 μg/ml). Supernatant cytokine and chemokine levels were determined by Bio-Plex or ELISA. (A) illustrates cumulative results from four separate experiments. Asterisks represent significant differences between untreated and s-dectin-1–containing wells (p < 0.05). Data are expressed as mean pg/ml + SEM.
(B) Cytokine and chemokine levels in clarified BAL fluid from untreated and soluble (s-dect)–treated mice was measured by Bio-Plex. (B) illustrates representative results from three independent experiments (n = 5–7 mice per group). Asterisks represent significant differences between untreated and s-dectin-1–treated mice (p < 0.05). Data are expressed as mean pg/ml + SEM.
(C) Total cell counts (Total) in BALF fluid as enumerated on a hemacytometer. Neutrophil (PMN) concentrations were determined by calculating the percentages of neutrophils in three to five sets of 100 cells and multiplying the percentage with the total BALF cell number.
(D) Lungs were excised from non-lavaged–untreated and s-dectin-hFc–treated mice, homogenized, followed by serial 1:10 dilutions, and plated onto potato dextrose agar. CFU/lung were determined after incubating the plates for 24 h at 37 °C. (D) illustrates representative results from three independent experiments (n = 5–7 mice per group).
A. fumigatus–beta-glucan moieties induce inflammation, both in vitro and in vivo, which is to our knowledge the first such report. We identified the receptor on alveolar macrophages responsible for the inflammatory response as the beta-glucan receptor dectin-1. Our studies show that ligation of dectin-1 on alveolar macrophages to unmasked beta-glucan moieties of live A. fumigatus swollen and early germlings leads to the potent induction of a variety of proinflammatory cytokines and chemokines, which is involved in the recruitment of neutrophils. These results provide critical insight into one of the earliest recognition events after inhalation of A. fumigatus and show the importance of alveolar macrophage–associated, beta-glucan–initiated, dectin-1 signaling in generating the appropriate inflammatory signals in response to A. fumigatus.
Materials and Methods
Mice.
Male C57BL/6 mice, 6–8 wk of age, were purchased from National Cancer Institute, National Institutes of Health (Bethesda, Maryland, United States). Toll-like-receptor-2–deficient (TLR2−/−) mice were generated on the 129SvJ × C57BL/6 background and were backcrossed to the C57BL/6 strain as previously described [41]. All animals were housed in a specific pathogen-free facility and handled according to institutionally recommended guidelines.
Preparation of A. fumigatus conidia.
A. fumigatus isolate 13073 (ATCC, Manassas, Virginia, United States) was maintained on potato dextrose agar for 5–7 d at 37 °C. Conidia were harvested by washing the culture flask with 50 ml of sterile, phosphate-buffered saline supplemented with 0.1% Tween 20. The conidia were then passed through sterile gauze followed by passage through a sterile 40 μm nylon membrane to remove hyphal fragments, and then enumerated on a hemacytometer.
Macrophage populations.
RAW 264.7 macrophages over-expressing dectin-1 were generated and maintained as previously described [23]. For alveolar macrophage isolation, male C57BL/6 or TLR2−/− mice were anesthetized with intraperitoneal pentobarbital and sacrificed by exsanguination. Thereafter, lungs were lavaged through an intratracheal catheter with pre-warmed (37 °C) calcium- and magnesium-free PBS supplemented with 0.6 mM EDTA. A total of 10 ml was used in each mouse in 0.5-ml increments with a 30-s dwell-time. The lavage fluids were pooled and centrifuged at 300 × g for 10 min, and the cells collected for A. fumigatus co-culture. To ensure that each cell preparation was enriched for macrophages, 2.5 × 104 cells were cytospun onto slides and stained with hematoxylin and eosin. Cell preparations were >98% enriched for alveolar macrophages.
A. fumigatus-macrophage co-culture for cytokine and chemokine induction.
Alveolar macrophages or RAW-dectin macrophages [23] (3 × 105) were pre-treated with the anti–dectin-1 antibody 2A11 (5 μg/ml) or isotype for 30 min [26] and thereafter co-cultured with A. fumigatus conidia at a ratio of 1:1 for various times in a 96-well plate at 37 °C, 5% CO2. Controls included alveolar or RAW macrophages cultured in medium alone. Thereafter, the contents of each well were collected and the supernatants analyzed for IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-10, IL-12p40, IL-12p70, IL-17, IFN-γ, GM-CSF, G-CSF, TNF-α, MIP-1α, RANTES, and KC levels using the Bio-Plex Protein Array System (Bio-Rad, Hercules, California, United States) as per manufacturer's instructions. MIP-2 concentrations were determined using a commercially available ELISA kit (R&D Systems, Minneapolis, Minnesota, United States) as per manufacturer's instructions. To determine the response to SC, A. fumigatus RC were cultured for 6 h at 37 °C, 5% CO2 and allowed to swell [10,31,42]. Thereafter, macrophages were added for an additional 4 h. Controls for these studies included alveolar macrophages added to RC for 4 h. To determine the response to A. fumigatus hyphae, A. fumigatus conidia were cultured for 24 h at 37 °C, 5% CO2 prior to the addition of macrophages for 4 h, 8 h, or 24 h. Spontaneous cytokine and chemokine production in unstimulated cultures was subtracted from stimulated cultures in order to calculate the net concentration induced by A. fumigatus. To determine whether macrophage cell death occurred in co-cultures, supernatants were analyzed for LDH levels using an LDH kit (Sigma, St. Louis, Missouri, United States) as per manufacturer's instructions. Caspase 3 levels in cell lysates were also analyzed using the EnzChek Caspase 3 Assay Kit containing the rhodamine 110 bis-(N-CBZ-L-aspartyl-Lglutamyl-L-valyl-L-aspartic acid amide) (Z-DEVD–R110) substrate (Molecular Probes, Eugene, Oregon, United States) as per manufacturer's instructions. In specific experiments, macrophages were pretreated with cytochalasin D (10 μM), 250 μg/ml mannan (both from Sigma, St. Louis, Missouri, United States), or 100 μg/ml glucan phosphate [23,26] for 30 min at room temperature prior to addition to RC or SC. For analysis of responses to heat-killed A. fumigatus, A. fumigatus conidia were cultured for 3 h, 6 h, or 9 h at 37 °C, 5% CO2 followed by heat-killing at 100 °C for 10 min [10]. Thereafter, heat-killed A. fumigatus were co-cultured with alveolar macrophages at a ratio of 1:1 for 6 h at 37 °C, 5% CO2 in a 96-well plate. Some experiments employed A. fumigatus organisms killed by 70% ethanol treatment for 30 min at room temperature. All killed A. fumigatus were plated onto potato dextrose agar at 37 °C for 48 h to confirm negative growth. In specific experiments, RAW 264.7 macrophages were cultured with LPS (100 ng/ml) or Pam(3)Cys (10 μg/ml) (both from InvivoGen, San Diego, California, United States) in the presence or absence of 2A11 for 16 h, followed by analysis of cytokine and chemokine levels in supernatants.
Analysis of A. fumigatus internalization.
Alveolar macrophages were isolated as described above and adhered to poly-L-lysine-coated glass slides (Polysciences Inc., Warrington, Pennsylvania, United States) for 60 min at 37 °C. After being washed, separated slides were incubated with dimethyl sulfoxide (DMSO, vehicle) or cytochalasin D (10 μM) for 60 min at 37 °C, followed by incubation for 60 min with fluorescein isothiocyanate-labeled A. fumigatus conidia (0.1 mg/ml FITC for 60 min at room temperature) [20]. After being washed, slides were counterstained with 4,6-diamidino-2-phenylindole,dihydrochloride (DAPI, Molecular Probes, Eugene, Oregon, United States) nucleic-acid stain (0.4 μg/ml, 10 min at room temperature), followed by application of Prolong (Molecular Probes) mounting media. Slides were analyzed on a Zeiss Axioplan 2 upright fluorescent deconvolution microscope (Carl Zeiss, Oberkochen, Germany), and images were captured using 3I Slidebook Version 4.0 software (Optical Analysis, Nashua, New Hampshire, United States).
s-Dectin-Fc constructs.
Two soluble fusion proteins consisting of the extracellular domain of murine dectin-1 fused with either the heavy chain of murine IgG1 (s-dectin-mFc) or a mutated Fc portion of human IgG1 (s-dectin-hFc) were constructed. For s-dectin-mFc, cDNA encoding the extracellular domain of dectin-1, consisting of amino acids 69 to 244 [25], was amplified from a PCR 3.1 plasmid encoding the full-length murine dectin-1 receptor using the primers GGGTACCGACGACACAATTCAGGG and GGATCCACGCGGAACCAGCAGTTCCTTCTCACAG. The cDNA encoding CH2-CH3 murine IgG1 regions were amplified using the primers CTGGTTCCGCGTGGATCCGTGCCCAGGGATTGTGGT and GAATTCTCATTTACCAGGAGAGTG from the pACCKP2 plasmid containing the TNF receptor extracellular domain linked to murine IgG1 heavy chain [43]. For the s-dectin-hFc construct, the pSecTag2 (Invitrogen) plasmid containing a mutated Fc portion of human IgG1 [38] was used. The products were combined at a 1:1 ratio, and PCR was performed using the 5′ dectin-1 primer and the 3′ IgG1 primer. The chimeric PCR product was isolated and purified via gel extraction and was subcloned into the TOPO-TA Vector (Invitrogen). Using M13 Forward and Reverse primers, the s-dectin-Fc DNAs were amplified via PCR and digested with KpnI and EcoRI and inserted in-frame into the multiple cloning site of pSecTag2 C mammalian expression vector (Invitrogen), containing the Igκ-leader sequence, facilitating protein secretion. To verify fusion protein expression, the pSecTag2 s-dectin-mFc or s-dectin-hFc constructs were transfected into HEK293T cells using Lipofectamine 2000 (Invitrogen). Western blotting of supernatants from transfected cells revealed a 120-kD product on non-reducing SDS-PAGE that reacted with either anti-human IgG1 or anti-murine IgG1. For analysis of the effects of s-dectin-hFc on cytokine and chemokine production, alveolar macrophages were co-cultured with A. fumigatus conidia at a ratio of 1:1 for 24 h in the presence or absence of s-dectin-hFc (10 μg/ml) in a 96-well plate at 37 °C, 5% CO2. Controls included alveolar macrophages cultured in medium alone. Thereafter, the contents of each well were collected and the supernatants analyzed for cytokines and chemokines by Bio-Plex (Bio-Rad, Hercules, California, United States).
Analysis of A. fumigatus, beta-glucan exposure.
A. fumigatus RC were adhered for 2 h, 6 h, 10 h, or 24 h to sterile, round glass coverslips and incubated in the presence or absence of conditioned media containing s-dectin-mFc followed by Cy3-conjugated, goat anti-mouse IgG1. After being washed, the coverslips were mounted onto glass slides and Prolong mounting media (Molecular Probes) was applied. The coverslips were analyzed on a Zeiss Axioplan 2 upright fluorescent deconvolution microscope (Zeiss), and images were captured using 3I Slidebook Version 4.0 software.
In vivo A. fumigatus challenge.
Mice were lightly anesthetized with isoflurane and held in a vertical, upright position. A. fumigatus conidia, 5 × 106 in a volume of 50 μl, in the presence or absence of s-dectin-hFc (40 μg/ml) was administered to mice via the caudal oropharynx. At 24 h post-inoculation, mice were anesthetized with intraperitoneal pentobarbital, sacrificed by exsanguination, and a BAL was performed. The first ml of BAL fluid was collected, the supernatant clarified by centrifugation, and stored at −80 °C until use in Bio-Plex (Bio-Rad) assays. For total BAL-fluid cell determinations, the cell pellet from each individual sample was resuspended in 1 ml of tissue culture media and enumerated by a hemacytometer using trypan blue dye exclusion. To determine neutrophil counts in BAL fluid, 2.5 × 104 cells from each lavage pellet was cytospun onto slides and stained with Diff-Quik (Fisher Scientific, Pittsburgh, Pennsylvania, United States). Thereafter, percentages of lymphocytes, macrophages, and neutrophils were determined in blinded fashion. To determine A. fumigatus lung burden, lungs were excised from non-lavaged untreated and s-dectin-hFc–treated mice and homogenized using a Polytron PT1200E tissue homogenizer (Kinematica, Newark, New Jersey, United States). Serial 1:10 dilutions were plated onto potato dextrose agar, and CFU/lung were determined after 24 h at 37 °C.
Statistics.
Data were analyzed using StatView statistical software (Brainpower, Calabasas, California, United States). Comparisons between groups were made with analyses of variance and appropriate ad hoc testing. The two-tailed unpaired t test or the two-tailed nonparametric Mann-Whitney test was employed. Significance was accepted at p < 0.05.
Supporting Information
Figure S1 Internalization of A. fumigatus by Alveolar Macrophages
(142 KB PDF)
Click here for additional data file.
We acknowledge Jean-Paul Latge and Oumaima Ibrahim-Granet, Unité des Aspergillus, Institut Pasteur, Paris, France, for valuable insight. This work was supported by the American Lung Association, the Parker B. Francis Foundation, and Public Health Service grant HL080317 (CS), Public Health Service grants HL61721 and HL62052 (JKK), and the Wellcome Trust and the Edward Jenner Institute for Vaccine Research (GDB). GDB is a Wellcome Trust Senior Research Fellow in Biomedical Science in South Africa.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. CS, RRR, AM, and SMP performed the experiments. CS, SMP, and GDB analyzed the data. CS, RRR, DLW, SG, JKK, and GDB contributed reagents/materials/analysis tools. CS conceived and designed the experiments, and wrote the paper.
Abbreviations
BALbronchoalveolar lavage
CSFcolony stimulating factor
GMgranulocyte monocyte
ILinterleukin
IPAinvasive pulmonary aspergillosis
LDHlactate dehydrogenase
MIPmacrophage inflammatory protein
RCresting conidia
SCswollen conidia
s-dectinsoluble dectin
TNF-αtumor necrosis factor α
TLRtoll-like receptor
==== Refs
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1633604810.1371/journal.pbio.0030364EssayAnimal BehaviorEvolutionNoneEvolution for Everyone: How to Increase Acceptance of, Interest in, and Knowledge about Evolution EssayWilson David Sloan 12 2005 13 12 2005 13 12 2005 3 12 e364Copyright: © 2005 David Sloan Wilson.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 success story about teaching evolution: when presented as unthreatening, explanatory, and useful, evolution can be easily appreciated by most people, regardless of their religious and political beliefs or prior knowledge of evolution.
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Evolution is famously controversial, despite being as well established as any scientific theory. Most people are familiar with the dismal statistics, showing how a large fraction of Americans at all educational levels do not accept the theory of evolution [1], how efforts to teach evolution often fail to have an impact [2], and how constant vigilance is required to keep evolution in the public school curriculum [3]. Even worse, most people who do accept the theory of evolution don't relate it to matters of importance in their own lives. There appear to be two walls of resistance, one denying the theory altogether and the other denying its relevance to human affairs.
This essay reports a success story, showing how both walls of resistance can be surmounted by a single college course, and even more, by a university-wide program. It is based on a campus-wide evolutionary studies program called EvoS (http://bingweb.binghamton.edu/~evos/), initiated at Binghamton University in 2002, which currently includes over 50 faculty members representing 15 departments. Enthusiasm at all levels, from freshmen students to senior administrators, makes EvoS a potential model for evolution education that can be duplicated; the basic ingredients are present at most other institutions, from small colleges to major universities.
In this essay, I will briefly describe the basic ingredients at both the single-course and program levels. First, however, it is important to document the claim that evolution can be made acceptable, interesting, and powerfully relevant to just about anyone in the space of a single semester.
Demonstrating Success
The single course is titled “Evolution for Everyone” and does not require any prerequisites. The students who enrolled in fall 2003 came from majors as diverse as anthropology, art, biology, business, chemistry, cinema, computer science, creative writing, economics, education, engineering, english, history, human development, linguistics, management, mathematics, nursing, philosophy, physics, political science, and psychology. The 2003 course was assessed with the help of two experts on evolution education: Dr. Brian Alters, Director of the Evolution and Education Research Center at McGill University (Montreal, Canada), and Dr. Craig E. Nelson, Professor of Biology at Indiana University (Bloomington, Indiana, United States) [4–7]. Information gathered on each student at both the beginning and end of the course included religious and political orientation, prior exposure to evolution education, and an assessment of general thinking skills without reference to specific subject matter. In addition, students wrote short essays throughout the course that were submitted electronically and analyzed for words associated with cognitive operations using the software Linguistic Inquiry and Word Count [8,9]. Finally, students assessed the course anonymously in addition to providing information associated with their identity. The details of the assessment are available from the author upon request, and the major results are summarized here.
Acceptance of, interest in, and knowledge about evolution
Figure 1 shows the distribution of anonymous responses to the question, “How much has this class changed your views on evolution and its relevance to human behavior, on a scale from −10 (negative change) to +10 (positive change)?” There was a large shift in the positive direction, so much that almost no one who took the course remained unmoved or shifted in the negative direction. The anonymous verbal evaluations speak more eloquently than the numbers: “This course provides evidence that evolution is evident in everything. It revolutionized my way of viewing problems.” “I have always agreed with evolution but I did not know how much of everyday life was affected by it.” “I came into the class not knowing a lot about evolution. I now have an entirely new outlook on how evolution can be applied to many aspects of life.” The positive anonymous evaluations are also reflected in the before-and-after measurements gathered on each student, and become even more interesting when related to the background variables.
Figure 1 Changed Views on Evolution
Anonymous response to the question “How much has this class changed your views on evolution and its relevance to human behavior, on a scale from −10 (negative change) to +10 (positive change)?”
Political orientation
Evolution has often been used to support conservative political ideologies, to the dismay of liberal thinkers. It might seem that politically conservative students would embrace the course material more enthusiastically than the liberals, but this was not the case. The course was equally effective across the political spectrum.
Religious orientation
The average student was moderately religious, and variation spanned the range from committed atheists to committed believers. Numerous students wrote at length about their religious upbringing and values in their first assigned essay on the topic, “What I know about evolution and its relevance to human affairs.” Surely, the famous tension between evolution and religion should be reflected in the course assessment measures. Remarkably, it was not. The course was effective across the spectrum of religious belief.
Prior evolution and science education
The average student had at least some exposure to evolution in high school, and variation spanned the range from no exposure to prior college courses. There was also extreme variation in exposure to science education, as had been expected from the diversity of majors. Remarkably, the course was again effective across the entire spectrum. What the students gained from the class did not require, and was not provided by, prior science and evolution education.
General cognitive development
As outlined in more detail below, the course involved first teaching a set of basic principles and then applying them to a broad range of topics. This experience increased general thinking skills as well as specific knowledge about evolution, according to before-and-after measurement of critical thinking and increase in the frequency of words indicative of cognitive operations in the essays over the course of the semester. Anonymous verbal evaluations such as “this course…has revolutionized my way of viewing problems” clearly reflect more than a body of facts learned about a particular subject.
The assessment did not include a comparison with another course, because it is difficult to know what an appropriate control group would be. The most relevant comparisons are provided by the internal analysis, especially the before-and-after comparisons for single individuals and comparison of individuals who differ in their background variables. Undoubtedly, there are students who didn't take the course that would have been less receptive to the material, including some committed to creationism, but the course clearly comes close to living up to its name, “Evolution for Everyone.” Now that the success of the course has been documented, we can examine the ingredients that make it work.
How It Works
Alters and Nelson have written on the need for science education to go beyond strict lecture mode and to teach the scientific process in addition to factual material [5–7]. “Evolution for Everyone” employs as many of these techniques as possible, some of which will be described below. However, the main ingredients of success involve teaching a sequence of ideas.
Beginning with implications
The main problem with accepting evolution involves implications, not facts. Threatening ideas are like other threats—the first impulse is to run away or attack them. Make the same ideas alluring, and our first impulse is to embrace them and make them our own. Neither impulse is very respectable scientifically. After all, scientists are supposed to accept ideas when they are true, regardless of their consequences. Nevertheless, the key to making evolution a subject that anyone can understand and everyone should want to understand is to focus first on the implications. A good theory should do two things. First, it should explain the world as it has existed in the past and exists in the present. Second, it should provide ways to improve the world in the future. The first major idea to convey is that evolution is a good theory by both of these standards.
This requires a discussion of past threatening associations, even before the theory is presented. Evolution has been associated with immorality, determinism, and social policies ranging from eugenics to genocide. It has been used to justify racism and sexism. All of these negative associations must be first acknowledged and then challenged. It's not as if the world was a nice place before Darwin and then became mean on the basis of his theory. Before Darwin, religious and other justifications were used to commit the same acts, as when the American colonists used the principle of divine right to dispossess Native Americans, and men claimed that women were designed by “God and Nature” for domestic servitude. These beliefs are patently self-serving and it should surprise no one that an authoritative scientific theory would be pressed into the same kind of service. It is the job of intellectuals to see through such arguments and not be taken in by them. Moreover, the deep philosophical issues associated with topics such as morality, determinism, and social equality are increasingly being approached from a modern evolutionary perspective and are among the topics to be discussed in the course. When these issues are discussed at the beginning of the course, students put their own threatening associations with evolution on hold and become curious to know how a subject that they associate with science (evolution) can shed light on a subject that they associate with the humanities (philosophy). Students who indicate exceptional interest are referred to books that are both authoritative and accessible, such as Daniel Dennett's Darwin's Dangerous Idea [10–15].
Adaptationism—A third way of thinking
The next task is to formally present the concept of natural selection. The principles of phenotypic variation, corresponding variation in fitness, and heritability are so simple and seemingly inevitable in their consequences that the main question is not “What are they?” or “Are they true?” but “Why should they be regarded as such a big deal?” To answer this question, I ask the students to imagine how someone would explain the properties of an organism before Darwin's theory of evolution. Only two options would be available; theological (God's handiwork) or material (explaining the properties of the whole from the properties of the component parts). The big deal about natural selection is that it provides a third explanatory framework, different from both theology (this is already obvious to the students) and materialism (this is not). To the extent that the material composition of organisms results in heritable variation, it becomes a kind of living clay that can be molded by environmental forces that influence survival and reproduction. The most interesting properties of a clay sculpture are caused by the molding action of the artist, not the physical properties of clay. In the same way, evolutionary biologists routinely make predictions about the properties of organisms (such as “many prey organisms match their background to avoid detection by predators”) without any reference to the physical materials of the organisms, including their genes.
This is the fundamental distinction between proximate and ultimate causation in evolutionary biology, and it is the second major idea in the sequence that I attempt to convey to students. The distinction is important because it has such predictive value. Knowing only a little about an organism and its environment, one can make predictions about its properties that are not certain to be correct, but which are likely to be correct. In mundane terms, they are good guesses. I make this point with a class exercise of the sort recommended by Alters and Nelson. Choosing the subject of infanticide, I say that superficially it might seem that organisms would never evolve to kill their own offspring, but with a little thought the students might be able to identify situations in which infanticide is biologically adaptive for the parents. I ask them to form small groups by turning to their neighbors to discuss the subject for five minutes and to list their predictions on a piece of paper.
After the lists are collected, I ask the students for some of their predictions to list in front of the whole class. They are eager to talk, and reliably identify the three major adaptive contexts of infanticide: lack of resources, poor offspring quality, and uncertain paternity, along with less likely possibilities, such as population regulation, that can be set aside for future discussion. I conclude by attempting to convey the simple but profound message of the exercise: How can they, mere undergraduate students, who know almost nothing about evolution and (one hopes) know nothing at all about infanticide, so easily deduce the major hypotheses that are in fact employed in the study of infanticide for organisms as diverse as plants, insects, and mammals? That is just one example of the power of thinking on the basis of adaptation and natural selection.
One explanatory framework, many applications
The next major idea to convey is that the same reasoning can be applied to an infinite number of topics. Why are males larger than females in some species and the reverse in others? Why are there two sexes in the first place? Why are males and females born in equal proportions in some species but not others? Why do some organisms reproduce once and then die, while others reproduce at repeated intervals? Why do some plants live for three weeks and others for 3,000 years? Why are some organisms social and others solitary? Among social organisms, why do some individuals cooperate and others exploit? Predictions based on natural selection provide a starting point for inquiry on all of these subjects, just as with infanticide. Evolutionary theory provides an escape from the extreme specialization that characterizes so much of the rest of science. It transcends taxonomic boundaries because organisms as different as plants, insects, and mammals can be similar in terms of their adaptations to similar environmental problems, for infanticide and many other subjects. It transcends subject boundaries because the problem of how to select food (for example) is very similar to the problem of how to select a mate. Evolutionary biologists sometimes take it for granted that they possess a common language that can be spoken across so many domains of knowledge. It is an extraordinary fact and needs to be presented as such to students learning about evolution for the first time.
Humans in addition to the rest of life
One of the biggest tactical errors in teaching evolution is to avoid discussing humans or to restrict discussion to remote topics such as human origins. The question of how we arose from the apes is fascinating and important, but is only one of any number of questions that can be asked about humans from an evolutionary perspective—including infanticide. If evolutionary theory can make sense of this subject for organisms as diverse as plants, insects, and mammals, what about us? If we operate by different rules than all other creatures for this and other subjects, why should this be so? The most common answer to this question is “learning and culture,” but what exactly are these things? Do they exist apart from evolution, or do they themselves need to be explained from an evolutionary perspective? I raise these issues early in the course, not to answer them, but to emphasize how much is “on the table” as part of the course.
For millennia, humans have regarded themselves as categorically different from other creatures in their mental, moral, and aesthetic abilities. We are obviously unique in some respects, but in exactly what way needs to be completely rethought. Nonhuman species have been discovered to be vastly more sophisticated and behaviorally flexible than most people imagined even 30 years ago. They solve the recurrent problems of their environments as well as, or better than, humans. They can change not only their behaviors but their entire bodies and life histories in response to environmental change. Something happened several million years ago to give our species a special kind of behavioral flexibility, and the ability to socially transmit behaviors in a cumulative fashion (culture). A sophisticated knowledge of evolution is required to discover exactly what happened. As for the consequences of these new mental capacities, they do not necessarily cause our species to play by a different set of rules than other species. Perhaps they enable us to play the evolutionary game better and faster than other species. For a specific topic such as infanticide, it all boils down to an empirical question: Do people commit infanticide under the same environmental conditions as other species? It turns out that there is a sizeable literature for this subject, to be reviewed later in the course along with a more general discussion of the nature of human learning and culture. Students who become exceptionally interested are directed to a growing genre of accessible and authoritative books, such as Jared Diamond's Guns, Germs, and Steel [16–22].
It might seem that boldly discussing subjects such as human infanticide (which the students quickly connect to the contemporary issue of abortion), along with other topics such as sex differences and homosexuality later in the course, is the ultimate in political incorrectness. However, I have taught this material for many years in prior courses without a single complaint, and the assessment of “Evolution for Everyone” demonstrates an overwhelmingly positive response across the religious and political spectrum. Clearly, there is a way to proceed that arouses intense interest without animosity or moral outrage. In the case of infanticide, evolutionary theory doesn't say that it's right—it is used to make an informed guess about when it occurs. All of the students want to know if the guess proves to be correct for humans in addition to other creatures, regardless of their moral stance on abortion. Moreover, they see that the information can be useful for addressing the problem, whatever particular solution they have in mind. The importance of culture is not denied, but becomes part of the evolutionary framework rather than a vaguely articulated alternative. The picture that emerges makes sense of cases of infanticide that appear periodically in the news (typically young women with few resources and under the influence of a male partner who is not the father) and that previously seemed inexplicable. Nearly everyone values this kind of understanding and thinks that it can be put to positive use, as demonstrated by the quantitative assessment. More generally, including humans along with the rest of life vastly increases students' interest in evolution and acceptance to the degree that it seems to lead to understanding and improvement of the human condition.
Not everything is adaptive
Readers of this essay familiar with evolutionary theory might be wondering why my sequence of ideas relies so heavily upon adaptation and natural selection up to this point. Isn't there more to evolution than natural selection, as Stephen Jay Gould cautioned at every opportunity [23–25]? The answer is “yes,” but this point needs to come later in the sequence, after the basic concept of adaptation and its explanatory power have been established. There are many reasons why organisms are not perfectly adapted to their environment. There might be insufficient time, especially when the environment changes, as it does with a vengeance in our own species. The living clay of heritable variation is by no means infinitely malleable. There are hidden connections among traits based on genetics and development, such that selection for one trait drags others along. Gene frequencies change by drift and mutation in addition to selection. The list goes on and on, and mature research programs in evolutionary biology pay attention to all of these factors.
If so, then why should adaptation and natural selection enjoy a special status? The answer is quite practical: It is usually much easier to make a prediction based on knowledge of the organism in relation to its environment than predictions based on the other factors. In the case of infanticide, my students easily derived the major adaptationist predictions, but would be at loss to derive predictions based on phylogeny, developmental and genetic constraints, neural mechanisms, and so on. This asymmetry in the ease of making predictions, combined with the admitted importance of the hard-to-predict factors, leads to proper understanding of the adaptationist program [26]. It is not a claim that everything is adaptive, but an effective method of scientific inquiry that begins with an adaptationist hypothesis as the best first guess, with the full expectation that it will be partially wrong due to the many hard-to-predict factors. Partial failures are then used as guide for the identification of other factors. This is not the only way to conduct evolutionary science, but I have used it as an effective way to order the sequence of ideas pedagogically. The Gouldian paradigm does not come first, but it does occupy center stage for a section of the course with the intention of making it a permanent part of the conceptual framework being built.
Evolutionary adaptations are not always benign
Even when organisms are highly adapted to their environments, their properties do not always correspond to the intuitive notion of adaptation. Everyone can agree about the impressive design of a butterfly that exactly resembles a leaf, or a fish shaped to cruise effortlessly though the water, but how about a species that degrades its own habitat or a social partner who fails to cooperate? Fitness is a relative and local concept. It doesn't matter how well an organism survives and reproduces, only that it does so better than other organisms in its vicinity. As a result, many evolutionary adaptations appear selfish and shortsighted in human terms, creating problems at larger temporal and spatial scales.
If behaviors regarded as immoral in human terms are adaptive and “natural,” then aren't all the fears about evolution justified? No—because behaviors that are regarded as moral in human terms are also adaptive and “natural” under the right circumstances, which can be illustrated with the following exercise of the sort suggested by Nelson and Alters. First, the class is asked to list the behaviors that they associate with morality. The most common items include altruism, honesty, love, charity, sacrifice, loyalty, bravery, and so on. Then they are asked to list behaviors that they associate with immorality, and respond with opposite items such as selfishness, deceit, hatred, miserliness, and cowardice. With these lists in mind, the students are asked three questions: (1) What would happen if you put a single moral individual and a single immoral individual together on a desert island? (The students quickly conclude that the moral individual would become shark food within days.) (2) What would happen if you put a group of moral individuals on one island and a group of immoral individuals on another island? (The students are equally quick to conclude that the moral group would work together to escape the island or turn it into a little utopia, while the immoral group would self-destruct.) (3) What would happen if you allow one immoral individual to paddle over to Virtue Island? (The answer to this question is complex because it is a messy combination of the straightforward answers to the first two questions.)
This exercise is simple and entertaining, but profound in its implications. It shows that most of the traits associated with human morality can be biologically adaptive. Groups of moral individuals are likely to survive and reproduce better than any other kind of group. The problem with morality is its vulnerability to subversion from within. To the extent that natural selection is based on fitness differences within groups, behaviors associated with immorality are the expected outcome. To the extent that natural selection is based on fitness differences among groups, behaviors associated with morality are the expected outcome (these statements apply to all evolutionary models of cooperation and altruism when the relevant groups are appropriately defined, including inclusive fitness theory, evolutionary game theory, and multilevel selection theory) [27,28]. The discerning student quickly perceives a disturbing corollary: Can't behaviors that count as moral within groups be used for immoral purposes among groups? The answer to this question is “yes,” which means that moral conduct among groups is a different and more difficult evolutionary problem to solve than moral conduct within groups [14,27].
The important point is that evolutionary theory can potentially explain the evolution of behaviors associated with morality and immorality. This is vastly different than the usual portrayal of evolution as a theory that explains immorality but leaves morality unaccounted for. The average student is well aware that immoral behaviors usually benefit the actor, that human groups have a disturbing tendency to confine moral conduct to their own members, and so on. When evolutionary theory is presented as a framework for understanding these patterns in all their complexity, including the good, the bad, the beautiful, and the ugly, it is perceived as a tool for understanding that can be used for positive ends, rather than as a threat. These issues are discussed in more detail later in the course. In the initial sequence of ideas, it is important to establish that evolutionary adaptations are not always adaptive in the everyday sense of the word, and that societal adaptations in particular require special conditions to evolve.
Using the framework
At this point (about mid-semester), the students are told that they have acquired a conceptual framework that can be used to study virtually any subject in biology and human affairs, which will be used to study particular topics for the rest of the semester. There is great flexibility in the topics that can be chosen, which is facilitated by having the students read, rather than a textbook, well-chosen articles from the primary scientific literature. I begin with the subject of Darwinian medicine; it is intrinsically interesting, illustrates a number of general principles, and is directly relevant to students preparing for careers in the health sciences.
The health sciences are enormously sophisticated in the study of proximate mechanisms but often ignorant of evolutionary principles, as pointed out by G.C. Williams and R. Nesse in their influential scientific article, “The Dawn of Darwinian Medicine” [29] and popular book Why We Get Sick [30]. Simply put, most doctors and medical researchers don't know what the students have learned during the first half of the course. I begin by assigning two articles from the primary scientific literature, one on pregnancy sickness [31] and the other on the anti-microbial properties of spices [32]. Both topics strike the students as arbitrary, as if they were pulled out of a hat. Yet each turns out to be a fascinating scientific detective story enlightened by the evolutionary principles that they learned during the first half of the course. The tendency of women to become nauseated during the first trimester of pregnancy has been treated by doctors as a sickness to be cured with medicine. In fact, it is an important biological adaptation that causes the mother to avoid foods that would damage her developing fetus. The use of spices to flavor food seems like an aspect of culture without any biological basis. In fact, most spices have important antimicrobial properties and their use within and among cultures is proportional to the likelihood of food spoilage. Both articles are authored by Paul Sherman, an evolutionary biologist without any prior training in either specific subject. Remarkably, both of his co-authors were undergraduate students when the papers were researched and written. How could they make such important contributions to knowledge without years of specialized training? They used evolutionary theory to ask the right questions, just as my students were able to do for the subject of infanticide.
After these two articles on specific topics, I assign the more general article on Darwinian Medicine by Williams and Nesse [29]. I also have the students make their own search for scientific articles using the key words “Darwinian medicine,” and ask them to post abstracts on the course Web site. This section of the course reveals the existence of a new scientific field that the students can understand and to which they can potentially contribute, even as undergraduates.
The section on Darwinian medicine is followed by sections on other topics, including violence, sexuality, personality, and culture. Like medicine, these subjects are voluminous and sophisticated in their own ways but are often ignorant of basic evolutionary principles, enabling foundational insights to be made that the students can easily appreciate. They realize that they have started to approach the study of humans in the way that evolutionary biologists approach the rest of life, with a common language that can be spoken across many domains of knowledge.
A subject of their own
The final vital ingredient of the course is to have the students choose their own topic to explore from an evolutionary perspective. This can be done in several ways depending upon class size and available resources. In my case, I form the students into small groups supervised by undergraduate teaching assistants, culminating in a poster session that emulates a scientific conference at the end of the semester. Most of the students become highly motivated to study “their” topic from an evolutionary perspective; in 2003, topics included adoption, alcoholism, attractiveness, body piercing, depression, eating disorders, fashion, fear, hand dominance, homosexuality, marriage, play, sexual jealousy, sibling rivalry, social roles, suicide, video games, and yawning. The topics were posted on the course Web site and students visited each other's posters during the session, providing yet another demonstration of how evolutionary theory can be used to approach a diversity of subjects.
To summarize, “Evolution for Everyone” works by establishing a general conceptual framework through a sequence of ideas. The framework is then strengthened and consolidated by applying it to a number of specific topics. Virtually all students respond to the class because they cease to be threatened by evolutionary theory and begin to perceive it as a powerful way to understand and improve the world. Once the theory becomes alluring, the only remaining obstacle to learning is the intrinsic difficulty of the subject. That, it turns out, is not much of an obstacle either. Almost anyone can master the basic principles of evolution and incorporate them into their own thinking, providing both a foundation and an incentive to advance their knowledge in subsequent courses.
From a Single Course to a Campus-Wide Program
Students who “catch the evolution bug” are usually eager to pursue their newfound interests. At Binghamton University we were able to assist them by creating a campus-wide program that can be replicated at other institutions—in spirit if not in each and every detail. The best way to learn about EvoS is by visiting its Web site (http://bingweb.binghamton.edu/~evos/), but the most basic ingredients can be summarized as follows.
An initial faculty core
Most colleges and universities have at least some faculty who are already teaching and conducting research from an evolutionary perspective. At Binghamton, we had core groups in the biology, anthropology, and psychology departments, and single individuals in other departments such as economics and philosophy. An initial faculty core can get the program going and can benefit personally by enhancing interactions with each other.
Organize existing resources
Core faculty already teach permanent courses from an evolutionary perspective, although students in a given department are usually unaware of offerings in other departments. In addition to their permanent courses, most active faculty teach special topic seminars on new subjects that interest them, involve students in their research, and so on. Most departments and higher administrative units provide modest funds for seminar speakers and new educational initiatives. These existing resources can be organized so that the whole is much more than the sum of the parts.
Maximize accessibility and minimize additional workload
At Binghamton it is possible for both undergraduate and graduate students to take a course of study that results in a certificate that accompanies their degree. Unlike a second major or a minor, both of which impose severe additional course loads on the student, a certificate program allows a given course to simultaneously count toward the certificate and one's major or graduate degree. This is important because many EvoS students are already “turned on” in other respects, with a minor or double major that would prevent them from adding still more. A certificate program is relatively easy to implement administratively (at least at Binghamton), can be integrated with existing course requirements, and is accessible to all students.
Depth and breadth
EvoS is designed both to increase competence in one's chosen subject area (depth) and to transcend disciplinary boundaries (breadth). Breadth is achieved in part through the EvoS seminar series that will be described in more detail below. Depth is achieved by having an EvoS faculty advisor help each student develop a curriculum tailored to his or her interests from the menu of offerings.
Growing the program
Expanding the program beyond its initial core requires confronting some uncomfortable truths about the status of evolutionary theory in academia. Earlier, I said that there are two walls of resistance to evolution, one that denies its validity altogether and another that denies its relevance to human affairs. It is easy for academics to ridicule the first wall (creationism and its born-again cousin, intelligent design), but the second wall has existed within academia for most of the 20th century, shaping the history of all human-related subjects. The wall is still staunchly maintained in some quarters, but even the most open-minded scientists and scholars are handicapped by the barriers separating evolutionary theory from their disciplines in the past. The most important developments in human-related research from an evolutionary perspective have taken place within the last 20 years, and weren't even on the radar when many faculty members were receiving their own graduate training. Expanding the program therefore requires faculty training in addition to student training.
Fortunately, it is possible to do this without imposing an unacceptable additional workload on the faculty. EvoS faculty participants are not all experts on evolution. Even better, they include some experts and others who have adopted the same receptive attitude as the students, resulting in the accumulation of expertise as the program develops. EvoS has already stimulated teaching and research activities in new subject areas, involving faculty members who were not part of the initial core. When the evolutionary perspective proves its worth to a faculty member, achieving a professional level of competence becomes a priority that contributes to rather than detracting from their career goals.
The university as a single intellectual community
The spirit of our campus-wide program is perhaps best represented by the EvoS seminar series, which brings an external speaker to campus at approximately two-week intervals. Table 1 lists a sample of speakers from the 2004–2005 academic year, which illustrates three points. First, the speakers span the length and breadth of the biological sciences, the human behavioral and social sciences, and the humanities. Second, the speakers include some of the most distinguished members of their respective fields in addition to up-and-coming young scientists. Third, the seminars are not “watered down” for a general audience, but are much the same as the speakers would give in departmental seminars at other universities. Nevertheless, all of the seminars are attended, understood, and enjoyed by a single audience of undergraduate students, graduate students, and faculty representing all departments on campus. The only thing that makes this possible is theoretical integration. The speakers and audience alike share a common conceptual framework that enables them to transcend disciplinary boundaries.
Table 1 A Sample of Speakers in the Campus-Wide EvoS Seminar Series during the 2004–2005 Academic Year
The EvoS seminar series simultaneously performs a number of important functions. It is advertised campus-wide, and speakers are usually cohosted with the most relevant department, which means that any given talk is attended by a mix of EvoS participants who attend all the talks and non-participants who are attracted by a particular speaker or topic and who encounter the evolutionary perspective for the first time. It facilitates research collaborations between Binghamton University faculty and graduate students, and outside experts. Finally, it is used as the basis for a two-credit course that must be taken twice to earn the EvoS certificate. Students in the course read one or more papers and post an electronic commentary in preparation for each seminar, attend the seminar, and attend an informal dinner and continuing discussion with the speaker that follows each seminar. The dinner and continuing discussion provide a rich social and intellectual experience that is repeated for a different specific topic with each seminar. Little wonder that many EvoS students regard EvoS as their academic “home” rather than their particular department. As one student put it, “EvoS provides a stimulating atmosphere within which biologists, psychologists, anthropologists, philosophers, social scientists, and even those in the arts can transcend traditional academic boundaries and collaborate in addressing mutually interesting questions. It creates a think-tank atmosphere of sorts, and it's a beautiful thing!”
In many ways, this type of experience approaches the ideal of a liberal arts education. It should be especially appealing to small colleges that have difficulty achieving a critical mass in single subject areas. Evolutionary theory is not the only common language, but it is a very good one that will eventually become part of the normal discourse for all subject areas relevant to human affairs and the natural world. Much can be done to facilitate this process, and EvoS provides one effective model. When it comes to evolution and teaching evolution, the future can be different from the past.
I thank the many students whose enthusiasm for learning evolution in my courses amply repays the effort. I also thank the many faculty and administrators at Binghamton University who have made the creation of EvoS a pleasure. Finally, I thank my colleagues at other institutions that have taken a special interest in this project, including Wyatt Anderson, Brian Alters, Mike Bell, Rick Firenze, Glenn Gehrer, Steve Hubble, Patty Gowaty, Craig Nelson, John Orbell, and Will Provine.
Citation: Wilson DS (2005) Evolution for everyone: How to increase acceptance of, interest in, and knowledge about evolution. PLoS Biol 3(12): e364.
David Sloan Wilson is with the Departments of Biology and Anthropology, Binghamton University, Binghamton, New York, United States of America. E-mail: [email protected]
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References
Harris Interactive The Harris poll #52: Nearly two-thirds of U.S. adults believe that human beings were created by God 2005 July 6 Available: http://www.harrisinteractive.com/harris_poll/index.asp?PID=581 . Accessed 20 October 2005
National Science Board Science and engineering indicators 2000 Washington (District of Columbia) Government Printing Office Report Number NSB-00-1. Available: http://www.nsf.gov/statistics/seind00/access/toc.htm . Accessed 18 October 2005
Meikle E Evolution still OK in Oklahoma (for now) Reports of the National Center for Science Education 2003 23 4 5
Alters BJ Alters SM Defending evolution: A guide to the creation/evolution controversy 2001 Sudbury (Massachusetts) Jones and Bartlett 272
Alters BJ Nelson CE Perspective: Teaching evolution in higher education Evolution 2002 56 1891 1901 12449476
Nelson CE Bosworth K Hamilton S Critical thinking and collaborative learning Collaborative learning and college teaching 1994 San Francisco (California) Jossey-Bass 45 58
Nelson CE Skehan JW Nelson CE Effective strategies for teaching evolution and other controversial topics The creation controversy and the science classroom 2000 Arlington (Virginia) NSTA Press 19 50
Pennebaker JW Francis ME Booth RJ Linguistic inquiry and word count: LIWC 2001, 2nd ed 2001 Mahway (New Jersey) Lawrence Erlbaum Associates computer program
Pennebaker JW Mehl MR Niederhoffer K Psychological aspects of language use: Our words, our selves Annu Rev Psychol 2003 54 547 577 12185209
Dennett DC Darwin's dangerous idea 1995 New York Simon and Schuster 592
Dennett DC Freedom evolves 2003 New York Viking 352
Boehm C Hierarchy in the forest: Egalitarianism and the evolution of human altruism 1999 Cambridge (Massachusetts) Harvard University Press 304
Sterelny K Thought in a hostile world 2003 Malden (Massachusetts) Blackwell 288
Wilson DS Darwin's cathedral: Evolution, religion, and the nature of society 2002 Chicago University of Chicago Press 268
Wilson EO Consilience: The unity of knowledge 1998 New York Knopf 384
Diamond J Guns, germs, and steel 1997 New York Norton 480
Calvin WH A brain for all seasons 2002 Chicago University of Chicago Press 352
Daly M Wilson M Homicide 1988 New York Aldine de Gruyter 340
Deacon TW The symbolic species 1998 New York Norton 527
Nisbett R Geography of thought: How Asians and Westerners think differently, and why 2003 New York Free Press 288
Richerson PJ Boyd R Not by genes alone: How culture transformed human evolution 2004 Chicago University of Chicago Press 342
Ridley M Nature via nurture: Genes, experience, and what makes us human 2003 New York Harper Collins 336
Gould SJ The panda's thumb 1981 New York Norton 344
Gould SJ Wonderful life: The Burgess Shale and the nature of history 1989 New York Norton 256
Gould SJ The structure of evolutionary theory 2002 New York Belknap 1464
Orzack SH Sober E Adaptationism and optimality 2001 Cambridge (United Kingdom) Cambridge University Press 420
Sober E Wilson DS Unto others: The evolution and psychology of unselfish behavior 1998 Cambridge (Massachusetts) Harvard University Press 394
Hamilton WD Fox R Innate social aptitudes of man: An approach from evolutionary genetics Biosocial anthropology 1975 New York John Wiley and Sons 133 155
Williams GC Nesse RM The dawn of Darwinian medicine Q Rev Biol 1991 66 1 22 2052670
Nesse RM Williams GC Why we get sick: The new science of Darwinian medicine 1995 New York Crown 304
Flaxman SM Sherman PW Morning sickness: A mechanism for protecting mother and embryo Q Rev Biol 2000 75 113 148 10858967
Billing J Sherman PW Antimicrobial function of spices: Why some like it hot Q Rev Biol 1998 73 3 49 9586227
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1633605010.1371/journal.pbio.0030421PrimerBiochemistryEubacteriaSelenoproteins—Tracing the Role of a Trace Element in Protein Function PrimerStadtman Thressa C 12 2005 13 12 2005 13 12 2005 3 12 e421This 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.2005This 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.
Different Catalytic Mechanisms in Mammalian Selenocysteine- and Cysteine-containing Methionine-R-Sulfoxide Reductases
Selenium Speeds Reactions
Selenium is an important component of several enzymes, replacing sulfur in cysteine residues. Its discovery and significance are described in this primer.
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In retrospect, the history of selenium biochemistry does not differ greatly from the study of a number of other trace elements in that it occurred over many years, progressing from periods of little general interest to widespread concern regarding toxicity problems and eventually to recognition of selenium as an essential nutrient for many forms of life. My involvement in studies on selenium metabolism is a classic example of serendipity. I was studying an interesting enzyme from an anaerobic bacterium that utilized glycine as substrate (glycine reductase), but the amounts of pure protein I could isolate were very limited because it seemed to be produced only during the very early stages of bacterial cell growth. The rich culture medium supported continued luxuriant growth of the bacterium, but the level of the desired enzyme in the cells merely underwent dilution during the process. Finally, after testing many known growth stimulatory supplements to no avail, my colleagues and I tried two inorganic nutrients, molybdate and selenite. This approach was prompted by the report that addition of these inorganic compounds to a medium used for anaerobic growth of Escherichia coli supported synthesis of the enzyme formate dehydrogenase [1]. Much to my delight, addition of selenite to our growth medium resulted in greatly increased levels of the glycine reductase enzyme, and synthesis of the protein continued throughout the entire growth period. Thus, a common belief among microbiologists that a so-called rich culture medium high in tryptone and yeast extract content was nutritionally adequate is incorrect if growth of the organism depends on ability to synthesize a selenium-containing enzyme. In our case other amino acids in the medium could support growth even when the trace of selenium was depleted and synthesis of glycine reductase stopped. With this unexpected finding, we had an ideal biological system for unraveling details of the role of selenium in the anaerobic metabolism of glycine.
The element selenium was discovered by the Swedish chemist Berzelius in 1817, and the few organic selenium-containing compounds that were prepared during the subsequent 100 years were considered primarily as chemical curiosities. Finally, in the 1930s, selenium was recognized as the potent toxic substance present in various types of plants that, when ingested by grazing animals, caused chronic symptoms of poisoning. Determination of the selenium contents of several native plants from Wyoming and South Dakota revealed that members of the genus Astragalus accumulated extremely high levels of selenium, as much as several thousand parts per million. During periods of drought, when appreciable amounts of these selenium accumulator plants were consumed in spite of their unpleasant odor, animals exhibited symptoms of acute poisoning, such as “blind staggers,” loss of appetite, paralysis, and finally death. In the arid regions of the western United States, selenium accumulation in soils is much higher than in areas that have normal rainfall, and as a result even ordinary plants, such as cereal grains, from this area contain unusually high levels of selenium. From 1930 to the mid-1950s, many investigators attempted to determine the chemical form(s) of the selenium present in the toxic plants, and animal nutritionists tested the effects of various inorganic and a few organic selenium compounds administered to animals. However, beyond the observation that much of the selenium in plant materials was protein-bound, actual identification of the toxic selenium compounds present was not accomplished [2]. In retrospect, this is not surprising because, as my coworkers and I learned firsthand much later, selenium-containing amino acids in proteins are notoriously unstable, especially when exposed to oxygen during isolation procedures. It took many years for scientists to learn how to identify and determine quantitatively the organic selenium compounds normally present in biological materials, and prior to the mid-1950s, these compounds, especially in selenium accumulator plants, were known only for their acute toxic effects. As is the case with other elements known originally only as toxins and later shown to be required by particular living organisms, selenium eventually was found to be an essential nutrient for animals and several species of bacteria.
In 1957, Klaus Schwarz, a German scientist working at the US National Institutes of Health in Bethesda, Maryland, reported that selenium was the essential component of a dietary preparation termed Factor 3 that prevented severe liver necrosis in rats [3]. Factor 3 preparations isolated from brewer's yeast and from casein were especially effective sources of the active selenium component. During this same period, an important disease in young chickens and turkeys known as exudative diathesis was recognized by Nesheim and Scott [4] as a symptom of selenium deficiency. Administration of Factor 3 or inorganic forms of selenium prevented development of the deficiency syndrome. However, the basic defect responsible for the selenium deficiency symptoms remained undefined for several more years.
Finally, in 1973, it was reported that the catalytic activities of two different enzymes depended on the presence of selenium in these proteins. One of these enzymes was my favorite glycine reductase from anaerobic bacteria [5], and the other was mammalian glutathione peroxidase, studied by investigators at the University of Wisconsin [6]. It was shown that when radioactive selenium (75Se) was provided, it was incorporated into both proteins during in vivo synthesis. After much effort spent in learning how to handle oxygen-sensitive selenium compounds, we finally could identify the selenium compound in our glycine reductase protein as selenocysteine, an analog of the sulfur-containing amino acid cysteine [7]. The selenocysteine occurs at a specific position in the protein polypeptide chain. Employing our methods for selenocysteine identification, Tappel and coworkers [8] at the University of California at Davis showed that the essential selenium in glutathione peroxide was also present as a selenocysteine residue in the protein.
Eventually scientists began to understand how proteins are built in living organisms and how the exact location of a naturally occurring amino acid in a protein is determined by the genetic code, but the inclusion of unusual amino acids remained a matter of speculation. Largely through the efforts of a young German scientist named August Böck and his students at the University of Munich, the question of how an amino acid such as selenocysteine could be inserted in a highly specific location in a protein could be answered. The first report in 1986 was followed by an impressive series of investigations dealing with the biosynthesis of selenoproteins in E. coli (reviewed in [9]). As it turned out, a sequence in the nucleic acid message that normally told the protein-synthesizing machinery to terminate the process—the stop codon UGA—now triggered a different set of instructions, namely, to direct selenocysteine insertion at the UGA codon. To achieve this reprogramming, cells have acquired an impressive number of additional chemical steps. A new amino acid transfer nucleic acid (tRNA) that could read the UGA codon was discovered. Selenocysteine derived from a serine initially bound to this tRNA is inserted into a growing protein chain. In E. coli, the selenium donor required for conversion of the serine on the tRNA to selenocysteine proved to be selenophosphate, an energy-rich compound in which selenium is bonded directly to the phosphorus atom [10]. This oxygen-labile selenium compound is known to be produced by a number of mammals and bacteria that synthesize specific selenoenzymes, and, based on present knowledge, a biological role for this reactive selenium compound as a “universal selenium donor” may be correct.
A new paper in this issue of PLoS Biology addresses the question of why selenium substitution for sulfur at the active site of an enzyme can be an advantage and in many cases has been preserved in nature [11]. Kim and Gladyshev address the catalytic mechanism of mammalian methionine-R-sulfoxide reductase (MsrB) 1, which contains selenocysteine at the active site, and compare it to the catalytic mechanism of the two other forms of the mammalian enzyme (MsrB2 and MsrB3) in which cysteine is found instead. Reduction of the methionine sulfoxide substrate to methionine by the active site cysteine residue in MsrB2 or MsrB3 converts the cysteine thiol to a sulfenic acid derivative (SOH), which can be reduced by thioredoxin to regenerate cysteine. In contrast, reduction of the sulfoxide substrate by MsrB1 converts the ionized selenocysteine residue to a selenenic acid derivative (SeOH), which is not reduced directly by thioredoxin (Figure 1). However, regeneration of MsrB1 by thioredoxin can occur after the SeOH group is converted to a selenosulfide intermediate by reaction with a unique cysteine residue in MsrB1.
Figure 1 Schematic Illustrations Comparing the Enzymatic Reactions of Three Mammalian MsrBs
MsrB1 is a selenium-containing protein, whereas MsrB2 and MsrB3 contain cysteine in the active site.
(Illustration: Rusty Howson, sososo design)
An impressive number of mutant forms of the genes corresponding to the three MsrB enzymes were constructed, cloned, and expressed in E. coli, and the catalytic activities and substrate affinities of the purified gene products were determined in detail by the authors. The native MsrB2 and MsrB3 enzymes contain three highly conserved amino acid residues—namely, His-77, Val- or Ile-81, and Asn-97—that are part of the active sites of these enzymes. Completely different amino acid residues, Gly-77, Glu-81, and Phe-97, are found at these positions in the fully active form of MsrB1. A series of mutant constructs in which the amino acids at positions 77, 81, and 97 were switched individually or in groups between native selenocysteine and cysteine forms of MsrB, and also between selenocysteine- or cysteine-substituted enzymes, were analyzed in detail. Several of these mutants exhibited marked changes in the ability to utilize the normal electron donor, thioredoxin, for enzyme regeneration. Replacement of the active site selenocysteine in MsrB1 with cysteine resulted in greatly decreased catalytic activity, which could be partially restored by introduction of His-77 and Asn-97 at the active site. Mutants that were modified extensively to allow insertion of selenocysteine at the active site in place of cysteine were also generated, and their characteristics were determined. In general, the replacement of cysteine with selenocysteine frequently resulted in increased activity with dithiothreitol as reductant, but regeneration of active enzyme in these constructs by the natural electron donor, thioredoxin, was not possible. Based on the reported findings, it is clear that different critical amino acids in the active sites of the selenocysteine and cysteine enzymes are required for their maximal catalytic activities, and these also determine electron donor specificity for enzyme turnover.
The last few years have seen the emergence of numerous important physiological roles for the trace element selenium. In contrast, it took many years from Berzelius's discovery of this new element in 1817, which he named after Selene, the goddess of the moon in ancient Greece, until it attracted growing interest, whereupon it gained a bad reputation as a toxic substance. Even after its recognition as a required nutrient for mammals, the role of selenium as an essential component of important antioxidant enzymes synthesized in our cells is not widely appreciated. Further studies of the unique properties of selenium will help us to understand the selective advantage imparted to cells by their investment in selenoprotein biosynthesis and its retention during evolution.
Citation: Stadtman TC (2005) Selenoproteins—Tracing the role of a trace element in protein function. PLoS Biol 3(12): e421.
Thressa C. Stadtman is at the Laboratory of Biochemistry, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America. E-mail: [email protected]
Abbreviations
MsrBmethionine-R-sulfoxide reductase
==== Refs
References
Lester RL DeMoss JA Effects of molybdate and selenite on formate and nitrate metabolism in Escherichia coli
J Bacteriol 1971 105 1006 1014 4926673
Stadtman TC Selenium biochemistry Science 1974 183 915 922 4605100
Schwarz K Foltz CM Selenium as an integral part of Factor 3 against dietary necrotic liver degeneration J Am Chem Soc 1957 79 3292 3293
Nesheim MC Scott ML Nutritional effects of selenium compounds in chicks and turkeys Fed Proc 1961 20 674 678 13728400
Turner DC Stadtman TC Purification of protein components of clostridial glycine reductase system and characterization of protein A as a selenoprotein Arch Biochem Biophys 1973 154 366 381 4734725
Rotruck JT Pope AL Ganther HE Swanson AB Hafeman DG Selenium: Biochemical role as a component of glutathione peroxidase Science 1973 179 588 590 4686466
Cone JE Martin del Rio R Stadtman TC Chemical characterization of the selenoprotein component of clostridial glycine reductase: Identification of selenocysteine as the organoselenium moiety Proc Natl Acad Sci U S A 1976 73 2659 2663 1066676
Forstrom JW Zakowski JJ Tappel AL Identification of the catalytic site of rat liver glutathione peroxidase as selenocysteine Biochemistry 1978 17 2639 2644 678534
Böck A Hatfield DA Selenium metabolism in bacteria Selenium: Its molecular biology and role in human health 2001 Boston Kluwer Academic Publishers 7 22
Glass RS Singh WP Jung W Veres Z Scholz TD Monoselenophosphate: Synthesis, characterization, and identity with the prokaryotic biological selenium donor, compound SePX Biochemistry 1993 32 12555 12559 8251472
Kim HY Gladyshev VN Different catalytic mechanisms in mammalian selenocysteine- and cysteine-containing methionine-R -sulfoxide reductases PLoS Biol 2005 3 e375 16262444
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030425Book Reviews/Science in the MediaCardiology/Cardiac SurgeryHomo (Human)The Heart of Medicine Book Review/Science in the MediaGlimcher Paul W 12 2005 13 12 2005 13 12 2005 3 12 e425Copyright: © 2005 Paul W. Glimcher.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.In De Motu Cordis, William Harvey described the circulation of the blood and, in the process, built the first comprehensive biomedical theory on an experimental base.
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In the early 16th century the practice of medicine and the discipline of physiology bore almost no relation to the sciences we know today. Medieval scholasticism had entrenched the writings of ancient authors as the arbiters of truth and almost completely suppressed novel inquiry in the biological sciences. The result of this entrenchment was that in European biomedical centers like the Universities of Bologna, Padua, or Montpellier, the writings of ancient medical authors were taught as fact. Direct observations on tissue, and hypothesis-testing experiments of the type routinely performed today, were essentially unknown.
Preeminent among the ancient medical writers for European and Arab scholars was the second century Greek physician Claudius Galen, whose works served as the unquestioned authority on all matters of physiology. Perhaps the most ironic aspect of this cultural devotion was Galen's own rejection of purely scholastic traditions. As he wrote in On the Natural Faculties: “The student must learn thoroughly all that has been said by the most illustrious of the ancients. And when he has learnt this, then, for a prolonged period, he must test and prove it, observing what point is in agreement, and what is in disagreement, with obvious fact; thus will he choose this and turn away from that.” Until the sixteenth century, however, the biomedical disciplines rested upon a slavish devotion to the writings of Galen, and his exhortation to perform experiments was ignored.
This Scholastic devotion to Galen was first challenged by Andreas Vesalius, the 16th century father of modern anatomy. Vesalius's brilliant anatomical woodcuts were scrupulously drawn from his own dissections and are probably known today to all students of medicine and physiology. Vesalius's drawings and dissections were specifically meant to challenge the authority of Galen, and his stated goal was an outright overthrow of the hegemony of Scholasticism in the medical academy. What is surprising about Vesalius's work, however, is that it is almost purely descriptive. Nowhere in his work do we see the formal tradition of hypothesis testing by experiment upon which modern biomedical science rests. That tradition begins instead almost a hundred years later in the hands of William Harvey. Perhaps surprisingly, despite the fact that modern biological science is cast almost entirely in the image of Harvey's model, his works are much less familiar than those of Vesalius to modern practitioners of science. With that in mind, it seems particularly timely to review Harvey's master work: De Motu Cordis, or On the Motion of the Heart and Blood in Animals, a brief and easily read work upon which the bulk of modern medical science and method rests.
William Harvey
(Image: University of Texas Libraries, University of Texas at Austin)
William Harvey was born in England in 1578 into a large and well-to-do merchant family. He received a traditional English education focused on Latin and Greek classics before enrolling in Gonvil-Caius College at Cambridge. After completing a bachelor's degree there at the age of 19, Harvey moved to Padua, Italy, to undertake his medical training under several of the leading physicians of his day, including his personal mentor Fabricius of Aquapendente. After five years at medical school Harvey returned to England and set about building his practice as a physician and conducting a series of private researches.
Of particular interest to Harvey was the mystery of the pulse and its relationship to the beating of the heart. It had been recognized for centuries that the arteries expanded and contracted at regular intervals, and that if a tourniquet was placed on a limb the arteries distal to the tourniquet ceased to pulse and the limb grew cold. Also it was known that all arteries were connected to the left side of the heart and that this organ was connected to the lungs. From this, it was generally concluded that the arteries themselves, by some form of muscular action, expanded and drew either blood or air into themselves from the heart or through the skin. The heart was widely presumed to contract in phase with the arteries. The distinct venous system was believed to mediate the passing of nutrition from the gut to the periphery via the blood. Food was transformed in the gut to chyle, which entered the liver via the portal circulation and then passed to the venous system for distribution throughout the body.
Harvey undertook a series of experiments designed to test this set of assertions, and it is a description of these tests that form the first six chapters of the De Motu Cordis. In the Proeme (Introduction) of the book, Harvey sets out to demonstrate that this contemporary description of the pulse simply cannot be correct. Immerse a man in a bath of oil, taking his pulse before and after immersion. The strength of his pulse is undiminished by the immersion even though the ability of air to penetrate the skin must in some degree be reduced by this immersion. Section an artery, and you observe not only that blood pulses out of the proximal side of the cut but that no air rushes into the distal side, nor is a pulse observed on the distal side. In short, perform a set of simple demonstrations aimed at testing this standard view and you are driven to the conclusion that it cannot be correct.
In the six chapters that follow Harvey presents a series of descriptive observations that yield an entirely novel conclusion: that the blood is driven into the arteries by a contraction of the left ventricle of the heart and that this blood is supplied to the left ventricle by the right ventricle, from the lungs. This blood, in turn, comes from the vena cava via the right side of the heart, which acts to drive blood through the lungs. To prove this, Harvey presents a series of very compelling observations made under a wide range of conditions. He observes that amphibian or reptilian hearts, which have been chilled to reduce the speed at which they beat, contract at the same time as the aorta to which they are connected fills. He notes that the heart looks like nothing so much as a muscle and that it becomes rigid when contracted. He notes that in the embryo the foramen ovale directly connects the right and left side of the heart, bypassing the lungs and providing a sure and simple passage between the venous and arterial systems. From these he concludes that “the pulsations of the arteries arises from the impulsion of the blood from the left ventricle; just so, as when one blows into a glove, he shall see all the fingers swell up together and assiumulate [sic] this pulsation.”
These first six chapters of the book are without a doubt worth reading, but to my mind what is most striking about them is how unmodern they feel. There is no doubt that Harvey is challenging Galenic doctrine, but he does so with a series of observations that a modern scientist cannot possibly consider experiments. These chapters are good, but not great. Apparently, these chapters reflected a preliminary series of observations that Harvey probably first presented as a series of lectures in anatomy in London and likely wrote up as a very short pamphlet that was never published independently. What probably prevented the publication of that pamphlet was Harvey's recognition that his observations raised more questions than they answered. If blood is pumped by the heart into the arteries, where does it go? If the venous system supplies blood to the heart, from where does this blood come? Harvey must have recognized that his observations yielded a system that was only slightly more logical than the one he was arguing against. It is in a reaction to this puzzle that Harvey's real genius showed itself and invented what we think of today as the biomedical scientific method.
Driven by these observations, in the second half of the book, Harvey sets out to prove a simple hypothesis by quantitative experiment. To us that seems a clear strategy, but in 17th century Europe the concept of experiment was almost unknown and the use of quantitative experimental strategies was only just starting to be used by Galileo in Italy. The first compelling experiment Harvey provides is to measure the output of the left ventricle at each contraction. He then goes on to very conservatively measure the volume of blood pumped by the heart into the arteries with each contraction. Making conservative estimates of cardiac rate, he then computes the total volume of blood moved by the heart in a day. This volume, he concludes, exceeds the weight of a man. In another experiment he measures the volume of blood that passes out of a cut carotid artery and concludes that it accounts for the entire blood volume of an animal in only 15 minutes.
Figure from On the Motion of the Heart and Blood in Animals (1628)
(Image: National Library of Medicine)
In the second half of the book Harvey presents a number of experiments like this that are absolutely delightful to read. In perhaps the most famous of these experiments he examines the actions of tourniquets and investigates the functions of the valves within veins during the cardiac cycle. In that investigation, which he encourages his reader to attempt, a tourniquet is placed on the upper arm. As the veins distend, small bumps along the veins become visible, which he identified as “little swellings” made by the venous valves, or “portals.” “If you draw down blood with your thumb or finger from [one node to the next] you see that [no blood] can follow … and yet [the vessel is] full enough above the knot…. Hence, since a man may make experiment in many places, it appears that the function of the portal in the veins is the same as that of the [the sigmoid valves of the heart]…to wit that they should be closely shut up, lest they should hinder the blood to return back again.” An experimental passage aimed at proving the unidirectional flow of blood in the veins as required by his hypothesis.
These experiments are all lovely to read, not simply because they invented modern biomedical science but because they provide such an elegant proof of a complex hypothesis. Indeed, Harvey even concludes his experimental sections with a model for the modern discussion section of a scientific paper: “It must of necessity be concluded that the blood is driven into a round by a circular motion in creatures, and that it moves perpetually; and hence does arise the action and function of the heart, which by pulsation it performs; and lastly, that the motion and pulsation of the heart is the only cause.”
By the time Harvey had written the second half of the De Motu Cordis, he had risen to the top of British medical practice. At that time he was physician to King Charles I, as he had been to Charles's father, James I. The De Motu Cordis was, however, not immediately accepted, and Harvey reported to a later biographer that his medical practice suffered terribly after the book's publication. His life was further complicated by the English civil war, which brought Cromwell to power and cost Charles his head. Harvey was, by all accounts, close to Charles and was deeply wounded by his sovereign's execution. Despite his ties to the crown before the civil war, however, Harvey was lionized towards the end of his life. He was, for example, elected president of the Royal College of Physicians (an office he declined) and widely hailed throughout Europe as the preeminent biomedical scientist of his period. It was clear by the end of Harvey's life what he had accomplished; he not only had solved the puzzle of the blood, the heart, the lungs, and the pulse but had done something much more significant than even that. He had described a method by which hypotheses could be tested by quantitative experiment. Historians often credit Francis Bacon with this accomplishment because he wrote at the same time about how scholars ought to do experiments, but it was Harvey, not Bacon, who built the first comprehensive biomedical theory on an experimental base.
The De Motu Cordis is striking because it accomplishes this in about a hundred pages. What I like best about the work, however, is the way it really is two books in one. The first is a not terribly exciting early Enlightenment scientific tract that is well worth reading if you are a historian of science. The second half is a model, or rather the model, of experimental hypothesis testing, and it is this half that really soars. For the English-language reader, there are basically two translations of the De Motu Cordis from the original Latin available today. The first is an Elizabethan translation produced during Harvey's lifetime and the second is Robert Willis's translation, produced in the mid-1800s. Both have their virtues. Willis's translation is definitely the more readable to a modern biologist, but to my mind, the Elizabethan translation, which sounds almost Shakespearian, captures an excitement that the more modern translation misses. One can only hope, regardless of the translation, that as each generation of biomedical scientists turns towards the history of their field, Harvey will be rediscovered and emulated in the same way Harvey rediscovered the true meaning of Galen.
Supporting Information
Text S1 On the Motion of the Heart and Blood in Animals
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Box 1
Harvey W (1628) On the motion of the heart and blood in animals. Willis R, translator. In: Scientific papers (physiology, medicine, surgery, geology). Salt Lake City: Project Gutenberg. Available: http://onlinebooks.library.upenn.edu/webbin/gutbook/lookup?num=5694. Accessed 18 October 2005. Also available as Text S1.
Citation: Glimcher PW (2005) The heart of medicine. PLoS Biol 3(12): e425.
Paul W. Glimcher is at the Center for Neural Science, New York University, New York, New York, United States of America. E-mail: [email protected]
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030427Book Reviews/Science in the MediaScience PolicyNonePromoting Science Literacy by Engaging the Public Book Review/Science in the MediaLiem Anna 12 2005 13 12 2005 13 12 2005 3 12 e427Copyright: © 2005 Anna Liem.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.The Science Adventure Center at Hawaii's Bishop Museum draws visitors into the excitement of scientific discovery and knowledge.
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When you're standing inside a volcano, it's hard not to pay attention to what's around you. And whose interest wouldn't be captured by guiding remotely operated vehicles through a hydrothermal vent or pumping magma into a volcano and watching it erupt? The new Science Adventure Center at the Bishop Museum in Honolulu, Hawai'i, captures the imagination in a bid to increase science literacy. In doing so, the center provides a valuable opportunity not just for its visitors, but for all those interested in science education. Both the center's mission and its design inspire critical questions: What is science literacy? How does a person become science-literate? What roles can museums, schools, and other institutions play in promoting science literacy? And what can teachers learn from museums, and vice versa, about science education?
What Is Science Literacy?
Science literacy is much more than the memorization or even comprehension of scientific facts and principles. The American Association for the Advancement of Science defines the science-literate person as “one who is aware that science, mathematics, and technology are interdependent human enterprises with strengths and limitations; understands key concepts and principles of science; is familiar with the natural world and recognizes both its diversity and unity; and uses scientific knowledge and scientific ways of thinking for individual and social purposes” [1]. Thus defined, science literacy encompasses not only the knowledge and understanding of scientific ideas and processes, but also, crucially, the ability and desire to apply those ideas and processes. Increasing the public's science literacy requires much more than the transmission of information; it must also change people's attitudes and actions.
The Science Adventure Center at the Bishop Museum in Honolulu, Hawai'i
(Image: Bishop Museum)
Promoting Science Literacy: Engagement
To effect change, one must first attract interest. At the Science Adventure Center, large-scale multi-sensory displays do just that: they grab attention. At the Hot Spot Theater, a furnace used to melt lava rock creates a literal hot spot that visitors can feel from the other end of the room, while ceiling panels with a rippled texture and moving red lights create the impression of standing inside a volcano. The more senses you stimulate, the more likely you are to engage your audience; science teachers use this same philosophy when designing lessons with visual, auditory, and/or tactile elements. And when resources permit, primary school classrooms are usually enriched with posters, three-dimensional displays, and student work, a strategy that has also been adopted by secondary school teachers and is slowly filtering into college classrooms.
At museums, visitors also gravitate towards interactive exhibits like the center's wave-making tank, where museum-goers can trigger wind-generated surf, earthquake-generated tsunamis, and landslide-generated mega-tsunamis. In the classroom, students are likewise more apt to be engaged by interactive lessons—hence the popularity of hands-on activities. In the most complete form of student-directed learning, students ask and investigate their own questions, perhaps by searching for information on the Internet or by designing and executing their own experiments.
The Science Adventure Center also engages people's interest by tapping into their prior knowledge. A familiar context not only helps to engage one's attention, it also creates an experience more likely to produce lasting change. Constructivist learning theory suggests that new knowledge and understanding is built upon prior knowledge. Since it's located in Hawai'i, the center focuses on the islands' unique natural environment and draws on residents' experience of news stories about Kilauea volcano, the daily surf report, and even the rainbow decorating the state's license plates. Teachers do the same thing by selecting topics that are relevant to their students. Even within the framework of the National Science Education Standards, teachers can meet national standards with topics of local interest. For example, teachers in Hawai'i might address the standard of “biological evolution” with a study of the Hawaiian honeycreepers, an outstanding example of adaptive radiation.
The activation of prior knowledge is a critical part of any educational experience, whether that experience seeks to replace, alter, or augment an individual's existing understanding [2]. For example, the center confronts people's stereotyped images of volcanos with side-by-side representations of a shield volcano (Mauna Loa) and a composite volcano (Mount Ngauruhoe, which visitors may recognize as Mount Doom from Peter Jackson's movie adaptations of The Lord of the Rings). The display simply wouldn't work if it didn't trigger visitors' prior images of volcanoes.
Partnerships between Museums and Schools
Engaging the interest of students and the public is a necessary component to promoting science literacy, but it is not sufficient by itself. Museum exhibits, while often extremely attention-grabbing, are also usually limited in duration; visitors, particularly school groups, frequently visit an exhibit only once or twice a year. A highly engaging exhibit might draw people back again and again or inspire visitors to learn more after they leave, but museums can also partner with schools to create more extensive learning experiences.
The designers of the Science Adventure Center deliberately chose topics that align with the state's science content standards, and geared many of their displays towards the associated grade level. They solicited input from teachers in Hawai'i during the design process, and teachers and students were also involved in the actual construction of the Origins Tunnel, an exploration of Native Hawaiian creation myths. In this black-lit corridor, the walls are decorated with student-created artwork that illustrates the Hawaiian chants vibrating through the tunnel. The Bishop Museum also frequently produces specific curriculum materials for use with their exhibits, and often involves teachers in the generation of those materials. Close partnerships between museums and schools mean that students can become engaged by visiting an exhibit and then deepen their knowledge and understanding in the classroom—the ideal combination.
Citation: Liem A (2005) Promoting science literacy by engaging the public. PLoS Biol 3(12): e427.
Anna Liem is a high school science teacher in Honolulu, Hawai'i, United States of America. E-mail: [email protected]
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References
American Association for the Advancement of Science Science for all Americans 1989 New York Oxford University Press Available: http://www.project2061.org/publications/sfaa/online/sfaatoc.htm . Accessed 13 October 2005
National Resource Council How people learn: Brain, mind, experience, and school, expanded ed 2000 Washington (D.C.) National Academy Press 374
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1633605310.1371/journal.pbio.0030429FeatureEcologyEvolutionNoneEco-Defense against Invasions FeatureGewin Virginia 12 2005 13 12 2005 13 12 2005 3 12 e429Copyright: © 2005 Virginia Gewin.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.Characterizing patterns of invasion across space, time, and taxonomic group will help reveal how invasive species affect ecosystem function and individual native species
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Once thought only science fiction, alien invasions are one of today's major scientific challenges. The “aliens” in question are nonnative, or exotic, species capable of outcompeting natives and, ultimately, taking over the ecosystems to which they are introduced (Figure 1). Invasive alien species are a worldwide problem, now found in every ecosystem on Earth. Staving off the further advance of invasions—identified by the Millennium Ecosystem Assessment (http://www.millenniumassessment.org/en/index.aspx), a United Nations–backed audit of ecosystem health, as one of the most important drivers of ecosystem change—is a key environmental priority.
Figure 1 Many Species Commonly Grown throughout the World Can Become Invasive When Introduced to New Ecosystems
(A) The California poppy (Eschscholzia californica) has invaded many parts of the world (e.g., Chile, Turkey, and New Zealand), and is commonly grown in many European and American gardens.
(B) The blue gum tree (Eucalypus globulus)—featured here in a plantation in Spain—is one of two of the most commonly planted trees in the world, the other being the Monterey Pine.
(Photos: Dov Sax)
Complicating matters, not all exotic species are invasive, or likely to cause harm to ecosystems or human health. But when introduced species exploit a specific species trait or fill a vacant niche in an ecosystem in order to invade, they cross the line from exotic to weed or pest. For example, European cheat grass (Bromus tectorum), accidentally introduced into western United States in contaminated seed, grows in the winter. Perennial grasses native to the west die off in the winter, giving cheatgrass the advantage of spreading healthy seed early in the growing season. Subsequently, Cheatgrass has altered the normal functioning of western ecosystems. Fires, once common in the region only every 60–100 years, now burn every three to four years.
Animal introductions can lead to species extinctions if they upset existing predator–prey relationships. When the poisonous brown tree snake (Boiga irregularis) was accidentally introduced to the originally snake-free island of Guam in the Pacific Ocean, as a cargo ship stowaway in 1952, it ultimately caused the demise of twelve native bird species. The ecosystem's unfilled predatory niche offered the brown tree snake the perfect opportunity to invade. Over 50 years later, the brown tree snake not only poses a human health risk (it's poisonous), but it also routinely disrupts power by crawling on electrical lines—both of which necessitate costly ongoing management.
The majority of introductions occur accidentally, as a result of human movement. From the exotic ornamental garden species to the pest control agent to the hitchhiker, these introduced nonnative species can be arbiters of economic and ecological mayhem. Governments so far have been fighting a costly, losing battle against invasions. The containment, removal, and control of invasive species costs $137 billion per year in the US alone, according to David Pimentel, economist at Cornell University in Ithaca, New York.
Conservationists say reliable reconnaissance—identifying the potential invasives early enough to implement cost-effective eradication efforts—is the best defense against invasions. But that's easier said than done. With a notorious lack of funding for this growing environmental threat, conservation managers, not surprisingly, want to focus efforts on those areas that appear most vulnerable. But even identifying those areas has proven elusive. While pragmatic, predictive approaches for identifying likely invasions must be developed, the underpinning ecological mechanisms of invasion still need to be worked out. Understanding how invasive species affect ecosystem function and individual native species will help land managers fight back. Ecologists are consequently working on several fronts to (1) identify those species most likely to be harmful, (2) resolve the spatial and temporal scale dynamics of the invasion, and (3) sharpen surveillance.
Predictive Predicament
Despite the thousands of new exotic species introduced globally each year, only a few ever become severe problems. Carla D'Antonio, ecologist at University of California (UC), Santa Barbara, points out that many nonnatives have no impact on an ecosystem's structure or function. Once introduced, these nonnatives live among the natives without taking over their resources or crowding them out. “I'd like to see more emphasis on understanding what makes a big player and how to resolve its impact rather than simply identifying its presence,” she says. Therefore, among the many challenges in invasion biology is the development of tools to identify which exotic species are going to become invasive and where. As Jay Stachowicz, ecologist at UC Davis, puts it, “Our ability to predict invasions to date largely remains qualitative.”
Currently, prediction relies on correlative lists of known invaders and matching habitats—to create a regional “most wanted” list of likely perpetrators. But this “invasion profiling” approach isn't working, says Shahid Naeem from Columbia University in New York City. It's not simply any one unique trait, such as high reproductive rate, that allows invasives to outcompete natives as was once thought. Growing evidence suggests that an exotic species' geographic range, body size, and abundance are often similar to those of natives. But given the paucity of ecological knowledge of native species, it may not serve as a good predictor of behavior elsewhere.
“In the end an invasion is an interaction between the species' attributes and ecosystem—and the outcomes of interactions are intrinsically hard to predict,” says Mark Lonsdale, Assistant Chief of Entomology at the Commonwealth Science and Industry Research Organization in Canberra, Australia. Lonsdale, like many land managers, is skeptical of any attributes assumed capable of predicting invasibility. “A lot of people are focused on trying to find general characteristics that might distinguish invasives from noninvasives,” says Richard Duncan, ecologist at Lincoln University in Canterbury, New Zealand. “We're unlikely to find those things,” he adds. With no universal traits to search for, the task is eminently more complex. “Despite lots of desire to have a rigorous risk assessment system, there's a lot of ecology that tells us prediction is fraught with difficulty,” says Lonsdale.
But the news is not all bad, and progress is being made. David Richardson, ecologist at the Centre of Excellence for Invasion Biology in Stellenbosch, South Africa, has been successful in identifying life history attributes, from growth rate to genome size, that accurately separate invasive from noninvasive pine species. It's proved to work well for other conifers and trees in general. Indeed, many scientists believe building more general models or grouping weed species into functional groups—such as leguminous shrubs—would help determine the mechanisms behind invasions. Richardson says that researchers have been unrealistic in expecting to develop a single set of answers that would apply to all introduced species in all habitats, suggesting instead that robust rules will emerge on a regional basis.
The most unpredictable variable remains the impact of the primary mode of introduction—human activity. “Invasions are human-assisted dispersal events,” says Steven Chown, Director of Centre of Excellence for Invasion Biology. However, a proxy measure—propagule pressure, or the supply of dispersible eggs, seeds, or larvae—has emerged as an equally, if not more, important predictor of invasion success. Repeated introductions or massive movements of individuals grant a species multiple chances to escape and invade.
Understanding how many organisms it takes to establish an invasive population remains a critical element of assessing risk. Propagule pressure is widely accepted as the major driver of marine coastal invasions, resulting from the dumping of a ship's ballast water (Figure 2). Discharging ballast can transport hundreds to thousands of both individuals and species at once into coastal ecosystems, but it is feasible to reduce that pressure by removing organisms via treatments such as filtration or ultraviolet radiation, thus mitigating the risk of the invasives taking hold. “It's challenging, if not impossible, to prescreen all potentially invasive marine species associated with a ship's ballast, so we must knock down the concentration of everything,” says Gregory Ruiz, invasion biologist at the Smithsonian Environmental Research Center in Edgewater, Maryland. Even more uncertain is how regional coastal influences will affect the outcome of ballast introductions. Ruiz adds that the same inoculum dumped in five different bays will have five different outcomes. “The challenge for basic ecology and management is understanding how low we need to reduce the overall density of propagules to achieve an acceptable level of risk,” says Ruiz.
Figure 2 Discharging Ballast—As This Ship Is Doing off the Coast of North America—Can Transport Hundreds to Thousands of Individuals and Species at Once
(Photo: Esther Collinetti)
Scientists are finding that no region on Earth is free from the risk of invasion, even isolated islands. We tend to think islands are easier to invade because, with fewer species, there is less competition, says Dov Sax, invasion biologist at UC Santa Barbara. But islands may have more exotic species simply because there have been so many introductions from humans drawn to go there. Even the most remote islands have been riddled with invaders (Box 1). Ironically, little work has yet been conducted in the tropical regions where most islands reside. Extrapolating findings from the better-studied temperate zones is almost certain to mask important regional mechanisms at work. For similar reasons, many are also concerned about extrapolating from one spatial scale to another.
Box 1. Isolation: No Defense for Islands
Gough Island in the South Atlantic Ocean is about as isolated a spot on the Earth as one can get (Figure 3). Described as one of the least disrupted ecosystems of its kind, even Gough is not free of invasion. Of the 233 recorded landings since human landfall in the 1600s, over 71 species of insects have been established through introductions. There are only 99 species of insect on the island. In other words, a new nonindigenous species is established every three to four landings. Steven Chown, Director of South Africa's Centre of Excellence for Invasion Biology, and his colleagues have calculated that the rate of human-facilitated introduction is two to three orders of magnitude greater than natural colonizations. “Alien species are getting to places never before imaginable thanks to human transport,” says Chown.
Resolving Scale and Impact
How to scale up findings is an issue that tends to plague ecology. Invasion biology is no different. At small scales, native diversity appears to defend against invasions, but at larger scales, it is the more species-rich areas that often have the most invasions—the so-called invasion paradox. Many think that the mechanisms at work at different scales are difficult to determine because they might balance each other out. However, “although small-scale mechanisms may be masked at larger scales, it doesn't mean they are not important to understand,” says Carla D'Antonio, ecologist at UC Santa Barbara. For example, Stachowicz found that the effect of native diversity on invasion success was actually driven largely by resource availability. To tease out the possibly counteracting forces at work, a growing number of scientists are conducting experimental and observational studies at multiple scales.
In many cases, the smaller scales provide the detail necessary to define a species' preferred habitat. For example, when trying to determine the habitat requirements of two nonnative species of thistle (Carduus nutans and Carduus acanthoides), researchers found that both existed in most of the US at the state level. But on inspection of smaller-scale county data, the areas of overlap were surprisingly small between the two species. “If you had only looked at the larger spatial scale, you would have thought these two species live quite happily together,” says Katriona Shea, theoretical ecologist at Pennsylvania State University in University Park, Pennsylvania. These very similar species may have very different dispersal and distribution patterns that wouldn't have been noticed at larger scales. The greater resolution at smaller scale allows conservationists to identify risk areas for long-term monitoring and prevention.
Equally important is a better understanding of the temporal scales at which invasions bloom. “The idea that we need to not only think spatially but temporally to understand invasion pattern mechanisms has been largely ignored,” says Melinda Smith, plant community ecologist at Yale University in New Haven, Connecticut. More urgently, understanding the temporal scale is important to identify the “sleeper cells” in the environment—species that can remain apparently innocuous for years until exploding into a problem when prompted by an environmental change.
Just as intriguing are species, such as an invasive giant African land snail (Achatina fulica), that end up being empty threats. Some spontaneous population crashes of seemingly invasive species, such as this snail, are likely due to disease, but other causes of such invasive species “time lags” remain a mystery. Smith's work has shown that highly diverse communities also experience the greatest turnover of species over time, providing temporal opportunities for exotics to invade. By resolving the time scales at work for species invasions, science could help prioritize invasive species management.
While understanding the mechanisms at play at different spatial and temporal scales is important, David Lodge, ecologist at University of Notre Dame in Indiana, points out that a more important metric—and one much harder to quantify—is the impact a species has on ecosystem goods and services. “Ecologists have to be more attuned to values of society and economic costs,” he says. While one of the most studied and costly invaders, the zebra mussel (Dreissena polymorpha) in the Great Lakes region, hasn't directly caused any species extinctions, it has displaced native clams. More importantly to natural resource managers though, the mussels clog pipelines and power plants throughout the Great Lakes region. Conservative estimates put annual zebra mussel control and removal in the Great Lakes at $200 million.
Sharpen Surveillance
Unfortunately, invasion success cannot be whittled down solely to individual attributes or profiles; effective management will depend on adequate surveillance and monitoring. The often piecemeal approach to managing invasive species, whereby different government agencies work on different invaders, is inefficient primarily because resources aren't easily shared. Effective invasive species management will require a centralized, coordinated approach linking university research to state and local agencies responsible for prevention and control efforts. It will take, says Dan Simberloff, from University of Tennessee in Knoxville, Tennessee, a national coordinating center equivalent to US Centers for Disease Control.
Tom Stohlgren, scientist at the US Geological Survey's Fort Collins Science Center, says the silver bullet is simply rapid online data sharing. “As scientists and agencies, we've forgotten how to share,” he says. Indeed, he's witnessed first hand the value of access to data, specifically absence data—clear indications of where the species is not found. His team partnered with NASA to develop a potential habitat model of the tamarisk (Tamarix ramosissima), a short riparian shrub also known as salt cedar (Figure 4). With data on only the presence of species, their model wasn't working in eastern US. Once they incorporated absence points from VegBank (http://www.vegbank.org) however, their model accurately predicted potential salt cedar habitat. “If we have both presence and absence data, we can get good predictive maps that are over 90% accurate,” he says. Online databases, such as that provided by the Global Invasive Species Information Network (http://www.gisinetwork.org/), are popping up as portals for sharing information.
Figure 4 Researchers Have Teamed with NASA to Determine the Range of Potential Tamarisk Habitat
(A) A computer model of Hackberry Canyon (Grand Staircase Escalante National Monument, US) that illustrates varying levels of above ground biomass of tamarisk (salt cedar) infestation.
(B) Field crew using a portable spectrometer to record spectral signatures of tamarisk which will help calibrate satellite sensors.
(C) Tamarisk resprouting, following a flood in Hackberry Canyon.
(Figures: Paul Evangelista)
Indeed, for birds—one of the few better-studied mobile invasive species—such long-term records of introduction success are invaluable. Often, records of bird introductions are kept so that one can determine what failed to establish and what successfully invaded. “The only way to understand characteristics of successful invaders is to have these two groups of species to compare,” says Tim Blackburn, ecologist at University of Birmingham, United Kingdom.
In lieu of meticulous historical records, scientists must use every resource available to unravel the mix of ecological forces at work. Lodge says ecologists have an important role to play in determining how to design the most efficient surveillance systems. Questions that need to be answered include the following: where should we specifically target efforts? what are reasonable detection limits? what nonnative species abundances should be of concern? when is it best to respond with eradication? and when is it more rational to simply slow the spread?
Prospects
Every ecosystem in the world faces the threat of invasive species. Lonsdale suggests that, on average, roughly 20% of the established plant species are invasive weeds. The immediate and urgent need to prevent invasions on a global level is stimulating much-needed research. The patterns have not yet revealed all the processes at work, but the science is making strides. Most scientists now agree that it is the high biodiversity areas that are most prone to invasion—due to heavy human traffic and more favorable growth conditions—and most in need of protection. Research has also honed in on the value of understanding scale-dependent mechanisms. Moving forward, predictive tools capable of identifying potentially invasive species remain the goal. And amassing—and sharing online—the existing and future data such that it can be mined efficiently for clues is the first step.
Characterizing patterns of invasion across space, time, and taxonomic group will help dissect the ecological mechanisms at work—and none too soon. In the face of climate change, introduced species will have yet another opportunity to invade. Pinpointing species distribution limits and range shifts as the climate changes is critical to control efforts. “Understanding how ecosystems respond to exotic invasions will help us solve more applied problems, like dealing with climate change,” says Sax. Researchers in South Africa, home to many global biodiversity hot spots, are tackling this issue head on. They find that in the southern hemisphere, invasive species are weedy—grow fast and have high reproductive ability—in an environment characterized by slow growing natives. “We need to look at what predicted climate changes can do to currently present invaders and native species,” says Chown. Research efforts are underway to model potential shifts in species resulting from different climate scenarios.
Obviously, there is much yet to be learned. “The fact that so many invasions are surprisingly successful is a humbling reminder that we don't truly understand the fundamental rules of nature,” cautions Sax. A better understanding of how nature works will undoubtedly help society defend against invasions.
Figure 3 Gough Island, Remotely Located in the South Atlantic Ocean, Isn't Free from Invasive Species
(A) View from the top of the island.
(B) Lower down on the island, showing the tree fern Blechnum palmiforme and Phylica arborea, one of only two true trees, on the island.
(C) The Atlantic yellow-nosed albatross (Thalassarche chlororhynchos), which breeds on the island, is classified as endangered (EN A4bd) on the World Conservation Union Natural Resources Red List 2004. Although originally endangered as a result of longline fishing, albatross chicks are now under threat from a mouse (Mus musculus) introduced to the island.
(Photos: Steven Chown)
Citation: Gewin V (2005) Eco-defense against invasions. PLoS Biol 3(12): e429.
Virginia Gewin is a freelance science journalist based in Portland, Oregon, United States of America. E-mail: [email protected]
Abbreviation
UCUniversity of California
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Further Reading
Gaston K Jones A Hanel C Chown S Rates of species introduced to a remote oceanic island Proc R Soc Lond B Biol Sci 2003 270 1091 1098
Kennedy T Naeem S Howe K Knops J Tilman D Biodiversity as a barrier to ecological invasion Nature 2002 417 636 638 12050662
Levine J Vila M D'Antonio C Dukes J Grigulis K Mechanisms underlying the impacts of exotic plant invasions Proc Biol Sci 2003 270 775 781 12737654
Lonsdale WM Concepts and synthesis: Global patterns of plant invasions, and the concept of invasibility Ecology 1999 80 1522 1536
Sax D Stachowicz J Gaines S Sax DF Stachowicz JJ Gaines SD Capstone: Where do we go from here? Species invasions: Insights into ecology, evolution and biogeography 2005 Sunderland (Massachusetts) Sinauer Associates 457 480
Sax D Brown J The paradox of invasion Glob Ecol Biogeogr 2000 9 363 371
Schmitz D Simberloff D Needed: A national center for biological invasions 2001 Issues Sci Technol. Available: http://www.issues.org/issues/17.4/schmitz.htm . Accessed 19 October 2005
Shea K Chesson P Community ecology theory as a framework for biological invasions Trends Ecol Evol 2002 17 170 177
Simberloff D Gibbons L Now you see them, now you don't—Population crashes of established introduced species Biol Invasions 2004 6 161 172
Stachowicz JJ Fried H Osman RW Whitlatch RB Biodiversity, invasion resistance, and marine ecosystem function: Reconciling pattern and process Ecology 2002 83 2575 2590
Stohlgren T Barnett D Kartesz J The rich get richer: Patterns of plant invasions in the United States Front Ecol and Environ 2003 1 11 14 Available: http://www.nrel.colostate.edu/projects/stohlgren/index.html . Accessed 19 October 2005
Verling E Ruiz GM Smith LD Galil B Miller AW Supply-side invasion ecology: Characterizing propagule pressure in coastal ecosystems Proc Biol Sci 2005 272 1249 1257 16024389
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-5-391625962610.1186/1471-244X-5-39Study ProtocolResource-oriented music therapy for psychiatric patients with low therapy motivation: Protocol for a randomised controlled trial [NCT00137189] Gold Christian [email protected] Randi [email protected] Leif Edvard [email protected] Trond [email protected] Lars [email protected] Brynjulf [email protected] Faculty of Health Studies, Sogn og Fjordane University College, 6823 Sandane, Norway2 Nordfjord Psychiatry Centre, 6770 Nordfjordeid, Norway3 University of Bergen, 5020 Bergen, Norway4 Stavanger University Hospital, 4068 Stavanger, Norway2005 31 10 2005 5 39 39 12 9 2005 31 10 2005 Copyright © 2005 Gold et al; licensee BioMed Central Ltd.2005Gold 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
Previous research has shown positive effects of music therapy for people with schizophrenia and other mental disorders. In clinical practice, music therapy is often offered to psychiatric patients with low therapy motivation, but little research exists about this population. The aim of this study is to examine whether resource-oriented music therapy helps psychiatric patients with low therapy motivation to improve negative symptoms and other health-related outcomes. An additional aim of the study is to examine the mechanisms of change through music therapy.
Methods
144 adults with a non-organic mental disorder (ICD-10: F1 to F6) who have low therapy motivation and a willingness to work with music will be randomly assigned to an experimental or a control condition. All participants will receive standard care, and the experimental group will in addition be offered biweekly sessions of music therapy over a period of three months. Outcomes will be measured by a blind assessor before and 1, 3, and 9 months after randomisation.
Discussion
The findings to be expected from this study will fill an important gap in the knowledge of treatment effects for a patient group that does not easily benefit from treatment. The study's close link to clinical practice, as well as its size and comprehensiveness, will make its results well generalisable to clinical practice.
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Background
Music therapy is defined as a systematic process where the therapist helps the client to promote health, using musical experiences and the relationships that develop through them [1]. It is often perceived as a psychotherapeutic method where musical interaction, in addition to verbal discussion, is used as a means of communication and expression. The aim of music therapy is to help people with mental health problems to develop relationships and to address issues they may not be able to by using words alone. Results from a Cochrane review showed that music therapy helps people with schizophrenia to improve their global state, mental state and social functioning in the short to medium term [2]. The review suggested that there is a need for studies examining the effects of music therapy over a longer term. Furthermore, studies are needed to examine the effectiveness of music therapy in clinical practice, and to further explore the psychological 'mechanisms' through which music therapy works.
Music therapy is usually not tailored to a specific diagnosis. Rather, contents of therapy are negotiated with the patient within the process of therapy, based on a variety of individual traits. It has been suggested that factors unrelated to psychiatric diagnosis, specifically therapy motivation, be considered when specifying, prescribing, and evaluating psychotherapy [3]. Psychotherapy may not work if patients are not motivated for it [4-6]. In music therapy, the use of music (i.e. playing or listening to music) itself can often be a motivating factor for patients who may otherwise not be motivated for psychotherapy [7]. Therefore, a low motivation for (other) therapy can become a reason for referral of a patient to music therapy, and such factors may at times be more important than the patient's primary diagnosis. However, there is a scarcity of research addressing the effects of music therapy for patients with low therapy motivation. We found only one randomised study on music therapy for depression where the authors described that the majority of the participants had previously failed to respond to verbal psychotherapy [8].
The problem of low motivation may sometimes be due to a lack of insight and will often lead to poor therapy outcome. It has been described for a variety of disorders, including schizophrenia [9-12], depression and bipolar disorder [11,13], and psychosomatic disorders [14,15]. Music therapy is often recommended for such patients and may have something unique to offer which is worth exploring. A randomised study is needed to examine the potential of music therapy for this under-researched but clinically important population.
Resource-oriented music therapy for people with mental health problems is oriented towards the client's resources, strengths and potentials, rather than primarily on problems and conflicts, and emphasizes collaboration and equal relationships [16,17]. Such a perspective to music therapy builds on a contextual understanding of therapeutic processes [6,18,19], the philosophy of empowerment [20,21], and positive psychology [22]. In music therapy, music may be seen as a central resource for the patient, but a resource-oriented approach will also emphasise the patient's resources in the verbal discussions taking place within the music therapy sessions [17]. Goals of resource-oriented music therapy with people with mental health problems include, among others, the ability to feel and express emotions, to build and sustain relationships to others, and to develop interest and motivation. Therefore the goals of the therapy are closely related to what has been described as negative symptoms in mental health research [2,23].
Objectives
The objectives of this study are as follows:
1.) To determine whether resource-oriented music therapy helps psychiatric patients who have a low therapy motivation and a willingness to work with music to reduce their level of negative symptoms (primary study outcome).
2.) To determine whether the therapy helps the patients to improve in the following secondary outcomes:
(a) secondary outcomes of general relevance for the patient: general symptoms; general functioning; clinical global impressions.
(b) secondary outcomes specifically linked to the assumed mechanisms of the therapy: interest in music; motivation for change; self-efficacy; self-esteem; vitality; affect regulation; relational competence; actual social relationships.
3.) Provided that significant effects are found: To determine whether general outcomes are mediated by specific outcomes.
Methods
Participants
The study will include adult patients with mental disorders who have a low motivation for therapy, as specified below. Criteria for in- and exclusion will be assessed by the ward psychiatrist, based on information collected by the clinical team on the ward.
Inclusion criteria
(a) Diagnosis F1 to F6
Participants must have a non-organic mental disorder (F1 to F6 according to ICD-10), as assessed by a psychiatrist at a participating centre. The inclusion of such a broad range of mental disorders is based on the finding that mechanisms of psychotherapy are not specifically linked to diagnosis [6]. This broad range of diagnoses will also improve external validity, which is often not optimal in randomised trials which have too narrow inclusion criteria [24].
(b) Low therapy motivation
This is the main inclusion criterion for the study. Patients are often referred to music therapy because they have a low therapy motivation and music can be motivating for them. The specific reasons for this low therapy motivation may vary. Some patients may have insufficient insight into having a mental health problem. Others may have insight about having a problem but fail to acknowledge psychosocial components. These patients may demand a 'medication cure' and state that they do not believe in talking. Other patients may state that they do not feel comfortable with talking about emotions and personal problems. Patients may also have low therapy motivation because they did not improve from therapy previously.
(c) Willingness to work with music
Participants will be included if they show a willingness to work with music in music therapy. They do not need to have an established interest in music, such as having learnt an instrument or enjoying listening to music, although this may be the case for some of them.
Exclusion criteria
(a) Severe mental retardation
The outcome measures include some self-reports and therefore participants who are unable to complete these cannot be included. Participants need to be cognitively able to complete a self-report questionnaire.
(b) Severe life-threatening somatic illness
Participants with a severe life-threatening somatic illness will not be included because the dynamics of such illness would have such a strong influence on the course of therapy that it would be highly questionable to pool them with other patients.
Interventions
Participants will be randomly assigned to two groups (details in next section). The interventions for both groups will be provided and monitored over the course of three months from randomisation.
Experimental group
(a) Music therapy
Participants assigned to the experimental group will receive individual sessions of resource-oriented music therapy. Two sessions per week will be offered, lasting each 45 minutes. Over the course of three months this corresponds to a maximum of 26 sessions. Previous research suggests that at least about 20 sessions are needed for music therapy to have an effect [2]. In cases where it is not possible to provide the maximum number of sessions, therapists should try to ensure that at least 18 sessions will be given within the three-month period. This may be the case when outpatients live too far from the centre to attend two times per week throughout the study period.
Music therapy will be provided in accordance with the principles of resource-oriented music therapy [16,17]. These principles describe general therapeutic attitudes and behaviours (e.g. focusing on the client's strengths and potentials) as well as specific attitudes within the musical interaction (e.g. tuning into the client's musical expression). Attitudes that should be avoided are also described, as well as attitudes that are acceptable but not necessary. Adherence to these principles and competence in their application [16,17,25] will be monitored in two ways. Therapists will rate their own behaviour at the end of every session. This is an efficient way of monitoring the complete course of therapy. To control for a possible subjective bias in these self-reports, randomly selected sessions will be videotaped and the therapist's adherence and competence will be assessed by independent raters. Half of all participants in the experimental group will be randomly selected for videotaping of one therapy session which will also be selected randomly.
(b) Standard care
Patients will continue to receive treatment as usual while receiving music therapy. What kind and what dose or frequency of other treatment they receive will be monitored by the ward clinician before randomisation and after 1, 3, and 9 months.
Control group
(a) Standard care
Patients will receive treatment as usual during the three-month study period. What kind and what dose or frequency of treatment they receive will be monitored by the ward clinician before randomisation and after 1, 3, and 9 months.
(b) After the study period: Optional music therapy
For ethical reasons and in order to keep participants in the control group motivated, they will be offered music therapy after the three-month study period. Setting (i.e. individual or group), frequency and duration need not be equivalent to the experimental therapy, but will be set according to clinical needs and possibilities. Adherence and competence will not be monitored.
Study design
The study will use a single-blind (assessor blinded) randomised design with two parallel groups of equal size. Outcomes will be assessed at pretest (directly after inclusion, before randomisation), at an early intermediate time point (1 month after randomisation), posttest (3 months after randomisation), and six-month follow-up (9 months after randomisation).
The required sample size was calculated for the primary outcome, negative symptoms. We assumed an effect size slightly smaller than medium (f = 0.20, equivalent to d = 0.40), based on the results of our Cochrane Review [2]. For a one-way ANCOVA with one covariate (pretest values), α = 0.05, 80% power, and 36% variance explained by the covariate, the required sample size available for analysis (total number of valid cases) needs to be N = 2 * 65 = 130. In order to allow for 10% drop-outs, the total sample size will need to be N = 144. The actual power of the study may then be greater than 80% because of the additional intermediate assessment points [26].
After inclusion in the study and pretest assessment, the participants will be allocated to conditions using a computerised randomisation procedure, stratified by treatment centre and type of disorder (psychotic versus non-psychotic). This will be done by the principal investigator who has no direct contact to the patients in order to conceal the allocation from the involved clinicians. An overview of the study design is shown in Figure 1.
Figure 1 Flow chart of the study design. Abbreviations: C – ward clinician; M – music therapist; I – principal investigator; A – blind assessor; MT – music therapy.
The following professionals will be involved in conducting the study and collecting data:
1. Ward clinician (C): the clinician who has the primary responsibility for the patient at the hospital unit.
2. Music therapists (M): academically qualified music therapists with clinical experience in music therapy in psychiatry and specifically trained in the use of the treatment principles for resource-oriented music therapy.
3. Principal investigator (I): The first author (CG).
4. Blind assessor (A): an experienced clinician who is not involved in the daily work at the patient's ward/hospital unit and therefore not aware of the patient's assigned treatment condition. The assessor will have received training in the use of the assessment instruments and will conduct a one-hour patient interview for each assessment. The success of blinding is verified with a separate question in the blind assessor questionnaire.
5. A local co-ordinator will help with the administrative side of the data collection. This person will supervise and facilitate the data collection process and ensure the reliable and timely transferral of information between hospital staff and principal investigator.
6. Other music therapists will assess treatment fidelity (adherence and competence) on the basis of the video recordings.
Outcomes
The study will use blind ratings as well as self-reports. Standardised instruments with demonstrated validity, reliability and sensitivity to change will be applied whenever possible.
Primary outcome: Negative symptoms
The concept of negative symptoms has originally been developed mainly in relation to psychotic disorders but is considered relevant for other mental disorders as well [27,28]. Including affective flattening and blunting, poor social interaction and lack of interest, among others, it is reasonable to assume that processes within music therapy are directly linked to negative symptoms [2]. This outcome will be evaluated by a trained blind assessor using the Scale for the Assessment of Negative Symptoms (SANS) composite score [23]. Validity of the SANS scale has been demonstrated for a variety of mental disorders [27,28]. Interrater reliability, test-retest reliability, internal consistency and sensitivity to change following music therapy have been demonstrated for schizophrenic patients. In order to achieve reliable ratings, the assessor must be trained in the use of this scale.
Secondary outcomes of general relevance for the patient
• General symptom level will be assessed using the BSI-18 self-report scale with 18 items addressing anxiety, depression, and somatic complaints [29]. It has demonstrated concurrent and predictive validity as well as internal consistency in clinical and community samples.
• General functioning will be measured using a blind rating with the GAF [30]. The GAF is a widely used single-item scale which has demonstrated good predictive validity and interrater reliability.
• Global clinical impressions will also be evaluated by a blind assessor using the CGI scale [31]. It consists of two items and has been widely used to assess treatment outcomes in mental health because of its simplicity and intuitiveness.
Secondary outcomes specifically linked to the assumed mechanisms of music therapy
• Interest in music: We were unable to find a published scale that was appropriate for this outcome. Therefore we developed a self-report scale to measure interest in music. The scale has 11 Likert-scaled items assessing preferences for various uses of music, actual behaviours, and emotional responses to music. The scale is face-valid; its reliability will be determined from the study sample.
• Motivation for change will be measured using a modified version of the two URICA subscales precontemplation and contemplation [32,33]. Predictive validity, reliability, and sensitivity for change of this scale have been shown for a variety of mental disorders [34,35]. The scale includes 19 items. It will be used as a straightforward continuous measure in order to avoid the conceptual problems that are associated with the stages of change model [36]. In addition to this self-report instrument, a blinded assessment of the patients' general motivation will be included in the SANS avolition/apathy scale [23].
• Self-efficacy will be assessed using the modified Norwegian version of the General Perceived Self-Efficacy Scale [37]. This is a self-report measure with 10 Likert-scaled items that has demonstrated test-retest reliability and internal consistency in both clinical and non-clinical samples.
• Self-esteem will be measured using the Rosenberg Self-Esteem Scale [38], a self-report measure with 10 items. The scale has been used in many studies. Discriminant validity, test-retest reliability and internal consistency have been shown for patients with mental disorders.
• Vitality will be assessed using the vitality subscale of the SF-36 scale [39]. This is a self-report scale with 4 items. It has demonstrated discriminant validity and sensitivity to change in schizophrenic patients, and internal consistency and test-retest reliability have also been confirmed.
• Affect regulation will be measured with a blind rating using the SANS subscale affecting flattening and blunting [23]. The seven-item scale has good internal consistency. Interrater reliability is moderate. Sensitivity to change following music therapy has been demonstrated in schizophrenic patients.
• Relational competence will be assessed using the IIP-32 [40]. This self-report scale contains 32 items describing a variety of interpersonal problems. It has demonstrated internal consistency in psychotherapy patients and test-retest reliability in a non-clinical sample.
• Actual social relationships will be measured using both a self-report and a blinded assessment. The Q-LES-Q social relationships subscale [41] will be used in self-reports. It has 11 face-valid items and has demonstrated sensitivity to change, test-retest reliability and internal consistency in major depression. In addition, the blind assessor will complete the SANS anhedonia/asociality subscale [23]. The 5-item scale has demonstrated satisfactory interrater reliability, internal consistency, and sensitivity to change following music therapy in schizophrenic patients.
In total, the self-report questionnaire will consist of 114 items. The blind assessor will check the completed questionnaire for completeness and help the patient if necessary. The blind assessor questionnaire will consist of 28 items to be rated on the basis of a 1-hour clinical interview.
Statistical analyses
The effects of treatment (Objectives 1 and 2) will be analysed using analysis of covariance methods and effect sizes with confidence intervals. Subgroup analyses are planned for psychotic versus non-psychotic disorders. No stopping rules or interim analyses are planned for this study. The primary analysis will be intention-to-treat.
Mediational processes (Objective 3) will be examined using structural equation modelling [42,43], which will address mediation of simultaneous as well as of subsequent change. Factor analysis will be used to examine structures in the adherence and competence ratings.
Ethical issues
The study has been approved by the Regional Committees for Medical Research Ethics Western Norway (REK Vest).
Time scale
The overall time frame for this project is from January 2004 to December 2007. Data collection is scheduled from April 2005 to June 2007, and we expect to be able to report the study's main results by December 2007.
Discussion
Relevance of the expected findings
The findings that can be expected from this study will be highly relevant because of its size and comprehensiveness and because of its close link to clinical practice. This study will have a much greater sample size than all previous studies on music therapy in the field of psychiatry to date. This will enable a more precise estimation of the effect of music therapy. The study will also be more comprehensive than previous studies in terms of how the treatment is defined and treatment fidelity measured, and in terms of the inclusion of potential mediator variables. This comprehensiveness will allow an evaluation of the processes and mechanisms leading to therapeutic change at a greater level of detail than in previous studies in the field. The close link to clinical practice will be ensured through the application of the flexible therapy manual, but also through the choice of the study population. The inclusion criteria for this study define a population that is often being referred to music therapy, and one that is in need of special attention. The results of the study will therefore be well generalisable to and relevant for clinical practice.
Limitations
The main limitations of this study will include the lack of an alternative or 'placebo' therapy, the partial reliance on self-reports, and the broadness of the sample. Due to the lack of a placebo therapy, it could be argued that we did not control for the effect of receiving attention from a caring person. However, there are several arguments why a placebo therapy would not be adequate for this study. Most importantly, it has been argued that the metaphor of a placebo is conceptually inadequate in psychotherapy research, considering the discussion on common factors in psychotherapy [6]. It is therefore more appropriate to examine the specific processes and mechanisms of therapy by other means, such as the assessment of treatment fidelity and the assessment of mediator variables that we included in the design.
It may also be criticised that self-reports may be biased because of expectancy or social desirability effects. However, for some mental health outcomes, such as self-esteem, there is no alternative to self-reports. The main outcome for this study will however be rated by a blind assessor. Blinding of participants is not possible in psychotherapy studies.
A final criticism might concern the broad inclusion criteria. The study sample will be broad with regard to diagnoses, but the main inclusion criterion will be unrelated to diagnosis and well linked to an important clinical question. As this will improve generalisability, this last limitation can also as be regarded as one of the strengths of this study.
Competing interests
CG, RR and BS are clinically trained music therapists.
Authors' contributions
CG developed the background and design of the study, did the power calculation, helped to develop the therapeutic principles, and drafted the manuscript. RR conceived of the study and helped to draft the manuscript. LA helped with the design of the study and the power calculation. TA and LT participated in the design and the coordination of the study. BS helped in the conception and design of the study and in drafting the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study is supported by The Research Council of Norway, Oslo, Norway, by Helse-Vest RHF, Stavanger, Norway, and by Helse Førde, Førde, Norway. Professor Alv A. Dahl, MD, PhD, Department of Clinical Cancer Research, Rikshospitalet-Radiumhospitalet Trust, Oslo, Norway, provided valuable feedback concerning the design of the study.
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1633604710.1371/journal.pbio.0040002Research ArticleBioinformatics/Computational BiologyMicrobiologySystems BiologyConserved and Variable Functions of the σE Stress Response in Related Genomes σE-Mediated Envelope Stress ResponseRhodius Virgil A
1
Suh Won Chul
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¤aNonaka Gen
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¤bWest Joyce
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Gross Carol A [email protected]
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1 Department of Microbiology and Immunology, University of California, San Francisco, California, United States of America2 Department of Cell and Tissue Biology, University of California, San Francisco, California, United States of AmericaEisen Jonathan A. Academic EditorThe Institute for Genomic ResearchUnited States of America1 2006 20 12 2005 20 12 2005 4 1 e217 3 2005 13 10 2005 Copyright: © 2006 Rhodius et al.2006This 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.
Finding Sigma-Controlled Promoters
Bacterial Stress Responses: What Doesn't Kill Them Can Make Them Stronger
Bacteria often cope with environmental stress by inducing alternative sigma (σ) factors, which direct RNA polymerase to specific promoters, thereby inducing a set of genes called a regulon to combat the stress. To understand the conserved and organism-specific functions of each σ, it is necessary to be able to predict their promoters, so that their regulons can be followed across species. However, the variability of promoter sequences and motif spacing makes their prediction difficult. We developed and validated an accurate promoter prediction model for Escherichia coli σE, which enabled us to predict a total of 89 unique σE-controlled transcription units in E. coli K-12 and eight related genomes. σE controls the envelope stress response in E. coli K-12. The portion of the regulon conserved across genomes is functionally coherent, ensuring the synthesis, assembly, and homeostasis of lipopolysaccharide and outer membrane porins, the key constituents of the outer membrane of Gram-negative bacteria. The larger variable portion is predicted to perform pathogenesis-associated functions, suggesting that σE provides organism-specific functions necessary for optimal host interaction. The success of our promoter prediction model for σE suggests that it will be applicable for the prediction of promoter elements for many alternative σ factors.
A model for predicting the variable promoter sequences associated with the bacterial stress response is developed and used to identify constituents of the transcriptional response to σE.
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Introduction
Induction of alternative sigma (σ) factors is an important strategy for coping with environmental stress in bacteria. Indeed, there is a rough correlation between the apparent complexity of the environment and the number of alternative σ factors, e.g., Mycoplasma sp., which are obligate intracellular pathogens, contain only the housekeeping σ and no alternative σ's; Escherichia coli, which inhabits the relatively constant environment of its host organisms but can also survive in vitro, has six alternative σ's; and Streptomyces coelicolor, which inhabits a hostile and changing soil environment, has 62 alternative σ's. Therefore, the ability to predict promoters recognized by alternative σ's would significantly improve our capacity for understanding how bacteria adapt to stress.
It is challenging to predict bacterial promoters, which are composed of two conserved sequences centered at about −10 and −35 from the start point of transcription. Some promoters also have an “upstream element” (UP) upstream of the −35 sequence and/or an “extended −10” element immediately upstream of the −10. The fact that these promoters are composed of multiple, weakly conserved elements separated by less conserved, variable length spacer sequences makes their prediction a difficult bioinformatics problem. Such attempts have a long history, mostly directed at predicting promoters recognized by σ70 (b3067), the housekeeping σ in E. coli, using hidden Markov models, neural networks [1–4], and position weight matrices (PWMs) [5–8]. While these methods detect promoters with a moderate degree of success, they suffer from high false-positive rates (FPRs) in genomic sequences. In addition, promoter consensus and mismatch searches have also been employed to identify promoters for the Group IV factor, σW (Bsu0173), in Bacillus subtilis [9]. However, these approaches are not as effective as using PWMs that better describe the natural variability of target sites. Here, we consider only PWMs because their success is comparable to more complex models [3]. Staden [5] used three matrices (describing the −35, −10, and +1 promoter motifs) and one spacer penalty (for the −35 to −10) to predict σ70 promoters; variations of this approach were later explored by Hertz and Stormo [7]. Huerta and Collado-Vides describe the most accurate prediction method to date for σ70 promoters using multiple matrices for the −35 and −10 motifs, with one spacer penalty for the intervening spacer [6]. Although this method successfully identifies known promoters with high sensitivity (86%; true positives/total promoters), it suffers from many false predictions resulting in low precision (20%; true positives/total predictions), reducing its utility as a prediction tool to identify new promoters.
Alternative σ factors usually turn on a group of genes synchronously in response to a particular stress, and hence use very few activators. As a consequence, promoters recognized by alternative σ factors are somewhat less variable and might have higher information content than those recognized by the housekeeping σ factor, making them more amenable to bioinformatic analysis. We chose to test this proposition by determining the feasibility of predicting promoters of E. coli σE (b2573), both in E. coli K-12 and in related bacteria. σE, a Group IV (extracytoplasmic, ECF) σ factor [10,11], mediates the envelope stress response [12,13], is essential in E. coli K-12 [14], and is important for virulence in related bacteria [15–22]. We first identified σE regulon members and their promoters using genome-wide expression analysis and transcript start site mapping in the E. coli K-12 genome. We derived a model for these σE promoters by building upon approaches pioneered for σ70 promoters, and used this model to make predictions in related genomes. By comparing promoter predictions from the actual genome with those from “randomized” genomes, we were able to identify those promoters that are unlikely to occur by chance alone. In addition, we adapted cross-genome approaches utilized for transcription factors [23–25] as an additional way of predicting promoters in E. coli and related pathogenic genomes. We tested all predictions in E. coli K-12 and Salmonella typhimurium and unique predictions in E. coli CFT073. These tests demonstrated that the model works with high precision.
Our studies reveal that the extended regulon of 89 predicted transcription units (TUs) is predicted to consist of a core set of genes conserved in most organisms and another group of more poorly conserved genes. Remarkably, each of these gene sets has a coherent function. The core genes coordinate the assembly and maintenance of lipopolysaccharide (LPS) and outer membrane porins (OMPs), the two key structures of the outer membrane of Gram-negative bacteria, in response to environmental change. A majority of the variable σE regulon members perform functions known to be important for a pathogenic lifestyle. We suggest that induction of such determinants at the first sign of stress facilitates bacterial adaptation to the host environment.
Results
Identifying σE-Dependent Genes by Transcription Profiling
σE-dependent genes were initially identified using genome-wide transcription profiling, comparing a wild-type E. coli K-12 strain that has a low level of σE, with a strain overexpressing σE (following induction of its gene, rpoE, from an inducible promoter by IPTG). This strategy is preferable to comparison with an rpoE− strain because: (1) many σE-transcribed genes have multiple promoters, so that the change in transcriptional signal upon loss of σE is often small; and (2) rpoE− strains (which require an uncharacterized suppressor for viability [14]) grow slowly, invalidating the direct comparison between rpoE+/− strains. We monitored changes in gene expression in four separate time-courses after induction and used statistical analysis of microarrays (SAM) [26] to identify 75 significantly induced and eight significantly repressed genes (Figure 1; see Materials and Methods). Some of these genes are part of operons in which other gene members were clearly induced but were not marked as significant in our strict selection criteria. Therefore, to fully describe the σE regulon we expanded this set by using the statistics from SAM to analyze the reproducibility and significance of the expression ratios of all the genes adjacent to and in the same orientation as the highly significant genes. This gave 96 genes organized in 50 σE-dependent TUs, of which 42 were induced and eight were repressed (Figure 1).
Figure 1 Expression Profiles of σE Regulon Members
Significantly regulated genes identified from genome-wide transcription profiling following comparison of rpoE overexpressed (CAG25197) versus wild-type (CAG25196) E. coli K-12 MG1655 cells. The color chart illustrates the expression level for each gene from an average of four time-course experiments (see Materials and Methods). Red denotes induced, and green denotes repressed genes in CAG25197 following rpoE induction. Fold change of mRNA levels (rpoE overexpressed/wild-type) is indicated by the scale at the bottom of the figure; time in minutes after induction of rpoE in the time-course experiments is indicated at the top of the figure. Genes are identified by their unique ID and name (Gene ID) and are listed in chromosomal order to illustrate the TUs; the direction of transcription is indicated.
Identification of σE Promoter Motifs Upstream of Induced TUs
To determine which of our induced genes might have σE promoters, we used rapid amplification of cDNA ends (5′ RACE; see Materials and Methods) to identify start points of each TU, comparing mRNAs from rpoE overexpressed versus rpoE− cells. This analysis indicated that 28 of the 42 induced TUs contained σE-dependent transcription start sites (unpublished data). The remaining promoterless TUs identified in transcriptional profiling may be indirectly regulated by σE, especially since most were only weakly induced.
Bacterial promoters are located immediately upstream of their start sites. We therefore searched small blocks of sequences directly upstream of the 5′ RACE determined transcription starts for conserved σE motifs using the algorithm WCONSENSUS (see Materials and Methods). By testing several different search-window positions and widths, we found that a 16-nt search window (−1 to −16) was optimal for identifying the conserved −10 motif (T/
CGGTCAAAA), and that a 16-nt search window starting 9 nt upstream of the −10 element was optimal for locating the −35 motif (
GGAACTTTT). Although there were no other highly significant motifs, we found a 30-nt window of generally A/T-rich sequences directly upstream of the −35 motif with two conserved A/T-rich elements at positions −48/−49 and −57/−58. These correspond closely to the two information peaks in the SELEX-derived consensus sequences for the UP element of the rrnB P1 promoter [27]. In addition, the initiation nucleotide of the 28 promoters exhibited a strong preference for a purine (A/G) and weak conservation of sequences directly upstream.
The sequence logos of the conserved sequence motifs upstream of the 28 σE-dependent transcription start sites, together with their information content, are displayed in Figure 2A. The fact that all of the sequences contained good −35 and −10 promoter motifs indicated that we had successfully mapped σE-dependent transcription initiation sites. Note that most of the total information content of the promoter motifs (22.8 bits) was contributed by the well-conserved −10 and −35 motifs. Figure 2B–2D displays histograms of the distance distributions of the promoter elements from each other: most promoters preferred a 5/6-nt discriminator region between the −10 and +1 (Figure 2D), while the spacing between the −10 and −35 varied from 15–19 nt, with 16 nt strongly preferred (Figure 2C). Interestingly, individual promoters displayed an inverse correlation between the length of these two spacers: promoters with a long −10/−35 spacer tended to have a short discriminator, and vice versa. Consequently, the range of distances between the −35 and +1 for all the promoters is quite small: 25–28 nt, with most promoters preferring a 26/27-nt spacer (Figure 2B). The identified promoter sequences are listed in section A of Table 1.
Figure 2 Sequence Logos and Spacer Histograms of σE Promoter Motifs
Motifs were identified upstream of the 28 mapped transcription starts in E. coli K-12.
(A) Sequence logos (http://weblogo.berkeley.edu/; [78]) of the −35, −10, and +1 start site motifs and the A/T rich UP sequences. The information content (Iseq) of each motif is indicated (see Materials and Methods).
(B–D) Histograms of the number of promoters versus distances between the motifs identified in (A): (B) +1 start and −35 motifs; (C) −10 and −35 motifs; and (D) +1 start and −10 motifs. Distances between the −35, −10, and +1 start motifs are from the conserved
GGAACTT,
TCAAA, and A/G sequences, respectively, as marked in (A). Note that the weakly conserved spacer sequence appeared to associate with the −10 motif and was therefore incorporated into PWM−10.
Table 1 σE Regulon Members in E. coli K-12
Table 1 Continued
Genome-Wide Predictions of σE Promoters
The sequence alignments for the UP, −35, −10, and +1 sequences were used to build four PWMs (see Materials and Methods); each PWM spans the complete sequence illustrated in each logo in Figure 2A. Each promoter was then scored by summing the individual PWM scores and incorporating penalties for suboptimal spacing between the motifs to generate a distribution of known promoter scores with mean (μk) and standard deviation (σk). High-scoring promoters were composed of more highly conserved promoter elements at optimal spacings, and low-scoring promoters contained less well-conserved elements at suboptimal spacings.
We searched the E. coli K-12 MG1655 sequence for σE promoters in which each individual PWM scored ≥μ−2σ, and where the distance between motifs was within the range observed for the 28 RACE-identified promoters. These constraints allow potential promoters to have a combination of weak and strong motifs and the variable spacings characteristic of known E. coli K-12 σE promoters. Genome-wide predictions with PWM-35 identified 98,113 sites (Table 2). Sequences flanking these sites were then searched for UP, −10, and +1 motifs within the spacing range of our validated promoters to create a library of candidate promoters (note that the order of the searches does not affect the final library). The total promoter score of each candidate was calculated using the same procedure described above for the known promoters and then converted to a z-score (the number of standard deviations [σk] of the candidate score from the mean score of the known promoters [μk]). In cases where promoters overlapped such that the +1 motifs were within 4 nt of each other, only the highest scoring promoter was selected. This generated a library of 553 candidate promoters that includes 27 of the 28 RACE-identified promoters (Table 2), missing only the ybfG promoter that fails due to a poor start motif (<μ−2σ) despite having a relatively high total promoter z-score (−0.03).
Table 2 Genome-Wide σE Promoter Predictions in E. coli K-12
Identifying Significant σE Promoters From the Promoter Prediction Library
The vast majority of the 553 predicted promoters were low scoring and randomly distributed, in contrast to the 5′ RACE validated promoters, which were high scoring (> −1) and located near target genes (Figure 3A). To identify significant (i.e., functional) promoters from our library, we compared predictions from the actual genomic sequence (Figure 3B) with those from 100 randomized genomes generated in silico (Figure 3C). The randomized genomes maintain the location of all open reading frames (ORFs), average codon, and nucleotide content, but now contain only nonspecific sequences. Hence, predictions from these genomes indicate the number of predictions occurring by chance alone. This allows us to determine both a FPR and a probability score that the prediction arose by chance (p-value) for every prediction in the actual K-12 genome. Using a cutoff of FPR <0.5 and p < 0.05 for each bin (a bin describes a group of promoters with similar scores and positions relative to the gene) and an additional distance and z-score constraint to remove spurious predictions (see Materials and Methods), we generated 39 highly significant predictions. Their combined FPR is 0.22, which means that 8.6 of 39 predictions would be expected by chance alone. Of the 39 significant predictions, 24 were of previously validated promoters located upstream of genes that were induced in transcriptional profiling. The remaining 15 predicted promoters were not upstream of genes that were induced in transcriptional profiling. Interestingly, one promoter is upstream of ompX (b0814), which is repressed in the transcription profiling, but is oriented away from the gene. Thirteen of 15 promoters (including ompX) were confirmed either by in vitro transcription or in vivo promoter assays (sections A and C in Table 1), giving a total of 37 of 39 verified significant predictions.
Figure 3 σE Promoter z-Scores versus Distance Upstream of the Nearest Gene in Actual and Randomized E. coli K-12 Genomes
Only promoters less than 2,000 nt upstream of target genes are shown.
(A) Scatter plot of predicted (diamonds) and known (circles) σE promoters in E. coli K-12 MG1655.
(B) Topographic plot of predicted σE promoters in E. coli K-12 MG1655. The x and y axes are divided up into 200-nt and 1 unit bins, respectively, and the number of predictions falling within each bin are indicated colorimetrically as shown in the scale. Note that the data in this plot are the same as the predictions in (A). Bins containing significant predictions are indicated by yellow ovals.
(C) Topographic plot indicating average number of predicted σE promoters made from 100 randomized E. coli K-12 MG1655 genomes in silico (see Materials and Methods). Each bin illustrates the average number of predictions made from 100 separate randomized genomes that fall within the parameters of that bin.
How Well Does Our σE Promoter Model Perform in E. coli K-12?
To determine the performance of our model in identifying significant promoters, we need to know the total number of validated σE promoters in E. coli K-12. We used several approaches to identify the 49 promoters that comprise the σE regulon in this organism (all promoters are listed in Table 1). (1) We identified 28 promoters by transcriptional profiling coupled with 5′ RACE and 13 additional promoters from our significant promoter model to give 41 promoters. (2) We searched our library of 553 promoters for any new predictions upstream of genes that were induced in our transcriptional profiling experiments. We found two low-scoring promoters located upstream of genes (malQ [b3416] and lpp [b1677]); these were validated in vitro to give 43 promoters. Note that, similar to ompX, lpp is repressed in the transcription profiling and the σE promoter is upstream but oriented away from the gene. (3) We noticed that several validated predictions are located far upstream of the nearest gene (dsbC [b2893], yhbG [b3201], lhr [b1653], and wzb [b2061]; Table 1) and are in fact internal and very close to the 5′ end of the adjacent ORF, suggesting that these ORFs may be misannotated. Searching our promoter library, we found a high-scoring promoter located upstream of narW (b1466) just beyond our distance cut-off that was very close to the beginning of narY (b1467). We confirmed this promoter in vitro to give 44 promoters. (4) Two genetic screens [28,29] identified additional putative σE- dependent promoters; we validated the five additional promoters identified by Rezuchova et al. to give 49 validated promoters, but were unable to validate any of the eight new promoters proposed by Dartigalongue et al. We note that most of the Dartigalongue et al.–proposed promoters contain poorly conserved sequence elements separated by a wide range of spacer lengths, suggesting they might not be functional. Table 3 shows all validated E. coli K-12 σE regulon members divided into functional categories.
Table 3 Functional Classification of the σE Regulon Members in E. coli K-12
Of the 39 highly significant predictions, 37 were validated, giving our promoter model a precision of 95% (validated predictions/number of predictions; Table 2 and Figure 4). This promoter model also successfully identified 37 of 49 known σE promoters, giving a sensitivity of 76% (validated predictions/known promoters). Averaging the sensitivity and precision scores gives an estimate of the total performance, or accuracy, of the σE prediction model (85%; Table 2). True promoters that remained undetected by the highly significant prediction model did so for a variety of reasons: five promoters failed because either their UP, −35, −10, or +1 motifs scored less than μ − 2σ; five promoters failed because of low total promoter scores, making them difficult to distinguish from the many other low-scoring nonfunctional promoters; and two failed because they were located far upstream of the nearest gene. Given the variety of reasons that they failed, this suggests that they were outliers rather than a fault with a particular predictive step of the model.
Figure 4 Venn Diagram of Predicted and Known σE Promoters in E. coli K-12
39 predictions from the promoter library were identified as highly significant, of which 37 were confirmed. A total of 49 known σE promoters were confirmed from the literature and additional experiments, of which 37 were successfully identified by the promoter prediction model (see text; Table 2).
Predictions of σE Promoters in Closely Related Genomes
Given the success of our promoter model in E. coli K-12, we extended it to eight genomes of closely related organisms in which the DNA binding determinants of the σE orthologs are identical or very similar to those in E. coli K-12 σE (Figure S1). This determination is based on the demonstration that the structure of Domain 2 (which recognizes the −10 conserved promoter sequence) and of Domain 4 (which recognizes the −35 conserved promoter sequence) of E. coli σE can be overlaid with that of σ70, the housekeeping σ, indicating that the structure of these two domains is conserved across σ's [30]. The −10 and −35 promoter recognition determinants in σ70 have been thoroughly mapped [31]. We assumed that comparable residues in σE carried out −10 and −35 recognition and identified eight organisms in which these residues were highly conserved.
We applied the promoter prediction model developed in E. coli K-12 to these eight genomes to generate a library of promoter predictions for each organism. We then identified all putative regulon members in TUs by assuming that the downstream genes formed an operon if they were in the same orientation and the intervening intergenic region (IG) was less than 50 nt [32]. Significant promoters were identified as described above for E. coli K-12 by comparison to predictions from random genomes (constructed specifically for each real genome to account for their structure, average codon, and nucleotide contents). To prevent spurious results in some genomes, significant promoters (FPR < 0.5; p < 0.05) were also filtered for z-score > −2 and distance < 1,100 nt upstream of genes.
As a second method, a significant prediction in any one genome was used to search the relevant promoter library for promoters upstream of conserved orthologs in the other species (see Materials and Methods). The matching promoter did not have to satisfy a minimum p-value or FPR, enabling the detection of less well-conserved orthologous promoters. However, to prevent spurious results, predicted σE promoters were required to have a z-score > −2 and to be within 1,100 nt upstream of the orthologous gene or TU. For each significant prediction upstream of a conserved ortholog, the probability of identifying a matching promoter in each genome by random chance from the promoter libraries is approximately 0.03, suggesting that the matches we identified were highly significant. In addition, we found that the vast majority of matching promoters were at similar distances upstream of the orthologs as the original search promoter, further increasing the significance of the matches. The results of these procedures are summarized in Table 4 and are presented in a database of conserved predicted σE promoters and regulon members across all nine genomes (Table S1).
Table 4 Genome-Wide σE Promoter Predictions in Nine Related Genomes
These two computational approaches, together with experimentally identified promoters in E. coli K-12, generated an “extended σE regulon” across nine genomes, which consisted of 89 unique TUs (Table 4). Interestingly, there are no TUs predicted to be regulated by σE in all nine genomes; however, a core of 19 TUs is present in at least six genomes. The conserved members of the regulon predominantly carry out related functions (Table 5) involving the outer membrane and the regulatory strategy to maintain the σE response. The majority of the remaining σE-controlled TUs are not highly conserved, but most control cell envelope functions (Table 5; see Table S2 for a list of all the extended regulon members in each functional category).
Table 5 Predicted Core σE Regulon Members
Among the nine organisms, E. coli O157:H7 has the most predictions (49) and Yersinia pestis the least (nine) (Table 4). Genomes may have fewer significant σE predictions because they have a reduced σE regulon. Alternatively, the promoter model may not perform well in that organism. We believe that Yersinia is an example of an organism with a reduced σE regulon, making it difficult to detect its promoters with the random genome approach that relies on identifying overrepresented sequences. In support of this idea, the σE DNA–binding determinants in both organisms are essentially conserved (see Figure S1), and eight of nine Yersinia promoters with reasonable promoter scores were identified using the conserved ortholog approach (see Table 4). This may also be true for Erwinia and Photorhabdus, which also have only a few significant promoter predictions (one and eight, respectively). However, they also contain four and six amino acid changes, respectively, near the DNA-binding determinants of regions 2.4 and 4 (see Figure S1), so there is a possibility that there is a slight deviation of the optimal promoter sequence that is not captured by the E. coli promoter prediction model. We note, though, that these genomes still share many highly conserved σE regulon members, indicating that many of our predictions in these genomes should be functional. In more divergent genomes, where σE orthologs had amino acid changes at critical DNA-binding positions (Shewanella oneidensis, Vibrio cholerae, and Pseudomonas aeruginosa; unpublished data), our model was unsuccessful. Interestingly, loss of P. aeruginosa σE is complemented by E. coli σE [21], and likewise, both σE consensus sequences are similar ([33] and references therein). However, few promoters match consensus, and the σE orthologs may tolerate different variations in their target promoter sequences.
Validation of the σE Promoter Model in S. typhimurium and E. coli CFT073
To determine the validity of our predictions, we experimentally tested all predictions made in S. typhimurium. In addition, we tested all unique predictions made in E. coli CFT073 (conserved predictions were not tested because their promoters were virtually identical to those found in E. coli K-12). Promoter function was tested both by in vivo promoter assay (see Materials and Methods) and in vitro transcription (see Tables S1 and S2). Although both of these assays used E. coli K-12 RNA polymerase and σE, we do not think there are any functional differences from the E. coli CFT073 and S. typhimurium σE holoenzymes since their subunits are virtually identical and differ only in a few nonessential positions, with at least 99.72% and 98.58% sequence identity, respectively, with the E. coli K-12 subunits. These assays revealed a high success rate. For S. typhimurium, we made a total of 29 predictions, composed of 22 significant predictions based on the random genome model and seven predictions based on the conserved ortholog approach. Sixteen of 22 (73%) of the significant predictions and four of seven (59%) of the conserved orthologs were validated, for an overall success rate of 69%. For CFT073, of the 40 predictions, we have validated 29 of 38 (76%) significant predictions and two of two conserved ortholog predictions, for an overall success rate of 78%. We note that unconfirmed predictions may still be functional in vivo, as they might require a coregulator not present in our assay conditions or in E. coli K-12. These results suggest that our promoter prediction strategies provide a reasonably accurate picture of the σE regulon in organisms closely related to E. coli K-12.
Discussion
The goal of this work was to follow the responses mediated by alternative σ's across organisms to determine whether these responses have changed. This required us to develop methods that accurately predict promoters recognized by alternative σ's. We have developed a successful strategy to predict the σE regulon in E. coli K-12 and related organisms and have validated predictions in three organisms. We report the first comprehensive analysis of the conservation and variation of a σ factor regulon across genomes, identifying an “extended” σE regulon in nine genomes comprised of 89 unique TUs. Of these, only 19 are highly conserved. The highly conserved TUs maintain appropriate cellular levels of LPS and OMPs, two unique constituents of the outer membrane of Gram-negative bacteria, thereby identifying the core function of the regulon. The less-conserved regulon members perform multiple pathogenesis-associated functions, suggesting that the σE regulon has been co-opted to provide organism-specific functions necessary for optimal interaction with the host.
Promoter Predictions
We chose to employ de novo promoter prediction as our primary method for cross-genome analysis because it can identify promoters unique to a particular genome. This is an important attribute, given the variability of bacterial genomes. For example, the three sequenced E. coli genomes share only 40% of their coding sequence. As a secondary approach, we searched for weakly conserved predictions upstream of orthologous genes, thereby identifying additional promoters too weak to pass the first filter (e.g., the latter method identified seven new S. typhimurium promoters, four of which were validated in vitro, and eight new Yersinia promoters). Our σE promoter model performed considerably better (precision = 95%; accuracy = 85%; see Table 2) than the housekeeping σ70 promoter model (precision = 20%) upon which it is based [5–7], primarily because the combined information content for σE is much higher than that for σ70 (I
seq = 22.8 bits versus 12.56 bits). In addition, performance was improved by comparison to a random genome to reduce false positives and our secondary approach of searching for conserved orthologs. Interestingly, σ70 promoters, but not σE promoters, were often embedded in predicted clusters of overlapping sites [6]. This distinction may result from the differences in specificity of the two models or reflect a fundamental distinction in promoter recognition mechanisms of housekeeping and alternative σ's. We note that a simple prediction model having a single-weight matrix and a fixed-length spacer suffices to predict promoters of another family of σ factors (σ54; RpoN) unrelated to the σ70 family [34–36]. In contrast, our promoter prediction model should be applicable for the prediction of promoters elements for the many alternative σ70 family members that bind to promoter elements separated by variable spacers, and especially Group IV σ's that tend to bind to more highly conserved promoter sequences [11].
Many σE promoter predictions were limited to particular subgroups. In some cases, the orthologs themselves had limited distribution. This particularly interesting case suggests that the ortholog has an organism or species-specific role. For example, the highly related E. coli and Shigella genomes contained three predictions upstream of orthologs exclusive to at least three of four of these genomes, and the two Salmonella species contained two predictions upstream of orthologs unique to Salmonella (see Table S1). In other cases, the orthologs themselves were widely distributed, but σE promoters were identified for only some orthologs. For example, ten predicted σE promoters are found only upstream of genes in E. coli and Shigella, and five σE promoters are found only upstream of genes in Salmonella (see Table S1). These cases may identify examples of regulon evolution, where σE promoters are created or lost in response to the requirements of the organism. Alternatively, we may have failed to detect σE promoters because one or more of their motifs failed our cutoff criteria. Finally, when σE promoters regulate long polycistronic TUs, some downstream TUs may no longer be classified as σE regulated in related genomes, either because of gene shuffling or because their intergenic distance was >50 nt (our cut-off for genes in an operon). In this latter case, σE might still regulate the downstream genes.
The Core σE Regulon
The core σE regulon consists of 19 TUs and 23 proteins, of which 20 have known functions (Table 5; Figure 5). Amazingly, at least 60% of the core regulon members (~75% of proteins with known functions) ensure the synthesis and assembly of LPS and OMPs, or encode the transcriptional circuitry to maintain the homeostasis of these two key constituents of the outer membrane of Gram-negative bacteria. The proper ratio of OMPs and lipid A contributes to the impermeability of the outer membrane [37].
Figure 5 Functions of the Highly Conserved σE Core Regulon Members
Stresses such as heat lead to the accumulation of unassembled OMPs; this activates the sequential proteolysis of the membrane-spanning antisigma RseA [12,54]. The inner membrane proteases DegS [b3235] and RseP [b0176] release the cytoplasmic portion of RseA, which is then degraded by the cytoplasmic proteases ClpX [b0438] and Lon [b0439] ([85]; R. Chaba unpublished data) to release free σE, which then binds to RNA polymerase core to regulate the expression of target regulon members. σE up-regulates functions required for synthesis, assembly, and/or insertion of both OMPs and LPS, the most abundant components of the outer membrane, as well as envelope-folding catalysts and chaperones. σE also up-regulates expression of itself and its negative regulator RseA and enhances expression of GreA [b3181] and σ32 [b3461]. Importantly, σE down-regulates OMP expression, thereby reducing the accumulation of unassembled OMPs, which presumably limits the duration of the response.
Five members of the core regulon are involved in the synthesis or assembly of LPS. Four members (Lpx A, B, D, and PlsB) promote the synthesis of lipid A, the hydrophobic anchor of the LPS, and a fifth (BacA) contributes to LPS assembly [38,39]. Lipid A comprises the outer leaflet of the outer membrane. The high resistance of Gram-negative bacteria to hydrophobic compounds is in large part due to the high density of saturated fatty-acid chains and potential for many lateral interactions in lipid A, which together dramatically slow diffusion of hydrophobic compounds through the outer membrane [40].
OMPs are trimeric β-barrel proteins that form channels in the outer membrane to permit access of small solutes. These abundant proteins comprise about 25% of the surface area of the bacteria [37] and have a complex assembly pathway. Six members of the core regulon promote the OMP assembly: two lipoproteins (YfiO and YraP) [41,42], three chaperones (Skp, FkpA, and DegP) [41,43], and YaeT (Omp85), which is generally implicated in insertion of β-barrel proteins into the outer membrane of many species [44–46] and may also do so in E. coli [45,46]. YaeT functions in a complex with three lipoproteins (YfiO, YfgL, and NlpB) [42], of which only YfiO is in the core regulon. However, the other two lipoproteins may also turn out to be part of the conserved regulon as YfgL is predicted to be driven by a σE promoter in five organisms and, at least in K-12, NlpB (b2477) is induced by overexpression of σE through an unknown mechanism. The complex assembly pathways of LPS and porins are not completely known, but it is clear that the two are mutually dependent [47–52]. Thus, some conserved regulon members may actually function in both assembly pathways.
Intriguingly, FtsZ, a member of the core regulon, is involved in initiating cell division (reviewed in [53]). This raises the possibility that the σE regulon may be needed to synthesize the excess outer-membrane components required at the time of septation. Thus, its primordial function may have been to facilitate passage through the cell cycle. However, as these core components are essential for the integrity of the outer membrane, this response could easily be used as a primary defense mechanism to protect the barrier function of the cell in the face of environmental stress.
The core regulon also encodes the transcriptional circuitry that allows the cell to detect and respond to imbalances in LPS and OMPs to maintain envelope homeostasis. Unassembled OMPs activate the proteolytic cascade that degrades RseA (b2572) [54], the membrane-spanning antisigma factor that inhibits σE function (reviewed in [12]). As LPS intermediates participate in OMP assembly [47–52], the unassembled OMP signal reports on the status of both LPS and OMP maturation [55–60]. Two notable features of the transcriptional circuit encoded by the core regulon ensure a rapid and sensitive response to imbalances in OMP assembly. First, the rpoErseABC operon has two highly conserved σE promoters, one upstream of the entire operon and the second upstream of rseA (see Table S1). As a consequence of this arrangement, σE positively autoregulates itself, thereby ensuring a rapid increase in proteins required for OMP/LPS homeostasis, and up-regulates RseA to set up a negative feedback loop (Table 5; Figure 5). The fact that RseA synthesis is driven from two promoters is likely to dampen the response, reduce oscillation, and provide a sufficient excess of RseA to ensure rapid down-regulation following a decrease in unassembled OMPs. A second important feature of the response is a homeostatic loop that prevents further buildup of unassembled OMPs (Figure 5). At least in E. coli K-12, OmpA (b0957), OmpC (b2215), OmpF (b0929), and OmpX are down-regulated upon induction of σE, thereby decreasing the flow of OMPs to the envelope. Down-regulation may be accomplished by production of σE-regulated antisense small RNAs transcribed divergently from their negatively regulated OMPs (V. Rhodius, unpublished data). Intriguingly, the σE promoter divergent from ompX is a member of the core regulon (see Table S1 and Table 5), raising the possibility that OMP down-regulation is a conserved feature of the response.
The Extended σE Regulon
More than 60 of the unique σE-controlled TUs we have predicted are present in fewer than six of the nine genomes we have scanned; many are present in only a small subset of these genomes (see Table S1 and Table 4). However, the majority of those with known functions carry out a coherent theme: adaptation of the organism to the conditions encountered when the bacterium interacts with its eukaryotic host (Table S2; Table 6). This idea is presaged by two functions in the core regulon: an iron acquisition system (YecI) to facilitate growth in the iron-deficient host environment and a component of alkyl reductase (AhpF) to detoxify lipid hydroperoxides that may be generated during exposure to macrophages.
Table 6 Predicted Properties of σE Regulon Members across Nine Genomes
The predicted extended regulon encodes multiple functions related to pathogenesis. Among these, several have already been validated in at least one organism. These include synthesis of capsule, a viscous polysaccharide layer that facilitates adhesion and protects against macrophage ingestion; recombination functions to resolve DNA lesions that could be generated by the respiratory burst (RecJ/O/R); and metabolic components for nitrate/nitrite respiration (NarW/V) that facilitate adaptation to the anaerobic/microaerophilic host environment. In addition, the regulon is predicted to encode components that produce colanic acid and chorismate and that modify the core and O-antigen portion of LPS, although no predictions in these classes have yet been validated. That the extended σE regulon encodes many pathogenesis-related functions explains why cells lacking σE are defective in pathogenesis [15–22], and suggests that the extended σE regulon may serve as an early adaptation system to facilitate survival in vivo. In addition, although the bacteria discussed here occupy diverse hosts, many pathogenic determinants apply broadly, even across the plant–animal divide [61–63].
Why is a response devoted to monitoring the status of OMPs and LPS also used for pathogenesis-related functions? Possibly, interaction with host cells alters the status of these σE regulators, thereby triggering the σE response. Using the core regulon as a base, organisms might then add additional members to the σE regulon that improve their viability in their hosts. This would explain why many of the pathogenesis functions are unrelated either to the core function of the regulon or even to the envelope itself. The variability of the σE regulon suggests that it may be easier to adapt the function of an existing regulator by changing the location of its binding sites than to evolve new regulators. Because environmental change is likely to generate envelope stress, it may be generally true that regulators sensing the envelope will contain organism-specific regulon members that facilitate the response for the particular ecological niche of the bacterium. Interestingly, σE is a member of the Group IV σ family, many of which also respond to stress in the envelope. It will be interesting to determine whether organism-specific variation in regulon function is characteristic of other Group IV σ's.
Materials and Methods
Media, strains, and plasmids
M9 complete minimal media was prepared as described [64], supplemented with 0.2% glucose, 1 mM MgSO4, vitamins, and all amino acids (40 μg/ml). The media was supplemented with 100 μg/ml ampicillin, 10 μg/ml tetracycline, and/or 20 μg/ml chloroamphenicol as required.
Bacterial strains and plasmids used in this study are listed in Table 7. Strain CAG25195 was constructed by using a lambda lysate from CAG16037 (MC1061 [ΦλrpoH P3::lacZ] ΔlacX74) to lysogenize MG1655 as described by [65]. P1 vir-mediated transductions were carried out as described by [66].
Table 7 Bacterial Strains and Plasmids Used in This Study
Plasmid pLC245 was used to overexpress rpoE from the strong IPTG-inducible trc promoter and was constructed as follows: the rpoE gene was amplified by PCR from genomic MG1655 DNA using the primers RPOE1 (5′-
CATATGAGCGAGCAGTTAACGGAC-3′) and RPOE2 (5′-
GCAAGGATCCTCAACGCCTGATAAGCGGTT-3′), which encodes a BamHI site (underlined). The PCR product was digested with BamHI to create one overlapping end, and then ligated into vector DNA prepared from pTrc99A by digesting with EcoRI, treating with Klenow enzyme to produce a blunt end, and then digesting the vector with BamHI. The final construct was confirmed by sequencing.
Strain growth and probe preparation for microarray analysis
To identify genes that alter their expression upon overexpressing σE, time-course microarray experiments were performed with the strain CAG25196 (MG1655 ΔlacX74 [ΦλrpoH P3::lacZ]) carrying the control vector, pTrc99A, versus CAG25197, which carries the IPTG-inducible rpoE overexpression vector, pLC245 (Table 7). Samples containing the control vector were labeled with Cy3 (green), and rpoE overexpression samples were labeled with Cy5 (red). Cells were grown in M9 complete minimal media with appropriate antibiotics in order to maximize the number of genes expressed, rather than in a rich media such as LB (luria broth) [67]. 500-ml conical flasks containing 100 ml of media were inoculated from fresh overnight cultures to a final OD450 = 0.03 or 0.035 for strains carrying the plasmid pTrc99A due to the fractionally slower growth rate. Cultures were grown aerobically at 30 °C in a gyratory water bath (model G76 from New Brunswick Scientific, Edison, New Jersey, United States) shaking at 240 rpm until OD450 = 0.3. Cultures were then induced with a final concentration of 1 mM IPTG and incubation resumed as before. Immediately prior to induction, and at 2.5, 5, 10, 15, 20, 30, and 60 min after induction, 1-ml and 8-ml samples were removed for microarray analysis.
Culture samples for microarray analysis were added to ice-cold 5% water-saturated phenol in ethanol solution, centrifuged at 6,600 g, and the cell pellets flash-frozen in liquid N2 before storing at −80 °C until required. Labeled probe for microarray analysis was prepared as described in [68]. Briefly, total RNA was isolated from the stored cell pellets using the hot phenol method, and labeled Cy3 and Cy5 cDNA was prepared from 16 μg of total RNA with 10 μg of random hexamer (Integrated DNA Technologies, Coralville, Iowa, United States) using the indirect labeling method.
DNA microarray procedures
Relative mRNA levels were determined by parallel two-color hybridization to glass slide cDNA microarrays [69]. PCR products of 4,110 ORFs representing 95.8% of E. coli ORFs were prepared according to [70] using primers from SigmaGenosys (The Woodlands, Texas, United States). The products were spotted onto glass slides to make DNA arrays as described in protocols on http://derisilab.ucsf.edu/core/resources/index.html. Samples were hybridized to the arrays and scanned as described in [68]. The resulting TIFF images were analyzed using GenePix 3.0 software (Axon Instruments, Union City, California, United States) and the data stored on an AMAD database (software available from http://derisilab.ucsf.edu/core/resources/index.html).
Expression data analysis
Expression data were normalized using the assumption that the quantity of initial mRNA was the same for both samples [71]. To correct for intensity (dye)–dependent biases, we used intensity-dependent normalization [72,73]. For each gene spot on an array, the green (Cy3) fluorescent intensity was defined as G = (F532Median – B532) and the red (Cy5) fluorescent intensity was defined as R = (F635Median – B635), where the local background intensity (B532, B635) is subtracted from the median foreground intensity (F532Median, F635Median). The data were filtered to exclude all R and G values less than 3 × local background. For each microarray experiment, an “MA-plot” was used to represent the (R,G) data, where M = log2
R/G and
. A local A-dependent normalization was performed by fitting a normalization curve using the robust scatter plot smoother “lowess” implemented in the statistical software package R, such that:
where c(A) is the lowess fit to the MA-plot. The fraction of data used for smoothing each point was 50%.
Statistically significant differentially expressed genes were identified from replicate microarray experiments using the SAM software ([26]; http://www-stat.stanford.edu/~tibs/SAM/index.html). SAM employs gene-specific t tests and by analyzing permutations of the t scores from the dataset derives a false discovery rate (percentage of genes identified by chance) for a user-selected cutoff threshold (the lowest false discovery rate at the median percentile). The rpoE time-course expression data revealed that genes that altered their expression in response to rpoE did so within 10 min after induction. Therefore, in each of the four time-courses time points from 10 min onwards were considered replicates and averaged to create four independent datasets. These data were then filtered for presence in at least 75% of datasets and significant genes identified using a stringent cutoff of the lowest false discovery rate (0.95%) at the median percentile.
5′ RACE PCR
The 5′ ends of σE-dependent transcripts were mapped using new 5′ RACE adapted from [74]. We chose this method because (1) it is highly sensitive, facilitating the detection of weakly expressed transcripts; and (2) sequencing the RACE products enables the precise identification of mRNA 5′ ends. Total RNA was extracted as described for microarray analysis from strains CAG25197 (rpoE
+; Table 7) 1 h after induction with 1 mM IPTG and CAG22216 (rpoE
−; Table 7). Both strains were grown under identical conditions as for the microarray experiments in M9 complete minimal media with appropriate antibiotics to OD450 = 0.3; samples from CAG22216 were harvested, while CAG25197 was induced with 1 mM IPTG for 1 h before harvesting. Fourteen micrograms of total RNA was treated with 5 U tobacco acid pyrophosphatase (TAP; Epicentre Technologies, Madison, Wisconsin, United States) to remove the 5′ γ and β phosphates from the RNA, and the samples cleaned by organic extraction and ethanol precipitation. One hundred picomoles RNA oligo (5′-
GAGGACU
CGAGCU
CAAGC-3′; MWG Biotech, Ebersberg, Germany) was then ligated onto the 5′ ends of the TAP-treated RNA using 5 U T4 RNA Ligase (Epicentre Technologies), and the samples again cleaned by organic extraction and ethanol precipitation. The oligo-ligated RNA was then used as template for reverse transcription reactions using 200 U SuperScript II RT (Invitrogen, Carlsbad, California, United States). In each series of experiments, 20 ng each of up to 40 gene-specific primers (GSP1; sequences available on request) were used in the same reaction to generate a library of cDNAs corresponding to the mRNAs of up to 40 putative σE-regulated genes. The production of full-length cDNAs was increased by reducing RNA 2° structure from incubating the reaction at increasingly higher temperatures: 37 °C for 1 h, 42 °C for 30 min, and 50 °C for 10 min. A dilution of the reverse-transcription reaction was then used as template for PCR amplification in the presence of a DNA primer containing a sequence complementary to the ligated RNA oligo sequence, and a second gene-specific primer (GSP2) for each gene that is closer to the promoter. A separate PCR reaction was performed with each GSP2 primer and the products visualized by 7.5% PAGE. Most of the tested genes contained multiple PCR products, suggesting multiple promoters. Thus, to identify σE-dependent transcripts for each gene, PCR products were compared from cDNA generated from CAG25197 (rpoE
+) and CAG22216 (rpoE
−) cells; products present from only the rpoE
+ reactions were considered σE-dependent transcripts. These products were gel-purified from 7.5% PAGE gels, electroeluted, and sequenced using the appropriate GSP2 primer. The transcription start site was defined as the nucleotide immediately preceding the sequence corresponding to the ligated RNA oligo sequence. In some cases, two adjacent start sites could be discerned by the appearance of a second RNA oligo sequence 1 nt out of frame from the first after reading the genome sequence.
Identifying σE promoter elements upstream of transcription starts mapped by 5′ RACE
WCONSENSUS [75] was used to identify the different conserved σE promoter elements using a method similar to [6]. We note that BioOptimizer is also a suitable alternative since it can identify two-block motifs separated by a variable spacer [76]. WCONSENSUS generates optimal matrices of aligned sequence motifs based on maximizing information content and minimizing the expected frequency of finding the matrix by chance given the known sequences. Matrices were selected using the second cycle in which every sequence contributes to the final alignment. A range of sequence windows of different widths were searched to identify optimal matrices describing −10 and −35, start site, and upstream elements. Optimal matrices for the −10 motif were identified by searching sequence windows −1 to −16, and for the −35 by searching a 16-nt window 9 nt upstream of the identified −10 motif.
σE promoter predictions using PWMs
The information content (Iseq) of aligned σE promoter motifs was calculated using:
where i is the position within the site, b refers to each of the possible bases, fb,i is the observed frequency of each base at that position, and pb is the frequency of base b in the entire genome (in E. coli taken to be 0.25 for A/G/C/T). The aligned σE promoter sequences were visualized using sequence logo ([78]; http://weblogo.berkeley.edu/).
PWMs (Wb,i) for each of the σE promoter elements (PWMUP, PWM−35, PWM−10, and PWM+1) were built using the method of [79]:
where nb,i is the number of bases b at position i in the aligned sequences and N is the total number of aligned sequences. A pseudo count of 0.1 was added for each base b for the Bayesian estimate. The relative binding affinity of σE to a DNA sequence of length L (equal to the length of the PWM) is given by the score:
(where b corresponds to the nucleotide at position i within the sequence fragment of length L), such that a high score corresponds to a high-affinity site with a close match to the consensus sequence, while a low score corresponds to a low-affinity site with a poor match to the consensus. The PWM was calibrated by scoring all the sequences used to build the matrix (Ew), and the distribution of the scores is described by their mean (uw) and standard deviation (σw). Potential σE target sites in the E. coli genome were identified by calculating the score Eg of every possible sequence window of length L in both strands of the genomic sequence and computing the mean (ug) and standard deviation (σg) of the distribution. Predicted sites were made by selecting all genomic scores Eg greater than a cutoff, S0, of two standard deviations below the mean of the PWM scores (uw – 2σw).
A penalty score adapted from the methods of [5] and [7] was applied to predicted promoters for suboptimal spacing between the +1, −10, and −35 motifs based on the observed spacing frequency for the known σE promoters. The spacer penalty was determined by taking the natural logarithm of an approximated spacer frequency normalized by the approximated frequency of the most frequently occurring spacer class. For each promoter, this was calculated for three spacers and summed to give a total spacer penalty: +1 to −10 (discriminator); −10 to −35 (spacer); and +1 to −35 (total).
A total score was calculated for each predicted promoter (Sp):
The predicted promoter scores, Sp, were calibrated by scoring the known promoter sequences used to build the matrices (Sk) to derive a distribution with mean (μk) and standard deviation (σk). The Sp scores were then converted to a promoter z-score: Zp = (Sp – μk)/σk.
In vitro transcription assays
Single-round in vitro transcription assays were employed to test predicted σE promoters. DNA templates were prepared by PCR from genomic DNA (primer sequences available on request) to create fragments with the promoter of interest contained within flanking sequences 100 nt downstream and 200 nt upstream of the predicted transcription start point. RNA polymerase core enzyme was purified as described in [80], and His6-tagged σE was purified using a Qiagen Ni2+ affinity column per manufacturer's instructions (Valencia, California, United States). The transcription assays were performed as described in [81] with the following modifications: Binding reactions (12 μl) contained 50 nM template DNA, 250 nM core RNA polymerase, 500 nM σE, 5% glycerol, 20 mM Tris (pH 8.0), 300 mM KAc, 5 mM MgAc, 0.1 mM EDTA, 1 mM DTT, 50 μg/ml BSA, and 0.05% Tween. Single-round transcriptions were initiated with 4 μl of “NTP + heparin mix” (to give a final concentration of 200 μM each NTP and 100 μg/ml heparin in 1× binding buffer), incubated for 5 min at 37 °C, and then terminated with 8 μl of 25 mM EDTA. The reactions were extracted with phenol and chloroform, precipitated with ethanol, and resuspended in 8 μl of H2O. The RNA transcripts were then used as templates in labeled reverse-transcription reactions using a primer ∼100 nt downstream of the predicted transcription start point (same as the downstream PCR primer used to create the template DNA). Primers were annealed by incubating with the template for 10 min at 70 °C before chilling on ice. The reverse transcription reactions (15 μl) contained 8 μl of template RNA, 10 μM primer, 1× StrataScript RT Buffer, 50 U StrataScript RNase H-RT (Stratagene, La Jolla, California, United States), 200 μM dCTP/dGTP/dTTP, 10 μM dATP, 6 μCi [α-32P] dATP (3,000 Ci/mmol; 110 TBq/mmol), and 8 U RNase Inhibitor (Boehringer Mannheim, Mannheim, Germany). Reactions were incubated at room temperature for 10 min and then at 42 °C for 1 h 50 min, before terminating with 9 μl of stop solution (95% deionized formamide, 25 mM EDTA, 0.05% [w/v] bromophenol blue, and 0.05% [w/v] xylene cyanol FF). The cDNA transcripts were resolved by electrophoresis after heating at 90 °C for 2 min and loading 8 μl on a 6% denaturing polyacrylamide sequencing gel together with DNA sequencing reactions that functioned as size markers. Transcripts were visualized using a Molecular Dynamics Storm 560 Phosphorimager scanning system (Sunnyvale, California, United States).
In vivo promoter assays
Promoters to be validated were cloned on XhoI-BamHI fragments into the green fluorescent protein (GFP) reporter plasmid, pUA66 (Table 7; [82]) upstream of the gene GFPmut2 [83]. The promoter fragments were generated by PCR from genomic DNA in which the upstream and downstream primers contained an XhoI and BamHI site, respectively, and amplified genomic promoter sequence from −65 to +20 with respect to the predicted transcription start point. Cloned promoter constructs were confirmed by sequencing. Reporter strains were generated by transforming the plasmids constructs into strains CAG25196 and CAG25197 carrying the pTrc99a vector and the rpoE expression plasmid, pLC245, respectively (Table 7). Promoter assays were performed by direct inoculation of Luria broth supplemented with appropriate antibiotics from frozen glycerol stocks. One hundred fifty–microliter cultures were grown in covered 96-well U-bottom tissue culture plates overnight at 30 °C with shaking at 400 rpm. The cultures were then diluted 1:50 into fresh 96-well plates containing Luria broth supplemented with appropriate antibiotics and 1 mM IPTG. Cultures were grown as before for up to 23 h and fluorescence measured in a Spectra Max Gemini XS 96-well fluorometer and OD600 measured in a Spectra Max 340 96-well spectrophotometer (Molecular Devices, Sunnyvale, California, United States). σE-dependent promoter activity was determined by first subtracting the background fluorescence/OD600 readings of CAG25196 and CAG25197 cells bearing a promoterless GFP vector from the readings of CAG25196 and CAG25197 cells carrying the same promoter construct, and then subtracting the CAG25196 from the CAG25197 readings for each promoter. Four independent assays were performed for each promoter construct. A promoter was judged to be σE dependent if the standard deviation of the four assays did not overlap with those of the promoterless GFP vector; this translated to a σE-dependent signal at least three times greater than background. This approach was validated by confirming σE-dependent activity of 42 of 49 verified E. coli K-12 σE promoters.
σE promoter predictions in related genomes
Promoter predictions were made in genomes as described for E. coli K-12 using genome sequence files (*.fna) and annotation files (*.ptt) downloaded from the NCBI FTP database (ftp://ftp://ftp.ncbi.nih.gov/genomes/Bacteria/) on 6 August 2004. For each genome promoter predictions were plotted as a function of promoter z-score versus distance upstream of the nearest ORF in the same direction (see Figure 3A). A topographic plot of promoter z-score versus distance upstream was then constructed in which the x and y axes were divided into 200-nt and 1 unit bins, respectively, and the number of predictions falling within each bin (PA) determined (see Figure 3B). Significant predictions were identified by comparing against predictions made in genomes containing randomized sequences. Randomized genomes were constructed to mimic the structures of real genomes but in which the nucleotide sequence of each structure was randomized. For each genome, the percentage nucleotide content was determined for all divergent IGs, convergent IGs, IGs less than 50 nt in the same direction as adjacent ORFs (short IGs), and IGs greater than 50 nt in the same direction as adjacent ORFs (long IGs). Finally, for each genome the average codon usage was determined for all ORFs. Randomized genomes of identical sizes were then constructed in which the size, orientation, and location of all the genomic structures were maintained but in which the nucleotide sequences were randomized while maintaining the average codon usage for all ORFs and the average nucleotide content for all dIGs, cIGs, long IGs, and short IGs. For each genome, promoter predictions were made from 100 randomized genomes, and, using the same bins as for the actual genomes, an averaged topographic plot was constructed that recorded the average number of predictions within each bin (P¯R; see Figure 3C). For each bin of the actual genome topographic plot, a FPR was calculated that compared the average number of predictions in the 100 randomized genomes (P¯R) with the number of predictions in the actual genome (PA):
In addition, for each bin, the significance of obtaining the observed number of predictions from the actual genome (PA) given the average number of prediction from the randomized genomes (P¯R) was calculated based on Poisson distribution to derive a p-value. All promoter predictions in actual genomes were assigned a FPR and p-value based on the bin where they were located. Promoter predictions for an actual genome were determined significant if, in general, FPR < 0.5 and p < 0.05, with the FPR cutoff being the stricter filter. Additional filters of promoter z-score > −2, distance upstream <1,100 nt were also applied to prevent spurious results in some genomes.
Conserved σE promoter predictions
A database of protein orthologs across the genomes was constructed using the program BLAST and the NCBI protein sequence files (*.faa) for each genome. Orthologs were defined as the highest scoring hit in a target genome, which, when the matching sequence was used to search the original genome, identified the same search sequence as the highest scoring match. All coding sequences in the genomes were organized into putative TUs defined as all adjacent ORFs in the same orientation separated by less than 50 nt [84]. Using the protein ortholog database, conserved TUs across genomes were identified by containing at least one protein ortholog. In some instances, a TU in one genome may match more than one TU in other genomes due to the location of constituent ORFs becoming separated. Conserved promoter predictions were defined as predictions from the promoter prediction libraries less than 1,100 nt upstream of all orthologous TUs and scored in general promoter z-score > −2, distances upstream < −1,100 nt, FPR <0.5, and p < 0.05 in at least one genome. Given that each promoter library contains approximately 150 predictions with z-score > −2 at distances <1,100 nt upstream, and each genome contains on average 4,500 genes, a matching promoter occurring by random chance for a particular search promoter = 150 of 4,500, or 0.033.
Supporting Information
Figure S1 Amino Acid Sequence Alignments of Conserved DNA-Binding Regions of σE across Eight Genomes
The RpoE (σE) sequences are aligned against RpoD (σ70) based on the structural alignment in [30]. Residues inferred to be involved in DNA interactions are based from σ70 [31] and are highlighted in yellow.
(A) Alignments of conserved regions 2.2–3.0 involved in −10 promoter recognition.
(B) Alignments of conserved regions 4.1–4.2 involved in −35 promoter recognition.
K-12, E. coli K-12; CFT073, E. coli CFT073; O157, E. coli O157:H7 EDL933; Sfl, Shigella flexneri 2a str. 2457T; Sty, Salmonella enterica subsp. enterica serovar Typhi str. CT18; Stm, Salmonella typhimurium LT2; Plu, Photorhabdus luminescens subsp. laumondii TTO1; Eca, Erwinia carotovora subsp. atroseptica SCR11043; Ype, Yersinia pestis CO92.
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Table S1 Highly Significant and Conserved σE Promoter Predictions across Nine Closely Related Genomes
Orthologous TUs are displayed on the same row; note that only one gene in each TU needs to be an ortholog. Genes within a TU are separated by “=” in the following fields: Unique ID (unique identification number from NCBI ptt file); Gene (Gene name); Function (Gene description from NCBI ptt file). Promoter predictions are given in the fields Distance (number of nucleotides of +1 position upstream of translation start point of the first gene in the TU) and Score (total promoter z-score; see Materials and Methods). If there is no promoter prediction for that TU, these two fields just contain “–.” Promoter predictions for E. coli K-12, E. coli CFT073, and S. typhimurium highlighted in gray in the distance and score fields have been validated by in vitro transcriptions and/or in vivo promoter assays. Promoter predictions in E. coli CFT073 that are conserved with E. coli K-12 are presumed functional based on their high level of conservation and were not tested. See Figure S1 for abbreviations.
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Table S2 σE Regulon Members in Nine Closely Related Genomes Organized into the Functional Categories Displayed in Table 5
Orthologous proteins are displayed on the same row. Proteins in parenthesis are part of TUs observed to be regulated in E. coli K-12 and based on TU conservation are assumed to be part of the regulon in the related genomes. Validated predictions for E. coli K-12, E. coli CFT073, and S. typhimurium are highlighted in gray. Predictions in E. coli CFT073 that are conserved with E. coli K-12 are presumed functional based on their high level of conservation and were not tested. See Figure S1 for abbreviations.
(27 KB XLS).
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Accession Numbers
The National Center for Biotechnology (NCBI) (http://www.ncbi.nlm.nih.gov/) accession numbers for the bacteria discussed in this paper are Erwinia carotovora subsp. atroseptica SCRI1043 (NC_004547); E. coli K-12 MG1655 (NC_000913); E. coli CFT073 (NC_004431); E. coli O157:H7 EDL933 (NC_002655); Photorhabdus luminescens subsp. laumondii TTO1 (NC_005126); Salmonella enterica subsp. enterica serovar Typhi str. CT18 (NC_003198); Salmonella typhimurium LT2 (NC_003197); Shigella flexneri 2a str. 2457T (NC_004741); and Yersinia pestis CO92 (NC_003143). Raw and normalized microarray expression data are available on the NCBI GEO Web site (http://www.ncbi.nlm.nih.gov/geo/) under the accession code GSE3437.
We thank Joe DeRisi, Sydney Kustu, Nick Cozzarelli, Brian Peter, and Dan Zimmer for help and advice with constructing the E. coli DNA microarrays; Hao Li and Jeff Chuang for advice with DNA binding site and BLAST analysis; Jeff Cox for advice on the biological functions of the regulon members; Carla Bonilla for setting up the 5′ RACE experiments; and Melissa Riegert for setting up the in vitro transcription experiments. This work was supported by National Institutes of Health (NIH) grants GM57755 and GM32678 (to CAG).
Competing interests. The authors have declared that no competing interests exist.
Author contributions. VAR, WCS, and CAG conceived and designed the experiments. VAR, WCS, and GN performed the experiments. VAR analyzed the data. VAR and JW contributed reagents/materials/analysis tools. VAR and CAG wrote the paper.
¤a Current address: Central Research and Development, DuPont Company, Wilmington, Delaware, United States of America
¤b Current address: Ajinomoto Company, Chuo-ku, Tokyo, Japan
Citation: Rhodius VA, Suh WC, Nonaka G, West J, Gross CA (2006) Conserved and variable functions of the σe stress response in related genomes. PLoS Biol 4(1): e2.
Abbreviations
5′ RACErapid amplification of cDNA ends
FPRfalse-positive rate
GFPgreen fluorescent protein
IGintergenic region
LPSlipopolysaccharide
O-PSouter polysaccharide
OMPouter membrane porin
ORFopen reading frame
PWMposition weight matrix
RNAPRNA polymerase
SAMstatistical analysis of microarrays
TUtranscription unit
UPupstream element
==== Refs
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PLoS BiolPLoS BiolpmedplosmedPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0040013SynopsisBioinformatics/Computational BiologyMicrobiologySystems BiologyEubacteriaFinding Sigma-Controlled Promoters Synopsis1 2006 20 12 2005 20 12 2005 4 1 e13Copyright: © 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.
Conserved and Variable Functions of the σE Stress Response in Related Genomes
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For bacteria, a change in the environment often causes physiological stress. Bacteria cope with that stress by altering the expression of suites of genes, and manufacturing new proteins, which may allow the cell to repair damage or protect itself in the future. This stress-induced gene expression response is often mediated by proteins called sigma factors. Sigma factors bind to the gene-transcribing machinery and direct this machinery to the promoter sites of the target genes. Identifying the binding sites for sigma factors that direct stress responses thus provides an important window on understanding how bacteria behave. Unfortunately, sigma factor binding sequences (otherwise known as promoters) vary from gene to gene, and current identification methods are tedious and prone to error. In a new study, Virgil Rhodius, Carol Gross, and their colleagues describe a novel model that finds these sites quickly and accurately. Their results show that in Escherichia coli bacteria, the main targets for one type of sigma factor, called sigma factor E, are genes that increase production of cell envelope components (inner membrane, periplasm, and outer membrane).
The authors first found sigma factor E sites the hard way—by comparing gene expression in two strains of E. coli that differed in their intrinsic level of sigma E–induced activity. They looked for genes whose expression in the high sigma E activity strain differed most from genes in the low sigma E activity strain, and then searched upstream of these genes for promoters containing these sigma sites (the region to which the transcription machinery binds). This method, called expression profiling, led them to 28 genes. This formed a “starter set,” which could be used to make their model.
By determining the nucleotide sequences and spatial arrangements that were most common at these sites, Rhodius et al. constructed a “position weight matrix,” a prediction tool with which to discover and analyze putative sigma E sites on other genes. Applied to the entire genome of bacterium E. coli K-12, the matrix identified 553 potential sites, which included 27 of the 28 sites identified through expression profiling. However, most of these sites were likely to be false positives. A series of increasingly stringent selection rules was then applied to eliminate those sites that were likely to arise by chance alone, whittling the list down to 39, including 24 of the original 28 sites. Of these 39, the authors confirmed that 37 were actual sigma factor E sites. Using a variety of other screening methods, they determined that the K-12 genome actually contained a total of 49 sigma E binding sites.
Escherichia coli (visualized with transmission electron microscopy, above) was used as a model system to predict the regulatory DNA targets of sigma factors, bacterial proteins induced by stress. Image: CDC/Elizabeth H. White, M.S
So how good are these results? A predictive model such as this is judged by two measures: sensitivity and precision. Sensitivity, the ratio of validated predictions (“hits”) to total actual sites, indicates how well the model finds true positives. Precision, the ratio of validated predictions to total predictions (hits plus misses), indicates how well the model screens out false positives. A model that claimed that every sequence was a promoter would indeed identify all the real ones, and have a sensitivity of 100%, but the rate of false positives would make the model useless. Similarly, a model so conservative that it only made predictions guaranteed to be right would have a precision of 100%, but might predict so little as to be of no use either.
The Rhodius et al. model has a sensitivity of 37/49, or 76%, and a precision of 37/39, or 95%, in E. coli K-12. The average of these two represents the total performance, or accuracy, of the predictive model, and was 85%, considerably better than previous models.
Finally, the authors used their results to predict the genes activated by sigma E in response to stress in a variety of bacteria. They found that the genes most commonly activated by sigma E primarily affect the integrity of the outer membrane, through promoting the synthesis, assembly, and homeostasis of the two major components of the membrane: lipopolysaccharide and proteins called porins. Other regulated genes promote pathogenic behavior of the bacteria. Experimental validation of the predictions in two different species of bacteria indicated that the model performed well, having a precision of about 75%. Improved ability to find such genes and to understand how they are activated has clear potential for reducing the burden of bacterial infections.—Richard Robinson
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Prev Chronic Dis
Preventing Chronic Disease
1545-1151
Centers for Disease Control and Prevention
PCDv14_04_0021
Original Research
PEER REVIEWEDAddressing Tobacco in Managed Care: Results of the 2002 Survey
McPhillips-Tangum Carol MPH CMT Consulting
106 Geneva Street, Decatur, GA 30030 404-377-4061 [email protected]
Bocchino Carmella MBA, RN America’s Health Insurance Plans (AHIP), Washington, DC
Carreon Rita America’s Health Insurance Plans (AHIP), Washington, DC
Erceg Caroline MJ America’s Health Insurance Plans (AHIP), Washington, DC
Rehm Bob MBA America’s Health Insurance Plans (AHIP), Washington, DC
10 2004
15 9 2004
1 4 A042004
Introduction
In the United States, tobacco use is the leading preventable cause of death and disease. The health and cost consequences of tobacco dependence have made treatment and prevention of tobacco use a key priority among multiple stakeholders, including health plans, insurers, providers, employers, and policymakers. In 2002, the third survey of tobacco control practices and policies in health plans was conducted by America's Health Insurance Plans' technical assistance office as part of the Addressing Tobacco in Managed Care (ATMC) program.
Methods
The ATMC survey was conducted in the spring of 2002 via mail, e-mail, and fax. A 19-item survey instrument was developed and pilot-tested. Of the 19 items, 12 were the same as in previous years, four were modified to collect more detailed data on areas of key interest, and three were added to gain information about strategies to promote smoking cessation. The sample for the survey was drawn from the 687 plans listed in the national directory of member and nonmember health plans in America's Health Insurance Plans.
Results
Of the 246 plans in the sample, 152 plans (62%) representing more than 43.5 million health maintenance organization members completed the survey. Results show that health plans are using evidence-based programs and clinical guidelines to address tobacco use. Compared to ATMC survey data collected in 1997 and 2000, the 2002 ATMC survey results indicate that more health plans are providing full coverage for first-line pharmacotherapies and telephone counseling for smoking cessation. Plans have also shown improvement in their ability to identify at least some members who smoke. Similarly, a greater percentage of plans are employing strategies to address smoking cessation during the postpartum period to prevent smoking relapse and during pediatric visits to reduce or eliminate children's exposure to environmental tobacco smoke.
Conclusion
The results of the 2002 ATMC survey reflect both tremendous accomplishments and important opportunities for health plans to collaborate in tobacco control efforts. With appropriate support, analytical tools, and resources, it is likely that health plans, clinicians, providers, and consumers will continue to evolve in their efforts to reduce the negative consequences of tobacco use.
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Introduction
In the United States, tobacco use is the leading preventable cause of death and disease. Smoking kills more than 440,000 people in the United States each year, with most deaths occurring from lung cancer, ischemic heart disease, and chronic airway obstruction (1). Yet approximately 23% of American adults continue to smoke cigarettes (2). In 2000, it was estimated that approximately 8.6 million persons in the United States were living with at least one condition attributed to smoking (3).
The health consequences of tobacco use are accompanied by a staggering economic burden. Smoking caused more than $157 billion in annual health-related economic losses between 1995 and 1999, including $81.9 billion in smoking-related productivity losses and $75.5 billion in excess medical expenditures (1). Smoking-attributable neonatal expenditures were estimated at $366 million in 1996, or $704 per maternal smoker (1). Together, the consequences and costs of tobacco dependence have made treatment and prevention of tobacco use a key priority among multiple stakeholders, including health plans, insurers, providers, employers, and policymakers.
In 1997, The Robert Wood Johnson Foundation established a collaborative program, Addressing Tobacco in Managed Care (ATMC). This program is based on the understanding that health plans' comprehensive benefits, sophisticated information systems, and defined populations, as well as their ongoing partnerships with health care providers, are well suited to implement, evaluate, and sustain tobacco control interventions. ATMC includes a National Program Office based at the University of Wisconsin Medical School's Center for Tobacco Research and Intervention, and a national technical assistance office (NTAO) managed by America's Health Insurance Plans (AHIP), formerly known as the American Association of Health Plans (AAHP). The mission of the NTAO is to advance the integration of tobacco cessation and prevention programs into routine health care by increasing the number and quality of tobacco control initiatives within health plans.
The NTAO provides resources to health plans and insurers striving to develop tobacco control programs; conducts a benchmarking awards program to highlight exemplary health plan tobacco control initiatives; promotes best practices and partnerships through national conferences; and oversees the development of a business case model for smoking cessation. The NTAO has also conducted three surveys of health plans over the past six years to assess practices and policies related to tobacco control.
The ATMC baseline survey was conducted in 1997, followed by a similar survey in 2000. The results of both surveys were published in peer-reviewed journals in 1998 and 2002 (4,5). The purpose of this paper is to present the results of the 2002 ATMC survey; highlight changes from 1997 to 2002; cross-reference the findings with national guidelines and recommendations; and explore these findings and trends in light of the changing environment in which health plans operate and the public's attitude toward tobacco use.
Methods
A 19-item survey instrument was developed and pilot-tested in the fall of 2001. The instrument was designed to assess new trends, barriers, and opportunities related to addressing tobacco control in health plans, identify new models or frameworks of care, and assess changes in health-plan–based tobacco control activities between 1997 and 2002. The sample for the survey was drawn from the 687 plans listed in AHIP's national directory of member and nonmember health plans. The directory was stratified based on health plan enrollment size, and a random sample of 246 health plans was selected. The sample size enables the detection of a 5% difference between proportions at α = .05 and β = .80.
The ATMC survey was conducted in the spring of 2002. As in 1997 and 2000, the 2002 survey was conducted via mail, e-mail, and fax, with telephone follow-up with nonrespondents at two, four, and six weeks after initial contact. The sample included large national plans that have local plans in multiple states. As in previous years, the corporate office of each national plan was asked to review the questionnaire and determine whether they would respond on behalf of their local plans or ask local plans to complete the questionnaires individually. Three of four national plans opted to respond on behalf of their local plans and their responses reflect 64% (97/152) of the responses.
The 2002 survey questionnaire was similar to the 2000 survey. Of the 19 items in the 2002 questionnaire, 12 were the same as in previous years, four were modified to collect more detailed data on areas of key interest (i.e., pharmaceutical coverage and system-level interventions), and three were added to gain information about strategies to promote smoking cessation. Based on feedback provided during pretesting, the majority of survey questions focused on smoking cessation despite recognition that tobacco cessation or tobacco control is a more encompassing term. Although we recognize that the preferred provider organization (PPO) product has grown in popularity, the 2002 ATMC survey asked respondents to answer all questions based on their best-selling commercial health maintenance organization (HMO) product to preserve the ability to make comparisons with previous years.
All analyses were performed with SPSS software (SPSS, Inc, Chicago, Ill). Chi-square tests and t-tests were used for comparisons, and results of these tests were considered statistically significant when the corresponding P value was ⩽ .05. Consistent with previous years, the data are unweighted to best describe the policies and practices of health plans.
Results
Of the 246 plans in the sample, 152 (62%) completed and returned the survey. Collectively, the 152 plans represent more than 43.5 million HMO members. Respondent plans were predominantly independent practice association, network, and mixed models. Fifty-one percent were for-profit and publicly held; 24% were for-profit and privately held; 23% were not-for-profit; and 2% were mutual companies. A comparative analysis of respondents and nonrespondents to the 2002 survey indicated that there were no significant differences in size, tax status, or predominant model type between respondents and nonrespondents.
Among plans that responded to the 2002 ATMC survey, 71% reported having written clinical guidelines for smoking cessation. The majority of plans reported having guidelines that had been internally developed by the plan; few plans reported using the 2000 U.S. Public Health Service Clinical Practice Guideline on Tobacco Use and Dependence or the 1996 Agency for Health Care Policy and Research (now the Agency for Healthcare Research and Quality [AHRQ]) Practice Guideline on Tobacco Cessation (Table 1).
Nearly three quarters of all plans indicated that they could identify at least some individual plan members who smoke (Table 1). Among those plans that reported being able to identify individual smokers, the most common data sources are health risk appraisals and telephone interviews. Only 6% of plans use enrollment data to identify individual smokers.
The vast majority of health plans that responded to the survey reported that they provide full coverage for at least one type of pharmacotherapy used for tobacco cessation (Table 1). Bupropion, in the form of Wellbutrin, was the most commonly covered pharmacotherapy. Only 11% of plans reported that provision of full coverage for tobacco cessation pharmacotherapies is dependent on enrollment in a counseling or cessation program.
Full coverage for at least one type of behavioral intervention used for tobacco cessation was reported by the vast majority of health plans (Table 1). Telephone counseling was the most commonly covered behavioral intervention, followed by face-to-face counseling and self-help materials.
Health plans reported having a variety of strategies to encourage members to stop smoking during times that might be considered important teachable moments. The majority of health plans reported having a specific strategy to address smoking cessation during pregnancy and during treatment for chronic illnesses (Table 1).
Plans reported that a variety of strategies are used with providers and their office staff to promote smoking cessation among plan members. The majority of plans reported offering provider education and offering prompts and reminders to providers (Table 1). Provider prompts and reminders were coupled with provider education by 44% of plans. Few plans reported offering incentives to providers and their staff to promote smoking cessation.
Health plans reported that they require providers to carry out a variety of assessments and activities related to smoking that are in accordance with the clinical model of the 5 As: Ask, Advise, Assess, Assist, and Arrange (6). (The 2002 ATMC survey was fielded before the development of Assess willingness to quit.) The majority of plans require providers to ask new patients about smoking status and include smoking status as a vital sign (ask about smoking status at every visit) (Table 1). Fewer plans reported requiring providers to carry out activities aimed at advising, assisting, and following up with patients trying to quit smoking.
Although health plans reported a variety of barriers that limit their ability to effectively address tobacco control, the most common barriers relate to resources (e.g., inadequate staff, funding, competing priorities) and system issues (e.g., poor data collection, reporting, record maintenance). Other barriers included lack of patient demand, lack of purchaser demand, and delayed economic return on investment.
Tobacco control activities used by larger health plans are different from those used by smaller plans (Table 2). Based on the enrollment distribution of health plans in our sample, we defined larger plans as those with more than 250,000 members and smaller plans as those with less than or equal to 250,000 members. Larger plans were more likely than smaller plans to have written clinical guidelines for smoking cessation P < .001) and to have a specific strategy to address smoking cessation during specific times, such as adolescence, pregnancy, postpartum visits, and hospitalization P ranged from < .001 to .02). Smaller plans, more likely to be staff and group-model plans, were more likely to be able to identify individual plan members who smoke P < .001) and provide full coverage for some prescription pharmacotherapies used for smoking cessation P ranged from < .001 to .02).
Although the ATMC survey instruments used in 1997, 2000, and 2002 were not identical, the majority of core questions on pharmacotherapies, behavioral health, and smoking cessation strategies remained unchanged. The percentage of plans that provide full coverage for any type of pharmacotherapy used for smoking cessation more than tripled from 1997 to 2002 (P < .001) (Table 3). The percentage of plans able to identify individual smokers also increased P < .001). More plans reported providing full coverage for telephone counseling P = .04) and face-to-face counseling P = .011) in 2002 compared with both previous surveys.
From 1997 to 2002, there were large increases in the percentage of plans with strategies to address relapse prevention during the postpartum period (P = .02) and smoking cessation during treatment for chronic illness P = .002) and following a heart attack P = .004) Table 3).
Health plan performance on measures related to requiring providers to adhere to four of the 5 As varied in both directions between 2000 and 2002 (Table 3). Although comparable data on these variables were not collected in 1997, the percentage of plans that require providers to ask new patients about smoking status (P = .02) and strongly advise all smokers to quit (P = .02) decreased from 2000 to 2002, and the percentage of plans that require providers to include smoking as a vital sign (i.e., ask about it at every visit) (P = .28) and assist smokers by referring them into appropriate treatment (P = .33) increased modestly.
Discussion
The results of the 2002 ATMC survey indicate that health plans are using evidence-based programs and clinical guidelines to address tobacco use. Clinical guidelines detail the most effective options for helping patients to quit smoking, and using strategies recommended in clinical guidelines is associated with greater success in helping smokers to quit (6,7). Although a large percentage of health plans reported having written clinical guidelines for tobacco cessation, it is possible that even more plans address tobacco cessation within other clinical guidelines used for managing or treating conditions in which tobacco use is identified as a comorbidity or risk factor (e.g., heart disease, diabetes, asthma). It is also noteworthy that more than half of the plans reported adopting internally developed guidelines, as opposed to guidelines developed by federal agencies and expert panels such as the U.S. Public Health Service (USPHS) and AHRQ. However, it is possible that plans reviewed such guidelines and integrated many or all of the key components into their own guidelines.
Plans showed remarkable improvement in 2002, compared with previous years, in identifying individual plan members who smoke. The ability to identify smokers is an important indicator of a plan's ability to remind or prompt providers to discuss and/or advise patients about smoking cessation. Such provider reminders are considered an effective strategy for supporting smoking cessation and are recommended by the Task Force on Community Preventive Services (7). The survey question, however, assesses the percentage of plans that can identify any members who smoke (rather than all members who smoke), and the methods that plans report using to identify smokers are most likely to identify subgroups of smokers (i.e., those that respond to health risk appraisals or surveys). Indeed, the ability of health plans to identify smokers is contingent upon members actively providing information about their smoking status during some interaction with the health plan, whether during enrollment, through a survey, or via some other point of contact.
The number of health plans providing full coverage for any type of pharmacotherapy for tobacco cessation more than tripled in 2002, compared with previous years. In the 2002 ATMC survey, nearly nine out of 10 plans reported providing full coverage for at least one type of pharmacotherapy for tobacco cessation. Consistent with recommendations based on the effectiveness of various prescription and over-the-counter tobacco cessation first-line pharmacotherapies (6), the majority of plans reported providing full coverage for bupropion. The significant increase in the number of plans that provide full coverage for at least one type of pharmacotherapy related to tobacco cessation is well aligned with the growing body of literature indicating that reduced out-of-pocket cost is associated with greater use of tobacco cessation programs and services (8-12) and may lead to increased rates of cessation (10,11).
Consistent with literature citing the effectiveness of telephone counseling and that smokers are more likely to use telephone counseling than to participate in individual or group counseling sessions (13,14), approximately half of plans surveyed provide full coverage for telephone counseling. It is possible that even more smokers have access to telephone counseling through the availability of state-sponsored quit lines. Less than 25% of plans impose an annual or lifetime limit on coverage for tobacco cessation treatments, indicating widespread acceptance of the USPHS guideline recommending coverage for repeated, intensive tobacco dependence counseling and pharmacotherapy (6).
The results of the 2002 ATMC survey also suggest that plans are paying close attention to pregnancy and the postpartum period to assist women to quit smoking. The large percentage of plans reporting strategies to address smoking cessation during and after pregnancy to prevent relapse may reflect greater health plan awareness of research that has demonstrated the cost-effectiveness of offering smoking cessation programs to pregnant women (15).
Overall, our results indicate the greatest improvement in tobacco control activities is at the health plan level as opposed to the physician level. For example, more plans report providing full coverage for pharmacotherapies than report requiring providers to carry out activities in support of the 5 As. This may be because most health plans (especially those that are not staff-model HMOs) find changing physician behavior to be a challenge. Although more plans are beginning to experiment with performance feedback as a way to change physician behavior, prompts, reminders, and provider training are more common strategies.
Health plans continue to report that resource limitations, including insufficient staff and inadequate funding, are leading barriers to adequately addressing tobacco control. Health plans may benefit from developing a business case model that stresses the importance of tobacco cessation to purchasers and advocates for resources to implement and maintain evidence-based tobacco cessation programs. Research supported by the NTAO is underway to provide an estimated return on investment for smoking cessation interventions, based primarily on smoking-attributable costs for health plans.
The ATMC survey and its findings have limitations. The response rate of approximately 60% is respectable, but leaves open the possibility of selection bias. Even though no significant differences were detected between respondents and nonrespondents on three key characteristics (size, tax status, predominant model type), respondents possibly differed from nonrespondents in ways that were not measured. Another limitation to the ATMC survey is that the psychometric properties of the questionnaire were not tested to assess reliability or validity. However, the survey design process did include substantial pretesting to increase the probability of including questions that were reliable and likely to yield valid responses. Additionally, we identified a potential limitation of the 1997 survey — it did not include a frame of reference for product type (e.g., HMO, PPO). When the survey does not specify product type, respondents tend to answer for the HMO product. Respondents were explicitly asked to answer for the HMO product in 2000 and 2002. However, the possibility remains that the change in frame of reference contributes to some differences in survey findings from 1997 to 2000 or 2002 (but not from 2000 to 2002).
Aside from the ATMC surveys, few surveys have assessed tobacco control practices and policies of health plans. Some surveys have focused on plans operating in a single state (9,16), some have included a narrow subset of plans (i.e., well-established nonprofit plans with a history of offering tobacco cessation programs) (17), and others have collected information about subsets of smokers within a plan (i.e., pregnant women) (18,19). Nevertheless, a 1999 survey of California health plans reported results comparable to our results: 85% of HMOs in the California survey covered at least one form of pharmacotherapy; 77% covered bupropion; 46% covered telephone counseling; and 54% covered individual counseling (16). However, the limited availability of comparable data prohibits comparisons of our findings with other surveys and underscores the importance of ATMC data for an adequate understanding of health plan tobacco control practices and policies at the national level.
The results of the 2002 ATMC survey indicate that an increasing number of health plans are using evidence-based approaches and strategies to address tobacco use. However, in light of competing priorities for limited resources, health plans may be challenged to sustain the improvements they have made from 1997 to 2002. Cost modeling and the development of a business case model for smoking cessation may hold promise by assisting some plans to leverage the body of literature that supports the cost-effectiveness of tobacco cessation treatment (6,20-23).
Just as challenges lay ahead, so do many important and potentially exciting opportunities. Health plans are in a key position to implement operational policies and programs that can reduce the prevalence of tobacco use and positively impact the health of millions of individuals. Health plans have the opportunity to sustain and expand access to tobacco cessation treatments and services such as pharmacotherapies and counseling services. As new evidence emerges, health plans have the flexibility to model new tobacco cessation benefits and promote them widely to their membership. They also have the opportunity to influence large purchasers of health care services by communicating the value of tobacco cessation services and expanding their field of influence from the clinical and provider setting to the broader community. By participating in community-wide campaigns and policy initiatives that support tobacco cessation and prevention, stakeholders can influence and help control tobacco use.
In summary, the results of the 2002 ATMC survey reflect both tremendous accomplishments and important opportunities for health plans to collaborate in tobacco control efforts. With appropriate support, analytical tools, and resources it is likely that health plans, clinicians, providers, and consumers will continue to evolve in their efforts to reduce the negative consequences of tobacco use.
The authors thank The Robert Wood Johnson Foundation for the unrestricted educational grant that made this survey possible.
Figures and Tables
Table 1 Results from the 2002 Addressing Tobacco in Managed Care Survey (N = 152), United States
% Yes
Plan has written clinical guidelines for smoking cessation 71.1
Plan uses internally developed clinical guidelines for smoking cessation 56.6
Plan uses the 2000 U.S. Public Health Service Clinical Practice Guideline 5.3
Plan uses the 1996 Agency for Health Care Policy and Research Guideline 3.3
Plan uses guidelines from some other source 5.9
Plan is able to identify individual members who smoke 71.7
Data sources used by plans to identify individual members who smoke (among plans that can identify smokers):
Health risk appraisal 89.9
Telephone survey 74.1
Sample of medical records 60.6
Administrative data review 53.2
Mail-based survey 48.6
Electronic medical record 48.6
Enrollment information 6.4
Plan provides full coverage for:
Bupropion (as Wellbutrin) 79.2
Bupropion (as Zyban) 41.1
Prescription NRTa nasal spray 35.8
Prescription NRT inhaler 35.8
NRT over-the-counter patches 8.6
NRT over-the-counter gum 4.6
Plan provides full coverage for:
Telephone counseling 51.7
Face-to-face counseling 41.1
Self-help materials (e.g., booklets, videos, audiotapes, tailored mailings) 25.8
Individual counseling of pregnant women 19.2
Group counseling or classes 15.9
Plan has annual or lifetime limits on coverage for smoking cessation interventions 15.1
Plan allows patients to self-refer to smoking cessation services 59.3
Plan requires providers to:
Ask new patients about their smoking status 61.2
Include smoking status as a vital sign (i.e., ask about and document status at every visit) 54.3
Strongly advise all patients who smoke to quit 44.1
Refer smokers to intensive treatment as appropriate 33.6
Arrange for follow-up with patients trying to quit smoking 30.3
Plan has specific strategy to address smoking cessation during:
Pregnancy 56.6
Treatment for other chronic illness 52.0
Post-myocardial infarction 46.7
Postpartum visits (relapse prevention) 46.7
Adolescence 28.9
Pediatric visits (secondhand smoke) 28.3
Hospitalization 7.2
Plan has guidelines, protocols, or pathways to address smoking cessation during:
Pregnancy 65.1
Treatment for other chronic illness 61.8
Post-myocardial infarction 57.2
Adolescence 57.2
Pediatric visits (secondhand smoke) 55.3
Postpartum visits (relapse prevention) 53.3
Hospitalization 36.2
Plan funds a full- or part-time tobacco control program staff position 19.1
Plan used the following strategies with providers and/or their office staff in the past year to promote smoking cessation:
Provider education 69.8
Providing prompts and reminders to encourage providers to address tobacco control 53.2
Elimination of pre-authorization requirements for smoking cessation interventions 40.1
Increased reimbursement for smoking cessation counseling/assistance 34.2
Incentives for providers and their staff to effectively address tobacco 4.6
Increased amount of time that providers can spend with patients 2.0
Barriers limiting plan’s ability to address tobacco control:
Resource barriers (e.g., staff, funding, competing priorities) 73.5
System barriers (e.g., poor data collection, reporting, record maintenance) 40.7
Lack of patient demand 39.7
Lack of purchaser demand 38.4
Delayed economic return on investment 33.1
a NRT indicates nicotine replacement therapy.
Table 2 Tobacco Control Activities by Size of Health Plan: 2002 Addressing Tobacco in Managed Care Survey (N = 152), United States
⩽250,000 Members
(N = 102)
% Yes >250,000 Members
(N = 50)
% Yes Pa
Plan has a written clinical guideline for smoking cessation 62.4 90.0 <.001
Plan provides full coverage for:
NRTb over-the-counter gum 3.0 8.0 .17
NRT over-the-counter patches 7.9 10.0 .67
NRT inhaler 42.6 22.0 .01
NRT nasal spray 42.6 22.0 .01
Bupropion (as Zyban) 47.5 28.0 .02
Bupropion (as Wellbutrin) 80.0 77.6 .73
Plan provides full coverage for:
Telephone counseling 62.4 30.0 <.001
Face-to-face counseling 52.5 18.0 <.001
Group counseling or classes 14.9 18.0 .62
Individual counseling of pregnant women 14.9 28.0 .054
Self-help materials 20.8 36.0 .04
Plan has annual or lifetime limits on coverage for smoking cessation interventions 17.0 28.6 .10
Plan allows patients to self-refer to smoking cessation services 68.3 40.8 .001
Plan requires providers to:
Ask new patients about smoking status 48.0 88.0 <.001
Include smoking status as a vital sign (i.e., ask about and document status at every visit) 39.2 85.7 <.001
Strongly advise all patients who smoke to quit 46.1 83.3 .001
Refer smokers to intensive treatment as appropriate 37.3 26.0 .17
Arrange for follow-up with patients trying to quit smoking 35.3 20.0 .054
Plan able to identify individual members who smoke 90.2 34.0 <.001
Plan has a specific strategy to address smoking cessation during:
Adolescence 5.9 76.0 <.001
Pregnancy 42.2 86.0 <.001
Postpartum visits (relapse prevention) 33.3 74.0 <.001
Pediatric visits (secondhand smoke) 5.9 74.0 <.001
Post-myocardial infarction 33.3 74.0 <.001
Treatment for other chronic illness 39.2 78.0 <.001
Hospitalization 3.9 14.0 .02
Plan has guidelines, protocols, or pathways to address smoking cessation during:
Adolescence 52.0 68.0 .06
Pregnancy 56.9 82.0 .002
Postpartum visits (relapse prevention) 46.1 68.0 .01
Pediatric visits (secondhand smoke) 48.0 70.0 .01
Post-myocardial infarction 50.0 72.0 .01
Treatment for other chronic illness 53.9 78.0 .004
Hospitalization 48.0 12.0 <.001
Plan funds a tobacco control program staff position 14.7 28.0 .05
a Boldface indicates a significant difference.
b NRT indicates nicotine replacement therapy.
Table 3 Comparison of Data from the 1997, 2000, and 2002 Addressing Tobacco in Managed Care Surveys, United States
1997
(N = 323)
(% Yes) 2000
(N = 85)
(% Yes) 2002
(N = 152)
(% Yes) Pa
Plan provides full coverage for:
Any pharmacotherapy for smoking cessation 25.0 59.2 88.8 <.001
Zyban 17.6 37.2 41.1 .57
Any over-the-counter NRTb 6.6 14.9 8.6 .004
NRT only with program enrollment 25.0 26.0 10.8 .004
Plan provides full coverage for:
Telephone counseling 32.8 36.8 51.7 .04
Face-to-face counseling 26.6 23.6 41.1 .01
Group counseling or classes 35.7 37.0 15.9 <.001
Self-help materials 54.1 56.6 25.8 <.001
Plan provides full coverage for any behavioral or pharmacotherapy 75.0 94.4 98.0 .28
Plan requires providers to:
Ask new patients about smoking status NAc 74.1 61.2 .02
Include smoking status as a vital sign (i.e., ask about and document status at every visit) NA 43.5 54.3 .28
Strongly advise all patients who smoke to quit NA 68.3 44.1 .02
Refer smokers to intensive treatment as appropriate NA 24.7 33.6 .33
Arrange for follow-up with patients trying to quit smoking NA 36.5 30.3 .15
Plan able to identify individual members who smoke 14.9 27.1 71.7 <.001
Plan has a specific strategy to address smoking cessation during:
Adolescence 17.6 24.2 28.9 .46
Pregnancy 45.0 59.0 56.6 .72
Postpartum visits (relapse prevention) 13.6 30.5 46.7 .02
Pediatric visits (secondhand smoke) 15.8 17.3 28.3 .06
Post-myocardial infarction 21.7 27.2 46.7 .004
Treatment for chronic illness 22.6 31.3 52.0 .002
Plan funds a full- or part-time tobacco control program staff position 7.7 23.5 19.1 .15
a Boldface indicates a significant difference.
b NRT indicates nicotine replacement therapy.
c NA indicates data not available because question was not included in 1997 ATMC survey.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
Suggested citation for this article: McPhillips-Tangum C, Bocchino C, Carreon R, Erceg C, Rehm B. Addressing tobacco in managed care: results of the 2002 survey. Prev Chronic Dis [serial online] 2004 Oct [date cited]. Available from: URL: http://www.cdc.gov/pcd/issues/2004/oct/04_0021.htm
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1 Annual smoking attributable mortality, years of potential life lost, and economic costs – United States, 1995-1999 MMWR Morb Mortal Wkly Rep 2002 51 14 300 303 12002168
2 Centers for Disease Control and Prevention. State-specific prevalence of current cigarette smoking among adults—United States, 2002 MMWR Morb Mortal Wkly Rep 2004 52 53 1277 1280 14712175
3 Centers for Disease Control and Prevention. Cigarette smoking – attributable morbidity, United States, 2000 MMWR Morb Mortal Wkly Rep 2003 52 35 842 843 12966360
4 McPhillips-Tangum C Results from the first annual survey on addressing tobacco in managed care Tob Control 1998 7 suppl S11 S13 10093188
5 McPhillips-Tangum C Cahill A Bocchino C Cutler C Addressing tobacco in managed care: results of the 2000 Survey Preventive Medicine in Managed Care 2002 3 3 85 94 Available from: URL: http://www.chpcare.com/downloads/ATMC-2000SurveyResults.pdf
6 Fiore MC Bailey WC Cohen SJ Dorfman SF Goldstein MG Gritz ER Treating tobacco use and dependence: clinical practice guideline U.S. Department of Health and Human Services, Public Health Service Rockville (MD) 2000 6
7 Task Force on Community Preventive Services Recommendations regarding interventions to reduce tobacco use and exposure to environmental tobacco smoke Am J Prev Med 2001 20 2Suppl 10 15
8 Curry SJ Grothaus LC McAfee T Pabiniak C Use and cost-effectiveness of smoking cessation services under four insurance plans in a health maintenance organization N Engl J Med 1998 339 10 673 679 9725926
9 Schauffler HH McMenamin S Olson K Boyce-Smith G Rideout JA Kamil J Variations in treatment benefits influence smoking cessation: results of a randomized controlled trial Tob Control 2001 10 175 180 11387540
10 Cox JL McKenna JP Nicotine gum: does providing it free in a smoking cessation program alter success rates? J Fam Pract 1990 31 3 278 280 2391458
11 Hughes JR Wadland WC Fenwick JW Lewis J Bickel WK Effect of cost on the self-administration and efficacy of nicotine gum: a preliminary study Prev Med 1991 20 486 496 1908080
12 Johnson RE Hollis JF Stevens VJ Woodson GT Patterns of nicotine gum use in a health maintenance organization DICP 1991 25 730 735 1949928
13 McAfee T Sofian N Wilson J Hindmarsh M The role of tobacco intervention in population-based health care Am J Prev Med 1998 14 46 52 9566937
14 McAfee T Increasing the population impact of quitlines The North American Quitline Conference Phoenix, AZ 2002
15 Marks JS Koplan JP Hogue CJ Dalmat ME A cost-benefit/cost-effectiveness analysis of smoking cessation for pregnant women Am J Prev Med 1990 6 5 282 289 2125228
16 Halpin Schauffler HH Mordavsky JK McMenamin S Adoption of the AHCPR Clinical Practice Guideline for Smoking Cessation: a survey of California’s HMOs Am J Prev Med 2001 21 3 153 161 11567834
17 Rigotti NA Quinn VP Stevens VJ Solberg LI Rosenthal AC Tobacco control policies in 11 leading managed care organizations: progress and challenges Eff Clin Pract 2002 May–Jun 130 136 12088292
18 Pickett KE Abrams B Schauffler HH Savage J Brandt P Kalkbrenner A Coverage of tobacco dependence treatments for pregnant smokers in health maintenance organizations Am J Public Health 2001 91 9 1393 1394 11527766
19 Barker DC Robinson LA Rosenthal AC A survey of managed care strategies for pregnant smokers Tob Control 2000 9 Suppl. III iii46 iii50 10982905
20 Elixhauser A The costs of smoking and the cost effectiveness of smoking cessation programs J Public Health Policy 1990 11 2 218 237 2114423
21 Pronk N Goodman MJ O’Connor PJ Martinson BC Relationship between modifiable health risks and short-term health care changes JAMA 1999 282 23 2235 2239 10605975
22 Tsevat J Impact and cost-effectiveness of smoking interventions Am J Med 1992 93 1A 43S 47S 1497003
23 Warner KE Cost effectiveness of smoking cessation therapies Pharmacoeconomics 1997 11 6 538 539 10168094
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BMC GeriatrBMC Geriatrics1471-2318BioMed Central London 1471-2318-5-141628588010.1186/1471-2318-5-14Study ProtocolPredicting the risk of falling – efficacy of a risk assessment tool compared to nurses' judgement: a cluster-randomised controlled trial [ISRCTN37794278] Meyer Gabriele [email protected]öpke Sascha [email protected] Ralf [email protected]ühlhauser Ingrid [email protected] University of Hamburg, Unit of Health Sciences and Education, Martin-Luther-King-Platz 6, D-20146 Hamburg, Germany2 Institute for Quality and Economic Efficiency in Health Care, Dillenburger Straße 27, D-51105 Köln, Germany2005 10 11 2005 5 14 14 6 10 2005 10 11 2005 Copyright © 2005 Meyer et al; licensee BioMed Central Ltd.2005Meyer 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
Older people living in nursing homes are at high risk of falling because of their general frailty and multiple pathologies. Prediction of falls might lead to an efficient allocation of preventive measures. Although several tools to assess the risk of falling have been developed, their impact on clinically relevant endpoints has never been investigated. The present study will evaluate the clinical efficacy and consequences of different fall risk assessment strategies.
Study design
Cluster-randomised controlled trial with nursing home clusters randomised either to the use of a standard fall risk assessment tool alongside nurses' clinical judgement or to nurses' clinical judgement alone. Standard care of all clusters will be optimised by structured education on best evidence strategies to prevent falls and fall related injuries. 54 nursing home clusters including 1,080 residents will be recruited. Residents must be ≥ 70 years, not bedridden, and living in the nursing home for more than three months. The primary endpoint is the number of participants with at least one fall at 12 months. Secondary outcome measures are the number of falls, clinical consequences including side effects of the two risk assessment strategies. Other measures are fall related injuries, hospital admissions and consultations with a physician, and costs.
==== Body
Background
Fall prevention in the elderly has been recognised as an important area of research and intervention [1]. Numerous studies have presented combinations of fall related risk factors and a number of risk assessment tools have been developed [2,3]. However, few of these tools are based on rigorous research. The minority has been adequately tested for accuracy [4]. Reproducibility and transportability have been rarely investigated [5]. An own recently conducted systematic review including 27 publications on 25 non-laboratory fall risk assessment tools found that only 13 instruments have been validated in different settings [6]. Treatment paradox has been discussed by only four publications although it seems to be an important source of bias in accuracy studies which use the number of fallers as reference standard. However, there is no other measure to use as the gold standard for determining the validity of a fall risk assessment tool. Treatment paradox is difficult to overcome as it would be unethical to discourage fall prevention measures in the clinical setting in order to test risk assessment tools. None of the publications included in the systematic review [6] reported or even discussed side effects of fall risk assessment like the application of physical restraints.
A German national nursing guideline on fall prevention discourages the use of a risk assessment tool [7]. However, as shown in a national survey these instruments are increasingly used throughout different nursing settings [8].
Only three tools have been repeatedly evaluated in geriatric populations: the Tinetti Test, the Mobility Interaction Fall Chart (MIF) and the Downton Index [9-11]. The Tinetti Test [9] and the MIF [10] are not suitable for routine nursing assessment in nursing homes as they are time-consuming and require special training whereas the Downton Index has been described to be easily administered by nurses [11]. Few accuracy studies compared fall risk assessment tools to nurses' clinical judgement [4,10,12,13]. Predictive values were unsatisfactory for both risk assessment strategies. None of the tools was superior to nurses' clinical judgement.
The impact of the use of a fall risk assessment tool on clinically relevant endpoints has never been investigated within a methodologically rigorous trial. Therefore, we designed a randomised controlled trial to compare the clinical efficacy and consequences of the use of a standard fall risk assessment tool alongside nurses' clinical judgement with nurses' clinical judgement alone. We chose the Downton Index as comparator to single nurses' clinical judgement since it has been validated in a nursing home population and its predictive value is comparable to other instruments. The Downton Index is easy to administer and is comparable to those non-validated tools already in use in German nursing homes.
Methods
Study design and setting
The study is a cluster-randomised controlled trial over 12 months with nursing homes randomised either to optimised standard care and the use of the Downton Index or optimised standard care alone. A full economic evaluation is also being conducted.
Ethical considerations
The protocol has been approved by the ethics committee of the Hamburg chamber of physicians and the regional data protection office.
Study interventions
A structured single education session of 60 to 90 minutes will be provided for all clusters to optimise standard care and to minimise possible centre effects. The education programme will cover information on best evidence strategies to prevent falls and fall related injuries. The programme is based on principles of social learning theory [14]. The development of the curriculum followed approaches we have successfully tested for other education programmes [15,16].
After randomisation nurses of the intervention group (IG) will be introduced to the use of the Downton Index. A nominated nurse in charge of each cluster will then be responsible for the monthly application of the tool. No further intervention will be carried out in the control group.
Identification of clusters and participants
Nursing homes in the cities Hamburg and possibly Bremen, Germany, and respective catchment areas will be invited to participate. A cluster is defined as a nursing home by itself or an independently working ward of a large nursing home. In a first step, a serially numbered list of all residents of each cluster will be produced by the nurse in charge. In a second step 20 residents fulfilling the predefined inclusion criteria (≥ 70 years old, not bedridden, and living in the nursing home for more than three months) will be selected using investigators' random table. Descriptive data on the cluster and participating residents will be collected by the nurse in charge supported by the external investigators. The figure shows the summary of the trial design.
Randomisation
We will use computer generated randomisation lists for concealed allocation of clusters. To obviate disparate sample sizes permuted blocks will be used. Clusters will be allocated by an external researcher immediately after collection of baseline data and administration of the education programme.
Clinical outcomes measures
The primary outcome is the number of participants with at least one fall at 12 months. Secondary outcome measures are the number of falls, clinical consequences, i.e. fall and injury prevention measures applied, and side effects of the two risk assessment strategies. Side effects are defined as the application of physical or pharmacological restraints. Injuries and fractures related to falls, hospital admissions and consultations with a physician related to falls irrespective of the reason for falls, and costs will also be recorded.
Nursing staff will have to fill in a specially developed documentation sheet in case of a fall event, also a documentation sheet on measures used to prevent falls once a month.
Data will be checked monthly during personal visits of the investigators. It is not possible to objectify the documented falls. Nevertheless, in Germany, nurses are legally required to document falls in nursing homes. Interrater reliability of the Downton Index will be determined in a subgroup of nurses of the IG clusters.
Sample size calculation
It is assumed that about 45% of the participants in the control group will experience at least one fall in 12 months with an intra-class correlation coefficient of ICCC = 0.075. A cluster-randomisation with about 20 participants in each cluster leads to a variation inflation factor of VIF = 2.425. Assuming that 20% of the participants will not complete follow-up and furthermore assuming an absolute difference of 15% (incidence of fallers in the Downton Index-group: 30%) to reach a significant result to the level of alpha = 0.05 with a power of 80% a total sample size of n = 1,080 participants is needed. Therefore the sample size comprises a total of 54 clusters with about 20 participants each.
Statistical analysis
The main outcome is the proportion of persons with at least one fall and will be analysed by using a chi-square test adjusted for cluster randomisation [17]. The effect of the risk assessment tool will be expressed as relative risk, difference in absolute risk, and number needed to treat. For risk differences 95% confidence intervals will be calculated using a method appropriate for cluster-randomised trials [18]. For all other follow up data the cluster will be used as unit of analysis. For statistical comparisons between the groups the Wilcoxon rank sum test will be performed. Two-sided p ≤ 0.05 will be regarded as significant. Interrater reliability will be calculated using kappa statistics.
Economic evaluation
The economic evaluation will adopt the viewpoint of the German health and nursing care insurance, adding up all costs and savings relevant from the viewpoint of health care insurers and party payers. Assessment will include costs for optimisation of usual care for both groups, costs for using the fall risk assessment tool in the IG as well as medical and nursing care costs following falls for both groups. Costs for the interventions will be estimated based on information from trial records. The analysis will adopt an incremental approach such that data collection will concentrate on resource use differences between study groups. The process of collecting data on resource use will be undertaken separately from data collection on unit costs. Resource use data due to fall related health care will be collected by the investigators during the personal visits using a cost component protocol. The documentation sheet has been successfully evaluated in a recent economic evaluation of a randomised controlled trial [19]. Unit costs will be collected from different sources including published data, health insurances, and various health care providers.
At present, it is not possible to state with certainty which form of economic analysis will be employed, since this will be driven in part by the clinical study results. If a difference in the primary endpoint is observed, then a cost efficacy analysis will be conducted. Otherwise a cost comparison analysis will be conducted. Since cost data will be available for the duration of the trial only, the appropriate effectiveness measure is the one that allows treatment effects during the trial only. A sensitivity analysis on the key variables that might influence the result of the economic evaluation will be carried out.
Time plan
The pilot phase of the study started in September 2005. Consecutive recruitment of clusters has started at the same time and is expected to last 4 to 6 months.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Gabriele Meyer, Sascha Köpke and Ingrid Mühlhauser were responsible for identifying the research question, and equally contributed to the development of the protocol and study design. Gabriele Meyer and Sascha Köpke were responsible for the drafting of this paper, Ingrid Mühlhauser commented on the drafts and approved the final version. Ralf Bender contributed as statistician.
Figure 1 Summary of trial design.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The study is funded by a grant of the German Ministry of Education and Research within the Northern Germany Nursing Research Network.
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Eagle DJ Salama S Whitman D Evans LA Ho E Olde J Comparison of three instruments in predicting accidental falls in selected inpatients in a general teaching hospital J Gerontol Nurs 1999 25 40 45 10476130
Bandura A Social learning theory 1977 Englewood Cliffs, NJ: Prentice-Hall
Mühlhauser I Verbesserung der Behandlungsqualität der chronischen Krankheiten Diabetes mellitus, arterielle Hypertonie und Asthma bronchiale durch strukturierte Therapie- und Schulungsprogramme 1993 Munich: Urban und Schwarzenberg
Meyer G Warnke A Bender R Muhlhauser I Effect on hip fractures of increased use of hip protectors in nursing homes: cluster randomised controlled trial Br Med J 2003 326 76 78 12521969
Donner A Klar N Methods for comparing event rates in intervention studies when the unit of allocation is a cluster Am J Epidemiol 1994 140 279 289 8030631
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Meyer G Wegscheider K Kersten JF Icks A Mühlhauser I Increased use of hip protectors in nursing homes: economic analysis of a cluster randomized, controlled trial J Am Geriatr Soc
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Aust New Zealand Health PolicyAustralia and New Zealand Health Policy1743-8462BioMed Central London 1743-8462-2-271627448910.1186/1743-8462-2-27ResearchDevelopment of a health care policy characterisation model based on use of private health insurance Moorin Rachael E [email protected] C D'Arcy J [email protected] Australian Centre for Economic Research on Health (ACERH), School of Population Health, The University of Western Australia, Perth, Australia2 Centre for Health Services Research, School of Population Health, The University of Western Australia2005 8 11 2005 2 27 27 29 6 2005 8 11 2005 Copyright © 2005 Moorin and Holman; licensee BioMed Central Ltd.2005Moorin and Holman; 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
The aim of this study was to develop a policy characterisation process based on measuring shifts in use of private health insurance (PHI) immediately following implementation of changes in federal health care policy.
Method
Population-based hospital morbidity data from 1980 to 2001 were used to produce trend lines in the annual proportions of public, privately insured and privately uninsured hospital separations in age-stratified subgroups. A policy characterisation model was developed using visual and statistical assessment of the trend lines associated with changes in federal health care policy.
Results
Of eight changes in federal health care policy, two (introduction of Medicare and Lifetime Health Cover) were directly associated with major changes in the trend lines; however, minor changes in trends were associated with several of the other federal policies. Three types of policy effects were characterised by our model: direction change, magnitude change and inhibition. Results from our model suggest that a policy of Lifetime Health Cover, with a sanction for late adoption of PHI, was immediately successful in changing the private: public mix. The desired effect of the 30% rebate was immediate only in the oldest age group (70+ years), however, introduction of the lifetime health cover and limitations in the model restricted the ability to determine whether or if the rebate had a delayed effect at younger ages.
Conclusion
An outcome-based policy characterisation model is useful in evaluating immediate effects of changes in health care policy.
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Introduction
Private health insurance (PHI) is one of the foundations of the Australian health system [1]. Unlike the Unites States, however, the Australian Government provides universal access to free public hospital care, with ambulatory care and pharmaceuticals being available subject to limited client co-payments via Medicare and the Pharmaceutical Benefits Scheme [2]. The return of a Liberal federal government to Australia in 1996 marked a resurgence of policy interest in the uptake of PHI [3]. The justification for the policies introduced was that falling PHI membership, observed since the introduction of Medicare in 1984, was thought to have increased the demand on the public system [4,5] and, therefore, promoting growth in the private sector would take the pressure off public hospitals and restore balance to the health care system [6]. Subsequent policy initiatives concentrated on increasing PHI coverage by a mixture of 'carrots' (the private health insurance incentive scheme in 1997, partially replaced by a 30% non-means tested rebate on PHI premiums in 1999) and 'sticks' (a Medicare levy surcharge in 1997 for high income earners who did not take out PHI; and Lifetime Health Cover in 2000, whereby higher premiums were paid by those who delayed taking out PHI until after the age of 30 years) [7].
To date, analyses of the effects of policies aimed at supporting PHI in Australia have primarily centred on changes in the proportion of the population covered by PHI [2,4,7,8]. However, a distinction between uptake of PHI and use of PHI is archetypal of the distinction between outputs and outcomes, where outputs document the amount, quality or volume of use of a services product and outcomes reveal the impact the service has on its participants (change in behaviour, attitude or condition) [9]. Changes in the uptake of PHI are necessary but do not of themselves provide sufficient evidence to evaluate outcomes of policies aimed at reducing the pressure on the public system. Rather, the effectiveness of such policies would be better judged by changes in PHI use.
The aim of this study was to use changes in the utilisation of payment classifications for in-patient hospitalisation to develop a process capable of characterising policy changes according to their observable outcomes. The intention being to aid in the analysis of the effects of health care policies directed towards reducing the pressure on the public hospital system.
Methods
The WA Data Linkage System [10] was used to extract all hospital morbidity data from 1 January 1980 to 31 December 2001 for the State of Western Australia (population 1.8 million), comprising encrypted patient identifiers and episode numbers, age, gender, date of admission, date of separation and payment classification (public, uninsured private, insured private, or "other").
The proportion of the total number of separations in each relevant payment category in each year was calculated according to gender and age group (0–16 yrs, 17–39 yrs, 40–69 yrs, 70+ yrs). The "other" payment categories, which included workers compensation, motor vehicle, defence force personnel and Veteran Affairs patients, were removed from the analysis, leaving only the categories of public, private insured and private uninsured. This was done because the study was principally concerned with elective shifts between private insurance and public categories; not prescribed payment classifications due to mandatory funding arrangements.
Development of the Policy Characterisation Model
The annual relative proportion of episodes in each payment classification (public, private insured and private uninsured) were graphed as segmented trend lines stratified by age group and gender. The development of a model to characterise the policies was undertaken by analysing the interaction of the gender and age specific segmented trend lines with the major changes in federal health care policy, termed 'cut points' (see table 1) for each payment classification. The process developed is shown in figure 1 with each component described below.
Table 1 Federal health care policy changes (cut points)
Federal Health Policy "Cut Points"
Cut Point Commencement (and duration) of initiative* Description of Initiative
1 Sept 1981 (- Jan 1984) Abolition of free public hospital care
2 Feb 1984 (- Oct 1986) Medicare introduced (Universal bulk billing and free public hospital care restored)
Out of hospital rebate set at 85% of scheduled fee
Maximum rebate set at $10
Levy set at 1%
3 Nov 1986 (- June 1993) Medicare levy increased to 1.25%
Out of hospital rebate @ 85%/$20
GAP set at $150/annum
In hospital rebate set at 75% with no maximum
Private hospital insurance to cover remaining 25%
4 1993 (- 1995) Medicare Levy increased to 1.4%
5 1995 (- 1997) Medicare Levy increased to 1.5%
0.2% Surcharge introduced to pay for a guns "buy back" following Port Arthur massacre
6 1997 (-1999) Private Health Insurance Incentive Scheme: Surcharge of 1% introduced for high income household without PHI.
GAP cover policies allowed (No GAP and known GAP)
Simplified billing (use of billing agents)
7 Jan 1999 (- June 2000) Uncapped 30% PHI† rebate for hospital and ancillary benefits with no means test
8 July 2000 (- Present) Lifetime Health Cover: Differential premiums allowed based on age at initial premium.
Informed Consent: Patients provided with quotes on costs prior to procedure commencement
* Financial year unless otherwise indicated
† Private Health Insurance
Figure 1 The policy characterisation process.
Stage 1: Identification and classification of inflection points in adjacent trend segments
For each policy change trend segments included in the analysis were determined in the following manner (refer to figure 2):
Figure 2 Schematic of the identification of the trend segments included in the analysis.
• Trend segment one was defined as the segmented trend line connecting the proportion of episodes two years prior with that one year prior to the policy change (series points 1 and 2).
• Trend segment two was defined as the segmented trend line connecting the proportion of episodes one year prior with that in the year of implementation of the policy change (series points 2 and 3).
Trend segments one and two were assessed visually to determine the occurrence and classification of inflections (changes in the magnitude or direction of the slope). Inflections were classified as either:
1. Not observed (no appreciable difference in either the magnitude of the slope or direction of trend section two relative to trend section one).
2. Magnitude changing (the slope of trend section two was appreciably different in magnitude to that of trend section one)
3. Direction changing (the direction of trend section two was different to that of trend section one).
Where inflections were not observed, the policy change was deemed to have had no effect on the trend in utilisation and no further analysis was undertaken (refer to figure 1). However, if an inflection was observed the process continued to stage two, as detailed below.
Stage 2: Determination of a significant difference in the proportion of episodes
Where an inflection point was identified significance testing of the equality of the proportion of episodes for series points two and three (the year immediately prior to the policy change and the year of implementation of the policy change, refer to figure 2) was performed using a z test based on the normal approximation to the binomial distribution. This test used the z statistic to test the two sided alternative that two proportions were the same.
Stage 3: Outcome of the significance testing
Characterisation of those policies deemed to have had an impact was undertaken depending upon the results of the significance testing. A non-significant difference between series points 2 and 3 (p value greater than 0.05) resulted in the policy being deemed as an inhibitory policy (type 3). However, a significant difference between series points 2 and 3 (p value less than or equal to 0.05) required the classification of the direction of the inflection to be integrated into the analysis.
Stage 4: Integration of the classification of the inflection
Those policy changes associated with inflections classified by stage one as direction changing were subsequently termed direction changing policies (type 1). While those policy changes associated with inflections classified by stage one as magnitude changing were subsequently termed magnitude changing policies (type 2).
Quantification of the rates of change associated with inflections
So as to investigate in more detail changes in utilisation associated with observed inflections a separate analysis was conducted quantifying changes in the rate of change of the annual proportion of episodes associated with the introduction of those policies identified in stage 1 as showing an observable inflection. This was achieved by representing each segmented trend segment as a straight line having the following mathematical properties y = a+bx (where 'a' is the intercept and 'b' is the slope). This analysis was carried out for trend segments 1 and 2 (see figure 2). Differences in the rate of change (slope of the trend segment expressed as percentage change per year) for all payment classifications by gender and age group were calculated
Results
Figure 3 shows the temporal positions of the eight federal health care policy cut points overlaid on the segmented trend lines of the proportions of annual episodes in each payment classification in each age group in males and females.
Figure 3 The eight federal cut points overlaid on trend line data for gender and age group.
Observation and quantification in changes in trend
In general the shape of the trends was similar in males and females. In some age groups, particularly the 17–39 years age group, there was a near-constant difference in proportion between the genders. Given this finding to simplify the analysis the genders were combined. The shape of the segmented trend lines; however, varied significantly across age groups, with the two younger age groups experiencing the largest changes in payment classification mainly over the early part of the observation period. The oldest age group had the least annual differences and a more stable overall trend.
Federal policy initiatives that were associated with major rate changes or inflections in the trend lines were federal cut points 2 (the re-introduction of free public hospital care via Medicare) and 8 (Lifetime Health Care). Federal cut point 2 was associated with acceleration in the rate of decline in the proportion of privately insured episodes and a greater rate of increase in the proportion of public episodes in all four age groups.
Federal cut point 8 was another major inflection point associated with a surge in the private insurance payment classification in all except the oldest age group. For the younger three age groups the shift in direction was of similar magnitude as shown in table 2. The magnitude of the change in rates associated with the introduction of Lifetime Health Cover was smaller in absolute terms, as well as in the opposite direction to that associated with the introduction of Medicare.
Table 2 The rates of change of the proportion of public and private insured episodes pre and post federal cut points 2 (Medicare) and 8 (Lifetime Health Cover).
Age Group Gender Payment Classification Rate of change in proportion
(% change in 1 year) Change of Direction Difference in Rate (% change in 1 year)
Federal cut point 2 1982–83 1983–84
0–16 years M Public 2.14 22.45 NO 20.312
F 2.60 19.73 NO 17.135
M Private Insured -2.99 -20.32 NO 17.333
F -3.35 -17.65 NO 14.296
17–39 years M Public 4.27 22.82 NO 18.549
F 3.10 15.83 NO 12.730
M Private Insured -5.17 -18.88 NO 13.709
F -3.87 -14.46 NO 10.590
40–69 years M Public 1.38 14.87 NO 13.489
F 1.88 10.52 NO 8.647
M Private Insured -1.99 -13.35 NO 11.362
F -2.44 -9.45 NO 7.002
70+ years M Public 0.17 5.96 NO 5.797
F 1.40 2.80 NO 1.401
M Private Insured -0.68 -5.35 NO 4.674
F -1.91 -2.28 NO 0.370
Federal cut point 8 1998–99 1999–00
0–16 years M Public 0.90 -2.49 Yes 3.398
F 0.74 -1.95 Yes 2.691
M Private Insured -1.13 3.09 Yes 4.219
F -1.06 3.08 Yes 4.143
17–39 years M Public 0.35 -1.85 Yes 2.196
F 1.44 -1.03 Yes 2.475
M Private Insured -0.43 2.81 Yes 3.232
F -1.69 1.98 Yes 3.672
40–69 years M Public 2.04 -2.74 Yes 4.787
F 2.12 -1.01 Yes 3.129
M Private Insured -2.17 3.20 Yes 5.362
F -1.97 1.63 Yes 3.608
Less marked changes in the trends, in addition to the major ones described above, were observed to coincide with all federal cut points to some degree, although none was seen consistently in all combinations of payment classification and age group. The largest of these minor rate changes was associated with federal cut point 3 (see table 1) in the youngest age group. These changes involved inflections in the segmented trend lines with absolute differences slightly in excess of 3.5 percent per year. The remaining observable changes ranged from 2.3 percent to 0.7 percent per year.
Significance testing in those cut points deemed to be associated with inflections
Significance tests of the equivalence of the proportion of episodes one year prior to and in the year of implementation for federal policy cut points associated with observable inflections are summarized in tables 3 and 4. Most federal policy initiatives that showed an observable change in trend were also associated with a significant change (p < 0.05) in private: public mix. The most notable exception to this occurred in the elderly age group. In those aged 70+ years, cut points 7 (designed to increase the proportion of persons holding private health insurance by making it more affordable) was not associated with a significant difference.
Table 3 Federal cut points associated with significant (p < 0.05) changes in the proportion of episodes and inflections or substantial changes in trend by age group
Federal Healthcare Policy Cut Points
Age Group Public Private Insured Private Uninsured
Age 0–16 yrs 2,5,6,8 2,5,8 2
Age 17–39 yrs 2,3,5,7,8 2,3,7,8 8
Age 40 – 69 yrs 2,4,5,7,8 2,4,5,7,8 2,8
Age 70+ yrs 2,6 2,6
Shaded areas = no cut points associated with significant changes and inflections or trend changes for the age group/couplet type combination
Table 4 Federal cut points associated non-significant (p > 0.05) changes in the proportion of episodes and inflections or substantial changes in trend by age group
Federal Healthcare Policy Cut Points
Age Group Public Private Insured Private Uninsured
Age 0–16 yrs 3 3,6 8
Age 17–39 yrs 2,5
Age 40 – 69 yrs
Age 70+ yrs 7 7 2
Shaded areas = no cut points associated with non-significant changes and inflections or trend changes for the age group/couplet type combination
The privately uninsured payment classification was the least affected by policy changes over time. However, the two most influential policies, being the introduction of Medicare (cut point 2) and Lifetime Health Cover (cut point 8), were both associated with significant reductions in the proportions of private uninsured patients in several age groups, albeit that the shifts were towards the public and private insured payment classifications respectively. Once again in the oldest age group neither of these cut points was associated with a significant difference.
Characterisation of policy effects
Four types of policies were identified by the policy characterisation model. Those that had no effect; type 1, those that affected the direction of the trend; type 2, those that affected the magnitude of the trend, but not its direction; and type 3, those that inhibited the trend (the pre policy trend was positive or negative, but significance testing indicated no-significant difference in the proportions post policy). It should be noted that type 3 policies prevented (or subdued) a pre-existing trend from continuing. The results of the characterisation of federal policies from 1980 to 2001 related to age group are detailed in table 5.
Table 5 Classification of federal policy effects on trends in age group related annual proportion of episodes by payment classification
Federal cut point Age Group
0 – 16 Years 17 – 39 Years 40 – 69 Years 70+ Years
Public Private Insured Private Uninsured Public Private Insured Private Uninsured Public Private Insured Private Uninsured Public Private Insured Private Uninsured
F1 No trend data available prior to 1980*
F2 Type 2 Type 2 Type 2 Type 2 Type 2 Type 3 Type 2 Type 2 Type 2 Type 2 Type 2 Type 3
F3 Type 3 Type 3 Type 2 Type 2
F4 Type 1 Type 1
F5 Type 2 Type 2 Type 1 Type 3 Type 2 Type 2
F6 Type 1 Type 3 Type 1 Type 1
F7 Type 1 Type 2 Type 1 Type 2 Type 3 Type 3
F8 Type 1 Type 1 Type 3 Type 1 Type 1 Type 1 Type 1 Type 1 Type 1
* Since no data was available about the trend 1979–1980 the classification of this policy was not possible. However, in the known historical setting sudden removal of free public hospital care it would be most likely that this policy was a type 1 (direction changing).
Type 1: Direction changing, Type 2: Magnitude changing, Type 3: Inhibitory
Shaded areas indicate no observable effect from the policy change.
Discussion
In free markets consumers and suppliers are left alone to interact and balance supply and demand for services. It is generally accepted that governments need to intervene in health markets to provide certain services and regulate the market. This intervention occurs via specific policy action [11]. In Australia the Commonwealth Government's decision to subsidise PHI has meant that it has increased its stake in the private sector alongside its existing stake in the public sector.
Controversy has raged about the success of the Commonwealth Government's policies with regard to supporting PHI in order to reduce the pressure on the public sector. The major debate has centered around the effectiveness of the 30% rebate and more recently the effectiveness of the Lifetime Health Care policy [4,7,2-15]. However, in most cases, commentators have used evidence relating to the changing prevalence of PHI membership, pre and post policy implementation. This may not be an accurate method to assess the effectiveness of such policies, because the policies themselves may promote the uptake of PHI for non-health related reasons, such as to avoid a tax penalty in high income households (cut point 6). This coupled with the finding that since 1998 the proportion of PHI fund members with high front-end deductibles has significantly increased [4] means that uptake of PHI may not necessarily lead to the expected changes in use of the public and private systems. This is quite apart from the debate about the price elasticity of demand for PHI and the assumption that demand for hospital care is a fixed commodity [4]. Our study has developed and used a policy characterisation model based on measuring shifts in the actual use of PHI at the time of receiving hospital services. This may be a more appropriate methodology for evaluating likely changes in the pressure on the public system affected by particular policies.
The results of our analysis indicate that federal cut point 2, the re-introduction of free public hospital care via Medicare, was a magnitude changing policy. This was an unexpected finding since it has been previously assumed that the introduction of Medicare, following on from an era when free public hospital care was abolished, would be a direction changing policy. However, our data indicate that a reversal in trend in favour of the public system occurred one year prior to the introduction of Medicare. Federal cut point 8, Lifetime Health Cover, was classified by our model as a direction changing policy in the younger three age groups with no effect observed in the oldest age group (individuals born prior to 1 July 1934 are exempt from Lifetime Health Cover). This finding was thus consistent with the objective of the policy, which was to reverse the declining trend in possession and use of PHI to reduce the burden on the public system. It would appear that this was achieved immediately post-implementation.
The effects of the 30% rebate (federal cut point 7) on levels of PHI have been one of the most hotly contested political issues surrounding heath care policy in recent times. Commentators have argued for and against this policy initiative mainly on a cost-benefit platform [4, 12, 14, 16]. Our analysis found that the effect of federal cut point 7 was related to age. This policy was associated immediately in time with a change in the magnitude of the existing negative trend (PHI) or a negative to positive change of direction (public) in the middle two age groups, and an inhibitory effect on the downward trend in the oldest age group, with no effect observed in the youngest age group. Thus the 30% rebate appears to have had the desired effect on PHI use (ie reducing the pressure on the public system) in the oldest age group, but no immediate desired effect in the younger age groups. To some extent this can be explained because younger members are not as likely to be hospitalised compared with older members, thus reducing the likelihood of an immediate effect on use. While older Australians are not only more likely to be hospitalised and therefore have more opportunity to use PHI, but are also more likely to be attracted to purchase PHI due to reduced cost because the price elasticity of PHI is different for younger and older individuals.
Limitations of the model
For practical reasons the immediate effects of the policies were the only effects able to be characterised by our model due to the plethora of federal health care policy changes, especially from 1993 onwards. To try to characterise the changes in trends over an extended period would have resulted in evaluation of the mix of effects produced by more than one policy. This is especially relevant when examining the effects of the 30% rebate and Lifetime Health Cover, where only one year separated the two policy changes.
A second limitation of this policy characterisation method is that it cannot accurately take into account enforced waiting periods, which are mandatorily applied to individuals taking out PHI who have a previous history of an illness or condition. Thus some underestimation of the effects of policies may be inherent in the model. Extending the model over two years post policy initiation is problematic, as discussed above, because the effect observed would then be confounded by subsequent policy changes. Another issue to be taken into account is the timing and extent of marketing of the policy to the public by government and the private insurance industry. In the case of Lifetime Health Cover, exhaustive marketing, the 'Run for Cover Campaign', was undertaken over several months leading up to its implementation. It is reasonable to assume that changes in behaviour, in this case purchasing of PHI, were likely to have been made prior to the policy implementation date, thus some of the waiting period, if applicable, would have been served prior to the policy implementation date. Conversely, advantage could be taken of the 30% rebate at any time after, but not before January 1999. The net result of these two limitations on our policy characterisation model may be that of cancelling each other out in the case of Lifetime Health Cover and causing a latent period between cause and effect in the case of the 30% rebate.
In addition, it could be argued that since the waiting period only applies to pre-existing conditions, those wishing to use newly acquired PHI for such a condition would be doing so to facilitate a more expedient health intervention than could be achieved in the public sector. As such these episodes of care would not normally have been observed in the public system over the same period, but rather at a later time. Under these circumstances the waiting time for benefits may serve to enhance the validity of a characterisation model employing a latent period.
Finally, our policy characterisation model does not allow for the possibility of an earlier policy initiative synergising with a subsequent initiative. Thus it is possible that the immediate effect of Lifetime Health Cover may have been less potent in the absence of the pre-existing 30% rebate.
Conclusion
Our study has developed and applied a policy characterisation model based on measuring shifts in use of PHI immediately prior to, and immediately following implementation of changes in federal health care policy. Our results indicate that Lifetime Health Cover was associated with an immediate increase in patients in hospital using PHI. While the 30% rebate for PHI introduced 18 months earlier did not have an immediate desired effect, the limitations of the model are such that we cannot be certain what, if any, latent contribution to the change in private: public mix may have occurred. From this study we conclude that an outcome-based policy characterisation model is useful in evaluating immediate effects of changes in health care policy.
Competing interests
Professor D'Arcy Holman is an independent director of HBF Health Funds inc which is the largest provider of private health insurance in Western Australia.
Authors' contributions
The manuscript has been read and approved by all authors and the requirements for authorship have been met as outlined below. REM was responsible for the conception and design of the study; analysis and interpretation of the data; and drafting and revising the paper. CDJH was responsible for conception and design of the study; interpretation of the data; and revising the paper.
Acknowledgements
The initial construction of the Data Linkage System was funded by the Western Australian Lotteries Commission. This study was undertaken as a part of a Collaborative Research and Development Venture funded by HBF Health Funds Inc and the WA Department of Health. We would also like to thank the WA Department of Health for on-going support of the Data Linkage Unit.
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Immunome ResImmunome Research1745-7580BioMed Central London 1745-7580-1-31630573710.1186/1745-7580-1-3ResearchIMGT, the international ImMunoGeneTics information system®: a standardized approach for immunogenetics and immunoinformatics Lefranc Marie-Paule [email protected] IMGT, the international ImMunoGeneTics information system®, Université Montpellier II, Institut Universitaire de France, Laboratoire d'ImmunoGénétique Moléculaire LIGM, UPR CNRS 1142, Institut de Génétique Humaine IGH, 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France2005 20 9 2005 1 3 3 21 6 2005 20 9 2005 Copyright © 2005 Lefranc; licensee BioMed Central Ltd.2005Lefranc; 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.
IMGT, the international ImMunoGeneTics information system®, was created in 1989 by the Laboratoire d'ImmunoGénétique Moléculaire (LIGM) (Université Montpellier II and CNRS) at Montpellier, France. IMGT is a high quality integrated knowledge resource specialized in immunoglobulins (IG), T cell receptors (TR), major histocompatibility complex (MHC) of human and other vertebrates, and related proteins of the immune system (RPI) of any species which belong to the immunoglobulin superfamily (IgSF) and to the MHC superfamily (MhcSF). IMGT consists of five databases, ten on-line tools and more than 8,000 HTML pages of Web resources. IMGT provides a common access to standardized data from genome, genetics, proteome and three-dimensional structures. The accuracy and the consistency of IMGT data are based on IMGT-ONTOLOGY, a semantic specification of terms to be used in immunogenetics and immunoinformatics. IMGT-ONTOLOGY comprises six main concepts: IDENTIFICATION, CLASSIFICATION, DESCRIPTION, NUMEROTATION, ORIENTATION and OBTENTION. Based on these concepts, the controlled vocabulary and the annotation rules necessary for the immunogenetics data identification, classification, description and numbering and for the management of IMGT knowledge are defined in the IMGT Scientific chart. IMGT is the international reference in immunogenetics and immunoinformatics for medical research (repertoire analysis of the IG antibody sites and of the TR recognition sites in autoimmune and infectious diseases, AIDS, leukemias, lymphomas, myelomas), veterinary research (IG and TR repertoires in farm and wild life species), genome diversity and genome evolution studies of the adaptive immune responses, biotechnology related to antibody engineering (single chain Fragment variable (scFv), phage displays, combinatorial libraries, chimeric, humanized and human antibodies), diagnostics (detection and follow up of residual diseases) and therapeutical approaches (grafts, immunotherapy, vaccinology). IMGT is freely available at .
IMGTantibodyimmunoglobulinT cell receptorsuperfamilyMHCHLAontologydatabaseinformation systemknowledge resourceimmunoinformaticsimmunogeneticsCollier de Perlesthree-dimensional3D structurepolymorphismannotation
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Introduction
IMGT, the international ImMunoGeneTics information system®[1,2], was created in 1989, by Marie-Paule Lefranc, at the Laboratoire d'ImmunoGénétique Moléculaire (LIGM) (Université Montpellier II and CNRS) at Montpellier, France, in order to standardize and manage the complexity of the immunogenetics data. Fifteen years later, IMGT is the international reference in immunogenetics and immunoinformatics, and provides a high quality integrated knowledge resource, specialized in the immunoglobulins (IG) and T cell receptors (TR), major histocompatibility complex (MHC) of human and other vertebrates, and related proteins of the immune systems (RPI) of any species which belong to the immunoglobulin superfamily (IgSF) and to the MHC superfamily (MhcSF)[3-13]. The number of potential protein forms of the antigen receptors, IG and TR, is almost unlimited. The potential repertoire of each individual is estimated to comprise about 1012 different IG (or antibodies) and TR, and the limiting factor is only the number of B and T cells that an organism is genetically programmed to produce. This huge diversity is inherent to the particularly complex and unique molecular synthesis and genetics of the antigen receptor chains. This includes biological mechanisms such as DNA molecular rearrangements in multiple loci (three for IG and four for TR in humans) located on different chromosomes (four in humans), nucleotide deletions and insertions at the rearrangement junctions (or N-diversity), and somatic hypermutations in the IG loci (see FactsBooks[3,4] for review). Although IMGT was initially implemented for the IG, TR and MHC of human and other vertebrates [6], data and knowledge management standardization, based on the IMGT unique numbering [14-19], has now been extended to the IgSF [15-17,20-22] and MhcSF [18,23,24] of any species. Thus, standardization in IMGT contributed to data enhancement of the system and new expertised data concepts were readily incorporated.
IMGT, the international ImMunoGeneTics information system® consists of five databases, ten on-line tools and Web resources [1,2]. Databases include sequence databases (IMGT/LIGM-DB, IMGT/PRIMER-DB and IMGT/MHC-DB), one genome database (IMGT/GENE-DB) and one three-dimensional (3D) structure database (IMGT/3Dstructure-DB) [1,2] (Figure 1). Interactive tools are provided for sequence analysis (IMGT/V-QUEST, IMGT/JunctionAnalysis, IMGT/Allele-Align, IMGT/PhyloGene), genome analysis (IMGT/LocusView, IMGT/GeneView, IMGT/GeneSearch, IMGT/CloneSearch and IMGT/GeneInfo) and 3D structure analysis (IMGT/StructuralQuery) [1,2] (Figure 1). Web resources ("IMGT Marie-Paule page") comprise more than 8,000 HTML pages of synthesis [IMGT Repertoire (for IG and TR, MHC, RPI)], knowledge [IMGT Scientific chart, IMGT Education (IMGT Lexique, Aide-mémoire, Tutorials, Questions and answers), IMGT Medical page, IMGT Veterinary page, IMGT Biotechnology page, IMGT Index], and external links [IMGT Immunoinformatics page, IMGT Bloc-notes (Interesting links, etc.) and IMGT other accesses (SRS, BLAST, etc.)] [2]. Despite the heterogeneity of these different components, all data in the IMGT information system are expertly annotated. The accuracy, the consistency and the integration of the IMGT data, as well as the coherence between the different IMGT components (databases, tools and Web resources) are based on IMGT-ONTOLOGY[5], which provides a semantic specification of the terms to be used in immunogenetics and immunoinformatics. IMGT-ONTOLOGY, the first ontology in the domain, has allowed the management of knowledge in immunogenetics [2,25] and provided standardization for immunogenetics data from genome, genetics, proteome and 3D structures [3-13]. IMGT-ONTOLOGY concepts are available, for the biologists and IMGT users, in the IMGT Scientific chart[2], and for the computing scientists, in IMGT-ML which uses XML (eXtensible Markup Language) Schema [26].
Figure 1 IMGT, the international ImMunoGeneTics information system® . Databases and tools for sequences, genes and structures are in green, yellow and blue, respectively. The IMGT Repertoire and other Web resources are not shown. Interactions in the genetics, genomics and structural approaches are represented with dotted, continuous and broken lines, respectively.
IMGT-ONTOLOGY concepts and IMGT Scientific chart rules
The IMGT Scientific chart[2] comprises the controlled vocabulary and the annotation rules necessary for the immunogenetics data identification, description, classification and numbering and for knowledge management in the IMGT information system. Standardized keywords, labels and annotation rules, standardized IG and TR gene nomenclature, the IMGT unique numbering, and standardized origin/methodology were defined, respectively, based on the six main concepts of IMGT-ONTOLOGY: IDENTIFICATION, CLASSIFICATION, DESCRIPTION, NUMEROTATION, ORIENTATION and OBTENTION[2,5] (Table 1). The IMGT Scientific chart is available as a section of the IMGT Web resources (IMGT Marie-Paule page). Examples of IMGT expertised data concepts derived from the IMGT Scientific chart rules are shown in Table 1.
Table 1 IMGT-ONTOLOGY concepts, IMGT Scientific chart rules and examples of IMGT expertised data concepts.
IMGT-ONTOLOGY main concepts 5 IMGT Scientific chart rules [2] Examples of IMGT expertised data concepts [2]
IDENTIFICATION Standardized keywords [5] Species, molecule type, receptor type, chain type, gene type, structure, functionality, specificity
CLASSIFICATION Reference sequences
Standardized IG and TR gene nomenclature (group, subgroup, gene, allele) [5] Nomenclature of the human IG and TR genes (entry in 1999 in GDB, HGNC [27] and LocusLink at NCBI) [3, 4]
Alignment of alleles [3, 4]
Nomenclature of the IG and TR genes of all vertebrate species
DESCRIPTION Standardized labels and annotations [5] Core (V-, D-, J-, C-REGION) Prototypes [5]
Labels for sequences
Labels for 2D and 3D structures
NUMEROTATION IMGT unique numbering [14-18] for: V- and V-LIKE-DOMAINs [16]
C- and C-LIKE-DOMAINs [17]
G- and G-LIKE-DOMAINs [18] Protein displays
IMGT Colliers de Perles [19]
FR-IMGT and CDR-IMGT delimitations [16]
Structural loops and beta strands delimitations [16, 17]
ORIENTATION Orientation of genomic instances relative to each other Chromosome orientation
Locus orientation
Gene orientation
DNA strand orientation
OBTENTION Standardized origin
Standardized methodology [2]
The IMGT Scientific chart rules, based on the IMGT-ONTOLOGY concepts [5], are used in the three major IMGT biological approaches, genomics, genetics and structural approaches [2], and corresponding data (Genes, Sequences, 3D structures) are available in the IMGT components (databases, tools and Web resources) [1,7-13].
IMGT sequence databases, tools and Web resources
IMGT sequence databases, tools and Web resources correspond to the IMGT genetics approach that refers to the study of genes in relation with their polymorphisms, mutations, expression, specificity and evolution (Table 2). The IMGT sequence knowledge management and the IMGT genetics approach heavily rely on the DESCRIPTION concept (and particularly on the V-REGION, D-REGION, J-REGION and C-REGION core concepts for the IG and TR), on the CLASSIFICATION concept (gene and allele concepts) and on the NUMEROTATION concept (IMGT unique numbering [14-18]).
Table 2 The IMGT sequence databases, sequence analysis tools and Web resources
IMGT sequence databases [1] IMGT sequence analysis tools [1] IMGT Repertoire"Proteins and alleles" section [2](2)
IMGT/LIGM-DB [7]
IMGT/PRIMER-DB [1]
IMGT/MHC-DB [28] IMGT/V-QUEST [10]
IMGT/JunctionAnalysis [11]
IMGT/Allele-Align
IMGT/PhyloGene [12]
IMGT/Automat [29, 30] (1) Alignments of alleles IG and TR [3, 4]
Alignments of alleles RPI [22]
Protein displays IG and TR [3, 4, 16, 17]
Protein displays MHC [18]
Protein displays RPI [16-18, 21]
Tables of alleles IG and TR
Tables of alleles RPI [22, 24]
Allotypes Isotypes, etc.
(1) IMGT/Automat [29, 30] is an integrated internal IMGT Java tool which automatically performs the annotation of rearranged cDNA sequences that represent the half of the IMGT/LIGM-DB content. So far 7,418 human and mouse IG and TR cDNA sequences have been automatically annotated by the IMGT/Automat tool, with annotations being as reliable and accurate as those provided by a human annotator.
(2) IMGT publications from the IMGT Repertoire "Proteins and alleles" section are available as pdf in IMGT Locus in Focus , in IMGT Index (see also [2]).
IMGT sequence databases
IMGT/LIGM-DB
IMGT/LIGM-DB is the comprehensive IMGT database of IG and TR nucleotide sequences from human and other vertebrate species, with translation for fully annotated sequences [7]. It was created in 1989 by LIGM (Montpellier, France), and is on the Web since July 1995 [6]. In August 2005, IMGT/LIGM-DB contained more than 96,500 sequences of 150 vertebrate species [7]. The unique source of data for IMGT/LIGM-DB is EMBL, which shares data with the other two generalist databases GenBank and DNA DataBank of Japan (DDBJ). Based on expert analysis, specific detailed annotations are added to IMGT flat files. The annotation procedure includes the IDENTIFICATION of the sequences, the CLASSIFICATION of the IG and TR genes and alleles, and the DESCRIPTION of all IG and TR specific and constitutive motifs within the nucleotide sequences. The Web interface allows searches according to immunogenetic specific criteria and is easy to use without any knowledge in a computing language. Selection is displayed at the top of the resulting sequences pages, so the users can check their own queries. Users have the possibility to modify their request or consult the results with a choice of nine possibilities. The IMGT/LIGM-DB annotations (gene and allele name assignment, labels) allow data retrieval not only from IMGT/LIGM-DB, but also from other IMGT databases. Thus, the IMGT/LIGM-DB accession numbers of the cDNA expressed sequences for each human and mouse IG and TR gene are available, with direct links to IMGT/LIGM-DB, in the IMGT/GENE-DB entries. IMGT/LIGM-DB data are also distributed by anonymous FTP servers at CINES and EBI and from many Sequence Retrieval System (SRS) sites . IMGT/LIGM-DB can be searched by BLAST or FASTA on different servers (EBI, IGH, INFOBIOGEN, Institut Pasteur, etc.).
IMGT/PRIMER-DB
IMGT/PRIMER-DB[1] is the IMGT oligonucleotide primer database for IG and TR, created by LIGM, Montpellier in collaboration with EUROGENTEC S.A., Belgium, on the Web since February 2002. In August 2005, IMGT/PRIMER-DB contained 1,827 entries. IMGT/PRIMER-DB provides standardized information on oligonucleotides (or Primers) and combinations of primers (Sets, Couples) for IG and TR. These primers are useful for combinatorial library constructions, scFv, phage display or microarray technologies. The IMGT Primer cards are linked to the IMGT/LIGM-DB flat files, IMGT Colliers de Perles and IMGT Alignments of alleles (IMGT Repertoire) of the IMGT/LIGM-DB reference sequence used for the primer description.
IMGT/MHC-DB
IMGT/MHC-DB[28] comprises databases hosted at EBI and includes a database of human MHC allele sequences or IMGT/MHC-HLA, developed by Cancer Research UK and maintained by ANRI, London, UK, on the Web since December 1998, and a database of MHC sequences from non human primates IMGT/MHC-NHP, curated by BPRC, The Netherlands, on the Web since April 2002.
IMGT sequence analysis tools
The IMGT sequence analysis tools comprise IMGT/V-QUEST[10], for the identification of the V, D and J genes and of their mutations, IMGT/JunctionAnalysis[11] for the analysis of the V-J and V-D-J junctions which confer the antigen receptor specificity, IMGT/Allele-Align for the detection of polymorphisms, and IMGT/PhyloGene[12] for gene evolution analyses.
IMGT/V-QUEST
IMGT/V-QUEST (V-QUEry and STandardization) is an integrated software for IG and TR [10]. This tool, easy to use, analyses an input IG or TR germline or rearranged variable nucleotide sequence. The IMGT/V-QUEST results comprise the identification of the V, D and J genes and alleles and the nucleotide alignments by comparison with sequences from the IMGT reference directory, the FR-IMGT and CDR-IMGT delimitations based on the IMGT unique numbering, the translation of the input sequence, the display of nucleotide and amino acid mutations compared to the closest IMGT reference sequence, the identification of the JUNCTION and results from IMGT/JunctionAnalysis (default option), and the two-dimensional (2D) IMGT Collier de Perles representation of the V-REGION [10] ("IMGT/V-QUEST output" in IMGT/V-QUEST Documentation).
IMGT/JunctionAnalysis
IMGT/JunctionAnalysis[11] is a tool, complementary to IMGT/V-QUEST, which provides a thorough analysis of the V-J and V-D-J junction of IG and TR rearranged genes. IMGT/JunctionAnalysis identifies the D-GENEs and alleles involved in the IGH, TRB and TRD V-D-J rearrangements by comparison with the IMGT reference directory, and delimits precisely the P, N and D regions [11] ("IMGT/JunctionAnalysis output results" in IMGT/JunctionAnalysis Documentation). Several hundreds of junction sequences can be analysed simultaneously.
IMGT/Allele-Align
IMGT/Allele-Align is used for the detection of polymorphisms. It allows the comparison of two alleles highlighting the nucleotide and amino acid differences.
IMGT/PhyloGene
IMGT/PhyloGene[12] is an easy to use tool for phylogenetic analysis of variable region (V-REGION) and constant domain (C-DOMAIN) sequences. This tool is particularly useful in developmental and comparative immunology. The users can analyse their own sequences by comparing with the IMGT standardized reference sequences for human and mouse IG and TR [12] (IMGT/PhyloGene Documentation).
IMGT sequence Web resources
The IMGT sequence Web resources are compiled in the IMGT Repertoire "Proteins and alleles" section that include Alignments of alleles, Proteins displays, Tables of alleles, Allotypes, Isotypes, etc. (Table 2). Standardized IMGT criteria for amino acid sequence analysis are described in [31].
IMGT gene databases, tools and Web resources
IMGT gene databases, tools and Web resources correspond to the IMGT genomics approach that refers to the studies of the genes within their loci and on their chromosome [2] (Table 3).
Table 3 The IMGT gene database, genome analysis tools and Web resources
IMGT genome database [1] IMGT genome analysis tools [1] IMGT Repertoire"Locus and genes" section [2] (1)
IMGT/GENE-DB [8] IMGT/LocusView
IMGT/GeneView
IMGT/GeneSearch
IMGT/CloneSearch
IMGT/GeneInfo [13] Chromosomal localizations [3, 4]
Locus representations [3, 4]
Locus description
Gene exon/intron organization
Gene exon/intron splicing sites
Gene tables
Potential germline repertoires
Lists of genes
Correspondence between nomenclatures [3, 4]
(1) IMGT Web resources (IMGT Marie-Paule page) also include IMGT Index, IMGT Education (IMGT Lexique, Aide-mémoire, Tutorials, Questions and answers), The IMGT Medical page, The IMGT Veterinary page, The IMGT Biotechnology page, The IMGT Immunoinformatics page, IMGT Bloc-notes (Interesting links, etc.) [2] which are not detailed in this paper.
(2) IMGT publications from the IMGT Repertoire "Locus and genes" section are available as pdf in IMGT Locus in Focus , in IMGT Index (see also [2]).
IMGT/GENE-DB, the IMGT gene database
Genomic data are managed in IMGT/GENE-DB, which is the comprehensive IMGT genome database [8]. IMGT/GENE-DB, created by LIGM (Montpellier, France) is on the Web since January 2003. In August 2005, IMGT/GENE-DB contained 1,377 genes and 2,207 alleles (673 IG and TR genes and 1,209 alleles from Homo sapiens, and 704 IG and TR genes and 998 alleles from Mus musculus, Mus cookii, Mus pahari, Mus spretus, Mus saxicola, Mus minutoïdes). All the human and mouse IG and TR genes are available in IMGT/GENE-DB. Based on the IMGT CLASSIFICATION concept, all the human IMGT gene names [3,4] were approved by the Human Genome Organisation (HUGO) Nomenclature Committee HGNC in 1999 [27], and entered in IMGT/GENE-DB [8], Genome DataBase GDB (Canada) [32], LocusLink and Entrez Gene at NCBI (USA) [33], and GeneCards [34]. Reciprocal links exist between IMGT/GENE-DB, and the generalist nomenclature (HGNC Genew) and genome databases (GDB, LocusLink and Entrez at NCBI, and GeneCards). All the mouse IG and TR gene names with IMGT reference sequences were provided by IMGT to HGNC and to the Mouse Genome Database (MGD) [35] in July 2002. Queries in IMGT/GENE-DB can be performed according to IG and TR gene classification criteria and IMGT reference sequences have been defined for each allele of each gene based on one or, whenever possible, several of the following criteria: germline sequence, first sequence published, longest sequence, mapped sequence [2]. IMGT/GENE-DB interacts dynamically with IMGT/LIGM-DB [7] to download and display gene-related sequence data. As an example ans as mentioned earlier, the IMGT/GENE-DB entries provide the IMGT/LIGM-DB accession numbers of the IG and TR cDNA sequences which contain a given V, D, J or C gene. This is the first example of an interaction between IMGT databases using the CLASSIFICATION concept.
IMGT gene analysis tools
The IMGT gene analysis tools comprise IMGT/LocusView, IMGT/GeneView, IMGT/GeneSearch, IMGT/CloneSearch and IMGT/GeneInfo. IMGT/LocusView and IMGT/GeneView manage the locus organization and the gene location and provide the display of physical maps for the human IG, TR and MHC loci and for the mouse TRA/TRD locus. IMGT/LocusView allows to view genes in a locus and to zoom on a given area. IMGT/GeneView allows to view a given gene in a locus. IMGT/GeneSearch allows to search for genes in a locus based on IMGT gene names, functionality or localization on the chromosome. IMGT/CloneSearch provides information on the clones that were used to build the locus contigs displayed in IMGT/LocusView (accession numbers are from IMGT/LIGM-DB, gene names from IMGT/GENE-DB, and clone position and orientation, and overlapping clones from IMGT/LocusView). IMGT/GeneInfo[13] provides and displays information on the potential TR rearrangements in human and mouse.
IMGT gene Web resources
The IMGT gene Web resources are compiled in the IMGT Repertoire "Locus and genes" section that includes Chromosomal localizations, Locus representations, Locus description, Gene exon/intron organization, Gene exon/intron splicing sites, Gene tables, Potential germline repertoires, the complete lists of human and mouse IG and TR genes, and the correspondences between nomenclatures [3,4] (Table 3). The IMGT Repertoire "Probes and RFLP" section provides additional data on gene insertion/deletion.
IMGT structure database, tool and Web resources
The IMGT structural approach refers to the study of the 2D and 3D structures of the IG, TR, MHC and RPI, and to the antigen or ligand binding characteristics in relation with the protein functions, polymorphisms and evolution (Table 4). The structural approach relies on the CLASSIFICATION concept (IMGT gene and allele names), DESCRIPTION concept (receptor and chain description, domain delimitations), and NUMEROTATION concept (amino acid positions according to the IMGT unique numbering [14-18]).
Table 4 IMGT structure database, analysis tool and Web resources
IMGT structural database [1] IMGT structural analysis tool [1] IMGT Repertoire"2D and 3D structures" section [2]
IMGT/3D structure-DB [15] IMGT/StructuralQuery [15] 2D Colliers de Perles IG and TR [3, 4, 16, 17, 19] (1)
2D Colliers de Perles MHC [18, 36]
2D Colliers de Perles RPI [16-18, 21, 22, 24, 37]
IMGT classes for amino acid characteristics [31]
IMGT Colliers de Perles reference profiles [31]
3D representations (1)
(1) Cover of the Nucleic Acids Research 1999 database issue
Structural and functional domains of the IG and TR chains comprise the variable domain or V-DOMAIN (9-strand beta-sandwich) which corresponds to the V-J-REGION or V-D-J-REGION and is encoded by two or three genes [3,4], the constant domain or C-DOMAIN (7-strand beta-sandwich), and, for the MHC chains, the groove domain or G-DOMAIN (4 beta-strand and one alpha-helix). A uniform numbering system for IG and TR V-DOMAINs of all vertebrate species has been established to facilitate sequence comparison and cross-referencing between experiments from different laboratories whatever the antigen receptor (IG or TR), the chain type, or the species [14-16]. In the IMGT unique numbering, conserved amino acids from frameworks always have the same number whatever the IG or TR variable sequence, and whatever the species they come from. As examples: Cysteine 23 (in FR1-IMGT), Tryptophan 41 (in FR2-IMGT), hydrophobic amino acid 89 and Cysteine 104 (in FR3-IMGT) (Figure 2). This numbering has been applied with success to all the sequences belonging to the V-set of the IgSF [20], including non-rearranging sequences in vertebrates (human CD4, Xenopus CTXg1, etc.) and in invertebrates (drosophila amalgam, drosophila fasciclin II, etc.) [15,16,21]. The IMGT unique numbering, initially defined for the V-DOMAINs of the IG and TR and for the V-LIKE-DOMAINs of IgSF proteins other than IG and TR, has been extended to the C-DOMAINs of the IG and TR (Figure 2B), and to the C-LIKE-DOMAINs of IgSF proteins other than IG and TR [17]. An IMGT unique numbering has also been implemented for the groove domain (G-DOMAIN) of the MHC class I and II chains (Figure 3), and for the G-LIKE-DOMAINs of MhcSF proteins other than MHC [18].
Figure 2 IMGT Colliers de Perles of a V-DOMAIN (A) and of a C-DOMAIN (B) (code PDB 1mcd in IMGT/3Dstructure-DB [9]). IMGT Colliers de Perles are shown on one layer (on the left hand side) and on two layers with hydrogen bonds (on the right hand side). (A) The IMGT Collier de Perles of a V-DOMAIN is based on the IMGT unique numbering for V-DOMAIN and V-LIKE-DOMAIN [16]. The CDR-IMGT are limited by amino acids shown in squares, which belong to the neighbouring FR-IMGT. The CDR3-IMGT extends from position 105 to position 117. CDR-IMGT regions are colored as follows on the IMGT site: CDR1-IMGT (blue), CDR2-IMGT (bright green), CDR3-IMGT (dark green) and hydrogen bonds are shown as green lines. (B) The IMGT Collier de Perles of a C-DOMAIN is based on the IMGT unique numbering for C-DOMAIN and C-LIKE-DOMAIN [17]. Amino acids are shown in the one-letter abbreviation. Arrows indicate the direction of the beta strands that form the two beta sheets of the immunoglobulin fold [3, 4]. Hatched circles correspond to missing positions according to the IMGT unique numbering [16, 17]. In the IMGT Collier de Perles on the IMGT Web site hydrophobic amino acids (hydropathy index with positive value) and Tryptophan (W) found at a given position in more than 50 % of analysed IG and TR sequences are shown in blue, and all Proline (P) are shown in yellow.
Figure 3 IMGT Colliers de Perles of the two G-DOMAINs of MHC class I (A) and of MHC class II (B) proteins (codes PDB 1bd2 and 1aqd, respectively, in IMGT/3Dstructure-DB [9]). The IMGT Collier de Perles of a G-DOMAIN is based on the IMGT unique numbering for G-DOMAIN and G-LIKE-DOMAIN [18]. (A) The two MHC-I G-DOMAINs, G-ALPHA1 (top) and G-ALPHA2 (bottom), form the groove of the MHC class I chain (I-ALPHA). (B) The two MHC-II G-DOMAINs, G-ALPHA (top) of the MHC class II alpha chain (II-ALPHA) and G-BETA (bottom) of the MHC class II beta chain (II-BETA), form the groove of the MHC class II protein [36]. Amino acids are shown in the one-letter abbreviation. Hatched circles correspond to missing positions according to the IMGT unique numbering [18]. Positions in colour correspond to the IMGT contact sites provided, for each peptide/MHC 3D structure, in IMGT/3Dstructure-DB [36].
IMGT/3Dstructure-DB, the IMGT 3D structure database
IMGT/3Dstructure-DB is the IMGT 3D structure database, created by LIGM, and on the Web since November 2001 [9]. In August 2005, IMGT/3Dstructure-DB contained 946 atomic coordinate files. IMGT/3Dstructure-DB comprises IG, TR, MHC and RPI with known 3D structures [9,36,37]. Coordinate files extracted from the Protein Data Bank (PDB) [38] are renumbered according to the standardized IMGT unique numbering [16-18]. The IMGT/3Dstructure-DB card provides, on-line, the complete information for each IMGT/3Dstructure-DB entry. The IMGT/3Dstructure-DB card shows a summary table and a menu that gives access to five sections: "Chain details", "Contact analysis", "Visualization with Jmol", "Renumbered file" and "References and links". The "Chain details" section provides chain description, IMGT gene and allele names, IMGT chain and domain labels, domain delimitations, amino acid positions according to the IMGT unique numbering, IMGT Colliers de Perles [16-19]. The "Contact analysis" section provides contact types and categories between domains (in IMGT/3Dstructure-DB Domain contacts) and atom contacts at the residue and position level (in IMGT/3Dstructure-DB Residue@Position contacts) [37]. (IMGT/3Dstructure-DB Documentation). The "Renumbered file" section downloadable provides renumbered IMGT/3Dstructure-DB flat files.
IMGT/StructuralQuery tool
The IMGT/StructuralQuery tool [9] analyses the interactions of the residues of the antigen receptors IG and TR, MHC, RPI, antigens and ligands. The contacts are described per domain (intra- and inter-domain contacts) and annotated in term of IMGT labels (chains, domain), positions (IMGT unique numbering), backbone or side-chain implication [37]. IMGT/StructuralQuery allows to retrieve the IMGT/3Dstructure-DB entries, based on specific structural characteristics: phi and psi angles, accessible surface area (ASA), amino acid type, distance in angstrom between amino acids, CDR-IMGT lengths.
IMGT structure Web resources
The IMGT stucture Web resources are compiled in the IMGT Repertoire "2D and 3D structures" section which includes 2D representations or IMGT Colliers de Perles [16-19], 3D representations, FR-IMGT and CDR-IMGT lengths [16], amino acid chemical characteristics profiles [31], etc. In order to appropriately analyse the amino acid resemblances and differences between IG, TR, MHC and RPI chains, eleven IMGT classes were defined for the 'chemical characteristics' amino acid properties and used to set up IMGT Colliers de Perles reference profiles [31]. The IMGT Colliers de Perles reference profiles allow to easily compare amino acid properties at each position whatever the domain, the chain, the receptor or the species. The IG and TR variable and constant domains represent a privileged situation for the analysis of amino acid properties in relation with 3D structures, by the conservation of their 3D structure despite divergent amino acid sequences, and by the considerable amount of genomic (IMGT Repertoire), structural (IMGT/3Dstructure-DB) and functional data available. These data are not only useful to study mutations and allele polymorphisms, but are also needed to establish correlations between amino acids in the protein sequences and 3D structures and to determine amino acids potentially involved in the immunogenicity.
Conclusion
In order to allow any IMGT component to be automatically queried and to achieve a higher level of interoperability inside the IMGT information system and with other information systems, our current objectives include the modelling of the three major IMGT biological approaches, genomics, genetics and structural approaches, the analysis of the IMGT components (databases, tools and Web resources) in relation with the concepts, and the development of Web services [2]. They are the first steps towards the implementation of IMGT-Choreography [2], which corresponds to the process of complex immunogenetics knowledge [25] and to the connection of treatments performed by the IMGT component Web services. IMGT-Choreography has for goal to combine and join the IMGT database queries and analysis tools. In order to keep only significant approaches, a rigorous analysis of the scientific standards [3,4], of the biologist requests and of the clinician needs [39-42] has been undertaken in the three main biological approaches: genomics, genetics and structural approaches. The design of IMGT-Choreography and the creation of dynamic interactions between the IMGT databases and tools, using the Web services and IMGT-ML, represent novel and major developments of IMGT, the international reference in immunogenetics and immunoinformatics. IMGT-Choreography enhances the dynamic interactions between the IMGT components to answer complex biological and clinical requests.
Since July 1995, IMGT has been available on the Web at . IMGT has an exceptional response with more than 140,000 requests a month. The information is of much value to clinicians and biological scientists in general. IMGT databases, tools and Web resources are extensively queried and used by scientists from both academic and industrial laboratories, from very diverse research domains: (i) fundamental and medical research (repertoire analysis of the IG antibody sites and of the TR recognition sites in normal and pathological situations such as autoimmune diseases, infectious diseases, AIDS, leukemias, lymphomas, myelomas), (ii) veterinary research (IG and TR repertoires in farm and wild life species), (iii) genome diversity and genome evolution studies of the adaptive immune responses, (iv) structural evolution of the IgSF and MhcSF proteins, (v) biotechnology related to antibody engineering (single chain Fragment variable (scFv), phage displays, combinatorial libraries, chimeric, humanized and human antibodies), (vi) diagnostics (clonalities, detection and follow up of residual diseases) and (vii) therapeutical approaches (grafts, immunotherapy, vaccinology).
Citing IMGT
If you use IMGT databases, tools and/or Web resources, please cite [1] and this paper as references, and quote the IMGT Home page URL address, .
Acknowledgements
I am very grateful to Véronique Giudicelli, Chantal Ginestoux, Joumana Jabado-Michaloud, Géraldine Folch, Elodie Duprat, Denys Chaume, Quentin Kaas, and Gérard Lefranc for their expertise, constant motivation and helpful discussion. I am thankful to Wafae El Alaoui, Aurélie Frigoul, Lamia Zaghloul, François Ehrenmann, Arnaud Henry, Emmanuel-Jean Servier, our "2005" students, for their enthusiasm, and to the many IMGT users who have expressed their encouragement and support. IMGT is a registered mark of Centre National de la Recherche Scientifique (CNRS). IMGT has obtained the National Bioinformatics Platform RIO label since 2001 (CNRS, INSERM, CEA, INRA). IMGT was funded in part by the BIOMED1 (BIOCT930038), Biotechnology BIOTECH2 (BIO4CT960037) and 5th PCRDT Quality of Life and Management of Living Resources (QLG2-2000-01287) programmes of the European Union and received subventions from Association pour la Recherche sur le Cancer (ARC) and from the Génopole-Montpellier-Languedoc-Roussillon. IMGT is currently supported by the CNRS, the Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche MENESR (Réseau National des Génopoles, Université Montpellier II Plan Pluri-Formation, Institut Universitaire de France, ACI-IMPBIO IMP82-2004 and BIOSTIC-LR2004 Région Languedoc-Roussillon) and GIS AGENAE (contrat AD2351 2005–2007).
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-1511626907910.1186/1471-2164-6-151Research ArticlecGMP-independent nitric oxide signaling and regulation of the cell cycle Cui Xiaolin [email protected] Jianhua [email protected] Penglin [email protected] Daniela E [email protected] Ilana G [email protected] Kelly J [email protected] Jennifer J [email protected] Peter J [email protected] Ana del Pilar [email protected] J Philip [email protected] Shuibang [email protected] Robert L [email protected] Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA2 Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, Maryland, USA3 Flow Cytometry Core Facility, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA4 Intensive Care Unit of the Military 309th Hospital, Haidian District of Beijing, People's Republic of China2005 3 11 2005 6 151 151 1 8 2005 3 11 2005 Copyright © 2005 Cui et al; licensee BioMed Central Ltd.2005Cui 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
Regulatory functions of nitric oxide (NO•) that bypass the second messenger cGMP are incompletely understood. Here, cGMP-independent effects of NO• on gene expression were globally examined in U937 cells, a human monoblastoid line that constitutively lacks soluble guanylate cyclase. Differentiated U937 cells (>80% in G0/G1) were exposed to S-nitrosoglutathione, a NO• donor, or glutathione alone (control) for 6 h without or with dibutyryl-cAMP (Bt2cAMP), and then harvested to extract total RNA for microarray analysis. Bt2cAMP was used to block signaling attributable to NO•-induced decreases in cAMP.
Results
NO• regulated 110 transcripts that annotated disproportionately to the cell cycle and cell proliferation (47/110, 43%) and more frequently than expected contained AU-rich, post-transcriptional regulatory elements (ARE). Bt2cAMP regulated 106 genes; cell cycle gene enrichment did not reach significance. Like NO•, Bt2cAMP was associated with ARE-containing transcripts. A comparison of NO• and Bt2cAMP effects showed that NO• regulation of cell cycle genes was independent of its ability to interfere with cAMP signaling. Cell cycle genes induced by NO• annotated to G1/S (7/8) and included E2F1 and p21/Waf1/Cip1; 6 of these 7 were E2F target genes involved in G1/S transition. Repressed genes were G2/M associated (24/27); 8 of 27 were known targets of p21. E2F1 mRNA and protein were increased by NO•, as was E2F1 binding to E2F promoter elements. NO• activated p38 MAPK, stabilizing p21 mRNA (an ARE-containing transcript) and increasing p21 protein; this increased protein binding to CDE/CHR promoter sites of p21 target genes, repressing key G2/M phase genes, and increasing the proportion of cells in G2/M.
Conclusion
NO• coordinates a highly integrated program of cell cycle arrest that regulates a large number of genes, but does not require signaling through cGMP. In humans, antiproliferative effects of NO• may rely substantially on cGMP-independent mechanisms. Stress kinase signaling and alterations in mRNA stability appear to be major pathways by which NO• regulates the transcriptome.
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Background
Nitric oxide (NO•) plays a pivotal role in vascular biology through both cGMP-dependent and -independent mechanisms. In health, NO• regulates vascular tone by activating soluble guanylate cyclase [1-3]. However, other important effects of NO• in the vasculature such as cytoprotection and anti-adhesion appear to occur independent of cGMP signaling [4-6]. Likewise, NO• regulation of inflammation has frequently been associated with signal transduction events that do not involve cGMP [7,8]. NO• induces TNFα in human cells by decreasing intracellular levels of cAMP, thereby removing cAMP-mediated repression of the TNFα promoter through a proximal Sp element [9,10]. Analogs of cAMP and Sp site mutation both block, while antagonists of cAMP-dependent protein kinase simulate the effect of NO• on TNFα. [9,11]. In contrast to TNFα, NO• induces interleukin-8 (IL-8) [12] through a distinct post-transcriptional mechanism that is both cGMP- and cAMP-independent. IL-8 mRNA is stabilized by NO• activation of p38 MAPK, increasing its half-life and translation [13]. These and other reports [14-16]. suggest that cGMP-independent gene regulation by NO• occurs through multiple pathways.
Similar to the regulation of blood pressure and inflammatory responses, NO• regulation of cell proliferation is of central importance to circulatory health. Failure of this regulatory pathway has been linked to atherosclerosis and other forms of vascular dysfunction [17-19]. Despite extensive investigation, the relative contribution of cGMP-independent NO• signaling in the regulation of cell cycle genes remains controversial. In rats, NO• has been shown to activate transcription through cGMP-dependent effects on AP-1 promoter sites [20]. Also in rodents, a NO•-cGMP-PKA-ERK1/2 signal transduction pathway has been described that inhibits cell proliferation [21,22] and increases expression of p21/Waf1/Cip1 [23,24]. A master regulatory gene, p21 directly inhibits Cdk complexes [25,26] and represses the transcription of many cell cycle genes through CDE/CHR (cell cycle dependent element/cell cycle gene homology region) promoter elements [27,28]. In contrast to rodents, NO• regulation of cell cycle genes in humans, including regulation of p21, appears to occur, at least in part, independent of cGMP [19,29]. However, a global examination of cGMP-independent NO• effects on the transcriptome in general or on cell cycle genes specifically has not been undertaken in either rodents or humans.
Here, oligonucleotide microarrays and human U937 cells that lack soluble guanylate cyclase [9,30] were used to globally characterize the cGMP-independent effects of NO• on gene expression. Differentiation with PMA was employed to render cells capable of cytokine production [9]. This treatment also forced >80% of cells into the G0/G1 phase of the cell cycle, which facilitated the analysis of cell cycle gene regulation. Since NO• lowers cAMP levels in U937 cells [9] and cAMP is known to affect cell proliferation, NO• effects were also tested in the absence and presence of a cell permeable cAMP analog. For genes affected by NO•-induced decreases in cAMP, cAMP analog would be expected to produce an apposite effect. Hypotheses generated from microarray results were further investigated by examining downstream changes in protein expression and signal transduction pathways.
Results
Functional distribution of NO•-regulated genes and hypothesis generation
Of 110 NO•-responsive genes, 71 were induced, and 39 were repressed; the majority were not previously known to be NO•-responsive. Both naïve and differentiated U937 cells lack NO•-sensitive soluble guanylate cyclase [9,30], and therefore gene regulation by NO• in these cells can be attributed to cGMP-independent mechanisms. Genes were annotated into functional categories (Fig. 1) [see Additional files 1 and 2 for complete gene lists]. NO• had broad biological effects independent of cGMP. Heme oxygenase 1 (HMOX1), a known NO•-responsive gene, had the second largest fold change among up-regulated genes. TNFα and IL-8, cytokines previously associated with specific cGMP-independent mechanisms of NO• regulation [8,9,12], were also detected as differentially regulated by the microarray analysis. NO•-regulated genes annotated disproportionately to the cell cycle [[31] of 106 (29%) compared to 407 of 4870 genes (8%) on the microarray, as annotated in the Gene Ontology [GO] Biological Process database; P = 0.0001] (Table 1). In particular, a large majority of NO• down-regulated genes annotated specifically to the cell cycle (21/38, as annotated in the GO Biological Process database; P = 0.0001). Additional annotation using PubMed identified 47 of 110 genes (43%) as cell cycle or cell proliferation related. Of 39 down-regulated transcripts, 27 (69%) were ultimately annotated specifically to the cell cycle.
Figure 1 Distribution of NO•-regulated genes. Of 110 differentially regulated genes, 71 were up-regulated (red) and 39 (green) were down-regulated. Genes were classified into functional categories using NIH-DAVID [83] and PubMed [52]. Data are from seven independent microarray experiments.
Previously, we demonstrated that NO• activates p38 MAPK and thereby stabilizes IL-8 mRNA through effects on AU-rich elements [ARE] in 3' untranslated regions [UTR] [13]. Therefore, the ARE database was used to identify ARE-containing genes among those regulated by NO•. Twenty-two of 110 genes contained ARE (20%) compared to 540 ARE genes of 5086 on the microarray (11%; P = 0.008). An additional 11 ARE-containing genes were identified in PubMed for a total of 33 (Table 2). Nearly half of these genes (14/33; 42%) have been reported to be p38 MAPK regulated (Table 2). Importantly, for these 14 genes, p38 MAPK activation produces responses that are in the same direction as those observed here for NO•. The broad influence of NO• on cell cycle-related genes and ARE-containing transcripts independent of cGMP was unexpected, as was the strong association of these effects with p38 MAPK. Therefore, further experiments were performed to confirm these results and to define underlying regulatory mechanisms that might link NO• effects on the cell cycle with post-transcriptional gene regulation through ARE sites.
Table 2 NO•-regulated genes containing AU-rich elements
GenBank Symbol Regulation by NO• Reported regulation by p38 MAPKa
X54150 FCAR UP
L06633 PSCDBP UP
K03195 SLC2A1 UP
X59834 GLUL UP
U03398 TNFSF9 UP
M59465 TNFAIP3 UP UP [77]
U15174 BNIP3 UP
D86962 GRB10 UP
M60278 DTR UP UP [77]
M69043 NFKBIA UP
M92357 TNFAIP2 UP
U48807 DUSP4 UP
M16750 PIM1 UP UP [77]
X89398 UNG UP UP [77]
S49592 E2F1 UP UP [66]
U70426 RGS16 UP
X02910 TNF UP UP [87, 88]
X04500 IL1B UP UP [77, 87]
M20681 SLC2A3 UP
M28130 IL8 UP UP [77, 89]
M57731 GRO2 UP UP [77]
U71203 RIT UP
S81914 IER3 UP
J04076 EGR2 UP
D90070 PMAIP1 UP UP [77]
J04111 JUN UP UP [90]
U09579 CDKN1A UP UP [42]
D16532 VLDLR UP
U14518 CENPA DOWN
U67369 GFI1 DOWN
U22376 c-Myb DOWN
U66838 CCNA1 DOWN DOWN [91]
M25753 CCNB1 DOWN DOWN [74]
a As reported in the literature; citations shown in parentheses
Validation of NO•-regulated genes
To determine whether NO• and cAMP effects on mRNA, as measured by microarray, produced downstream effects on secreted protein, TNFα, IL-8 and IL-1β were measured in supernatants collected from parallel cell cultures incubated for 24 h. S-nitrosoglutathione (GSNO) significantly increased TNFα, IL-8, and IL-1β protein [see Additional file 3, part A (P < 0.0001 for all)]. In contrast, cAMP decreased TNFα (P < 0.0001), increased IL-1β (P = 0.003), and had no significant effect on IL-8. NO•-induced changes in transcript abundance as determined by microarray were consistent with these results (see Additional file 3, part B].
Real-time RT- PCR (TaqMan®) was used to validate NO•-mediated changes in mRNA levels (Fig. 2A and 2B). Of 18 selected genes, 13 were NO• up-regulated, and 5 were down-regulated. Fold changes from microarray experiments strongly correlated with results from RT-PCR (R = 0.95, P < 0.0001).
Figure 2 Validation of microarray results by real-time, reverse transcription (RT) PCR and Western blotting. (A) Fold changes for microarray and RT-PCR at 6 h comparing S-nitrosoglutathione (GSNO; 400 μM) to glutathione (GSH; 400 μM) incubated cells. (B) Correlation of fold change comparing the two methods after logarithmic transformation. Results are means ± SE of the last three microarray experiments for which material was available for RT-PCR (performed in triplicate). (C) Representative Western blots of cell cycle genes detected by specific antibody and enhanced chemiluminescence. Differentiated U937 cells (1 × 107) were incubated with PBS, GSH (400 μM) or GSNO (400 μM) for 12 h and then lysed for Western blotting. (D) Western blot results quantified with laser densitometry and expressed as ratios relative to PBS control values. Data are means ± SE of three or four independent experiments.
Western blotting of key cell cycle genes regulated by NO• was performed to test whether microarray results accurately predicted changes in protein expression (Fig. 2C and 2D). Three induced genes, E2F transcription factor 1 (E2F1), p21/Waf1/Cip1 (Cdk inhibitor; CDKN1A), and cell division cycle 6 (CDC6) were examined. E2F1 and p21 are well-characterized master regulatory proteins that control the cell cycle. Four repressed genes, cyclin A1 (CCNA1), cyclin B1 (CCNB1), polo-like kinase (PLK) and cyclin F (CCNF) were also measured by Western blotting. In all cases, directional changes in protein expression were consistent with the differential effect of NO• on corresponding transcripts as determined by microarray analysis.
NO•-regulation of cell cycle genes independent of cAMP
NO• induces TNFα by decreasing intracellular cAMP; dibutyryl-cAMP (Bt2cAMP), a cell permeable cAMP analog, blocks this effect [9,10]. Moreover, cAMP is an omnipresent second messenger that affects cell proliferation and the cell cycle in a variety of contexts [31-33]. Therefore, Bt2cAMP was added to some conditions to test for the cAMP-dependence of NO•-mediated effects. Of 106 Bt2cAMP-responsive genes, 16 of 103 (16%) compared to 407 of 4870 on the microarray (8%), as annotated in the GO Biological Process database, were cell cycle related, but this effect was not statistically significant (P = 0.5). Only one additional cAMP-responsive gene was subsequently annotated to the cell cycle by searching PubMed [see Additional file 4]. However, like NO•, cAMP-regulated genes did contain ARE more frequently than expected [[24] of 106 (23%) compared to 540 of 5086 (11%) on the microarray; P = 0.0003]. This finding was consistent with the known ability of cAMP to stabilize transcripts that contain ARE [34].
To further compare the effects of NO• and cAMP, a hierarchical cluster analysis was performed using the 35 cell cycle genes regulated by NO• (Fig. 3). For each of the 5 cell cycle genes significantly affected by both NO• and Bt2cAMP [c-Myb, B-cell translocation gene 1 (BTG1), dual specificity phosphatase 4 (DUSP4), growth factor independent 1 (GFI1), and cyclin A1 (CCNA1)], the direction of regulation was the same. Further, for cell cycle genes regulated by NO•, cAMP analog either had no effect on or produced expression changes that were similar to and additive with those observed for NO• (Fig. 3). These results suggest that NO• effects on cell cycle genes are independent of its interference with cAMP signaling, since cAMP analog (the opposite signal) was not antagonistic to the actions of NO•.
Figure 3 Heat map of NO• and cAMP effects on the expression of NO•-regulated cell cycle genes. Differentiated U937 cells were incubated with glutathione (GSH; 400 μM) or S-nitrosoglutathione (GSNO; 400 μM) in the absence or presence of dibutyryl-cAMP (Bt2cAMP; 100 μM). Color intensity reflects fold change from differentiated cells at 0 h; up-regulation is shown in red and down-regulation in green. Fold changes were computed from the mean results of seven independent microarray experiments.
Analysis of NO• effects on the cell cycle
NO• causes arrest in either the G1 or G2/M phase of the cell cycle [19,35-37]. However, the mechanisms underlying this effect are not well characterized. Annotation of NO•-regulated genes to their respective phase of the cell cycle revealed that expression changes were not random (Table 1). Most NO• up-regulated genes (7/8) were G1/S associated, while down-regulated genes were strikingly G2 and G2/M phase associated (24/27). The latter included topoisomerase II alpha (TOP2A), cyclin B1, PLK, and CDC25B, genes that are necessary factors for mitosis. These results show that NO• suppresses the cell cycle in early G2/M by triggering a highly integrated program of gene regulation that does not require soluble guanylate cyclase or cGMP.
Table 1 NO•-regulated cell cycle related genes
GenBank Symbol Functional category Fold changea
G1/S
J04111 JUN Regulation of cell cycle 3.68 ± 0.76
U09579 CDKN1A (p21) CDK inhibitor 2.06 ± 0.58
U77949 CDC6 DNA replication 1.95 ± 0.48
M74093 CCNE1 Cell cycle control 1.77 ± 0.31
X89398 UNG DNA repair 1.68 ± 0.30
S49592 E2F1 G1 phase of mitotic cell cycle 1.60 ± 0.19
X61123 BTG1 Negative regulation of cell proliferation 1.60 ± 0.28
L13689 BMI1 Modifies chromatin 0.67 ± 0.09
U67369 GFI1 G1/S-specific transcription in mitotic cell cycle 0.54 ± 0.06
U22376 c-Myb Cell cycle control 0.30 ± 0.10
G2
U66838 CCNA1 Regulation of CDK activity 0.60 ± 0.12
Z36714 CCNF Regulation of cell cycle 0.60 ± 0.11
J04088 0.57 ± 0.14
L47276 TOP2A b Spindle assembly, chromsome condensation 0.42 ± 0.07
X05360 CDC2 Start control point of mitotic cell cycle 0.57 ± 0.11
U28386 KPNA2 Cytoskeleton organization and biogenesis 0.51 ± 0.05
D14678 KIFC1 Spindle assembly, chromsome condensation 0.44 ± 0.10
U14518 CENPA Chromosome organization and biogenesis 0.38 ± 0.06
G2/M
U48807 DUSP4 Regulation of cell cycle 1.40 ± 0.35
U63743 KIF2C Microtubule motor activity 0.72 ± 0.10
U83115 AIM1 Tumor supressor, cytoskeleton 0.71 ± 0.07
M34458 LMNB1 Cytoskeletal anchoring 0.66 ± 0.16
D63880 CNAP1 Mitotic surveilance 0.63 ± 0.05
X65550 MKI67 Chromatin/chromosome structure 0.61 ± 0.09
S78187 CDC25B Cell cycle control 0.60 ± 0.05
D38553 BRRN1 Chromatid separation 0.59 ± 0.08
U73379 UBE2C Protein degradation 0.54 ± 0.08
U30872 CENPF Mitosis 0.53 ± 0.07
Z15005 CENPE Mitotic chromosome movement 0.53 ± 0.10
U29343 HMMR Mitotic surveilance, cell motility 0.51 ± 0.11
U05340 CDC20 Ubiquitin-dependent protein degradation 0.49 ± 0.07
M86699 TTK Spindle assembly/mitotic checkpoint 0.47 ± 0.05
U01038 PLK Mitosis 0.44 ± 0.04
M25753 CCNB1 Mitotic checkpoint 0.43 ± 0.07
D38751 KIF22 Mitosis 0.40 ± 0.18
U04810 TROAP Cell adhesion 0.24 ± 0.03
a Fold change comparing glutathione (GSH) to S-nitrosoglutathione (GSNO)-treated cells, expressed as the mean ± SE (N = 7)
b Represented by more than one probe set on the microarray that reached statistical significance; each result is shown
To further test this hypothesis, cell cycle analysis was performed on U937 cells using flow cytometry. PMA-differentiation significantly increased the portion of cells in G0/G1 (P < 0.0001), while decreasing cells in S (P = 0.0007) and G2/M (P = 0.003) compared to a naïve, undifferentiated cell population (Fig. 4A). Differentiated cells were then treated with glutathione (GSH) or GSNO in the absence or presence of Bt2cAMP. Consistent with NO•-induced changes in mRNA expression at 6 h, cell cycle analysis at 24 h demonstrated that NO• increased the portion of cells in G2/M (P = 0.0004), and in combination with Bt2cAMP, NO• synergistically increased G2/M phase cells (Fig. 4B; P = 0.008).
Figure 4 Cell cycle analysis of U937 cells. (A) U937 cells (1 × 106) were differentiated with PMA (100 nM) for 48 h and then compared with naïve cells for cell cycle distribution using propidium iodide staining. The cell cycle distribution of stained cells was examined by flow cytometry. The percentage of cells in G0/G1, S, and G2/M was determined using ModFit software. (B) U937 cells (1 × 106) differentiated as in A were treated for 24 h with medium alone, medium with glutathione (GSH; 400 μM) or medium with S-nitrosoglutathione (GSNO; 400 μM) in the absence or presence of dibutyryl-cAMP (Bt2cAMP; 100 μM). Cell cycle distribution data, presented as fold change (percentage of cells in each phase of the cell cycle relative to medium alone), are means ± SE of four independent experiments.
NO• induction of p21, a master cell cycle regulator; dependence on p38 MAPK and role of mRNA stabilization
The Cdk inhibitor, p21 is known to induce cell cycle arrest in late G1 or early G2/M, [38-40] effects similar to those of NO•. NO• increased both p21 mRNA and protein expression in the current experiments. We have previously shown that NO• activates p38 MAPK in U937 and THP-1 cells [13,41]. Activation of p38 MAPK induces p21 in other cell types, [42] and like NO• here, can trigger G2/M cell arrest [40]. We therefore reasoned that NO• might up-regulate p21 by activating p38 MAPK in the present system.
GSNO was first confirmed in PMA-differentiated U937 cells to dose-dependently increase p38 MAPK activation. This effect reached significance at the lowest (100 μM) GSNO concentration tested (P ≤ 0.0001; Fig. 5). Next, three chemically distinct NO• donors were tested for their ability to up-regulate p21 protein in PMA-differentiated U937 cells. GSNO, S-nitroso-N-acetylpenicillamine (SNAP), and DETA-NONOate similarly increased p21 expression in these cells compared to degraded controls (Fig. 6A; P < 0.05 for all). Thus, independent of cGMP and type of donor molecule, NO• consistently increased the expression of p21 protein in U937 cells. A specific p38 MAPK inhibitor (SB202190) was used to determine whether blocking this pathway could prevent NO• induction of p21. As shown in Fig. 6B, SB202190 dose-dependently reduced NO•-induced p21 protein expression (P = 0.0005). Collectively, these results suggest that NO• induces p21 through p38 MAPK activation. Finally, we investigated the effects of NO• and p38 MAPK inhibition on the stability of p21 mRNA, which harbors ARE in its 3' UTR [43]. After 8 h of PMA exposure, p21 expression increased almost 100 fold compared to naïve U937 cells (Fig. 6C). NO• stabilized p21 mRNA in the absence of SB202190 (Fig. 6D;P = 0.004), but had no effect in the presence of SB202190 (P = 0.5).
Figure 5 Effect of NO• on p38 MAPK phosphorylation. Differentiated U937 cells (5 × 10 6) were incubated with S-nitrosoglutathione (GSNO; 0–400 μM) for 30 min; cells were then lysed for Western blotting to detect total (p38) and phosphorylated forms (pp38) p38 MAPK. (A) Representative gel for Western blotting. (B) Western blotting results were quantified with laser densitometry and expressed as ratios relative to control values (GSNO = 0 μM). Data are means ± SE of three independent experiments.
Figure 6 Effect of NO• donors and p38 MAPK inhibition on p21 expression and mRNA stabilization. (A) Differentiated U937 cells (1 × 107) were incubated with NO• donors, S-nitrosoglutathione (GSNO; 400 μM), S-nitroso-N-acetylpenicillamine (SNAP; 400 μM), or DETA-NONOate (1 mM) or their degraded controls. Western blotting was then performed to detect p21 expression after 12 h of incubation. Results were quantified with laser densitometry and expressed as ratios relative to their appropriate degraded control. Data are means ± SE of four independent experiments. (B) Differentiated U937 cells (1 × 107) were incubated with increasing concentrations of the p38 inhibitor SB202190 (0 nM to 25 nM) for 30 min, then exposed to PBS, glutathione (GSH; 400 μM) or GSNO (400 μM) for 12 h. Western blotting was performed to detect p21 expression. Results were quantified with laser densitometry. Data, presented as fold change relative to PBS control values, are means ± SE of three independent experiments. Next, TaqMan® RT-PCR was used to quantify p21 mRNA levels normalized to GAPDH mRNA. (C) Changes in p21 mRNA levels during differentiation of U937 cells (1 × 107) with PMA. Data, presented as fold change relative to mean mRNA level in naïve cells, are means ± SE of three independent experiments. (D) NO• stabilization of p21 mRNA is dependent on p38 MAPK. U937 cells (1 × 107) were differentiated with PMA for 8 h. After 30 min pretreatment with actinomycin D (2.5 μg/ml) without and with SB202190 (0.1 μM), cells were further incubated with GSH (400 μM) or GSNO (400 μM) for 2 to 4 h. At the specific time points, cells were harvested for total RNA extraction. Data, presented relative to mRNA level at 0 h (arbitrarily set to 100%), are means ± SE of three independent experiments.
NO• regulation of the cell cycle through E2F1 and p21
The E2F family of transcription factors and p21 act as master regulatory switches that control the cell cycle. E2F1 regulates target genes through E2F-binding sites and thereby plays an essential role in DNA synthesis and the G1/S transition [44-47]. Some p21 effects are mediated by inhibition of E2F factor binding, while other downstream targets contain cell cycle dependent element/cell cycle gene homology region [CDE/CHR] repressor sites within their promoters [27,28,39,48-50]. Protein binding to CDE/CHR sites, triggered by p21 expression, causes repression of a diverse group of cell cycle genes and subsequent late G1 or early G2/M phase arrest, responses that are highly similar to NO• effects shown here. The ability of NO• to increase the expression of E2F1 and p21 may explain much of its broad control over the cell cycle that ultimately involves dozens of gene products. We therefore identified NO•-regulated genes that contain E2F or CDE/CHR promoter sites by searching TRANSFAC [51] and PubMed[52]. Sixteen NO•-regulated genes contain apparent E2F sites (Table 3A). Seven of these are annotated to the G1/S phase of the cell cycle, six of which have reported E2F1 responses that are concordant with NO• effects in the current experiment. Notably, of 10 NO• down-regulated transcripts with possible E2F-binding sites, only c-Myb is a G1/S phase gene. Further, 5 of these genes are known to also contain a CDE/CHR binding site that appears to be functionally dominant (Table 3B). Moreover, of the 27 cell cycle genes down-regulated by NO•, 8 are known targets of p21 repression (all G2/M associated), including 6 genes with putative CDE/CHR sites (Table 3B) and two others, lamin B1 (LMNB1) and centromere protein F (CENPF) [39].
Table 3A Specific promoter elements associated with NO•-regulated cell cycle genes
3A. Genes with E2F sites
GenBank Symbol Cell cycle phase Regulation by NO• Regulation by E2F1a
J04111 JUN G1/S Up Up [63]
U09579 CDKN1A G1/S Up Up [59]
U77949 CDC6 G1/S Up Up [61]
M74093 CCNE1 G1/S Up Up [63]
X89398 UNG G1/S Up Up [64]
S49592 E2F1 G1/S Up Up [61]
U22376 c-Myb G1/S Down Up [63, 92]
U66838 CCNA1 b G2 Down Up [93]
J04088 TOP2Ab G2 Down Up [62]
L47276 TOP2A b G2 Down Up [62]
X05360 CDC2 b G2 Down Up [62, 64]
X65550 MKI67 G2M Down Up [63]
U14518 CENPA b G2/M Down
S78187 CDC25B G2/M Down
M86699 TTK G2/M Down
U01038 PLK b G2/M Down
M25753 CENPE G2/M Down
a As reported in the literature; citations shown in parentheses
b These genes also contain CDE/CHR sites as shown in Table 3B
Table 3B Specific promoter elements associated with NO•-regulated cell cycle genes
3B. Genes with CDE/CHR sites
GenBank Symbol Cell cycle phase Regulation by NO• Reported regulation by p21a
U66838 CCNA1 b G2 Down Down
J04088 TOP2A b G2 Down Down
L47276 TOP2A b G2 Down Down
X05360 CDC2 b G2 Down Down
U14518 CENPA b G2/M Down Down
U01038 PLK b G2/M Down Down
M25753 CCNB1 G2/M Down Down
a As reported in the literature [39]
b These genes also contain E2F sites as shown in Table 3A
Next, electrophoretic mobility shift assays (EMSA) were performed to test whether NO• altered protein binding to E2F and CDE/CHR consensus sequences. PMA-differentiated U937 cells were treated with PBS, GSH, or GSNO followed by preparation of nuclear extract. NO• increased binding to both E2F (Fig. 7A and 7B) and CDE/CHR probes (Fig. 7C and 7D). Site-directed mutagenesis of each consensus sequence abolished competition (Fig. 7) and E2F1-directed antibody blocked complex formation with labeled E2F probe (Fig. 7A and 7B).
Figure 7 NO• increases protein binding to E2F and CDE/CHR promoter sites. (A) Representative gel for protein binding to the E2F probe detected by ECL. (B) Results for the E2F probe quantified with laser densitometry. Data, presented as ratios relative to PBS values (set to 1), are means ± SE of six independent experiments. (C) Representative gel for protein binding to CDE/CHR probe detected by ECL. (D) Results for the CDE/CHR probe quantified with laser densitometry. Data, presented as ratios relative to PBS values (set to 1), are means ± SE of six independent experiments. Differentiated U937 cells were incubated with glutathione (GSH; 400 μM) or S-nitrosoglutathione (GSNO; 400 μM) for 3 h. Nuclear extract (15 μg) was prepared and incubated with double-stranded, biotin-N4-CTP labeled DNA probe representing the E2F or CDE/CHR concensus sequence from the E2F1 or PLK promoter, respectively. Protein binding was then determined by electrophoretic mobility shift assay. Complexes were competed with 100-fold molar excess of cold probes, site-directed mutagenesis of the consensus sequence, and for the E2F probe, E2F1-directed antibody as indicated to test for binding specificity.
Summary
NO•, independent of cGMP, regulated a diverse subset of genes involved in inflammation, metabolism, apoptosis, the cell cycle, proliferation, signal transduction, and transport. Notably, genes associated with the cell cycle and proliferation, including the master cell cycle regulatory genes E2F1 and p21, were over-represented. Further, NO•-regulated transcripts had ARE (post-transcriptional regulatory sites) in their 3' UTR and were p38 MAPK responsive more frequently than expected. E2F1 induction by NO• was associated with up-regulation of several genes involved in G1/S transition that contain E2F-binding sites. NO• also induced p21, an ARE-containing gene, through p38 MAPK activation and mRNA stabilization. This was associated with the down-regulation of G2/M phase genes, at least in part, through changes in protein binding to CDE/CHR promoter sites. Collectively, these results demonstrate that NO•, independent of cGMP and cAMP, triggers a specific and highly coordinated genetic program that alters the G1/S transition and induces arrest in early G2/M (Fig. 8). MAPK pathways and mRNA stability are major mechanisms by which NO• regulates the transcriptome.
Figure 8 Schematic representation of NO• regulation of the cell cycle. Genes differentially regulated in this investigation are shown in color; red signifies up- and green down-regulation. At the bottom, NO• is depicted as allowing progression to G2 where it induces cell cycle arrest. KEGG pathway [86] reproduced and Modified with permission.
Discussion
NO• has potent anti-tumor and anti-atherosclerotic effects that are closely associated with its ability to block cell proliferation [18,53]. This activity of NO• has been ascribed to both cGMP-dependent and -independent mechanisms. Experiments in rodents have found, with a few notable exceptions [54,55], that NO• controls the cell cycle through cGMP. These studies have focused on the importance of a NO•-cGMP-PKA-ERK 1/2 signal transduction pathway [22-24]. Accordingly, cAMP itself has also been reported to inhibit cell proliferation through activation of PKA and/or ERK 1/2 with the up-regulation of p27 or p21 in a cell-specific manner [31-33,56]. In contrast, the anti-proliferation effects of NO• in human cells have been frequently associated with cGMP-independent signaling [19,29]. Here, a transcriptome-wide approach revealed that NO• exerts broad control over the cell cycle through p38 MAPK activation and mRNA stabilization.
In a previous study, we found that NO• up-regulates TNFα by decreasing cAMP, an effect antagonized by cAMP analogs. Therefore Bt2cAMP was used in this investigation to explore whether some effects of NO• on the transcriptome could be attributed to its interaction with cAMP signaling. However, our results indicate that NO•-cAMP signaling appears to be a minor pathway, regulating less than 6 of the affected transcripts in U937 cells (data not shown). These included TNFα, as well as pim-1 oncogene (PIM1), TNFα-induced protein 2 (TNFAIP2), and glutathione reductase (GSR). Importantly, for cell cycle genes, NO• and Bt2cAMP consistently had the same directional effect on transcripts, although NO• regulated more genes overall. Thus, decreases in intracellular cAMP appear unrelated to NO• effects on the cell cycle. Furthermore, treatment with both NO• and Bt2cAMP synergistically provoked cell cycle arrest in G2/M, suggesting that NO•-induced decreases in cAMP may attenuate some of its effects on the cell cycle. Although this experiment also provides useful intormation on gene regulation by cAMP in U937 cells, the reader should keep in mind that Bt2cAMP was the only analog studied and some effects may have been caused by its butyryl component.
U937 cells were PMA-differentiated in the current experiments to render them capable of producing TNFα and IL-8, two cytokines previously identified as NO•-responsive [7-13]. Further, this treatment also reduced cell proliferation and forced >80% of the cells into the G0/G1 phase of the cell cycle, allowing for a more coherent analysis of cell cycle regulation (Fig. 4). However, PMA itself had large effects on NO•-regulated genes such as p21 (Fig. 6D) and the findings here cannot be extrapolated directly to naïve U937 cells. Fortunately, Turpev and colleagues have recently reported selected microarray results from NO• exposure of undifferentiated U937 and Mono Mac 6 cells [57]. Of interest, a number of key genes identified by this group were also found to be similarly regulated by NO• in PMA-differentiated cells including HMOX1, IL-8, activating transcription factor 4 (ATF4), BCL2/Adenovirus E1B 19 kD-interacting protein 3 (BNIP3), and importantly p21/Waf1/Cip1.
The NO• donor GSNO was found to activate p38 MAPK in U937 cells, which was consistent with our previous result using SNAP, another NO• donor [41]. Furthermore, three different NO• donors were shown here to consistently increase p21 protein expression, indicating that this effect is NO•-specific and donor independent. Importantly, very low concentrations of the p38 MAPK inhibitor SB202190 were found to block the induction of p21 protein by NO•, further establishing the role of p38 MAPK as an intermediary signal transduction event. Finally, p21 mRNA was measured serially by RT-PCR after transcriptional blockade in the absence or presence of SB202190 showing that this transcript is stabilized by NO• through a p38 MAPK-dependent mechanism.
Others have found that NO• increases p21 mRNA and protein expression in human vascular smooth muscle independent of cGMP [58]. In addition, p38 MAPK activation has been shown to increase p21 expression by both transcriptional activation and protein stabilization [42]. E2F1 is also known to induce p21 transcription [59] providing another mechanism by which NO• may have increased p21 expression in the current experiment. Conversely, as already discussed, NO• decreases cAMP, reducing the ability of Sp1 to bind to GC box elements and thereby repressing the transcription of Sp1-dependent genes such as eNOS [11]. Interestingly, p21 is highly dependent on Sp1 for transcription [60] and is induced by cAMP [56], findings consistent with the possibility that p21 may be transcriptionally repressed by NO•-cAMP-Sp1 signal transduction. Nonetheless, NO• induction of p21 demonstrates that other mechanisms dominate over any negative effects of NO• on Sp1 binding to the p21 promoter. Here, we focused on mRNA stabilization, because of the strong indication in our microarray data that this is a major mechanism of gene regulation by NO•. However, other mechanisms such as changes in transcription, translation, or protein stability may have contributed substantially to the net effects of NO• on p21 expression.
The E2F family of transcription factors play important roles in G1/S phase transition. E2F1 up-regulates many G1/S phase genes including itself, cyclin E1 (CCNE1), CDC6, uracil-DNA glycosylase (UNG), JUN, p21 and c-Myb [46,47,61-65]. Except for c-Myb, all were up-regulated by NO• in the present study, suggesting that NO• may drive differentiated U937 cells through the G1/S transition by inducing E2F1 expression. This conclusion is further supported by EMSA showing that NO• increases E2F1 binding to E2F consensus sequence. However, increased E2F1 expression may not be the only mechanism contributing to these observed changes in DNA binding. NO• activates p38 MAPK in U937 cells and p38 MAPK has been shown to increase E2F1 binding to E2F sites [66]. Further, cyclin A1 was down-regulated by NO• and has been shown to turn off E2F1 target genes by decreasing E2F1 DNA binding [47,67].
Notably, c-Myb and a number of G2 or G2/M phase genes that contain E2F sites were down-regulated by NO•. E2F sites can function as repressors in some genes and their disruption by mutation leads to promoter activation [46,61,68]. Further, NO•-responsive genes with both E2F elements and CDE/CHR repressor sites were uniformly down-regulated. Promoters with CDE/CHR motifs are repressed by p21 [28,39], which was also induced by NO•. Therefore, even for promoters activated by E2F1, repression through CDE/CHR sites appears to be the dominant action of NO• in this cellular context. Moreover, E2F and CHR sites may cooperate as co-repressors [69]. Although CDC25B lacks an identifiable CDE/CHR site, it does have a proximal repressor and its regulation is similar to CDE/CHR-containing genes [70].
Cell cycle arrest induced by p21 occurs in late G1/S [40,71] or early G2/M [38] and is mediated, at least in part, by the repression of target genes with CDE/CHR sites [28]. CDE/CHR sites are present in the promoters of cyclin A1 [50], CDC2 [48], cyclin B1 [72], and TOP2A [49], centromere protein A (CENPA) [73] and PLK [27]. All of these genes are G2 or G2/M related and are down-regulated by p21, results consistent with the effects of NO• observed here. EMSA confirmed the hypothesis that NO• regulates protein binding to CDE/CHR sites. Collectively, these findings suggest that NO• regulates many G2/M phase cell cycle genes through p21. However, NO• may also regulate some of these downstream p21 targets through additional mechanisms. For example, c-Myb, cyclin A1, cyclin B1, and CENPA have ARE in their 3' UTR, indicating that NO• might alter the stability of these transcripts. Notably, cyclin A1 and cyclin B1 are down-regulated by p38 MAPK, a signal transduction pathway that was activated by NO• in the current experiments. Importantly, p38 MAPK has been shown to induce cell cycle arrest at the G2 checkpoint through mechanisms that were only partially dependent on p21 [74].
ARE in 3' UTR have been implicated in the control of transcript stability and have an important post-transcriptional impact on transcriptome content [11,75-77]. We previously demonstrated that independent of cGMP, NO• up-regulates IL-8, but not TNFα post-transcriptionally through p38 MAPK activation [13]. In the current investigation, ARE- containing genes including IL-8 and p21 were over-represented among NO•-regulated genes. Nearly half of these ARE genes have been reported to be regulated by p38 MAPK (Table 2). Notably, NO• responses were all in the same direction as those reported for p38 MAPK activation. Previous microarray experiments that globally tested mRNA stability found that 10% of transcripts were associated with p38 MAPK-dependent regulation [77]. The over-representation of p38 MAPK-regulated genes in our experiments indicates that this stress kinase is an important target of NO•.
Conclusion
The present investigation was focused on understanding cGMP-independent gene regulation by NO•. Major themes within the identified gene list were the predominance of cell cycle-related genes and ARE-containing transcripts. NO• was found to trigger a specific and coordinated cell cycle arrest independent of both cGMP and cAMP. E2F1 induction up-regulated target genes involved in G1/S transition through E2F sites. NO• stabilization of p21 mRNA was p38 MAPK dependent and led to increased protein binding to CDE/CHR promoter sites and the down-regulation of G2/M phase genes. The cell cycle is a major target of NO•-mediated gene regulation. Importantly, p38 MAPK and mRNA stability are major intermediary mechanisms through which NO• affects the human transcriptome.
Methods
Reagents and cell culture
PMA, GSNO, S-nitroso-N-SNAP, Bt2cAMP and SB202190 were purchased from Calbiochem (San Diego, CA). DETA-NONOate was obtained from Cayman (Ann Arbor, Michigan); actinomycin D and GSH were from Sigma-Aldrich (St. Louis, MO). U937 cells (ATCC, Rockville, MD), a human monoblastoid line devoid of NO• sensitive guanylate cyclase, [9,30] were cultivated and then differentiated with PMA (100 nM) for 48 h as described previously [9].
Microarray experiments
NO• donor, GSNO (400 μM), or its precursor GSH (400 μM) was added into differentiated U937 cells in the absence or presence of Bt2cAMP (100 μM) followed by incubation at 37°C for 6 h (N = 7). Cells were then washed three times with ice cold PBS. Total RNA was extracted using RNeasy Mini kits (Qiagen, Valencia, CA) and reverse transcribed (10 μg) using the SuperScript II® custom kit (Invitrogen, Carlsbad, CA). Resulting cDNA (1 μg) was in vitro transcribed into biotin-labeled cRNA using the BioArray high yield RNA transcript labeling kit (Enzo Life Sciences, Farmingdale, NY). After fragmentation, biotin-labeled cRNA (20 μg) was hybridized to Affymetrix HuGeneFL 6800® microarrays [> 5,000 unique transcripts after masking uninformative probe sets [Affymetrix Website, #106] following the Affymetrix protocol [78]. After staining with streptavidin phycoerythrin (Molecular Probes) and enhancing with anti-streptavidin (0.5 mg/ml, Vector Laboratories, Burlingame, CA), microarrays were scanned using Agilent GeneArray Scanner.
Cytokine assay and TaqMan® Real time RT-PCR
Supernatants were collected from duplicate cell cultures after 24 h of incubation. TNFα, IL-8 and IL-1β production were measured using ELISA kits (R & D Systems, Minneapolis, MN) according to the manufacturer's instructions.
The TaqMan® Real time RT-PCR system (Applied Biosystems, ABI, Rockville, MD) was employed to quantify mRNA levels. Gene-specific TaqMan®probes and PCR primers were designed using Primer Express 1.0 (ABI) and their sequences are provided in supplemental data [see Additional file 5]. The High-capacity cDNA Archive kit (ABI, Foster City, CA) was employed to prepare cDNA from 2 μg of total RNA. The resulting cDNA was used for RT-PCR in triplicate according to the standard ABI protocol. The average quantities of target gene mRNA relative to GAPDH mRNA was determined for each sample. The target gene/GAPDH ratio in GSH treated cells was arbitrarily set at 1 and results from all other samples were expressed relative to that standard.
Western blotting
Polyclonal antibodies detecting p21, cyclin A1, cyclin B1, CDC6, E2F1 and cyclin F were obtained from Santa Cruz Biotechnology (Santa Cruz, CA). PLK antibody was purchased from BD Transduction Laboratories (San Diego, CA). Aliquots of 1 × 107 cells were incubated with PBS, GSH (400 μM), or GSNO (400 μM) for 12 h to prepare whole cell lysates. Separate experiments were conducted to detect total and phosphorylated p38 MAPK using anti-p38 and anti-pp38 (Promega, WI). All Western blotting was performed using 20 μg of whole cell lysate as previously described [13].
Cell cycle analysis
Cells were harvested and stained with propidium iodide and the cell cycle distribution of stained cells was determined by flow cytometry (FACS Calibur, Becton Dickinson). The percentage of cells in G0/G1, S, and G2/M was determined using ModFit (Verify Software House Inc., Topsham, ME) and expressed as relative change compared to PMA-differentiation alone. Naïve U937 cells were compared to cells (1 × 106) incubated with PMA for 48 h to examine the effects of differentiation on the cell cycle. As expected, PMA differentiation pushed cells into G0/G1 arrest (>80% of cells). These cells were then treated with GSH (400 μM) or GSNO (400 μM) without or with Bt2cAMP for 24 h and processed for cell cycle analysis as described above.
EMSA
Differentiated U937 cells were cultured for 3 h with GSH (400 μM) or GSNO (400 μM). EMSA were performed with 15 μg of nuclear extract and double-stranded DNA probes labeled with biotin-N4-CTP according to manufacturer's instructions (Pierce, Rockford, Illinois). Probes purchased from Sigma-Genosys (The Woodlands, TX) were as follows: E2F probe (5'-CGTGGCTCTTTCGCGGCAAAAAGGA-3') representing the -39 to -15 section of the E2F1 promoter and CDE/CHR probe (5'-GTTCCCAGCGCCGCGTTTGAATTC-3') representing the -10 to +14 section of human PLK promoter. Binding complexes were competed using 100-fold molar excess of cold probes. Sequences for these cold probes are available in [see Additional file 6].
Microarray data analysis, gene annotation, and statistics
Images were analyzed using Microarray Suite 4.0 (Affymetrix). Global scaling was set at 100. Data were transformed and analyzed using the MSCL Analyst's Toolbox written in the JMP scripting language (SAS Institute, Cary, NC). Average difference (AD) values were standardized and transformed using the Symmetric Adaptive Transform [79-81] yielding quantile-normalized, homogenous variance scaled results. Differentially regulated genes were identified from 7 independent experiments using a combination of consistency tests set at a 4% false discovery rate (FDR) and an average AD above 20 for at least one condition. One of 7 experiments was an outlier for some genes, but was not allowed to eliminate genes found significant in the other six. Fold change in gene expression was calculated directly from AD results after raising negative values to 10, and likewise adding 10 to all positive values.
Genes were annotated by searching NIH-DAVID [82,83] and PubMed [52]. Over-representation of gene categories among differentially expressed transcripts was tested using Expression Analysis Systematic Explorer [84,85]. EASE scores (penalized Fisher exact test), corrected for multiple comparisons using bootstrap resampling with 10,000 iterations, are reported as P-values. These analyses and tests of significance relied on databases within EASE and therefore did not include additional genes that were annotated to particular functional categories using PubMed.
All data not derived from microarrays are presented as mean ± standard error (SE) of at least three independent experiments. All P-values are two-sided unless noted otherwise, and considered significant if less than 0.05. To compare treatment effects on cytokine secretion, a two-way ANOVA with blocking for experiment was carried out on the logarithm of the measured concentrations for TNFα, IL-8 and IL-1β (supplemental Fig. 1A). A linear regression of RT-PCR log fold change versus microarray log fold change was generated to evaluate the validity of the microarray data (Fig. 2B). To determine whether NO• affected the protein expression of various cell cycle genes, paired t-tests, unadjusted for multiple comparisons, were performed for GSH versus GSNO, after log normalization to PBS (Fig. 2D). Log percentages of naïve and PMA-differentiated U937 cells in each phase of the cell cycle were compared by paired t-tests, unadjusted for multiple comparisons (Fig. 4A). Two-way ANOVAs with blocking were performed on log percentage of cells in each phase of the cell cycle to assess the significance of the NO• effect, cAMP effect, and their interaction (Fig. 4B). Effects of NO• on p38 MAPK phosphorylation (Fig. 5) were investigated with a one-way ANOVA comparing the log fold change of laser densitometry intensity (pp38/p38) over different concentrations of GSNO. A post-hoc Dunnett's test was carried out to determine the lowest concentration at which the effect became significant compared to control. The expression of p21 in the presence of GSNO, SNAP, or DETA-NONOate was compared to that in the presence of their respective degraded controls with paired t-tests, unadjusted for multiple comparisons (Fig. 6A). The dose effect of SB202190 on NO•-induced p21 protein expression normalized to PBS was analyzed using a one-way ANOVA (Fig. 6B). NO• stabilization of p21 mRNA over time (with and without SB202190) was assessed using constrained one-way analysis of covariance, after natural log transformation of relative mRNA amounts (Fig. 6D).
List of abbreviations
ARE: AU-rich elements; Bt2cAMP: dibutyryl-cAMP; CDE/CHR: cell cycle dependent element/cell cycle gene homology region; EMSA: electrophoretic mobility shift assays; GO: Gene Ontology; GSH: glutathione; GSNO: S-nitrosoglutathione; NO•: nitric oxide; SNAP: S-nitroso-N-acetylpenicillamine; UTR: untranslated regions.
Authors' contributions
Xiaolin Cui, Shuibang Wang, and Robert L. Danner: framing the question and designing experiments; developing downstream hypotheses; writing the manuscript. Jianhua Zhang: developing, designing, and performing the real time RT-PCR and EMSA experiments; raising questions that contributed to the scientific process. Xiaolin Cui and Penglin Ma: Designing and performing experiments to test downstream hypotheses. Performing Western blotting. Final annotation of gene lists. Daniela E. Myers, Ilana G. Goldberg and Ana del Pilar Cintron: developing methodology and performing bench work for microarray experiments including preparation of total RNA, reverse transcription, labeling, hybridization, and scanning. Preliminary annotation of gene lists. Kelly J. Sittler, Peter J. Munson and Jennifer J. Barb: developing computation tools and data normalization methods; detecting differentially regulated genes and performing statistical analysis. J. Philip McCoy: developing, designing, and performing the cell cycle analysis experiments. Editing the manuscript.
Supplementary Material
Additional File 1
Classification of NO•-Upregulated Genes. Complete list of genes upregulated by NO• Genes are classified by function and fold change from control is shown.
Click here for file
Additional File 2
Classification of NO•-Downregulated Genes. Complete list of genes downregulated by NO• Genes are classified by function and fold change from control is shown.
Click here for file
Additional File 3
Confirmation of NO• effects on TNFα, IL-8 and IL-1β (A) NO• up-regulated secreted TNFα, IL-8 and IL-1β protein at 24 h as measured by ELISA. Dibutyryl cAMP (Bt2cAMP) decreased TNFα, increased IL-1β, and had no effect on IL-8. Data are means ± SE of six independent experiments. (B) NO• effect on TNFα, IL-8 and IL-1β mRNA at 6 h as measured by microarray (N = 7) were similar to changes in secreted protein.
Click here for file
Additional File 4
Classification of cAMP-Regulated Genes. Complete list of genes regulated by cAMP. Genes are classified by function and fold change from control is shown.
Click here for file
Additional File 5
RT-PCR Primers and Probes. List of genes tested by RT-PCR including the sequence of primers and probes used in the assays.
Click here for file
Additional File 6
Electrophoretic Mobility Shift Assay (EMSA) Probes. List of genes from which E2F and CDE/CHR promoter sequences were derived for testing by electrophoretic mobility shift assay (EMSA). For each gene, the EMSA probe sequence is shown.
Click here for file
Acknowledgements
The authors would like to thank Joel Moss, Anthony Suffredini, and James Shelhamer for their helpful suggestions. This work was supported by intramural NIH funds from the Critical Care Medicine Department, Clinical Center and the Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience, Center for Information Technology.
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-2651627113710.1186/1471-2105-6-265Methodology ArticleCross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes Warnat Patrick [email protected] Roland [email protected] Benedikt [email protected] Department of Theoretical Bioinformatics, German Cancer Research Center, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany2 Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biology, University of Heidelberg, Im Neuenheimer Feld 364, D-69120 Heidelberg, Germany2005 4 11 2005 6 265 265 29 3 2005 4 11 2005 Copyright © 2005 Warnat et al; licensee BioMed Central Ltd.2005Warnat 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 extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods.
Results
In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85%) were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis.
Conclusion
Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies. Predictive models generated by this approach are better validated than those generated on a single data set, while showing high predictive power and improved generalization performance.
gene expression profilingDNA microarraycross-platform analysisclassificationcancer
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Background
Gene expression profiling by DNA microarrays has become an important tool for studying the transcriptome of cancer cells, and has been successfully used in many studies of tumour classification and of identification of marker genes associated with cancer [e.g. [1-3]]. With an increasing number of microarray data becoming available, the comparison of studies with similar research goals, e.g. to identify genes being differentially expressed in normal versus tumour tissue, has gained high importance. In general, the evaluation of multiple data sets promises to yield more reliable and more valid results since these results are based on a larger number of samples and the effects of individual study-specific biases are weakened. However, the comparison of results from different microarray studies is hampered by the fact that different studies use different protocols, microarray platforms and analysis techniques. The question whether the results of gene expression measurements obtained by different platforms can be compared has been addressed in several studies [4-7]. It has been found that results derived from the measurements like lists of tumour subtype marker genes [5] or measures of intra-study correlation of gene expression patterns [6] can be compared and thus inter-validated between different platforms. However, the measures of gene expression themselves could not be directly compared between different platforms [4,7]. Some studies propose methods for meta-analysis of microarray data with the goal to identify significantly differentially expressed genes across studies by using statistical techniques that avoid the direct comparison of gene expression values [8-14].
The goal of this study is to investigate the benefit of performing supervised classification analyses across disparate sources of microarray data. Methods of supervised classification analysis render it possible to automatically build classifiers that distinguish among specimens on the basis of predefined class label information (phenotypes), and in many cancer research studies [e.g. [1-3]] the application of these methods has shown promising results of improved tumor diagnosis and prognosis. However, as pointed out by several authors, there is a strong need for independent validation of these results, and an increase in sample size is recommended for future studies [15,16]. We therefore chose to explore how gene expression data from different studies can be directly combined, especially for an integrated classification analysis. Such an integrated analysis promises to be a valuable tool for validation of classification results obtained in a single study, and might yield improved results because it is based on a larger number of samples.
Recently, Wright et al. [17] have proposed a statistical method based on Bayes' rule to classify cancer specimens by their gene expression profiles. They were able to classify oligonucleotide microarray data from one study with a predictor derived from cDNA microarray data from a different study. Here, we evaluate the feasibility of building predictors from and classifying microarray data independent of the platform used for expression profiling. The general approach to first derive numerically comparable measures of gene expression from different platforms (data integration) and then to apply supervised classification on the integrated data was successfully applied in first attempts to classify cancer microarray data generated with multiple array platforms [18,19].
We adopt this approach and demonstrate the use of two data integration methods, namely median rank scores, which has already been successfully applied for comparability assessment of five different breast cancer microarray data sets [19], and quantile discretization which has not been used in the context of microarray data analysis before. For supervised classification analysis, we use support vector machines (SVM), a well-established machine learning technique for classification of microarray data [20,21]. Integrated cross-platform classification of cancer is demonstrated for three pairs of publicly available data from microarray studies on different types of cancer [22-27]. To investigate the hypothesis that an integrated analysis of data from different microarray studies can yield results not obtained by a single study, we chose to investigate two leukemia data sets in more detail and studied differences in gene expression profiles between the cytogenetically defined subgroups t(15;17), t(8;21) and inv(16), all associated with a favourable prognosis [28,29], and samples with normal karyotype lacking mutations in FLT3 or RAS, thought to belong to an intermediate risk group [30-32]. While differences between the first three groups are prominent and were detected in multiple studies [33,26,27], evidence about the homogeneity of the normal karyotype group and the associated genes is still lacking. The list of genes selected in an integrated analysis of both studies is compared to the lists of genes selected in two analyses performed separately on either study.
Results
We investigated six publicly available cancer microarray gene expression data sets to perform cross-platform supervised classification analysis. We selected three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer and acute myeloid leukaemia, respectively. All pairs of studies allowed for either classification of cancer versus normal tissue or cancer subtype differentiation. Each pair was chosen to consist of one study using cDNA arrays and one study based on oligonucleotide arrays. We studied how to combine pre-processed data sets measured with different microarray platforms for an integrated classification analysis. The process can be divided into the following main parts: First, we determined the overlap of genes common to both platforms using the UniGene database. Next, we derived numerically comparable quantities from the expression values of both platforms by application of median rank scores or quantile discretization. Then, the support vector machine algorithm, an approved method for supervised classification analysis, was applied to different classification settings.
Data integration
Figure 1 shows for all three study pairs the number of common UniGene clusters (genes) represented on both platforms. Since there is only a moderate overlap of UniGene clusters for the pairs of array platforms, many probes cannot be used for cross-platform analysis. The number of microarray features used for cross-platform analysis is further reduced by averaging expression values of probes on the same platform that map to the same UniGene cluster. As a result, only 40–50% of genes are retained for cross platform analysis.
Figure 1 Barplot of the number of UniGene clusters represented in each data set. Grey coloured bars indicate the proportion of UniGene clusters common to a pair of studies.
As the next step, we applied the median rank scores (MRS) method or quantile discretization (QD). In order to check whether the comparability of the data from different platforms is improved after data transformation by these methods, we compared the distribution of gene expression values per microarray between arrays of different studies. We selected one microarray per study and produced a quantile-quantile plot (QQ-plot) for every pair of microarrays from corresponding studies as shown in Figure 2. In every QQ-plot the quantiles of all gene expression values from a first microarray are plotted against the quantiles of all gene expression values from a second microarray. If the gene expression values of the two different microarrays share the same distribution, the points in the plot should form a straight line. As can be seen in Figure 2, the distribution of expression values of microarrays of different studies is much more similar after application of MRS in comparison to non-integrated data. As an effect of QD, the quantiles of the expression values of all microarrays in the integrated studies are equal by definition, resulting in points in the plots forming a straight line.
Figure 2 Quantile-quantile-plots (QQ-plots) comparing the distribution of gene expression values from microarrays of all investigated studies before and after the respective application of MRS or QD. One microarray per study was selected and a quantile-quantile plot (QQ-plot) for every pair of microarrays from corresponding studies was produced. In every QQ-plot the quantiles of all gene expression values of a first microarray are plotted against the quantiles of all gene expression values of a second microarray. If the gene expression values of the two different microarrays share the same distribution, the points in the plot should form a straight line. Abbreviations: MRS, median rank scores; QD, quantile discretization
Classification analysis
After data integration by the median rank scores method or quantile discretization, respectively, two different types of cross-platform classification analyses were performed: training of a classifier on only one data set of a pair followed by classifier evaluation on the other data set, and classifier training and testing on data instances randomly chosen by a cross validation from the combined data set.
The first type of analysis was performed on non-integrated data and on integrated data, respectively. Evidently, without data integration, a classifier created on one set cannot correctly classify data instances of the other set (Figure 3). This is clearly indicated by prediction accuracies being similar to or worse than the prior prediction rates, i.e. the prediction accuracy of a classifier which always predicts a data instance to be an element of the dominating class. The only exception is the prostate cancer data, where high classification accuracy was achieved after training on the data set of Welsh et al. and classification of the data of Dhanasekaran et al. Data integration improves the results in cases of the prostate and breast cancer studies (p-values < 0.01, except for classification of the data of Dhanasekaran et al, where a high classification accuracy was already achieved on the non-integrated data set). We conclude for these two pairs of studies that data integration enables the successful application of classifiers trained on one data set to a comparable data set generated with a different platform. This conclusion does not hold for the AML studies. Here, only the result for building a classifier based on the data of Bullinger et al. and classifying the data of Valk et al. improved after application of median rank scores or quantile discretization (p-value < 0.1).
Figure 3 Barplot of results from a classification analysis using SVM classifiers. Barplot of results from a classification analysis where all data from one study are used to built a classifier (training), which is then used to classify all samples of the other study (test), using SVM classifiers. The names below the bars indicate which study was used for classifier training (left name) and testing (right name). The bars represent the achieved classification accuracies, i.e. the fraction of samples correctly classified. The colour of a bar indicates the method used for data integration. P-values are obtained by statistical testing with the null hypothesis that the two marked classification approaches perform equally well on the given test set (see Methods for details). The target variable for classification analysis of the prostate cancer data was 'type of tissue' (normal vs. tumor tissue), for the breast cancer data the estrogen receptor (ER) status (ER positive vs. ER negative), and for the leukemia data the karyotype of the samples (one of the chromosomal aberrations t(8;21), t(15;17), inv(16) or normal karyotype, respectively). Abbreviations: MRS, median rank scores; QD, quantile discretization, SVM, support vector machine.
Except for the pair of breast cancer microarray data sets, the application of the MRS versus QD showed no significantly different effect on the achieved classification accuracies. For the training on the data set of Gruvberger et al. and classification of the data of West et al., the classification result was significantly better after application of QD in comparison to the result obtained after using the MRS method. In all other cases both methods can be considered equivalent.
In addition to the above mentioned separated training and validation, cross-validation analyses were performed on combined data sets. High classification accuracies were achieved with training and testing on data instances randomly chosen from both data sets (> 85%; see table 2). Although the integrated classifiers only operated at less than 50% of all genes, classification accuracies for integrated classifiers were nearly as high or even markedly improved in comparison with classification accuracies achieved for single data sets only. In the case of the breast cancer studies, the results were better than the accuracies achieved by cross-validation on each of the pre-processed single sets with all available microarray features.
Table 2 Classification results observed by cross validation using SVM classifiers. Figures represent achieved classification accuracies, i.e. the fraction of samples correctly classified. The upper table shows results for cross validation analysis of both data sets of a pair, where samples for training and testing are selected randomly from both studies. For this, data sets were integrated by either MRS or QD. The bottom table contains the results of a cross-validated classification analysis performed separately on each study, using all available gene expression data after pre-processing (without applying MRS or QD). Abbreviations: MRS, median rank scores; QD, quantile discretization, SVM, support vector machine.
both data sets integrated
MRS QD
Prostate cancer 97.67 % 97.56 %
Breast cancer 87.01 % 88.97 %
Acute myeloid leukemia 90.60 % 90.20%
original data
Prostate cancer Dhanasekaran et al. Welsh et al.
95.28 % 99.09 %
Breast cancer Gruvberger et al. West et al.
80.52 % 86.73 %
Acute myeloid leukemia Bullinger et al. Valk et al.
68.53 % 99.90 %
In order to check whether similar classification results could be obtained with another method of supervised classification analysis, we repeated the above described experiments using the method of nearest shrunken centroids classification (also known as "Prediction Analysis of Microarrays", PAM) [34]. As presented in Additional Files 1 and 2, the classification results obtained with PAM are similar to those obtained by SVM.
Selection of genes with discriminative expression patterns
To show the potential of an integrated cross platform analysis, we generated lists of genes forming discriminative expression patterns by means of recursive feature elimination (RFE) analysis for the leukemia studies (see Methods for details). We generated six lists of genes, two lists for an analysis of the combined leukemia studies, integrated by MRS or QD, and two lists for each of the two leukemia studies analysed separately, using only samples of the either the MRS or QD data which belong to one study. A number of 512 elements was selected for each list, which corresponded to minimal cross-validated error rate in the integrated analyses of data from both leukemia studies. Interestingly, the intersection of the lists generated in analyses using only data of one of the two leukemia studies comprises only about 40 UniGene clusters, independently of whether MRS or QD was used (Figure 4). In the sets generated by an analysis of both studies together, integrated by MRS or QD, many genes were selected that were lost in the analyses based on a single study (Figure 4). These include important genes with regard to the biology of leukemia, like RXRA, PBX3, ABL2, SOCS1, and EGR2 (see Additional File 3 for annotated lists of selected genes; Additional File 4 contains all six gene lists ordered by gene ranks as determined in RFE analysis).
Figure 4 Venn diagrams showing the overlap between lists of genes generated by RFE analysis. Venn diagrams showing the overlap between lists of genes generated by RFE analysis based on single sets (Bullinger et al. or Valk et al.) and based on both data sets integrated by MRS or QD. Abbreviations: MRS, median rank scores; QD, quantile discretization, RFE, recursive feature elimination.
Finally, we used hierarchical clustering as a visualization method to display coherence in gene expression of the genes selected by RFE in the leukemia studies. We first clustered the data of both leukemia studies separately based on the genes selected by RFE on either set. As shown in Figure 5(a,b), the samples of Valk et al. were perfectly grouped according to their karyotype while in the data of Bullinger et al. samples with karyotype t(8, 21) and inv(16) were not grouped homogeneously. Then, we clustered the data of Valk et al. using only genes found to be discriminative on the data of Bullinger et al. (Figure 5c). Figure 5d shows the reverse case, a clustering of the data of Bullinger et al. based on the gene selection on the data of Valk et al. For the selected groups of genes, coherence in gene expression between samples of the same karyotype was weak when results of an analysis solely based on one leukemia data set are transferred to the other leukemia data set, as samples of the same karyotype were not grouped homogeneously. Figure 5e and 5f show clustering results on all samples of both studies using gene lists integrated either by MRS or QD. Here we can observe a much more consistent grouping of the samples according to their karyotype than that observed in Figure 5c and 5d. Still, both methods of data integration are not able to fully eliminate study specific self-similarity of samples, as the samples form clusters according to study origin.
Figure 5 Hierarchical clustering of leukemia samples. Hierarchical clustering of leukemia samples based on expression values of genes selected by RFE analysis. The colored bars indicate the true class affiliations of every sample, the black and white bars below indicate study origin. (a) Clustering result for data from Valk et al. or (b) Bullinger et al. using only genes selected by RFE on this data set. (c) Clustering of data from Valk et al. after data integration by MRS algorithm using only expression values of genes selected by RFE on the data of Bullinger et al. (d) Clustering of data from Bullinger et al. based on genes selected on data from Valk et al. Data integrated by QD or non-integrated data yielded results similar to those here (data not shown). (e) Clustering results of all samples of both studies using gene lists generated on the combined set integrated by MRS or (f) QD. Abbreviations: MRS, median rank scores; QD, quantile discretization, RFE, recursive feature elimination.
Discussion
In this study we showed that classification of cancer microarray data can be markedly improved by cross-platform classification analysis of gene expression data from different studies with similar focus. Key techniques for cross-platform classification analysis were data integration methods rendering microarray data numerically comparable across platforms in combination with well-established machine learning techniques for generation of predictive models. An obvious advantage of an integrated classification analysis is the improved generalization performance and reliability of the resulting predictive models (classifiers) since they are found and validated on a larger number of samples, thus the effect of study-specific biases can be reduced. For all study pairs used here, we achieved high classification accuracies when using data samples randomly chosen from both data sets of a comparison pair for classifier building and testing. Our findings endorse the encouraging results of first attempts of multi-platform microarray classification analysis [18,19].
For integration of microarray measurements from different platforms, Bloom et al. [18] used a scaling approach based on measurements for one common reference RNA sample. As hybridization results for such a common reference RNA sample are normally not available for different microarray studies and platforms (especially in the case of custom made cDNA arrays), we applied the median rank scores method [19] and quantile discretization for data integration. Besides the problem of integrating microarray data that have been measured with different platforms, a general problem in combining measurements from different gene expression studies is the variability between results of different studies. This is primarily due to biological differences among the samples of different studies, differences in the technical procedures to obtain gene expression measurements, and random variation. The use of methods providing an abstraction of data like ranks or discretized values reduces this variability at the price of reduced information. Therefore, data sets processed by MRS or QD can not be considered as a suitable input for every kind of analysis purpose. However, for the aim of cross-platform classification analysis, the combination of such abstraction methods with a sophisticated machine learning technique like the support vector machine used here helps to compensate for this loss of precision, and can yield useful results.
Even when a classifier is built on one data set of a pair of compared studies and the samples of the other study are classified, good classification results can be observed for the prostate and breast cancer studies. In this case, the generalization ability of the classifier is sufficient to correctly classify most of the samples of the other study, and thus the classifier obtained on the data of one study can be validated by the data of another study. In contrast, the results for the AML studies indicate that the generated classifiers based on only one of these studies are too specific. This might be due to fact that the sample sets of either study are not representative enough to cover all characteristic transcriptional features observable for the investigated phenotypes. Indeed, the results for the cross-validation analysis using samples from both AML studies show that classifiers with better generalization performance can be obtained underlining the potential of a cross-platform classification analysis.
Selection of discriminative gene expression signatures is an important task frequently performed in microarray studies. Here, we applied RFE analysis for selecting subsets of genes with distinctive expression patterns on the data of the leukemia studies of Bullinger et al. [26] and Valk et al. [27]. For visualization of the coherence in gene expression of the genes selected by RFE in the different studies we performed hierarchical clustering. Gene sets selected only on data of one study show poor coherence in gene expression for the karyotype groups of samples on the other set. Clustering results observed for gene sets selected on the combined set are more consistent. Therefore, these discriminative gene sets are apparently of more general validity. On the other hand, cluster analysis showed that neither of the two methods of data integration was able to entirely overcome study specific self-similarity of the leukemia samples. For cross-platform classification analysis, however, the MRS and QD algorithms yielded good results.
The analysis of gene lists obtained by RFE indicated that gene signatures can be generated on a combined set that comprise important genes that were not part of gene signatures generated on either set alone. Notably, the intersection of lists from the Bullinger and Valk data sets with the list from the combined set contained only a few genes, none of them to be known of high importance in the context of AML. Similarly, the intersection between the Bullinger and Valk data sets was not large (Additional File 3). In contrast, the list obtained from the combined data set contained many genes well known to be involved in leukemia pathogenesis, like PBX3 [35], the retinoid receptor X [36], the ABL2 tyrosine kinase [37] or early growth response 2 [38]. In addition, many genes in the combined list are prominent oncogenes or tumour suppressors, like BCL2 [39] or ERBB3 [40]. Most notable is the inclusion of human telomerase TERT, which has been found by Hahn et al. to be one of three necessary factors for transforming a normal cell into a tumour cell [41].
We compared the gene lists generated by the RFE method to the result of a meta-analysis approach as described by Rhodes et al. [8]. This method aims at identifying genes that show reproducible standardized differences in mean expression between phenotype groups across studies. For this, a p-value is calculated for every gene in both leukemia studies separately, in order to quantify the significance of differences in mean expression between phenotype groups within a study. Then, the study specific p-values are combined to a test statistic S and significance values for this test statistic by a permutation approach are calculated (for details see [8]). At a significance level of p = 0.01, 43 genes were selected by this meta-analysis approach. Of these 43 genes, 12 genes were also found in the list generated by an RFE analysis of the data of both studies integrated by MRS, 19 were also found in the list generated by an RFE analysis of the data of both studies integrated by QD. This result shows that the gene lists selected by RFE analysis also contains genes that would have been found in an independent meta-analysis, but that also many different genes are selected. This is not surprising, as there are essential differences in both approaches. The meta-analysis performed here applies a univariate statistical test to find genes with a significant difference in group means of expression values, whereas the SVM based RFE analysis is a multivariate approach which also considers combinations of genes and selects genes with maximum influence on the discriminative performance of a classifier. While interpreting a gene list generated in a RFE analysis, one has to keep in mind that the main goal of methods like the SVM based RFE approach used here is to generate signatures that allow for accurate classification of phenotypes. These gene signatures are unlikely to contain all and only genes that are most relevant to the genetic differentiation between complex disease phenotypes. The task to find the complete set of only those relevant genes out of gene expression data is much more demanding and might pose an irresolvable challenge as the changes of gene expression profiles recorded by microarrays are mostly secondary and tertiary effects and not the primary ones. With microarrays one observes the avalanche of gene expression changes, not necessarily the small pebble causing it. First promising concepts and methods to work on the task to find the set of relevant genes have been proposed [42], but their usefulness to address biological questions has still to be thoroughly investigated. However, the finding that RFE signatures generated by an integrated analysis of both leukemia studies contained genes that are described as being relevant for tumor biology in the literature, and that were not found in either single set analyses, shows the potential of cross-platform microarray data integration to be useful not only to improve results for phenotype classification but also for generation of gene signatures that contain more biologically interesting genes.
Considering integrated classification analyses in general, a limiting factor for future application is posed by inconsistencies in biological phenotype annotation across studies. In many cases, it is hard to obtain consistent annotation on the samples used that would allow to form comparable groups for classification analysis. This is due to lack of ontologies for description, or the use of categories that are based on subjective evaluation such as histological grading or borderline expression of a molecular marker as determined by immunohistochemistry. In such respect, it would be highly desirable to introduce systems for annotation of samples that are analogous to the MIAME standard for description of technical details of hybridization [43]. Until such a system exists, one has to focus on studies where consistency can be guaranteed by expert evaluation, as is the case for the data sets investigated here.
More study results will be needed to validate our findings. Cross-platform analyses have to be conducted considering more than two studies at a time. Here, the problem of having relatively few genes in common between all studies will gain increasing importance. Methods to make use of gene expression values only available on some platform(s) but not on others will be required. For this, the adaption of a recent approach by Guo et al. [44] could be a first step. Guo et al. use functional expression profiles (FEP) instead of gene expression profiles (GEP) for their classification analysis and generate the FEP by averaging the expression levels of genes mapping to the same Gene Ontology (GO) annotation. For integrating data from different microarray platforms, mapping of such functional summary measurements as FEP rather than the actual gene expression measurements between different chip platforms might result in an increased number of measurements (in terms of the number of genes) having an influence on the analysis results. However, by this approach the amount of information in the data is also reduced, as for example anti-correlated genes mapping to the same GO annotation would countervail each other. Further research is required to evaluate the impact of these two effects on the results of an integrated cross-platform classification analysis. The general improvement of matching genes between different platforms would also be beneficial in order to avoid false or missing mappings. Such developments are under way in our laboratory.
Conclusion
Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies. Predictive models generated by this approach are better validated than those generated on a single data set, while showing high predictive power and improved generalization performance. The results presented here for the three sample study pairs indicate that this approach bears the potential to become a widely applicable technique for inter-validation of studies performing classification of microarray gene expression data.
Methods
Gene expression data collection and preprocessing
All data for this study were downloaded from public web sites (Table 1) and were pre-processed by software packages included in the R-project [45] or Bioconductor [46], respectively. For all studies where raw microarray data were available, pre-processing was performed as follows. Microarray features with more than 20% missing values across all arrays per study were not considered for further analysis. Missing values for all remaining features were replaced by median values per gene. Normalization was carried out using either the vsn [47] or loess [48] algorithms with default parameters as implemented in the Bioconductor software packages vsn and marray. Data were base-two log-transformed where applicable.
Table 1 Key characteristics of used microarray data. The figures in curly brackets denote the number of samples belonging to each category. The number of probes comprises all probes for which data were available and which have not been filtered out in the preprocessing of the data (see methods for details). Abbreviations: ER, estrogen receptor status; AML, acute myeloid leukemia; t(A;B), balanced translocation of genetic material between chromosomes A and B; inv(16), inversion of a segment of chromosome 16; NN, normal karyotype.
Study Cancer Platform Samples Probes Target Variable of Classification Analysis
Dhanasekaran et al[22] Prostate cancer cDNA 53 7769 Tissue: tumor{34}, normal{19}
Welsh et al[23] Prostate cancer oligo 33 9023 Tissue: tumor{24}, normal{9}
Gruvberger et al[24] Breast cancer cDNA 58 3300 ER-status: positive{28}, negative{30}
West et al[25] Breast cancer oligo 49 5435 ER-status: positive{25}, negative{24}
Bullinger et al[26] AML cDNA 52 14776 Karyotype: t(8;21){11},t(15;17){12},inv(16){15}, NN{14}
Valk et al[27] AML oligo 97 13250 Karyotype: t(8;21){22}, t(15;17){19},inv(16){23}, NN{33}
Data integration
The UniGene database (Build 171) [49] was used to match cDNA clones and Affymetrix probe sets between platforms. Each transcript from the different microarrays was mapped to a UniGene cluster. The overlap of genes was determined by forming the intersection of the respective UniGene cluster sets. Within each study, expression values corresponding to probes of the same UniGene cluster were averaged. Genes that did not map to any UniGene Cluster and genes not mapping to a UniGene cluster obtained for the other microarray platform were not considered for cross-platform analysis.
In the case of the breast cancer data sets [24,25], all probes corresponding to the estrogen receptor gene (UniGene cluster Hs.1657) have been removed for further analysis since, for these data sets, the estrogen receptor status of the samples should be predicted independently of the expression of the estrogen receptor gene.
For the comparison of the leukemia microarray data sets [26,27], we selected only those samples belonging to one of the following karyotypes being represented in both data sets: t(8;21), t(15;17), inv(16) and normal karyotype, respectively.
To derive numerically comparable measures of gene expression for different microarray platforms we used either median rank scores or quantile discretization. Before either of these methods was applied to the preprocessed data, all expression values of oligonucleotide arrays were divided by the median expression value per array to scale absolute intensity values to relative ratio values.
Median Rank Scores
(MRS) [19] The basic idea of this method is to transform gene expression values of different microarray platforms to a common numerical range by replacing numerical values of one study by numerical values from the other study, with respect to the relative ranks of expression values within each study. Therefore, one of the microarray data sets to be compared is chosen as a reference set. For each gene, the median expression value over all microarrays of the reference study is calculated, and the resulting vector of median gene expression values is sorted in ascending order. Next, for every microarray of the non-reference set, the relative rank of each gene expression value is determined. An expression value with rank n is then replaced by element n of the sorted median expression vector. Thus, the gene expression values of all microarrays of the non-reference sets are replaced by surrogate values with comparable numerical range relative to the reference data set. Therefore, the study comprising most microarrays should be designated as the reference set. Under certain circumstances it might make sense to chose the reference set according to another criterion than sample size, e. g. when the largest data set shows an inferior expression data quality in comparison to the smaller sets. Note that the only information being preserved for the non-reference set are the relative ranks of gene expression values. To keep our analyses comparable with regard to the selection of the reference set, we always selected the study using a cDNA microarray as reference data set because for two of the three investigated pairs of studies the study using a cDNA microarray contained more samples (microarrays) than the corresponding study that used an oligonucleotide microarray.
Quantile discretization
(QD) This method is based on equal frequency binning [50]. Here, the expression values of all arrays are discretized into a predetermined number of bins b (b = 8) for all our analyses. For each experiment, b subsets with equal number of values are determined using the quantiles of the array expression values as cut points, where a cut point is here defined as the expression value separating an ordered set of expression values into two subsets. The two central bins with the median value as cut point are merged into one bin yielding one central interval. Every expression value is replaced by an integer value corresponding to the bin it falls into, where zero is assigned to central bin and all other bins are numbered consecutively beginning with the bins next to the central one, using positive integers for bins containing values above the median and negative integer values for the others. Both methods were implemented using the R software for statistical computing [45]. Code is available upon request.
Classification analysis
For each pair of studies, classification analyses were performed on the UniGene matched gene expression values. We investigated how well a classifier trained on one data set predicts class labels of the other data set after application of MRS and QD, respectively, compared to no application of MRS or QD. For each pairwise combination of these three approaches, a statistical test with the null hypothesis of equal performance in classification of the given test set was realized according to Salzberg [51]: For comparing the performance of two classification approaches A and B on a given test set, the number of test samples n for which one of the two approaches gave a correct classification and the the other approach gave a wrong classification is determined. If both approaches perform equally well, then among these n samples the proportion p of samples for which approach A gave a correct classification should be equal to the proportion q of samples for which approach B gave a correct classification. Therefore, the null hypothesis of equal classification performance of A and B can be tested by a binomial test with null hypothesis p = q = 0.5.
In addition, we examined the class prediction accuracies by 10-times repeated (i.e. 10 resampling replicates) 10-fold cross-validation. Arrays of both studies were chosen randomly for training and testing after data integration by the median rank scores method and by quantile discretization, respectively. Finally, we performed a cross-validated classification analysis on each data set alone using all available pre-processed gene expression values.
We used support vector machines (SVM) for supervised classification analysis, applying the libsvm implementation by Chang and Lin with a polynomial kernel function [52]. Hyperparameters C and degree were tuned by cross-validating parameter combinations in a grid search over a two-dimensional parameter space with ranges from 2-5 to 210 and 1 to 3, respectively.
For classification with nearest shrunken centroids (PAM), we used the corresponding R package pamr, available on the Bioconductor website [46]. The hyperparameter delta (threshold for centroid shrinkage) was tuned over the default parameter range given in the pamr package.
Parameter tuning for both classification methods was done by a three-fold cross-validation and was repeated for cross-validation in each single iteration (nested cross-validation). No variable pre-selection was performed on the preprocessed data prior to classifier construction. The scheme of our workflow for calculating class prediction accuracies is shown in Additional File 5.
The whole process of cross-platform classification analysis in comparison to a meta-analysis approach is summarized in Figure 6.
Figure 6 Flow diagram of the presented cross-platform classification approach. Flow diagram of the presented cross-platform classification approach (see Methods for details) compared to a meta-analysis approach.
Selection of genes with discriminative expression patterns
Independently of the classification analysis described above, we applied a SVM based Recursive Feature Elimination (RFE) method [53] for selection of genes with discriminative expression patterns in case of the leukemia studies by Bullinger et al. (2004) and Valk et al. (2004). We used an implementation of the method in R [54]. As the magnitude of the internal SVM classifier feature weights represent the influence of a feature on a classification decision by that classifier, the approach suggested by Guyon et al. [53] uses the internal feature weights of an SVM classifier to generate a feature ranking. This is realised by repeatedly fitting an SVM model to given data and iteratively eliminating features from this model. We generated six lists of genes, two lists for an analysis of both leukemia studies together, integrated by MRS or QD, and two lists for each of the two leukemia studies analysed separately, using only samples of either the MRS or QD data which belong to one study. Note that in the integrated analyses as well as in the analyses based on single study data only expression data for only those genes were used that were present on both microarray platforms used in the two studies. For generation of gene lists with RFE, we first performed a 10-fold cross-validation once on every given data set for optimizing the number of selected genes, where we only considered gene lists containing a number of genes equal to a power of two but less than the total number of genes. For the two integrated analyses of data from both leukemia studies, a number of 512 elements corresponded to the minimal cross-validated error rate. We next applied RFE to every dataset (without cross-validation) resulting in one ranking of all genes per data set. We then selected the 512 most highly ranked genes for every data set and finally compared the six different lists of 512 genes.
Moreover, we visualized results from RFE analysis by performing hierarchical clustering of the leukemia data based on the generated gene lists. For hierarchical clustering, we used the method "hclust" of the R package mva, applying the following parameter settings: Manhattan distance function was performed on data transformed to zero mean and unit variance, and clustering was done using a complete linkage algorithm [55].
List of abbreviations
AML: acute myeloid leukemia
FEP: functional expression profile
GEP: gene expression profile
GO: gene ontology
MIAME: minimum information about a microarray experiment
MRS: median rank scores
PAM: prediction analysis of microarrays
QD: quantile discretization
QQ-plot: quantile-quantile plot
RFE: recursive feature elimination
SVM: support vector machine
Authors' contributions
PW conceived of the study, carried out the analyses and drafted the manuscript. RE participated in the design of the study and helped to draft the manuscript. BB coordinated the study, participated in its design, performed the hierarchical cluster analysis and helped to draft the manuscript. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Barplot of results from a classification analysis where all data of one study is used to built a classifier (training), which is then used to classify all samples of the other study (test), using PAM classifiers. The names below the bars indicate which study was used for classifier training (left name) and testing (right name). The bars represent the achieved classification accuracies, i.e. the fraction of samples correctly classified. The colour of a bar indicates the method used for data integration. P-values are obtained by a statistical test with the null hypothesis that the two marked classification approaches perform equally well on the given test set (see methods for details). The target variable for classification analysis of the prostate cancer data was 'tissue type' (normal vs. tumor tissue), for the breast cancer data the estrogen receptor (ER) status (ER positive vs. ER negative), and for the leukemia data the karyotype of the samples (one of the chromosomal aberrations t(8;21), t(15;17), inv(16) or normal karyotype, respectively). Abbreviations: MRS, median rank scores; QD, quantile discretization, PAM, prediction analysis of microarrays.
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Additional File 2
Classification results observed by cross validation using PAM classifiers. Figures represent achieved classification accuracies, i.e. the fraction of samples correctly classified. The upper table shows results for cross validation analysis of both data sets of a pair, where samples for training and testing are selected randomly from both studies. For this, data sets were integrated by either MRS or QD. The bottom table contains the results of a cross-validated classification analysis performed separately for each study, using all available gene expression data after pre-processing (without application of MRS or QD). Abbreviations: MRS, median rank scores; QD, quantile discretization, PAM, prediction analysis of microarrays.
Click here for file
Additional File 3
The overlap between lists of genes found by RFE analysis based on single sets (Bullinger et al. or Valk et al.) and based on both data sets integrated by MRS or QD. Abbreviations: MRS, median rank scores; QD, quantile discretization, RFE, recursive feature elimination.
Click here for file
Additional File 4
All six lists of genes found by RFE analysis (see Methods for details). In every list, the corresponding UniGene identifiers of the genes are ordered according to their rank as determined in the RFE analysis. Abbreviations: RFE, recursive feature elimination.
Click here for file
Additional File 5
Workflow for calculation of the presented class prediction accuracies. (a) Classifier performance evaluation on an independent data set as applied for calculation of the results presented in Figure 3 and Additional File 1. (b) Classifier performance evaluation by repeated cross validation as applied for calculation of the results presented in Table 2 and Additional File 2.
Click here for file
Acknowledgements
The authors acknowledge financial support by the BMBF (BioFuture; 0311880A), and the National Genome Research Network (01 GR 0450). PW receives a stipend from the DFG Graduiertenkolleg 886.
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-1551627765710.1186/1471-2164-6-155Methodology ArticleSSH adequacy to preimplantation mammalian development: Scarce specific transcripts cloning despite irregular normalisation Bui LC [email protected]éandri RD [email protected] JP [email protected] V [email protected] UMR Biologie du Développement et de la Reproduction. INRA 78350 Jouy en Josas. France2005 8 11 2005 6 155 155 9 6 2005 8 11 2005 Copyright © 2005 Bui et al; licensee BioMed Central Ltd.2005Bui 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
SSH has emerged as a widely used technology to identify genes that are differentially regulated between two biological situations. Because it includes a normalisation step, it is used for preference to clone low abundance differentially expressed transcripts. It does not require previous sequence knowledge and may start from PCR amplified cDNAs. It is thus particularly well suited to biological situations where specific genes are expressed and tiny amounts of RNA are available. This is the case during early mammalian embryo development. In this field, few differentially expressed genes have been characterized from SSH libraries, but an overall assessment of the quality of SSH libraries is still required. Because we are interested in the more systematic establishment of SSH libraries from early embryos, we have developed a simple and reliable strategy based on reporter transcript follow-up to check SSH library quality and repeatability when starting with small amounts of RNA.
Results
Four independent subtracted libraries were constructed. They aimed to analyze key events in the preimplantation development of rabbit and bovine embryos. The performance of the SSH procedure was assessed through the large-scale screening of thousands of clones from each library for exogenous reporter transcripts mimicking either tester specific or tester/driver common transcripts. Our results show that abundant transcripts escape normalisation which is only efficient for rare and moderately abundant transcripts. Sequencing 1600 clones from one of the libraries confirmed and extended our results to endogenous transcripts and demonstrated that some very abundant transcripts common to tester and driver escaped subtraction. Nonetheless, the four libraries were greatly enriched in clones encoding for very rare (0.0005% of mRNAs) tester-specific transcripts.
Conclusion
The close agreement between our hybridization and sequencing results shows that the addition and follow-up of exogenous reporter transcripts provides an easy and reliable means to check SSH performance. Despite some cases of irregular normalisation and subtraction failure, we have shown that SSH repeatedly enriches the libraries in very rare, tester-specific transcripts, and can thus be considered as a powerful tool to investigate situations where small amounts of biological material are available, such as during early mammalian development.
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Background
Molecular analysis during the early period of embryonic development has long been prevented in mammals because of the scarcity of biological material, whatever the species considered. In recent years, however, technical improvements in the analysis of messenger RNAs from tiny amounts of cells have revealed the complexity of the genome expressed during the preimplantation period [1,2]. This complexity has been highlighted in recent publications which reported the isolation of new sequences in different mammalian species [3-6]. Although the transient and tissue-specific expression of such sequences during later development cannot formally be ruled out, they are more likely to be specifically expressed during the preimplantation stages, which has thus prevented their identification until now [7]. This underlines the need for dedicated transcriptomic tools to investigate these developmental stages. Such tools now exist for studies in mice [8], but still have to be developed for other species. Of the different strategies available to establish cDNA libraries, Suppression Subtractive Hybridisation (SSH) [9] is an efficient and widely-used PCR-based method to obtain subtracted libraries and isolate differentially expressed genes. The procedure involves two successive tester-driver hybridisation steps, the first of which induces a normalisation of tester-specific molecules, thus allowing the subsequent cloning of rare, tester-specific transcripts. Because SSH can be initiated using PCR-amplified cDNAs, it seems particularly well-suited to mammal preimplantation stage embryos which contain only a few tens of picograms of mRNAs. Furthermore, because SSH does not require previous knowledge of gene sequences, it may also be suitable for species where only a small number of sequences are available in databases.
Although it has already been used to get insight into early embryo transcriptome [10,4,6,11], SSH performance in this area has not been widely documented. This is because authors usually establish only one dedicated library and provide information restricted to a few biologically relevant clones isolated from this library. Because we were interested in the more systematic establishment of SSH libraries dedicated to the analysis of early mammalian embryo development, we designed a procedure involving exogenous reporter transcripts that mimic either tester-specific or tester-driver common transcripts to enable the large scale assessment of the quality of such libraries. This procedure was applied to four independent libraries and provided information on their quality and the repeatability of the SSH procedure applied to early-stage embryos. These data were further validated by the sequencing and clustering of about 1600 clones isolated from one of these libraries. Our results show that when applied to preimplantation mammal embryos, the cDNAs of which had been pre-amplified using the SMART (Clontech patent) procedure, SSH-generated libraries repeatedly provided access to very scarce, tester-specific transcripts, despite irregular normalisation and some subtraction failures.
Results and discussion
Three of the libraries we established aimed to analyse embryonic genome transcriptional activation in rabbit and cattle (the so-called rab1, rab3 and bov1 libraries), while the fourth library (rab2) was designed for studies on early cell differentiation at the blastocyst stage in the rabbit.
In order to achieve a broad appraisal of the quality of the libraries, we decided to array several thousand clones from each library and analyse the abundance of tester-specific and tester/driver common transcripts in the subtracted libraries after bacteria transformation. However, neither strict tester-specific transcripts nor tester/driver common and equally expressed transcripts are identified at these stages in bovine and rabbit embryos, so we introduced exogenous A. thaliana transcripts into our biological material prior to pre-amplification and subtraction (see Fig. 1). We screened 768, 4608, 2683 and 4608 clones from the rab1, bov1, rab2 and rab3 libraries, respectively, for the presence of rare, tester-specific transcripts, using hybridisation with probes corresponding to the exogenous transcripts added. Whatever the lowest abundance of the reporter RNA (0.001% or 0.0005% of messenger RNAs), we found clones which encoded for the scarce, tester-specific transcript in the libraries. These clones represented more than 0.1% of the colonies (Table 1). This result was in agreement with the initial findings of Diatchenko's group [9], but disagreed with the results published more recently by Ji et al. [12] that suggest that only abundant targets (0.1% of messenger RNA) underwent efficient enrichment by SSH PCR, thus precluding the detection of transcription factors and receptors. Such divergent conclusions may have been due to differences in analytical sensitivity, because Ji et al. only considered subtracted cDNA smears after agarose gel electrophoresis [12], whereas we applied the hybridisation of specific, radiolabelled probes to thousands of bacterial colonies. We also found that exogenous reporter RNAs were differentially represented among the bacterial colonies, in line with their initial abundance in the tester material (Table 1 and Fig. 2). While clones encoding rare and moderately abundant tester-specific transcripts represented 0.2 to 0.5% of the clones in the libraries, abundant tester-specific transcripts were very frequently represented in the subtracted libraries (5 to 10 % of clones). These results thus show that rare and moderately abundant transcripts are roughly normalised by the SSH procedure while abundant transcripts are not. This is inconsistent with the conclusions reached by both Diatchenko et al [9] and Ji et al [12], suggesting that all or nothing differentially expressed cDNAs were enriched to a similar final level, irrespective of their initial concentration. Here again, these divergent conclusions could be explained by differences in the experimental procedures: during our study, reporter transcripts were added to a complex biological material containing both commonly and differentially expressed transcripts, whereas tester-specific reporter DNA were added to a common cDNA used as tester and driver material by Diatchenko [9] and Ji [12]. The hybridisation kinetics during our study were probably more representative of most biological situations. Moreover, two other experimental approaches allowed us to conclude that the unequalized abundance of exogenous transcripts in our libraries was representative of the behaviour of endogenous transcripts.
Figure 1 Experimental procedure designed to check SSH efficiency.
Table 1 Proportion of reporter transcripts encoding clones in the subtracted libraries
Library Initial abundance (%)
Tester specific (bold)
Tester/Driver Common (standard) Hybridised/analysed clones Proportion in subtracted library (%)
Rare transcripts rab1 # # #
bov1 0,001 13/4608 0,28
rab2 0,0005 3/2683 0,11
rab3 0,0005 14/4608 0,3
Moderate transcripts rab1 0,005 5/768 0,65
0,005 0/768 -
bov1 0,005 24/4608 0,52
0,005 3/4608 0,06
rab2 0,002 22/2683 0,82
0,002 0/2683 -
rab3 0,002 11/4608 0,24
0,002 2/4608 0,043
Abundant transcripts rab1 0,05 82/768 10,7
0,05 10/768 1,3
bov1 0,05 195/4608 4,2
0,05 4/4608 0,086
rab2 0,01 147/2683 5,48
0,01 0/2683 -
rab3 0,01 256/4608 5,55
0,01 25/4608 0,54
#: not introduced in library
-: no clone detected among the screened clones
Figure 2 Abundance of reporter-transcript encoding clones in the bov1 subtracted library. Screening of bov1 bacteria macroarray with reporter RNA probes. Because of independent repeated hybridisations (of some membranes), only encircled clones on the left hand side (middle row) membrane should be considered as hybridising with the relevant probe.
First of all, we picked seven clones at random from the rab2 library that did not encode for exogenous transcripts, and analysed their abundance in the library by hybridising each radiolabelled insert to 2683 clones from the library. Three of them were present only once, the others (3L22, 3P11, 3I20 and 3C24) were found 29, 63, 152 and 362 times, respectively. The most abundant of these clones (3C24) thus represented 13.5% of the clones in the rab2 library. Its sequence corresponded to a fragment of rabbit mitochondrial 16S rRNA (nucleotide 1402 to 1899, Accession number AJ001588).
Secondly, we systematically sequenced 1920 clones from the rab2 library that did not hybridise to the 3C24 insert. From these clones, 1582 "good quality" sequences were assembled into 651 distinct contigs, of which 447 were singlet contigs. The depth of the 204 remaining contigs ranged from 2 (98 contigs) to 135 (1 contig). Only 14 contigs contained more than 10 sequences, two of them corresponding to A. Thaliana tester-specific exogenous transcripts (initial abundance 0.01 and 0.002%). These 14 contigs totalized 638 sequences. Surprisingly, the deepest contig – that containing 135 (8.5%) of the sequenced clones – encoded for the complete mitochondrial 16S rRNA, with a high prevalence of sequences bordering the 3C24 fragment. Finally, about 22% of the rab2 library clones encoded for this mitochondrial cDNA.
Sequence data validated the results obtained by hybridisation for tester-specific exogenous transcripts. We found 86/1582 (5.43%) clones encoding for the abundant reporter tester-specific transcript, 14/1582 (0.88%) clones encoding for the moderately abundant one and 6/1582 (0.38%) for the rare one, these results being in agreement with hybridisation results (Table 1). Sequencing also confirmed the results obtained for common transcripts in the rab2 library, since no sequenced clone corresponded to these exogenous transcripts (Table 1). However, "commonly expressed transcript" elimination seemed somewhat variable in the four libraries we analysed (Table 1). In the worst case (rab3 library, moderate transcript initial abundance), with respect to transcripts of the same initial abundance, we observed five-fold less representation in the library for the tester/driver commonly expressed transcript when compared with the tester-specific transcript (Table 1).
The normalisation failures observed for abundant exogenous reporter-transcripts probably reflected the results concerning endogenous transcripts: the coexistence of a majority of singlet contigs (68%) with a few, very deep contigs.
In order to obtain more information about highly redundant endogenous sequences in the libraries, we returned to the 3L22, 3P11, 3I20 and 3C24 transcripts. Their high level of representation in the library suggested that they encode for abundant transcripts, but the mitochondrial nature of 3C24 rendered tester-specific expression highly unlikely. We thus analysed the relative abundance of these four cDNAs in tester and driver unsubtracted materials. Semi-quantitative analysis (see methods) revealed that these four clones encode for very abundant cDNA in the tester material : they represented about 0.11, 0.6, 2.45 and 3.7% (for 3P11, 3I20, 3L22 and 3C24 respectively) of blastocyst cDNAs. They thus constituted a fourth category of transcripts that we did not mimic with the exogenous A. thaliana transcripts. Two of them (3P11 and 3I20) were tester-specific, but the others (3L22 and 3C24) were expressed in both tester and driver, with double their relative abundance in tester than in driver (3L22 and 3C24 representing respectively about 1.2 and 1.5% of morula driver cDNAs). It thus appears that very abundant tester-specific transcripts escape normalisation, whereas very abundant transcripts expressed in both tester and driver escape both subtraction and normalisation.
Under these conditions, the few deep contigs we obtained by sequencing the library could correspond to either abundant or very abundant tester-specific transcripts escaping normalisation, or to very abundant commonly expressed transcripts escaping both subtraction and normalisation. These normalisation (and possibly subtraction) failures resulted in a significant proportion of highly redundant sequences (638/1582, or 40%) in the library.
In view of the considerable concordance between sequencing and hybridisation data, we assumed that the addition of exogenous reporter transcripts and analysis of their representation in the library would ensure reliable monitoring of SSH performance. However, the very high representation of mitochondrial 16S rRNA in the library showed that the frequency we chose to mimic abundant transcripts underestimated the representation of certain transcripts in our biological material. 16 SrRNA was found to represent a high proportion (1.5 to 3.7%) of unsubtracted cDNAs, which could be correlated to the very rapid growth [13] and marked metabolic activity of the blastocyst in the rabbit species, which certainly requires strong mitochondrial activity during the early stages of development. Mitochondrial transcripts have been shown to represent as much as 23% of polyadenylated RNAs in mouse blastocysts [14]. Such quantitative data are not available in the rabbit.
Our results show that application of the SSH procedure to early development permits the isolation of scarce tester-specific transcripts despite irregular normalisation and some subtraction failures concerning very abundant transcripts.
Taken together, these data confirmed and extended previous reports showing that whatever the origin of the clones picked from SSH constructed libraries – random choice or selection by differential screening – some of them are heavily represented among the sequenced clones [[9,10,4,15], and [16]]. Since this unequalized representation has been reported in both SMART-preamplified-cDNA subtracted libraries [10,4,16] and non preamplified cDNA libraries [15], it should not be considered as a consequence of the cDNA preamplification step but rather as an intrinsic defect in the SSH procedure.
Conclusion
This study showed that SSH libraries dedicated to early mammalian development are greatly enriched in tester-specific transcripts. They are only partially normalized, with transcript equalization being restricted to rare and moderately abundant transcripts. Very abundant transcripts common to both tester and driver may escape both normalisation and subtraction, giving rise to abundant background clones in these libraries. The differential expression of genes represented by redundant clones in subtracted libraries should therefore be checked very carefully.
These conditions were however compatible with the isolation of very rare tester-specific transcripts (0.0005% of messenger RNA) in the libraries. Under these conditions, SSH produced reproducible results in terms of rare stage-specific transcript isolation and can thus be considered as a tool of considerable potential when studying the onset of mammalian development.
Methods
Developmental stages used for the construction of the libraries
The following stages were used in tester-driver subtractions:
- for rabbit embryonic activation: early morulae (16–32 cell stage) – one cell stage (rab1 library) and early morulae – 4 cell stage (rab3 library).
- for cattle embryonic activation: early morulae – four-cell stage (bov1 library)
- for early cell differentiation in rabbit: blastocyst – late morulae (32–64 cell) (rab2 library).
Embryo recovery
Cattle embryos were obtained by in vitro oocyte maturation, fertilization and embryo culture as described by Pavlok et al. [17] and Parrish et al. [18]. Four-cell and morulae stage embryos were recovered at 41 and 120 hours post-insemination respectively, from early two-cell-cleaved embryos picked up at 32 hours post-insemination.
For rabbit embryos, all tester materials contained embryos produced both in vivo and in vitro, whereas driver materials contained only embryos produced in vivo. In vivo one-cell, four-cell, early morulae, late morulae and blastocyst stage embryos were recovered at 19, 32, 50, 65 and 90 h post-coitum (hpc), respectively. In vitro embryos were recovered at the one cell stage (19 h post-coitum) from superovulated females treated as described by Henrion et al. [19]. They were cultured from the one cell stage onwards (19 hpc) until the early morula (58 hpc) and blastocyst (100 hpc) stages respectively in four different culture media: B2 medium (Laboratoire C.C.D.), B2 medium plus 2.5% foetal calf serum, ISM1–ISM2 sequential media (Medicult.) and G1–G2 sequential medium (JCD sa). In the latter two cases, the sequence used for embryo culture mimicked that used in human IVF in terms of the timing of genome transcriptional activation: embryos were cultured in the first medium until the 8 cell stage in ISM1 and until the early morula stage in G1, i.e. just before and just after the onset of embryonic genome transcription respectively, then transferred to the second medium until the stage of interest.
RNA extraction
Total RNA was extracted from batches of embryos (n = 200 to 450 embryos) using the RNeasy Mini Kit (Qiagen, CA, USA) with a DNAse I treatment (37°C, 30 min). Arabidopsis Thaliana RNAs (Stratagene, Spike RNA 201, 204, 205, 208, and 209) were added as reporter exogenous RNAs at different concentrations, either specifically in tester material or in both tester and driver materials. These exogenous RNAs were added either before (rab1 and bov1 libraries) or after (rab2 and rab3 libraries) total RNA extraction.
cDNAs amplification, SSH and PCR amplification of subtracted products
The tester to driver hybridisation steps in the SSH procedure require one hundred nanograms of tester and driver cDNA, whereas a preimplantation embryo only contains a few picograms of mRNAs. For this reason, we adopted the SMART PCR cDNA amplification procedure (SMART-PCR cDNA Synthesis Kit: Clontech, Palo Alto, CA, USA) starting from embryonic total RNA. The optimum numbers of PCR cycles, checked as suggested in the SMART-PCR kit protocol, were 22 (rab1), 20 (bov1 and rab2), 23 (rab3), respectively.
SSH was performed with the PCR Select cDNA Subtraction Kit (Clontech, Palo Alto, CA, USA). The first hybridisation was performed with 15 ng amplified tester cDNA and 450 ng amplified driver cDNA for 10 hours at 68°C. Following the first hybridisation, 100 ng of fresh denatured driver cDNA was added to the sample and a second hybridisation was performed at 68°C overnight. The final hybridisation sample was diluted in 200 μl 20 mM Hepes pH 8.3, 50 mM NaCl, 0.2 mM EDTA buffer. Selective PCR was performed according to the recommendations of the kit manufacturer, except that the cycle numbers in the first and nested PCR were modified as mentioned in the text.
Checking "selective PCR"
In order to check subtraction efficiency after selective PCR, relative amounts of tester-specific and tester/driver common exogenous A. Thaliana transcripts were estimated in the subtracted and control unsubtracted cDNA populations. As suggested by the kit manufacturer, this was achieved by semi-quantitative PCR amplification (gradually increasing the numbers of PCR cycles, and an analysis of amplicon intensity after agarose gel electrophoresis and ethidium bromide staining). We therefore used primers specific to A. Thaliana transcripts:
5'-201 TGGGTTAAGGCTCAGGAATG
3'-201 GCCAAGTGAGTTGCCAAGTT
5'-204 AACACAATGGCTTTCGCTTT
3'-204 CAAAGCCATCAAGACAAACAAA
5'-205 TTATTAGCCGTGTGCCTGGT
3'-205 CTAGCAAACCAATGCCCTCA
5'-209 TTCTGTCAATGGAGGCAACA
3'-209 TGTCAAACCAGAGCTCACGA
Establishment and analysis of subtracted libraries
PCR-amplified subtracted products (about 17 ng) were cloned into the pGEM-T-Easy vector (Promega) and 1/10 of the ligation volume was used to transform competent DH5α Max-Efficiency E.coli bacteria (Invitrogen). After overnight culture at 37°C, the colonies were picked and arrayed in 384 well plates. Replicates of these arrayed libraries were spotted onto nylon membranes (Hybond N+ Amersham) placed on agar plates, and grown up for 12 hours at 37°C. After bacteria denaturation and DNA fixation treatments, these "macroarrays" were hybridised with 32P radiolabelled probes corresponding to either exogenous RNAs or endogenous transcripts. Hybridisation was carried out overnight at 65°C in Church buffer [20].
Sequence analysis and clustering were performed as described in [21].
Semi-quantitative analysis of endogenous transcript abundance in tester and driver materials
Varying amounts (100, 300 500 ng) of unsubtracted SMART-amplified tester (blastocyst) and driver (morula) cDNAs used to establish the SSH library were slot blotted on Brightstar TM-Plus membranes (Ambion). On the same membranes, various quantities (ranging from 0.01 ng to 6.25 ng) of cDNA inserts (3P11, 3C24, 3L22 and 3I20, respectively) were slotted. DNA inserts (3P11, 3C24, 3L22 and 3I20) were labelled by random priming (Rediprime TM II Amersham). Hybridization was performed in UltrahybTM buffer (Ambion), at 42°C, for 22 hours. Washings were performed twice in 2 × SSC, 0.1% SDS at 42°C for 10 minutes, then twice in 0.1 × SSC, 0.1% SDS, 42°C, for 30 minutes. Membranes were exposed to a phosphoscreen (Phosphorimager Amersham) for 6 hours, and hybridization signals were quantified using Imagequant (Amersham).
Authors' contributions
Véronique Duranthon is the supervisor of Linh Chi Bui and Roger Dominique Léandri, two doctoral students in the laboratory who participated directly in the experimental work. Jean Paul Renard is Director of the Laboratory.
Acknowledgements
We would like to thank Annie Chastellier and François Piumi (CRB, Jouy en Josas, France) for library arraying and bacteria membrane preparations, Cédric Cabau (Agenae) for contig assembly and Catherine Faure for her technical assistance. We also are indebted to members of the UCEA responsible for our rabbit colonies and Yvette Lavergne (BDR, INRA) for bovine embryo production.
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Ko MS Kitchen JR Wang X Threat TA Hasegawa A Sun T Grahovac MJ Kargul GJ Lim MK Cui Y Sano Y Tanaka T Liang Y Mason S Paonessa PD Sauls AD DePalma GE Sharara R Rowe LB Epigg J Morrell C Doi H Large-scale cDNA analysis reveals phased gene expression patterns during preimplantation mouse development Development 2000 127 1737 49 10725249
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Robert C Gagne D Bousquet D Barnes FL Sirard MA Differential display and suppressive subtractive hybridization used to identify granulosa cell messenger rna associated with bovine oocyte developmental competence Biol Reprod 2001 64 1812 20 11369613 10.1095/biolreprod64.6.1812
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-1461624203910.1186/1471-2164-6-146Research ArticleIdentification of genes involved in Ca2+ ionophore A23187-mediated apoptosis and demonstration of a high susceptibility for transcriptional repression of cell cycle genes in B lymphoblasts from a patient with Scott syndrome Kozian Detlef [email protected] Valérie [email protected] Almut [email protected] Marie [email protected] Marie-Carmen [email protected] Beatrice [email protected] Dominique [email protected] Matthias [email protected] Jean-Marie [email protected] Danièle [email protected] Aventis Pharma Germany (Sanofi-Aventis group), Therapeutic Department Thrombosis and Angiogenesis, Industriepark Hoechst, Building H831, 65926 Frankfurt, Germany2 INSERM Unité 143, Hôpital de Bicêtre, 80 rue du Général Leclerc, 94276 Le Kremlin-Bicêtre, France3 Institut d'Hématologie et Immunologie, Faculté de Médecine, 4 rue Kirschleger, 67085 Strasbourg, France2005 21 10 2005 6 146 146 14 6 2005 21 10 2005 Copyright © 2005 Kozian et al; licensee BioMed Central Ltd.2005Kozian et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
In contrast to other agents able to induce apoptosis of cultured cells, Ca2+ ionophore A23187 was shown to elicit direct activation of intracellular signal(s). The phenotype of the cells derived from patients having the hemorrhagic disease Scott syndrome, is associated with an abnormally high proportion of apoptotic cells, both in basal culture medium and upon addition of low ionophore concentrations in long-term cultures. These features are presumably related to the mutation also responsible for the defective procoagulant plasma membrane remodeling. We analyzed the specific transcriptional re-programming induced by A23187 to get insights into the effect of this agent on gene expression and a defective gene regulation in Scott cells.
Results
The changes in gene expression upon 48 hours treatment with 200 nM A23187 were measured in Scott B lymphoblasts compared to B lymphoblasts derived from the patient's daughter or unrelated individuals using Affymetrix microarrays. In a similar manner in all of the B cell lines, results showed up-regulation of 55 genes, out of 12,000 represented sequences, involved in various pathways of the cell metabolism. In contrast, a group of 54 down-regulated genes, coding for histones and proteins involved in the cell cycle progression, was more significantly repressed in Scott B lymphoblasts than in the other cell lines. These data correlated with the alterations of the cell cycle phases in treated cells and suggested that the potent effect of A23187 in Scott B lymphoblasts may be the consequence of the underlying molecular defect.
Conclusion
The data illustrate that the ionophore A23187 exerts its pro-apoptotic effect by promoting a complex pattern of genetic changes. These results also suggest that a subset of genes participating in various steps of the cell cycle progress can be transcriptionally regulated in a coordinated fashion. Furthermore, this research brings a new insight into the defect in cultured Scott B lymphoblasts, leading to hypothesize that a mutated gene plays a role not only in membrane remodeling but also in signal transduction pathway(s) leading to altered transcriptional regulation of cell cycle genes.
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Background
Several signaling pathways have been identified which promote the characteristic features of apoptotic cell death, including cell shrinkage, translocation of phosphatidylserine from the inner to the outer leaflet of the plasma membrane, internucleosomal DNA fragmentation and budding leading to disintegration into apoptotic bodies [1-3]. The biochemical and cellular mechanisms involved depend on the apoptogenic stimulus and may also be specific to the experimental model [4]. In contrast to other agents able to induce apoptosis, Ca2+ ionophore A23187 elicits direct activation of intracellular signal(s). A23187 provokes caspase-independent apoptosis in Jurkat cells, which differs from Fas cross-linking by antibodies [3]. However, the apoptotic pathways induced by A23187, dependent on extracellular Ca2+ ions [5,6], remain a matter of debate [7]. Although the effect of A23187 on gene transcription had been documented for various genes, there were no experimental data available on the overall pattern of gene expression in cells grown in the presence of this agent.
Scott syndrome is an extremely rare hereditary defect of swift egress (scrambling) of phosphatidylserine to the cell surface of stimulated platelets and blood cells, when challenged by stimuli resulting in rapid elevation of cytosolic Ca2+ concentration [8-10], and the clinical phenotype is hemorrhagic [9]. Functional studies performed with B lymphocytes immortalized by Epstein-Barr virus (EBV)-transformation (B lymphoblasts) derived from three unrelated Scott syndrome patients demonstrated the same deficiency of rapid membrane response and suggested alteration in (a) Ca2+-dependent transduction pathway(s) [11-15]. Another characteristic of cultured Scott B lymphoblasts is a spontaneous tendency to apoptosis that is enhanced when A23187 is added to the culture medium [12,13,16,17]. We previously demonstrated using several methods that addition of 200 nM A23187 for 48 hours in the culture medium induced apoptosis in Scott B lymphoblasts more markedly than observed in control B cell lines [16]. It has been currently observed that cells originating from patients with genetic diseases or acquired pathologies may exhibit either spontaneous susceptibility or resistance to apoptosis, allowing the study of the role of the defective genes in the corresponding apoptotic processes [18-20]. The Scott B lymphoblasts thus appeared to constitute a unique model for the global investigation of changes in gene expression patterns during apoptosis and search of mutation(s) possibly accounting for the hemorrhagic phenotype associated with the syndrome.
In order to understand what controls the apoptogenic potential of the ionophore we examined by DNA microarray analysis whether A23187 provoked changes in the transcriptional patterns for 12,000 different mRNA sequences. We simultaneously checked if differential modulation of gene expression could account for the enhanced susceptibility of Scott B lymphoblasts to apoptosis as compared to B lymphoblasts derived from the patient's daughter or unrelated individuals. A change of expression of 109 genes was observed, including the coordinated repression of a set of 54 genes mostly involved in cell cycle progress. Our data illustrated that A23187-induced apoptosis correlates with transcriptional regulation of multiple genes and that global profiling analyses are important approaches for a better understanding of the involved mechanism(s). The results also showed a more pronounced gene repression in Scott B lymphoblasts, suggesting that the defect implies an element involved in (a) signal transduction pathway(s) promoting A23187-induced transcriptional regulation of cell cycle genes.
Results
Apoptotic characteristics of Scott B lymphoblasts
Spontaneous cell mortality of Scott B lymphoblasts in vitro has been previously shown [13,17] as well as their high susceptibility to 200 nM Ca2+ ionophore A23187-induced apoptosis [16]. We confirmed using the cells cultured for the present study, the apoptogenic effect of the treatment for all B lymphoblasts (Table 1) and the much higher amount of apoptotic Scott B lymphoblasts in basal medium and upon addition of A23187.
Table 1 Effect of A23187 on apoptosis. The B lymphoblasts were cultured in basal (X-VIVO15) medium in the absence or presence of 200 nM A23187 for 48 hours. Percentages of FITC-annexinV positive cells were determined as described in Methods. Two Scott B lymphoblasts independently immortalized from the patient's lymphocytes, one daughter's and two control B lymphoblasts from unrelated individuals were used for these studies. Each cell line was analyzed twice. Results are means ± SD from the four experiments for Scott and control B lymphoblasts respectively and the two experiments for daughter's cells.
% of annexin V positive cells
untreated + A23187
Control 18.5 ± 10.7 33.7 ± 17.6
Daughter 14.9 ± 8.6 29.6 ± 0.3
Scott 48.7 ± 7.6 64.7 ± 12.9
Identification of 109 genes that are transcriptionally modulated in B lymphoblasts cultured in the presence of A23187
The selection criteria for analyses of the hybridization data as defined in the Methods section enabled identification of 109 genes that were differentially expressed with a two-fold change or more of gene expression, after treatment of B lymphoblasts with 200 nM Ca2+ ionophore A23187 for 48 hours [see Additional file 1]. Hierarchical clustering [21] (Fig. 1) and comparison of the fold change values (Tables 2 and 3), showed one group of 55 genes characterized by transcriptional activation (Fig. 1A, Table 2) and another group of 54 genes by repressed transcription (Fig. 1B, Table 3).
Figure 1 Cluster analysis to compare the fold changes in gene expression in cells treated by A23187. Cluster analysis allowed to directly compare the fold changes in gene expression for each one the three cell lines respectively (C: control B lymphoblasts; D: daughter's B lymphoblasts; S: Scott B lymphoblasts). Ratios determining the fold changes in gene expression due to treatment for 48 hours with 200 nM A23187 were examined for each one of the three cell types. The hierachical clustering of the selected genes (fold change of gene expression >2 with a P-value of <0.02 in at least one of the three cell types, see additional file 1) was performed. Without changing their respective hierarchical order, the genes were then grouped by functional category. Differential gene expression in A23187-treated versus non-treated cells is color coded as indicated at the bottom of the figure (fold-changes < 0.5 [see Additional file 1] correspond here to <-1/0.5, i.e. <-2). (A) Cluster analysis of up-regulated genes. (B) Cluster analysis of down-regulated genes. For selected genes represented on the U95Av2 GeneChips by more than one Probe Set ID [see Additional file 1], data corresponding to a single Probe Set ID were used for the cluster analysis and the gene lists in Tables 2 and 3.
Table 2 Fold change of induced gene expression in B lymphoblasts treated with ionophore A23187. Genes listed here showed a 2-fold increase or more in gene expression in at least one of the three B lymphoblasts cultured for 48 h with 200 nM A23187. Data are fold changes calculated from hybridization values in treated versus non treated cells for each one of the B lymphoblasts respectively (see additional file 1). For each functional group, the genes were ordered by using a program for gene clustering. *Genes basally expressed differently in the cell lines (see additional file 2).
Accession no. control daughter's Scott Gene definition
Chemokines
D90144 2.05 2.79 1.80 LD78 alpha
M26383 12.28 15.78 8.70 MONAP
D43768 11.58 30.28 11.87 SCM-1*
NM002984 5.46 8.43 4.63 SCYA4
U83171 9.70 2.44 2.29 macrophage-derived chemokine*
U02020 3.79 4.71 3.37 PBEF
X13274 4.19 3.20 9.17 IFN-gamma
D12614 2.83 3.91 3.01 TNF-beta
Cell proliferation
AB044548 7.36 1.83 1.77 P/OKcl.6*
X79067 2.40 1.98 1.56 ERF-1
X59892 4.29 4.83 4.71 IFN-inducible gamma 2
AB021663 7.95 12.89 5.23 leucine-zipper protein
D84224 7.47 10.92 7.44 methionyl tRNA synthetase
X52560 4.32 4.10 3.70 NF-IL6
AF064105 3.44 3.00 2.67 Cdc14B3 phosphatase
AF022375 2.86 2.22 2.63 VEGF
X77366 2.66 2.51 2.42 HBZ17
M91196 2.02 3.70 3.47 DNA-binding protein
J04076 3.67 3.88 3.41 EGR2
U09587 2.01 2.13 2.20 glycyl-tRNA synthetase
U12767 3.84 4.00 7.02 MINOR
U27655 3.70 1.76 3.60 RGP3
U88964 -1.14 1.14 2.57 HEM45*
D87116 1.67 1.79 2.71 MAP kinase kinase 3b
Metabolism
U08997 2.03 2.41 1.82 glutamate dehydrogenase
X01630 12.51 17.87 15.17 argininosuccinate synthetase
M55543 6.11 4.96 4.47 GPB-2*
M37721 2.20 2.27 2.77 peptidylglycine alpha-amidating monooxygenase
AF081195 2.48 3.98 2.18 CalDAG-GEFII
U53347 2.05 2.21 2.25 neutral amino acid transporter B
M77836 2.86 3.08 2.36 pyrroline 5-carboxylate reductase
X05908 2.18 3.45 2.77 Lipocortin*
X92720 2.05 2.10 2.34 phosphoenolpyruvate carboxykinase
AK000379 2.82 2.91 3.10 similar to asparagine synthetase
X76488 2.43 2.48 2.44 lysosomal acid lipase*
S52784 3.36 3.86 4.85 cystathionine gamma-lyase
U26398 1.98 3.69 2.77 inositol polyphosphate 4-phosphatase
AF002020 1.03 1.33 3.00 Niemann-Pick C disease protein
M11233 1.23 1.21 3.59 cathepsin D*
Metallothionein family
M13003 2.85 10.83 1.90 metallothionein-I F*
K01383 3.62 4.60 3.90 metallothionein-I A
D13365 2.74 2.46 2.36 GIF
X64177 1.91 2.79 1.82 metallothionein
M10942 1.93 2.49 2.17 metallothionein-I E
Micellaneous
M31516 5.63 4.00 3.57 decay-accelerating factor
D90224 4.84 3.93 2.16 GP 34*
M27492 2.28 3.60 3.07 IL-1 receptor*
AF104032 2.43 3.06 2.83 LAT1
X03444 2.79 2.02 3.66 lamin A*
U33017 1.90 2.62 1.56 SLAM
U16954 1.97 2.35 2.81 AF1q*
U64863 4.14 4.57 3.41 PD-1*
Y14039 2.25 3.48 2.51 CASH alpha*
U76248 2.47 2.81 2.68 SIAH2
M80899 2.13 1.33 2.69 AHNAK*
Table 3 Fold change of repressed gene expression in B lymphoblasts treated with ionophore A23187. Genes listed here showed a decreased gene expression (2-fold or more) in at least one of the three B lymphoblasts cultured for 48 h with 200 nM A23187. Data are fold changes calculated from hybridization values in treated versus non treated cells for each one of the B lymphoblasts respectively (see fold-changes < 0.5 in additional file 1 corresponding to <-1/0.5 in this table). For each functional group, the genes were ordered by using a program for gene clustering. *Fourteen genes are basally under-expressed in Scott B lymphoblasts compared to control cells.
Accession no. control daughter's Scott Gene definition
Histones
X57129 -3.79 -3.59 -6.54 H1.2*
X00088 -6.80 -3.41 -3.62 H2b/r*
Z80779 -4.49 -2.11 -2.31 H2B/g*
Z80782 -2.96 -2.42 -3.41 H2B/k*
Z83336 -2.98 -2.12 -2.08 H2B/d*
L19779 -2.97 -2.01 -1.66 H2A.2*
AJ223352 -2.57 -1.71 -2.57 H2B/a*
Z83740 -2.92 -1.69 -1.83 H2B/c*
U91328 -2.81 -1.31 -1.65 H2A-like protein*
AJ223353 -3.12 -1.92 -1.27 H2B/b
Z83738 -3.08 -1.88 -1.98 H2B/e*
Z80780 -3.06 -1.84 -2.85 H2B/h*
X14850 -1.45 -1.40 -3.20 H2A.X
Cell cycle and proliferation
AF041248 -2.58 -1.78 -1.84 CDKN2C*
X04757 -2.53 -2.55 -1.31 alpha-tubulin
J00139 -2.35 -2.35 -5.23 dihydrofolate reductase
U05340 -3.12 -2.44 -6.60 p55CDC
U37426 -2.07 -1.41 -5.10 HKSP
AF067656 -2.18 -1.26 -4.08 ZW10 interactor
AF011468 -2.20 -1.97 -6.05 BTAK
M25753 -2.13 -1.91 -7.30 cyclin B
Z36714 -2.20 -1.94 -3.74 cyclin F
D55716 -2.04 -1.57 -3.25 P1cdc47
D14678 -1.92 -2.05 -3.63 kinesin-related protein
X76771 -1.85 -1.17 -2.75 flap endonuclease-1
X62534 -1.38 -1.15 -3.20 HMG-2
X54942 -1.43 -1.48 -2.79 ckshs2
L37747 -1.65 -1.63 -2.84 lamin B1
L25876 -1.91 -1.69 -3.63 CIP2
D14657 -1.82 -1.27 -3.35 mRNA for KIAA0101 (p15-PAF)
L16991 -1.92 -1.78 -4.08 CDC8
AB024704 -1.76 -1.86 -4.60 fls353
X13293 -1.93 -1.69 -5.74 B-myb*
X05360 -1.84 -1.04 -4.12 CDC2
X51688 -1.62 1.05 -5.23 cyclin A
U63743 -2.00 -1.96 -6.59 centromere-associated kinesin
AF017790 -1.85 -1.55 -4.40 retinoblastoma-associated protein
AF004022 -1.92 -1.62 -5.11 protein kinase STK12
U14518 -2.00 -1.65 -5.02 CENP-A
U30872 -1.68 -1.49 -5.00 mitosin
J04088 -1.70 -1.22 -5.16 top2
AF035933 -1.80 -1.21 -4.54 protein kinase SSK1
K02581 -1.91 -1.63 -5.02 thymidine kinase*
U73379 -1.79 -1.66 -8.96 cyclin-selective ubiquitin carrier
Micellaneous
U49184 -11.24 -6.64 -4.10 occludin
L02950 -5.26 -4.49 -3.85 mu-crystallin
AB047079 -2.20 -2.54 -4.21 IRE1b
U29343 -2.11 -1.58 -4.07 hyaluronan receptor
D13633 -2.21 -1.72 -6.35 putative gene KIAA0008
AF057557 -1.75 -3.43 -3.45 TOSO
X59350 -1.95 -1.06 -3.43 CD22
AF053641 -1.46 -1.45 -2.65 CSE1
AF274943 -1.20 -1.06 -2.45 PNAS-18
M91438 -1.18 -1.02 -3.41 HUSI-II
Up-regulation of 55 genes
The activation of transcription for each one of the 55 up-regulated genes respectively appeared to be mostly of comparable amplitude in the three Scott, daughter's and control B lymphoblasts cell lines. These genes belong to various functional categories, coding for cytokines, transcription or growth factors and proteins of cell metabolism (Fig. 1A, Table 2). Hybridization ratios comparing the expression levels, in the absence of A23187, in Scott and daughter's relative to the control B lymphoblasts [see Additional file 2], suggested that in some cases, such as for expression of SCM-1 or macrophage-derived chemokine, different up-regulation values for a given gene reflected variable basal expression. The effect of the treatment (Table 2) then appeared to be more effective when the basal expression was low. Sixteen up-regulated genes were basally expressed differently [see Additional file 2] and the others were similarly expressed in the three cell lines before A23187 treatment.
Down-regulation of 54 genes mostly coding for histones and proteins involved in the cell cycle
A significant fold change for repression of the 54 down-regulated genes mostly occurred in A23187-treated Scott B lymphoblasts when compared with control B lymphoblasts for which repression was of lower amplitude (Table 3). These genes mostly code for histones and for proteins involved in the progression of the cell cycle. Sets of genes coding for essential components of the molecular mechanisms involved in movements of organelles, microtubules or chromosomes and for proteins participating in the cell cycle at the level of DNA replication, repair and recombination or nucleotide synthesis were also down-regulated in A23187-treated cells. Fold change values found in daughter's cells were either intermediate when compared to Scott and control B lymphoblasts, or similar to control.
The comparison of the expression levels of these genes in Scott and daughter's relative to control B lymphoblasts in the absence of A23187 indicated a reduced basal expression of a subset of 14 genes in Scott B lymphoblasts [see Additional file 2]. These genes code for 11 histones and for CDKN2C, B-myb and thymidine kinase (marked by an asterisk in Table 3). The other down-regulated genes were similarly transcribed in the three cell lines in basal culture conditions.
Independent verification of array findings
To validate the microarray results, we measured the relative expression of an arbitrarily chosen subset of nine of the identified genes by real-time quantitative RT-PCR on new RNA preparations and RT (Fig. 2). Results confirmed the up-regulation of expression in the presence of A23187 of the genes coding for IFN-inducible gamma 2 (WARS), argininosuccinate synthetase (ASS) and SIAH2. The expression differences were of the same range values as those measured by microarrays analysis for WARS and SIAH2. Much higher values (for instance a fold change of 91 ± 17 for control B lymphoblasts compared to 12.5 from microarrays) were measured for ASS, demonstrating that for determining high difference levels the microarrays data saturate at lower values than those resulting from quantitative RT-PCR. Determination of mRNA levels using quantitative RT-PCR for six down-regulated genes confirmed the fold change values found in the microarrays analyses and demonstrated the stronger repression in Scott B lymphoblasts (Fig. 2). These data provide validation of the gene expression changes identified by microarrays.
Figure 2 Quantitative RT-PCR validation for a subset of genes differentially expressed by treatment with A23187. RTs were performed with new RNA prepared from the treated and untreated cell lysates. For each PCR experiment, RT samples were analyzed at least in triplicate for the expression of a gene in parallel with GAPDH and 18S rRNA as described in Methods. Results are means ± SD from two independent PCR experiments with different RTs (each analyzed at least in triplicate) for the up-regulated genes and three independent PCR experiments for the down-regulated genes. The fold changes in expression level, i.e. 2-ΔΔCT for the up-regulated genes calculated as described in Methods and -1/2-ΔΔCT for the down-regulated genes (to compare with the values given in Table 3), are represented on a logarithmic scale. Some of the gene symbols listed in Table 2 and 3 have been recently renamed by Affymetrix [54] and the latest symbols were used for ordering the Assays-on-Demand. WARS: IFN-inducible gamma 2, Ac. N° X59892; ASS: argininosuccinate synthetase, Ac. N° X01630; SIAH2, Ac. N° U76248; CDC20: p55CDC, Ac. N° U05340; CCNB1: cyclin B, Ac. N° M25753; AURKB: protein kinase STK 12, Ac. N° AF004022; UBE2C: cyclin-selective ubiquitin carrier, Ac. N° U73379; H2H2AA: H2A.2, Ac. N° L19779; H1H2BM: H2B/e, Ac. N° Z83738.
Treatment with A23187 alters cell cycle progression
Cell cycle analyses were performed in order to check whether the decrease in expression of histones and cell cycle-related genes in A23187-treated cells correlated with changes in cell cycle progression. The cells continued to cycle in the presence of 200 nM A23187, although 10% decrease of populations in S phase was observed, suggesting partial blockade from G1 to S in all of the three cell lines (Fig. 3). However, cell cycle profiles for A23187-treated Scott, daughter's and control B lymphoblasts were differently affected. Scott cells exhibited a 2-fold increase of cells in G2/M and a very low enhancement of cells in G1. Conversely, with few changes in the number of cells in G2/M, the number of control B lymphoblasts in G1 was increased in the presence of A23187, coinciding with diminished proportion of cells in S phase. Intermediate values were found for daughter's B lymphoblasts. Therefore, it appears that treatment with A23187 modifies the cell cycle with, in Scott cells, a marked increase in G2/M populations in correlation with blocked progression to G1.
Figure 3 Effect of A23187 on B lymphoblasts cell cycle. The B lymphoblasts cultured in the absence or presence of 200 nM A23187 for 48 hours were stained with PI and analyzed for the proportion of cells in the different phases of the cycle by flow cytometry as described in Methods. Two independent experiments were performed with each one of five cell lines (two Scott B lymphoblasts independently immortalized from the patient's lymphocytes, one daughter's and two control cell lines from unrelated individuals). Data are means ± SD from the four independent experiments for Scott and control B lymphoblasts respectively and the two experiments for daughter's cells. For a given phase of the cell cycle and a cell type, P reflects the statistical differences between the percent of cells in the presence versus absence of A23187. * P = 0.0038, # P = 0.0016, † P = 0.0344, ★ P = 0.004, ◆ P = 0.0134.
Discussion
The spontaneous tendency to apoptosis is presumably a consequence of the still unknown mutation in Scott B lymphoblasts rather than due to EBV transformation. Other cases have been described, showing that EBV-transformed B cell lines, independently of viral transformation, retain the original characters of the B lymphocytes from which they originate [22]. Indeed, several B lymphoblast cell lines derived from the patient with Scott syndrome having provided the cells for this study as well as from the first described isolated case, all present a spontaneous tendency to apoptosis in culture [12,16,17]. Therefore, we expected that a better knowledge of the mechanisms underlying the spontaneous and A23187-induced apoptosis in Scott B lymphoblasts would be helpful for the identification of candidate gene(s) for the mutation(s).
Previous reports on the effects of A23187 on a variety of cultured cells pointed at apoptosis associated with induction of early response genes partly dependent only on caspase/Bcl-2 pathways [3,23,24]. However the signaling pathways controlling A23187-induced cell death are not known. Several studies have demonstrated that Ca2+ ionophores are able to regulate gene transcription in vitro but the effects varied according to concentration, time course of the treatment and cell type [25-28]. In this study, although a possible effect of A23187 treatment for 48 h on the stability of the various mRNAs cannot be excluded, the fact that groups of genes were modulated in the same directions suggests transcriptional regulation. These genes could be, at least in part, regulated as downstream targets for early response genes coding for transcription factors or regulators.
Execution of apoptosis can be regulated by specific transcriptional factors and further modulated by cytokine-triggered signaling pathways [29]. The cytokines up-regulated in this study are markers of lymphocyte activation and mediators of inflammation [30-32] and may play a role in the modification of the B lymphoblasts leading to cell death. Additional genes up-regulated by A23187-treatment (see Table 2) code for proteins involved in cell proliferation [33], metabolism, protein synthesis, translation, tumor cell proliferation or transcription and may either promote cell death [23,34] or be markers of phenotypic changes. The up-regulated genes mostly appear to be similarly expressed and modulated in all of the three B cell lines and one could assume at least a partial role for these genes in the apoptotic characters of the treated cells although the importance of a specific gene cannot be deduced from this study. For instance, all the proteins coded by the three up-regulated genes analyzed to validate the microarrays data (WARS, ASS, SIAH2) were all demonstrated in other studies to be either involved in apoptosis or anti-proliferation [35,36] and/or markers of induction by interferon gamma [36,37].
Several mechanisms may simultaneously participate in the specific effects of A23187 on gene regulation, depending on the transcription machinery of individual genes. Our study further demonstrated that the treatment of Scott B lymphoblasts with A23187 provokes a down-regulation of a restricted set of 54 different genes. The transcription level of these genes was repressed more than two-fold in A23187-treated Scott B lymphoblasts and also decreased, although to lower extent, in daughter's and control B lymphoblasts.
Interestingly, at least 31 out of these 54 genes code for proteins involved in the formation of essential complexes during the onset of cell cycle or in cell cycle progress, and 13 of them code for histones. Genes expressing positive and negative regulators of the cell cycle were repressed, suggesting a complex effect of the treatment on cycle progression. Among the repressed genes are those coding for the cyclin-dependent serine-threonine kinase cdc2 and the subunit cyclins A and B, each being involved in the control of entry into mitosis and cell cycle advancement [38,39]. Remarkably, the gene encoding the cyclin-selective ubiquitin carrier, which participates in the destruction of cyclins A and B promoting exit from mitosis into G1 of the next cell cycle [40], is also repressed. Several down-regulated genes, coding for serine/threonine kinases (STKs in Table 3), are implicated in the segregation of chromosomes during mitotic cell division [41]. Other genes code for transcription factors or regulators, such as B-myb, which are important for chromosome segregation [42].
Decrease of histones H1.2, 3 H2A and 9 H2B expression is a characteristic feature observed in A23187-treated cells, including daughter's and control B lymphoblasts. Histones, of which genes are transcriptionally modulated as the cell cycle progresses, play key roles in the structural and transcriptional properties of the chromatin [43]. If the repressed genes are regulated at the transcriptional level in a coordinated fashion, one may hypothesize that common promoter elements may direct the observed pattern of regulation. Remarkably, expression of a large portion of the down-regulated genes is controlled by member(s) of the E2F family transcription factors. Among these genes are histones, cyclins A and B, B-myb, Cdc2, Bub1, thymidine kinase, dihydrofolate reductase, top2, Flap endonuclease1 [44]. E2F-DP1 heterodimers, essential for the G1/S phase transition bind to promoter elements and are negatively regulated by the hypo-phosphorylated retinoblastoma protein Rb [45]. A role for E2F in regulating gene expression of several histones subtypes correlates with the presence of consensus sequences for E2F binding in the shared H2A/H2B promoters [46]. However, the location of these sequences in the promoter domain is closer to the H2A side rather than to that of H2B (corresponding to most of the histones genes repressed in this study). This observation, together with the fact that only a restricted fraction of the known E2F target genes were repressed, suggests complex regulatory mechanisms for transcriptional regulation of genes down-regulated in the presence of A23187.
The data suggest a link between G0/G1 or G2/M cell cycle arrests and apoptosis. Similar relationships were previously demonstrated through various studies (see [47] and as shown for the effect of several anticancer agents [48-50]). Furthermore, the individual inhibition or knock-down of several genes repressed in the presence of A23187, such as top2 or lamin B1, was shown to induce apoptosis in HeLa cells [47,51,52]. Our results suggest that the down-regulation of cell cycle genes explains, at least partly, the apoptogenic effect of A23187. Although the analysis of the cell cycle phases did not show significant differences between the three B cell lines under basal conditions, A23187 treatment clearly changed the cell cycle profiles for all cell types. The presence of A23187 in culture medium diminished the transcription of genes important for progression of the cell cycle at the G1 phase correlating with an increase of cells in G1 and decrease of S phase cells. This was observed in all cell types, correlating with the down-regulation of cell cycle genes, although with lower change levels in daughter's cells and controls. Moreover, significant blockade of cells in G2/M phase was mainly observed for Scott and (at a lesser extent) daughter's B lymphoblasts.
Networks linking signaling and cell proliferation have now been widely described, connecting for instance Ras effector/extracellular signal-regulated kinase pathway with the Rb/E2F pathway, controlling either cell proliferation, differentiation, cell growth arrest or apoptosis [53]. These observations suggest that the expression pattern of the genes down-regulated by A23187 is associated with the apoptotic tendency of the cultured Scott B lymphoblasts. The present work leads to the hypothesis that a mutation in Scott cells would reside in a signaling pathway component shared by the membrane remodeling process and the transcriptional regulation of a subset of cell cycle genes upon addition of the Ca2+ ionophore.
Conclusion
The apoptogenic effect due to the addition to cultured cells of 200 nM A23187 for 48 hours correlates with an overall pattern of gene regulation involving at least a hundred genes and demonstrating the coordinated regulation of sets of them. Analysis of gene regulation upon treatment also represented a new approach to understand the still unexplained defective mechanism(s) in Scott cells. The results orientate the future work toward the exploration of a signaling defect in Scott B lymphoblasts upstream of the transcriptional machinery for genes participating in cell cycle progress and down-regulated with the treatment.
Methods
Cell lines
The cases of the propositus with Scott syndrome and of her daughter have been previously described and familial study has suggested a homozygous status for the propositus [13]. Control B lymphocytes were obtained from consenting and informed volunteers unrelated to the patient. All B lymphoblasts used in this study were B lymphocytes transformed in our laboratory by EBV-infection into proliferating cell lines as already reported [13]. The Scott phenotype, i.e. a lack of exposure of procoagulant phosphatidylserine due to defective membrane remodeling, was constantly observed in independently EBV-infected B cells from the propositus [13]. The B lymphoblasts derived from the propositus' daughter (daughter's B lymphoblasts in this study) exhibited in vitro functional properties coinciding with heterozygous status, although this individual denied any hemorrhagic tendency [13]. B lymphoblasts were routinely seeded at 2 × 105 cells/ml and expanded to 6 × 105 cells/ml in X-VIVO15 culture medium (BioWhittaker, Cambrex Bio Science, Verviers, Belgium, [Ca2+]free = 1.8 mM), without any other additive.
Culture conditions in the presence of Ca2+ ionophore A23187
The B lymphoblasts (8 × 107 cells) were seeded at 5 × 105 cells/ml in X-VIVO15 medium. To treat the cells, A23187 (200 nM final concentration, Calbiochem, La Jolla, CA) was added to the cultures four hours later. Treated and non-treated cells were further cultured for the indicated times before harvesting.
Assessment of apoptosis
Detection and quantification of apoptosis were performed on 6 × 105 cells, washed twice with Hanks' balanced salt solution (Sigma-Aldrich), and diluted in 300 μl of the same solution supplemented with 1 mM CaCl2. Fluorescein isothiocyanate (FITC)-Annexin V solution (BD Biosciences, Pharmingen) was added to 5% vol/vol. The cells were then incubated for 20 min at room temperature before analysis to quantify the apoptotic cells that expose PS with the CELL Quest software using a FACSscan flow cytometer (BD Biosciences).
Cell cycle analysis
For cell cycle studies, 3 × 106 cells were collected and incubated in the dark at 4°C for 20 min in 500 μl of phosphate buffer saline supplemented with 0.1% Triton X-100, 0.5 mg/ml RNase A (type I, Sigma) and 50 μg/ml propidium iodide (PI, CN Biosciences, Nottingham, UK). Data acquisition were performed by flow cytometry analysis by gating on an appropriate area to exclude cell debris and aggregate with the CELLQuest software using a FACSscan flow cytometer (BD Biosciences, San Jose, CA, USA). Quantification of cells in the different phases of the cycle was then performed using the ModFit LT™ software (Verity Software House Inc., Topsham, ME, USA). Results are expressed as percentage of cells in each phase of the cell cycle.
RNA extraction, cRNA preparation and microarray hybridization
B lymphoblasts were cultured for 48 h in the presence or absence of 200 nM A23187. Total RNA from cultured cells was isolated for double-stranded cDNA synthesis using Trizol™ reagent (Invitrogen Ltd, Paisley, UK) according to the manufacturer's instructions. RNA was further purified with RNeasy columns (Qiagen, Hilden, Germany). Ten μg of total RNA were subjected to first strand cDNA synthesis reaction using an oligo(dT)-primer with a T7-promoter sequence added to the 5'-end (Superscript Choice System, Invitrogen Ltd). After second-strand synthesis, double-stranded cDNA was purified by phenol/chloroform extraction, precipitated and diluted in nuclease-free water. Biotin-labeled cRNA was made by in vitro transcription using ENZO Bioarray High Yield Transcription kit (Affymetrix Inc, Santa Clara, CA, USA). The resulting cRNA was fragmented at 94°C for 35 minutes in 40 mM Tris-acetate buffer, pH 8.1, 100 mM K-acetate and 30 mM Mg-acetate. Human genome U95Av2 GeneChip® microarray (see the updated list in [54]) was used to analyze gene expression patterns. For hybridization with GeneChip®, 12 μg of fragmented cRNA was incubated with 50 pM control oligonucleotide B2 (Affymetrix Inc.), 1X eukaryotic hybridization control (Affymetrix Inc.), 0.1 mg/ml herring sperm DNA and 0.5 mg/ml acetylated BSA and 1X hybridization buffer according to the manufacturer's instructions for 16 to 18 h at 45°C. Washing and staining were performed in Affymetrix GeneChip® Fluidics Station using Affymetrix antibody staining and washing protocol. GeneChip® microarrays were scanned with Agilent Gene Array (Affymetrix Inc.) scanner (100% PMT settings). Results for a given sample originate from two independent sample preparations, in vitro transcriptions and hybridization reactions performed in order to avoid any bias due to variations in sample treatment.
Analysis of hybridization data
Scanned images were analyzed using Microarray Suite 4.0 (Affymetrix Inc.), which assigns an intensity that is a measure of the corresponding transcript abundance. Replicates were combined by computing the median of the replicate intensity. For each Probe Set ID, the expression ratios were obtained using both intensity and noise data through the PFOLD algorithm [55]. This provides an estimate of the expression ratio and also a P-value, which quantifies its statistical significance. All statistics and graphics related to Affymetrix analysis were performed using the GECKO software [21]. Ratios determining the fold changes in gene expression due to A23187 treatment were determined for each cell line, ie: Scott, daughter's and control B lymphoblasts. All further analyses were performed with genes displaying a 2-fold change or more in expression level and a P-value <0.02 for at least one of the three cell lines (see additional file 1). Data are deposited in NCBI's GEO (Gene Expression Omnibus site) [56] with accession N° GSE1028.
Quantitative RT-PCR
Reverse transcription (RT) followed by quantitative real-time polymerase chain reaction (quantitative RT-PCR) was performed on new RNA preparations of the B lymphoblasts lysates to validate the microarray data. RT was performed with 4.5 μg of total RNA (100 μl final reaction) using the High-Capacity cDNA Archive Kit (Applied Biosystems, Foster City, CA). Quantitative PCR was performed using an ABI Prism 7000 Sequence Detector (Applied Biosystems). PCR was performed with 5 μl of 10 times diluted RT samples mixed in 96-well optical reaction plates with 20 μl of a solution containing the TaqMan Universal PCR master mix and a TaqMan Assay-on-Demand (Applied Biosystems) according to the manufacturer's instructions. The TaqMan Assays-on-Demand include the primers and a fluorescent TaqMan (6-FAM dye-labeled) probe allowing the specific amplification of a cDNA and its quantification via the determination of the threshold cycle (CT) value. The thermal cycling conditions were: 50°C for 2 min and 95° for 10 min, followed by 40 cycles of 95° for 15 s and 60° for 1 min. For a PCR experiment, each RT sample was analyzed at least in triplicate for every analyzed cDNA. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, for which gene expression was not regulated by A23187 treatment) and 18S rRNA were amplified from the same RT in separate reactions to normalize the results. The PCR experiments were repeated three times for the genes down-regulated by treatment with A23187 and twice for the up-regulated genes. Relative quantification of gene expression was carried out using the 2-ΔΔCT method . The average (CT) value for GAPDH or 18S was subtracted from the average (CT) value for the analyzed cDNA (ΔCT). Difference in (ΔCT) values between A23187 treated and untreated samples (-ΔΔCT) were used to calculate the relative change in gene expression ( = 2-ΔΔCT).
Authors' contributions
DK coordinated the hybridization experiments, collected the statistical data and contributed to initiate and design the study and to draft the manuscript. VP performed the analyses by flow cytometry to determine apoptosis and cell cycle progression. AN carried out the statistical analyses of the hybridization values. MG and MCM made preliminary experiments having originated the design of the study. BS conducted the microarray hybridizations. DM, MH and JMF were crucial to initiate and support the study and participated in its design and coordination. DKN contributed to initiate, design, and coordinate the study, carried out the cultures, preparation of cell extracts and quantitative RT-PCR, analyzed the significance of the genes regulations and drafted the manuscript. All authors participated in the final drafting of the manuscript. DK and VP contributed equally to the work.
Supplementary Material
Additional File 1
Excel file containing the 109 genes displaying in at least one of the three cell lines a two fold change or more in expression level due to A23187 treatment . The list shows the Affymetrix Probe Set IDs, GenBank accession numbers, GenBank definitions, fold changes and p values. Results are sorted by GenBank definition, allowing the assemblage in the list of the different Probe Set IDs corresponding to a single gene.
Click here for file
Additional File 2
Relative basal gene expression of the genes up-regulated by A23187 treatment (Table A) or down-regulated by A23187 treatment (Table B), in Scott B lymphoblasts or daughter's B lymphoblasts versus control B lymphoblasts.
Click here for file
Acknowledgements
We acknowledge Angelina Siard for help in RT-PCR experiments and Dr. Kenneth Martin for checking the language of the manuscript.
==== Refs
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Wheeler DL Barrett T Benson DA Bryant SH Canese K Church DM DiCuccio M Edgar R Federhen S Helmberg W Kenton DL Khovayko O Lipman DJ Madden TL Maglott DR Ostell J Pontius JU Pruitt KD Schuler GD Schriml LM Sequeira E Sherry ST Sirotkin K Starchenko G Suzek TO Tatusov R Tatusova TA Wagner L Yaschenko E Database resources of the National Center for Biotechnology Information Nucleic Acids Res 2005 33 D39 45 15608222 10.1093/nar/gki062
Livak KJ Schmittgen TD Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method Methods 2001 25 402 408 11846609 10.1006/meth.2001.1262
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PLoS Comput BiolPLoS Comput. BiolpcbiplcbploscompPLoS Computational Biology1553-734X1553-7358Public Library of Science San Francisco, USA 1636207110.1371/journal.pcbi.001006805-PLCB-RA-0140R3plcb-01-07-04Research ArticleBioinformatics - Computational BiologyCell BiologyEubacteriaSaccharomycesThe Activity Reaction Core and Plasticity of Metabolic Networks Metabolic Network CoreAlmaas Eivind 12Oltvai Zoltán N 3*Barabási Albert-László 241 Microbial Systems Division, Biosciences Directorate, Lawrence Livermore National Laboratory, Livermore, California, United States of America
2 Center for Complex Network Research and Department of Physics, University of Notre Dame, Notre Dame, Indiana, United States of America
3 Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
4 Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard University, Boston, Massachusetts, United States of America
Segre Daniel EditorBoston University, United States of America* To whom correspondence should be addressed. E-mail: [email protected] 2005 16 12 2005 2 11 2005 1 7 e6827 6 2005 2 11 2005 Copyright: © 2005 Almaas et al.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.Understanding the system-level adaptive changes taking place in an organism in response to variations in the environment is a key issue of contemporary biology. Current modeling approaches, such as constraint-based flux-balance analysis, have proved highly successful in analyzing the capabilities of cellular metabolism, including its capacity to predict deletion phenotypes, the ability to calculate the relative flux values of metabolic reactions, and the capability to identify properties of optimal growth states. Here, we use flux-balance analysis to thoroughly assess the activity of Escherichia coli, Helicobacter pylori, and Saccharomyces cerevisiae metabolism in 30,000 diverse simulated environments. We identify a set of metabolic reactions forming a connected metabolic core that carry non-zero fluxes under all growth conditions, and whose flux variations are highly correlated. Furthermore, we find that the enzymes catalyzing the core reactions display a considerably higher fraction of phenotypic essentiality and evolutionary conservation than those catalyzing noncore reactions. Cellular metabolism is characterized by a large number of species-specific conditionally active reactions organized around an evolutionary conserved, but always active, metabolic core. Finally, we find that most current antibiotics interfering with bacterial metabolism target the core enzymes, indicating that our findings may have important implications for antimicrobial drug-target discovery.
Synopsis
Although cellular metabolism is among the most investigated cellular functions, it is not well understood how it changes and adapts on a systems level in response to environmental variations. In this study, the authors take advantage of the reconstructed metabolic networks of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae and the computational method of flux-balance analysis to investigate the functional plasticity of these three metabolic networks for a large number of simulated growth conditions.
Within this approach, the authors identify the metabolic cores of these organisms. These metabolic cores represent connected sets of reactions that are used in all tested environments. When cross-correlating the predicted cores with experimental data, the authors find that the cores display a significantly higher fraction, both of essential and evolutionary conserved enzymes, than their noncore counterparts. Additionally, the extended mRNA half-lives of the core enzymes give further support to the notion that core reactions represent main integrators of metabolic activity.
Since most of the metabolic core reactions are indispensable for the growth of the microorganism, and several existing antibiotics target select bacterial enzymes in the core, the authors suggest that the metabolic core may have important applications for antimicrobial drug-target discovery.
Citation:Almaas E, Oltvai ZN, Barabási A (2005) The activity reaction core and plasticity of metabolic networks. PLoS Comput Biol 1(7): e68.
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Introduction
Constraint-based modeling approaches, such as flux-balance analysis (FBA) [1,2], have proved highly successful in analyzing the capabilities of cellular metabolism, including its capacity to predict deletion phenotypes, the ability to calculate the relative flux values of metabolic reactions, and the capability to identify properties of alternate optimal growth states in a wide range of simulated single-carbon-source environmental conditions [3–5]. Recent analyses also indicate that the deletion phenotype of a substantial subset of metabolic enzymes is growth-condition-dependent [6], arguing for a selective use of metabolic reactions in distinct environments.
Results
To thoroughly examine the utilization and relative flux rates of each metabolic reaction in a wide range of simulated environmental conditions, we sampled 30,000 randomly and uniformly chosen optimal growth conditions, as well as all single-carbon-source minimal medium conditions sufficient for growth, using FBA [1,2] on the reconstructed metabolic networks of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae [7–9] (see Materials and Methods). We found that when assuming optimal growth of the three microorganisms, their metabolism adapts to environmental changes through two distinct mechanisms. The more common mechanism is flux plasticity, involving changes in the fluxes of already active reactions when the organism is shifted from one growth condition to another. For example, changing from glucose- to succinate-rich media alters the flux of 264 E. coli reactions by more than 20%. Less commonly, environmental changes can also induce structural plasticity, resulting in changes in the metabolism's active wiring diagram, turning on previously zero-flux reactions and inhibiting previously active pathways. For example, when shifting E. coli cells from glucose- to succinate-rich media, 11 previously active reactions are turned off completely, while nine previously inactive reactions are turned on.
The two types of response mechanisms described by flux and structural plasticity imply the possible existence of a group of reactions that are not subject to structural plasticity, being active under all environmental conditions. Indeed, some metabolic reactions were found to carry non-zero fluxes in S. cerevisiae under nine different growth conditions [6]. In itself, however, this finding is compatible with a scenario in which the active reactions are randomly distributed: if typically a q fraction of the metabolic reactions are active under a specific growth condition, for n distinct conditions one can predict a non-zero overlap encompassing at least qn fraction of the reactions. Indeed, we find that as we increase the number of inspected conditions, n, the number of reactions carrying non-zero flux in each condition decreases rapidly. However, it eventually saturates at a constant value (Figure 1A–C), identifying a group of reactions that have non-zero flux under all 30,000 simulated growth conditions. Specifically, we find that in 138 of 381 H. pylori (36.2%), 90 of 758 E. coli (11.9%), and 33 of 1,172 S. cerevisiae (2.8%), metabolic reactions are always active (Figure 1D and 1E). While these reactions respond to environmental changes only through flux-based plasticity, the rest of the reactions are only conditionally active, being turned on only in specific growth conditions and thus being subject to both structural- and flux-based plasticity.
Figure 1 The Emergence of the Metabolic Core
(A–C) The average relative size of the number of reactions that are always active as a function of the number of sampled conditions (black line) for (A) H. pylori, (B) E. coli, and (C) S. cerevisiae. As the number of conditions increases, the curve converges to a constant denoted by the dashed line, identifying the metabolic core of an organism. The red line denotes the number of reactions that are always active if activity is randomly distributed in the metabolic network. The fact that it converges to zero indicates that the real core represents a collective network effect, forcing a group of reactions to be active in all conditions.
(D and E) The number of metabolic reactions (D) and the number of metabolic core reactions (E) in the three studied organisms.
Figure 2 displays those metabolic reactions of E. coli that remain active in all 30,000 simulated growth conditions. The striking feature of this diagram is the fact that these reactions form a single connected cluster, encompassing each of the 90 reactions. This is not a unique feature of E. coli; in H. pylori, all 138 reactions form a single cluster, and in S. cerevisiae all 33 reactions are connected. Given the relatively low number of reactions that are always active, the likelihood that they form a single large cluster by chance alone is negligible, with p < 1e−6 for H. pylori and E. coli, and p ~ 2e−6 for S. cerevisiae. Given the compact and clustered nature of the group of reactions that are always active, we will refer to them collectively as the metabolic core.
Figure 2 The Metabolic Core of E. coli
All reactions that are found to be active in each of the 30,000 investigated external conditions are shown. Metabolites that contribute directly to biomass formation [8] are colored blue, while core reactions (links) catalyzed by essential (or nonessential) enzymes [13] are colored red (or green). (Black-colored links denote enzymes with unknown deletion phenotype.) The blue dashed lines indicate multiple appearances of a metabolite, while links with arrows denote unidirectional reactions. Note that 20 out of the 51 metabolites necessary for biomass synthesis are not present in the core, indicating that they are produced (or consumed) in a growth-condition-specific manner. See Protocol S1 and Table S1 for the abbreviations of metabolites and a list of core reactions for E. coli, H. pylori, and S. cerevisiae. The folate and peptidoglycan biosynthesis pathways are indicated by blue and brown shading, respectively, and the white numbered arrows denote current antibiotic targets inhibited by: (1) sulfonamides, (2) trimethoprim, (3) cycloserine, and (4) fosfomycin. Note that a few reactions appear disconnected since we have omitted the drawing of cofactors.
The metabolic core contains two types of reactions: The first type consists of those that are essential for biomass formation under all environmental conditions (81 out of 90 reactions in E. coli), while the second type of reaction is required only to assure optimal metabolic performance (Table S1). In case of the inactivation of the second type, alternative sub-optimal pathways can be used to ensure cellular survival. Interestingly, in the compact core of S. cerevisiae, all 33 reactions were predicted to be indispensable for biomass formation under all growth conditions. Moreover, when assuming a 10% reduction in the growth rate (compared to optimal growth), the size and identity of the E. coli metabolic core remains largely unchanged, retaining 83 of the original 90 reactions, of which two are nonessential. This indicates that the concept of the metabolic core is valid under both optimal and suboptimal growth, although with some difference in the identity of individual reactions. Of note, the metabolic core represents a subset of the minimal reaction sets [10,11] and the overlap of alternative (i.e., degenerate) minimal reaction sets [4,5]. The minimal reaction set of [11] contains the metabolic core in addition, however, to reactions necessary for the sustained growth on any chosen substrate, whereas the minimal reaction set of [5] consists of the 201 reactions that are always active in E. coli for all 136 aerobic and anaerobic single-carbon-source minimal environments capable of sustaining optimal growth. The latter finding is in excellent agreement with Figure 1B (upper curve), and demonstrates that the analysis of a low number of growth conditions will significantly overestimate the set of reactions that are always active.
To identify some of the factors that determine the size of the metabolic core, we note that the number of core metabolic reactions systematically decreases as we move from H. pylori to S. cerevisiae (Figures 3A and 1E), the relative size of the core decreasing from 36.2% of all reactions in H. pylori to 11.9% in E. coli and 2.8% in S. cerevisiae. This trend can be explained as a collective network effect: the relatively small size of the H. pylori metabolic network leaves little flexibility for biomass production, requiring the continuous activity of a high fraction of the available metabolic reactions. Indeed, we find that on average approximately 61% of the H. pylori reactions are active in a given environmental condition. The larger number of metabolic reactions present in E. coli offers a higher degree of metabolic flexibility, allowing for a significant fraction of the biomass to be produced by alternative pathways. Indeed, the average utilization of the E. coli metabolic network in a given growth condition is only 35.3%. For S. cerevisiae, whose metabolic network size significantly exceeds that of both H. pylori and E. coli, there is an even higher metabolic flexibility, and the activity of only 19.7% of the reactions are required in a typical environment.
Figure 3 Characterizing the Metabolic Cores
(A) The number of overlapping metabolic reactions in the metabolic core of H. pylori, E. coli, and S. cerevisiae.
(B) The fraction of metabolic reactions catalyzed by essential enzymes in the cores (black) and outside the core in E. coli and S. cerevisiae.
(C) The distribution of average metabolic fluxes for the core and the noncore reactions in E. coli.
The fact that the core reactions are active under all investigated environmental conditions suggests that they must play a key role in maintaining the metabolism's overall functional integrity. Therefore, the absence of individual reactions that are part of the metabolic core may lead to significant metabolic disruptions. Indeed, using genome-scale deletion-phenotype data obtained in rich growth media [12,13], we find that 74.7% of those E. coli enzymes that catalyze core metabolic reactions (i.e., core enzymes) are essential, compared with a 19.6% lethality fraction characterizing the noncore enzymes. The gap between the core and noncore enzymes is also significant for S. cerevisiae, for which essential enzymes catalyze 84% of the core reactions, whereas the average essentiality of the conditionally active enzymes is only 15.6% (Figure 3B). Note that the likelihood of a random concentration of so many essential enzymes in the core is extremely small, with p-values of 3.3e−23 and 9.0e−13 for E. coli and yeast, respectively, even if we presume the presence of errors in the deletion-phenotype data (Protocol S1). Taken together, these results indicate that an organism's ability to adapt to changing environmental conditions rests, to a large extent, on the continuous activity of the metabolic core, regardless of the environmental conditions.
Intuitively, one could assume that the core represents a subset of high-flux reactions characterizing the activity of metabolic networks [3]. However, our measurements indicate that, on average, the fluxes of the core and noncore reactions are highly comparable (Figure 3C). Alternatively, we could also assume that the main biological role of the metabolic core is to ensure the continuous production of biomass under all growth conditions. In contrast, we find that 20 out of the 51 metabolites (39%) considered necessary for biomass production in E. coli [8] are not produced by any of the core reactions; instead—in a growth-condition-dependent fashion—they are produced by various alternative metabolic pathways. The core, however, contains a large number of reactions for selected anabolic pathways, including those of membrane lipid-, cell envelope-, and peptidoglycan-biosynthesis pathways (Table S1). These core reactions represent network bottlenecks, being the only paths for the synthesis of certain biomass components. Therefore, it appears that the composition of the metabolic core is determined by two factors. First, those metabolic reactions that directly contribute to biomass production tend to be part of the metabolic core of the organism. Second, however, this tendency is offset by network-induced redundancy: reactions or pathways whose end-products can be synthesized by at least two alternative pathways show environmental redundancy and structural plasticity, and are thus eliminated from the core. Therefore, the more reactions a metabolic network possesses (see Figure 1D), the stronger is the network-induced redundancy, and the smaller is the core (Figure 1E).
Given the important functional role played by the metabolic core in a given organism, one would expect significant parts of the core to be conserved in different organisms. Indeed, the E. coli and H. pylori cores have 63 reactions in common, and 18 of the 33 core reactions in S. cerevisiae are present in both the E. coli and the H. pylori metabolic cores (see Figure 3A). Also, when considering enzyme orthologs among 32 divergent bacteria [13], we find that the metabolic core enzymes of E. coli display a high degree of evolutionary conservation, the average core enzyme having orthologs in 71.7% of the reference bacteria (p < 1e−6). In contrast, the conditionally active noncore enzymes have an evolutionary retention of only 47.7% [13]. Given the observed correlation between evolutionary retention and the essentiality of gene products [13], this difference may be a simple consequence of the high-lethality fraction of the core enzymes. However, further analysis indicates that this is not the case: random selection of 90 enzymes with a 74.7% lethality ratio has an average evolutionary retention of only 63.4%. Taken together, these results indicate that major portions of the metabolic core have been conserved, displaying a higher evolutionary retention than the individual essentiality of its participating enzymes would indicate, suggesting that maintaining the core's integrity is a collective need of the organism.
The requirement for the continuous activity of the core reactions may well also impact the regulation of its catalytic enzymes. We would expect that the activity of core reactions—for which only the flux magnitude needs to be modulated—should display a higher degree of stability and a different regulatory control than the reactions outside of the core. Evidence for such regulatory effects is provided by two measurements. First, we inspected the experimentally determined mRNA half-lives of the E. coli metabolic enzymes when cells were grown in Luria-Bertani medium [14]. We found that the distribution of mRNA half-life times for the core enzymes displays a shift to higher values, with the average half-life time of their mRNAs being 14.0 min compared with 10.5 min for the noncore mRNAs (p = 0.016). Furthermore, only 2% of the core enzymes have corresponding mRNA half-life times shorter than 5 min, compared to 23% for the ones catalyzing conditionally active noncore reactions (see Materials and Methods). Therefore, the continuous metabolic need for these reactions has apparently affected the mechanisms responsible for mRNA decay of their catalytic enzymes, providing a higher dynamic inertia under environmental changes.
Additionally, for each enzyme-encoding operon, we counted the number of activating and repressive regulatory links in the E. coli transcriptional regulatory network [15,16]. As regulatory interactions are currently known for only 13 of the core enzyme-encoding operons, we considered the extended core, representing the set of 234 reactions that are active in more than 90% of the 30,000 simulated growth conditions. The results indicate that the fraction of repressive regulatory links in the extended core is 52.3%, while the fraction of activating interactions is only 35.7%, and the remaining 12% represents regulatory links that can either activate or suppress the enzyme's mRNA synthesis rate. In contrast, for noncore enzyme-encoding operons, there is no difference between the fraction of activating and repressing links, both representing 45% of the regulatory interactions. These results offer evidence for the co-evolution of the core metabolic network and its corresponding regulatory network: given the requirement for the continuously active nonredundant core, regulatory mechanisms have shifted both the mRNA decay rate and the nature of the regulatory interactions, offering a higher regulatory stability for the core enzymes.
The finding that the core reactions form a single cluster would suggest that the activity of the participating reactions is highly synchronized, with changes in the flux of one reaction affecting the flux of other reactions as well. Thus, we should be able to discover the core by inspecting the correlations between the activities of all metabolic reactions. To test this hypothesis, we calculated the flux correlation coefficient, Cij, for each reaction pair in the E. coli metabolism [17] by inspecting the flux values of all reactions under each of the 30,000 simulated growth conditions. Using the Cij matrix as the metric of a hierarchical clustering algorithm [18], we observed the emergence of a group of reactions whose fluxes change simultaneously under environmental shifts. Interestingly, these highly correlated reactions significantly overlap with the metabolic core (Figure 4A), indicating that when the environmental conditions are altered, the fluxes of the core reactions are increased or decreased in a synchronized fashion.
Figure 4 Correlations among E. coli Metabolic Reactions
(A) We calculated the Pearson correlation using flux values from 30,000 conditions for each reaction pair before grouping the reactions according to a hierarchical average-linkage clustering algorithm. The values of the flux-correlation matrix range from −1 (red) through 0 (white) to unity (blue). The horizontal color bar denotes if a reaction is a member of the core (green), and the vertical color bar denotes whether the enzymes catalyzing the reaction are essential (red).
(B) Distribution of Pearson correlation in mRNA copy numbers from 41 experiments [14]. The correlations of the core reactions are clearly shifted towards higher values, with an average correlation coefficient of <C> = 0.23 compared with the average noncore coefficient of <C> = 0.07
The same synchrony is observed when we inspect experimental mRNA data among the core reactions during changes in environmental conditions. Calculating the correlations between core and noncore enzymes using their corresponding mRNA copy number data (see Materials and Methods) for 41 experiments [19], we find that the correlations among mRNA copy numbers of the core enzymes are systematically higher than the observed correlations among their noncore counterparts (Figure 4B), with an average correlation of <C> = 0.23 for core enzymes and <C> = 0.07 for noncore enzymes (p < 1e−4). Notably, this finding is different from the average correlation of mRNA expression found in 66 “correlated reaction sets” (groups of between two and nine reactions that are turned on and off together), where the frequency with which a correlated reaction set is used does not affect its mRNA correlation [5].
Discussion
Previous studies have firmly established that environmental changes induce flux plasticity, altering the flux levels of individual reactions (reviewed in [20]). Similarly, the fact that metabolism displays structural plasticity, turning on and off some reactions as the growth conditions are altered, has been observed before [3,4,6]. However, our demonstration of a group of reactions predicted to be active in all environmental conditions and forming a connected metabolic core could substantially improve our understanding of the organization and utilization of metabolic networks. The emergence of the core represents a collective network effect, channeling the production of some indispensable biomass components to a few key reactions that cannot be replaced by alternative pathways under any environmental conditions. The collective origin of the core is supported by the observed changes in the core size. While the number of biomass components that the metabolism needs to produce is comparable in the three organisms (49 for H. pylori, 51 for E. coli, and 44 for S. cerevisiae), the number of metabolic reactions contributing to them differs. The larger size of a metabolic network significantly increases its capacity and redundancy, decreasing the size of the metabolic core. Therefore, the metabolic core contains reactions that are necessary for optimal cellular performance regardless of the environmental conditions, while the conditionally active metabolic reactions represent the different ways in which the cell is capable of optimally utilizing substrates from its environment.
The identification of the metabolic core also has important practical implications: given the continuous activity and high degree of essentiality of the core enzymes, they represent potential targets for antimicrobial intervention. Specifically, while many bacterial and yeast gene products are essential, a high fraction of them are essential only in specific environments. For example, recent measurements indicate that 76% of the S. cerevisiae genes that are inactive in nutrient-rich conditions are in fact not only active, but are also essential in some other growth conditions [6]. However, an effective antimicrobial drug needs to be able to kill its target organism under all physiological conditions in which it can exist. Thus pharmacological interventions targeting a specific pathway will not be effective in environments where the pathway is not needed and its corresponding enzyme is turned off. Instead, the most effective antimicrobials must target the activity of the core reactions, as their disruption will have an impact on the microorganism's ability to function under all environmental conditions. Indeed, among the currently used antibiotics, fosfomycin and cycloserine act by inhibiting cell-wall peptidoglycan, while sulfonamides and trimethoprim inhibit tetrahydrofolate biosynthesis, both pathways being present in the H. pylori as well as the E. coli core (see Figure 2). In addition, core pathways involved in the synthesis of flavin adenine dinucleotide, 2-dehydro-3-deoxy-d-octonate, and lipopolysaccharide are among potential antibiotic targets [21].
This indicates that the core enzymes essential for biomass formation, both for optimal and suboptimal growth (Table S1), may prove effective antibiotic targets given the cell's need to maintain the activity of these enzymes in all conditions. The fact that not all such core reactions are shared by all bacteria offers the possibility to identify bacterium-specific drug targets. Finally, our results pertaining to the existence of the core and its characteristics are by no means limited to the three studied organisms, but the analysis can be carried over to all organisms for which high-quality metabolic reconstructions are available. Given the large number of sequenced bacterial genomes, such studies could open up new avenues for rapid in silico antimicrobial drug-target identification.
Materials and Methods
FBA.
Starting from the published stoichiometric matrices of the reconstructed E. coli MG1655, H. pylori, and S. cerevisiae metabolic networks [7–9], the steady-state concentrations of all the internal metabolites of an organism satisfy
where Sij is the stoichiometric coefficient of metabolite Ai in reaction j, and νj is the flux of reaction j. We use the convention that if metabolite Ai is a substrate (or product) in reaction j, Sij < 0 (Sij > 0). Any vector of fluxes {νj} which satisfies equation 1 corresponds to a state of the metabolic network, and hence, a potential state of operation of the cell. Using linear programming, we choose the solution that maximizes biomass production of the respective organisms. Reactions that are never active, likely reflecting annotation errors or incomplete data, are ignored in our flux analysis.
Metabolic core identification.
We model all possible cellular environments in the investigated FBA models, as described before [3]. Briefly, for each metabolic-uptake reaction, we fix the uptake rate to a randomly chosen value of between 0 and 20 mmol/g/h (dry weight) before calculating the optimal fluxes for this configuration using linear programming. Each reaction is subsequently tagged as either active (non-zero flux) or inactive (zero flux). Since there are a very large number of possible combinations of the selected uptake rates, we repeat this process 30,000 times. In addition, we calculate the optimal fluxes for all single-carbon-source configurations on a minimal-uptake medium consisting of unlimited ammonia, sulfate, phosphate, carbon dioxide, potassium, and restricted oxygen for E. coli, H. pylori, and S. cerevisiae, plus unlimited ergosterol and zymosterol for S. cerevisiae. For each reaction, we assign a value, qi, of between zero and unity, representing the relative number of conditions for which reaction i is active. The preliminary metabolic core is defined as the set of reactions that are active in all sampled conditions (qi = 1). We determine the final metabolic core by removing from the preliminary core those reactions that, when their flux rate is constrained to zero over the total set of initial conditions, leave the growth rate unchanged.
Correlations and clustering.
We evaluate the FBA flux correlations by calculating the Pearson coefficient for all possible reaction pair combinations in the 30,000 different simulated growth conditions. We subsequently group the reactions by employing the correlation values as the metric in a standard hierarchical average-linkage clustering algorithm [22].
Data analysis.
The annotated metabolic FBA models specify the enzymes and genes catalyzing the various metabolic reactions. We represent the mRNA activity level or half-life time of a metabolic enzyme by averaging the available experimental values, Ek, for all the catalyzing gene products, as
where Ni is the number of genes. We assess the essentiality of a reaction by considering it essential if at least one of its catalyzing enzymes (or gene products) is essential for the survival of the organism (see Protocol S1 for error analysis).
Core and noncore essentiality.
The deletion phenotype of E. coli enzymes was taken from Gerdes et al. [13]. For nonlethal core reactions, we cross-checked the deletion phenotype with the PEC database (http://www.shigen.nig.ac.jp/ecoli/pec) as given in Table S2 of Gerdes et al. [13], resulting in a total of 62 essential enzymes and seven enzymes for which we could not determine the deletion phenotype. The deletion phenotype of the yeast enzymes was taken from the CYGD database [12].
Supporting Information
Protocol S1 Supplementary Analyses and List of Metabolite Abbreviations
(628 KB DOC)
Click here for additional data file.
Table S1 Metabolic Core Reactions
(57 KB XLS)
Click here for additional data file.
We thank S. Mobashery and S. Vakulenko for discussions. Work at the University of Notre Dame and at the University of Pittsburgh was supported by the US Department of Energy, the National Institutes of Health, and the National Science Foundation.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. EA, ZNO, and ALB conceived the study. EA and ALB designed the study. EA performed all the computational work. EA, ZNO and ALB wrote the paper.
A previous version of this article appeared as an Early Online Release on November 2, 2005 (DOI: 10.1371/journal.pcbi.0010068.eor).
Abbreviation
FBAflux-balance analysis
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PLoS Comput BiolPLoS Comput. BiolpcbiplcbploscompPLoS Computational Biology1553-734X1553-7358Public Library of Science San Francisco, USA 1636207210.1371/journal.pcbi.001007105-PLCB-RA-0244R2plcb-01-07-05Research ArticleBioinformatics - Computational BiologyMicrobiologyEukaryotesComparing the Dictyostelium and Entamoeba Genomes Reveals an Ancient Split in the Conosa Lineage Two Amoebozoa Genomes ComparedSong Jie 1Xu Qikai 23Olsen Rolf 4Loomis William F 4Shaulsky Gad 23Kuspa Adam 12Sucgang Richard 1*1 Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, United States of America
2 Department of Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
3 Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas, United States of America
4 Section of Cell and Developmental Biology, Division of Biology, University of California San Diego, La Jolla, California, United States of America
Bourne Philip EditorUniversity of California San Diego, United States of America* To whom correspondence should be addressed. E-mail: [email protected] 2005 16 12 2005 7 11 2005 1 7 e7119 9 2005 7 11 2005 Copyright: © 2005 Song et al.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.The Amoebozoa are a sister clade to the fungi and the animals, but are poorly sampled for completely sequenced genomes. The social amoeba Dictyostelium discoideum and amitochondriate pathogen Entamoeba histolytica are the first Amoebozoa with genomes completely sequenced. Both organisms are classified under the Conosa subphylum. To identify Amoebozoa-specific genomic elements, we compared these two genomes to each other and to other eukaryotic genomes. An expanded phylogenetic tree built from the complete predicted proteomes of 23 eukaryotes places the two amoebae in the same lineage, although the divergence is estimated to be greater than that between animals and fungi, and probably happened shortly after the Amoebozoa split from the opisthokont lineage. Most of the 1,500 orthologous gene families shared between the two amoebae are also shared with plant, animal, and fungal genomes. We found that only 42 gene families are distinct to the amoeba lineage; among these are a large number of proteins that contain repeats of the FNIP domain, and a putative transcription factor essential for proper cell type differentiation in D. discoideum. These Amoebozoa-specific genes may be useful in the design of novel diagnostics and therapies for amoebal pathologies.
Synopsis
Most single-celled eukaryotes were lumped together in a single catchall classification until molecular sequencing revealed that they are a very diverse group that illustrates the different paths eukaryotic evolution has taken. Comparing a representative subset of genes indicates that one group in particular, the Amoebozoa, are a sister group to the animals and fungi, even more closely related than the plants. Despite their diversity, few simple eukaryotes have been the subject of complete genome sequencing. The genomes of two amoebozoa, Dictyostelium discoideum (a free-living social amoeba) and Entamoeba histolytica (a pathogenic amoeba), were recently completed. The authors compared the predicted proteins encoded by each organism to each other, and to other representative eukaryotes, and built a phylogenetic tree using not just a few representative genes, but the entire genomes of 23 organisms. The resulting tree closely re-created the relationships predicted from the sampled genes, including reinforcing the close relationship between the amoebozoa and the animals and fungi. The authors also found very few genes that are exclusively inherited by amoebozoa. Since some amoebozoa are important clinical pathogens, these genes are likely good targets for therapeutic agents that will not affect the animal host.
Citation:Song J, Xu Q, Olsen R, Loomis WF, Shaulsky G, et al. (2005) Comparing the Dictyostelium and Entamoeba genomes reveals an ancient split in the conosa lineage. PLoS Comput Biol 1(7): e71.
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Introduction
Comparative genomics of the bacteria and archea is well developed, has provided many insights, and has promoted the development of numerous analytical tools. The comparative genomics of eukaryotes is still in its infancy due to a relative paucity of completely sequenced eukaryotic genomes. However, genomic comparisons from species as divergent as man and the nematode Caenorhabditis elegans have provided important insights into the functional aspects of each genome [1]. Comparing genomes from organisms along a common evolutionary lineage and of varying phylogenetic distances has been particularly informative, and the recent sequencing and comparison of five hemiascomycete yeast genomes best illustrates this. These studies showed how the hemiascomycete lineage was shaped through the forces of massive genome duplication, reductive evolution, and gene dispersion [2]. The comparison of the first two sequenced Drosophila species, D. melanogaster and D. pseudoobscura, has proven so fruitful that 12 additional Drosophila genomes are being sequenced [3].
Although most eukaryotic genome sequencing efforts are focused on animals, fungi, and plants, the simple eukaryotes or “protists” represent a major component of the diversity of eukaryotes. Single-celled eukaryotes lack extensive fossil records, but phylogenetic trees built using exhaustive sampling of small subunit rRNA genes and selected protein coding genes have revealed a previously unappreciated diversity deep in the roots of eukaryotic ancestry [4]. Notable is the positioning of the Amoebozoa as a sister clade to the opisthokonts (animals and fungi). To date, only two Amoebozoa species have had their genomes extensively sampled, although more species are being sequenced [5]. The genome of the social amoeba Dictyostelium discoideum has been completely mapped and sequenced [6], and the genome of the amitochondriate human pathogen Entamoeba histolytica has been subjected to deep shotgun sampling and assembly into unordered scaffolds [7]. Because the Amoebozoa do not exhibit strong morphologic traits that can be used for taxonomic categorization, classification has relied heavily on sequence comparison.
Due to similarities in lifestyle, the genome of Entamoeba has been compared with that of other parasitic eukaryotes such as Giardia, Trichomonas, or Leishmania [7], but analyses of 100 representative genes have clustered Dictyostelium and Entamoeba as genera of a common phylum [8], each one, in turn, representing the two major arms of the Conosa lineage: the free-living Mycetozoa and the amitochondrial Archamoeba, respectively [8]. Both organisms have unusually A+T-rich genomes that have confounded sequencing and assembly, and analyses from the genomic sequences have implicated significant contributions of genes from putative horizontal gene transfer events from bacteria into the physiology of each organism [5]. We have taken advantage of having two related genomes among the Amoebozoa, and have compared the predicted proteomes of D. discoideum and E. histolytica. Although we found a sizeable number of gene families in common between the two, most of those are shared with other eukaryotes such as plants, animals, and fungi. In fact, less than 45 gene families defined the amoeba-specific proteins, which is consistent with a deep evolutionary divergence between the two amoebae as indicated by a tree constructed from the complete proteomes of 21 additional eukaryotes.
Results
Shared Proteins between Dictyostelium and Entamoeba
Using the complete predicted protein sets of each organism, we ran reciprocal BLASTP analyses to identify putative orthologs between E. histolytica and D. discodeum, using only proteins that hit a cognate with an e-value of ≤ 10−5, and requiring that each protein return its cognate from the other genome as a best hit when used as a query. This method, referred to as reciprocal best hits (RBHs), was adapted from the construction of the Clusters of Orthologous Genes (COG) database at the National Center for Biotechnology Information (NCBI) [9]. A set of 1,607 proteins passed these criteria as orthologs between E. histolytica and D. discoideum; loosening the stringency of the cutoff value did not appreciably change the number of pairs detected. To distinguish which members of this set are unique to the Amoebozoa lineage, we filtered out orthologs found also in model organisms representing plants, animals, and fungi. Using the Homo sapiens, C. elegans, D. melanogaster, Saccharomyces cerevisiae, and Arabidopsis thaliana genomes as the representative model genomes for the other sequenced eukaryotes, we determined that 1,545 of the shared orthologs between D. discoideum and E. histolytica also matched orthologs with the other major eukaryotes, with 1,199 genes being universally conserved among all seven representative eukaryotic genomes. Only 62 genes appear exclusive to the amoebozoan genomes relative to the other eukaryotes.
Lineage-Specific Genes
The number of putative lineage-specific genes appears to be much lower among the amoebozoans than among other related species. Comparing five hemiascomycete yeast genomes identified 800 gene families out of 2,014 shared gene families [2]. However, since the species chosen were relatively closely related, the fact that a higher proportion of their genomes are shared is not surprising. Although not a perfect alternative, the divergence between D. discoideum and E. histolytica is better approximated by the greater divergence between S. cerevisiae and Schizosaccharomyces pombe, members of the Hemiascomycetes and Archaeascomycetes, respectively [10]. The Hemiascomycetes and Archaeascomycetes are major diverging branches of the Ascomycota lineage. A total of 3,281 genes in the S. pombe genome were described as having orthologs with the S. cerevisiae genome [11]. Using the C. elegans genome as the outgroup, 2,512 genes were predicted to be specific to the yeasts. The criteria used in this early analysis were significantly less stringent than our methodology, and having only C. elegans as the model outgroup significantly weakens the argument for lineage specificity. We updated the study by processing and comparing the two yeast genomes using the same RBH strategy that we used for the amoebozoan genomes. Despite the smaller proteomes of the yeast species, they share almost twice as many orthologs compared to the two amoebae (Table 1). Moreover, when compared against the five other completely sequenced model eukaryotic genomes, 372 orthologous genes were identified as being specific to these two divergent yeast species—five times more than the lineage-specific genes among the amoebae.
Table 1 Lineage-Specific Genes in Amoebas versus Yeasts
Shared Paralogous Families
While the RBH method is a commonly used means of identifying orthologs between two genomes, it works best when the genomes being compared are not rich in recent gene duplication events. Expanded paralog sets within each genome can confound the method, resulting in some spurious elimination of orthologous sets. While this is not an issue with relatively compact genomes such as those of prokaryotes, both the D. discoideum [6] and E. histolytica [7] genomes were shaped by significant contributions from gene duplication. We feared that missing data from the paralogs might skew the estimates for the number of lineage-specific genes. We adapted the Markov clustering algorithm [12] used in comparing the five hemiascomycete yeast lineages [2] for identifying and clustering common gene families between the two species. While Markov clustering exhibits good specificity in identifying gene families, it is best used on species of a relatively close phylogenetic distance. The sensitivity required to detect orthologs between divergent species will be overwhelmed by stronger similarity to paralogs within the same genome, and will be omitted in the clustering. We generated an optimized result by supplementing the results from the Markov clustering with groupings generated by the more sensitive RBH method. Less than 0.2% of the gene families identified by Markov clustering contradicted the RBH results. Manual inspection of some these gene families indicated that they possibly can be merged because RBH detected a structural similarity missed by the Markov clustering. Loosening the stringency of the clustering would have merged these families but would have most likely created spurious groupings as well; we considered this an acceptable error rate.
The combined results identified a set of 1,510 gene families or “archetypes” shared between the two amoeba, representing 3,216 genes in D. discoideum and 3,833 genes in E. histolytica. Of these, 1,132 gene families are shared with all the other model eukaryotes (Figure 1), with only 63 gene archetypes representing the amoeba-exclusive set. Thus, even with the inclusion of paralogs, the number of lineage-specific genes for the Conosa is remarkably small.
Figure 1 Shared Gene Archetypes between Amoeba and Other Eukaryotes
The combined RBH and TribeMCL clustering identified 1,510 gene archetypes between E. histolytica and D. discoideum, with all but 63 shared with five other model eukaryotes. This Venn diagram illustrates how the shared archetypes are distributed with other eukaryotic genomes; the amoeba-specific genes are not displayed here. Animals are represented by H. sapiens, C. elegans, and D. melanogaster. Plant is represented by A. thaliana, and yeast by S. cerevisiae.
The 63 gene archetypes translate to 78 genes in the D. discoideum genome. We used them as queries against the NCBI nonredundant protein database (nr; as of April 2005, downloaded from http://www.ncbi.nlm.nih.gov/Database/) for matches in other organisms not represented in our model outgroups. Of these genes, 48 (representing 40 gene families) failed to match anything significant in the database. Of the remaining gene families, one matched an actin-binding protein previously identified in Physarum polycephalum—another Amoebozoa. A second family is enriched for proteins containing repeats of the protein domain FNIP. Until this comparison, the FNIP domain was described exclusively in D. discoideum and distributed among 154 proteins ranging from putative kinases to transcription factors [6]. The FNIP domain appears to be related to leucine-rich repeats, a protein motif involved in setting up protein–protein interactions [13]. In addition to the FNIP-containing proteins in E. histolytica, 16 FNIP-containing proteins are encoded in the genome of mimivirus, the largest virus on record [14]. Mimivirus infects Acanthamoeba polyphaga—itself another amoebozoan. All together, these 42 gene families are exclusively found in the Amoebozoa and represent the lineage-specific cohort of genes for this clade.
Among the remainder, we found families of ADP-ribosylglycohydrolases with an ortholog in Neurospora crassa, a fungus that we had not included as part of our model organism outgroup. Six of the gene families were matched primarily on the basis of alignment to a conserved domain, and not throughout the protein—we did not consider these passing criteria as orthologs, although we cannot discount the possibility that they arose from a common gene prototype. Aside from three proteins that have orthologs in Leishmania and Plasmodium (early diverging eukaryotes), the rest are orthologs retained from the prokaryotic ancestry. In no case was a match to proteins from plants or animals detected.
Divergence between Dictyostelium and Entamoeba
Given the small number of orthologs identified that is distinctive to the lineage based on comparing five genomes, we sought to estimate the phylogenetic distance represented by Dictyostelium and Entamoeba. We had earlier used the proteome content of 17 eukaryotes to establish that D. discoideum had diverged later from the opisthokont lineage than the plants did [6]. Supplementing the data with the proteomes of four organisms in addition to E. histolytica, the expanded tree demonstrates that the divergence between E. histolytica and D. discoideum is even deeper than between the animals and fungi (Figure 2). Note that, although the revised tree used significantly more data in its construction, the topology is essentially identical to the tree built using 100 sample genes [8], and remains unchanged with regards to the divergence of the Amoebozoa from the opisthokonts as a later event than the divergence of plants from that lineage.
Figure 2 Proteome-Based Phylogeny of Eukaryotes
Abbreviations for organisms are as follows: Ag, A. gambiae; At, A. thaliana; Ce, C. elegans; Cr, C. rheinhardtii; Ci, C. intestinales; Cp, C. parvum; Cm, C. morolae; Dd, D. discoideum; Dm, D. melanogaster; Eg, E. gracilis; Eh, E. histolytica; Fr, F. rubripes; Gl, G. lamblia; Hs, H. sapiens; Lm, L. major; Nc, N. crassa; Os, O. sativa; Pf, P. falciparum; Sc, S. cerevisiae; Sp, S. pombe; Tt, T. thermophila; Tc, T. cruzi; and Zm, Z. mays. 1 Darwin = 1/2000 of the divergence between S. cerevisiae and H. sapiens. Branch thickness is proportional to the size of each clade. The tree was constructed by full maximum likelihood with clusters of orthologs generated from whole proteomes from each of the organisms. A phylogeny program used for constructing a new amino acid replacement model (23) determined the individual nodes and branch lengths.
Universal Common Eukaryotic Genes
Given the phylogenetic distances represented by the seven model organism genomes in this comparison, the 1,132 gene families that are shared among all the model eukaryotes may define a core set of eukaryotic genes. Using D. discoideum as the reference genome for this analysis, we chose to explore the available annotations in this organism. The 2,726 D. discoideum genes represented by the “universal” ortholog set were enriched for Gene Ontology (GO) terms [15] as a consequence of receiving functional assignments extrapolated from the study of the orthologs in other organisms. The relative enrichment of GO terms in these annotations permitted the use of the automated GOAT tool [16] to recognize the major functions of this collection of proteins. The full results are available from the Supporting Information. As expected, the genes in this group encode proteins that regulate and propagate the cell cycle, components of DNA replication, RNA transcription, and protein synthesis, as well as a significant number predicted to be involved in cellular transport. Nevertheless, 257 of the orthologous eukaryotic gene families have no GO annotation.
Horizontal Gene Transfer
Horizontal gene transfer (HGT) is a major force in the evolution of prokaryotic genomes [17], but its impact on eukaryotic genomes is not as easily detected or determined. Basic methods for identifying HGT candidates rely on seeking out proteins or protein domains in a eukaryotic genome that are statistically predicted to have a bacterial source (and can be eliminated from possible contamination during the course of genome sequencing) and are unlikely to have been inherited from the ancestor. Significant numbers of HGT candidates were reported in both D. discoideum and E. histolytica annotations, all of them reportedly contributing strongly to the physiology of each organism. However, the methods used to identify HGT candidates were peculiar to the respective organisms.
Comparing the gene content of two related species can serve to detect false positives among the putative products of HGT—a gene that was acquired through recent lateral transfer is unlikely to share an ortholog in a relatively closely related organism. Of the 18 HGT candidates identified from the annotation of the D. discoideum genome, only one, DDB0204031, annotated as a beta-eliminating lyase, was found among the 1,510 orthologous gene families in common with E. histolytica. This gene is likely to have been inherited xenologously.
Discussion
Given the relative paucity of sequenced genomes among the Amoebozoa, the availability of two sampled genomes presents an important first look at the distinctive physiology and evolution of this sister clade to the opisthokonts. The two representative organisms in this clade, E. histolytica and D. discoideum, despite dramatic differences in physiology and life modes, share distinct similarities on the genome scale. Both genomes are extremely A+T rich, which led to great difficulties in sequencing and assembly, and both genomes are also relatively gene rich, with small predicted introns. We have identified 1,132 gene families conserved across seven genomes, representing the major phyletic branches of the eukaryotes: D. discoideum, E. histolytica, H. sapiens, D. melanogaster, C. elegans, A. thaliana, and S. cerevisiae. The gene families in this collection fall into expected categories: proteins known to be involved in housekeeping functions such as transcription, translation, and replication. However, a significant number of genes involved in organogenesis, cell migration, and environmental response are conserved across all these diverse phyla, even in organisms that do not form organs, or are nonmotile.
The 1,132 “universally” conserved orthologs represent 1,967 genes in the S. cerevisiae genome; we cross-referenced this list against the list of 1,189 genes essential for growth on rich medium [18]. While the number of essential genes in yeast comprises 18.8% of the 6,298 genes in the S. cerevisiae genome, 667 of them are among the conserved orthologs. This represents enrichment to 34%, indicating that the genes in this set are ancient, conserved gene archetypes that may serve fundamental functions in all eukaryotes. However, 523 of the yeast orthologs to “universally” conserved genes are not vital. Moreover, 257 of the conserved gene families as yet do not have GO assignments. Elucidation of their functions will have profound implications for our understanding of all eukaryotes. When the predicted proteomes from each amoeba genome are compared, we find 1,510 orthologous gene families, but only 63 of these families were not found among the five model eukaryotic genomes we had chosen. More detailed inspection revealed that 42 of these families appear to be exclusively carried by amoebae, and most of the rest are ancient genes retained from prokaryotes. The very small number of Amoebozoa lineage-specific genes was surprising; we entertained the possibility that it could be an artifact of differences in gene prediction algorithms. The methods used in this comparison relied on using the predicted protein sequences of each genome project, and trusting that each respective project has chosen the appropriate criteria peculiar to that organism to generate the best possible predictions. Orthologs will not be found if they are not predicted as coding regions in one or the other organism. The exon-dense nature of both genomes makes this scenario unlikely. However, expansion of the comparison into undetected open reading frames of each respective genome can prove useful in detecting hidden lineage-specific orthologs. A preliminary scan of the noncoding regions of the D. discoideum region using TBLASTN has yielded nothing more than a few pseudogenes (unpublished data), so we do not think that the differences in gene prediction algorithms had a major effect on this estimate. Alternatively, the physiology of the Amoebozoa may be more strongly influenced by RNA-based effectors than other eukaryotes. Earlier scans for short noncoding RNAs in D. discoideum identified novel species unreported in other organisms [19]. Perhaps a closer inspection of the nonprotein coding regions of the genome will unearth conserved motifs indicative of strongly conserved RNA-based physiology distinct from other eukaryotes. Barring these alternative explanations, anything distinctive in the physiology of the Conosa lineage, if not for the entire Amoebozoa, lies among these 42 lineage-specific gene families.
The construction of an expanded phylogenetic tree using the complete proteomic content of 23 eukaryotic genomes yielded a general topology that is essentially identical to the earlier grouping of D. discoideum and E. histolytica as sharing a common ancestor [8], but the distances indicate a divergence almost as ancient as that between fungi and animals. Since these two represent but one subphylum of the Amoebozoa, this suggests that the diversity among this clade is very large indeed. Sequencing additional genomes from this clade will undoubtedly return rich veins of information about presently unexplored physiology.
In D. discoideum, 51 genes represent the 42 amoeba-specific gene families. Three other genes are found among only the amoebae and two pathogenic primitive eukaryotes, Leishmania and Plasmodium. For most of these genes, we cannot draw from studies in orthologs found in other organisms to interpret their functional roles due to their lineage-specific nature. We can, however, infer the putative functions of these genes based on structural features, and independent mutagenesis experiments. Only three of the lineage-specific genes have been mutated among the more than 900 genes that are being systematically mutated in D. discoideum (unpublished data and personal communication, http://www.dictybase.org), and one of these is the putative transcription factor cudA, which is necessary for the entry of D. discoideum into terminal differentiation [20]. Given that E. histolytica lacks a multicellular stage but retains an ortholog argues that cudA has a more vital role in most amoebae beyond regulating social behavior and cell-type differentiation. Other amoeba-specific genes include a histidine kinase gene family, a bZIP transcription factor, and a calcium-binding protein of unknown function. These lineage-specific genes may represent the distinctive physiologic elements of all amoebae. Future experiments into these key genes using the easily tractable and nonpathogenic D. discoideum can be extrapolated to the physiology of the E. histolytica, an important human pathogen with more laborious culturing requirements [21]. These proteins are likely to be the best substrates for drugs that target E. histolytica, as well as other pathogenic amoebae such as Acanthamoeba, without affecting the vertebrate host.
Materials and Methods
Comparison algorithms.
All work was done based on version 2.0 of the D. discoideum genome (http://www.dictybase.org) and the latest release of the E. histolytica genome as of April 2005. RBH was performed by generating BLAST databases from the predicted proteins in each respective genome, and performing a BLASTP analysis (NCBI BLAST v2.2.1), using the following parameters: sequence filtering by SEG with default settings; Matrix BLOSUM62; gap opening penalty = 11; and gap extension penalty = 1. The minimum high-scoring segment pair (HSP) was set at 50 residues, and the minimum identity of the longest HSP was set at 20%. Results were filtered for hits with e-value scores less than 10−5. A successful RBH ortholog returns the same protein as the best hit when the query is reversed, and the querying genome is now used as the subject database. The same parameters were used in updating the comparison between S. cerevisiae and S. pombe. Sources for the relevant genome databases are listed and linked in the Web site provided in the Supporting Information.
Clustering into gene families used a modification of the TribeMCL-based method described in Dujon et al. (http://www.ebi.ac.uk/research/cgg/tribe/) [2]. Briefly, both protein databases were pooled and subjected to an all-versus-all BLASTP comparison, with the minimum identity of the longest HSP set at 25%, and the required HSP length at least 50% of the query length (all other parameters were as described above). We had empirically determined these cutoff parameters that would maximize overlap with the RBH results and minimize inclusions due to alignments of short HSPs. The results were processed into gene families using TribeMCL, with the parameter “Inflation = 4.0.” This is the default value; increasing or decreasing it did not affect the composition or number of proteins being clustered appreciably, only how they were grouped. The final set of orthologous gene families is the union of the TribeMCL and RBH results; where TribeMCL clustering “broke” an RBH pairing, we retained the TribeMCL families. The set of D. discoideum genes represented by gene families excluded from overlaps with the model animal, plant, and yeast outgroups (putatively “amoeba-specific”) were in turn compared via BLASTP against the NCBI nonredundant database to compare against all other organisms not used in the set of model eukaryotic genomes, and manually inspected to categorize them into appropriate bins. Custom Perl and Unix shell scripts were written to parse results as necessary.
Construction of phylogenetic tree.
The phylogenetic tree is an expansion of the construction described in Eichinger et al. [6]. Tree rooting was done with a set of clustered orthologs generated from the proteomes of the seven archaea (Aeropyrum pernix, Archaeoglobus fulgidus, Halobacterium sp., Pyrococcus abyssi, Methanococcus jannaschii, Sulfolobulus solfataricus, and Thermoplasma acidophilum) by the COG methodology [9]. The phylogenetic relationships of these Archaea were previously established [8]. The clusters were BLAST aligned [22] against the proteins of eight eukaryotes: A. thaliana (At), Oryza sativa (Os), S. cerevisiae (Sc), S. pombe (Sp), D. melanogaster (Dm), Anopheles gambiae (Ag), H. sapiens (Hs), and Fugu rubripes (Fr). Proteins that could be easily aligned over more than half their length were considered appropriate for rooting. Rooting was done with a set of 159 clusters with at least one member from each of the major groups: plants, fungi, and animals. All possible root positions among these groups were tested. Bootstrap values were highest for rooting in the interval between plants and fungi, with animals diverging after yeast (96/100). The second-highest values were found for the interval between yeast and animals, with plants diverging after animals. The positions of a chordate, Ciona intestinales (Ci), another fungus, N. crassa (Nc), a nematode, C. elegans (Ce), and corn, Zea mays (Zm), were then established by maximum likelihood on databases of clusters of likely orthologs or evolutionary clusters of orthologs (ECOs) generated by a multimatrix model of protein divergence [23]. The favored position of the archaebacterial root for this set of 12 organisms remained at the junction of plants and fungi (93/100). We then tested the position of the malarial parasite, Plasmodium falciparum (Pf), and found that it diverged before the plant/fungal split. When the archaebacterial root was determined with this set of 13 organisms, the interval between plants and fungi received 100/100 bootstraps with Plasmodium as an early diverging organism. The root position was then fixed. D. discoideum (Dd), Leishmania major (Lm), Giardia lamblia (Gl), and Chlamydomonas rheinhardtii (Cr) were then added to the tree [6]. Using the same approaches, we added the red alga Cyanidioschyzon morolae (Cm), the alveolates Cryptosporidium parvum (Cp), Tetrahymena thermophila (Tt), and the euglenoids Trypanosoma cruzi (Tc) and Euglena gracilis (Eg). The positions of the nodes for the newly added organisms were all supported by 100/100 bootstraps. Finally, the position of E. histolytica was determined using the complete set of 5,908 ECOs. The length of the Entamoeba branch was computed with 987 ECOs.
Supporting Information
We have made all gene lists and raw BLAST reports available for query and download at http://dictygenome.org/supplement/rsucgang/song_2005. Analyses about the HGT candidates between D. discoideum and E. histolytica, and the clustering of the “universally” conserved orthologs in D. discoideum, are also available for download.
Accession Numbers
The InterPro (http://www.ebi.ac.uk/interpro) accession number for the protein motif FNIP is IPR008615, and for LRR1 is IPR001611.
We thank Shelly Sazer and Aleks Milosavjevic for critical feedback on the manuscript. This work was supported by a grant from the National Institutes of Health (GM62350) and a National Science Foundation Biocomplexity Grant (MCB0083704) to WFL, and by grants from the Institute of Child Health and Development, National Institutes of Health, to AK (RO1 HD35925, PO1 HD39691), GS (PO1 HD39691), and RS (PO1 HD39691).
Competing interests. The authors have declared that no competing interests exist.
Author contributions. RO, AK, and RS conceived and designed the experiments. JS, RO, and RS performed the experiments. JS, QX, RO, WFL, and RS analyzed the data. JS, QX, RO, WFL, and GS contributed reagents/materials/analysis tools. GS participated in intellectual discussion and supervised experiments. RS supervised the work. WFL, AK, and RS wrote the paper.
A previous version of this article appeared as an Early Online Release on November 7, 2005 (DOI: 10.1371/journal.pcbi.0010071.eor).
Abbreviations
GOGene Ontology
HGThorizontal gene transfer
HSPhigh-scoring segment pair
NCBINational Center for Biotechnology Information
RBHreciprocal best hit
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Olsen RM Loomis WF 2005 A collection of amino acid replacement matrices derived from clusters of orthologs J Mol Evol 61 659 665 16245010
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PLoS Comput BiolPLoS Comput. BiolpcbiplcbploscompPLoS Computational Biology1553-734X1553-7358Public Library of Science San Francisco, USA 1636207310.1371/journal.pcbi.001007305-PLCB-RA-0245R2plcb-01-07-07Research ArticleBioinformatics - Computational BiologyEvolutionMammalsPrimatesSelective Constraint on Noncoding Regions of Hominid Genomes Noncoding Conservation in HominidsBush Eliot C Lahn Bruce T *Howard Hughes Medical Institute, Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of AmericaHaussler David EditorUniversity of California Santa Cruz, United States of America* To whom correspondence should be addressed. E-mail: [email protected] 2005 16 12 2005 11 11 2005 1 7 e7314 9 2005 9 11 2005 Copyright: © 2005 Bush and Lahn.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.An important challenge for human evolutionary biology is to understand the genetic basis of human–chimpanzee differences. One influential idea holds that such differences depend, to a large extent, on adaptive changes in gene expression. An important step in assessing this hypothesis involves gaining a better understanding of selective constraint on noncoding regions of hominid genomes. In noncoding sequence, functional elements are frequently small and can be separated by large nonfunctional regions. For this reason, constraint in hominid genomes is likely to be patchy. Here we use conservation in more distantly related mammals and amniotes as a way of identifying small sequence windows that are likely to be functional. We find that putatively functional noncoding elements defined in this manner are subject to significant selective constraint in hominids.
Synopsis
A major goal of human evolutionary biology is to understand what genetic changes make humans unique. One influential idea is that changes in gene expression are most responsible for unique human characteristics. Regulatory elements in noncoding DNA play a key role in controlling gene expression, so one approach is to study human–chimpanzee differences in these elements. Here we use conservation in more distantly related mammals and amniotes as a way of identifying small sequence windows that are likely to be functional. We find that putatively functional noncoding elements defined in this manner are subject to significant selective constraint in hominids. Contrary to some previous reports, these results argue that hominid noncoding regions are not evolving free of constraint.
Citation:Bush EC, Lahn BT (2005) Selective constraint on noncoding regions of hominid genomes. PLoS Comput Biol 1(7): e73.
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Introduction
Thirty years ago, King and Wilson [1] raised a key question in human evolutionary genetics: Given that humans and chimpanzees have extremely similar genomes, what can account for the large biological differences between the two species? They proposed the provocative hypothesis that changes in the regulation of gene expression have played a central role in defining these differences.
Since then, some progress has been made toward understanding the genetic basis of human–chimpanzee differences. Most studies have focused on protein coding regions [2–10]. In comparison, much less progress has been made toward understanding the functional significance of noncoding sequence evolution, and as a result, it has been hard to assess King and Wilson's hypothesis.
An important step in assessing that hypothesis is to examine the level of selective constraint in hominid noncoding regions. The approach taken in a recent study was to divide noncoding sequences upstream of genes into 500-bp blocks [11]. Divergence in these blocks was then compared with divergence in putatively neutral regions. This analysis suggested that hominid noncoding regions are essentially evolving free of selective constraint.
Here we take a different approach. Functional elements in noncoding regions can be quite small. As a result, significant variation in hominid divergence may occur on relatively small scales, e.g., less than 50 bp. To capture variation in divergence on this fine scale, we use conservation in more distantly related mammals and amniotes to identify small sequence windows that are likely functional. Interspecies comparisons between different orders of mammals and between mammals and other vertebrates have been used for many years to identify potentially functional noncoding sequences [12–14]. Using this method, we find that that human–chimpanzee divergence is highly correlated with the degree of conservation across mammals and amniotes. That is, using conservation in more distantly related species allows us to find regions that are under strong constraint in hominids. Our results argue that hominid noncoding regions are not evolving free of constraint.
Results/Discussion
We examined alignments of 10-kb upstream noncoding sequence for 5,547 human–chimpanzee orthologous gene pairs [15] (see Materials and Methods). For each pair we also obtained 10 kb of upstream noncoding sequence for the corresponding mouse, dog, and chicken orthologs. We included only genes for which the 10-kb upstream sequences do not contain any other genes as annotated in the Ensembl database [16]. Using these data, we examined, one small sequence window at a time, how the level of nucleotide divergence in the human–chimpanzee alignment was influenced by the degree of conservation among more distantly related species.
We first used human–mouse–dog three-way comparisons to obtain information on mammalian conservation. We used exhaustive ungapped comparisons to obtain a conservation score for every 16-bp window in the human sequence. Scores were integers between 10 and 16, with higher numbers indicating stronger conservation (see Materials and Methods). We then located each human 16-bp window in the human–chimpanzee alignment and examined the single nucleotide positions immediately to its left and right. This allowed us to calculate the level of human–chimpanzee divergence next to windows of a particular conservation score. For example, we took every window in the human–chimpanzee alignment with a score of 12 as defined by the human–mouse–dog three-way comparisons and determined whether nucleotide sites adjacent to it were the same or different between human and chimpanzee. We then divided the number of sites that showed a difference by the total number of sites, which produces a fraction that indicates the level of human–chimpanzee divergence at conservation score 12. We tabulated human–chimpanzee divergence for each of the conservation scores between 10 and 16.
The motivation for examining adjacent sites, rather than sites that are part of the 16-bp window itself, is to avoid ascertainment bias. If we used sites within the 16-bp conservation window to calculate human–chimpanzee divergence, it would cause bias since the same human nucleotides would contribute to both scores. By using adjacent sites, we avoid this problem. For a description of simulations that illustrate that our method is unbiased, see Materials and Methods and Figure S1.
Figure 1A shows that broader mammalian conservation is tightly correlated with conservation in hominids (see Table S1 for raw data). Sites next to highly conserved windows are about 40% as likely to have a human–chimpanzee difference as sites next to windows with low scores. That is, mammalian conservation is a good predictor of human–chimpanzee conservation.
Figure 1 Levels of Human–Chimpanzee Divergence for Different Conservation Scores
Conservation scores are calculated using either human–mouse–dog three-way comparisons (A) or human–mouse–chicken comparisons (B). Error bars represent 95% confidence intervals.
We next substituted chicken sequences for dog and repeated the analysis (see Materials and Methods). We found that the conservation score in the human–mouse-chicken comparison is an even better predictor of human–chimpanzee divergence (Figure 1B; also see Table S2 for raw data). These results suggest that many sequences in hominid noncoding regions are highly constrained.
A possible explanation for this pattern is that the upstream regions used in the study contain some surreptitious genes. Our 10-kb sequences do not contain any genes as annotated in the Ensembl database [16]. However, it is possible there are some genes present that are not annotated in Ensembl. To address this possibility, we used transcript predictions from ab initio prediction programs [17,18] in order to eliminate all upstream sequences that contained predicted transcripts (see Materials and Methods). These programs have a relatively liberal definition of genes [19], which allowed us to be more stringent in identifying sequences that do not contain genes in them. Our more stringent set included 2,390 genes. The relationship between human–chimpanzee divergence and conservation score for this set was not appreciably different from that for the whole data set (Figure S2A).
Another possible explanation is that errors in identifying transcription start sites might have somehow contributed to our results. We therefore divided the 10-kb upstream region into two equal halves and repeated the analysis with each. We found that the 5′ and 3′ halves did not differ significantly (Figure S2B). In addition, we tried restricting our analysis to 627 genes whose transcription start site is annotated in the Vertebrate Genome Annotation (Vega) database [20], which is manually annotated. The results for this subset did not differ from those for the whole set of genes (Figure S2C). This indicates that our results are unlikely due to errors in assigning transcription start sites.
We also repeated our analysis by examining only non-CpG sites (i.e., sites that did not overlap a CG dinucleotide in chimpanzee or human), and the pattern remained the same (Table S3).
For comparison, we repeated our analysis with mouse–rat alignments (Figure 2; also see Tables S4 and S5 for raw data). An obvious difference with Figure 1 is that divergence values are much higher for the murids, reflecting the greater time since their last common ancestor and the faster substitution rate in these lineages. As in the hominids, divergence declines with increasing conservation score. However, in murids the rate of this decline is about twice as high as in hominids, which is similar to that observed by others [21]. This is true even at the most highly conserved sites (e.g., near windows that are the same in human, mouse, and chicken). There are a number of possible explanations for this difference, including sequencing errors in the chimpanzee, relaxation of constraint, positive selection, or the more recent common ancestry of hominids. Errors in the draft chimpanzee genome sequence are likely to be random relative to conservation score and would therefore tend to bring the human–chimpanzee plot closer to random expectation (i.e., make the plot more shallow). Using computer simulations, we found that an error rate of roughly 3 × 10−3 would be sufficient to account for the different relative rates of decline in hominids and murids (data not shown). The error rate in the chimpanzee genome sequence is thought to be an order of magnitude less than this and therefore is not enough to account for the hominid–murid differences [15]. Based on polymorphism data from human populations, relaxation of constraint seems preferable to positive selection [11,15,21]. However, there is one other explanation to consider. Because of the recent common ancestry of hominids, approximately 14% of the single nucleotide differences between the human and chimpanzee genomes are at sites polymorphic in one or both species, a value that is likely to be substantially greater than the number for murids [15]. Such sites include some mildly deleterious mutations that have not yet been selected out of the population. This would tend to make the hominid plot shallower than the murid plot. The relative contribution of this factor versus other factors such as relaxation of constraint and positive selection can be better accessed as we get a better appreciation on the nature of sequence polymorphisms in human populations. Also, as more mammalian species are sequenced, we will have other examples of closely relates species that can be compared to hominids.
Figure 2 Levels of Mouse–Rat Divergence for Different Conservation Scores
Conservation scores are calculated using human–mouse–dog three-way comparisons (A) or human–mouse–chicken comparisons (B). Error bars represent 95% confidence intervals.
We also sought to confirm our results using a second, alignment-based method. We downloaded multiple alignments to 1-kb upstream noncoding regions of human genes from the University of California Santa Cruz (UCSC) Genome Informatics Web site (http://hgdownload.cse.ucsc.edu/goldenPath/hg17/multiz8way/). We then examined human–chimpanzee divergence in these relative to the alignments of mouse, dog, and chicken. Table 1 shows our results. We divided nucleotide sites in the human–chimpanzee alignment into several categories: (1) sites where mouse or dog do not align, (2) sites where they do but have differing bases, (3) sites where mouse and dog align and are the same, and (4) sites where mouse, dog, and chicken all align and are the same. Human–chimpanzee divergence decreases as we move from category 1 to category 4. This is also true if we only examine non-CpG sites (Table S6). This is consistent with the results of our previous method, suggesting that sites under constraint among mammals or amniotes generally also tend to be under constraint in hominids.
Table 1 Human–Chimpanzee Divergence Relative to Mouse, Dog, and Chicken Alignments
The simplest explanation of our results is that purifying selection is at work in hominid noncoding sequences. A possible alternative, however, is that those sites which are conserved in amniotes tend to have a low mutation rate and that this low mutation rate alone explains the lower human–chimpanzee divergence. To test this, we made use of the fact that mutation rate variation occurs on a relatively large spatial scale of tens of kilobases [22]. In contrast, purifying selection can act on a much finer spatial scale. In the results given in Table 1, we took a position in the multiple alignment, examined the mouse and dog bases at that site, and then looked at the human–chimpanzee bases at the same site. To test the mutation rate hypothesis, we modified this procedure and instead of looking at the corresponding site for human and chimpanzee, we looked at sites 1 or 15 bases away. If the mutation rate explanation is correct, then the divergence at sites close by should not differ sharply from the rate at the site corresponding to the mouse–dog position. As Table 2 shows, however, there is in fact a substantial difference as we move away in the alignment. The state of mouse and dog at a given alignment position has much more predictive power for the corresponding position in chimp and human than it does for positions 1 or 15 bp away. Again, the same is true if we restrict the analysis to non-CpG sites (Table S7). These results argue that our earlier observations are due to purifying selection rather than low mutation rate.
Table 2 Human–Chimpanzee Divergence at Adjacent Sites in Multiple Alignments with Mouse and Dog
Our findings differ from the results of Keightley et al. [11]. Their analysis suggested that hominid noncoding regions have been evolving under exceptionally weak, if any, selective constraint. In contrast, we find evidence that many small sequence windows have evolved under strong constraint. Keightley and colleagues' analysis involved dividing upstream regions into 500-bp blocks. In Figure 3, we present a plot of a similar analysis done with our data, which clearly shows that the range of divergence is much smaller. This likely results from the fact that in large 500-bp blocks, functional elements that are under constraint are mixed with large sections of nonfunctional DNA, which are not under constraint. Because of this, we think that our method of using small sequence windows is a more sensitive way to detect constraint in hominid noncoding regions.
Figure 3 Human–Chimpanzee Divergence in 500-bp Blocks over Our 10-kb Upstream Noncoding Sequences
Y axis range is the same as in Figure 1.
In our data, the difference between murids and hominids is present but is much smaller than that suggested by Keightley and colleagues. We conclude that hominid noncoding regions are subject to significant amounts of selective constraint, though the magnitude of such constraint may not be equal to that observed in other lineages such as murids.
Materials and Methods
Acquisition of sequences and alignments.
We obtained a list of human–mouse-dog orthologs via Ensmart (http://www.ensembl.org) and selected the trios that were unique reciprocal best hits [16]. We then used the Ensembl Perl api to identify genes among these whose 5′ upstream regions do not contain another Ensembl gene within 10 kb. For each ortholog trio for which this was true in all three species, we downloaded 10 kb of upstream sequence from the human [23,24], mouse [25], and dog (The Broad Institute, Cambridge, Massachusetts, United States, and Agencourt Bioscience, Beverly, Massachusetts, United States) genomes via Ensembl. All sequences were premasked for repetitive sequence using Repeat Masker (http://www.repeatmasker.org). For the same set of genes, we also obtained a copy of the UCSC human–chimpanzee and mouse–rat blastz alignments via Ensembl's perl api. There were 5,547 ortholog trios for which we obtained a human–chimpanzee alignment and 5,434 trios for which we obtained a mouse–rat alignment. We also repeated this process to get a set of human–mouse–chicken orthologs and downloaded the corresponding chicken genome sequence from Ensembl [26]. There were 3,223 human–mouse–chicken ortholog trios with a human–chimpanzee alignment.
Calculating conservation scores.
To calculate conservation scores for human noncoding sequences, we used human–mouse–dog three-way comparisons for mammalian conservation and, separately, human–mouse–chicken three-way comparisons for amniote conservation. We chose to use exhaustive ungapped comparison methods [27–29], which have been shown to be highly effective in finding cis-regulatory elements [30–34]. Such methods were particularly attractive here because of their simplicity, lack of assumptions, and suitability for producing a distribution of scores. In particular, this approach makes no assumption about the size, amount of similarity, or relative positions of functional elements in the noncoding sequence of various species [29]. For these reasons, the method can detect conserved elements even if their positions have been scrambled during evolution, as long as these elements still lie in the general vicinity of the gene.
We implemented the method using the python interface to the open source Paircomp library developed by Brown et al. [29]. We made use of several functions in that library to calculate a maximum transitive threshold (MTT) conservation score (see Figure S3 for an illustration). Take the example of a 16-bp window from a 10-kb sequence upstream to a human gene. To obtain the MTT conservation score of this window in mammals, we compared it against sequences upstream to the mouse and dog orthologs (10 kb each). We first compared it against all possible 16-bp windows (and their reverse complements) in these two species. For each comparison we obtained a score. In our data, scores are integers between 10 and 16, with higher scores indicating more similarity (e.g., 16 for perfect identity, 15 if there was one mismatch, and so on). Windows with scores under 10 were lumped with score 10 windows. We then considered every combination of three windows where one window comes from each species. For each combination we look at the three pairwise comparisons between species and take the minimum similarity score (e.g., if human–mouse and human–dog are 12, but dog–mouse is 11, then we take 11). We then find the combination of three windows that has the largest minimum similarity score. This score is the MTT, which is a measure of mammalian conservation for the human window. Another way to say this is: we identify the maximum threshold we can set where the given human window has hits in both mouse and dog, and where the mouse and dog hits also hit each other above threshold. Figure S4 shows a plot of these scores, giving the probability of various scores as a function of position relative to the gene.
For comparing 10-kb sequences across distantly related species, previous studies have found a window size of 20 to be suitable [30–34]. We chose to use a slightly smaller window size of 16. This choice represents a tradeoff between two considerations: (1) smaller windows are sensitive to smaller features in the sequence and (2) smaller windows increase the probability of obtaining high scores by random chance. Simulations showed that under our parameters (10-kb sequences and 16-bp windows), high MTT scores are highly unlikely by chance. In a simulation of over 5,000 repetitions where random sequences were compared, there were no windows with an MTT of 15 or 16, and the bulk of scores were 10 or 11 (Table S8). We also tried performing our analysis with nonorthologous human, mouse, and dog upstream sequences (Table S9). The results show that there are essentially no high scoring windows compared with data from orthologous upstream sequences (see Table S1). This shows that our MTT scores reflect conservation rather than random or nonorthologous similarity.
Correlating human–chimpanzee divergence with conservation score.
For every 16-bp window in the human sequence, we calculated a conservation score as described above. We then located this window in the human–chimpanzee alignment and determined whether the sites immediately to the left and right (not overlapping with the window) were the same or different between human and chimpanzee (Figure S5). We omitted windows that had repetitive sequences in them. For each conservation score, we tabulated the total number of adjacent sites that were the same or different and calculated human–chimpanzee divergence as the number of sites that differed between the two species divided by the total number of sites compared.
Analysis and simulations.
The 95% confidence intervals for the level of divergence were calculated by bootstrapping over genes 10,000 times. We performed several simulations to check our method for bias and help interpret the results.
We set up a random mutation simulation, which showed that our method is unbiased. We started with our sets of mouse–dog–human orthologs, taking 10 kb of upstream sequence from each. We then took the human sequence, and applied random mutations to it to create two new “species.” We used a Jukes-Cantor substitution model, applying enough substitutions on average so that our new species would differ by about as much as chimpanzee and human. We then treated these two new species the same way we treated human–chimpanzee in the real analysis. We took one of the two new species, species A, and calculated conservation between it and mouse and dog. Then we examined the alignment between species A and species B. Just as we did with the real data, we tabulated divergence scores at sites adjacent to 16-bp conservation windows. The results for 5,547 ortholog sets are shown in Figure S1. Unlike the plots in Figure 1, this plot is flat. That is, divergence is no different at sites next to windows with a 16 conservation score than it is at sites next to windows with a 10 score. This shows that our method is not biased.
We also performed a simulation to assess the potential effect of errors in the chimpanzee genome sequence. We again made two new “species” by applying mutations to human sequences. But this time, we applied them nonrandomly, taking mammalian conservation into account in determining the probability of a mutation at a given nucleotide. We then applied random errors to one of the species (in analogy to sequencing errors in the chimpanzee). By applying different amounts of these random errors, we explored the potential effect of different levels of sequencing errors.
Analysis of multiple alignments.
Multiple alignments of chimpanzee, mouse, dog, rat, chicken, zebrafish, and fugu to 1 kb of human upstream sequence were downloaded from the UCSC Genome Informatics Web site. For consistency with our initial analysis, we used only mouse, dog, chimp, and chicken. The genes in this data set all have annotated 5′ UTRs. We used the UCSC table browser [35] to identify the subset of these upstream sequences that do not overlap with genes in the UCSC Known Genes track. We then divided the alignment positions into the categories given in Table 1 and calculated human chimpanzee divergence at each. In order to examine whether mutation rate variation might explain our results, we modified the above analysis, this time examining human–chimpanzee sites 1 or 15 bp away. We repeated this analysis with non-CpG sites by eliminating all sites that overlapped a CG dinucleotide in either human or chimpanzee. Analysis was done with a combination of perl and python scripts.
Supporting Information
Figure S1 Results of a Simulation Illustrating Our Method Applied to Truly Random Substitutions
Unlike the plots in Figure 1, this plot is flat, i.e., divergence values next to windows with a 16 score are not different from those next to windows with 10 scores. This shows that our method is not biased (see Materials and Methods for details of the simulation).
(21 KB PDF)
Click here for additional data file.
Figure S2 Plots of Proportions As in Figure 1
(A) Data for our full set of 5,547 genes plotted along with those for a stringent “no gene” set of 2,390 genes. For this set we used more stringent criteria in eliminating upstream sequence that might contain a gene.
(B) We divided our 10-kb sequence in half. Here we plot data for the 5′ and 3′ regions separately.
(C) Data for our full set of genes plotted along with a subset of 627 that were manually annotated in Vega.
(29 KB PDF)
Click here for additional data file.
Figure S3 Calculating an MTT for the Window on Top (e.g., from Human) against Two Longer Sequences (e.g., from Mouse and Dog)
Consider all possible combinations of three 16-bp windows where one window comes from each species. Here we highlight two such combinations in red and blue. For each combination we consider the three pairwise comparisons and take the minimum similarity score. For the window combinations indicated in red, this is 12, and for the combinations indicated in blue, it is 10. We then find the combination (or combinations) of three windows that has the largest minimum similarity score. This score is the MTT. Here the MTT for the window on top is 12. Note that we also consider all the combinations of reverse complements.
(23 KB PDF)
Click here for additional data file.
Figure S4 Plots of the Probability of MTT Scores 14, 15, and 16 as a Function of Position Upstream of the Transcription Start Site
This is for human–mouse–dog three-way comparisons. In this plot, the probability is averaged over 50-bp regions. Conservation increases significantly near to the gene; 26.7% of all 16 scores, 23.9% of all 15 scores, and 19.7% of all 14 scores occur within 500 bp of the transcription start site.
(56 KB PDF)
Click here for additional data file.
Figure S5 A Section of Aligned Sequence (Made Up for Illustrative Purposes)
We have already taken human upstream sequence and calculated MTT conservation scores for every 16-bp window. We now take all windows with a particular score and find them in the alignment. Imagine for example that the three windows we have highlighted in blue represent all the windows with a 13 score. We examine the positions adjacent to these, here highlighted in red, and count the number of nucleotides that are the same or different. For our windows with a 13 score, four are the same and two are different. We repeat this for the other possible window scores, creating a table such as Table S1.
(21 KB PDF)
Click here for additional data file.
Table S1 Human–Chimpanzee Differences Relative to Mammalian Conservation
(17 KB PDF)
Click here for additional data file.
Table S2 Human–Chimpanzee Differences Relative to Amniote Conservation
(17 KB PDF)
Click here for additional data file.
Table S3 Non-CpG Version of Table S1: Human–Chimpanzee Differences Relative to Mammalian Conservation
(17 KB PDF)
Click here for additional data file.
Table S4 Mouse–Rat Differences Relative to Mammalian Conservation
(17 KB PDF)
Click here for additional data file.
Table S5 Mouse–Rat Differences Relative to Amniote Conservation
(17 KB PDF)
Click here for additional data file.
Table S6 Non-CpG Version of Table 1
(26 KB PDF)
Click here for additional data file.
Table S7 Non-CpG Version of Table 2
(27 KB PDF)
Click here for additional data file.
Table S8 MTT Scores Based on Random Sequence
(17 KB PDF)
Click here for additional data file.
Table S9 MTT Scores from 10 kb of Upstream Human Noncoding Sequence Compared to Nonorthologous 10-kb Upstream Sequences from Mouse and Dog
(17 KB PDF)
Click here for additional data file.
We thank Su Yeon Kim, Rizvan Mamet, Nathan M. Pearson, Jonathan Pritchard, and Eric J. Vallender for helpful discussions. We are grateful to Titus Brown for timely support on the Paircomp library. Conversations with Peter McCullagh and Chris Hart contributed to the early direction of the project.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. ECB conceived and carried out the project and was chiefly responsible for writing the paper. BTL participated in critical discussions and contributed to writing the paper.
A previous version of this article appeared as an Early Online Release on November 11, 2005 (DOI: 10.1371/journal.pcbi.0010073.eor).
Abbreviations
MTTmaximum transitive threshold
UCSCUniversity of California Santa Cruz
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Karolchik D Hinrichs AS Furey TS Roskin KM Sugnet CW 2004 The UCSC Table Browser data retrieval tool Nucleic Acids Res 32 D493 D496 14681465
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PLoS Comput BiolPLoS Comput. BiolpcbiplcbploscompPLoS Computational Biology1553-734X1553-7358Public Library of Science San Francisco, USA 1636207410.1371/journal.pcbi.001007505-PLCB-RA-0090R3plcb-01-07-06Research ArticleBioinformatics - Computational BiologyEvolutionNoneGenome Trees from Conservation Profiles Species Trees from Phylogenetic ProfilesTekaia Fredj 1*Yeramian Edouard 21 Unité de Génétique Moléculaire des Levures (URA 2171 CNRS and UFR927 Univ. P.M. Curie), Institut Pasteur, Paris, France
2 Unité de Bio-Informatique Structurale (URA 2185 CNRS), Institut Pasteur, Paris, France
Bourne Philip EditorUniversity of California San Diego, United States of America* To whom correspondence should be addressed. E-mail: [email protected] 2005 16 12 2005 1 7 e752 5 2005 10 11 2005 Copyright: © 2005 Tekaia and Yeramian.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.The concept of the genome tree depends on the potential evolutionary significance in the clustering of species according to similarities in the gene content of their genomes. In this respect, genome trees have often been identified with species trees. With the rapid expansion of genome sequence data it becomes of increasing importance to develop accurate methods for grasping global trends for the phylogenetic signals that mutually link the various genomes. We therefore derive here the methodological concept of genome trees based on protein conservation profiles in multiple species. The basic idea in this derivation is that the multi-component “presence-absence” protein conservation profiles permit tracking of common evolutionary histories of genes across multiple genomes. We show that a significant reduction in informational redundancy is achieved by considering only the subset of distinct conservation profiles. Beyond these basic ideas, we point out various pitfalls and limitations associated with the data handling, paving the way for further improvements. As an illustration for the methods, we analyze a genome tree based on the above principles, along with a series of other trees derived from the same data and based on pair-wise comparisons (ancestral duplication-conservation and shared orthologs). In all trees we observe a sharp discrimination between the three primary domains of life: Bacteria, Archaea, and Eukarya. The new genome tree, based on conservation profiles, displays a significant correspondence with classically recognized taxonomical groupings, along with a series of departures from such conventional clusterings.
Synopsis
Since Darwin's Origin of Species and Haeckel's Tree of Life, systematic biology has attempted to classify species into “family trees.” Genomics has provided a new framework permitting descriptions of sibling relations between species on the basis of their complete genetic blueprints. While trees based on single genes (rRNA), or limited numbers of genes have been useful, genome trees derived from complete genome comparisons should lead to more complete pictures of phylogenetic relations between various organisms. In order to reach such a global vision, procedures to establish sibling relationships should depend on an overall comparison that captures the evolutionary fates of proteins jointly in multiple genomes. This paper aims to establish a methodological basis to use genuine multidimensional procedures in the construction of genome trees. This approach completes the derivation of trees based on more classical techniques of pair-wise comparison between species. The authors survey classification schemes emerging from this approach, which either supports traditional views, such as the separation between the three phylogenetic domains Bacteria, Archaea, and Eukarya, or challenges them by suggesting, for example, intermingled clusterings of Proteobacteria with various other bacterial species.
Citation:Tekaia F, Yeramian E (2005) Genome trees from conservation profiles. PLoS Comput Biol 1(7): e75.
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Introduction
Genomes contain many levels of phylogenetic information. As well as sequences of nucleotides and amino acids, complete genomes also contain structural information on the order of genes [1], nucleotide usage patterns [2], and amino-acid composition [3,4]. The evolution of genome content has become a central issue in comparative genomics revealing major evolutionary events including gene loss, gene acquisition through horizontal transfer [5–10], transfer of mitochondrial DNA sequences to the nucleus [11], and gene duplication [12–14]. Such events tend to undermine the concept of “the universal phylogenetic tree” since no single gene tree can reflect evolution in all species. Moreover, since single gene families represent only a minor fraction of genomic information, it has been argued that focusing on single genetic elements (such as rRNA genes) can be inadequate for an integrative analysis of complete character complexes and the construction of phylogenetic trees of whole organisms. Accordingly, various integrative procedures have been designed to overcome these difficulties [15–17]. For example, the construction of “phylogenomic trees” involves the use of longer and richer datasets, obtained by joining large sequence stretches or concatenated proteins common to several species [18,19]. In another direction, the construction of “supertrees” relies on several individual gene trees [20,21].
Genome trees integrate information of potential evolutionary significance, based on comparisons of gene similarities, from whole genome content. Thus, the various proposed genome trees reflect global similarities based on the presence or absence of genes, gene families, protein folds, amino acid patterns [22–28], or gene order [29,30]. More recently, genome trees have been based on protein domain contents [31] or “genome conservation” [32]. The rationale in making phylogenetic inferences from such information is that shared similarities in the organization of two genomes should correspond to inherited features from a common ancestor. The methods used to assess information from complete genomes rely on the occurrence of shared orthologs or shared gene families, as measures of similarity. However, despite their major advantages over single-gene trees, the derivation of genome trees still suffers from a series of limitations and difficulties, essentially relevant to the choice of the data, and to the adequacy of the methods used to analyze them.
The primary information used to construct genome trees reflects phylogenetic relations and evolutionary events relevant to gene transfer, gene loss, and acquisition. It has necessarily mixed origins. The construction of robust genome trees still remains in many ways an unachieved goal. The problems and limitations encountered in the construction of genome trees are of different origins. For the genome data, biodiversity is not homogeneously represented in the various branches of the three domains of life. The assessment and estimation of gene acquisition via duplication, horizontal transfer, or other processes [8,33,34] remains difficult despite recent reappraisals [13,35–39] and new methods adopted to better treat them (derivation of genomic trees [40,41], or genomic non-tree topologies [42,43]). Finally, tree building methodologies have so far not fully exploited the multidimensional nature of the evolutionary genomic information, obtained jointly across several species.
In the context of these limitations, we introduce here methods to derive genome trees based on “conservation profiles,” taking fully into account the high dimensional nature of the data and the multidimensional nature of the evolutionary histories of proteins. Indeed, the conservation profile of a given protein captures an evolutionary history, expressed as an n-component vector detailing the presence or absence of homologs, in each of the n considered species. Through the multidimensional structure of conservation profiles, the evolutionary history of proteins is thus observed jointly across n species: proteins with identical conservation profiles can be associated with identical evolutionary histories. From the complete set of considered proteomes, the set of distinct conservation profiles is indicative of the various evolutionary histories.
On methodological grounds, we used multivariate analysis for deriving genome trees from conservation profiles. Such derivations highlight some difficulties in the handling of conservation profiles, as representation of phylogenetic histories. These difficulties are discussed in some detail, paving the way for possible improvements. On the one hand, resorting to conservation profiles permits reduction of informational redundancy by retaining only distinct conservation profiles. On the other hand, analysis of conservation profiles from the proteomes of 99 complete genomes showed that many proteins (in the same or in different species) share identical evolutionary histories, leading to a very small set of shared distinct profiles (associated with at least two proteins from two distinct species). The criteria for the derivation of trees from profiles are thus not trivial, with various possible compromises on stringency. Stated otherwise, should we consider the full set of all distinct conservation profiles or retain only the core set of shared distinct conservation profiles? We explored these possibilities by constructing a genome tree based on the core set of shared distinct profiles. One step further, to reasonably relax the strict restriction to shared distinct profiles, we considered the whole set of distinct conservation profiles, resorting to Jaccard similarity scores between pairs of species (as calculated from the whole set of distinct conservation profiles), and also derived the corresponding tree.
Beyond the methodological derivations, for a first exploration of this new type of genome tree, we analyze in some detail the topology of the tree based on profiles obtained from Jaccard scores. This analysis is performed in parallel with the analysis of other trees obtained from the same set of genomic data: (a) a genome tree based on ancestral duplication and ancestral conservation weights (an updated version of the genome tree presented in [23]) and (b) a genome tree based on shared orthologs. This comparative analysis reveals very stable features and clusters, along with a series of variations following the trees. All trees discriminated clearly between the three phylogenetic domains of life. A series of variable features, amongst the trees, appears to reflect rather faithfully various alternative hypotheses associated with debated phylogenetic clusterings. This observation is interpreted in part in the light of possible interplays between phylogeny and genome dynamics.
Results
The large-scale predicted proteome comparisons (see Materials and Methods) permit determination of conservation profiles for each protein of n considered species (n = 99; Table S1 and Figure 1, steps 1 and 2). For each protein, the conservation profile is represented by an n-component vector of zeros and ones, which describes its conservation pattern across the n species (zero corresponds to the absence and one to the presence of a homolog in the various species). The conservation profile of a protein sequence can be associated with its evolutionary history in a multidimensional genome space. This mathematical definition of “conservation profile” is identical to that of “phylogenetic profile” [44–45] as it is based on the same vector. The terms “phyletic pattern” and “phylogenetic pattern” have also been used to describe the same vector [46]. Here, we prefer “conservation profile” since it refers explicitly to the comparison process. The “evolutionary profile” underlying such multidimensional comparisons, can indeed be associated with evolutionary processes (such as horizontal transfer or duplication) rather than purely vertical inheritance (i.e., phylogeny).
Figure 1 Determination of Distinct Conservation Profiles for Proteins
The flow chart details steps in the determination of distinct conservation profiles for proteins in 99 predicted proteomes. The steps are as follows:
(A) Step 1: Species-specific predicted proteome comparisons. Each protein sequence of species Si (see list in Table S1) was compared to each database of all proteins from each surveyed species, using the BLASTP program (See Materials and Methods). Best significant matches in each of the considered species were determined. The original 541,880 protein sequences, lead to 442,460 non-specific proteins (i.e., 81.7%). Fractions of ancestral duplication and ancestral conservation were determined. Each protein was then described by a vector whose components are zeros (no matches) or best significant matches whenever hits occur in each of the considered species. From the list of proteins and their corresponding best hits, pairs of orthologs were determined by looking for reciprocal best significant hits.
(B) Step 2: Protein conservation profiles. In each species Si, the conservation profile of each protein k, denoted gi,k, is represented by a n-component vector of ones and zeros describing its pattern of conservation across all species. Each vector associated with a conservation profile is of size 99, corresponding to the total number of surveyed species (in the order indicated in Table S1).
(C) Step 3: Distinct conservation profiles. In each species Si, identical conservation profiles were represented by a single representative, leading to the set of distinct conservation profiles. In this simplification, a “weight” is associated to a given conservation profile, as the total number of proteins with that profile. For example 3,154 distinct conservation profiles were found in S. cerevisiae, 5,690 in A. gambiae, 6,225 in H. sapiens, and 1,716 in P. falciparum.
(D) Step 4: Overall characterization of distinct conservation profiles. The overall set of distinct conservation profiles amounted to 184,130 profiles. The “conservation weight” of each conservation profile is determined, as the total sum of 1.
Distinct Conservation Profiles
The large-scale proteome comparisons for the 99 completely sequenced genomes analyzed involved a total of 541,880 proteins (Table S1). The comparisons led to 442,460 non-specific proteins with non-trivial conservation profiles (i.e., with at least one homolog outside their own proteome), resulting in 184,130 distinct conservation profiles, which retained only one representative from each set of identical conservation profiles (Figure 1, steps 3 and 4). Thus, distinct conservation profiles represent 41.6% of the total set of non-specific proteins. One step further, we consider the core subset of shared distinct conservation profiles, associated with at least two proteins from distinct species. This core subset reduces to 24,044 profiles, which represent only 5.4% of the whole set of non-specific conservation profiles and 13% of the set of distinct conservation profiles.
These data provide several possible choices for the derivation of trees from conservation profiles. Based on maximal redundancy reduction, we can adopt the core reduced subset of shared distinct conservation profiles. Alternatively, this choice could be seen as too reductive, since it discards information contained in the 160,086 distinct conservation profiles associated with only one species, which correspond to one or several proteins from that species. It is then possible to derive trees that consider the additional information in this set of profiles, with potential relevance to ancestry signals.
In the light of these different choices, it may be of interest to quantify the characteristics of information contained in the distribution of profiles. Thus, each of the 24,044 shared distinct profiles, associated with at least two species, involved an average of 11.9 proteins. The classification of profiles according to relative “conservation weights” (or the total number of occurrences of 1 in the given profile; this number could vary between 1 and 99), led to an average weight of 30 (SD = 25.3). For most conservation profiles, conservation weights ranged between three and seven. Overall, the distribution of the number of profiles decreased uniformly as conservation weights increased (Figure 2A and 2B). Finally, for the set of 184,130 distinct conservation profiles, similarities between pairs of species were evaluated from the Jaccard score (see formula in Materials and Methods).
Figure 2 Distinct Conservation Profiles and Corresponding Weights
(A) Distribution of the whole set of distinct conservation profiles (184,130) according to the 99 possible weight classes varying from one to 99.
(B) Similar distribution restricted to the subset of distinct conservation profiles (2,044) associated with proteins from at least two species.
Genome Trees: Similarity Matrices
The various genome trees considered here were derived using a common rationale, as shown in Figure 3. First, a data matrix T was constructed from similarity scores measuring the relatedness of each pair of species (see Materials and Methods): fractions of shared distinct conservation profiles, Jaccard scores, fractions of shared orthologs, and finally, ancestral duplication-conservation weights. Secondly, correspondence analysis was performed [47,48] to construct an orthogonal system, and to represent the n species in the corresponding factorial space of dimensions n–1. Finally, each resulting genome tree was derived, based on the reciprocal neighboring of the species, using Euclidean distances calculated from coordinates in the factorial space. We will consider in some detail the genome tree associated with Jaccard scores (that we term profiles tree), comparing it with the three other trees (minimal profiles tree,
Figure S1, based on shared distinct conservation profiles; orthologs tree,
Figure S2, based on shared orthologs; and conservation tree,
Figure S3, based on ancestral duplication and conservation weights). We highlight features that seem to be stable in these various trees and those that are most variable.
Figure 3 Genome Tree Construction
The flow chart details the three steps in genome tree construction. In the first step a data matrix is constructed, based on overall similarity scores between pairs of species (i.e., the fraction of shared distinct conservation profiles, Jaccard scores, fraction of shared homologs, or ancestral duplication conservation weights). In the second step, correspondence analysis is performed on the data tables, for constructing the corresponding factorial spaces (orthogonal systems of dimensions n–1, with n the number of lines in the considered matrices). In the third step the genome trees are derived based on the reciprocal neighboring of the species from their Euclidean distances, as calculated in the factorial spaces.
We note that all trees are derived from the same set of genomic data, and depend on multidimensional or pair-wise conservations, thus reflecting potentially different evolutionary relationships. More precisely, conservation profiles reflect detected evolutionary relationships across all surveyed species (multidimensional evolutionary signatures), whereas the orthologs and the ancestral duplications-conservations reflect detected evolutionary relationships between pairs of species. Also, following the terminology in [27], the conservation tree is relevant to the “homolog method” and the orthologs tree is relevant to the “ortholog method.” For the new profiles tree we could similarly refer to the “conservation profiles method.”
Tree Topologies and Clusterings of Species
The first striking observation is that the three domains of life are clearly separated in the profiles tree (Figure 4), with the branching of Archaea with Bacteria. This separation, as well as the Archaea-Bacteria branching, apparently corresponds to very stable features throughout the different trees (Figures S1, S2, and S3). At such a global level, the only difference between the various trees concerns variable levels of resolution. With this respect, as illustrated here for the profiles tree (Figure 4), enhanced resolutions can be achieved by considering partial trees, which can be associated, for example, with each one of the three domains of life, separately. In what follows, we consider two different types of such partial trees (see Materials and Methods for more details), in which we restrict the construction of the partial trees to the species of a given domain of life. Still taking into account the comparisons between all species in the three domains, the restrictions are only at the level of similarity matrices. Thus, from the similarity matrix of the profiles tree, by restricting ourselves to the lines associated with the species in the respective domains we derive the bacteria subtree, archaea subtree, and eukarya subtree (Figures 5, 6A, and 7A, respectively). By further restricting the matrix at the level of the columns as well (with lines and columns corresponding to species in a given domain), we define the archaea only subtree and eukarya only subtree (Figures 6B and 7B).
Figure 4 Genome Tree
Profiles tree based on Jaccard scores as obtained from the whole set of distinct conservation profiles.
Figure 5 Bacterial Branch
Bacteria subtree (see Materials and Methods), based on the restriction of the Jaccard scores matrix to the lines corresponding to bacterial species.
Figure 6 Archaeal Branch
The archaeal branch is represented in more detail with (A) archaea subtree (see Materials and Methods), based on the restriction of the Jaccard scores matrix to the lines corresponding to archaeal species, and (B) archaea only subtree based on the restriction of the Jaccard scores matrix to the lines and the columns corresponding to archaeal species.
Figure 7 Eukaryal Branch
The eukaryal branch is represented in more detail with (A) eukarya subtree (see Materials and Methods) based on the restriction of the Jaccard scores matrix to the lines corresponding to eukaryal species, and (B) eukarya only subtree based on the restriction of the Jaccard scores matrix to the lines and the columns corresponding to eukaryal species.
Bacterial Branch
General structure.
Clusters in the bacterial branch follow accepted taxonomical groupings rather closely, with only a few departures. The Mycoplasmas are the most distant cluster (as further illustrated in the bacteria subtree,
Figure 5). Beyond the out-branched Mycoplasmas, the bacterial branch splits into two nodes (B1 and B2, on the bacteria subtree,
Figure 5). Following this major organization, some classically accepted taxonomical groups are homogeneously clustered, whereas others —such as the Proteobacteria— are scattered throughout several nodes and branches. We next consider in more detail the organization of the bacterial branch according to classical taxonomical classifications. Due to various intermingled clusters the analysis follows the hierarchical structure of the tree, rather than strict taxonomical classifications.
The B1 node (Figure 5) is bifurcated, with two clearly separated branches at nodes B11 and B12. The B11 node clusters together three (homogeneous) subclusters: (a) the parasitic alpha and gamma Proteobacteria, (b) the Chlamydiae, and (c) the Spirochaetes. The B12 node clusters two clearly separated subgroups: (a) the Cyanobacteria and (b) a clustering of the epsilon species with a composite group comprising Thermoanaerobacter tengcongensis (underlined as separated from the other Firmicutes), Thermotoga maritima and a Aquifex aeolicus.
The B2 node (Figure 5) splits into three branches, at the nodes B21, B22, and B23. The B21 node joins together all Actinobacteria (with the exception of Streptomyces coelicolor, underlined in the B23 node). The B22 node unites all the Firmicutes (with the exception of T. tengcongensis, as already mentioned). The B23 node splits into two subclusters: (a) the node b'23 groups alpha Proteobacteria (with the underlined gamma species Pseudomonas aeruginosa) along with the association of S. coelicolor (Actinobacteria) and Deinococcus radiodurans (Deinococcus) and (b) the b''23 node unites gamma Proteobacteria. In this overall organization we note that the b'23 node joins a series of soil/plant associated bacteria, from different phylogenetic groups but with common lifestyle features. This clustering unites the free-living S. coelicolor (Actinobacteria; which has developed a large coding potential involving many proteins implicated in regulatory functions), with the pathogenic P. aeruginosa (with free-living capacities), and a series of rhibozomal microsymbionts (alpha Proteobacteria). This clustering overlaps rather sharply with those observed, for example, on the basis of transport capabilities [49], since the concerned organisms “have more ABC transporters than any other sequenced organisms” [49]. We also note (as in [49]) that such clustering is uncorrelated with genome size. The genome of D. radiodurans is about 3.3 Mega bases while that of S. coelicolor is about 6.2 Mega bases, for example.
Stabilities versus variabilities in the background of alternative phylogenetic hypotheses.
The out-grouping of the Mycoplasmas does not seem to be a stable feature across the trees we consider. In the minimal profiles tree as well as in the orthologs tree the most distant cluster concerns Actinobacteria (Figures S1 and S2). Also, at this level, the analyses are not consistent with other work, which suggests that either the Thermotogales or the Aquificales are the most out-grouped of the bacterial branch [18,26].
The scattering of the Proteobacteria at various nodes of the bacterial branch is found in all the trees considered here (see also Figures S1, S2, and S3). This feature is consistent with conclusions in many analyses [26,50], and contradicts monophyletic proteobacterial clusters observed in certain studies [18,31]. At a more detailed level, several associations between various Proteobacteria seem to be very stable, such as the association (node b'23, Figure 5) of the pathogenic P. aeruginosa (gamma species) with a series of rhibozomal microsymbionts (alpha species). This cluster seems to be systematically clustered with the free-living Actinobacteria S. coelicolor in all trees examined here. On the other hand, the association of D. radiodurans with this cluster varies according to the chosen tree. In the minimal profiles tree (Figure S1) and in the conservation tree (Figure S3), the Actinobacteria Mycobacterium leprae joins S. coelicolor, and surprisingly unites a highly decaying species with a series of species with extended repertoires for adaptation.
Other composite associations also seem to be very stable, such as that concerning the Spirochaetes, the Chlamydiae, and the parasitic Proteobacteria (node B11 in Figure 5). Interestingly, this association is observed not only in the various trees here, but also in other analyses [26].
Concerning the Firmicutes, we note that T. tengcongensis is separated from the other Firmicutes in all trees. This separation may reflect the ambiguous status of this species in traditional classifications. While empirical definitions suggest that it is gram-negative, analysis of the complete genome revealed that T. tengcongensis “shares many genes characteristic of gram-positive bacteria” [51]. Similar observations have been reported in trees in recent studies [50].
Archaeal Branch
General structure.
In the archaeal branch, the hyperthermophilic Nanoarchaeum equitans and the psychrophilic Methanogenium frigidum are out-grouped. We note that N. equitans has been assigned recently to a novel archaeal phylum (“Nanoarchaeota” [52]).
Beyond these out-grouped species, the archaeal branch displays little resolution in the profiles tree (Figure 4), but is bifurcated with the enhanced resolution of the archaea subtree and archaea only subtree (nodes A1 and A2, in Figure 6A and 6B). This bifurcated structure does not follow the Crenarchaeota/Euryarchaeota separation, even if the four Crenarchaeota species are clustered together.
The A1 node (Figure 6A and 6B) clusters the Crenarchaeota species together with the Thermoplasma. The organization of the A2 node varies between the archaea subtree (Figure 6A) and the archaea only subtree (Figure 6B). In the archaea subtree, the node A2 bifurcates with the node a21 clustering together a series of Methanogens with Halobacterium sp. and Archaeoglobus fulgidus, and the node a22 clustering together the Pyrococcus species with two Methanogen species (Methanopyrus kandleri and Methanopyrus janaschii). In the archaea only subtree, the Pyrococcus cluster shifts with respect to the archaea subtree, becoming out-branched from a mainly Methanogens cluster, joining the a21 node of the archaea subtree with the remaining two Methanogens (M. kandleri and M. janaschii).
Stabilities versus variabilities in the background of alternative phylogenetic hypotheses.
In terms of major clades, these analyses do not support the classification of the Archaea after the Crenarchaeota/Euryarchaeota separation, despite the co-clustering of the Crenarchaoeta species observed in the profiles tree. Clustering together of the Crenarchaoeta is not always observed in these trees (see for example the minimal profiles tree;
Figure S1). This conclusion on Crenarchaeota/Euryarchaeota is consistent with various other analyses (such as in [26], where Crenarchaoeta cluster with the Thermoplasma). In fact, recent genome tree studies have rarely supported Crenarchaeota/Euryarchaeota separation (moderately supported in [18], on the basis of a single species, Aeropyrum pernix). As for the novel archaeal phylum “Nanoarchaeota,” it is difficult to draw firm conclusions here since it concerns a single species N. equitans (out-grouped in the minimal profiles tree, but not in the orthologs tree; see Figures S1 and S2).
A more detailed study of the branch reveals an inconsistency between the archaea subtree (Figure 6A) and archaea only subtree (Figure 6B) for the positioning of the Pyrococcus. These data could therefore either support or contradict potential monophyly of Methanogens. This doubt about the appropriate position for the Pyrococcus is confirmed by the other trees. In the orthologs tree (Figure S2), for example, Pyrococcus joins the other node of the archaeal branch, with Crenarchaeota and Thermoplasma species. Of these possibilities, an out-grouping of Pyrococcus from a largely homogeneous Methanogens cluster, as in the archaea only subtree (Figure 6B), is consistent with the representation of [26]. A. fulgidus clusters with the Methanogens in all the trees considered here. In contrast, Halobacterium sp. does not cluster with the Methanogens in the minimal profiles tree or in the conservation tree (Figures S1 and S3). In the literature, the positioning of A. fulgidus relative to the other Archaea has been controversial, shifting from a deep-branching position toward a grouping with Methanomicrobiales and extreme halophiles [53], based on rRNA genes. However, with the completion of its genome, it was revealed that in A. fulgidus “all the enzymes and cofactors of methanogenesis are used, but the absence of methyl-CoM reductase eliminates the possibility of methane production by conventional pathways” [54], thus reinforcing the firm clustering consistently observed here.
Eukaryal Branch
General structure.
The eukaryal branch bifurcates with two clearly separated branches (Figure 4). This structure is preserved with the enhanced resolution in the eukarya subtree (Figure 7A) and eukarya only subtree (Figure 7B). In these representations of the eukaryal branch, the first node joins together the animals (Mammals, Nematodes, Arthropods, and the Chordate Ciona intestinalis), along with a composite cluster comprising a red algae, a plant, and a protist. The second branch unites various fungal species.
At a more detailed level, in the profiles tree (Figure 4), Encephaliltozoon cuniculi is out-grouped in the first node. In this profiles tree no separations are observed in the animals cluster. Better resolution, in the eukarya subtree and eukarya only subtree (Figure 7A and 7B, respectively) reveals an unstable positioning of E.cuniculi. In the eukarya subtree, E. cuniculi is distant from the red algae-plant–protist (Plasmodium falciparum) cluster at the E12 node, whereas in the eukarya only subtree, it is distant from all animals at the E1 node (as in the profiles tree). For the animals, in the eukarya subtree, a separation appears between Nematodes and the other animals (node E11, with Anopheles gambiae out-grouped), whereas in the eukarya only subtree (Figure 7B, node E11) we observe a more precise clustering following Vertebrates along with the Chordate C. intestinalis, the Nematodes, and the Arthropods. At the second node (E2), an increasing resolution appears between the profiles tree, the eukarya subtree, and the eukarya only subtree, respectively. A progressive resolution is apparent in the fungi branch with the separation of Schizosaccharomyses pombe from the other yeasts in the eukarya only subtree. In this tree we obtain essentially a separation of the fungi in clusters corresponding to Euascomycota, Archiascomycota (S. pombe), and Hemiascomycota. In this case, the genomic subtree reflects, rather faithfully, admitted phylogenies [55], either based on limited sets of orthologous proteins (Resources for Fungal Comparative Genomics: http://fungal.genome.duke.edu) or fungal mitochondrial genome projects (Global Fungal Phylogeny: http://megasun.bch.umontreal.ca/People/lang/FMGP/phylogeny.html), with the precise positioning of the out-grouped S. pombe indeed varying following the studies.
Stabilities versus variabilities in the background of alternative phylogenetic hypotheses.
The bifurcated structure of the eukaryal branch is found consistently in the various trees considered here. At the highest level, the only observed variance is that the red algae-plant–protist cluster joins with the fungi branch in the conservation tree (Figure S3). It is interesting to note that at present for eukarya, relations between plants, animals, and fungi “have not been conclusively resolved” [56]. None of the analyses here, with a bifurcated structure of the eukaryal branch, support recent phylogenetic analyses, which imply the definition of an Opisthokonta “super-taxon” that joins animals and fungi [57,58]. With additional genomes becoming available (notably plants), it will be important to see if this bifurcated structure is further confirmed. At this large organizational level, we note that none of the analyses would link the microsporidian E. cuniculi with the fungi, with the exception of the conservation tree, where the cluster with the fungi also includes plants and other protists (P. falciparum). This observation is inconsistent with the “general consensus” [56] on the relation of microsporidians to fungi. However, as noticed in [56], this consensus depends essentially on phylogenies of single proteins, and is still under debate.
At a more detailed level, the classical “Coelomata hypothesis,” suggests that Arthropods are closer to Vertebrates than to Nematodes, whereas the recent “Ecdysozoa hypothesis” suggests Nematodes should be clustered with Arthropods [56]. The various representations of the eukaryal branch in the profiles tree do not permit discrimination between these two hypotheses. Nonetheless, the minimal profiles tree (Figure S1) reveals a clear clustering of Nematodes with the Arthropods, while in the orthologs tree and in the conservation tree (Figures S2 and S3), the Nematodes are out-grouped, and the Arthropods are associated with the Vertebrates. Such instabilities suggest that contradictory theories reflect different interpretations of the same data. However, instabilities might instead derive from the quality of the data and notably of annotations. Plausibly such is the case for the variable positioning of A. gambiae (directly clustered with Drosophila melanogaster only in the eukarya only subtree, Figure 7B), and in the orthologs tree (Figure S2).
Discussion
The primary concern of our work was to derive methods to construct genome trees from conservation profiles. One challenging problem in constructing genome trees is to separate—as much as possible—phylogenetic signals from other evolutionary “noise,” deriving from gene acquisitions via horizontal transfer, duplication, and gene losses. Thus, information in protein conservation profiles may represent an especially accurate marker for genome classification, since it embeds the most conserved and meaningful evolutionary signals, captured jointly in the whole set of surveyed species. In addition, we have shown that the core set of distinct conservation profiles is associated with a significant reduction in informational redundancy as compared to the complete set of profiles. Potentially, this reduction in the redundancy may reflect, more or less directly, reductions in the contributions of gene acquisition and loss processes in the evolutionary histories as captured by the profiles.
Beyond the descriptive analysis of profiles, we have also tried to assess problems and difficulties encountered in the derivation of trees from profiles. Thus, a reduction in informational redundancy, which may be an advantage in some respects, can also be too drastic if we consider the set of shared distinct profiles. Such stringent requirements, based on the normalized number of shared distinct conservation profiles between species, leads to the minimal profiles tree (Figure S1). However, the scheme retains only a very small percentage of the set of distinct conservation profiles, and much potentially significant information is discarded. We therefore opted here for a reasonable compromise, of calculating similarities between pairs of species from Jaccard scores based on the set of all distinct conservation profiles.
In short, the approach developed here is probably just a first step in treatment of the intrinsically multidimensional evolutionary histories of proteins to derive genome trees. Possibly, other data handling schemes may provide improved compromises between the criteria of maximal retention of relevant information and maximal removal of redundancies. For example, such improvements might derive from methods to calculate distances or similarity scores between species from conservation profiles, as well as measures of relatedness between species (for example, Manhattan, Euclidean, Chebyshev, and Hamming distances; see [59] for discussion).
Biologically, the results in the new profiles tree are better appreciated with a parallel analysis of three other trees (Figures S1, S2, and S3) obtained from identical genomic data. One major conclusion in this comparative analysis is the simultaneous observation of certain stable features and clusterings, along with clusterings that are highly variable following the trees (and the underlying methods of data analysis).
At the most general level, all the trees considered here display, invariably, a robust clustering of the studied species into three well defined groups corresponding to the three domains of life, as defined on the basis of 16/18S rRNA sequences [53]. Moreover, all the trees group the Archaea together with Bacteria. Such branching is consistent with the overall trend observed in various proteome comparisons that reveal Archaea are closer to Eukarya in terms of informational genes (transcription, translation) but closer to Bacteria for operational genes [9,60,61]. As all trees here are based on overall proteome comparisons, this very stable result adheres to a higher proportion of operational genes, rather than informational ones. This sibling relation is also consistent with universal trees, with artifacts due to long-branch attraction eliminated, in which Archaea are also clustered with Bacteria [62,63].
More detailed analysis reveals a series of prominent features in the three domains. Whatever the details of bacterial branch clustering, the Proteobacteria never form a homogeneous branch. Even so, within the bacterial branch certain associations are highly stable between trees such as the one which unites the parasitic Proteobacteria (Rickettsia species and in three trees the Buchnera species) with the Chlamydiae and the Spyrochaetes. A surprising example of variability is the position of the highly decayed M. leprae [64], which either clusters with the other Actinobacteria, or is separated from them to join S. coelicolor (with a highly expanded genome [65], separated from the other Actinobacteria in all cases). Similarly, the archaeal branch displays both stable and variable features such as the systematic clustering of A. fulgidus with Methanogens, and the variability of Halobacterium sp. which joins this cluster in only two of the trees. An even more striking example of variability is the location within this branch of the Pyrococcus cluster. Similarly we note in the eukaryal branch that the composite cluster {Plant-red algae-Protists} is linked with the Animals in all trees, but with the Fungi in the conservation tree (Figure S3).
Some unstable features observed in the various trees might potentially derive from a lack of adequate information (such as the number of representatives for given clades). Alternatively unstable features might originate in true evolutionary signals, such as dynamic features reshaping the genomes, toward either decays or expansions, and providing distinct versions, when analyzed with different schemes of data handling.
As discussed for several examples, differences between trees could account for a series of alternative phylogenetic hypotheses (monophyly of Methanogens, Coelomata versus Ecdysozoa, microsporidians with animals or fungi, etc). In such a perspective, several present controversies might then simply represent different facets of the same evolutionary reality. Possibly, the only reasonable road toward a global view of the genomic clustering of species would involve a combination of pictures from different trees. In addition, it seems important to keep track of the evolution of the variability features from different pictures, as the number of available genomes increases. Such information may tend to cause certain variabilities to recede or disappear while other (intrinsic variabilities) will remain independent of the number of representatives for the concerned clades. We have noted this tendency, in preliminary observations, as we have increased the number of genomes included in the present work from preliminary observations with smaller numbers of species. Those intrinsic variabilities, following the different points of view associated with the different types of analyses, may ultimately be preferentially associated with genome dynamics features. For such studies, we plan to update the various trees here (based on 99 species) as new data become available.
Materials and Methods
Species-specific comparisons.
The methodology for large-scale proteome comparisons (the list of species in the analysis is given in Table S1) has been described in detail elsewhere [23,66]. Briefly, the proteome of each species considered was compared to that of each other species (Figure 1, step 1), using the BLASTP program [67], with the pam250 substitution matrix and the seg filter [68]. The significance threshold for the comparisons was set heuristically for each target species. For example, probability score limits were set at 10−9 for all eukaryotic species (for details concerning Saccharomyces cerevisiae see [69]). From intra-proteome comparisons only reciprocal significant hits were retained, eliminating 2% to 5% of initial significant hits (with significant score in one comparison direction [A,B], and the score associated with the reciprocal direction [B,A] being non-significant). The results of all bidirectional pair-wise comparisons for the predicted proteomes (step 1 in Figure 1) permit the estimation of (a) the level of ancestral duplication in each species, (b) the ancestral conservation, (c) the number of shared orthologs between pairs of species (following the working definition of putative orthologs in [70]), and (d) the conservation profile for each protein across all considered species.
Data tables and tree construction methods.
Figure 3 details the steps for the derivation of genome trees. For each data table considered, correspondence analysis [47,48] was used to plot species in a factorial space of dimensions n–1 (orthogonal system), with n the number of species. Species were then clustered according to their reciprocal neighborhood in the factorial space to obtain the genome tree. Correspondence analysis permits calculation of Euclidean distances using species coordinates in the factorial space.
The clustering process consists in grouping the two closest pairs of the n considered species (or terminal nodes), leading to (n–1) nodes. The two closest nodes among these (n–1) are then grouped to give (n–2) nodes, etc. This process is iterated (n–1) times until all species are grouped in a single node. The final tree shows the hierarchical clustering of all species in a decreasing order of neighborhood: closest species are clustered first and most distant last.
Shared distinct conservation profiles.
The data matrix is defined as T = {Tij = 100*sij/sjj; i = 1, n; j = 1, n}, where Tij represents the percent of shared distinct conservation profiles sij (see Figure 1, step 2) between species i and j relative to sjj, the total number of distinct conservation profiles in j. Note that among the total 184,130 distinct conservation profiles, only 24,044 are shared by at least two species, and the rest are unique to a given species. The corresponding tree is referred to as minimal profiles tree.
Jaccard similarity scores between species.
In order to relax the strict restriction leading to the definition of shared conservation profiles and for taking into account the relevant ancestral information in the whole set of 184,130 distinct conservation profiles, we resort to similarity scores between species based on the Jaccard score. In this case the species are defined by binary vectors in the space of distinct conservation profiles.
The Jaccard score sij between two species i and j is calculated following the formula: sij = aij/(aij + bij + cij), where for indexes i and j (column indexes for the various species) the values of aij, bij, and cij are given, respectively, by the total number of occurrences of (1,1), (0,1), and (1,0) along the lines of the 184,130 distinct conservation profiles.
Following this definition, a Jaccard score varies between one (i and j are related at each line position of the distinct conservation profiles by a (1,1) pair) and zero (not a single (1,1) pair in all lines of the conservation profiles). As such, a Jaccard score can be considered as a normalized indicator of mutual conservation between pairs of species.
In this case the data matrix is defined as T = {Tij = 100*sij; i = 1, n; j = 1, n}, with Tij expressing as a percentage the Jaccard score sij between species i and j. It can be noticed that the T matrix is symmetrical and that Tii = 100 (since sii = 1). The tree derived from this data matrix is referred to as profiles tree.
Partial trees associated with domains of life.
Two series of partial data tables were extracted from the previous table T, corresponding respectively to the bacterial, archaeal, and eukaryal domains. In the first series, restrictions concerned only the lines. For example, in such construction, the partial table associated with Eukaryotes is defined as TE = {Tij = 100*sij; i = 1, p; j = 1, n}, where i is limited to the p eukaryotic species. The tree derived from this partial table is referred to as eukarya subtree. In the same way we define a bacteria subtree and an archaea subtree.
In the second series, the restrictions concerned the lines as well as the columns (lines and columns restricted to the species in a given domain). The trees derived from the corresponding data matrices are referred to as bacteria only subtree, archaea only subtree, and eukarya only subtree, respectively.
Shared orthologs.
Two orthologous proteins are defined here as proteins with bidirectional best matches, in the comparison process. The central assumption in this approach is that orthologs display greater similarity to each other than to any other proteins from the respective genomes. The data matrix associated with shared orthologs is defined as T = {Tij = 100*sij/size(j); i = 1, n; j = 1, n}, where Tij represents the percentage of shared orthologous proteins sij between species i and j, relatively to size(j), the total number of proteins in j. The tree derived from this data matrix is referred to as orthologs tree.
Ancestral duplications and ancestral conservations.
The ancestral conservation sij is defined as the percentage of proteins in j that are conserved in i (i.e., proteins in j with at least one significant match in i), relative to the total number of proteins in j: sij = 100*(number of proteins in j that are conserved in i)/size(j). It can be noticed that for a given species j, sjj corresponds to the weight of ancestral duplication. With this definition of the weights sij, the ancestral duplication-conservation data matrix is: T = {Ti j= sij; i = 1, n; j = 1, n}. The tree derived from this data matrix is referred to as conservation tree. With the 99 genomes considered here, this tree corresponds in fact to an update of the tree derived previously from 15 genomes, as available in 1999 [23].
Supporting Information
Figure S1
Minimal Profiles Tree Based on Shared Distinct Conservation Profiles
See Materials and Methods.
(360 KB PDF)
Click here for additional data file.
Figure S2
Orthologs Tree Based on Shared Orthologs between Pairs of Species
See Materials and Methods.
(340 KB PDF)
Click here for additional data file.
Figure S3
Conservation Tree Based on Ancestral Duplication and Ancestral Conservation Weights
See Materials and Methods
(319 KB PDF)
Click here for additional data file.
Table S1 List of Predicted Proteomes Presented as They Appear in the Conservation Profiles and Corresponding References
(94 KB DOC)
Click here for additional data file.
We thank the anonymous referees whose remarks were at the origin of several new developments in the work (such as the derivation of the profiles tree, as compared to the original minimal profiles tree). We acknowledge many insightful discussions with Bernard Dujon and Antonio Lazcano. We thank Richard Miles for careful reading of the manuscript. This work was supported by the Institut Pasteur (Strategic Horizontal Programme on Anopheles gambiae) and the Ministère de la Recherche Scientifique (France): ACI-IMPBIO-2004–98-GENEPHYS program.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. FT and EY conceived and designed the experiments, analyzed the data, and wrote the paper.
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BMC Nucl MedBMC Nuclear Medicine1471-2385BioMed Central London 1471-2385-5-61631367510.1186/1471-2385-5-6Research ArticleA preliminary study of neuroSPECT evaluation of patients with post-traumatic smell impairment Eftekhari Mohammad [email protected] Majid [email protected] Majid [email protected] Mohsen [email protected] Armaghan Fard [email protected] Babak Fallahi [email protected] Ali [email protected] Davood [email protected] Research Institute for Nuclear Medicine, Tehran University of Medical Sciences, Shariati hospital, Northern Kargar St, 14114 Tehran, Iran2 Department of Otorhinolaryngology, Tehran University of Medical Sciences, Amiralam hospital, Sadi St, 13185-1678 Tehran, Iran2005 28 11 2005 5 6 6 22 7 2005 28 11 2005 Copyright © 2005 Eftekhari et al; licensee BioMed Central Ltd.2005Eftekhari 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
Most olfactory testings are subjective and since they depend upon the patients' response, they are prone to false positive results. The aim of this study was to use quantitative brain perfusion SPECT in order to detect possible areas of brain activation in response to odorant stimulation in patients with post-traumatic impaired smell in comparison to a group of normal subjects.
Methods
Fourteen patients with post-traumatic impaired smell and ten healthy controls were entered in this prospective study. All subjects underwent brain SPECT after intravenous injection of 740-MBq 99mTc-ECD and 48 hours later, the same procedure was repeated following olfactory stimulus (vanilla powder).
Results
In most of seven regions of interest (Orbital Frontal Cortex, Inferior Frontal Pole, Superior Frontal Pole, Posterior Superior Frontal Lobe, Parasagittal Area, Occipital Pole, and Cerebellar area) the post-stimulation quantitative values show increased cortical perfusion being more pronounced in normal volunteers than the anosmic patients (except cerebellar areas and the right occipital pole). Maximal activation was observed in orbitofrontal regions (right+ 25.45% and left +25.47%).
Conclusion
Brain SPECT is a valuable imaging technique in the assessment of post-traumatic anosmia and could be competitive as an alternative to other imaging techniques, especially when functional MRI is unavailable or unsuitable. However, this procedure may benefit from complementary MRI or CT anatomical imaging.
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Background
Although olfaction is the primal sense in animals, it has also an important role in the human life. Loss of this unique sensation could be extremely unpleasant and can be associated with deterioration of communicational functions of patients [1,2]. In fact, disorders of the sense of smell can be frustrating for both the patient and physician [3]. Quality of life studies have shown a general decrease in the level of satisfaction with life among those patients with continuing olfactory impairment[4]. Consequently, there is a growing interest into the investigation of smell disorders in both research and clinical practice and lots of efforts is being made to provide a noninvasive tool to elucidate the underlying pathology.
The ability to accurately measure loss of olfactory function is important not only for research purposes, but also to follow progression of the disease and for appropriate management of patients. The most powerful tools the clinician has in the diagnosis of olfactory disorders are only the patient's history and clinical assessment [5]. However, clinical definition and measurement of smell loss have been difficult to achieve, in part because the symptom is dependent upon patients' subjective complaints and in part because techniques used to demonstrate smell loss are based upon psychophysical measurements[6].
Most objective testings rely on measuring detection thresholds of a specific odorant and/or by measuring the ability to identify odorants by the patients[3,7]. Although it has been noted that these tests are capable of estimating various levels of decreased sense of smell and are also somewhat able to identify malingerers, but all of these methods have major limitations, which are widely acknowledged [3]. One of the main problems that stand in the way of analyzing olfactory disorders at present is that the majorities of methods are largely subjective and depend upon the patients' response. It is on this account that many of tests that have been conducted in this field do not carry much impact as they are not fit for quantification of these disorders. There is a strong likelihood that the patient is out to deceive the analyst by pretending malingering; in the case of the existence or nonexistence of post-traumatic impaired smell, which is a frequent complication of head injury[8]. Olfactory dysfunction following trauma is currently compensable according to existing American Medical Association guidelines and therefore either from the legal point of view or for planning an appropriate medical management, differentiating real impaired smell from affections of the patients is of unique importance. Unfortunately, up to now all methods which have been devised for differentiating these two groups have major limitations, not completely reliable and in the case of electrical olfactory evoked potentials, olfactometers or electroencephalograms were restricted to research centers and are nonpractical for general use[3,9].
A review of the literature revealed that only one study has evaluated the brain single photon emission tomography (SPECT) findings in patients affected by post-traumatic impaired smell[10] and information about the efficacy of this technique is extremely scarce. The results of this only study are partially confirmed in another study using positron emission tomography (PET). However, it was emphasized that further works should be undertaken to evaluate the role of SPECT and PET functional imagings as screening tools for the evaluation of this disorder. Among different and currently-available imaging techniques for the study of the olfactory system, functional magnetic resonance imaging (fMRI) has been more encouraging and is able to detect areas of brain activity in response to odorant stimulation in more detail than older methods[3,11,12]. However, its use is still limited to research centers. Therefore, we decided to use a similar rational by using SPECT in order to detect possible areas of brain activity in response to odorant stimulation and to evaluate the potential of SPECT imaging to identify real cases of impaired sense of smell by investigating the quantitative SPECT findings in post-traumatic patients and in comparison to a group of normal subjects.
Methods
From January 2004 to January 2005, twelve te fourteen patients with impaired sense of smell (8 men and 6 women) and ten healthy control volunteers (6 men and 4 women), all right handed, were entered in this prospective study. Each participant was healthy, not taking any medication, was able to breath normally in each naris, and had no subjective nasal pathology. The cases were selected from a population of patients referred to our otolaryngology department for evaluation and treatment of their post-traumatic impaired smell. Fourteen patients accepted to participate in our study. All participants had a history of mild to moderate head injuries followed by post-traumatic impaired smell. The time interval between the SPECT examination and the traumatic insult was 3 to 8 years (mean time interval, 4.6 years). None of patients had history of olfactory diseases or invasive therapeutic interventions on brain or nose before or after the traumatic insult. Control volunteers had also no history of mental disorder or head trauma.
The diagnosis of smell impairment was based on preliminary and limited olfactory stimulation testing using Cain's test, which revealed impairment in identifying common odors in all subjects. The findings however, are limited since this test is not adequate to fully evaluate the degree and type of smell loss [13]. The presence of normal olfactory perception in control group was also assessed by the same method. All subjects were free of remarkable mental disorders as assessed by a psychiatric interview at the time of examination.
All patients gave informed consent to participate in this study, which was approved by the committee on ethics at the faculty of medicine, university of Tehran.
Brain scintigraphy
A commercial ECD preparation was used. The labeling and quality control procedures were performed according to the manufacturer's instructions. All subjects had an intravenous line established while they were lying down, with their eyes closed and ears unplugged, in a quiet darkened room with low ambient sound and light. After approximately 30 min, each subject received a 740-MBq intravenous injection of tracer while they were still lying down in the same quiet darkened room.
One hour after IV injection of 750 MBq (20 mCi) 99mTc-ECD in a room with low level of ambient light and minimal background noise, SPECT procedure was performed. Scans were performed on a dual head ADAC camera, equipped with a pair of low energy, high resolution collimators. The full-width at half maximum (FWHM) of this system, as measured in-house, was 12-mm for 99mTc. Standard head positioning was based on uniform alignment of the external auditory meatus using automated table positioning and camera-to-head-detector ratio values. The total acquisition time was 35 minutes for each Study. Images were acquired in a 64 × 64 × 64 three-dimensional pixel matrix at 64 steps, 30 s each step. Before reconstruction of the images, attenuation correction of the images was carried out by the Chang method (attenuation coefficient 0.12 cm-1). Then the data were processed by back projection and filtered by Butterworth filter, using a Nyquist frequency cutoff of 0.5 and order of 5. Images were reconstructed and displayed in all three orthogonal planes.
48 hours later, the same procedure with all of the above-mentioned steps was repeated while vanilla powder stimulus was delivered in both nostrils. For olfactory stimulation during normal breathing, vanilla powder stimulus (10 minutes of saturated air in 2 seconds) was delivered in both nostrils simultaneously by an electronic pump every five inspirations, for a total 7 minutes. Such a long time interval was chosen to avoid adaptation effects. Seven minutes following the olfactory stimulus, 750 MBq (20 mCi) 99mTc-ECD was injected intravenously and subsequently, image acquisition was performed 60 minute post injection.
Statistical evaluation
The method of SPECT image analysis was similar to that of Varney and Bushnell[12]. Using a sagittal cut that bisected the frontal lobes at approximately level of the olfactory nerve at anterior end and level of the occipital pole posteriorly. Seven regions of interest (ROI) were drawn for quantitative analysis:
1. Orbital Frontal Cortex.
2. Inferior Frontal Pole.
3. Superior Frontal Pole.
4. Posterior Superior Frontal Lobe.
5. The Parasagittal Area.
6. The Occipital Pole.
7. The cerebellar area.
The mean activity in each of the seven ROIs in either hemisphere was calculated. Fourteen uptake indexes were obtained for each person (Fig 1). We evaluate the post-stimulation values of each segment as a fraction of the corresponding pre-stimulation values: [(post-stimulation counts minus pre-stimulation counts)/prestimulation counts] × 100.
Figure 1 The regions of interest at the sagittal cut used for the quantitative analysis: 1 = orbital frontal cortex, 2 = inferior frontal pole, 3 = superior frontal pole, 4 = posterior superior frontal lobe, 5 = parasagittal region, 6 = occipital pole, 7 = cerebellar region.
Statistical analysis
Statistical analysis was performed using Variance Analysis (ANOVA) and student's t-test for paired data and comparison of demographic data. SPSS for windows (Release 11.5.0) was used for statistical analysis. A probability of less than 0.05 was considered significant.
Results
The groups were comparable in regard to demographic data [mean age 37 (18–56) yr in the anosmic Vs 33 (22–42) yr in the normal control group].
Quantitative SPECT data for each region of anosmic and control subjects are shown in table 1. In most of the regions the mean post-stimulation values, which are expressed as the percentage of the pre-stimulation values, are significantly higher in the normal controls than the anosmic patients (P < 0.05) (Fig. 2). However, in both sides of cerebellum and in the right occipital region no statistically significant increase in the post-stimulation values is noted (P = 0.05). No statistically significant difference in the pattern of cerebral activation was identified regarding the patient's gender or right versus left hemispheric activity (P > 0.05).
Table 1 (Mean ± SD) Percentage Increases of Brain Perfusion in All Selected ROIs in Normal and patients Subjects
ROI Patients with smell impairment controls P value
Orbitofrontal Right 7.2 ± 38.46 25.45 ± 44.45 0.03
Left 8.16 ± 38.60 25.47 ± 44.48 0.03
Inferofrontal Right 9.14 ± 36.27 25.07 ± 46.02 0.04
Left 8.22 ± 38.39 24.09 ± 45.81 0.03
superofrontal Right 9.32 ± 39.91 24.06 ± 42.98 0.04
Left 7.99 ± 40.34 21.30 ± 39.64 0.03
Posterosuperofrontal Right 8.32 ± 40.38 20.70 ± 39.20 0.04
Left 8.10 ± 38.57 20.41 ± 40.79 0.04
Parasagital Right 6.68 ± 36.08 20.59 ± 43.49 0.03
Left 6.24 ± 36.02 18.57 ± 40.46 0.04
Occipital Right 7.42 ± 37.53 22.87 ± 41.58 0.05
Left 6.74 ± 35.95 23.27 ± 44.61 0.04
Cerebellum Right 4.34 ± 32.74 22.06 ± 44.02 0.05
Left 5.57 ± 33.80 23.19 ± 42.62 0.05
Figure 2 Pre-stimulation (upper rows) and Post-stimulation (lower rows) SPECT images of a volunteer control.
Discussion
Availability of an objective and noninvasive technique by which smell function can be readily demonstrated and quantitated is of significant medical and medicolegal importance. Efforts to establish objective techniques to measure hyposmia and to determine the existence or nonexistence of post-traumatic impaired smell have included EEG, olfactory evoked responses, and magnetoencephalography, but they were without particular success [6]. With the use of brain CT and MRI, some details of CNS pathology were obtained and measurements of olfactory bulb size and other anatomical structures in the CNS olfactory system have become possible in normal subjects and in patients with hyposmias and impaired smell of various causes14. However, these methods provided no information about functional olfactory performance [6,15]. In fact, up to now, the main available method for obtaining functional information about the olfactory system is functional MRI (fMRI). Since its introduction, functional MRI (fMRI) showed promise in defining brain activation in response to visual, auditory, and somatosensory stimuli. This technique has been applied to normal and anosmic subjects using olfactory stimuli to obtain quantitative data [6]. Although only limited functional imaging studies are available, fortunately these efforts have been promising and successful results are reported. In a recent study by Henkin and Levy [12] evaluated the role of fMRI to define brain activation in response to olfactory stimulation in patients who never recognized odors (congenital hyposmia). Brain activation in response to odors was present in patients with congenital hyposmia, but the activation was significantly lower than in normal subjects and patients with acquired hyposmia. One of the most widely referenced studies is that of Levy et al[6], in which the authors found that brain activation to three different olfactory stimuli (pyridine, menthone, amyl acetate) was lower in all nine brain sections in anosmic patients compared with normal subjects. This difference reached statistical significance for mean activation for each odor in six of the nine individual sections studied. In patients activation was found in regions associated with CNS processing of olfactory stimuli in normal subjects, but this activation was much less, particularly in inferior frontal and cingulate gyral regions of frontal cortex and in regions of medial and posterior temporal cortex. The authors concluded that quantitative CNS changes in response to olfactory stimuli in patients with hyposmia, demonstrate a novel, objective method by which these patients can be identified. In another study done by Levy et al[11], fMRI was obtained in 21 patients with Type I and II hyposmias. Patients with Type I hyposmia (who could detect but not recognize odors) had less activation than patients with Type II hyposmia (who could detect and recognize odors, albeit with less than normal acuity). Both patient groups had less activation than normal volunteers. The authors described fMRI as a simple, rapid technique that can be used in a practical clinical setting to identify patients with hyposmia and to differentiate patients with different types of olfactory loss. However, it should be noted that fMRI has some important drawbacks, which prompt us to use quantitative brain perfusion SPECT as another objective, noninvasive technique. In most places fMRI is more expensive and has more contraindications and scheduling difficulties.
Nuclear medicine procedures (SPECT and PET) has been considered as functional imaging modalities by which patients with smell loss can be identified, their abnormalities quantitated and compared with findings in normal subjects. In the study of Varney and Bushnell, neuroSPECT findings in patients rendered totally anosmic from head injury were investigated. The authors underscore the importance of orbital frontal hypoperfusion as a paraclinical sign of post-traumatic impaired smell, particularly in patients with mild head injury who have normal computed tomography and magnetic resonance imaging scans [10]. In the study of Varney et al [16] eleven patients with head injury resulting in severe impaired smell and 11 controls matched for age were investigated using quantitative positron emission tomography. The study showed that posttraumatic impaired smell is closely associated with cerebral perfusion abnormalities evident in cerebral PET images.
As to our knowledge, there is only a single similar study to determine impaired olfactory function following odorant stimulation evaluating the potential of SPECT imaging[17]. In the study of Di Nardo et al, 15 volunteers (including 10 healthy adults and 5 patients with post-traumatic impaired smell) underwent brain SPECT by m99Tc-HMPAO, before and after olfactory stimulation with lavender water. As to our results, variable degrees of cortical activation were detected. Gyrus rectus (+24.5%), orbito-frontal cortex (right +26.6%, left +25.6%), and superior temporal (right +9.9%, left +5.5%) cortical areas were remarkably activated, while only a slight increased perfusion was present in middle temporal (right +3.2%, left +2.1%) and parieto-occipital (right +0.4%, left +2%) regions. Those patients affected by posttraumatic impaired smell showed markedly less perfusion increment as low as 0.5% in every olfactory area. Similarly, our results demonstrate that patients with impaired smell exhibit decreased brain activation compared with normal subjects following olfactory stimulation. These results might be expected based upon patient complaints of lack of smell[6], but they have not been widely documented by objective criteria. It has been shown that all regions of cerebral cortex have blood flow increment after pure first nerve (CN1) stimulation. This technique allows identification and definition of olfactory areas, and makes it potentially of value to the clinicians. Although the findings of Di Namdo wee are confirmed by our larger series of patients, however few minor differences between two studies are present. In our study more activation in the parietal and occipital regions was observed, that could be explained by the different radiotracer (HMPAO vs. ECD) used in our study. In fact this difference could be presumably due to differences in pharmacokinetics of these two radiopharmaceuticals. As it was previously reported by Patterson et al, radiotracer activity of the parietal, occipital and superior temporal cortices were significantly lower with 99mTc-HMPAO than 99mTc-ECD[18]. Although both tracers have good results in depicting the cerebral blood flow (rCBF), as it was previously shown by Pupi et al[19], 99mTc-ECD uptake is significantly more linear with regard to rCBF and thus it has less back-diffusion and better correlation with blood flow.
Depending on the method of analysis (fMRI, PET or SPECT), nature, intensity of the stimulant or previous exposure of the subjects to the odorant, different patterns of cerebral activation are observed [17,20]. It seems that orbital and frontal regions are almost always activated. Behavioral evidence and imaging findings (PET and fMRI) have suggested that laterally specialized mechanisms for odor processing exist and that the right orbitofrontal region has a main role in the olfaction process [21,22]. However, the results of our study and that of the Di Nardo et al using SPECT did not show any statistically significant perfusion lateralization within the olfactory areas, which could be interpreted by the differences in methods of analysis[17,20].
Although it was previously found that women outperform men during odor identification [23, 24, 25), but our study has shown that the male and female have similar pattern of cerebral activation with no significant difference with respect to the extent and amount of the activated regions. Further investigation concerning the issue of gender effects on olfactory function seems warranted.
Similar to the fMRI technique, brain perfusion SPECT offers an objective approach, by which smell function can be assessed quantitatively without significant patient participation. Similarly, this technique offers another objective method by which differences between groups can be easily quantitated. However, SPECT procedure takes much more time in comparison to fMRI and involves radiation, and tends to be more expensive in many parts of the world. SPECT may be better than fMRI to visualize certain parts of the brain (such as the orbitofrontal cortex) that may be harder to see by fMRI due to signal distortions near the skull base. In comparison to SPECT, fMRI is faster, generally cheaper, can be repeated with different stimuli, and may be performed at the same time as conventional MRI to provide detailed anatomical images.
Finally it should be emphasized that our study is not free of drawbacks. There are some areas of brain unrelated to olfaction that appear to show activation. This remains unexplained and raises questions about the specificity of the SPECT images for olfaction. To answer this questions, experiments should be performed namely with no odor stimulation, with control of other process of olfactory stimuli such as memory, emotion or physiological blood pressure changes, etc. Additional conventional imaging such as MRI or CT would be useful to show the degree of brain encephalomalacia or atrophy. These regions would not be expected to activate, and may create falsely decreased activation in SPECT studies. In some cases of brain injury, despite minimal brain MRI changes, the apparently normal brain tissue may be functionally abnormal on SPECT. This distinction should be made in future studies when interpreting the SPECT images, since in regions of decreased SPECT activity, the corresponding degree of atrophy or encephalomalacia is not evident without the anatomical imaging. Also it should be noted that the Coin's test is not sufficient and completely reliable for confirming the presence of smell impairment and therefore further studies comparing brain perfusion SPECT with fMRI (as another reliable method in the assessment of smell impairment[26,27]) is warranted.
Conclusion
Our study demonstrates that brain SPECT is a valuable imaging technique and could be considered as an alternative to other imaging modalities (particularly fMRI) in the diagnostic management of patients complaining of post-traumatic impaired smell. SPECT is specially a useful alternative when fMRI is unavailable or unsuitable and it is beneficial when more accuracy is desired (when fMRI results are either inconclusive or conflict with other clinical data). However, brain SPECT may benefit from complementary MRI or CT anatomical imaging.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
ME participated in writing of the manuscript and interpretation of the scintigraphic results. MA participated in its design and coordination, supervised the acquisition process and participated in the interpretation of the scintigraphic results and performed the statistical analysis. MK carried out the olfactory test. MS, AFE and BFS supervised the acquisition process and interpreted the scintigraphic results. AG and DB supervised the acquisition process and interpreted the scintigraphic results. 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 Tehran University of Medical Sciences. We are indebted to Dr. Jan Mohammad Malek Zadeh and Seyed Reza Arabi for their fruitful and useful suggestions. The authors thank the technologists at our department for data acquisition and other technical support.
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Varney NR Bushnell D NeuroSPECT findings in patients with posttraumatic impaired smell: a quantitative analysis Head Trauma Rehabil 1998 13 63 72
Levy LM Henkin RI Lin CS Finley A Rapid imaging of olfaction by functional MRI (fMRI): identification of presence and type of hyposmia J Comp Assist Tom 1999 23 767 775 10.1097/00004728-199909000-00026
Henkin RI Levy LM Functional MRI of congenital hyposmia: brain activation to odors and imagination of odors and tastes J Comp Assist Tom 2002 26 39 61 10.1097/00004728-200201000-00008
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Varney NR Pinkston JB Wu JC Quantitative PET findings in patients with posttraumatic anosmia J Head Trauma Rehabil 2001 16 253 259 11346447
Di Nardo W Di Girolamo S Galli A Meduri G Paludetti G De Rossi G olfactory function evaluated by SPECT Am J Rhinol 2000 14 57 62 10711334
Patterson JC Early TS Martin A Walker MZ Russell JM Villanueva-Meyer H SPECT image analysis using statistical parametric mapping: comparison of technetium-99m-HMPAO and technetium-99m-ECD J Nucl Med 1997 38 1721 1725 9374340
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Bengtsson S Berglund H Gulyas B Cohen E Savic I Brain activation during odor perception in males and females Neuroreport 2001 12 2027 2033 11435941 10.1097/00001756-200107030-00048
Levy LM Henkin RI Lin CS Finley A Rapid imaging of olfaction by functional MRI (fMRI): identification of presence and type of hyposmia J Comput Assist Tomogr 1999 23 767 775 10524865 10.1097/00004728-199909000-00026
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BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-5-731629319510.1186/1472-6963-5-73Research ArticleA 6-months assessment of the alcohol-related clinical burden at emergency rooms (ERs) in 11 acute care hospitals of an urban area in Germany Baune Bernhard T [email protected] Rafael T [email protected] Gerhard [email protected] Annette [email protected] Susanne [email protected] Hildegard [email protected] Ulrike [email protected] Mental Health Epidemiology, Department of Psychiatry, University of Muenster, Germany2 School of Public Health, University of Bielefeld, Germany3 Clinic for Addiction, Westphalian Hospital Dortmund, University of Bochum, Germany4 City Council of Public Health, City of Dortmund, Germany2005 18 11 2005 5 73 73 14 4 2005 18 11 2005 Copyright © 2005 Baune et al; licensee BioMed Central Ltd.2005Baune 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 the study was to identify and to profile alcohol-related attendances to emergency rooms (ERs) of 11 hospitals of various medical specialties covering a large urban population, to assess risk factors associated with short-stay cases, repeat attendances and higher degree of alcohol consumption and to estimate their impact on the alcohol-related burden at ERs.
Methods
A 6-months study was carried out to obtain clinical and administrative data on single and multiple attendances at ERs in 11 governmental acute hospitals in a large city in Germany. All alcohol-related attendances at ERs of study hospitals were eligible. A broad definition of alcohol-related attendances independently from alcohol diagnosis and various demographic, clinical and administrative measures were used. Odds ratios for the associations of these measures with duration of stay, repeat attendances and higher degrees of alcohol consumption were derived from multivariate binomial and multinomial logistic regression models.
Results
1,748 patients with symptoms of alcohol consumption or withdrawal (inclusion rate 83.8%) yielded 2,372 attendances (3% of all medical admissions), and resulted in 12,629 inpatient-days. These patients accounted for 10.7 cases per 1,000 inhabitants. The average duration of inpatient stay was 10 days. 1,451 of all patients (83%) presented once, whereas the median of repeat attendances was three for the remaining 297 patients. Short-stay cases (<24 hours) were significantly linked with male gender, alcohol misuse, trauma (or suspicion of a trauma) and medical specialties. Increased levels of alcohol consumption at first attendance were significantly associated with repeat attendances in due course. In a multinomial logistic regression model higher degrees of alcohol consumption were significantly associated with male gender, trauma, short-stays, attendance outside regular working time, and with repeat attendances and self-discharge.
Conclusion
Apart from demographic factors, the alcohol-related clinical burden is largely determined by short-stay cases, repeat attendances and cases with higher levels of alcohol consumption at first attendance varying across medical specialties. These findings could be relevant for the planning of anti-alcoholic interventions at ERs.
==== Body
Background
Alcohol misuse constitutes a major problem in modern society and both physical and mental alcohol-related harm result in a large number of attendances at emergency rooms (ERs), imposing a significant burden on the workload and financial resources of the medical departments [1,2]. Excess alcohol consumption with attendance at ERs may occur as part of an alcohol or drug dependence, as a cause of trauma or as a co-morbid condition of psychiatric diseases [3-5]. Furthermore, alcohol-related withdrawal syndromes may play an important role in the management of patients attending ERs [6].
Most previous studies on the alcohol-related clinical burden of emergencies on health care settings were based on samples drawn from single practices, single hospitals or only certain departments (e.g. surgical, internal medicine, orthopaedics) of several hospitals [7-10]. Selection bias and lack of representativeness are often major limitations of these studies. Previous studies in this field also have reported on the impact of short stay cases and repeat cases on the clinical burden of ERs at general hospitals [8,11-13] and noted an increasing number of younger patients attending ERs in recent years in the UK and Ireland [14,15].
O'Farrell et al. reported in their study of an increase of alcohol-related admissions to ERs for each age group over time indicating a rising burden for hospitals between 1997–2001 in Ireland [15]. The importance of the alcohol-related clinical burden caused by younger people also has been demonstrated in a study performed in an inner-city hospital in the UK [14]. Hazardous alcohol consumption in the general population in Germany has increased in younger people aged 18–24 between the years 1995–2001 and has slightly decreased in older men and women in the general population in Germany [16]. It is unknown whether the increasing number of younger people with hazardous alcohol consumption in Germany receive alcohol specific interventions at all, and particularly present at ERs due to alcohol-related problems.
However, since duration of stay, number of attendances and degree of alcohol consumption are contributing to the alcohol-related burden at ERs, little knowledge has been obtained in previous studies whether this is true for a wide range of medical specialties in hospitals and which demographic and clinical factors are associated with these contributing factors. In this study, which should serve as a needs assessment of future alcohol-related interventions at ERs, a complete sample of alcohol-related attendances at ERs of all existing hospitals covering a wide range of medical specialties in an urban area of a known population size was assessed for the duration of 6 months.
Objectives
The aims of the study were as follows:
1. To identify alcohol-related attendances at emergency rooms (ERs) of 11 hospitals of various medical specialties covering a large urban population;
2. To profile the alcohol-related attendances;
3. To assess sociodemographic and clinical risk factors associated with short-stay cases, repeat attendances and higher degree of alcohol consumption and to assess their impact on the alcohol-related burden at ERs.
Methods
Sampling and setting of the study
A cross-sectional study of alcohol-related attendances at ERs was carried out in 11 ER of 11 hospitals of an urban area in Germany during a 6-months period (01 Sept 2000 – 28 Feb 2001). The study region was the city of Dortmund, with 587,000 inhabitants [17]. Through the initiative of the Dortmund City Council of Public Health all existing ERs in 11 hospitals serving the population of the city were included. The public health relevant purpose of this study was to obtain a complete assessment of the alcohol-related burden in a large city that was realized through a concerted initiative of all existing hospitals driven and led by the City Council of Public Health.
3,144 alcohol-related records were registered during the 6 months study period. Of these attendances about 10% (N = 314) across the study hospitals were direct admissions to inpatient units bypassing ER either via clinics or for pre-booked investigations. Our study focussed on the other 90% of alcohol-related attendances (N = 2,830) presented to the ERs first and that were contributed by 2,085 single patients for whom clinical charts were filled. In 310 (14.9%) of these patients the clinical charts were unusable, non-interpretable or completely missing and in further 27 patients (1.3%) the linkage of clinical and administrative information was impossible, leaving 1,748 (83.8%) single patients for this analysis.
Inclusion criteria
Independently from the type of alcohol diagnosis all patients with either clinical symptoms of alcohol consumption or withdrawal syndrome presenting at ERs of the study region were eligible for this study, even considering patients with alcohol use several hours before presenting to ERs or patients who left ERs after a short-stay.
Data sources
Two sources of data (clinical and administrative data) were combined for each individual patient. First, at ER the attending physician filled a chart on clinical routine data (clinical characteristics and diagnoses, degree of alcohol consumption, trauma, mode of referral and discharge, whether patient was accompanied by relatives or friends, and need for inpatient treatment) for each patient meeting the inclusion criteria. Second, administrative data (length of stay in the hospital, number of attendances, time and day of attendances and health care insurance) on each patient included into the study were obtained.
The clinical data were linked to the administrative data by the administrative departments and only anonymous data were further processed by the study group. Clinical data were entered on a sheet containing the patient's name before given to the administrative department who generated and entered an anonymous code on the sheet. The individual identifier (name and year of birth) on the sheet was then removed by the administrative department before the sheet was further processed by the study group. The administrative department destroyed the identifier and the list containing code and patient's name and year of birth. The study group received a blinded clinical data sheet which was entered into a data base. The administrative department provided for the study group an electronic data file containing administrative data on the hospital stay. The anonymous code on the clinical data sheet and the administrative data was used to merge both data sets.
While written informed consent was not able to be obtained, the information used was routine clinical and administrative data, which was provided for our use by the hospitals after patients had signed release of information forms.
Clinical measures
The level of alcohol consumption was assessed objectively with the same model of alcometer (breathalyser Model: "Draeger, Alco-Test, 7410 plus") across all hospitals and subjectively by the study physicians scoring each patient for the degree of alcohol consumption based on operational clinical criteria comprising 4 grades which were classified according to common clinical features related to alcohol consumption:
(1) withdrawal symptoms: physical / vegetative/psychological signs;
(2) low grade: inebriated, beginning of inhibitions, logorrhoea, euphoria, reduced concentration and attention, reduced self-criticism, alterations of balance;
(3) middle grade: increased intensity of symptoms of (2) plus beginning of abasia, blabber, reduced control over action;
(4) severe intoxication: successive increase of symptoms of (3) plus euphoria or depression, early signs of somnolence, loss of control;
Clinical alcohol diagnoses of acute alcohol intoxication, alcohol misuse, alcohol dependence and alcohol withdrawal were classified according to the criteria of the International Classification of Diseases, version 10 (ICD-10).
To make the study feasible in 11 hospitals over a period of 6 months no further information on e.g. drinking habits or number of drinks were obtained. Training of medical staff ensured the consistent use of operational clinical criteria of alcohol consumption or withdrawal and the use of breathalyser across all study hospitals. The clinical judgement of the degree of alcohol consumption was in good agreement with the alcohol breath test (Spearman-rho = 0.78). The inter-rater reliability (measured with Spearman's correlation coefficient, rho) showed values from rho = 0.87 – 0.53 across 11 hospitals and the intra-rater reliability had a similar range from rho = 0.87 – 0.62.
Emergency rooms
Each study hospital provides typically for the German health care system a 24 h service of emergency care to patients. Patients attending an ER present with the need of acute medical care. Doctors at ERs provide acute treatment and make the decision on what type of further treatment is needed and whether patients are either treated on an outpatient level or admitted to a ward. In contrast, medical clinics serve patients who have pre-booked regular appointments and who are not an emergency case.
Typically, the study hospitals within the single city where the study took place do not provide the whole range of medical specialties. Thus, the ERs in this study are part of the hospitals and serving the medical specialties of internal medicine, surgery, addiction, and general psychiatry of the respective hospital. However, the largest hospital of the city, which serves as a tertiary hospital, provides an emergency service to all medical specialties of that hospital.
Statistical analysis
The incidence of alcohol-related attendances at ERs and the number of inpatient-days related to these attendances at ERs was calculated for the six months study period and extrapolated for a 12 months period. For the calculation of the incidence all single and multiple attendances at ER during the study period were included.
All other analyses cover data from the study period of 6 months only. In the further analyses we allowed for only one attendance at ERs per patient (the first attendance for patients with multiple attendances). To make feasible an age comparison of the study sample with the general population of the City of Dortmund, age was classified into age groups according to the official statistics for the City of Dortmund.
Pearson's Chi2 test was used to compare proportions of categorical data (e.g., gender differences, medical specialties, etc.). Separate binomial logistic regression models were applied to measure the relationship between multiple attendances, short-stay cases, respectively, and the degree of alcohol consumption and several other clinical characteristics (tables 2, 3). In separate multinomial logistic regression models we calculated Odds ratios for the association between four clinical degrees of alcoholic consumption (patients with alcohol withdrawal syndrome acted as a reference group) and demographic and clinical characteristics of patients presenting to ER for the first time during the study period (table 4). The continuous flow of the average number of patients through a 24 hours cycle of the day and a cycle of a typical week are presented in figures 2 and 3. All analyses were performed using SPSS-software, version 11 [18].
Table 2 Association of level of alcohol consumption with single and multipleattendances among 1,748 patients on first attendance at ERs
Single attendance (N = 1,451 patients) Multiple attendances (N = 297 patients) OR for multiple attendances
Factors % % OR a (95% CI)
Gender b
Male (N = 1,313) 57.7 42.3 1.95 (1.57–2.44)
Female (N = 404) 72.6 27.4 1 (reference)
Age c
≤ 20 years (N = 58) 98.3 1.7 1 (reference)
21 – 44 years (N = 907) 80.6 19.4 13.3 (1.8–96.5)
45 – 59 years (N = 568) 82.2 17.8 12.1 (1.7–88.1)
60 – 64 years (N = 103) 85.6 14.4 9.5 (1.2–73.6)
≥ 65 (N = 77) 96.1 3.9 2.3 (0.2–22.9)
Clinical degree of alcohol consumption
High (N = 531) 55.0 45.0 2.62 (1.84–3.72)
Middle (N = 796) 62.6 37.4 1.94 (1.35–2.77)
Low (N = 231) 65.8 34.2 1.77 (1.19–2.65)
Symptoms of withdrawal (N = 190) 77.3 22.7 1 (reference)
Results of alcohol breath test in millilitres
> 400 (N = 32) 52.4 47.6 2.30 (1.17–4.50)
201 – 400 (N = 474) 51.1 48.9 2.38 (1.79–3.18)
101 – 200 (N = 968) 59.9 40.1 1.70 (1.21–2.38)
≤ 100 (N = 274) 72.4 27.6 1 (reference)
a separate logistic regression models calculated for each factor showing Odds ratios for multiple attendances, adjusted for age and sex; OR denotes Odds ratio; CI denotes confidence interval; b 31 missing values; c 33 missing values;
Table 3 Association of clinical characteristics and duration of stay < 24 h among 1,748 patients at first alcohol-related attendance at ERs
Duration of stay ≥ 24 h (N = 1,148 patients) Duration of stay <24 h (N = 600 patients) OR for duration of stay < 24 h
Clinical factors (%) (%) OR a (95% CI)
Repeat attendance
Yes (multiple), (N = 297) 56.3 43.7 1.24 (1.04–1.49)
No (single) (N = 1,451) 63.3 36.7 1 (reference)
Clinical degree of alcohol consumption
High (N = 531) 44.3 55.7 9.39 (5.63–15.64)
Middle (N = 796) 58.4 41.6 5.47 (3.28–9.14)
Low (N = 231) 71.8 28.2 3.04 (1.73–5.35)
Withdrawal (N = 190) 88.8 11.2 1 (reference)
Discharge mode b
Self-discharge (N = 207) 58.8 41.2 1.33 (0.97–1.81)
Medical discharge (N = 1,173) 66.0 34.0 1 (reference)
Breath test (millilitres)
>400 (N = 32) 48.4 51.6 6.7 (2.9–15.0)
201–400 (N = 474) 60.7 39.3 4.1 (2.7–6.3)
101–200 (N = 968) 55.6 44.4 5.2 (3.5–7.8)
≤ 100 (N = 274) 86.9 13.1 1 (reference)
Alcohol diagnosis at ER attendance c
Misuse (N = 497) 33.9 66.1 11.56 (7.67–17.43)
Dependence (N = 924) 68.3 31.7 2.81 (1.91–4.13)
Withdrawal (N = 300) 86.2 13.8 1 (reference)
Diagnosis or suspicion of trauma d
Yes (N = 263) 37.6 62.4 3.4 (2.51–4.60)
No (N = 1,383) 65.5 34.5 1 (reference)
a separate logistic regression models calculated for each clinical factor showing Odds ratios for the duration of stay below 24 h, adjusted for age and sex; b the category "referral to other hospital" was not considered in this table; see table 1 for information on missing values about mode of discharge; c missing information on diagnosis among 27 cases; d missing information on trauma among 102 cases; OR denotes Odds ratio; CI denotes confidence interval;
Table 4 Demographic and clinical factors associated with various degrees of alcohol consumption for 1,748 patients presenting at ERs for the first time during 6 month study period (OR a; 95% CI)
Categories Genderb Duration of stay ER attendancec ER attendanced Traumaf No of attendances Discharge modeg
Clinical degree/signs of alcohol consumption Male (N = 1,313) vs. Female (N = 404) <24 h (N = 600) vs. = 24 h (N = 1,148) Weekends (N = 1,234) vs. Weekdays (N = 479) Outside (N = 867) vs. Regular (N = 820) working timee Yes (N = 263) vs. No (N = 1,383) Multiple (N = 297) vs. Single (N = 1,451) Self-discharge (N = 207) vs. Medical discharge (N = 1,171)
High (N = 531) 2.21 (1.51–3.23) 9.42 (5.65–15.70) 1.73 (1.16–2.59) 8.56 (5.72–12.81) 3.17 (1.72–5.83) 2.62 (1.84–3.73) 1.93 (1.08–3.42)
Middle (N = 796) 1.61 (1.11–2.32) 4.93 (3.29–9.18) 1.39 (0.93–2.09) 6.38 (4.29–9.51) 3.52 (1.92–6.46) 1.94 (1.36–2.78) 1.59 (0.89–2.85)
Low (N = 231) 1.28 (0.84–1.95) 3.04 (1.73–5.35) 1.49 (0.95–2.36) 2.09 (1.33–3.27) 2.39 (1.22–4.71) 1.78 (1.19–2.66) 1.16 (0.57–2.35)
Withdrawal (N = 190) 1.0 (reference) 1.0 (reference) 1.0 (reference) 1.0 (reference) 1.0 (reference) 1.0 (reference) 1.0 (reference)
a separate multinomial regression models calculated for each category showing Odds ratios for the degrees of alcohol consumption, adjusted for age and sex;
b 31 missings; c 34 missings; d 52 missings; e regular time: 8 a.m. – 4 p.m.; f 102 missings; g the category "referral to other hospital" was not considered in this analysis; see table 1 for information on missing values about mode of discharge; OR denotes Odds ratio; CI denotes confidence interval;
Figure 1 Sampling and study population.
Figure 2 Flow of average number of patients during course of the day by time during the weekdays and at weekends. Note 1: based on 1,696 patients (52 missing) for whom the exact time of attendance was available
Figure 3 Average number of patients on different days of the week (Mon-Sun). Note 1: line – average number; scatter above line – 95th percentile; scatter below line – 5th percentile. Note 2: based on 1,714 patients (34 missing) for whom the exact date of attendance was available
Results
Study population
Overall, during the 6 months study period there were 105,006 medical admissions (annual incidence of 358 per 1,000 inhabitants) to the 11 study hospitals. These admissions contained 3,144 alcohol-related attendances (3%) to ERs plus direct admissions to inpatient units due to any type of primary or secondary alcohol diagnosis. These 3,144 alcohol-related attendances (including multiple attendances) and admissions yielded an extrapolated annual incidence of 10.7 cases per 1,000 inhabitants.
A total of 1,748 patients (75.1% male) attended ERs during the study period after the exclusion of 337 patients with either missing or non-interpretable (16.2% of the 2,085 eligible cases) charts (Figure 1). These patients contributed 2,372 alcohol-related attendances (male to female ratio: 3.85:1) to ERs. In total, 1,959 of all attendances (82.6%) led to inpatient admission resulting in 12,629 days of inpatient treatment (6.5 days on average per admission to inpatient units). The average duration of inpatient stay was 10 days (95% CI, 9.5–10.5; min. 1; max. 108; SD 0.26; median 9).
Demographic and clinical characteristics of the study population
About one quarter of the patients was female (23.1%) and the median age of all patients was 43 years (min: 14y; max: 85y; mean: 43.3y; SD: 0.29). Mean age of men was significantly lower than of females (42 years, SD 11.9 vs. 44 years, SD 12.8; Student-t-test p < 0.01). In comparison to the general population of the City of Dortmund we observed some systematic differences in age and gender for the study population. While about 3.3% of the patients were = 20 years of age, this age group accounted for 20.3% of the general population. Whereas 19% of the general population was 65 years of age or older only 4.4% patients were of that age in our study. As shown in Table 1 males were predominant in this study, while the male:female ratio in the general population was 0.9:1.
Table 1 Demographics and characteristics of patients (N = 1,748) on first alcohol-related attendance at ERs in this study
Study population N = 1,748
Age groupsa %
≤ 20 years 3.3
21 – 44 years 51.9
45 – 59 years 32.5
60 – 64 years 5.9
≥ 65 years 4.4
Missing 1.9
Gender
Male 75.1
Female 23.1
Missing 1.8
Medical specialties associated with ER
Internal medicine 46.7
Addiction 25.5
Mixed medical disciplines 15.2
Surgery 6.5
General Psychiatry 5.7
Other 0.4
Mode of discharge
Regular 67.1
Own request, against medical advice 11.8
Referral to other hospital 3.0
Missing b 18.1 b
a Age of study sample is grouped according to the official statistics of the City Council Dortmund, for the purpose of group comparison in this analysis.
b 65% of these patients stayed less than 24 h in hospital and 58% of these were not referred to inpatient units.
At attendance 53.7% of the patients presented with dependent alcohol use, 28.9% with non-dependent alcohol use and 17.5% with an alcohol withdrawal syndrome. About 94.5% of the patients with initial withdrawal syndrome were classified as dependent alcohol use at discharge. About 15% of the patients had a trauma or suspicion of a trauma at ER attendance.
First-time ER attendances in several medical specialties
The majority of the first-time attendances took place at ERs of internal medicine (46.7%) and addiction (25.5%), less often at ER serving mixed disciplines based at a general hospital (15.2%), and marginally at ERs providing surgery (6.5%), general psychiatry (5.7%), and others (0.4%).
Next, we compared repeat cases to single cases and found that the proportion of repeat cases was significantly highest at the ER of the clinic of addiction (21.6%), secondly at the ER serving mixed disciplines (19.3%), followed by the specialty of general psychiatry (12.7), the ERs serving internal medicine (12.6%) and ERs for surgery (5.6%), (Chi2-test: p < 0.0001).
We also compared the proportion of cases staying = 24 hours to cases staying <24 hours, and observed that the proportion of short-stays was highest at the ER serving mixed disciplines (95.2%), then at ERs serving surgery (42.0%), internal medicine (16.9%), general psychiatry (14.8%) and clinic of addiction (1.1%), (Chi2-test: p < 0.0001).
The proportion of patients with high degree of alcohol consumption was highest at ERs serving mixed disciplines (72.9%), then at ERs serving surgery (46.9%), followed by general psychiatry (37.7%), and ERs of internal medicine (31.8%), and was lowest at the clinic of addiction (27.7%), (Chi2-test: p < 0.0001).
Gender specific clinical characteristics
Females were significantly (p < 0.05) more likely to present with medical referral papers from a physician, to have a lower degree of alcohol consumption, to stay longer than 24 hours in hospital, to present once only at ER in 6 months and to be classified more often with the diagnose of alcohol withdrawal syndrome instead of non-dependent or dependent alcohol use compared to their male counterparts.
Furthermore, women were significantly (Chi2-test: p < 0.05) more often accompanied by friends or relatives and were less likely to use ambulance services for transportation, but they did not differ significantly from male patients in the proportion of inpatient treatment following attendance at ER, mode of discharge and having trauma or suspicion of a trauma.
Attendances at ERs by time and weekday
Variations of attendances at ER by time of the day and weekday may reveal peaks and lows of the alcohol-related clinical burden at ER indicating special need for personnel and other resources. Given the exact time of attendance, the flow of the average number of patients through a 24 hour cycle is presented in figure 2. Figure 3 shows the average number of patients per weekday through a 1 week period. On average, there were 9.7 attendances per day with some systematic variation between different weekdays and between different hours of the day (Figures 2 and 3). During weekends there was no time preference for attendances but during weekdays most attendances occurred between 8 a.m. to 4 p.m. Almost 28% of all attendances occurred on weekends (Sat. + Sun.). A decrease in attendances during winter months was observed.
Single vs. repeat attendance at ERs
In total, 1,451 (83%) patients were admitted once during the study period and 297 patients contributed the remaining 921 multiple attendances (min. 2; max. 63; mean 8; SD 0.5; median 3). Whereas most of the patients (76%) with repeated attendances had up to 5 attendances there were 10% with 27 or more attendances during the study period (3.2% had more than 45 attendances). Patients with multiple attendances (17% of all patients) were responsible for 39% of all attendances at ER indicating a huge clinical burden caused by this group.
Thus, we further looked at factors associated with multiple attendances. Patients aged between 31–50 years, patients non-fixed abode, and males were significantly more likely to present multiple times than younger or older patients (Chi2-test: p < 0.05).
Table 2 shows factors associated with multiple attendances at ERs. Separate logistic regression models revealed statistically significant increased odds for the association of multiple attendances with male gender, higher clinical degrees of alcohol consumption and higher alcohol concentrations assessed by the breath test. The latter two variables indicated dose-response effects between alcohol consumption and multiple attendances at ERs. High degrees of alcohol consumption at first ER attendance predicted repeat attendance in due course.
Factors associated with duration of stay at hospital
Duration of stay (< 24 hours versus = 24 hours) at ERs and hospital, respectively, gives an indication whether patients with alcohol-related problems presenting at ERs finally receive inpatient treatment and whether or how clinical and demographic characteristics differ among these groups.
About one third of the patients left hospital within 24 hours (40.8% males vs. 29.3% were females, Chi2-test: p < 0.0001), another third stayed between 1–7 days (19.6% males vs. 26.8% females, Chi2-test: p < 0.0001) and the remaining third of patients stayed for more than 7 days (39.6% males vs. 43.9% females, Chi2-test: p < 0.0001). The average length of stay was 10 days (95% CI, 9.5–10.5; min: <1 day; max: 108 days; SD 0.26). The length of stay varied by age: while we found 15 days on average for patients below 20 years of age, we observed 7.4 days on average for the 21–30 years old. The remaining age groups had a similar average duration of stay between 9–10 days.
Males, patients below the age of 30 years and above the age of 50 years, patients with no fixed abode and patients supported by social services were more likely to leave the hospital within 24 hours (Chi2-test: p < 0.05). Although most patients (79.7%) in this sample were admitted to inpatient units, about one third (31.1%) of them stayed less than 24 hours in hospital. While few of them self-discharged against medical advice (13.8%), the majority of (86.2%) left hospital within 24 h after regular medical discharge or transfer.
Table 3 presents clinical characteristics associated with short duration of stay (<24 hours) for patients with first alcohol-related attendance at ERs during study period. Statistically significant associations between short stay (<24 hours) at ERs were found for male gender, multiple attendances, patients with trauma or suspicion of trauma, alcohol dependence or alcohol misuse. Dose-response effects were seen for higher degrees of alcohol consumption assessed by clinical judgement and breath-test on short duration of stay. Self-discharge was not significantly associated with short duration of stay (Table 3).
Factors associated with clinical degree of alcohol consumption
Higher degrees of alcohol consumption mostly indicate the need of intensified medical care and often limit the motivational or psychological access to patients. Therefore we were interested in factors associated with the clinical degree of alcohol consumption of patients at ER.
Clinical categories were analyzed for their association with different degrees of alcohol consumption (according to the 4 clinical degrees of alcohol consumption) with separate (for each category) multinomial logistic regression models. We found statistically significant associations for higher degrees of alcohol consumption with male gender, trauma (or suspicion of trauma), duration of stay <24 hours, multiple attendances, discharge against medical advice, attendances on weekends and attendances outside regular working times (Table 4).
Significant linear dose-response relationships were seen for the effects of short duration of stay, multiple attendances, attendances outside regular working times, discharge against medical advice and male gender on increasing clinical degree of alcohol consumption on (Table 4). Age alone was without significant association with any of the clinical degrees of alcoholic consumption, neither as continuous nor as categorical variable (data not shown).
Discussion
In this study the demographic characteristics, clinical burden and profile of alcohol-related attendances at ERs was analyzed for 1,748 patients in 11 hospitals of an urban area. With this study design we considered a broad definition of alcohol-related attendances independently from alcohol diagnosis, different hospital settings and a wide range of medical specialties over a period of 6 months which is an advantage over previous studies in this field which concentrated on single settings or specialties,[7,8]. Since all hospitals of the urban region were involved in this study, the obtained estimates can be related to the population of the area. The prospective design of this study allowed for obtaining information on characteristics of single compared with repeat attendances.
Instead of tests such as CAGE, the Michigan Alcoholism Screening test (MAST) and the Munich Alcoholism Test (MALT) [19-21] we applied an objective measure of alcohol consumption using breath test and a standardized clinical measure. For the purpose of this study to estimate the alcohol-related clinical burden at ERs independently of specific alcohol diagnoses we found the applied criteria to be valid and feasible measures.
The proportion of alcohol-related attendances at ERs in relation to all medical attendances / inpatient admissions was 3% in this study. This figure is lower than in other studies such as in Liverpool, UK: Pirmohamed et al. reported a proportion of all alcohol-related hospital admissions of 6.2% [14] that is line with figures from the National Statistics for England and Wales (1 alcohol case in 16 hospital admissions) [22]. The proportion of 3% alcohol-related admissions in our study was twice as high as the corresponding figure of the National German Hospital Statistics (1 alcohol case in 64 hospital admissions), [23] indicating an over-representation of alcohol-related attendances and admissions to hospitals of the study region.
The high proportion of male gender in this sample reflects the well known greater likelihood of alcohol-related diagnoses for men than for women in the general population [24,25] and in clinical samples [26] as well. Female gender was associated with social behaviour better adapted to regular working times at ERs, regular medical procedures and with more social support by friends.
Patients of the age groups 21–44 years and 45–59 years with alcohol problems at ERs were over-represented in this study compared to the age distribution in the general population of Dortmund implicating intensive and dangerous alcohol consumption in these age groups. However, age was neither significantly associated with higher degrees of alcohol consumption nor with non-dependent alcohol use in this sample. This is controversial in light of findings reported in the German annual report on alcohol and drug use in the general population. They found an increase of hazardous alcohol consumption (women >20 g alcohol daily; men >30 g alcohol daily) in younger people aged 18–24 between the years 1995–2001 and a slight decrease in older men and women in the general population [27]. The cross-sectional findings in our study did not mirror these age-related national trends for the year 2001 (neither for men nor for women). Our finding is also contrary to studies in Ireland reporting highest rates of non-dependent alcohol use among the 20–29 years-old [15] and 18–29 years-old [28], respectively. O'Farrell et al. reported in an Irish study that 41% of the acute alcohol intoxication admissions to inpatient units of general hospitals were in young people under 30 years of age [15]. In our study this age group contributed only 13% attendances. Even when we restricted our analysis to the subgroup of patients with inpatient stay, the proportion changed only slightly.
However, the proportion of patients with alcohol use and their related pattern of alcohol misuse may differ depending on samples from the general population, in primary care and emergency rooms as reported by Cherpitel et al. [29].
The proportion of patients with short stay was 34.3% in this study, about 10% higher than the proportion of short stays in a similar study by O'Farrell et al. in Ireland [15]. This difference is probably due to the fact that O'Farrell et al. captured only inpatient hospital admissions (>24 hours attendance) in contrast to our study that also captured short stays less than 24 hour attendance. Another difference between these studies was related to the average duration of hospital stay (10 days in this study) that was more than 3 times higher than in O'Farrell's study (2.3 days on average) indicating a huge effect on the hospital resources [15]. These differences mirror the generally longer duration of stay of patients in inpatient units in Germany (nationwide 9.3 inpatient days on average in general hospitals) compared to the UK (nationwide 5.5 inpatient days on average in general hospitals in NHS beds) for the year 2001 [14,22,30].
In our study higher degrees of alcohol consumption were associated with attendances at ERs on weekends which is similar to findings reported in a study on emergency inpatient hospital admissions in Ireland [15]. The same effect was seen for patients with attendances at ERs outside regular working times. Both effects may reflect the harmful drinking pattern that occurs over the weekend period and during evening and night. Since harmful drinking patterns on weekends and at night time are found to be associated with alcohol-related traffic accidents in Germany [16], these patients presenting at ER are at higher risk to be involved than non-alcoholics.
Our results show that short-stay cases and cases with high degrees of alcohol consumption represent high risk groups for potential alcohol-related medical complications (e.g., trauma). Although no figures are available showing the proportion of patients involved in alcohol-related road accidents after discharge from ERs, especially short-stay cases and cases with higher degrees of alcohol consumption may represent a group with higher risk to themselves and in road traffic. A study by Cryer et al. in the general population supported the generally held view that heavy alcohol consumers are disproportionate users of acute medical services, but they are relative under-users of preventative medical care services [31]. The short-stay cases in our study would fit into this pattern of service use as they tend not to utilize the inpatient treatment facilities.
The findings in this study on the association of trauma with higher degrees of alcohol consumption confirm previous findings on this issue. Cherpitel et al. reported in a meta-analysis a moderate, but consistent association between higher blood alcohol concentration and injury in emergency room settings [32,33].
Limitations of the study
We reached the goal to include all ERs of the study region. However, due to possible moving of the patients outside and within the catchment area the study population was an open cohort. A proportion of 18% of the patients were not registered as living in the catchment area, and we did not obtain information if they moved into this area for the short-term. It remained unclear whether these patients are balanced by patients registered as living in the study region but presenting to ERs in other regions. Moreover, the proportion of people officially registered in the study area is unknown and also unknown is the proportion in the sub-sample of inhabitants with alcohol-related problems. Furthermore, patients also could have presented in two different study hospitals during the study period. Given the size of the city and the wide spread locations of the hospitals (one central hospital, several peripheral district hospitals, and hospitals in the outskirts of the town), we assumed multiple presenting of the same patient in different hospitals was not very likely in this study, because the study hospitals are not in easy reach of each other. All these effects could lead to either over- or underestimation of incidence in our study.
Because of the relatively short time (from September to February) covered by the study, we do not know the number of patients in the spring and summer months (March-August). The observed decrease of attendances at ERs during winter months could be explained by a diminished recruitment in the course of the study, but can also result from climatic conditions. On the other hand, based on official statistics on drinking patterns in Germany there is no indication of an increasing or declining trend of incidence of alcohol-related attendances at ER during the Christmas holiday period [27]. Given the observed trend in this study is true, our estimates of incidence of attendances at ERs and days of hospitalisation are rather too low.
We obtained no information on patients not included in the study. If particular characteristics were associated with the non-inclusion status, our estimates of associations could be distorted, however, the relatively high inclusion rate of 83.8% makes a strong influence of missing cases improbable.
Conclusion
Although the figure obtained in this survey is likely to be an underestimate of the true alcohol-related burden in hospitals, the results demonstrate a substantial need for tailored interventions at ERs. The presented trends on patterns of alcohol use in the general population support this view. In particular, short-stay cases, repeat attendances and cases with higher degrees of alcohol consumption largely determine the clinical burden at ERs serving several medical disciplines. Since suitable brief anti-alcoholic interventions have been proven to initiate change of drinking patterns [1,34,35] and have demonstrated their effectiveness in patients with alcohol problems in primary care settings [36-39] and in emergency departments as well [40], the design, implementation and evaluation of anti-alcoholic interventions applied to ERs would need to take into account the factors determining the alcohol-related clinical burden presented in this study.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
BB conceived the study, guided the statistical analysis and drafted the manuscript. RM performed the statistical analysis and contributed to drafting of the manuscript. GR conceived the study, designed and developed the questionnaire and co-ordinated the data collection in the addiction hospital. AD conceived the study and co-ordinated the data collection in the general hospitals. SF developed and tested the questionnaire, applied the questionnaire across the hospitals and carried out data collection and data management. HK planned the study and organized clinical and administrative data collection. US supervised data collection and data management throughout the study period. All authors revised and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors wish to thank the medical staff in the participating hospitals for data collection and administrative staff for the provision of administrative data on the patients.
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BMC Clin PatholBMC Clinical Pathology1472-6890BioMed Central London 1472-6890-5-101628866110.1186/1472-6890-5-10Technical AdvanceTouchdown General Primer (GP5+/GP6+) PCR and optimized sample DNA concentration support the sensitive detection of human papillomavirus Evans Mark F [email protected] Christine SC [email protected] Linda [email protected] Kumarasen [email protected] Department of Pathology, University of Vermont, Burlington, Vermont 05405, USA2005 16 11 2005 5 10 10 23 6 2005 16 11 2005 Copyright © 2005 Evans et al; licensee BioMed Central Ltd.2005Evans 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 GP5+/GP6+ PCR assay is a well-established HPV detection technique. This study has examined the effects of incorporating 'hot start' and 'touchdown' steps into the protocol. In addition, dTTP was substituted with dUTP to permit contamination control measures against carry-over PCR product.
Methods
Firstly, HPV-16 was amplified from SiHa cell DNA (0.1 ng–100 ng) diluted in a background of C-33A DNA (100 ng-2 μg). Secondly, the detection of small quantities (15ag-1.5pg) of HPV recombinant plasmids (types 16, 31, 33, 45, 51, 52, and 56) diluted in C-33A DNA was investigated. Thirdly, clinical sample DNA extracts (cervical smears, formalin-fixed vaginal lesions and breast tumors) were tested for HPV. Six different PCR protocols were assessed. HPV was detected by gel electrophoresis, and by Southern and dot blot hybridization.
Results
HPV detection sensitivity was dependent on the total amount of DNA in a PCR. Touchdown protocols supported HPV-16 detection from 1 ng or 0.5 ng SiHa cell DNA in a background of 2 μg or 1 μg C-33A DNA respectively, and from 0.1 ng of SiHa cell DNA (~28 copies HPV-16) in 500 ng or 100 ng background DNA. Under standard GP5+/GP6+ annealing conditions, HPV-16 went undetected when the DNA content of a PCR was 2 μg or 1 μg, and with 500 ng C-33A DNA the sensitivity limit was 1 ng SiHa cell DNA. HPV recombinant plasmids were each detected with high (albeit varying) sensitivity by a touchdown protocol. HPV-31 was better amplified under standard annealing conditions (1.5fg in 100 ng background DNA) than by a touchdown approach (15fg detection limit). HPV-52 was not amplified by the standard protocol at the dilutions tested. Seventeen different HPV types were demonstrated in 47/65 (72%) abnormal cytology samples recorded as HPV negative by standard GP5+/GP6+ conditions. Twenty-one different HPV types were recorded in 111/114 (97%) vaginal lesions. Multiple infections were also detectable using a touchdown approach. Of 26 breast tumors, 5 (19%) tested HPV positive by the standard assay and 15/26 (58%) using a touchdown protocol.
Conclusion
Touchdown modification of the GP5+/GP6+ PCR assay enables the detection of HPV undetected under regular assay conditions. The use of standardized DNA quantities in a PCR rather than standard sample volumes containing arbitrary amounts of DNA is supported. A touchdown approach may be beneficial as an analytical test for the re-evaluation of (apparently) HPV negative abnormal cervical cytological or histological samples, and for investigating the association of HPV with disease conditions at diverse organ sites. The clinical utility of a touchdown approach for HPV detection requires further investigation as increased assay analytical sensitivity may not necessarily equate with improved clinical sensitivity or specificity.
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Background
The association of human papillomaviruses (HPV) with invasive cervical carcinoma and its precursor lesions is well characterized [1,2]. There is also an emerging body of data indicating that HPV may contribute to tumor etiology at a variety of other anatomical sites [3]. For example, high-risk HPV types have been detected in up to 48% of breast carcinomas [4], although other studies have reported an absence of HPV in these tumors [5].
Clearly, any estimate of HPV prevalence amongst a tissue sample set is dependent on the detection method used. Commonly employed PCR based assays include the General Primer Mediated 5+/6+ (GP5+/GP6+) [6,7] and the MY09/MY11 [8] systems that amplify sequences from the L1 region of the HPV genome. Since the early/mid-90 s, when these assays were first developed a number of modifications that can improve PCR efficiency have been described. In addition, there have been improvements in thermal cycler specifications. This study has examined the effects of incorporating 'hot start' [9] and 'touchdown' [10] steps into the GP5+/GP6+ assay. Assays have been tested for use with dUTP instead of dTTP so that a uracil N-glycosylase (UNG) pre-PCR-incubation step can be included to degrade any contaminating carry-over PCR product present at reaction set up. The effect of the quantity of background DNA in an individual PCR on the limits of HPV detection has been specially investigated. Protocols have been tested on HPV recombinant plasmids, and DNA extracted from cervical cell lines, cervical cytology samples, and from formalin-fixed, paraffin-embedded (FFPE) vaginal intraepithelial neoplasia (VAIN) lesions and breast invasive ductal carcinomas (IDC).
Methods
Materials
All patient materials used in this study were obtained and analyzed with Institutional Review Board approval.
Cell lines
SiHa cells that contain one copy of the HPV-16 genome integrated at chromosome 13q21-31 [11], and C-33A cells derived from an HPV negative cervical carcinoma, were acquired from the American Tissue Culture Collection (ATCC), Manassas, VA.
HPV recombinant plasmids
HPV types 16, 45, and 51 were received courtesy of Dr. E-M de Villiers, Deutsches Krebsforschungszentrum (dkfz), Heidelberg, Germany. HPV-33 was received courtesy of Dr. Gerard Orth, Institut Pasteur, Paris, France. HPV-31, 52, and 56 were obtained from the ATCC.
Cervical cytology samples
Remnant cells (following cervical smear testing) were obtained from samples diagnosed as low-grade cervical squamous intraepithelial lesion (LSIL), abnormal squamous cells of undetermined significance (ASC-US), abnormal squamous cells cannot exclude HSIL (ASC-H), or, high-grade squamous intraepithelial lesion (HSIL).
Breast invasive ductal carcinomas
Twenty-six FFPE IDC samples were selected at random from Fletcher Allen Health Care Pathology (FAHC) archives.
Vaginal intraepithelial neoplasia samples
114 FFPE VAIN samples were retrieved from FAHC archives.
DNA extraction and quantification
DNA was extracted and purified from cultured SiHa and C-33A cells and from cytology samples by proteinase K digestion followed with a column clean-up method (Qiagen DNeasy Tissue Kit). DNA was extracted from FFPE tissues by digesting five 6 μm dewaxed sections with proteinase K as previously described [12]. DNA concentrations were estimated using a DyNA Quant 200 Fluorometer (Hoeffer Scientific).
PCR protocols
The PCR protocols tested are summarized in Table 1. Initially, the standard GP5+/GP6+ assay cycling conditions and four different touchdown protocols were compared. In all protocols dTTP was substituted with dUTP (ACGU dNTP mix [Sigma A5593]) and included a pre-PCR incubation step with 'heat-killed'-Uracil N-Glycosylase (HK™-UNG, Epicentre). HK™-UNG is a heat-labile form of UNG that has maximal activity at 50°C and is designed to be completely inactivated by 10 minute incubation at 65°C (Epicentre technical note). Samples were incubated at 37°C for 30 min with 0.2U HK™-UNG according to supplier's recommendations. A 'hot-start' step followed utilizing HotStarTaq DNA Polymerase (Qiagen), which requires incubation at 95°C for 15 min to activate the Taq. HotStarTaq was used at a concentration of 1U per 50 μl reaction. Magnesium chloride was included in reactions at a concentration of 4 mM. Other reaction conditions held constant for each protocol included 1X HotStarTaq buffer [Tris-HCl (pH8.7), KCl, (NH4)2S04], and each primer at a concentration of 1 μM, (GP5+ [5'-TTTGTTACTGTGGTAGATACTAC-3'] and GP6+ [5'-GAAAAATAAACTGTAAATCATATTC-3']). The Tm of the GP5+ primer is 45°C and the Tm of the GP6+ primer is 41°C, (Tm values are estimated for 50 mM salt conditions and are calculated from the equation Tm= 59.9 + 41 [%GC] - [675/Primer Length]).
Table 1 Details of the PCR protocols tested. All protocols included substitution of dTTP with dUTP and commenced with a uracil N-glycosylase (UNG) incubation step (37°C for 30 minutes) followed by a HotStarTaq DNA Polymerase (Qiagen) activation/UNG inactivation step (95°C, 15 min).
Protocol Denaturation Touchdown annealing cycles Additional annealing cycles Extension Final extension
GP5+/GP6+ 1 min, 94°C - 2 min, 40°C (40–50 cycles) 1.5 min, 72°C 4 min, 72°C
TDP1 1 min, 94°C 2 min, 45°C to 40°C in 0.5°C decrements (11 cycles) 2 min, 40°C (29–39 cycles) 1.5 min, 72°C 4 min, 72°C
TDP2 1 min, 94°C 2 min, 50°C to 40°C in 1.0°C decrements (11 cycles) 2 min, 40°C (29–39 cycles) 1.5 min, 72°C 4 min, 72°C
TDP3 1 min, 94°C 2 min, 50°C to 40°C in 0.5°C decrements (21 cycles) 2 min, 40°C (19–29 cycles) 1.5 min, 72°C 4 min, 72°C
TDP4 1 min, 94°C 2 min, 55°C to 40°C in 1.0°C decrements (16 cycles) 2 min, 40°C (24–34 cycles) 1.5 min, 72°C 4 min, 72°C
The standard GP5+/GP6+ amplification cycles comprise 1 min at 94°C, 2 min at 40°C, and 1.5 min at 72°C, with the final extension step prolonged to 4 min to ensure complete amplification of the 140–150 base pair products [6,7]. The standard denaturation and extension temperatures and times were retained in the four 'touchdown' protocols tested. The annealing period of 2 min was also retained, as was the 'final' annealing temperature of 40°C. Touchdown protocols (Table 1) with starting annealing temperatures of 45°C, 50°C, or 55°C, decreasing by 0.5°C or 1.0°C decrements per PCR cycle down to 40°C were evaluated. All PCR protocols were tried at 40 up to 50 cycles of amplification. Protocols were variously tested with 5 ng up to 2 μg of DNA per 50 μl reaction. Experiments were conducted using 0.2 ml PCR tubes (lightly coated with mineral oil to enhance heat conduction) and a Programmable Thermal Controller-100™ [PTC-100] (MJ Research, Inc. MA).
Following completion of the study the authors became aware of a GP5+/GP6+ protocol incorporating modified ramping times (MRT) [13]. The MRT GP5+/GP6+ conditions were tested on a subset of the samples. For the purposes of this study, the protocol was modified to include hot-start, dUTP, and 50 PCR cycles. Reaction ingredients were as above. The MRT GP5+/GP6+ cycling conditions were as follows:
HK™-UNG incubation step: 37°C 30 mins
Denaturation/HotStarTaq activation: 95°C 15 mins
Cycles (n = 50) 20 s at 94°C
In 24 s to 90°C
In 66 s to 48°C
In 30 s to 38°C
30 s at 38°C
In 18 s to 42°C
In 42 s to 71°C
80 s at 71°C
In 24 s to 69°C
In 90 s to 94°C
Final step: 4 min at 71°C
Control measures
PCR
Negative control PCR was performed using C-33A cells and reactions containing no template DNA. PCR amplification of β-globin sequences was performed to confirm sample fitness for PCR assay [14].
FFPE tissue-block sectioning
Measures to prevent potential cross-contamination of tissue during sample sectioning, included wiping the microtome blade with histoclear (xylene substitute) and 'DNA-Erase' [ICN] (a DNA contamination removal reagent) between blocks. Additionally, the first 10–20 sections cut from a specimen were discarded prior to collecting sections for DNA extraction from the specimen.
HPV detection
Protocol sensitivity was measured by the presence of a ~150 base-pair band after 10 μl PCR product had been sieved through a 2.5% agarose gel stained with 0.5 μg/ml ethidium bromide. Southern blot hybridization was performed to test that the ~150 base-pair PCR product represented HPV DNA. HPV typing was performed by dot blot hybridization of PCR products with up to 37 type-specific biotin-labeled oligonucleotide probes. Biotin was detected with streptavidin-alkaline phosphatase conjugate and the substrate nitrobluetetrazolium/5-bromo-4-chloro-3-indolyl phosphate [12], or by sequencing.
Protocol reliability
Touchdown protocols were assessed on two or more occasions on SiHa cell/C-33A DNA dilutions, and the TDP3 was also tested several times on recombinant HPV plasmid samples to determine data reproducibility. Clinical samples (cytology, FFPE VAIN and breast tumors) were tested once only with PCR protocols. Inter-laboratory tests of different PCR assays were not performed.
Results
Detection of HPV in SiHa cells
The ability of the protocols to amplify HPV-16 from 100 ng, 25 ng, 1 ng, 0.5 ng or 0.1 ng of SiHa cell DNA 'hidden' in a total quantity of either 2 μg, 1 μg, 500 ng or 100 ng DNA (made up with C-33A DNA) was examined. The results are summarized in Table 2, and Figure 1. Each touchdown protocol improved the detection of HPV sequences. The best protocols were the TDP3 and TDP4 that demonstrated HPV-16 amplification from 0.1 ng of SiHa cell DNA in a total DNA content of 100 ng or 500 ng (Figure 1). When the total DNA content was 1 μg, these protocols enabled successful HPV-16 amplification from 0.5 ng of SiHa DNA (Table 2). HPV-16 was amplifiable by the TDP3 protocol from 1 ng of SiHa cell DNA when the total reaction DNA was 2 μg (Table 2). The least successful protocol was the standard GP5+/GP6+ that did not amplify HPV-16 from SiHa cell DNA diluted in 1 μg or 2 μg of background DNA and was only able to demonstrate HPV-16 from 1 ng of SiHa cell DNA contained in a total of 500 ng DNA (Table 2, Figure 1). Data was confirmed by repeat PCR tests. The MRT GP5+/GP6+ protocol was also tested on SiHa cell DNA diluted in a background of 100 ng C-33A DNA and supported detection of HPV-16 from 0.1 ng SiHa cell DNA. Figure 2 details a Southern blot of PCR products from the TDP3 and MRT protocols hybridized with a biotin-labeled HPV-16 oligonucleotide probe.
Table 2 PCR protocol sensitivities for the detection of HPV-16 in SiHa/C-33A DNA mixtures. SiHa cell DNA (0.1 ng to 100 ng) was mixed with C-33A cell DNA to give final DNA quantities of 2 μg, 1 μg, 0.5 μg, or 0.1 μg. These preparations were tested with up to six different PCR protocols. HPV-16 detection was most successful using by the TDP3 and TDP4 conditions. Data are for protocols tested at 50 PCR cycles. HPV detected, HPV undetected, ND: not done.
Total DNA in PCR: 2 μg Total DNA in PCR: 1 μg
SiHa DNA 100 ng 25 ng 1 ng 0.5 ng 0.1 ng 100 ng 25 ng 1 ng 0.5 ng 0.1 ng
GP5+/6+
TDP1
TDP2
TDP3
TDP4 ND ND ND ND ND
MRT ND ND ND ND ND ND ND ND ND ND
Total DNA in PCR: 0.5 μg Total DNA in PCR: 0.1 μg
SiHa DNA 100 ng 25 ng 1 ng 0.5 ng 0.1 ng 100 ng 25 ng 1 ng 0.5 ng 0.1 ng
GP5+/6+
TDP1
TDP2
TDP3
TDP4
MRT ND ND ND ND ND
Figure 1 Detection of HPV-16 PCR product by agarose gel electrophoresis. SiHa cell DNA, 0, 0.1, 0.5, 1.0, 25, or 100 ng, was diluted in C-33A DNA to give a total of 0.5 μg or 0.1 μg DNA. The GP5+/GP6+, TDP1, TDP2, TDP3 and TDP4 assays were compared for the detection of HPV-16 product on an ethidium bromide (0.5 μg/ml) stained 2.5% agarose gel. RB: reagent blank, M: 50 base pair molecular weight ladder.
Figure 2 Southern blot of TDP3 and MRT GP5+/GP6+ PCR products. SiHa cell DNA, 0, 0.1, 0.5, 1.0, 25, or 100 ng, was diluted into a total of 0.1 μg DNA made up with C-33A DNA. After PCR with the TDP3 and MRT protocols and gel electrophoresis, products were electroblotted onto nylon membrane and hybridized with biotin-labeled HPV-16 oligonucleotide probe detected with streptavidin-alkaline phosphatase and NBT/BCIP. The data confirm the ~150 bp amplicons represent HPV sequences.
Detection of HPV recombinant plasmid DNA
The ability of the standard GP5+/GP6+ and TDP3 protocols (50 cycles each) to amplify 15ag, 150ag, 1.5fg, 15fg, 150fg, or 1.5pg of HPV-16, 31, 33, 45, 51, 52, or, 56 plasmid in a background of C-33A cell DNA (to a total of 100 ng DNA per PCR) was examined. The same set of 'master dilutions' were used in the two different PCR assays. The results are shown in Figures 3 and 4, and Table 3. With the TDP3 protocol HPV-16 amplification product was observed from (all three) 15ag recombinant plasmid dilutions, and HPV-45 from one of three 15ag HPV DNA dilutions. HPV-51 was detectable following amplification from 150ag DNA template; HPV-33, 52, and 56 from 15fg template; and, HPV-31 from 150fg recombinant HPV DNA. With the GP5+/GP6+ protocol HPV-16 was amplifiable from 150ag recombinant plasmid; HPV-45 from 1.5 g template; HPV-31 from 15fg; and HPV-33, 51, and 56 from 150fg template; HPV-52 was not detected. Data were confirmed up on repeat PCR.
Table 3 PCR sensitivities for the detection of seven different HPV types. HPV recombinant plasmids were diluted in a background of 100 ng C-33A DNA and amplified (50 cycles) by the GP5+/GP6+ protocol or by the TDP3 method. HPV detected, HPV undetected, Σm Total number of mismatches between the GP5+/GP6+ primers and the target HPV type.
GP5+/GP6+ TDP3
Σm HPV 1.5pg 150fg 15fg 1.5fg 150ag 15ag 1.5pg 150fg 15fg 1.5fg 150ag 15ag
2 16
3 33
3 45
4 31
4 56
7 52
10 51
Figure 3 Detection of recombinant HPV plasmid by GP5+/GP6+ PCR. HPV (types 16, 31, 33, 45, 51, 52, and 56) recombinant plasmids, 0, 15ag, 150ag, 1.5fg, 15fg, 150fg, 1.5pg, were diluted in 100 ng C-33A DNA and amplified (50 cycles) using the GP5+/GP6+ protocol. Positive control amplification of HPV-18 from 100 ng HeLa cell DNA is also shown.
Figure 4 Detection of recombinant HPV plasmid by TDP3 PCR. HPV (types 16, 31, 33, 45, 51, 52, and 56) recombinant plasmids, 0, 15ag, 150ag, 1.5fg, 15fg, 150fg, 1.5pg, were diluted in 100 ng C-33A DNA and amplified (50 cycles) using the TDP3 conditions.
Detection of multiple HPV types
To examine how a touchdown protocol affected the detection of multiple HPV types, dilutions of HPV-16, 45, 51, and 52 were combined in a background of 100 ng C-33A DNA. Five mixtures were compared: 1.5pg each HPV type combined in a PCR, 150fg of each type in a PCR, 15fg of each type in a PCR, 1.5fg of each type in a PCR, or 150ag of each type in a PCR. Following amplification using the TDP3 protocol, duplicate rows of 0.6 μl heat-denatured product was dot-blot hybridized with type-specific probes. The results are shown in Figure 5. HPV-16 was detectable following amplification of each dilution mixture. HPV types 45, 51, and 52 were detectable following amplification of mixtures containing 1.5pg, 150fg, 15fg, or 1.5fg, but not 150ag HPV DNA. Repeat PCR tests were not performed.
Figure 5 Dot blot hybridization detection of multiple HPV types after TDP3 PCR. HPV types 16, 45, 51, and 52 were combined (1.5pg, 150fg, 15fg, 1.5fg, or 150ag of each type) in a background of 100 ng C-33A DNA. After PCR, 0.6 μl of denatured product was spotted onto nylon membrane and hybridized with biotin-labeled HPV type specific oligonucleotide probe. Duplicate dot preparations were prepared.
Detection of HPV in cytology samples
In a previous study [15], GP5+/GP6+ PCR was used to determine HPV type and prevalence amongst a set of abnormal cytology samples and 290/355 (82%) were recorded as HPV positive. The TDP3 and TDP4 protocols were tested on 100 ng and 500 ng of DNA from samples that had tested HPV negative. Both protocols detected HPV in samples previously recorded as HPV negative, with the TDP3 representing a more efficient assay (Figure 6). In some instances, HPV sequences were only detectable when 100 ng of DNA was included in the PCR, and samples were apparently HPV negative when 1.0 μg or 500 ng of sample DNA was used in an assay (Figure 7). All 65 abnormal cytology samples previously recorded as HPV negative were repeated with 50 cycles of the TDP3 protocol using 100 ng of sample DNA as template and 47 (72%) samples were recorded as HPV positive. Seventeen HPV types (6, 11, 16, 18, 31, 33, 35, 40, 42, 52, 58, 66, 67, 73, 81 [CP8304], 84 [MM8], and cand91 [CP6108]) were detected amongst these 47 samples. Multiple infections were recorded in 9/47 (19%) samples.
Figure 6 Amplification of HPV from abnormal cervical cell DNA samples by TDP3 and TDP4. Seven samples that tested negative for HPV using standard GP5+/GP6+ cycling conditions were assessed with the TDP3 and TDP4 assays. Both TDP3 and TDP4 supported the detection of HPV in sample 4 when 100 ng DNA was subject to PCR but not when 500 ng DNA was in a reaction. HPV was detected in sample 2 by the TDP3 protocol with 100 ng or 500 ng template DNA and by the TDP4 protocol at 500 ng but not 100 ng of sample. RB: reagent blank.
Figure 7 Detection of HPV by TDP3 in abnormal cervical cell samples. Samples had previously tested negative for HPV using standard GP5+/GP6+ cycling conditions. HPV detection was dependent on the total amount of DNA included in the PCR.
Fifty-four of the 65 'HPV negative' samples were also tested with the MRT protocol. Of these 54 samples, 10 were HPV negative and 44 HPV positive with the TDP3 protocol. Of the 10 negative samples, 1 tested HPV-53 positive by the MRT method, and of the 44 positive samples, 16 (36%) were negative with the MRT method. Five HPV types (16, 18, 31, 35, and 81) went undetected by the MRT method in this sample. An additional 25 samples that were negative following primary screening by the TDP3 assay were identified; none tested HPV positive after PCR with the standard GP5+/GP6+ protocol (50 PCR cycles). Two of the 25 samples tested positive (for HPV-39, and for HPV-51) by the MRT protocol. Thus, of 44 samples negative with the standard GP5+/GP6+ assay, 44 (100%) tested positive with the TDP3 protocol, and 28 (64%) tested positive with the MRT conditions; and, of 35 samples negative by the standard GP5+/GP6+ assay and the TDP3 assay, 3 (8.6%) tested positive with the MRT protocol.
The TDP3 protocol has subsequently been tested on 799 abnormal cytological samples and 752 (94%) have tested positive for one or other of 37 types: HPV-6, 11, 16, 18, 31, 32, 33, 35, 39, 40, 42, 43, 44, 45, 51, 52, 54, 55, 56, 58, 59, 61, 62, 66, 67, 69, 70, 72, 73, 81, 82, 83, 84, 87, cand89, cand90, and cand91.
Detection of HPV in FFPE VAIN samples
The GP5+/GP6+ and TDP3 protocols (both 50 cycles) were compared for the detection of HPV in 5 ng, 20 ng, 50 ng and 100 ng of DNA extracted from six VAIN lesions (Figure 8). HPV sequences were detectable by TDP3 assay in all six samples at each sample DNA quantity. The GP5+/6+ assay detected HPV in all six cases, but electrophoretic band strength was strong only with 100 ng DNA in the PCR and tended to be weaker or absent at other template DNA quantities (Figure 8). Subsequent analysis (using the TDP3 conditions) of VAIN lesions from 114 patients demonstrated HPV in 111 (97.4%) samples. Twenty-one HPV types were identified: HPV types 6, 11, 16, 18, 31, 33, 35, 40, 42, 43, 45, 51, 54, 56, 58, 59, 73, 81, 83, 84, and cand90. Double or multiple infections were detected in 19 (13%) samples.
Figure 8 HPV detection in six VAIN lesions by GP5+/GP6+ and TDP3 assays. Sample DNA in PCR: 100 ng, 50 ng, 20 ng, or 5 ng. M: 50 bp molecular weight marker, RB: reagent blank.
Detection of HPV in FFPE breast invasive ductal carcinoma samples
A preliminary assay of 26 IDC samples (arbitrary 10 μl of DNA extract) by GP5+/GP6+ PCR demonstrated faint bands upon electrophoresis for five (19%) samples. Following DNA quantification, one of these samples was compared for detection of HPV by GP5+/GP6+ assay and TDP3 assay (both for 50 cycles), using 20 ng, 100 ng, 225 ng, 325 ng or 450 ng of DNA extract in the PCR (Figure 9). A weak band was obtained using the GP5+/GP6+ assay following amplification from 225 ng DNA template but HPV was not detected at any of the other sample concentrations. Bands were clearly visible using the TDP3 assay from 100 ng, 225 ng and 325 ng sample quantities. There is a faint band after amplification from 20 ng, but amplification is not evident for 450 ng sample DNA. The TDP3 assay was subsequently applied to the other 25 breast tumor samples (~200 ng sample DNA in the PCR) and HPV was detected in another 14 instances (Figure 10). Overall 15/26 (58%) breast tumors tested positive by this assay for HPV. Dot blot hybridization demonstrated fourteen samples were positive for HPV-16 and one for HPV-31.
Figure 9 Comparison of GP5+/GP6+ and TDP3 amplification of HPV sequences from a FFPE breast invasive ductal carcinoma. Sample DNA in PCR, A: 450 ng, B: 325 ng, C: 225 ng, D: 100 ng, E: 20 ng. The TDP3 assay facilitated detection of HPV in 100 ng, 225 ng and 325 ng sample, whereas with GP5+/6+ assay conditions a HPV was detectable (weak band) only when 225 ng sample was in the PCR. 50 cycles of PCR were used with each of the protocols. RB: reagent blank.
Figure 10 Demonstration of HPV in FFPE breast invasive ductal carcinomas following TDP3 amplification. 200 ng of sample DNA were subject to a total of 50 PCR cycles. Samples 1, 2, 3, 6, 7, 8, 9, 10, 11, 15, 16, 20, 21 and 25 were recorded as HPV positive following electrophoresis and dot blot hybridization. M: 50 bp molecular weight marker, RB: reagent blank.
Discussion
The main finding of this study is that touchdown general primer PCR coupled with control of the quantity of sample DNA in a PCR supports the detection of low-copy number HPV in an excess background of human DNA sequences. A high percentage of abnormal cervical cytological samples previously recorded as HPV negative tested HPV positive by this approach. It has also been demonstrated that a wide range of HPV types and multiple infections are detectable using a touchdown protocol.
Detection of minority HPV DNA
The potential of PCR to detect minority nucleic acid species is the genius of the technique. However, the presence of background DNA may compromise PCR efficiency by giving rise to non-specific primer annealing [16]. Assays such as the GP5+/GP6+ use one pair of primers to amplify a wide range of HPV types and necessarily each primer contains varying numbers of mismatches relative to any given HPV type [17]. Low-stringency primer-annealing conditions were originally defined for the GP5+/GP6+ assay [7] however, these conditions permit GP5+/GP6+ primer annealing to human sequences [6].
In this study a hot start step was introduced to prevent annealing that can occur at PCR set-up between primers and single-stranded DNA sequences produced during DNA extraction [9,16]. Four touchdown protocols were tested to assess how a touchdown annealing approach affects the amplification of HPV sequences. In the first instance, these protocols were tested on a model system consisting of small quantities of SiHa cell DNA 'hidden' in varying amounts of C-33A cell DNA.
The SiHa cell line is known to contain approximately one slightly truncated copy of the HPV-16 genome integrated in chromosome 13q21-31 [11]. The cell line is hyper-diploid but has been shown to be disomic with respect to chromosome 13 [11]. It is possible to approximate the number of copies of HPV-16 detected by the various protocols tested assuming for the purpose of the approximation that there are 6.6 × 109 DNA base pairs per SiHa cell. One base pair weighs 650 daltons and one dalton weighs 1.66 × 10-24g. Therefore, one diploid cell contains 7.12pg of DNA, and there will be one copy of the GP5+/GP6+ primer target per 3.56pg of SiHa cell DNA extract. It follows that the number of copies of HPV-16 in 0.1 ng of SiHa cell DNA will be ~28; in 0.5 ng SiHa DNA there will be ~140 copies; in 1 ng ~280 copies; in 25 ng ~7000 copies and in 100 ng ~28,000. HPV-16 was amplifiable from smaller quantities of SiHa cell DNA as the total quantity of DNA reduced from 2 μg to 500 ng for all protocols. Two touchdown protocols (TDP3, TDP4) were routinely able to detect 0.1 ng SiHa HPV DNA (~28 copies) in a total DNA quantity of 500 ng (approximately equal to DNA extracted from 70,000 cells [i.e. 1 copy HPV-16 detected per 2500 cells]). In contrast, the best performance of the GP5+/GP6+ protocol was the demonstration of HPV-16 from 1 ng of SiHa cell DNA (~280 HPV-16 copies) in a background of 500 ng total DNA. Unlike TDP3 and TDP4 protocols, the standard GP5+/GP6+ assay failed to demonstrate HPV when the total DNA content in a reaction was 1 μg or 2 μg. It is noticeable from Figure 1 that some of the protocols gave slightly more intense bands, ~150 bp in size, following amplification of HPV-16 sequences in a background of 500 ng DNA compared to the amplification in a background of 100 ng. This empirical finding was reproducible on repeat PCR. This observation is counter-intuitive; however, given the nature of the touchdown approach to annealing, the character of the dilutions, and the high number of PCR cycles, the PCR assays tested are essentially qualitative rather than quantitative.
The significantly improved detection of low-copy HPV-16 by touchdown protocols illustrates the important influence background DNA can have on detection sensitivity. The findings are particular noteworthy given that relative to the target HPV-16 sequence, the GP5+ primer sequence (Tm = 45°C) has just two mismatches and the GP6+ primer (Tm = 41°C) has no mismatches. The data show that under low-stringency annealing conditions and when the target is low-copy HPV, considerable non-specific primer annealing with human sequences must occur despite the near perfect homology of the primers for the HPV-16 L1 open reading frame target.
The MRT GP5+/GP6+ protocol was also found to detect low-copy HPV in a background of human sequences. This assay incorporates periods of slow temperature changes from the denaturation step to the annealing step and from the annealing step to the extension step. This approach may allow for better specific annealing of primers with HPV target during the gradual cooling to the final annealing temperature of 38°C than is possible with the standard conditions. Possibly, the express heating/cooling default settings of current generation thermal cyclers may compromise the efficiency of the GP5+/GP6+ PCR assay as developed using early generation machines [6,7].
Detection of HPV recombinant plasmid DNA
To examine the effect of a touchdown protocol on a wider range of HPV types, recombinant HPV/plasmid DNA samples were diluted in C-33A DNA and tested with the TDP3 protocol. There are three primer mismatches with respect to HPV-33, one in the GP5+ primer and two in the GP6+ primer. For HPV-45 there are three mismatches that are all in the GP5+ primer sequence. HPV-31 and 56 each have one mismatch in the GP5+ primer and three in the GP6+ primer. For HPV-52 there are seven mismatches, including five in the GP5+ primer. HPV-51 has the greatest number of mismatches of any HPV type amplified by the GP5+/6+ system: six mismatches in the GP5+ primer and four in the GP6+ primer. Not surprisingly, HPV-16 was the most efficiently amplified type (Figure 3). Amplification product was detectable from 15ag DNA (approximately equivalent to 1 copy of the recombinant HPV plasmid) in a background of 100 ng human DNA (equivalent to 14,000 cells). HPV-45 and 51 were amplifiable from 150ag (10 HPV/plasmid copies); types 33, 52, and 56 from 15fg (100 copies of HPV) DNA; and, HPV-31 from 150fg (1000 copies) of recombinant DNA. Clearly, for such small quantities of DNA, slight variations in pipetting accuracy and/or DNA concentration estimate might alter the final sensitivity data. Nonetheless, the data indicates firstly that a touchdown protocol can sensitively amplify a range of HPV types, and secondly that the efficiency of amplification may not be a simple function of the number of mismatches between the primer and target sequence. With the TDP3 protocol HPV-31 (4 mismatches) was less efficiently amplified than HPV-56 (4 mismatches), HPV-52 (7 mismatches), or HPV-51 (10 mismatches) (Figure 4, Table 3). The TDP3 amplified six of the seven HPV types tested with greater sensitivity than the standard GP5+/GP6+ conditions. HPV-31 was detectable as a faint band from 15fg template DNA following the standard PCR, whereas the detection limit with the TDP3 protocol was 150fg (Figures 3 and 4, Table 3). HPV-52 was not detectable by the standard protocol at the dilution range tested but was detectable from 15fg starting template using the TDP3 protocol. These data again indicate that the detection efficiency for different HPV types by a general primer method may not follow easily predictable rules. General primer annealing involves a dynamic inter-relationship depending on HPV copy number, primer affinity for different HPV types, human sequence concentration, and, primer affinity for human sequences at a given annealing temperature. The empirical data indicate that a touchdown annealing approach helps negotiate these variables to support sensitive detection of a wide range of HPV types.
Detection of multiple HPV types
This study also examined the ability of a touchdown protocol to amplify HPV types-16 (2 mismatches), 45 (3 mismatches), 51 (10 mismatches), and, 52 (7 mismatches) combined in one PCR (Figure 5). Again, HPV-16 was detected with the highest sensitivity; however, the other three types were identifiable following dot blot hybridization demonstrating that the touchdown protocol did not favor HPV-16 amplification to the exclusion of other types. An exhaustive assessment of the effect of different concentrations of HPV types within a PCR on the detection of multiple types was beyond the resources available for this study.
Detection of HPV in cervical smear samples
An excess of background human DNA is likely to be a common situation when performing PCR on DNA extracted from routine cervical smear samples. The extent to which normal or lesion tissues are scraped from the cervix cannot be controlled. For efficient HPV PCR amplification, the study data strongly support the use of standardized quantities of DNA and not regular volumes containing arbitrary amounts of DNA. Overall, the cell line and clinical sample studies indicate that in a 50 μl reaction, between 100 ng and 200 ng sample DNA supports the most efficient PCR. Using optimized amounts of DNA, the TDP3 protocol demonstrated one or other of seventeen different HPV types in 47 of 65 (72%) abnormal cytology samples HPV negative by the regular GP5+/GP6+ protocol. Multiple infections were found in 9/47 (19%) of the samples.
The MRT GP5+/GP6+ conditions were tested on 54 of the 65 GP5+/GP6+ negative samples. Twenty-eight (64%) of 44 samples positive by the TDP3 were also positive by the MRT protocol, and 1 of 10 samples negative by the TDP3 protocol was positive by the MRT. Of an additional 25 abnormal cytological samples negative for HPV by the TDP3 assay, none tested positive with the standard GP5+/GP6+ method, but two samples were positive by the MRT protocol. Again, these data show that HPV detection can be highly dependent on assay conditions and also demonstrate that two or more methods may be required for inclusive HPV screening. Nevertheless, the above comparison of methods together with the demonstration of HPV in 94% of 799 abnormal cytological samples indicates a touchdown approach supports wide-ranging and sensitive HPV detection.
Detection of HPV in FFPE samples
This study also examined the effect of touchdown PCR and sample DNA quantity on the amplification of HPV from FFPE tissues, where DNA quality is likely to be poor. VAIN lesions were chosen as an example of tissues from which HPV virions are actively shed and therefore where HPV DNA is in a relative abundance [18]. Amplification of HPV from VAIN lesions was readily accomplished by both the GP5+/GP6+ and TDP3 protocols although the TDP3 assay was able to amplify HPV from smaller quantities of template DNA than the GP5+/GP6+ assay (Figure 8). The data again illustrates the effect of DNA content on amplification efficiency. Assay of 114 VAIN lesions by TDP3 has demonstrated 21 different HPV types in 111 HPV positive samples, with double or multiple infections in 17 (15%) of the samples, again suggesting that a touchdown approach sensitively detects a wide range of HPV types.
Breast carcinomas were chosen for study as an example of a tumor where there are widely differing estimates of HPV prevalence. Most early studies of breast tumors found no evidence of HPV following PCR assay [5,19-21]. However, other investigations (by Southern blot hybridization, and by PCR) have reported high-risk HPV types (e.g. HPV-16, 18, or 33) in 24/50 (48%), 5/17 (30%), 19/41 (46%), 14/32 (44%), and 6/17 (35%) breast carcinomas [4,22-25]. de Villiers et al. PCR tested paired breast carcinoma and nipple tissues from 29 patients for anogenital and skin HPV types. HPV was detected in 25/29 (86%) carcinomas and in 20/29 (69%) nipple tissues [26]. In the present study, application of the GP5+/GP6+ assay using an arbitrary DNA quantity in the PCR indicated 5/26 (19%) invasive ductal carcinomas were HPV positive. Subsequent assays using the TDP3 protocol and 200 ng of sample indicated 15/26 (58%) tumors were HPV positive. These data add to the evidence that HPV sequences are present in breast tumor tissues. However, that HPV was detectable in most tumors only after use of a highly optimized PCR protocol may indicate that HPV is in a latent form and/or is confined to a subset of tumor cells or other cells associated with a tumor mass. Perhaps there may be parallels with Epstein Barr Virus (EBV) detection in breast tumors. EBV has been reported in up to 51% of breast carcinomas by sensitive PCR assay [27], but it has since been suggested the EBV detected is amplified from EBV positive tumor infiltrating lymphocytes and not from tumor cells per se [28]. Further studies are required to determine whether HPV detected in breast tumors represents an incidental 'passenger' or has causal significance. Additionally, given the disparate estimates of HPV prevalence in breast tumors, future studies might incorporate more rigorous control measures such as cutting sections of HPV negative tissues for DNA extraction and PCR in between successive breast tumor specimens to test for cross-contamination with HPV positive samples.
Carry-over contamination control
In this study dTTP was replaced with dUTP in all protocols in order to identify a sensitive PCR test that also includes a control measure against carry-over contamination. However, uracil N-glycosylase (UNG) is an enzyme that has a degree of thermostability and may also become reactivated after completion of a PCR protocol [29,30]. HK™-UNG (Epicentre) is designed to be heat-labile and free of such shortcomings, but it has been reported that even heat-labile forms of UNG may retain some residual activity and spoil PCR efficiency [29]. Further, dUTP is less efficiently incorporated into PCR amplicons than dTTP [30]. UNG/dUTP usage might therefore result in suboptimal PCR assay performance and even greater sensitivity for HPV might be possible for all the protocols tested using regular dNTPs.
Clinical utility
The impetus for this study was to determine whether abnormal cytological samples that tested HPV negative by a commonly used PCR assay were in fact HPV positive. Using a touchdown protocol, HPV has been demonstrated in a high percentage of samples previously recorded as HPV negative consistent with HPV representing a necessary cause of most (if not all) abnormal cervical cytological conditions. However, an assay with high analytical sensitivity for HPV might not be appropriate as a clinical test. The HPV status of cytological samples has been proposed as a marker to identify patients with underlying high-grade cervical intraepithelial neoplasia (CIN) [31] and current clinically approved HPV tests detect the bulk of high-grade CIN and invasive cervical carcinoma. Increased sensitivity for HPV might lead to reduced test specificity for high-grade CIN.
Conclusion
This study has demonstrated that a touchdown modification of the GP5+/GP6+ assay coupled with attention to the quantity of DNA in a PCR significantly improves the detection of low-copy HPV DNA without compromising the ability of the technique to detect a wide range of HPV types or multiple infections. A touchdown approach may be especially beneficial as an analytical test for the re-evaluation of (apparently) HPV negative abnormal cervical cytological or histological samples and for investigating the association of HPV with non-anogenital lesions and tumors. Further studies are required to determine the clinical utility of a touchdown PCR approach to HPV detection and to compare the performance with recently developed (highly sensitive) multi-primer HPV PCR assays such as the PGMY [32] and the SPF10 [33].
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
MFE conceived and designed the study, performed DNA extractions and PCR assays, and drafted the manuscript. CS-CA carried out DNA extractions, the bulk of the PCR assays, and dot blot and Southern blot hybridization analyses. LS-A performed DNA extractions, PCR, and HPV typing of VAIN tissues. KC contributed to the study conception and design.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors are grateful to Sharon L. Mount, M.D. for selecting the vaginal intraepithelial lesions examined, and to Jacalyn L. Papillo, B.S. and Timothy L. St. John, B.S. for the cervical smear samples. This study was supported by a Fletcher Allen Health Care Research and Development Fund Award.
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Gravitt PE Peyton CL Alessi TQ Wheeler CM Coutlée F Hildesheim A Schiffman M Scott DR Apple RJ Improved amplification of genital human papillomaviruses J Clin Microbiol 2000 38 357 361 10618116
Kleter B van Doorn LJ Schrauwen L Molijn A Sastrowijoto S ter Schegget J Lindeman J ter Harmsel B Quint WGV Development and clinical evaluation of a highly sensitive PCR-reverse hybridization line probe assay for detection and identification of anogenital human papillomavirus J Clin Microbiol 1999 37 2508 2517 10405393
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BMC PediatrBMC Pediatrics1471-2431BioMed Central London 1471-2431-5-431631367310.1186/1471-2431-5-43Research ArticleImpact of the introduction of pneumococcal conjugate vaccine on immunization coverage among infants Lin Nancy D [email protected] Ken [email protected] K Arnold [email protected] Xian-Jie [email protected] Eric K [email protected] Stanley [email protected] Feifei [email protected] John [email protected] Jeanne [email protected] Tracy A [email protected] Vaccine Safety Datalink Group 1 Department of Ambulatory Care and Prevention, Harvard Pilgrim Health Care and Harvard Medical School, Boston, MA, USA2 Harvard School of Public Health, Boston, MA, USA3 Kaiser Permanente, Denver, Kaiser Permanente, Denver, CO, USA4 HealthPartners Research Foundation, Minneapolis, MN, USA5 Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA6 National Immunization Program, Centers for Disease Control and Prevention, Atlanta, GA2005 28 11 2005 5 43 43 5 7 2005 28 11 2005 Copyright © 2005 Lin et al; licensee BioMed Central Ltd.2005Lin 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 introduction of pneumococcal conjugate vaccine (PCV) to the U.S. recommended childhood immunization schedule in the year 2000 added three injections to the number of vaccinations a child is expected to receive during the first year of life. Surveys have suggested that the addition of PCV has led some immunization providers to move other routine childhood vaccinations to later ages, which could increase the possibility of missing these vaccines. The purpose of this study was to evaluate whether introduction of PCV affected immunization coverage for recommended childhood vaccinations among 13-month olds in four large provider groups.
Methods
In this retrospective cohort study, we analyzed computerized data on vaccinations for 33,319 children in four large provider groups before and after the introduction of PCV. The primary outcome was whether the child was up to date for all non-PCV recommended vaccinations at 13 months of age. Logistic regression was used to evaluate the association between PCV introduction and the primary outcome. The secondary outcome was the number of days spent underimmunized by 13 months. The association between PCV introduction and the secondary outcome was evaluated using a two-part modelling approach using logistic and negative binomial regression.
Results
Overall, 93% of children were up-to-date at 13 months, and 70% received all non-PCV vaccinations without any delay. Among the entire study population, immunization coverage was maintained or slightly increased from the pre-PCV to post-PCV periods. After multivariate adjustment, children born after PCV entered routine use were less likely to be up-to-date at 13 months in one provider group (Group C: OR = 0.5; 95% CI: 0.3 – 0.8) and were less likely to have received all vaccine doses without any delay in two Groups (Group B: OR = 0.4, 95% CI: 0.3 – 0.6; Group C: OR = 0.5, 95% CI: 0.4 – 0.7). This represented 3% fewer children in Group C who were up-to-date and 14% (Group C) to 16% (Group B) fewer children who spent no time underimmunized at 13 months after PCV entered routine use compared to the pre-PCV baseline. Some disruptions in immunization delivery were also observed concurrent with temporary recommendations to suspend the birth dose of hepatitis B vaccine, preceding the introduction of PCV.
Conclusion
These findings suggest that the introduction of PCV did not harm overall immunization coverage rates in populations with good access to primary care. However, we did observe some disruptions in the timely delivery of other vaccines coincident with the introduction of PCV and the suspension of the birth dose of hepatitis B vaccine. This study highlights the need for continued vigilance in coming years as the U.S. introduces new childhood vaccines and policies that may change the timing of existing vaccines.
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Background
The addition of pneumococcal conjugate vaccine (PCV) to the U.S. recommended childhood immunization schedule in the year 2000 added three injections to the number of shots a child is expected to receive during the first year of life. Whereas seven to ten injections were recommended during the first year of life prior to introduction of PCV, between ten and thirteen injections are now recommended, depending on use of combination vaccines. With the addition of pneumococcal vaccination, the youngest children may receive up to five injections at a single office visit[1].
Simultaneous administration of vaccines is recommended to facilitate early protection against vaccine-preventable disease[2]. At the same time, administration of multiple injections may create distress for children and parents, and many parents and providers have previously expressed concern regarding the administration of four vaccines at a single visit [3-5]. It is unclear how increased crowding of the childhood immunization schedule and safety concerns about multiple injections related to the introduction of PCV have affected immunization delivery. Two regional provider surveys suggested that physicians who administer PCV may delay other vaccinations,[4,6] although a different, national survey found that most physicians who adopted PCV in their practices would administer four or more injections at the 2-month visit[7]. The objective of this study was to evaluate whether the introduction of PCV affected immunization delivery in actual practice among large populations of children in several provider groups.
Methods
Study population
This study included children enrolled in four large provider groups: Harvard Vanguard Medical Associates (Boston, MA), HealthPartners (Minneapolis, MN), Kaiser Permanente of Colorado (Denver, CO), and Kaiser Permanente Northwest (Portland, OR). These sites participate in the Centers for Disease Control and Prevention Vaccine Safety Datalink Project, in which individual-level vaccination, demographic, and medical data are shared to facilitate vaccine safety and other vaccine-related epidemiologic research[8].
We studied infants who were born into one of the four provider groups between October 1996 and November 2000 and had received at least one polio vaccination, where receipt of polio vaccination was used as an indicator that a child received immunizations that were recorded by the provider group information systems (n = 86,561). To ensure that the most complete immunization information was available, the study population was additionally restricted to children continuously enrolled throughout their first year of life (n = 38,588). The study protocol was approved by the institutional review boards at the four participating sites and the Centers for Disease Control and Prevention.
Definition of Post-PCV and Pre-PCV exposure cohorts
Each child was assigned to one of two birth cohorts based on the timing of their birth relative to the regulatory approval of PCV in February 2000[9]. Individuals who were born between October 1996 and January 2000 were assigned to the "pre-PCV" birth cohort. Children born between February and November 2000 were assigned to the "post-PCV" birth cohort.
While introduction of PCV added three new vaccine injections, use of the hepatitis B-Haemophilus influenzae type B (Hib) combination vaccine can offset the increase in vaccine injections a child requires to be fully immunized during the first year of life. Hepatitis B-Hib combination vaccine was available throughout the study period in one provider group and was implemented in two other provider groups during the study period; in the fourth provider group, it was not available at all. Table 1 describes how the expected number of injections varied based on provider group-specific availability of the hepatitis B-Hib combination vaccine during the pre-PCV and the post-PCV periods.
Table 1 Variation in the expected number of vaccine injections during the first year of life, by provider group and PCV policy period
Number of vaccine injections expected during the first year of life
Group Pre-PCV birth cohort Post-PCV birth cohort
Group A† 7* or 10 10* or 13
Group B‡ 10 13
Group C§ 10 10* or 13
Group D|| 7* or 10 10* or 13
A child born during the pre-PCV period is expected to receive 3 DTP vaccinations, 2 polio vaccinations, 3 Hib vaccinations, and 2 hepatitis B vaccinations during the first year of life. Introduction of PCV added three new vaccine injections.
* Replacement of the separate hepatitis B and Hib vaccines during the first year of life with 2 doses of hepatitis B-Hib combination vaccine reduces the number of vaccine injections expected during the first year of life to 7 shots in the pre-PCV period and 10 in the post-PCV period.
† Hepatitis B-Hib combination vaccine available throughout the study period.
‡ Hepatitis B-Hib combination vaccine never available during the study period.
§ Hepatitis B-Hib combination vaccine available starting in 2000 following PCV introduction, based on descriptive analyses.
|| Hepatitis B-Hib combination vaccine available starting in mid-1999 prior to PCV introduction, based on descriptive analyses.
Definition of immunization coverage measures
We assessed the impact of the introduction of PCV on two measures of immunization coverage at 13 months of age: (1) up-to-date status and (2) time spent underimmunized[10].
Vaccination histories were identified using the immunizations databases for each provider group. When vaccine entries of the same type were recorded within seven days of one another, the later entry was assumed to represent a duplicate record and was excluded (0.006% – 0.56%, by vaccine type). In addition, vaccinations that were administered before the minimum recommended age or earlier than the minimum recommended between-vaccination interval, allowing for a four-day grace period,[1,2] were considered to be invalid. Only the remaining vaccinations for eligible individuals were included in our analyses.
In general, a child was considered to be up-to-date for non-PCV recommended vaccinations at 13 months of age if they received all of the following: 3 diphtheria and tetanus toxoids and acellular or whole cell pertussis (DTP) vaccinations; 2 polio vaccinations; 2 hepatitis B vaccinations; and 3 Hib vaccinations. Children who received the hepatitis B-Hib combination vaccine were considered up-to-date for the hepatitis B and Hib vaccinations if they received 2 hepatitis B vaccine doses and 2 Hib vaccine doses by age 13 months.
The up-to-date measure includes only those vaccine doses with recommended age ranges contained wholly included within the 13-month individual follow-up period. Doses with recommended age ranges that spanned the 13-month birthday (e.g., third dose of hepatitis B vaccine recommended between 6 and 18 months of age) were not included because children who had not yet received these doses by age 13 months would not be considered late. As a result, the up-to-date definition corresponds to vaccinations recommended between birth and 6 months of age. PCV was not included in the outcome definition because the primary study objective was to evaluate whether addition of PCV affected adherence to existing vaccine recommendations.
The secondary outcome, time spent underimmunized, was defined as the number of days a child spent underimmunized for at least one non-PCV recommended vaccination by 13 months of age and is the complement of a previously documented outcome measure, cumulative time spent up-to-date[11]. Because it measures the amount of vaccination delay rather than immunization status at a single point in time, this outcome is expected to be more sensitive than up-to-date status at 13 months. Operationally, we calculated time spent underimmunized for each individual by assessing the child's up-to-date status for the non-PCV recommended vaccinations on each day from birth up to their 13-month birthday based on the U.S. recommended childhood immunization schedule[1]. We then summed the number of days on which the child was not up-to-date for at least one non-PCV recommended vaccination. When the recommended age range was specified in months, a vaccination was considered age-appropriate if it was given prior to the end of the maximum recommended month, where 30.5 days represented one month. Days spent underimmunized began to accumulate following the end of this 30.5-day grace period. For example, the first dose of diphtheria and tetanus toxoids and acellular pertussis combination vaccine (DTaP) is recommended at 2 months. A child who receives a valid DTaP by 91 days of age (i.e., (2 months* 30.5) + 30.5 day grace period = 91.5 days) is considered to have been vaccinated age-appropriately and does not accumulate any underimmunized time. By comparison, a child who receives their first DTaP at 94 days of age has accumulated 2 days of underimmunized time (i.e., underimmunized for days 92 and 93, and up-to-date on day 94 for the first dose of DTaP).
Description of covariates
In addition to the PCV exposure cohorts, other vaccine policy and temporal factors related to immunization coverage were considered. Between July and September 1999, providers were encouraged to delay initiation of hepatitis B vaccination for low-risk infants from birth to 2–6 months of age because of safety concerns about thimerosal[12]. While resumption of hepatitis B birth vaccination practices was recommended after regulatory approval of the first thimerosal-free hepatitis B vaccine formulation in September 1999,[13] reinstatement of universal birth vaccination policies occurred slowly [14-17]. Two indicators were included in the regression models to account for potential disruptions in immunization coverage during the pre-PCV period, related the temporary hepatitis B birth dose suspension ("HB delay" cohort: date of birth between July – September 1999) and to incomplete resumption of hepatitis B birth vaccination practices ("HB carryover" cohort: date of birth between October 1999 – January 2000).
Temporal trends in immunization coverage were modeled using four variables based on birth month cohort: a linear slope was fit for the entire study period, and three additional linear trends were included to estimate changes in slope during the HB delay, HB carryover, and post-PCV periods relative to the pre-PCV baseline trend.
An indicator variable was included to account for a potential change in the level of immunization coverage after integration of PCV into routine practice ("PCV routine": July – November 2000) compared to the initial PCV adoption period ("PCV adoption": February – June 2000). Selection of the July 2000 birth cohort as the transition point after which PCV entered routine use was based on descriptive analyses of the adoption of PCV in the participating provider groups.
Finally, seasonal variation in immunization scheduling could affect the timeliness of vaccination and was entered into the time spent underimmunized regression models using indicator variables for calendar month of birth.
Figure 1 illustrates how the hepatitis B and PCV policy variables are temporally related.
Figure 1 Timing of PCV introduction and hepatitis B policy periods. "Pre-PCV" and "Post-PCV" describe the timing of the exposure birth cohorts. The "pre-PCV" cohort includes children born prior to introduction of pneumococcal conjugate vaccine (date of birth between October 1996 – January 2000) while the "post-PCV" cohort includes children born after introduction of pneumococcal conjugate vaccine to the end of the study period (date of birth between February – November 2000). "HB delay" refers to children born during the temporary hepatitis B birth dose suspension (July – September 1999). "HB carryover" refers to children born after reinstatement of hepatitis B birth vaccination recommendations and before introduction of pneumococcal conjugate vaccine (October 1999 – January 2000). "PCV adoption" represents the first five months following introduction of pneumococcal conjugate vaccine and the period of initial uptake of pneumococcal conjugate vaccine in the four study sites (date of birth between February – June 2000). "PCV routine" represents the five-month period after adoption of pneumococcal conjugate vaccine had occurred (date of birth between July – November 2000).
Statistical analysis
Logistic regression was used to assess the association between PCV introduction and a child's probability of being up-to-date at 13 months.
For the second outcome, time spent underimmunized, we expected that the majority of children would be vaccinated age-appropriately (i.e., zero days spent underimmunized by 13 months), and a two-part modelling approach[18] was applied. First, a logistic regression model was used to assess the association between PCV introduction and a child's probability of having received all vaccines age-appropriately by 13 months of age. Then, among children who spent at least one day underimmunized, negative binomial regression was used to evaluate the impact of PCV introduction on the discrete outcome, number of days spent underimmunized by 13 months. Negative binomial regression accounts for overdispersion in the data[19] and provides relative rate estimates for the association between PCV introduction and the number of days spent underimmunized.
Analyses were stratified by provider group because differences in baseline immunization coverage, differing concern regarding multiple injections, and provider group-specific decisions to use combination vaccines may have differentially affected the impact of PCV introduction across the provider groups. To allow comparisons across sites, the set of variables included in the regression analysis for each outcome was fixed across the provider groups.
Based on the regression model, we estimated the effect of introduction of PCV at two points – (1) immediately following PCV introduction (February 2000 birth cohort) and (2) after PCV was integrated into routine use (July 2000 birth cohort) – comparing each to the outcome as predicted from the pre-PCV baseline trend. Comparison of the PCV routine use period to the predicted baseline trend was considered of primary interest because it measures the impact of addition of PCV, allowing for a period of adjustment to the new PCV policy. Figure 2 illustrates the calculation of these two contrasts for the up-to-date outcome as an example. Given that the baseline proportion of children who were up-to-date or who were vaccinated age-appropriately was expected to be high, under these conditions, odds ratios should not be interpreted as approximately the relative risk. Absolute differences in coverage were also provided, comparing the probability fitted from multivariate regression models based on the observed data ("fitted probability") to the "predicted probability" extrapolated from the baseline trend (e.g., absolute differenceFeb2000 = [fitted probability]Feb2000 - [predicted probability]Feb2000). All analyses were performed using SAS software, Version 8.2 of the SAS System for Windows (SAS Institute, Cary, NC).
Figure 2 PCV introduction and immunization coverage: illustration of primary contrasts. Graph uses data from Group C as an example. ---: coverage based on multivariate regression models and the observed data. - - - -: coverage predicted from the pre-PCV baseline trend. Time point a: February 2000 birth cohort, start of the PCV adoption period. Contrast a compares immunization coverage for the February 2000 birth cohort based on the observed data to that predicted from the pre-PCV baseline trend. Time point b: July 2000 birth cohort, start of the PCV routine period. Contrast b compares immunization coverage for the July 2000 birth cohort based on the observed data to that predicted from the pre-PCV baseline trend.
Results
Study population and adoption of PCV
In the four provider groups, 38,588 children met study inclusion criteria. Due to the identification of a potential disruption in the immunization tracking system during the early part of the study period in one of the provider groups, the study population for that site (Group D) was additionally restricted to children born between November 1998 and November 2000. This resulted in a final study population of 33,319 children.
Following its introduction in February 2000, the rate of adoption of PCV varied but was relatively rapid across the four sites. Among children born in July 2000, over 85% of children in each of three provider groups (Groups A, B, and D) received PCV at their 2-month visit. In Group C, while only 24% of the July 2000 birth cohort had received PCV at a 2-month visit, 76% in the August 2000 cohort had done so. Notwithstanding the age at which the PCV series was initiated, between 92% (Group C) and 96% (Group D) of the July 2000 birth cohort had received three shots of PCV by 13 months.
Impact of PCV recommendations on probability of being up-to-date at 13 months
Overall, 93% of children were up-to-date at age 13 months. In each provider group, the percent of 13-month-olds who were up-to-date either was maintained or increased slightly from the pre-PCV to the post-PCV cohorts (Figure 3).
Figure 3 Percent up-to-date for non-PCV recommended vaccines at 13 months. Children were grouped by the month and year of their birth. Note: Data between April – October 1998 were considered incomplete in Group D due to a potential disruption in their immunization information system during the pre-PCV baseline period.
In Group C, children born at the start of the PCV adoption period were less likely to be up-to-date at 13 months compared to the pre-PCV baseline after multivariate adjustment for the HB delay and carryover periods (OR = 0.5, 95% CI: 0.4 – 0.8) (Table 2). This decrease persisted even after PCV entered routine use (Group C: OR = 0.5, 95% CI: 0.3 – 0.8), representing 3% fewer children in Group C who were up-to-date compared to the predicted baseline trend. Additional analyses indicated that children in Group C were less likely to be up-to-date in the HB delay (OR = 0.4; 95% CI: 0.3 – 0.6) and HB carryover (OR = 0.25; 95% CI: 0.17 – 0.35) periods compared to baseline, preceding the introduction of PCV. This suggests that the decrease observed in post-PCV period in Group C may have been due to the lingering effects of the hepatitis B birth dose suspension. In contrast, PCV introduction was not significantly associated with a child's probability of being up-to-date at 13 months in the three other provider groups.
Table 2 PCV introduction and probability of being up-to-date at 13 months
Contrast a: PCV adoption vs. predicted baseline Contrast b: PCV routine vs. predicted baseline
Group Odds ratio 95% CI Fitted probability Predicted probability Odds ratio 95% CI Fitted probability Predicted probability
Group A 1.0 0.5 – 1.8 0.95 0.95 0.8 0.4 – 1.4 0.94 0.95
Group B 0.5 0.2 – 1.2 0.96 0.98 0.7 0.3 – 1.5 0.97 0.98
Group C 0.5 0.4 – 0.8 0.92 0.95 0.5 0.3 – 0.8 0.93 0.96
Group D 1.2 0.5 – 2.9 0.90 0.89 2.0 0.6 – 7.0 0.94 0.88
Contrast a compares a child's probability of being up-to-date at 13 months among the February 2000 birth cohort to that predicted from the pre-PCV baseline trend. Contrast b compares a child's probability of being up-to-date at 13 months among the July 2000 birth cohort to that predicted from the pre-PCV baseline trend. For each contrast, the "fitted probability" was the probability of being up-to-date as fitted from the multivariate regression models based on the observed data and the "predicted probability" was extrapolated from the pre-PCV baseline trend.
Impact of PCV recommendations on time spent underimmunized by 13 months
Overall, 70% of children received all non-PCV recommended vaccinations without incurring any underimmunized time. Among the 30% of children who were ever delayed for at least one vaccination, a median of 122 days (interquartile range: 33 – 212) was spent underimmunized during the first 13 months of life.
Multivariate-adjusted results are provided only for Groups A, B, and C because the truncated study period for Group D limited our ability to fit the full multivariate model for this population (Table 3). Children born at the start of the PCV adoption period were less likely to receive all vaccinations without delay in Group B (OR = 0.4, 95% CI: 0.3–0.5) and in Group C (OR = 0.33, 95% CI: 0.26 – 0.42). These differences persisted through the start of the PCV routine period (Group B OR = 0.4, 95% CI: 0.3 – 0.6; Group C OR = 0.5, 95% CI: 0.4 – 0.7), representing 16% (Group B) and 14% (Group C) fewer children among the July 2000 birth cohort who spent no time underimmunized compared to baseline. In Group A, no difference in timeliness of immunization delivery was noted following PCV introduction.
Table 3 PCV introduction and probability of never being underimmunized by 13 months of age
Contrast a: PCV adoption vs. pre-PCV baseline Contrast b: PCV routine vs. pre-PCV baseline
Group Odds ratio 95% CI Fitted probability Predicted probability Odds ratio 95% CI Fitted probability Predicted probability
Group A 1.1 0.7 – 1.6 0.83 0.82 1.1. 0.7 – 1.8 0.86 0.85
Group B 0.4 0.3 – 0.5 0.55 0.77 0.4 0.3 – 0.6 0.65 0.81
Group C 0.33 0.26 – 0.42 0.34 0.60 0.5 0.4 – 0.7 0.58 0.72
Contrast a compares a child's probability of spending zero days underimmunized by 13 months among the February 2000 birth cohort based on the observed data to that predicted from the pre-PCV baseline trend. Contrast b compares a child's probability of spending zero days underimmunized by 13 months among the July 2000 birth cohort based on the observed data to that predicted from the pre-PCV baseline trend. For each contrast, the "fitted probability" was the probability of spending no time underimmunized as fitted from the multivariate regression models and the "predicted probability" was extrapolated from the pre-PCV baseline trend.
Among children ever-delayed, children born during the PCV adoption period in Group C spent 1.5 times as many days underimmunized (CI: 1.3 – 1.8) compared to the predicted baseline trend. This increase in days spent underimmunized persisted among the PCV routine use cohort compared to the baseline trend (RR: 1.4, 95% CI: 1.2 – 1.8), representing a delay of 103 days versus 73 days on average, among children in Group C who were ever-underimmunized. In contrast, no significant difference in the number of days spent underimmunized was found in the other two Groups, for either the PCV adoption cohort (Group A RR: 1.1, 95% CI: 0.7–1.7; Group B RR: 1.1, 95% CI: 0.8–1.4) or the PCV routine use cohort (Group A RR: 1.4, 95% CI: 0.8–2.2; Group B RR: 0.9; 95% CI: 0.6–1.2), compared to the baseline trend.
Introduction of PCV and immunization coverage for individual vaccine series
Additional analyses suggest that the decrease in probability of being up-to-date observed in Group C was not driven by disruptions in one vaccine series alone. Children in Group C were less likely to be up-to-date at the start of the PCV adoption period for hepatitis B vaccination (OR = 0.4, 95% CI: 0.2 – 0.8) as well as for each of the other vaccine series (polio: OR = 0.48, 95% CI: 0.24 – 0.96; DTP: OR = 0.65, 95% CI: 0.41 – 1.03; Hib: OR = 0.60; 95% CI: 0.38 – 0.96). These effects persisted after PCV entered routine use for the polio (OR = 0.48, 95% CI: 0.23–1.01), DTP (OR = 0.5, 95% CI: 0.3–0.9) and Hib (OR = 0.5, 95% CI: 0.3–0.9) vaccine series compared to baseline. In the three provider groups where PCV introduction was not associated with changes in up-to-date coverage among 13-month-olds, no systematic differences in coverage for individual vaccine series were observed. Similar broader disruptions across individual vaccine series were observed for the impact of PCV on the time spent underimmunized by13 months (data not shown).
Discussion
In our study of four large provider group populations, the introduction of PCV was not associated with a substantial adverse impact on a child's probability of being up-to-date at 13 months for non-PCV recommended childhood vaccines. We did find moderate increases in time spent underimmunized in some provider groups, although these delays likely did not result in clinical harm. Our findings suggest that the timeliness of immunization for several vaccine series was likely adversely affected by either PCV introduction or the hepatitis B birth dose suspension, although it was not possible to disentangle the effects of these two policy changes. Overall, our findings support continued vigilance during changes in immunization policy in order to mitigate unintended delays in vaccine delivery that may arise due to concerns about multiple injections.
Our finding of no major impact of PCV introduction on up-to-date status is consistent with reports that increases in the number of vaccine injections have not led to clinically important reductions in immunization coverage[11]. While providers and parents have expressed concerns regarding multiple injections,[4,6,20,21] many parents still prefer that all recommended vaccines be given at one visit[20]. Recent surveys also suggest that providers are now more willing to administer multiple injections[7,21,22]. These findings are consistent with results from the 2003 National Immunization Survey,[23] which indicate that coverage levels for other childhood vaccines among children aged 19–35 months of age had been maintained during the adoption of PCV.
The heterogeneity in the response to PCV introduction observed across the sites may have arisen due to a variety of reasons. Responses to hepatitis B policy changes differed across the provider groups, and only those sites that experienced disruptions during the hepatitis B delay and carryover periods had lower immunization coverage following PCV introduction. If the effects of the hepatitis B vaccination policy reversals lingered as PCV entered routine use in our health plan populations (i.e., 10 months after reinstatement of the birth vaccination recommendations), our study would be unable to fully disentangle the relative contributions of these two policies. Availability of the hepatitis B-Hib combination vaccine may simplify immunization scheduling by reducing the total number of shots a child receives during the first year of life,[24] and the differential adoption of the combination hepatitis B-hib vaccine may have influenced the variation observed across provider groups. The routine use of the combination hepatitis B-Hib vaccine in Group A throughout the study period may have made immunization scheduling less sensitive to the temporary hepatitis B birth dose suspension or alleviated concerns about multiple injections related to the introduction of PCV.
Implementation of the hepatitis B-Hib combination vaccine during the study period in Group C and Group D may also have influenced the impact of PCV introduction on immunization coverage in these settings; however, the coincident timing of the implementation of the hepatitis B-Hib combination vaccine with the temporary hepatitis B birth dose suspension (Group D) and PCV adoption (Group D) limits our ability to determine separate effects for these policy changes in our analysis. We also compared immunization coverage for non-PCV vaccinations following PCV introduction with predictions of immunization coverage extrapolated from baseline trends. Dependent on the level of baseline immunization coverage, expectation of a continued constant trend in immunization coverage may have yielded an overestimate of the extent of disruption in immunization coverage in some settings. It is reassuring that PCV introduction was not associated with clinically important reductions in immunization coverage, even given expectations of a linear trend predicted from baseline. Finally, flexibility in immunization guidelines or expectations regarding the simultaneous administration of vaccines may vary across sites and influence variation in immunization scheduling.
This study evaluates the impact of introduction of the PCV policy and not whether administration of PCV to a given child affects that individual's probability of being up-to-date. Given the rapid adoption of PCV in the four provider groups, we are able to estimate the net effect of integration of PCV into the childhood immunization schedule. Information on certain demographic factors (e.g., race/ethnicity, socioeconomic status) was not routinely collected across these settings, and we were unable to directly assess the effects of these characteristics in the analysis. However, by comparing birth cohorts across the study period, only those characteristics whose distribution in the study population changed concurrently with the time of introduction of PCV can act as potential confounders. No major changes in the coverage plans offered by these health plans occurred at the time of PCV introduction, and we therefore would not expect the demographic characteristics of the enrolled populations in these provider groups to have changed dramatically concurrent with introduction of the new PCV policy.
Finally, this study was conducted in health plan populations, who are mostly privately insured and have good access to health care. The study population was additionally restricted to children who were continuously enrolled during the first year of life; these children are likely to have experienced less scattering of their immunization records and to have had more opportunities to catch-up on immunizations. While children in the study population are thus likely to have higher overall immunization coverage than might be expected for the general population, comparison across similarly restricted birth cohorts remains valid to evaluate the potential impact of the introduction of PCV. In addition, earlier evaluations of the impact of the transition from oral polio vaccine to inactivated (injected) polio vaccine have yielded similar findings of a lack of an adverse effect on immunization coverage among children enrolled in managed care populations[11] and among children receiving vaccinations in public clinics[20].
Conclusion
The continued development and addition of new vaccines to the childhood immunization schedule is likely to exacerbate concerns regarding simultaneous multiple injections. Our findings indicate that these provider groups remained capable of absorbing the most recent increases in multiple injections with minimal impact on up-to-date measures. However, the timeliness of delivery may have been affected by the introduction of PCV and the suspension of the birth dose of hepatitis B vaccine. This study highlights the continuing need to monitor the impact of new vaccines as well as vaccine-safety related policy decisions that affect immunization scheduling.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
NDL participated in the design of the study, analysis and interpretation of results, and manuscript preparation and revision. KK participated in the design of the study, interpretation of results, and manuscript revision. KAC participated in the design of the study, interpretation of results, and manuscript revision. XY participated in the acquisition of data, analysis and interpretation of the results, and manuscript preparation. EKF, SX, FW, and JM were involved in the acquisition of data, interpretation of results, and manuscript revision. JS was involved in the interpretation of results and manuscript revision. TAL participated in the design of the study, acquisition of data, analysis and interpretation or results, and manuscript revision.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was supported by the Centers for Disease Control and Prevention, Atlanta, GA, via cooperative agreement UR6/CCU117611 and contract 200-2002-00732 (the Vaccine Safety Datalink Project) with America's Health Insurance Plans. Dr. Lin's effort was supported in part by the Agency for Healthcare Research and Quality, National Research Service Award, grant number HS000028-19. Dr. Lin is currently an AHRQ Trainee at the Center for Health Policy, Stanford University, Stanford, California.
We gratefully acknowledge our colleagues at the Department of Ambulatory Care and Prevention, especially Megan O'Brien, MPH, for local coordination of the VSD project, Richard Fox, MA, for his management of the automated analytic databases, Jyotsna Dhall, MPH, for initial dataset preparation for this work, and Richard Platt, MD, MPH, for his senior leadership of the project. We appreciate the contributions of Nicolle Mode, MPH (HealthPartners Research Foundation), Karen Riedlinger, MPH (Kaiser Permanente Northwest), and Renae Smith-Ray (Kaiser Permanente Colorado) who prepared the analytic databases at each of the health plans in this study. We thank Robert Davis, MD, MPH, for thoughtful advice, and we appreciate the guidance of our other collaborators at the National Immunization Program, including Robert Chen, MD, David Shay, MD, MPH, and Frank DeStefano, MD.
The Vaccine Safety Datalink Team at the time of this study included Frank DeStefano, MD, MPH, Robert T Chen, MD, David Shay, MD, MPH, Philip H Rhodes, PhD, Margarette Kolzcak, PhD, Julianne Gee, MPH, Robert L Davis, MD, MPH, William Thompson, PhD, James Baggs, PhD, Brooke Barry, Carolyn Bridges, MD, Scott Campbell, Chris Casey, Jufu Chen, PhD, Charissa Denson, David Elswich, Gary Euler, Paul Gargiullo, Rafael Harpaz, Beth Hibbs, Aisha Jumaan, David King, Katrin S Kohl, MD, MPH, Piotr Kramarz, MD, Gina Mootrey, John Moran, Pekka Nuorti, Vitali Pool, MD, James Singleton, Eric Weintraub, Bruce Weniger, MD, MPH, Melinda Wharton, Fujie Xu, and Weigong Zhou, MD, PhD (Centers for Disease Control and Prevention, Atlanta, GA); M Miles Braun, MD, MPH, Robert P Wise, MD, MPH, Robert Ball, MD, MPH, Dale Burwen MD, MPH, David Davis, Hector Izurieta, Ann W McMahon, MD, MS, Fred Varricchio MD, PhD, and Jane Woo MD, MPH (Food and Drug Administration, Bethesda, MD); Lisa Jackson, MD, MPH, Patti Benson, MPH, Kari Bohlke, ScD, Barbara Carste, MPH, John Dunn, MD, MPH, Christi Hanson, BA, Mike Jackson, Darren Malais, BS, Jennifer Nelson, PhD, Kathleen Neuzil, MD, MPH, Troy J Scott, Neil Vora, Onchee Yu, MS, and Ann Zavitkovsky, MPH (Group Health Cooperative, Seattle, WA); Tracy Lieu, MD, MPH, Richard Platt, MD, MS, Emily Cain, Jyotsna Dhall, MS, Jonathan Finkelstein, MD, MPH, Richard Fox, MSW, Charlene Gay, Katherine Hohman, Ken Kleinman, PhD, Martin Kulldorff, PhD, Grace Lee, MD, Nancy Lin, MS, Megan O'Brien, MPH, Lisa Prosser, PhD, Virginia Rego, MS, MPH, Katherine Yih, PhD, and Xian-Jie Yu, MS (Harvard Pilgrim Health Care/Harvard Medical School, Boston, MA); Mike Goodman, PhD, Jim Nordin, MD, Feifei Wei, PhD, Susan Adlis, Jerry Amundson, Renner Anderson, Amy Butani, Kelly Fillbrandt, Olga Godlevsky, Peter Harper, Leslie Kuckler, A. Marshall McBean, MD, MS, Maribet McCarty, Beth Molitor, Andrew Nelson, MPH, Kristin Nichol, MD, MPH, MBA, Eugene Sesonga, and Lynn Taliaferro (HealthPartners Research Foundation, Minneapolis, MN); Vito Caserta, MD, MPH and Geoffrey Evans, MD (Division of Vaccine Injury Compensation, Health Resources and Services Administration, Rockville, MD); Eric France, MD, Marcia Blake MA, MSPH, Jason Glanz, MS, Simon Hambidge, MD, PhD, James Kaferly, David L McClure, MS, Ronald Norman, Marsha Raebel, PharmD, Debra P Ritzwoller, PhD, Renae Smith-Ray, MA, Stanley Xu, PhD, and Kristi Yamasaki, PharmD (Kaiser Permanente Colorado, Denver, CO); Edward Belongia, MD, Nicholas Berger, Carol Beyer, James Donahue, DVM, PhD, Robert Greenlee, PhD, Juanita Herr, Burney Kieke, MS, Katherine Konitzer, Jordon Ott, Peggy Peissig, MS (Marshfield Clinic Research Foundation, Marshfield, WI); Steven B Black, MD, Laura Bracken, Diane Carpenter, Lisa Croen, Robert Davis, MD, MPH, Ajit Gemunu de Silva, Bruce H Fireman, MA, Patti Hallam, John Hansen, BA, Ned Lewis, MPH, Regina L Mason, Roxana Odouli, Paula Ray, MPH, Pat Ross, Joan Schwalbe, Henry R Shinefield, MD, Stanley Watson, Cathleen Yoshida, and Lea Q Zhang (Kaiser Permanente of Northern California, Oakland, CA); John P Mullooly, PhD, Allison Naleway PhD, Steven L Balch, Michael Barrett, MD, Alan Bauck, BS, Cathy Briggs, Marina Britsky, Andrea Brown, Colleen Chun, MD, Brad Crane, MS, Lois Drew, BA, Shannon Edie, Terri Haswell, Benjamin Drew Horning, Weiming Hu, Jill Mesa, John A Pearson, MD, Karen Riedlinger, MPH, Roberleigh Schuler, MS, Jerry Slepack, MD, Loie Drew, Gayle Thomas-Monk, Mike Thornton, Kathryn Torvik, Amy Triebwasser, and Sheila Weinmann, PhD (Kaiser Permanente Northwest Region, Portland, OR); Joel I Ward, MD, Constance M Vadheim, PhD, Ken Zangwill, MD, Eileen Eriksen, MPH, Jennifer Lee, MS, Jennie Jing, MA, and Nancy Goff (Center for Vaccine Research, Harbor-UCLA Medical Center, Torrance, CA); S. Michael Marcy, MD, Marlene M Lugg, DrPH, Jag Batra, MD, Monique Bryher, MSPH, Susan Butler, Frederico Canton, Chung-Yin Chiu, MS, and Martin Lee, PhD (Southern California Kaiser Permanente, Los Angeles, CA).
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BMC Mol BiolBMC Molecular Biology1471-2199BioMed Central London 1471-2199-6-221632114710.1186/1471-2199-6-22SoftwareSiteFind: A software tool for introducing a restriction site as a marker for successful site-directed mutagenesis Evans Paul M [email protected] Chunming [email protected] Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, USA2 Sealy Center for Cancer Cell Biology, University of Texas Medical Branch, Galveston, USA2005 1 12 2005 6 22 22 4 8 2005 1 12 2005 Copyright © 2005 Evans and Liu; licensee BioMed Central Ltd.2005Evans 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
Site-directed mutagenesis is a widely-used technique for introducing mutations into a particular DNA sequence, often with the goal of creating a point mutation in the corresponding amino acid sequence but otherwise leaving the overall sequence undisturbed. However, this method provides no means for verifying its success other than sequencing the putative mutant construct: This can quickly become an expensive method for screening for successful mutations. An alternative to sequencing is to simultaneously introduce a restriction site near the point mutation in manner such that the restriction site has no effect on the translated amino acid sequence. Thus, the novel restriction site can be used as a marker for successful mutation which can be quickly and easily assessed. However, finding a restriction site that does not disturb the corresponding amino acid sequence is a time-consuming task even for experienced researchers. A fast and easy to use computer program is needed for this task.
Results
We wrote a computer program, called SiteFind, to help us design a restriction site within the mutation primers without changing the peptide sequence. Because of the redundancy of genetic code, a given peptide can be encoded by many different DNA sequences. Since the list of possible restriction sites for a given DNA sequence is not always obvious, SiteFind automates this task. The number of possible sequences a computer program must search through increases exponentially as the sequence length increases. SiteFind uses a novel "moving window" algorithm to reduce the number of possible sequences to be searched to a manageable level. The user enters a nucleotide sequence, specifies what amino acid residues should be changed in the mutation, and SiteFind generates a list of possible restriction sites and what nucleotides must be changed to introduce that site. As a demonstration of its use, we successfully generated a single point mutation and a double point mutation in the wild-type sequence for Krüppel-like factor 4, an epithelium-specific transcription factor.
Conclusion
SiteFind is an intuitive, web-based program that enables the user to introduce a novel restriction site into the mutated nucleotide sequence for use as a marker of successful mutation. It is freely available from
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Background
There are several methods available for mutagenesis: 1) to isolate single strand template DNA and then create the mutation with one complementary primer [1]; 2) design two sets of PCR primers that overlap the mutation site, amplify the template by two PCR reactions and then clone the two PCR fragments and the vector by three piece ligation [2]; 3) Site-directed mutagenesis using the QuikChange method [3-5]. All of these in vitro mutagenesis methods require careful design of one or more primers that cover the mutation site. Currently, QuikChange site-directed mutagenesis is the method of choice. This method requires two complementary oligonucleotide primers flanking the desired mutated nucleotide on both the sense and anti-sense strands. Furthermore, each primer must contain one to several base-pair changes within the desired region. PCR is then performed using these primers along with the gene of interest, which was previously inserted into a vector containing an antibiotic resistance gene. The extension step of the polymerase chain reaction is given sufficient time to replicate the entire circular DNA construct, with the reaction eventually ending where it started. After several rounds of PCR, the resulting mixture of newly-synthesized mutant constructs and template DNA is incubated with a methylation-specific endonuclease to remove the wild-type template DNA which contains methylated nucleotides. The mixture is then transformed into competent bacteria, plated on an antibiotic-containing medium, and grown overnight to in order to allow individual colonies to grow.
However, since the bacteria was transformed with a complex mixture of undigested template DNA, successful point mutant copies of the template, and PCR side-products, it becomes difficult to determine which colonies contain the desired mutant construct. Restriction enzyme digestion of plasmid DNA extracted from each colony can differentiate between correct and aberrant PCR products, but it cannot distinguish between bacteria transformed with template DNA and bacteria transformed the with desired point mutant. Instead, plasmid DNA extracted from each colony must be sent to a sequencing laboratory and the sequence manually scanned for a successful mutation. If the number of colonies containing template DNA is high relative to the total number of colonies, this can be an expensive and time-consuming process.
A simple method to confirm the presence of a point mutation prior to sequencing is to design the mutation of the sequence such that it introduces a novel restriction site, taking advantage of the redundancy of the genetic code [6-8]. Thus plasmid DNA extracted from each colony can be digested with the appropriate restriction enzyme and then run on a DNA gel to check for the presence of a band not found in the template DNA. However, finding the correct set of mutations to the DNA sequence in order to introduce a restriction site without disturbing its corresponding amino acid sequence is not always a trivial task, requiring the investigator to manually generate hundreds of possible DNA sequences and then scan them for restriction sites. Even for an experienced molecular biologist, it will take time and luck to find a suitable site. SILMUT, a program written and published several years ago, can be used to discover such diagnostic restriction sites [9]. The user enters a short amino acid sequence, and SILMUT determines if any of 30 of the most common, 6 bp restriction sites can introduced within that sequence. To make this task much faster and less error-prone, we wrote our own, web-based computer program, called SiteFind.
In some cases, however, silent mutations in the coding sequence can have a drastic effect on the translation rate. Thus, the user must be alert to the possibility of codon bias in the organism where this sequence will be expressed.
Implementation
SiteFind overview
The ultimate goal of SiteFind is to search a given nucleotide sequence for any possible restriction sites that can be introduced without disturbing the amino acid sequence that it codes for. For example, the sequence CTCGAA codes for the amino acid sequence LE, or leucine-glutamate, but does not possess any common restriction site. However, by simply changing the last Adenine to a Guanine, the sequence becomes CTCGAG, which is the restriction site for XhoI. At the same time, the amino acid sequence is preserved, since both GAA and GAG code for glutamate. For such a short sequence, the necessary mutations to introduce a restriction site may be obvious, but SiteFind can quickly search through much longer sequences, where potential restriction sites may be hidden in long sequence of nucleotides. We found that on the average end-user personal computer, SiteFind can handle sequences of up to approximately 400 bp.
SiteFind was designed with the purpose of introducing a restriction site into a nucleotide sequence as a marker for successful point mutation via site-directed mutagenesis. Consistent with this purpose, the user can specify which amino acids should be changed in the peptide sequence and then select the potential restriction site closest to the point mutation. Ideally, these two will overlap, but this is not always possible. A novel restriction site within a few nucleotides of the point mutation is often sufficient to use as a marker.
Algorithm optimizations
The redundancy in the genetic code means that as the length of a given amino acid sequence increases, the number of possible DNA sequences that can code for that sequence increases exponentially. Since the amino acid serine can be represented by six different codons, this means that a sequence of four serines can be represented by 64 (1296) different DNA sequences. To substantially reduce the number sequences to scan by our program, SiteFind uses a "moving window" algorithm (See Fig. 1A). The "moving window" algorithm effectively breaks up a long nucleotide sequence into a series of short, non-redundant sequences that can be then searched individually. Thus, a long amino acid sequence with millions of possible nucleotide sequences can be converted into 10 or so "windows", each with only a few hundred possible sequences.
The size of each "window" is determined by the length of the longest restriction site the user is searching for. In general, for a given restriction site of n nucleotides, the window must be at least 2n-1 nucleotides long. SiteFind then shifts the window only enough to ensure overlap between windows such that any possible restriction site is found, meaning that the window is shifted forward no more than n nucleotides (See Fig. 1B). This process is then repeated until the entire nucleotide sequence is traversed.
SiteFind was originally written in C++ as a simple command-line tool for in-house use. We subsequently rewrote the program as a Java applet embedded in a HTML web document, giving it a more intuitive, graphical interface and posted it on our institutional website. The source code to our Java applet is freely available and is released under the GPL [10]. SiteFind was written using TextPad v4.7.3 [11] and compiled with the Java 1.4.2 SDK [12]. The website was designed with Microsoft FrontPage.
Results
Using sitefind
SiteFind was designed to have an intuitive interface, with each step necessary to specify the search conditions presented in a separate window. A button labeled "Next" at the bottom right hand corner of each window allows the user to progress to the next step. The SiteFind applet loads in a browser once its webpage is visited and prints out a simple message identifying the program name and creator. To begin, the user clicks "Next". The first window prompts the user to enter a short segment (preferably at least 15 nucleotides) of the wild-type DNA sequence, covering the region where a mutation is desired. The user is then prompted to select the correct reading frame for the sequence. After clicking "Next", the properly translated sequence is given, as shown in Fig. 2A, The user then double-clicks the amino acid he wishes to mutate and selects from a drop-down list which residue it should be changed to. If the user wishes to mutate more than one amino acid, he can simply repeat this step. Each mutation is highlighted in red. In the next window, the user can select which restriction sites the program should search for. Currently, SiteFind has 131 restriction sites to choose from and uses them all by default, but the user is free to remove any of these or add new ones if so desired. Any restriction sites present in the wild-type sequence are removed from the search. The next window then displays a progress bar as it searches: in most cases, the search takes no more than a few seconds. Once finished, the user can click "Next" one last time, and the results are printed in a list. A list of potential restriction sites is given, For each site, the wild-type sequence displayed, with the necessary mutant sequence displayed just under it. Any differences between the two sequences are indicated. Below the mutant sequence, the location of the introduced restriction site is clearly marked. If there are multiple locations in the sequence where a given restriction site can be introduced, only the location closest to the desired point mutation is displayed. (See Fig. 2B). The user can then use this information to design the appropriate primers for performing site-directed mutagenesis.
Examples of its use
We used this tool routinely in our laboratory. For example, Krüppel-like factor 4 (KLF4) is a transcription factor implicated in colon cancer. Previous studies on KLF4 have shown that a single point mutation, R390S, can abolish its ability to enter the nucleus, where it is normally exclusively located [13,14]. In order to make such a construct, we entered the wild-type DNA sequence corresponding to amino acids 385–393 into SiteFind and then specified the desired mutation R390S. Using the default settings, SiteFind found 10 restriction sites that we could use as a marker. We chose BglII since no BglII site was present in our original construct, and it required the mutation of only three nucleotides. Using this information, we were then able to design the proper primers for site-directed mutagenesis.
After transformation of competent bacteria with the PCR product, we plated the cells on ampicillin-containing agar overnight. We then picked several colonies and isolated their plasmid DNA. The plasmid DNA was then digested with ClaI, which is present in the vector backbone, and BglII. Since BglII is neither present within the vector backbone nor the wild-type KLF4 sequence, BglII should only cut successfully mutated plasmid DNA, yielding a 1244 bp fragment (See Fig. 3A). As shown in Fig. 3B, wild-type plasmid DNA yields only one fragment, whereas successfully mutated DNA yields a second, 1.2 kb fragment.
To confirm that our mutant construct is expressed, we transfected 293T cells, lysed the cells 48-hours post-transfection, and performed an α-Flag Western blot with the lysate. Fig. 3C demonstrates that both the wild-type and mutant constructs express a protein of identical size, whereas transfection with an empty vector yields no Flag-tagged protein whatsoever. This is expected since a point mutation should have no detectable effect on the molecular weight. Finally we verified the mutant construct by sequencing (See. Fig. 3D).
To demonstrate that SiteFind can be used to design multiple point mutation, we produced a double point mutation of KLF4, mutating two successive lysines (K225/K229) to arginine. Using SiteFind, we decided to introduce an NheI site just 3' to the second point mutation. After PCR and plasmid purification, we digested the mutant construct with NheI and EcoRI. NheI should only cut the mutant construct, producing a 767 bp fragment (See Fig. 4A). As expected NheI cuts the mutant construct to produce a second fragment of approximately 750 bp, whereas the wild-type plasmid yields only one fragment (See Fig. 4B). We confirmed expression of this construct in 293T cells, and as expected, both wild-type and K225/229R mutant KLF4 produce bands of identical size (See Fig. 4C). Finally, we verified our construct by sequencing (See Fig. 4D).
Discussion
There are several programs available for designing primers for site-directed mutagenesis. Most of these programs are used to calculate the annealing temperature and to predict secondary structures. They cannot be used to design a restriction site. SiteFind is designed specifically for this.
In an easy-to-use, graphical interface, the user is prompted to enter the desired template nucleotide sequence. Then, the translated amino acid sequence is given and the user is able to select which amino acids to mutate. After that, the user can specify which restriction sites to search for, and even add additional sites if so desired. Finally, after a few seconds, a list of potential restriction sites is given. For each site, only the location closest to the desired point mutation and involving the fewest number of mutations is given. This substantially reduces the amount of information the user has to process prior to selecting the optimal sequence for site-directed mutagenesis, saving both time and money. Furthermore, SiteFind can be used for any type of mutagenesis and places no limits on the number of point mutations in the mutant sequence.
As the sequence length increases, when simply generating every possible nucleotide sequence for a given amino acid sequence and then searching for the presence of a restriction site, the time required for the search increases exponentially. If done in this manner, searches of longer than 15 bp quickly become infeasible. Our "moving window" algorithm is a novel way to drastically reduce the time required for a search, and yet does so without missing any potential sites. Because SiteFind implements this algorithm, it can process sequences up to 400 bp.
Shankarappa et. al. have published a computer program called SILMUT [9]. SILMUT is a simple command-line program that can search a short amino acid sequence for the 30 most common, 6 bp restriction sites. It does this by translating each restriction site in all three frames and compares every possible translation with the user-specified amino acid sequence. During preparation of this manuscript, we discovered another web-based program that performs a function similar to SiteFind, called the Primer Generator [15]. However, the Primer Generator requires the user to manually type in both the wild-type sequence and desired mutant amino acid sequence and to manually pick from hundreds of output sequences. Furthermore, it is not suitable for nucleotide sequences longer than 15 bp.
In contrast, SiteFind, automatically translates the input nucleotide sequence and allows the user to graphically select which residues to mutate. Furthermore, our window algorithm enables SiteFind to quickly and efficiently work with sequences of any length. For each restriction site, if multiple locations are found, SiteFind only gives the location closest to the desired point mutation: this means much less information for the user to parse in order to choose the best restriction site and sequence. Although not specifically designed for it, SiteFind could be used to make translational fusions between two different coding sequences. The user can specify that SiteFind give every location found for each restriction enzyme, and then run a search on a portion of both sequences. Then, through manual comparison, the user could select a restriction site found within both sequences and design the appropriate primers for introducing the necessary mutations.
Conclusion
SiteFind is a useful tool for performing site-directed mutagenesis, enabling the user to introduce a novel restriction site into the mutated nucleotide sequence for use as a marker of successful mutation. The "moving window" is a novel algorithm that enables SiteFind to work efficiently with sequences up to 400 bp. In order to demonstrate its utility, we introduced a point mutation, R390S, into the wild-type sequence of KLF4 while simultaneously introducing a novel BglII restriction site. This mutant DNA could be cut by BglII, as expected, and expressed a full-length protein in 293T cells. For a double point-mutation, K225/229R, we introduced a novel NheI restriction site. This mutant DNA could be cut by NheI, as expected, and expressed a full-length protein in 293T cells.
Materials and methods
Materials
pCS2-Flag-KLF4 was sub-cloned from pMT3-KLF4, kindly provided by Dr. Vincent Yang, and verified by sequencing (MCLab, San Francisco, CA). All restriction enzymes and ligase were obtained from New England BioLabs (Ipswich, MA). Anti-Flag monoclonal antibody (m2) was purchased from Sigma (St. Louis, MO).
Mutant preparation
SiteFind identified a potential BglII sequence overlapping with our desired R390S mutation of the KLF4 wild-type sequence [GenBank: BC010301]. Using the primer design guidelines included in the QuikChange II Site-Directed Mutagenesis Kit (Stratagene, La Jolla, CA), we chose forward primer 5'-CCAAAGAGGGGAAGATCTTCGTGGCCCCGG and reverse primer 5'- CCGGGGCCACGAAGATCTTCCCCTCTTTGG (BglII restriction site underlined). All primers were synthesized by Sigma-Genosys. PCR was performed using the Pfu Turbo DNA Polymerase (Stratagene, La Jolla, CA) according to manufacturer's instructions. The PCR product was then digested with DpnI to remove template DNA, followed by transformation of XL-10 competent bacteria. Bacteria were then plated on ampicllin-containing Luria-Bertani (LB) agar overnight at 37°C. Individual colonies were then grown in LB/Ampicillin medium for 12 hours at 37°C, and plasmid DNA was extracted using the Qiaprep Miniprep Kit (Qiagen, Valencia, CA). Purified DNA was then digested with BglII and ClaI and then run on an 0.8% agarose gel for 30 min at 120 V. Successful mutants, as determined by the presence of a second, 1244 bp band were grown in 100 mL LB/Ampicillin overnight and plasmid DNA extracted using the Qiagen Midiprep Kit (Qiagen, Valencia, CA). Purified DNA was then verified by sequencing. For our second mutant construct, K225/229R, SiteFind identified a potential NheI sequence. For this mutation, we chose forward primer 5'- CTGATGGGCAGGTTTGTGCTGAGGGCTAGCCTGACCACCCCTGGC and reverse primer 5'-GCCAGGGGTGGTCAGGCTAGCCCTCAGCACAAACCTGCCCATCAG (NheI restriction site underlined). We used similar methods to produce this construct, except that successful mutants were identified by restriction digest with NheI and EcoRI instead, which yields a 767 bp band.
Cell culture and western blot
HeLa and 293T cells were grown in DMEM media supplemented with 10% FBS and 1% penicillin/streptomycin, and split as needed. For Western blot, 293T cells were plated on a 12-well plate and transfected with 1ug of either pCS2 empty vector, pCS2-Flag-KLF4, pCS2-Flag-KLF4-R390S, or pCS2-Flag-KLF4-K225/229R using the calcium phosphate method. After 6 hours, the media was replaced and the cells allowed to grow for another 36 hours. Cells were lysed in standard RIPA buffer with 1% Triton X-100 and protease inhibitor cocktail. Lysate was boiled in SDS sample buffer and run on a 10% polyacrylamide gel at 180 V for 45 min, and transferred to an Immobilon membrane (Millipore, Billerica, MA) at 30 V overnight. After blocking in TBS-T with 5% milk for 1 hr, membrane was incubated with α-Flag primary antibody (1:1000) for 1 hr, washed, and incubated with α-mouse secondary antibody (1:10,000). Membrane was then visualized using ECL buffer and exposed to X-ray film.
Availability and requirements
Project name: SiteFind
Project home page:
Operating system: Platform independent (any system with Java installed)
Programming language: Java
Other requirements: SiteFind is freely available to both academic and commercial users as a webpage-embedded Java applet.
Source code: Available at
List of abbreviations used
bp: base pair
DNA: Deoxyribonucleic acid
HTML: Hypertext markup language
kb: One thousand nucleotide bases
PCR: Polymerase chain reaction
Authors' contributions
PME wrote both versions of SiteFind and was responsible for drafting this manuscript. In addition, PME performed all the experiments, including all PCR, restriction digests, Western blots, and immunostaining. CL originally suggested the idea and supervised the project.
Acknowledgements
The authors wish to thank Vincent Yang for KLF4 plasmid, as well as Wen Zhang, Xi Chen, and Jun Yang for helpful discussions. The software is housed in the Sealy Center for Cancer Cell Biology at UTMB. CL is supported by a John Sealy Memorial Fund Recruitment Award and by R21 CA112007 from the NIH.
Figures and Tables
Figure 1 Moving window algorithm. a) Example of how the algorithm is implemented with a 4 nucleotide restriction site. Each window is therefore 7 nucleotides and each successive window is shifted forward 4 nucleotides, ensuring minimal overlap. b) Example of all the possible sequences generated for each of the first two search windows using the moving window algorithm.
Figure 2 SiteFind Screenshots. a) Sample input, showing translated nucleotide sequence and a mutant residue highlighted in red. b) Sample output, showing a novel BglII site discovered within the sequence.
Figure 3 KLF4 R390S mutant has a novel BglII restriction site. a) pCS2-KLF4-R390S construct diagram. b) ClaI / BglII Restriction digest of both wild-type and successfully mutated plasmid DNA. c) α-Flag Western blot showing expression of mutant construct in 293T cells. d) Sequencing result of the mutation, mutated residue is highlighted in red.
Figure 4 KLF4 K225/229R mutant has a novel NheI restriction site. e) pCS2-KLF4-K225/229R construct diagram. f) NheI / EcoRI Restriction digest of both wild-type and successfully mutated plasmid DNA. g) α-Flag Western blot showing expression of mutant construct in 293T cells. h) Sequencing result of the mutation, mutated residues are highlighted in red.
==== Refs
Hutchison CA IIIPhilips M Edgell MH Gillam S Jahnke P Smith M Mutagenesis at a specific position in a DNA sequence J Biol Chem 1978 253 6551 6560 681366
Stemmer WP Morris SK Enzymatic inverse PCR: a restriction site independent, single-frame method for high-efficiency, site-directed mutagenesis Biotechniques 1992 13 214 220 1327007
Kunkel TA Rapid and efficient site-specific mutagenesis without phenotypic selection Proc Natl Acad Sci 1985 82 488 492 3881765
Hemsley A Arnheim N Toney MD Cortopassi G Galas DJ A simple method for site-directed mutagenesis using the polymerase chain reaction Nucleic Acids Res 1989 17 6545 6551 2674899
Papworth C Bauer JC Braman J Wright DA QuikChange site-directed mutagenesis Strategies 1996 9 3 4
Little JW Mount DW Creating new restriction sites by silent changes in coding sequences Gene 1984 32 67 73 6099315 10.1016/0378-1119(84)90033-7
Arentzen R Ripka WC Introduction of restriction enzyme sites in protein-coding DNA sequences by site-specific mutagenesis not affecting the amino acid sequence: a computer program Nucl Acids Res 1984 12 777 787 6320109
Shankarappa B Sirko DA Ehrlich GD A general method for the identification of regions suitable for site-directed site-mutagenesis Biotechniques 1992 12 382 384 1315141
Shankarappa B Vijayananda K Ehrlich GD SILMUT: a computer program for identification of regions suitable for silent mutagenesis to introduce restriction enzyme recognition sequences Biotechniques 1992 12 882 884 1322684
SiteFind Development Group
TextPad v4.7.3
Java 1.4.2 SDK
Shie JL Tseng CC A nucleus-localization-deficient mutant serves as a dominant-negative inhibitor of gut-enriched Krüppel-like factor function Biochem Biophys Res Comm 2001 283 205 208 11322789 10.1006/bbrc.2001.4762
Shields JM Yang VW Two potent nuclear localization signals in the gut-enriched Krüppel-like factor define a subfamily of closely related Krüppel proteins J Biol Chem 1997 272 18504 18507 9218496 10.1074/jbc.272.29.18504
Turchin A Lawler JF The primer generator: a program that facilitates the selection of oligonucleotides for site-directed mutagenesis Biotechniques 1999 26 672 676 10343904
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==== Front
BMC Musculoskelet DisordBMC Musculoskeletal Disorders1471-2474BioMed Central London 1471-2474-6-551627447710.1186/1471-2474-6-55Study ProtocolTOIB Study. Are topical or oral ibuprofen equally effective for the treatment of chronic knee pain presenting in primary care: a randomised controlled trial with patient preference study. [ISRCTN79353052] Cross Pamela L [email protected] Deborah [email protected] Geoff [email protected] Enid M [email protected] Louise [email protected] Suzanne [email protected] Anne E [email protected] Martin [email protected] TOIB Study Team [email protected] Centre for Health Sciences, Barts and The London, Queen Mary's School of Medicine and Dentistry, 2 Newark Street, Whitechapel, London E1 2AT, UK2 Medical Research Council General Practice Research Framework, Stephenson House, North Gower St, London NW1 2ND, UK3 Wolfson Institute of Preventive Medicine, Barts and The London, Charterhouse Square, London EC1M 6BQ, UK4 Department of Economics, Queen Mary's University of London, Mile End Road, London E1 4NS, UK2005 7 11 2005 6 55 55 25 8 2005 7 11 2005 Copyright © 2005 Cross et al; licensee BioMed Central Ltd.2005Cross et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Many older people have chronic knee pain. Both topical and oral non- steroidal anti-inflammatory drugs (NSAIDs) are commonly used to treat this. Oral NSAIDS are effective, at least in the short term, but can have severe adverse effects. Topical NSAIDs also appear to be effective, at least in the short term. One might expect topical NSAIDs both to be less effective and to have fewer adverse effects than oral NSAIDs. If topical NSAIDs have fewer adverse effects this may outweigh both the reduction in effectiveness and the higher cost of topical compared to oral treatment. Patient preferences may influence the comparative effectiveness of drugs delivered via different routes.
Methods
TOIB is a randomised trial comparing topical and oral ibuprofen, with a parallel patient preference study. We are recruiting people aged 50 or over with chronic knee pain, from 27 MRC General Practice Research Framework practices across the UK. We are seeking to recruit 283 participants to the RCT and 379 to the PPS. Participants will be followed up for up to two years (with the majority reaching one year). Outcomes will be assessed by postal questionnaire, nurse examination, laboratory tests and medical record searches at one and two years or the end of the study.
Discussion
This study will provide new evidence on the overall costs and benefits of treating chronic knee pain with either oral or topical ibuprofen. The use of a patient preference design is unusual, but will allow us to explore how preference influences response to a medication. In addition, it will provide more information on adverse events. This study will provide evidence to inform primary care practitioners, and possibly influence practice.
==== Body
Background
Osteoarthritis (OA) is a common condition, particularly in older people[1]. Around 36% of those aged over 50 suffer from knee pain[2-4], half of whom have severe difficulty with physical function or severe pain[5]. The vast majority of patients who seek care receive it in primary care. Since much of this pain is due to OA, and the only treatment convincingly shown to slow progression of OA is surgery, primary care management should target pain and disability[6]. Analgesics and non steroidal anti-inflammatory drugs (NSAIDs) are the most commonly prescribed drugs for knee pain in older people.
NSAIDs for knee pain
Oral NSAIDs reduce pain in those with knee osteoarthritis[7]. Despite the risks of gastro-intestinal side effects, renal insufficiency, hepatic toxicity, exacerbation of asthma, sodium retention, raised blood pressure and resistance to anti-hypertensive drugs[8], oral NSAIDs are widely used for the symptomatic treatment of OA in older people[9]. In 2003 over 20 million prescriptions for oral NSAIDs, at a cost of over £250 million, were dispensed in England[10]. There are few data on the direct, indirect and intangible costs and cost offsets from using NSAIDs in older people; the personal and economic costs of managing adverse effects are, however, large. Around 40% of hospital admissions with upper gastrointestinal bleeding, and 40% of associated deaths in older people, are related to NSAID use[11].
Topical NSAIDs
An alternative to using oral NSAIDs is to use topical NSAIDs, which may have fewer side effects as a result of lower serum concentrations[12]. In 2003, 4.5 million prescriptions for topical antirheumatics were dispensed in England, at a cost of £25 million[10].
There are data to show that topical applications of ibuprofen achieve therapeutic concentrations in deep compartments[13]. Thus they could have pharmacological effects on peri-articular and intra-articular structures, as well as having effects through peripheral and central sensitisation[14]. The continued popularity of rubefacients, with no active ingredient, supports the idea that patients' responses to topical NSAIDs may also be partly mediated through the act of rubbing the affected part and the patients' expectation of receiving a benefit[15].
A meta-analysis of studies using topical NSAIDs concluded that they were more effective than placebo ointments for chronic musculoskeletal disorders at up to two weeks of use[16]. Another meta-analysis considering longer periods of use found that topical NSAIDs were no more effective than placebo at three or four weeks of use[14].
If :
a) the combined effect of NSAID in the ointment, the act of rubbing, and the patients' expectation of benefit produces an effect on pain and disability, and
b) topical NSAID preparations have fewer adverse effects compared to oral preparations,
then topical preparations may be preferable to oral ones as routine treatment for older patients with knee OA, as there will be fewer side effects in those whose pain can be managed effectively by topical NSAIDs.
Choice of NSAID and chronic knee pain to study
There are compelling reasons for choosing ibuprofen to treat chronic knee pain when comparing topical and oral NSAIDs:
• different NSAIDs appear to be equally effective in the treatment of knee OA[17].
• a meta-analysis of the risk of gastrointestinal side effects found that low-dose ibuprofen had the lowest risk compared to other NSAIDs [18].
• the reduction in risk of gastrointestinal side effects is similar when comparing high-dose ibuprofen with either low-dose oral ibuprofen or with Cox-II inhibitors[19]. Ibuprofen is widely used both orally and topically for the treatment of osteoarthritis. In 2003 there were five million and one million prescriptions issued for oral and topical ibuprofen respectively, in England. These represent 25% of oral and 22% of topical NSAID prescriptions.
• most chronic knee pain is thought to be secondary to osteoarthritis[20]. There are problems in diagnosing OA, in that many older people have x-ray changes of OA without experiencing symptoms, and even when x-ray changes are present, OA may not be the cause of their pain.
• x-ray evidence of OA has little impact on pragmatic general practice management of knee pain in older people; indeed, most patients are treated without any x-rays being taken.
Objective of study
The main objective of this study is to evaluate the benefits and risks of oral and topical ibuprofen in older people with chronic knee pain. A secondary objective is to explore patients' attitudes to medication for knee pain.
Health economic objectives
We will look at the cost effectiveness of topical and oral ibuprofen in terms of three key research questions:
1. What are the societal costs and benefits of implementing the programme, in terms of the impact the programme has upon the NHS, patient and other service providers?
2. What is the cost effectiveness of the programme over a one-year period and how is this influenced by treatment compliance?
3. What is the predicted long-term cost effectiveness of the programme based on the likelihood and extent of major and minor side effects?
Methods
The randomised controlled trial (RCT) will evaluate, for people with chronic knee pain, the difference in effectiveness (primarily at one year), and the difference in side effects from general practitioner treatment with oral versus topical ibuprofen over the follow up period. As this is a study lasting over a year, we expect that many patients will change their treatment if pain relief is inadequate. If they do need to change, they are asked to keep to the same route, oral or topical, if possible. For this reason, we expect that the pain outcome at one year will reflect some, but not all, of the difference in effectiveness of the treatments.
Consequently this study has elements of both a difference study and an equivalence study[21]. Our hypothesis is that the oral treatment will have greater side effects. Patients for whom the oral and topical treatment would be equally effective at pain relief will be expected to have more side effects if they are allocated to oral rather than topical treatment. Patients who persist with topical treatment that is less effective than oral treatment may have more pain and fewer side effects. Patients who rapidly change if topical is less effective than oral are likely over the longer term to have similar side effects and pain to patients already on oral treatment. Because patients cannot be expected to remain on inadequate treatment, an intention-to-treat analysis is more appropriate than an on-treatment analysis. The latter would be required for a typical equivalence study. Understanding the results depends crucially on the pattern of combined benefits and harms from the two treatments. However, as the combined effects require a judgement to be made of the relative value of pain relief and side effects, we will first analyse pain and side effects separately. For pain we will be considering whether the outcomes are different or equivalent; for side effects we are interested only if they are different. Then we will seek to demonstrate whether overall patient outcomes (benefits and harms) are better, or worse, if general practitioners advise treatment with either topical or oral NSAIDs, for a range of weightings of pain and side effects.
Patient preference study
In addition to the RCT, there is a parallel patient preference study (PPS). This will enhance the external validity of the study because:
a) we can establish whether strong preferences affect the relative outcomes. The results of RCTs may not be generalisable if those with strong preferences for a particular treatment are excluded[22].
b) the difficulties of recruiting trial participants from primary care are well known. Allowing those who have a preference for one treatment to be recruited to a PPS will provide more observational data.
c) an RCT would have to be very large to identify any differences in serious adverse events. Including data collected from the PPS will provide further information.
Additionally we will:
a) explore study participants' perceptions of treatment using depth interviews with a theoretical sample of participants. This integration of qualitative data into the interpretation of the quantitative data may provide insights into any unexpected or anomalous findings[23,24].
b) collect information on treatment preferences prior to randomisation.
Participant inclusion and exclusion criteria
The inclusion and exclusion criteria are as follows:
Inclusion criteria
• aged 50 or over.
• have ever had pain in or around the knee on most days for at least a month and have experienced knee pain for more than three months out of the preceding year.
• GP consultation, or treatment, for knee pain in the preceding three years.
• informed consent.
• agreement to use chosen or allocated treatment.
• GP agreement to prescribe oral/topical ibuprofen.
• ability to complete postal questionnaires.
Exclusion criteria
• peptic ulceration (past or current).
• current moderate or severe indigestion.
• previous severe adverse reaction to NSAIDs.
• hypertension (systolic BP of 155 mm of Hg or more or a diastolic BP of 105 mm of Hg or more).
• uncontrolled heart failure.
• creatinine > 140 mmol/L.
• abnormal liver function sufficient to contraindicate use of NSAIDs (as liver function tests performed and reference ranges vary between different laboratories, this decision is at the discretion of the participant's GP).
• GP request not to include.
• serious psychological or psychiatric disorders (including dementia).
• previous knee replacement/s or awaiting knee surgery.
• inflammatory arthropathy.
• pain referred from hip or back.
• serious injury within six months.
• currently on anticoagulants or oral steroids.
• anaemia (Hb <12.4 g/L for men or <11.8 g/L for women).
• disseminated malignancy.
To meet the American College of Rheumatologists' (ACR) clinical criteria for osteoarthritis of the knee, patients need to have knee pain, as defined for this study, and meet three out of the following six criteria [25]:
• aged over fifty.
• less than 30 minutes morning stiffness.
• crepitus.
• bony tenderness.
• bony enlargement.
• no palpable warmth.
Measuring the proportion of our sample meeting each of these criteria will allow us to describe our sample more accurately, and assess whether any of these criteria affect outcome.
Participant identification and recruitment
Location
The study is taking place in 27 practices (plus two pilot practices) from the Medical Research Council General Practice Research Framework GPRF[26]. We sought to select practices that were nationally representative in terms of region, deprivation and type of locality (inner city/urban/suburban/rural).
Identifying potential participants
In order to maximise recruitment we are using three approaches to identify potential participants:
a) searching electronic medical records within general practices for patients aged 50 or over who have consulted with OA or knee/leg pain in the preceding 5 years.
b) searching electronic prescribing databases for all patients aged 50 or over who have received a prescription for oral/topical NSAIDs or a rubefacient over the preceding year.
c) during the study recruitment period, GPs are asked to notify the practice research nurse when potentially eligible patients consult.
After training, the practice-based research nurses perform a search on the practice computer using MIQUEST[27]. This program, which was obtained from the National Health Service Information Authority, will search nearly all GP software in current use. The search selects patients over 50 who either have a diagnosis of osteoarthritis or knee pain recorded within the last 5 years, or who have received a prescription for NSAIDs or a rubifacient over the last 12 months.
The output from this search generates a comma-delimited file, on a floppy disk, containing the patients' name and address data. A bespoke software program generates study ID numbers, personalised approach letters and participant registers for the research nurse. This program is sent out to practices on a laptop computer, with a printer and pre-printed study paperwork. The nurses use this computer, and the data generated from the practice computer system, to print names and addresses on the invitation letters. After printing, all patient data are removed from the study computer. This approach minimises access to the patient records for research purposes, ensures all patient-identifiable data remains within the practice until explicit consent has been given for it to be released to the study team, and automates the production of study paperwork.
Initial approach questionnaire
The list of potential participants is screened by the GP and those whom it would be inappropriate to approach, for example those patients with terminal illness or serious psychological disorders, are removed. Invitations to participate, trial information sheets, questionnaires to screen for eligibility and expression of interest forms are sent from and returned to the practice.
Initial assessment
The practice-based research nurse contacts interested patients who, from the initial approach questionnaire, appear eligible. At the initial assessment:
a) the trial is explained to the potential participant.
b) eligibility is confirmed.
For those potential participants who are still eligible and interested:
a) blood pressure and peak expiratory flow rate are measured.
b) blood is collected for full blood count, renal function, liver function and serum ferritin.
c) arrangements are made for a medical assessment prior to a baseline assessment one-two weeks later.
d) potential participants are asked not to use any topical or oral NSAIDs for one week before baseline assessment.
Medical assessment
Between the initial and baseline assessments potential participants attend for a brief clinical assessment by a general practitioner, to identify components of the ACR clinical criteria for knee OA. The general practitioner also confirms, in light of the laboratory results, the potential participant's eligibility for the study, and agrees that he or she will be willing to prescribe either oral or topical ibuprofen for this potential participant. A patient with contraindications to either oral or topical ibuprofen cannot enter the study, either in the RCT or the PPS.
Baseline assessment
Eligible and interested patients return one to two weeks later to complete baseline questionnaires, to have baseline blood pressure, peak expiratory flow and forced expiratory volume measured, and to complete consent forms. Immediately after baseline assessment those consenting to join the RCT are randomised. All participants are provided with a starter pack of their chosen/allocated treatment when randomised to ensure that they can start treatment immediately.
The assessment procedures are the same for patients in the RCT and PPS. Figure 1 shows the recruitment process, with the calculated recruitment targets.
Figure 1 Allocation and protection from bias
A remote telephone randomisation service, separate from the main study team, uses computer-based randomisation to register patients joining the study and to allocate RCT participants to treatment groups. Randomisation is stratified by practice, severity of pain, age and source of patient.
The main study team are blind to participants' chosen/allocated treatment. The trial statistician, who is not involved in data collection, has information on chosen/allocated treatment for the data monitoring and ethics committee.
At a practice level the study is not blinded. The main outcome measures are all based on self-completed questionnaires; clinical outcomes are measured at baseline, 1 and 2 years (or end of study if sooner). Blood is analysed in the practice's usual NHS laboratories. Blood pressures are the average of three readings using a Compact Dinamap (Johnson & Johnson). Respiratory function is an average of three readings made using a Clements Clarke one flow tester ATS 94 spirometer. Mortality data are collected from practices and the NHS central registry. Prescribing, selected diagnostic and hospital admission data are collected from patient records by practice nurses.
Interventions
The two interventions being compared are the GP's recommendation (either a prescription or advice to get an over-the-counter preparation) to use either topical or oral ibuprofen. For those whose chosen/allocated treatment is oral ibuprofen, practices are asked to use no more than 1.2 g per day. Treatments for knee pain other than NSAIDs may be used as each patient's doctor thinks appropriate.
Adherence with chosen/allocated treatment will be assessed using:
1. a summary of GP prescriptions issued for the trial participants, converted into Average Daily Quantities for topical/oral ibuprofen and other topical/oral NSAIDs.
2. participant self-report of the number of times they have used pain-killing tablets or rubbing ointments in the month previous to each of the questionnaires, plus information in the same questionnaires on whether they have changed treatment during follow-up.
Follow-up
Follow-up is organised centrally. Postal questionnaires consist of the same package of instruments collected at baseline. Participants are sent postal questionnaires three, six, 12 and 24 months after randomisation. One year and two years after randomisation participants are asked to visit the practice to have their blood pressure and respiratory function measured and blood taken for full blood count, serum ferritin, creatinine and liver function tests. The medical records are examined one year after randomisation to identify unplanned hospital admissions, and after two years (or at the end of the study) to collect health service activity data and confirm reported changes in medication and adverse effects. Follow-up procedures are summarised in Figure 2.
Figure 2 We are taking the following steps to keep loss to follow up to a minimum:
1. there are two reminders for each follow up questionnaire, the second by recorded delivery.
2. participants who are unable to attend surgery for annual follow-up will be visited at home by the practice nurse.
3. participants are flagged at NHS central registry to ensure that we identify all deaths, and changes of general practitioner. This will also allow us to locate participants who have moved house for follow-up.
4. participants who have withdrawn from treatment continue to be followed up, with their consent.
Qualitative study
Depth interviews will be conducted on a sample of participants during the study. A theoretic sampling strategy will be used to explore the theory that 'older' people's beliefs about the efficacy of topical and oral ibuprofen are shaped by their social role as non-economically productive members of the social order[28]. The theory to be explored postulates that older people do not act as consumers in respect of their use of topical and oral ibuprofen, but rather accept at face value 'expert' knowledge from health professionals.
A theoretic sample will be generated. Informants will be selected based on their age, severity of pain, treatment choice/allocation, and occurrence of adverse events. These interviews will be recorded, transcribed and analysed using the principles of theory- informed qualitative analysis to establish the veracity of the constructed theoretical model[29]. To avoid any possibility that the interviews could, themselves, affect the participants' responses to the main outcome measures, data from these participants will not be included in the main analysis.
Outcome measures
Data collection is the same for the RCT and PPS. The outcome measures include measures of pain and disability, quality of life, use of medication and adverse events. Health economic data will also be collected.
Patient pain or quality of health outcomes
Primary outcome measure
• the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire, which measures pain and disability in the preceding 48 hours[30].
Secondary outcome measures
• the postal version of the Chronic Pain Grade[31], which measures pain and disability over the preceding six months.
• the EQ5D[32,33], a measure of health-related quality of life.
• the SF-36 version 2[34], a different measure of health related quality of life.
• a question assessing satisfaction with treatment.
Major possible adverse effects
The proportion who die or have an unplanned hospital admission will be presented. A trial powered to show a difference in individual major adverse events would be unfeasibly large. For example, in the control arm of a trial of misoprostol for patients taking NSAIDs 1% of patients had a serious upper gastrointestinal complication over six months[35]. The rates of serious gastrointestinal complication in the control groups of the CLASS[36] and VIGOR[37] studies of Cox-2 inhibitors compared with NSAIDs were 0.6% and 1% respectively.
Ascribing causality for individual events to the medication will not usually be possible.
Deaths will be identified by practices when records are withdrawn and by flagging of records at NHS central registry. Unplanned hospital admissions will be identified from patient-completed questionnaires and annual medical record examination. Cause of admission will be ascertained from medical record. If necessary, the unplanned nature of an admission will be confirmed by the practice nurse contacting the participant.
Minor possible adverse effects
A composite binary measure of minor adverse events will be developed, consulting with general practitioners using the Delphi technique. We will define these as changes in selected parameters serious enough for a change of treatment to be advised. We will collect data on the following parameters indicative of 'minor side effects'; these will be reported individually, and they may also contribute to the overall composite measure:
• iron deficiency or iron deficiency anaemia. data from the Framingham study show that the prevalence of iron deficiency in NSAID users, measured by serum ferritin (2.7%), is over twice the prevalence of iron deficiency anaemia (1.2%) in a healthy elderly population[38]. Ferritin may therefore be a useful proxy for occult gastrointestinal bleeding.
• new diagnosis of hypertension or failure of existing anti-hypertensive treatment or increase in blood pressure during follow up.
• New diagnosis of asthma/chronic obstructive pulmonary disease (COPD), or a new prescription of either a beta-2 agonist or a steroid inhaler, or a 15% fall in peak flow. Explicit new diagnoses will be identified from the medical record. In a previous GPRF study, recording of new diagnoses of asthma in general practice record was found to be unreliable[39]. For this reason the issue of a beta-2 agonist inhaler to someone who has not had one in the preceding year will be used as an indicator of a new diagnosis of asthma/COPD. Deterioration in asthma/COPD control will be considered present if a patient is initiated on a steroid inhaler. Deterioration in lung function measures will also be used.
• Renal impairment The upper limit of the normal range for creatinine in older people is 160 mmol/L[40]. This is higher than in a younger population. Patients with a creatinine >140 mmol/L at baseline will not be included.
• Heart failure Few GPs have access to echocardiography to confirm the diagnosis of heart failure. For this reason any new diagnosis of heart failure in the practice records will be included.
• Indigestion An increase in recorded indigestion by more that one category on a five-point Likert scale.
Compliance
• decision to stop NSAIDs for any reason during the study period.
• use of other analgesics during the study period.
Health economic outcomes
The two main outcomes that arise from medication are alleviation of knee pain balanced against the adverse effects of medication. The main HE analysis uses a combined summary measure of quality of life using the EQ-5D and the SF-6D[41]. It is recognised, however, that our summary quality of life measures, taken at different time intervals, may not adequately capture adverse effects from medication due to their fluctuating and temporary nature. Initially we had proposed to model 'any presumed link' between minor and major effects and death. This no longer seems appropriate given that exposure to adverse effects of medication is likely to lead to a discontinuation or switching of medication. We will instead conduct secondary analysis based upon the clinical measures of pain and adverse effects. If the pain levels are similar in the two treatments, and only the adverse effects are statistically different, we will use the composite binary measure of minor adverse events to calculate the 'number needed to harm' for both oral and topical medication[42]. This calculation gives the number of patients who must receive medication before we expect to see one harmful case of adverse effects. If both pain and adverse effects are statistically different between the two medications, we will attempt to combine the measures of pain and adverse effects, informed by patients' preferences for medication based on patients' a) compliance to the regime and b) switching to the alternative medication. This secondary analysis will also draw upon the qualitative study of patients' attitudes towards medication.
Costs
Costs will be obtained by recording units of resources, e.g. GP consultations, drug purchases and hospital attendance, used in both groups and applying a tariff to each type of unit. Health service usage will be based upon patients' self-reported usage, validated against medical records. Where possible, local cost tariffs will be used with national sources as a comparator. The costs to participants and their families will be obtained from the patient questionnaire.
The incidence of serious adverse effects caused by ibuprofen is such that a much larger trial is required to identify an important difference between the two groups. However these events may have large financial and other costs. Particular care, therefore, will be made in measuring the financial impacts of side effects.
Sample size
The sample size estimate is based on the primary efficacy measurement at one year
Previous work has shown minimum differences in WOMAC pain and disability scales perceptible to patients are around 10–12 mm on a 100 mm visual analogue scale[43]. Typical standard deviations for the change between baseline and follow-up in knee OA trials are around 22 mm. The results will be presented for the difference between groups in the change from baseline in WOMAC mean score with their 95% confidence intervals. To show a difference of 10 mm with 90% power and 5% significance we need analysable data on 103 subjects in each group. Assuming a 75% follow-up rate at one year, this means we need to recruit 275 participants to the RCT. This will also show equivalence to within 10 mm at 80% power.
Early recruitment data for the PPS indicate a 3:1 preference for topical compared to oral treatment; allowing for this imbalance, we need to recruit 368 participants to the PPS to achieve 90% power.
When the study was first started, it was planned that we would recruit to both RCT and PPS from all participating practices. However early recruitment data indicated that twice as many people would join the PPS when compared to the RCT, suggesting that we would overshoot our PPS recruitment target whilst not reaching the more important RCT recruitment target. For this reason, with the agreement of the funders, the trial steering committee and the data monitoring and ethics committee, we are recruiting to the RCT only in the last seven practices to join the study.
It is usual in equivalence studies to do an on-treatment analysis rather than an intention-to-treat analysis. However, as this study is testing two approaches to managing knee pain, it was agreed that an intention-to-treat analysis would be appropriate, although on-treatment analyses of side effects will also be carried out.
Analysis
Initially the RCT and PPS will be analysed separately. The primary and secondary outcomes of pain and health status, side effects and compliance (which includes drug use and mode of delivery) will be described and analysed on an intention-to-treat basis. The first analyses will be on outcomes or changes in outcomes at one year. This is the period for which the most substantial amounts of data will be available. There will also be an on-treatment analysis of side effects, which will report results for oral or topical treatment both before and after adjusting for other drug use. Although the data on timing of side effects is limited, it will be possible to produce estimates of the rate of side effects and hazard ratios for the effect of the mode of different treatment as well as for dosage and other pain killers.
The joint distributions of pain at one year and side effects at or by one year will be plotted. The effect of a range of relative utilities of pain and side effects will be incorporated into the analysis to produce more information on the relative advantage of the oral vs topical approach.
Prior to any analysis, and blind as to the treatment arm, we will have checked and validated the data. Rules for classification of information on side effects, hospital admissions and drug usage, each of which can come from a number of sources, will have been set, implemented and checked. Missing data in the health scores will be dealt with as recommended in the relevant manuals.
Details of analyses
All results will be presented with 95% confidence intervals.
ITT analyses comparing topical and oral treatment
For binary outcomes, the differences in proportions, as well as the odds ratio (from unadjusted logistic regression) will be presented. The effect of any failure of the randomisation will be investigated for the RCT, but adjustment will be made for the expected difference between the groups in the PPS using logistic regression.
For ordinal data with 5 categories or fewer the results will be presented as tables of proportions.
All other scores will be treated as quantitative date and will be analysed using t-tests or multiple regression.
Normally the change in WOMAC score from baseline would be the most appropriate measure, adjusting as it does for baseline pain, but as absolute levels of pain may be more strongly associated with treatment dose, and hence side effects, this measure will be included as well. If the randomisation is successful then both should be unbiased.
Differences in measures of potential side effects will be used preferentially, although results for absolute values may be presented.
On-treatment analysis by mode of drug
The information available on drug usage over the year is not complete. Classification of average daily dose will incorporate the three measures – prescriptions over the period, and the measures of drug use over the previous month and two days as indicated in the questionnaires. Estimates will be obtained for the period covered by each questionnaire and over the first year. Where we know a side effect has resulted in a change of medication during the time a questionnaire covers, estimates of drug use prior to the change will be made from the previous questionnaire (if available) and/or prescribing data as appropriate.
The development of a side effect will be analysed using survival analysis methods allowing for changing predictors. As part of this analysis allowance will be made for other painkillers being taken by the participants. The pain killers will already have been classified according to their likely side effects.
On-treatment analysis of WOMAC scores is unlikely accurately to reflect the treatment effect, as patients with inadequate treatment are more likely to have changed treatment.
Combined analysis of WOMAC and side effects
The joint distributions of the WOMAC score at one year and side effects will be displayed graphically by study arm. In order to make judgements on the relative overall benefit of each treatment the results of a) the Delphi study into GPs' attitudes to side effects, and b) the qualitative study of patients' attitudes to them will be incorporated as utilities in a Bayesian analysis of the relative benefits for a range of values of the relative importance of pain and side effects.
If there is a similarity in the relationship between pain and side effects for the four groups, the relationship will be modelled.
Sensitivity analyses
The potential effect of withdrawals from the study and missing data will be investigated. Best and worst case scenarios will be given.
Comparison of RCT and PPS results
The effects of treatment (by intention-to-treat) will be tabulated for the two studies. Appropriate multiple analysis will be performed adjusting for those predictors expected to be different in the two studies (eg age, sex, attitude to treatment and possible troublesomeness of knee at baseline). Tests will be made for potential interaction of type of study with type of treatment.
The combined outcomes of WOMAC and side effects will be investigated graphically in the two studies to see whether the relationship appears to have similar characteristics. If it does, modelling of the relationship to allow for the different baseline characteristics will be attempted.
Health economics analysis
The study will estimate the distribution of costs and outcomes of treatment. This will allow us to investigate the probability that the intervention is cost effective and to establish a confidence interval around the cost effectiveness estimate. Given that there is a band of 'uncertainty' around the measure of adverse effects additional sensitivity analysis will be conducted around this measure to see the impact on the cost effectiveness result.
The secondary health economics analysis will be based upon the clinical measures of pain and adverse effects. If the main difference between topical and oral medications is that the former produce fewer adverse effects, we can estimate the cost of preventing adverse effects caused by oral medication. If the cost of prevention is less than the cost of treating one episode of adverse effects, then there is a clear case for preferring the use of topical medication. If the treatment cost is less than the cost of prevention, then the decision to implement topical mediation rests on a view of the value of preventing the pain and suffering from these adverse effects. The value of this prevention will be informed by both the Delphi study into GPs' attitudes to side effects and the qualitative study of patients' attitudes to them. If both pain and adverse effects are statistically different between the two medications, we will gauge patients' preference for the medications looking at a) compliance to the regime and b) switching to the alternative treatment regime. This secondary analysis will also draw upon the qualitative study of patients' attitudes towards medication[41].
Discussion
Recruitment started in April 2003. By April 2005 we had completed our recruitment with 276 participants in the RCT and 288 participants in the PPS. Early follow-up rates at one year are around 80–85%, suggesting that we will have ample data for our analyses of the RCT. While the PPS is a little underpowered, it will provide important information to the on-treatment analyses of side effects and effects of compliance.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
PC drafted the manuscript and carried out the Delphi study. MU is the principal investigator. DA participated in the study design and the analysis plan. EH drafted the analysis plan. GH planned and performed the qualitative study. SP assisted in the qualitative study and contributed to the manuscript. LL participated in the study design and contributed to the manuscript. AE participated in the study design and wrote the health economic sections 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
The research costs for this study have been provided by the NHS Health Technology Assessment Programme. NHS R&D have provided NHS excess treatment and service support costs. We are grateful to Goldshield Pharmaceuticals for starter packs of topical ibuprofen. We are grateful to Helen Tate for statistical advice on study design and to Miranda Mugford for advice on health economic analysis. TOIB is a collaborative project between the Centre for Health Sciences at Barts and The London and the Medical Research Council General Practice Research Framework. We would like to acknowledge the contributions made by the funding applicants (Ashby D, Feder G, Harding G, Martin J, Parsons S, Spencer A, Underwood M (PI), Vickers M), the study team (Cross P, Hennessy E, Letley L, Shah H, Underwood M, Whyte K), the Trial Steering Committee (Ashby D, Buszewicz M, Carr A, Cross P, Grimley-Evans J, Hay E. Hennessey E, Lemon S, Letley L, Little P, Martin J, Shah H, Shine U, Underwood M, Vickers M, Whyte K), and the Data Monitoring and Ethics Committee (Adebajo A, Hennessey E, Morris R, Smeeth L). We would also like to thank the GPRF lead GPs, nurses and regional trainers involved in the TOIB study.
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Laupacis A Sackett DL Roberts RS An assessment of clinically useful measures of the consequences of treatment N Eng J Med 1988 318 1728 33 3374545
Peloso PM Bellamy N Bensen W Thompson GT Harsanyi Z Babul N Darke AC Double blind randomized controlled trial of controlled release codeine in the treatment of osteoarthritis of the hip or knee J Rheumatol 2000 27 764 71 10743822
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BMC Musculoskelet Disord. 2005 Nov 7; 6:55
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-5-1501630956210.1186/1471-2407-5-150Case ReportUnusual explosive growth of a squamous cell carcinoma of the scalp after electrical burn injury and subsequent coverage by sequential free flap vascular connection – a case report Horch Raymund E [email protected] G Bjoern [email protected] Justus P [email protected] Department of Plastic and Hand Surgery, University Hospital of Erlangen-Nuernberg, Friedrich-Alexander-University Erlangen-Nuernberg, Germany2 Department of Plastic and Hand Surgery, University Hospital of Freiburg, Albert-Ludwigs-University Freiburg, Germany2005 28 11 2005 5 150 150 27 7 2005 28 11 2005 Copyright © 2005 Horch 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
Squamous cell carcinomos may arise from chronic ulcerating wounds in scars, most commonly postburn scars. Tumour growth usually takes place over months to years. Localization on the scalp is a relatively rare condition.
Case presentation
This report presents the case of a 63-year-old man with chronic ulceration of a postburn scar of the scalp due to an electrical burn 58 years ago. Sudden tumour growth started within weeks and on presentation already had extended through the skull into frontal cortex. After radical tumour resection, defect was covered with a free radial forearm flap. Local recurrence occurred 6 weeks later. Subsequent wide excision including discard of the flap and preservation of the radial vessels was followed by transfer of a free latissimus dorsi muscle flap, using the radial vessels of the first flap as recipient vessels. The patient received radiotherapy post-operatively. There were no problems with flap survivals or wound healing. Due to rapidly growing recurrence the patient died 2 months later.
Conclusion
Explosive SCC tumour growth might occur in post-burn scars after more than 50 years. As a treatment option the use of sequential free flap connections might serve in repeated extensive tumour resections, especially in the scalp region, where suitable donor vessels are often located in distance to the defect.
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Background
Malignant degeneration of burn scars has been reported as early as in the first century by Celsus. However, the classic description was published by Jean-Nicholas Marjolin in 1828, who indeed did not regard the lesion as malignant [1]. The entity „Marjolin's ulcer“, which is commonly used for carcinoma arising in post-traumatic scars, has been applied ever since the report of Da Costa in 1903, who first used the term [2]. Today the term "Marjolin's ulcer" is generally used for squamous cell carcinoma arising in posttraumatic scars. However squamous cell carcinoma has recently been reported to arise also from other skin lesions such as Fournier's gangrene[3]. There have been some case reports of burn scar carcinomas of the scalp [4-6], a localization not as common as the lower extremity but still making up approximately 14% of burn scar carcinomas[7]. Due to involvement of the skull and brain, wide excision can require complex reconstructions including free flap transfer. Utilizing previously anastomosed recipient vessels of an earlier transposed free flap as new donor vessels, may serve as an ideal tool for secondary free flap surgery in cases of local recurrence, as in the reported case.
Case presentation
A 63 year old male patient presented with a large 8 × 8 cm ulcerated and suppurating tumour of the scalp (Fig. 1). The patient had suffered from an electrical burn at the age of 5 with delayed and incomplete healing of the wound and subsequent baldness in the healed area. The wound had never been completely healed and ulcerated again 8 weeks before admission to the hospital. The patient, a professor of philosophy, wore a turban for decades to hide the chronic wound. Due to personal neglect and the circumstance that he had been living on his own for many years, without any relatives or a partner kept him from seeing a doctor. The disease only received medical attention when he suddenly experienced a complete aphasia during lectures.
Clinically there was a palpable mass adherent to the skull. Nuclear magnetic resonance and computed tomography imaging revealed a tumour with infiltration of the cranium and the brain (Fig. 2). Radical resection of the tumour including the cranium, as well as dura mater and the affected parts of the frontal cortex was performed. The dural defect was closed with an autologous fascia lata patch from the right thigh, and as recipient vessels, the superficial temporal artery and vein were dissected. A radial forearm flap with a 10 × 10 cm skin paddle was harvested with an adequately long vascular pedicle. Radial artery and vein were anastomosed end-to-end to the superficial temporal vessels above the zygomatic arch. The donor site was covered with a split thickness skin graft from the thigh (Fig. 3). Histology of the specimen revealed a squamous cell carcinoma and free resectional margins. Postoperative healing was uneventful and the patient regained his speech immediately after the operation (Fig. 4). Six weeks later, the patient was referred to our unit again with multiple satellite nodules around the radial forearm free flap (Fig. 5). Incision biopsy revealed recurrence of squamous cell carcinoma. In a second operation, radical excision of the scalp with wide margins was performed, requiring sufficient soft tissue coverage. The skin and soft tissue of the radial forearm flap was completely resected, but the proximal part of the radial vessels which were in sufficient distance from the tumour were preserved. Thus radial artery and vein served as recipient vessels for a latissimus dorsi free flap. After the second operation postoperative healing was uneventful again, and the patient recovered well initially for the first three weeks (Fig. 6 and 7). Postoperatively, radiation therapy with single focus dose of 14 Gray was administered and one cycle of combined chemotherapy was conducted with cisplatin 180 mg i.v. on day 1 and 5-fluorouracil 1800 mg i.v. on days 1–5 continuously. However despite these efforts, six weeks after the second resection the patient again developed multiple recurrent metastatic lesions around the latissimus dorsi flap, and cerebral symptoms. Clinically, the general condition of the patient dramatically worsened and he died within two more weeks.
Conclusion
In this case report we describe a surgical approach to a recurrent squamous cell carcinoma of the scalp that resulted in particular problems for recontructive surgery. Our approach involved the sequential transfer of a free radial forearm flap after first tumour resection and the subsequent use of the radial vessels as recipient vessels for a free latissimus dorsi flap after second tumour resection. In general treatment of squamous cell carcinoma developing in scars and chronic wounds includes wide local excision, which in many cases demands either extensive soft tissue coverage or even amputation if extremities are involved. Burn scar carcinomas of the scalp are relatively rare compared to extremity localization and primary intracranial malignancies have been falsely mistaken for those lesions [8].
When there is clinical involvement, lymph node dissection is necessary [9]. Marjolin's ulcers have been described as aggressive and are associated with high rates of nodal metastases and a poor prognosis. Two clinical types with different growth patterns are distinguished: on one hand the flat, indurated, infiltrative, ulcerative carcinoma, and on the other hand the exophytic papillary form, which is infrequent and generally less severe. It has also been advocated to subdivide the "Marjolin's ulcers" into 2 variants: acute with a latency of > one year and chronic with an average latency of 36 years[10]. A current meta-analysis determined an average interval between injury and development of Marjolin's ulcer of 32 years (+/- 19 years) [7]. The interval between the initial trauma and the onset of malignacy in our patient confirms this disease pattern of type one, variant two Marjolin's ulcer.
If an old burn scar begins to break down and ulcerate, it has been advocated to consider prophylactic wide local excision and simple skin grafting of the area [5]. For a long time Marjolin's ulcer had not been reported to arise from the site of a skin graft. However, burn scar carcinoma in a previously skin grafted area was described [11]. Deep second degree and in particular third degree burns should be grafted immediately, which can be addressed by different means like split and/or full thickness skin graft transplantation or in very extensive lesions by keratinocyte transplantation [12,13]. Thus lower contracture rates and more sustainable wound healing can be achieved. Burns with unsuccessful grafting procedures are more susceptible to scar cancer formation than burns which have been fully successfully grafted. In the reported case the patient had not received skin grafting during acute phase of the trauma, neither was he treated for chronic wound healing impairment by skin grafting during the following years. Especially in cases of electrical burns, like in the presented case, early estimation and appropriate treatment would have been crucial [14]. Skin grafts would also not have been an option at the stage the patient presented to our clinic. Due to depth of infiltration, resulting in a post-excisional defect that needed durable and immediate soft tissue coverage to seal the subdural space and protect underlying structures, transfer of vascularized soft tissue was mandatory. This concept is similar to other indications in problem wounds as previously reported [15,16].
In general, both size and depth have been recognized to be important factors conjuring up the need for free flap transplantations [17]. In our patient the free radial forearm flap was chosen because of the length of its vascular pedicle and the desirable flatness of a fasciocutaneous flap. Suitability of this flap for the given defect was advocated by other authors [18,19].
After the rapid development of the relapsing tumor we had to create an even larger defect after to achieve complete tumour resection. Thus we were facing the demand for a larger soft tissue cover than after the first tumour resection. The favoring of the free latissimus dorsi muscle flap and its coverage with split thickness skin grafts was based on three advantages of this muscle flap: first all muscle flaps may solve defect problems even under extremely difficult local conditions by their vascularization. Secondly the large diameter of the vessels of the latissimus dorsi flap and its long, anatomically constant vascular pedicle promote safety of this operative procedure. Thirdly, the dimensions and thickness of this large, but flat free muscle flap can be nicely adapted to the convex surface of the skull. Covering the surface of the muscle flap with split thickness skin grafts leads to resistance against chronic mechanical stress and has a better aesthetic result. The particular problem in the presented case was to obtain a sufficient pedicle length, since the contralateral temporal artery was not available for anastomosis after the first tumour resection. Therefore, besides using the latissimus dorsi free flap with its long pedicle, it had to be combined with the preservation and use of the former radial flap vessels as new donor vessels. The concept of utilizing the vascular pedicle of a previous free flap while removing the rest of the flap is an interesting alternative to gain length and freedom of positioning a second required microvascular free flap. One disadvantage in the case of neoplasms involving the first flap might be an increased risk of local recurrence when preserving parts of the first pedicle. In the presented case, we tried to avoid this possibility by only preserving the proximal part of the pedicle.
Only few case reports have described one-step reconstruction with different free flaps sequentially connected in mandibular reconstruction: a fibular osseous free flap was combined with either a radial forearm flap or a lateral arm flap in six patients, with the latter flaps being connected to the distal peroneal vessels [20]. This approach demonstrated the versatility of sequential free flap coverage in cases where vascular access is limited, such as following head and neck surgery and radiation [20]. Sequential free flap transfer has twice been described as an approach to hand reconstruction [21,22]. Serial multiple flap transfers as an operative sequence have also been described as an option in extensive burn reconstruction. In this case series, 2–3 flaps were preferably used from one donor site and transferred to one or two (extensive) defects during one operation [23]. However, these authors did not have to discard the first free flap and he flaps had not been connected consecutively but to two independent donor vessels.
In this case we demonstrate the feasibility of serial microvascular transplantations with removal of the primary flap while preserving the vessels to connect a secondary free flap in complex reconstructions of the scalp. The radial artery and concommitant vein were available as relatively large vessels, matching the thoracodorsal vessels. This approach might therefore be taken into account when planning complex microsurgical reconstructions, especially when discard of the first flap is mandatory like in the presented case.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
REH, the principal author was the main operating surgeon in charge of the case manuscript. GBS was the first surgical assistant and the consultant in charge of the case. JPB was the second surgical assistant. He contributed towards preparation of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Figure 1 63 year old male patient presenting with a large 8 × 8 cm measuring exulcerated suppurating tumor of the scalp (Marjolin's ulcer).
Figure 2 Nuclear magnetic resonance imaging revealing tumor infiltration of the cranium and frontal cortex.
Figure 3 Four weeks postoperative aspect of donor site at left forearm.
Figure 4 Uncomplicated healing of radial forearm flap on left hemicranium.
Figure 5 Six weeks after primary resection tumor relapse with multiple satellite metastases around the radial forearm flap.
Figure 6 Secondary wide resection and closure of defect with a skin grafted latissimus dorsi myocutaneous flap consecutively connected to the former radial artery and vein of the primary flap.
Figure 7 Uneventful early postoperative course after second free flap with uncomplicated donor site and no impairment of shoulder function.
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Marjolin NJ Ulcère Dictionnaire de medecine 1828 21 31
da Costa JC Carcinomatous changes in an area of chronic ulceration or Marjolin's ulcer Ann Surg 1903 37 496 502
Chintamani Shankar M Singhal V Singh JP Bansal A Saxena S Squamous cell carcinoma developing in the scar of Fournier's gangrene--case report BMC Cancer 2004 4 16 15113443 10.1186/1471-2407-4-16
Ozek C Celik N Bilkay U Akalin T Erdem O Cagdas A Marjolin's ulcer of the scalp: report of 5 cases and review of the literature J Burn Care Rehabil 2001 22 65 69 11227688 10.1097/00004630-200101000-00013
Ozek C Cankayali R Bilkay U Guner U Gundogan H Songur E Akin Y Cagdas A Marjolin's ulcers arising in burn scars J Burn Care Rehabil 2001 22 384 389 11761388 10.1097/00004630-200111000-00006
Clements B Lewis H McKinstrey S Gray J Byrnes D A late, fatal complication of a high energy thermal injury to the scalp Ann Plast Surg 1995 35 650 653 8748350
Kowal-Vern A Criswell BK Burn scar neoplasms: A literature review and statistical analysis Burns 2005 31 403 413 15896501 10.1016/j.burns.2005.02.015
Cavadas PC Baena-Montilla P Jorda-Cuevas J Vera-Sempere FJ Primary intracranial malignant tumour mistaken for a postburn scalp Marjolin's ulcer Burns 1996 22 331 334 8781733 10.1016/0305-4179(95)00147-6
Phillips TJ Salman SM Bhawan J Rogers GS Burn scar carcinoma. Diagnosis and management Dermatol Surg 1998 24 561 565 9598012 10.1016/S1076-0512(98)00022-3
Aydogdu E Yildirim S Akoez T Is surgery an effective and adequate treatment in
advanced Marjolin’s ulcer? Burns 2005 31 421 431 15896503 10.1016/j.burns.2005.02.008
Turegun M Nisanci M Guler M Burn scar carcinoma with longer lag period arising in previously grafted area Burns 1997 23 496 497 9429029 10.1016/S0305-4179(97)00041-7
Horch RE Debus M Wagner G Stark GB Cultured human keratinocytes on type I collagen membranes to reconstitute the epidermis Tissue Eng 2000 6 53 67 10941201 10.1089/107632700320892
Kopp J Jeschke MG Bach AD Kneser U Horch RE Applied tissue engineering in the closure of severe burns and chronic wounds using cultured human autologous keratinocytes in a natural fibrin matrix Cell Tissue Bank 2004 5 89 96 15241004
Kopp J Loos B Spilker G Horch RE Correlation between serum creatinine kinase levels and extent of muscle damage in electrical burns Burns 2004 30 680 683 15475142 10.1016/j.burns.2004.05.008
Horch RE Gitsch G Schultze-Seemann W Bilateral pedicled myocutaneous vertical rectus abdominus muscle flaps to close vesicovaginal and pouch-vaginal fistulas with simultaneous vaginal and perineal reconstruction in irradiated pelvic wounds Urology 2002 60 502 507 12350497 10.1016/S0090-4295(02)01823-X
Horch RE Stark GB The contralateral bilobed trapezius myocutaneous flap for closure of large defects of the dorsal neck permitting primary donor site closure Head Neck 2000 22 513 519 10897113 10.1002/1097-0347(200008)22:5<513::AID-HED12>3.0.CO;2-N
Horch RE Meyer-Marcotty M Stark GB Preexpansion of the tensor fasciae latae for free-flap transfer Plast Reconstr Surg 1998 102 1188 1192 9734443
Uzunismail A Marjolin's ulcer of the scalp after 45 years Plast Reconstr Surg 1995 95 198 199 7809241
Santamaria E Granados M Barrera-Franco JL Radial forearm free tissue transfer for head and neck reconstruction: versatility and reliability of a single donor site Microsurgery 2000 20 195 201 10980521 10.1002/1098-2752(2000)20:4<195::AID-MICR10>3.0.CO;2-W
Sanger JR Matloub HS Yousif NJ Sequential connection of flaps: a logical approach to customized mandibular reconstruction Am J Surg 1990 160 402 404 2221243
Dzwierzynski WW Sanger JR Yousif NJ Matloub HS Case report: sequential vascular connection of free flaps in the upper extremity Ann Plast Surg 1997 39 303 307 9326713
Whitney TM Lineaweaver WC Hing DN Alpert BS Buncke HJ Sequential multiple free flap transfers for reconstruction of devastating hand injuries Ann Plast Surg 1991 27 66 72 1872557
Mardini S Tsai FC Yang JY Double free flaps harvested from one or two donor sites for one or two-staged burn reconstruction: models of sequential-link and independent-link microanastomoses Burns 2004 30 729 738 15475151 10.1016/j.burns.2004.03.009
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BMC Cancer. 2005 Nov 28; 5:150
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BMC UrolBMC Urology1471-2490BioMed Central London 1471-2490-5-151630768610.1186/1471-2490-5-15Research ArticleDetection of human papillomavirus DNA and p53 codon 72 polymorphism in prostate carcinomas of patients from Argentina Leiros Gustavo J [email protected] Silvia R [email protected] Mario E [email protected] Tomas [email protected] Elisabeth [email protected] Kumiko [email protected] Catedra de Bioquimica e Inmunologia, Facultad de Medicina-Universidad del Salvador, Buenos Aires, Argentina2 Servicio de patología, Hospital Israelita, Buenos Aires, Argentina3 Deutsches Krebsforschungszentrum, Heidelberg, Germany4 Expert Team Life Sciences, Deutsche Bank AG, Frankfurt, Germany2005 24 11 2005 5 15 15 17 3 2005 24 11 2005 Copyright © 2005 Leiros et al; licensee BioMed Central Ltd.2005Leiros 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
Infections with high-risk human papillomaviruses (HPVs), causatively linked to cervical cancer, might also play a role in the development of prostate cancer. Furthermore, the polymorphism at codon 72 (encoding either arginine or proline) of the p53 tumor-suppressor gene is discussed as a possible determinant for cancer risk. The HPV E6 oncoprotein induces degradation of the p53 protein. The aim of this study was to analyse prostate carcinomas and hyperplasias of patients from Argentina for the presence of HPV DNA and the p53 codon 72 polymorphism genotype.
Methods
HPV DNA detection and typing were done by consensus L1 and type-specific PCR assays, respectively, and Southern blot hybridizations. Genotyping of p53 codon 72 polymorphism was performed both by allele specific primer PCRs and PCR-RFLP (Bsh1236I). Fischer's test with Woolf's approximation was used for statistical analysis.
Results
HPV DNA was detected in 17 out of 41 (41.5 %) carcinoma samples, whereas all 30 hyperplasia samples were HPV-negative. Differences in p53 codon 72 allelic frequencies were not observed, neither between carcinomas and hyperplasias nor between HPV-positive and HPV-negative carcinomas.
Conclusion
These results indicate that the p53 genotype is probably not a risk factor for prostate cancer, and that HPV infections could be associated with at least a subset of prostate carcinomas.
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Background
Prostate cancer is one of the most common malignancies in males, but little is known about the molecular events involved in its development [1]. The prostate could constitute a target for infection with human papillomaviruses (HPV) due to anatomical reasons, particularly by direct access of the viral particles through the urethra. Penile and urethral HPV lesions have been described [2], as well as an increased prostate cancer risk associated with sexual behaviour [3]. Several studies have shown the presence of HPV DNA in prostate carcinomas and hyperplasias [4,5], whereas others could not detect any [6]. Thus, the possible role of HPV in prostate carcinogenesis is still unclear.
The carcinogenic potential of high risk HPV types (such as HPV16 and HPV18) is largely determined by the two oncoproteins E6 and E7. A major function of E6 is to bind and to target the tumor-suppressor protein p53 for proteosomal degradation [7], whereas E7 inactivates the retinoblastoma protein pRb [8]. There exists a polymorphic sequence in the p53 gene at codon position 72 encoding either arginine (Arg) or proline (Pro) [9]. It has been reported that the p53 protein with Arg (p53-Arg72) is more susceptible to E6-mediated degradation than the proline form (p53-Pro72) and that the Arg allele is over-represented in cervical cancer patients [10]. The conclusion that the p53-Arg72 allele confers a higher risk for cervical cancer development than the p53-Pro72 allele has either been supported by subsequent studies [11] or not [12]. On the other hand, it has been shown that Pro homozygosity is associated with a reduced risk of prostate cancer [13], and therefore this allele could have some protective effect.
In this study we have analyzed prostate neoplasia samples of patients from Buenos Aires, Argentina, in order to evaluate the prevalence of HPV DNA and the distribution of p53 codon 72 alleles. We have tried to minimize the possibility of urethral HPV contaminations by using microdissection for further sample processing before DNA extraction.
Methods
Studied Population
89 caucasian men older than 60 years were studied from whom 41 had prostate adenocarcinoma diagnosis and the 48 remaining had hyperplasia diagnosis. All patients were from Buenos Aires city, Argentina.
Helsinki recommendations for tissue sampling were observed. In addition, we had scientific committee approvals from institutions involved in the present report.
Clinical samples
Samples of histopathologically confirmed adenocarcinomas and benign hyperplasias were obtained by biopsy (transrectal prostatic puncture method). Three to five specimens (puncture biopsy) were obtained from each patient, fixed in formaldehyde-phosphate buffer, embedded in paraffin, and slides from these pieces were stained with hematoxylin-eosin for histopathological analysis. Blood samples from each patient were also obtained by venous puncture and collected in tubes with EDTA.
Dissection of neoplastic tissue
Specimens with hyperplasias or infiltrated by adenocarcinoma cells were first selected. In a second selection process the areas corresponding to carcinoma or hyperplasia were microdissected in order to obtain samples with the highest percentage of neoplastic cells. Slides and hematoxylin-eosin staining from these new fragments were performed to confirm the success of the procedure. These steps were repeated as many times as necessary to obtain microscopic images showing more than 90% of neoplastic cells.
DNA extraction
Genomic DNA from deparaffinized tumor samples and peripheral blood cells was obtained by proteinase K digestion, followed by phenol-chloroform extraction and ethanol precipitation. To assess the quality of the isolated DNA for PCR, a 268 bp long segment of the β-globin gene was amplified by PCR using the primers GH20 and PC04. Only DNA samples showing specific amplification with this set of primers were used for HPV- and/or p53-specific PCR assays. Due to the small sizes of many biopsies and the low amounts of DNA extracted, it was not possible to perform both HPV and p53 PCR experiments with all samples.
Detection and typing of HPV DNA by PCR and hybridization
DNA samples of all 41 prostate carcinomas and of 30 prostate hyperplasias were available for HPV analysis. DNA of the 18 remaining hyperplasia samples was completely used up for the p53 PCR analysis. As described in Hoffmann et al. [14], DNA was first analyzed for the presence of HPV sequences by multiplex PCR with type-specific (TS) primers for HPV types 6, 11, 16 and 18 (Table 1). TS-PCR-negative samples and samples for which only very small amounts of DNA were available were subjected to PCR with the consensus L1 primers MY09 and MY11, able to recognize a wide range of mucosotropic HPV types (Table 1). PCR reactions were performed in a Peltier Thermal Cycler 2000 DNA Engine (MJ Research Inc., Watertown, Massachusetts, USA). The reaction conditions for TS-PCR were as follows: initial denaturation at 94°C for 5 minutes, 39 cycles with denaturation at 94°C for 1 minute, annealing at 54°C for 2 minutes and elongation at 72°C for 2 minutes. In the last cycle, the elongation step was extended to 10 minutes. The reaction conditions for PCR amplification with the consensus primers were identical, with the exception that annealing was performed at 55°C for 1 minute. In each PCR reaction we took precautions to an extreme in order to avoid contaminations with PCR products. For this purpose we manipulated both reagents and products in completely separated rooms, and used disposable materials and different sets of instruments. Furthermore, a negative control (water instead of DNA) was included in each set of PCR reactions.
Table 1 Oligonucleotides used as primers and radiolabelled probes for HPV type-specific and consensus PCR.
Oligonucleotide Nucleotide sequence Localization in HPV genome
TS-HPV6-1 +5'-TAGTGGGCCTATGGCTCGTC-3' E5: 4671–4690
TS-HPV6-2 -5'-TCCATTAGCCTCCACGGGTG-3' E5:4931–4950
TS-HPV6 probe +5'-CATTAACGCAGGGGCGCCTGAAATTGTGCC-3' E5: 4761–4790
TS-HPV11-1 +5'-GGAATACATGCGCCATGTGG-3' L1: 6841–6860
TS-HPV11-2 -5'-CGAGCAGACGTCCGTCCTCG-3' L1: 7181–7200
TS-HPV11 probe +5'-CGCCTCCACCAAATGGTACACTGGAGGATA-3' L1: 6977–7006
TS-HPV16-1 +5'-TGCTAGTGCTTATGCAGCAA-3' L1: 6028–6047
TS-HPV16-2 -5'-ATTTACTGCAACATTGGTAC-3' L1: 6160–6179
TS-HPV16 probe +5'-CAAACCACCTATAGGGGAACACTGGGGCA-3' L1: 6117–6146
TS-HPV18-1 +5'-AAGGATGCTGCACCGGCTGA-3' L1: 6903–6922
TS-HPV18-2 -5'-CACGCACACGCTTGGCAGGT-3' L1: 7100–7119
TS-HPV18 probe +5'-TGGTTCAGGCTGGATTGCGTCGCAAGCCCA-3' L1: 7021–7050
MY11 +5'-GCMCAGGGWCATAAYAATGG-3' (W = A+T, Y = C+T; M = A+C) L1: 6582–6601
MY09 -5'-CGTCCMARRGGAWACTGATC-3' (W = A+T; R = A+G; M = A+C) L1: 7033–7014
Consensus probe:
MY18 +5'-CTGTTGTTGATACTACACGCAGTAC-3' L1
MY46 +5'-CTGTGGTAGATACCACWCGCAGTAC-3' L1
MY57 +5'-CTGTGGTAGATACCACACGTAGTA-3' L1
WD147 +5'-CTGTAGTGGACACTACCCGCAGTAC-3' L1
For each experiment, 150 ng of DNA from the patient sample was used together with 50 pmoles of each primer, 0.01 μmoles of each dNTP, 1.5 mM of MgCl2 and 2 units of Taq DNA polymerase in reaction buffer (GIBCO BRL-Life Technologies Inc Gaithersburg, MD, USA). HPV-positive and negative control reactions were done in parallel in all experiments. In the HPV type-specific PCR assays, the HPV-positive controls included genomic DNA of SiHa (HPV16-positive cell line), and C4-I (HPV18-positive cell line), as well as cloned DNA of HPV6 and HPV11. In the consensus L1 PCR, genomic SiHa DNA was used as HPV-positive control. In both types of PCR assays, DNA from the HPV-negative cell line HaCaT was used as HPV-negative control. PCR products were subjected to electrophoresis on 2% agarose minigels, visualized by ethidium bromide staining and blotted on Type B positive nylon membranes (Fluka Chemie AG, Buchs, Switzerland). Southern hybridization was performed with the radiolabelled oligonucleotide probes shown in Table 1. Filter hybridization, washing, and exposure as well as 5'-end labelling of oligonucleotide probes were done as described [14].
PCR assays for p53 polymorphism at codon 72
PCR was performed as described in Storey et al. [10] with tumor and peripheral blood cell DNA. For the p53 PCR, DNA of 39 prostate carcinomas (the DNA of 2 samples was completely used up for the HPV-specific PCR assays) and 48 prostate hyperplasias was available. Two sets of primer pairs (Table 2) were used for detection of p53-Pro72 and p53-Arg72 sequences, respectively. The different variants could be discriminated by the different sizes of PCR products (Table 2). The PCR conditions were as follows for the p53-Pro72 allele: denaturation at 94°C for 5 minutes, then 35 cycles with denaturation at 94°C for 1 minute, annealing at 56°C for 1 minutes and elongation at 72°C for 1 minute. In the last cycle, the elongation step was extended to 10 minutes. For the p53-Arg72 allele, the PCR conditions were identical with the exception that annealing was performed at 62°C for 1 minute. The PCR products were separated in 3 % agarose gels. Possible assay outcomes were: 1) if a PCR product (136 bp) was obtained only with the arginine-specific primers, the patient was considered arginine homozygous, 2) if only a proline-specific primer product (178 bp) was obtained, the patient was considered proline homozygous, 3) if the sample showed amplification with both two primer sets, the patient was considered heterozygous (Arg/Pro).
Table 2 PCR primers used for the analysis of p53 polymorphism at codon 72.
Allele Primer Primer sequence PCR product size
p53-Pro72 p53 Pro + 5'GCCAGAGGCTGCTCCCCC3' 178 bp
p53- 5'CGTGCAAGTCACAGACTT3'
p53-Arg72 p53+ 5'TCCCCCTTGCCGTCCCAA3' 136 bp
p53 Arg- 5'CTGGTGCAGGGGCCACGC3'
A second assay for p53 polymorphism status was performed using a RFLP (restriction fragment length polymorphism) site for the enzyme Bsh1236I (5'-CGCG-3'), present in the Arg allele (CGC-G), but not in the Pro allele (CCC-G) [9]. PCR reactions were performed with the p53+ and p53- external primers (table 2), using p53-Pro72 allele PCR conditions, amplifying a product of 279 bp length. The PCR product was then digested with 10 U of Bsh1236I (Fermentas GmbH, St. Leon-Rot, Germany) during 90 minutes at 37°C. Digestion products were run in 3% agarose gels. In case of the Arg allele, cleavage products of 160 bp and 119 bp were obtained.
Statistical analysis
Statistical analysis was performed using exact Fisher's test with Woolf's approximation. Statistical analysis was performed with Statistica 5.0 software program (Stat Soft Inc Tulsa, OK, USA).
Results
Presence of HPV DNA in prostate tissues
To assess the presence of HPV DNA in prostate lesions, DNA of histopathologically confirmed samples of 41 prostate carcinomas and 30 prostate hyperplasias was analyzed. The tumor sections were obtained by microdissection in order to minimize contamination with stromal tissue. Using multiplex PCR and Southern blot for HPV types 6, 11, 16 and 18, five HPV16-positive and 2 HPV11-positive prostate carcinomas were detected, whereas all benign prostate hyperplasias were negative (Table 3). The HPV-negative samples were subjected subsequently to PCR with the MY09/MY11 consensus primers and Southern blot with consensus probes (Figure 1). This assay detected 10 additional HPV-positive carcinoma samples, whereas all benign hyperplasias remained negative (Table 3). Unfortunately, no HPV typing could be performed with the MY09/MY11-positive samples mainly due to the lack of additional DNA material. Statistical analysis indicated a significant association (Fischer's exact test with Woolf's approximation, p < 0.0001) between HPV DNA presence and prostate carcinomas.
Table 3 HPV DNA in prostate carcinomas and hyperplasias.
Samples HPV+ HPV-
HPV 16 HPV 11 HPV Consensusa HPV + total
Prostate carcinomas (n = 41) 5b 2 10 17c 24
Prostate hyperplasias (n = 30) 0 0 0 0 30
a samples were negative in the TS-PCR, but HPV-positive in the MY09/MY11 PCR.. b p = 0.068, Fischer's Exact Test with Woolf's approximation; c p < 0.0001, Fischer's Exact Test with Woolf's approximation.
Figure 1 HPV DNA detection in prostate carcinomas. PCR reactions using primers MY09 and MY11 were performed. After gel electrophoresis and transfer, the filter was hybridized with consensus probes. Positive (SiHa) and negative (HaCaT, water) controls were included in the experiment, but are not shown in the figure. The numbers above the lanes indicate the designation of the carcinoma samples. The bar on the left side indicates the position of the MY09/11 PCR product of approximately 450 bp length. Two hybridization signals are visible in the HPV-positive samples. The upper band corresponds to the MY09/11 PCR product. The additional lower band which was also seen in other experiments using these batches of the MY09/11 primers, appeared exclusively as a companion of the HPV-specific 450 bp product. We did not try to clarify the nature of this extra-band.
p53 polymorphism at codon 72
In parallel to the HPV studies, we have analyzed the p53 polymorphism at codon 72 (Arg, Pro or Arg/Pro) in the leukocyte and tumor DNA from 39 patients with prostate carcinomas and 48 patients with prostate hyperplasias by allele-specific PCR and PCR-RFLP analysis. The results of the allele-specific PCR are shown in Figure 2. The two methods gave consistent results for each DNA, and no differences were detected between the tumor and normal DNA of each patient. The data are summarized in Table 4. Among the 39 prostate cancer patients, 20 Arg homozygotes, 2 Pro homozygotes, and 17 Arg/Pro heterozygotes were identified. From the hyperplasia patients, 23 were Arg homozygotes, 2 Pro homozygotes, and the 23 remaining heterozygotes. For the statistical analysis, the p53 Pro allele-carrying patients (Pro homozygotes and Arg/Pro heterozygotes) were grouped together and compared with the Arg homozygotes, in order to evaluate the latter genotype as risk factor. In the frequency of p53 Arg homozygosity no significant differences (Fischer's exact test with Woolf's approximation, p = 0.831) could be detected between carcinoma and hyperplasia patients.
Figure 2 Analysis of p53 codon 72 polymorphism in prostate carcinomas and hyperplasias by allele-specific PCR. The PCR products were run on a 3 % agarose gel. Numbers above the lanes indicate the designation of the tumor samples. (H) and (C) denote prostate hyperplasia or carcinoma, respectively. A or P indicate the use of Arg or Pro specific primer sets, respectively. The bars on the left side indicate the positions of the PCR products for Arg allele (136 bp) and Pro allele (178 bp).
Table 4 Genotypes and frequencies of codon 72 p53 polymorphism variants in prostate carcinomas and hyperplasias.
p53 codon 72 allele Prostate Carcinomas Prostate Hyperplasias
Arg/Arg 20 (0.51) 23 (0.48)
Pro/Pro 2 (0.05) 2 (0.04)
Pro/Arg 17 (0.44) 23 (0.48)
Total 39 48
The data are a summary of all experiments because identical results were obtained for each DNA with the two methods used for p53 allele typing (allele specific PCR, and PCR-RFLP), and no differences were observed between the two DNA sample sources (peripherical blood leucocytes or neoplastic tissue) from the individual patients.
Next, we compared the p53 codon 72 allelic frequencies between patients with HPV-positive and HPV-negative carcinomas in order to evaluate whether an association between p53 Arg homozygosity and HPV-positivity might exist. From 17 patients with HPV-positive carcinomas, 9 were Arg homozygotes, 2 were Pro homozygotes and 6 were Arg/Pro heterozygotes. From the 22 patients with HPV-negative carcinomas, 11 were Arg homozygotes, none were Pro homozygotes and 11 were heterozygotes (Table 5). For the Fisher's test the samples were grouped in the same way as described above. No significant differences (Fischer's exact test with Woolf's approximation, p = 1.00) in the frequency of p53 Arg homozygosity could be observed between HPV-positive and HPV-negative prostate carcinomas.
Table 5 Genotypes and frequencies of codon 72 p53 polymorphism variants in HPV-positive and HPV-negative prostate carcinomas
p53 codon 72 allele Prostate Carcinomas
HPV (+) HPV (-)
Arg/Arg 9 (0.53) 11 (0.50)
Pro/Pro 2 (0.12) 0 (0)
Pro/Arg 6 (0.35) 11 (0.50)
Total 17 22
Discussion
In the analysed sample collection we have detected a great difference in HPV positivity between prostate carcinomas (17 out of 41 = 41,5 %) and hyperplasias (0 out of 30 = 0 %) From the 7 carcinomas with identified HPV type, 5 samples contained the high-risk HPV16 and 2 samples the low-risk HPV11. The presence of HPV16 DNA supports the assumption that high-risk HPV infections are associated with at least a subset of prostate cancers. The presence of HPV11 DNA points to the possibility that HPV can infect the prostate, but these infections have probably no influence on the carcinogenic process.
After more than 10 years of HPV DNA analysis in benign and malignant prostate samples, the causal involvement of HPV in prostate carcinogenesis is still a matter of controversial debate. The discrepant results and methodological problems of the earlier analyses have already been discussed in Cuzick [5] and Strickler et al [6]. It has been speculated that the discrepancies could be due to HPV contamination from nearby tissues during the sampling procedure since HPV DNA has been detected in urethral [15,16] and anal [17,18] tissues. Based on these data some authors recommended radical prostatectomy as tissue source, as well as an exhaustive microdissection of the neoplastic sample. For the present study radical prostatectomy samples could not be obtained. However, we have performed a microdissection approach to exclude contaminating anal tissue as well as to minimize stromal content from the samples. On the other hand, if a HPV contamination from anal epithelium is a common event during biopsy taking and sample manipulation, it would be expected that both carcinomas and hyperplasias show some degree of HPV detection. However, we could not detect any HPV DNA in the hyperplasias. In some recent studies, HPV DNA was either detected in prostate cancer samples [19] or not [20,21]. Possible explanations for the divergent frequencies of HPV-positivity in prostate cancer samples may be found in populational, geographical, environmental and genetic heterogeneities, beyond methodological detection problems.
In cervical cancer, several studies of the p53 codon 72 polymorphism have been performed after the initial report claiming a higher cancer risk associated with the Arg allele [10]. Some of them refute the original finding [22-24] whereas others support it [25,26]. In our analysis of the p53 polymorphism at codon 72, we could not find an indication that the Arg allele confers a higher risk for prostate cancer, including those tissues positive for HPV. The use of two different typing methods and polymorphism determination, in both blood and tumor samples, avoid misinterpretations due to methodological typing problems and LOH in cancer samples. A recent study came to the conclusion that the Pro/Pro genotype is associated with a reduced risk of prostate cancer [13]. We could not evaluate this hypothesis due to the extremely low populational frequency of the rare Pro/Pro genotype.
It will remain important issues for future studies of prostate carcinogenesis to assess the presence, expression and potential role of HPV and to further understand the contribution of p53 mutations and polymorphisms.
Conclusion
In the present work, HPV DNA was detected in 17 out of 41 (41,5 %) prostate cancer samples, whereas all 30 tested benign hyperplasias were HPV-negative. The results allow the conclusion that HPV infections might be associated with prostate carcinoma development, at least in a subset of cases. In addition, the allelic frequencies of the p53 codon 72 polymorphism (Arg, Pro or Arg/Pro) were determined in the patients with benign and malignant tumors in order to evaluate the possibility of increased cancer susceptibility associated with the Arg allele. However, no statistically significant differences in the Arg and Pro (Pro plus Arg/Pro) allelic frequencies could be detected, neither by comparing patients with carcinomas and hyperplasias nor between HPV-positive and HPV-negative carcinomas.
Competing interests
(1) The authors declare that they have no competing interests.
(2) Financial resources expended during the assays were supported by PICT0528 Grant from National Agency for Scientific and Technology Promotion (Argentina), ARG 99/029 mobility Grant from the International Bureau of the Federal Ministry of Education, Science, Research and Technology (BMBF, Germany) and the Secretary for Technology, Science and Productive Innovation (SETCIP, Argentina), and Alberto J Roemmers Foundation Grant.
Authors' contributions
GJL carried out the molecular genetic studies, performed the statistical analysis and drafted the manuscript. SRG carried out the pathological diagnosis and microdissected the biopsy samples. MES carried out the prostatic biopsies and contributed with clinical urologic knowledge. TK participated in the design of the study. ES participated in the design of the study, gave continuous technical support and helped to draft the manuscript. KE planned 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:
Acknowledgements
We wish to thank Claudia Lohrey, Steffen Hollmer, Inés Stella, Carolina Sofer Podestá, and Romina Montani for expert technical assistance. This work was supported in part by mobility grant ARG 99/029 from the International Bureau of the Bundesministerium für Bildung und Forschung (BMBF, Germany) and the Secretaría para la Tecnología, la Ciencia, y la Innovación Productiva (SETCIP, Argentina) and Alberto J Roemmers Foundation.
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BMC OphthalmolBMC Ophthalmology1471-2415BioMed Central London 1471-2415-5-271630955410.1186/1471-2415-5-27Research ArticleIntravitreal triamcinolone with transpupillary therapy for subfoveal choroidal neovascularization in age related macular degeneration. A randomized controlled pilot study [ISRCTN74123635] Agurto-Rivera Ricardo [email protected] Jose [email protected] Luis [email protected] Tamer A [email protected] Juner [email protected] Gabriela [email protected] Rama D [email protected] Susana [email protected] Jans [email protected] Hugo [email protected] Retina Service, Asociación Para Evitar la Ceguera (APEC), Mexico City, Mexico2 Department of Ophthalmology, Kasr El Aini Hospital, Cairo University, Cairo, Egypt3 Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, USA2005 25 11 2005 5 27 27 11 11 2004 25 11 2005 Copyright © 2005 Agurto-Rivera 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 assess the effect of intravitreal triamcinolone acetonide (iTA) as an adjunctive treatment to transpupillary therapy (TTT) for new subfoveal choroidal neovascular membranes (CNV) in age-related macular degeneration (AMD).
Methods
This prospective randomized controlled pilot study comprised 26 patients scheduled to receive TTT, due to either absent indications for photodynamic therapy or financial issues. Patients were assigned into; Group A (n = 14) received TTT alone and Group B (n = 12) received iTA (4 mg) followed by TTT within one week. Follow ups were at 2 weeks, and 1, 3 and 6 months for; best-corrected visual acuity (BCVA) by ETDRS chart at 4 meters, intraocular pressures (IOP), fluorescein angiography (FAG), and central foveal thickness by optical coherence tomography (OCT).
Results
All 26 patients completed 6 months of follow ups. The average age for both groups was 74 years. Occult CNV formed 64% and 41%; classis/predominately classic 21% and 16.6%; and minimally classic 15% and 42.4% of group A and B respectively. At baseline; the mean BCVA was 0.045 for group A and 0.04 for group B; mean CNV size was 6.15 disc diameter (DD) and 2.44 DD; mean OCT foveal thickness was 513 um and 411 um for group A and B respectively with no statistical differences (P = 0.8, 0.07, and 0.19). At six months the proportion of patients gained ≥ 1 lines was 14% and 25% (P = 0.136) and stabilization was 86% and 66% (P = 0.336); the mean size of the CNV was 5.63 DD and 2.67 DD (P = 0.162); rate of CNV closure was 64% and 83% (P = 0.275); and the mean OCT central foveal thickness was 516.36 um and 453.67 um (P = 0.341), for group A and B respectively.
Conclusion
The use of iTA as an adjunctive to TTT for new subfoveal CNV in AMD showed a tendency towards better functional results. However due to the small sample size of the study a statistically significant results could not be reached.
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Background
Age-related macular degeneration (AMD) is one of the leading causes of blindness in the western world; with the most common cause of visual loss is the formation of choroidal neovascularization (CNV) [1]. Laser photocoagulation has been proven to be more effective than the natural history of the disease process in both extra- and juxtafoveal CNV [2,3]. Currently, photodynamic therapy (PDT) with verteporfin and laser photocoagulation are the only proven therapies for the subfoveal CNV [4-8].
PDT has been proven beneficial for patients with both predominately classic CNV and with some benefits for occult with no classic CNV [4,6]. A retrospective review of 1000 consecutive patients with CNV in AMD showed that 17.1% had predominantly classic CNV [9]. In addition, analysis has shown that the treatment has minimal cost effectiveness, which is principally due to the high cost of the drug, the need of many retreatments, and the continuing visual decline that most patients experience even with re-treatments [10].
Transpupillary therapy (TTT) is a technique in which heat is delivered to the choroid and retinal pigment epithelium through the pupil using an 810-nm infrared diode laser. The diode laser has theoretical advantages over other wavelengths of light because there is little absorption in the xanthophyll layer and thus damage to the nerve fiber layer is minimized. Also, it is poorly absorbed by hemoglobin allowing an improved ability to treat through preretinal and subretinal hemorrhage [11]. The wavelength of the diode laser is mainly absorbed by melanin at the level of the choroid and retinal pigment epithelium, enabling treatment of choroidal lesions [12]. TTT is also significantly less expensive than PDT. It has been used to treat choroidal melanomas, and in preliminary trials to treat both classic and occult subfoveal CNV [13-17].
There is evidence suggesting that steroids may have a beneficial effect in patients with CNV. Eyes with CNV have histopathologic evidence of inflammation, and neovascularization is a frequent component of inflammatory processes [18-20]. Histopathological examination of CNV complexes has shown the presence of inflammatory cells [18-20]. In addition the amount of vascular endothelial growth factor (VEGF), the major cytokines involved in initiating angiogenesis, has been shown to be proportional to the amount of inflammatory cells present [21]. Corticosteroids also have in addition a direct antiangiogenic effects [22-25]. Triamcinolone acetonide and other steroids have been shown to be effective in inhibiting neovascularization in animal models [26,27]. Several clinical studies have shown an apparent beneficial effect where treated patients appeared to have a favorable effect on visual acuity and fundus appearance, although a significant proportion of patients still lost vision [28-30].
It may be possible to treat patients with CNV with TTT plus iTA, to combine the immediate effect of TTT with the longer-lasting, anti-inflammatory and possibly synergistic effect of intraocular triamcinolone. To help investigate this possibility we started a randomized controlled pilot study of combined TTT with iTA for CNV in patients with AMD.
Methods
Although a randomized, multicenter, prospective, placebo-controlled trial (TTT4CNV, preliminary results, ARVO 2005) is underway to investigate the value of TTT in the treatment of occult subfoveal CNV, we did not find any report in a medline search that evaluates the effect of intravitreal triamcinolone with TTT for subfoveal CNV. Considering recent reports that demonstrate beneficial effects when triamcinolone is associated to PDT, we decided to conduct a prospective randomized non-masked clinical study of combined TTT with iTA in patients with CNV secondary to AMD.
Approval for the study was obtained from the hospital's ethical committee which is in compliance with the Helsinki Declaration. All patients received a thorough explanation of the study design and aims, and were provided with written informed consent. Patients were seen at Asociación Para Evitar la Ceguera en Mexico, "Dr. Luis Sanchez Bulnes Hospital", Mexico City, Mexico. All patients had a baseline evaluation for the following:
- Best-Corrected Visual Acuity (BCVA) that was evaluated using an ETDRS chart, measured with refraction obtained at the beginning of the study and recorded as a decimal equivalent value. Special careful was taken to avoid extrafoveal fixation.
- Slit-lamp biomicroscopy
- Indirect ophthalmoscopy
- Flourescein angiography (FAG)
- Optical Coherence Tomography (OCT) measurement of central foveal thickness.
Eligibility criteria includes: 55 years or older, new CNV under the geometeric center of the fovea, and VA of <0.20. No restriction to the type of the membrane (classic, predominately classic, or occult) was made. The patient had to have a clear media and the ability and willingness to understand the informed consent. Patients were excluded if they had received previous treatment, have any condition other than AMD to account for the CNV or refuse follow up. Patients could not have pre-existing atrophy of the fovea or a rip of the retinal pigment epithelium (RPE). Patients were also excluded if they were using corticosteroids. They could not have any disease that would interefere with the treatment, increased risk of side effects, or been confused with side effects of the treatment (as anticoagulant treatment, hepatitis, prophyria, uncontrolled glaucoma, or sensitivity ot the drug or the flourescein dye used in the trial). Only one eye per patient was entered in the study.
Once the patients accepted to be part of the study, were randomly assigned by an unmasked investigator (RA) using a random number table, to one of two groups: Group A received transpupillary thermotherapy (TTT) alone, and Group B received TTT within a week of intravitreal triamcinolone acetonide injection (iTA). Same investigator (RA) treated the patients, knowing the assigned group before to apply TTT or inject iTA.
Transpupillary therapy
Transpupillary thermotherapy was delivered through a slit lamp using a modified infrared diode laser at 810 nm with an adjustable beam width of 1.2 mm, 2.0 mm, 3.0 mm and 4.3 mm (Iris Medical Instruments, Mountain View, CA). The treatment parameter was adjusted according to the CNV type and size. Topical 0.5% proparacaine was applied before placement of a three mirror Goldmann lens coated for use with the diode laser. Continues observation through the slit lamp ensured fixation. Treatment was initiated with one spot for 60 seconds' duration at a power setting ranging between 360 and 880 mW such that no visible change or a barely detectable light-gray appearance to the lesion was present at the end of the treatment. Power settings was proportional to the spot size with larger spots requiring higher energy levels. In general, for a 2-mm spot size, the initial power level was between 360 mW and 700 mW. The spot size was adjusted to be 500 um larger than the membranes' greater diameter, if the CNV is larger than 4300 um, then overlapping spots were used. If any retinal whitening was observed or patient felt any pain, the power of the laser was decreased by 100 mW. Treatment was re-initiated and if retinal whitening continued to be observed, the power setting was again decreased by 100 mW. Care was taken to ensure that the entire lesion border was covered with treatment beam.
Intravitreal triamcinolone injection
Intravitreal triamcinolone acetonide were given as follows: patients received several drops of topical proparacaine and one drop of Betadine 5% solution (Purdue Pharma, L.P., Stamford, CT). They were given then topical flouroquinolone drops once every 5 minutes for 30 minutes. A wire speculum was inserted in the eye. The patient reclined slightly and was instructed to look up. An injection of 0.1 cc triamcinolone (Kenalog 40 mg/ml, Bristol Myers Squibb, New York, NY) was given at the 6-o'clock postion 3.5 to 4 mm posterior to the limbus using a 27 gauge needle. During the injection the needle was inserted 3 to 4 mm into the eye. The intraocular pressure was measured 5 minutes afterwards. If the IOP exceeded 24 mmHg at any time during follow ups, patients were given topical medications. If IOP exceeds 40 mmHg an anterior chamber paracentesis using a 30-gauge needle to remove 0.1 cc of aqueous humor was performed.
Follow ups
Patients were give topical antibiotics to use four times per day for one week. Patients were seen in follow ups at 2 weeks, and 1,3 and 6 months after the treament. At each follow up visit, they had BCVA measurement, slit lamp biomicroscopy, intraocular pressure measurement (IOP), and indirect ophthalmoloscopy. FAG, and OCT measurement of foveal center were done at 3 and 6 months. Retreatment was performed using the same protocol if there was no change in subretinal elevation by clinical examination and OCT, together with persistent leakage on FAG at 3 and 6 months.
Outcome measures
Primary outcomes were BCVA, CNV size and rate of closure, and OCT measurements at the foveal center. Main secondary outcome was retreatment rates.
Results
There were 26 eyes of 26 patients enrolled in our study. The mean age was 74 years for both groups with no statistical difference in the age of the patients or other demographic data (Table 1) between the two groups. Baseline mean BCVA were 0.045 and 0.04 in groups A and B respectively, with no statistical significant difference (P = 0.885 Mann-Whitney). In group A there were 64% (9/14) occult CNV, 21% (4/14) classic/predominately classic CNV, and 15% (1/14) minimally classic CNV. In group B there were 41% (5/12) occult CNV, 16.6% (2/12) classic/predominately classic CNV and 16.6% (2/12) minimally classic CNV, 3 patients was undetermined. The breakdown of the type of the CNV showed no statistical difference between both groups (P = 0.095 Mann-Whitney). The mean CNV size at baseline was 6.15 disc diameter (DD) and 2.44 DD for group A and B respectively (P = 0.075 Mann-Whitney). The mean OCT foveal thickness was 513 um and 411 um for group A and B respectively at baseline with no statistical significant different (P = 0.190 Mann-Whitney).
Six-month data
The six months follow up was available for all 26 patients. Media BCVA was 0.045 for both groups. The proportion of patients gained ≥ 1 lines was 14% (1/14) and 25% (2/12) for group A and B respectively (P = 0.336 Wilcoxon test), and stabilized in 86% (12/14) and 66% (8/12) of patients in group A and B respectively (P = 0.26 Wilcoxon test) both of which were not statistically significant. The mean size of the CNV was 5.63 DD and 2.67 DD for patients in group A and B respectively (P = 0.162 Mann-Whitney). Rate of CNV closure was 64% (9/14) and 83% (10/12) for group A and B respectively at six months (P = 0.275 Chi Square). The proportion of patients with subretinal fluids was 60% and 50% with the mean OCT central foveal thickness was 516.36 um and 453.67 um, for groups A and B respectively (P = 0.341 Mann-Whitney). All the above mentioned data were not statistically significant when compared to baseline and between both groups.
Occult CNV had more lines gain, where 60% (3/5) of patients receiving iTA showed lines gain compared with 22% (2/9) of patients not receiving iTA. Also patients with occult CNV had a relatively better response to iTA than classic membranes where 66% of patients without iTA showed lines gain compared with 80% of patients with iTA. The retreatments rate was 36% and 17% for groups A and B respectively (P = 0.175 Chi Square).
Complications
No patient had evidence of endophthalmitis at any time point. An increase in intraocular pressure beyond 23 mmHg was experienced in 2 patients in group B, who did not have previous glaucoma. Intraocular pressure was controlled with topical medication using beta blockers in both patients. Mean IOP at baseline was 15.1 and 16.3 mmHg, and at six months was 15.0 and 16.3 mmHg for groups A and B respectively. However two weeks after triamcinolone injection the mean IOP in group B was 17.9 mm Hg. The lens status was not graded in this study by a formalized method such as the Lens Opacities Classification System (LOCS II) [31]. However progression of nuclear sclerosis was not seen in any patient.
Discussion
This single-center prospective comparative randomized pilot study examined the use of combined TTT with iTA for the treatment of CNV secondary to AMD. We found that although there was no statistical significant difference in the functional result between both groups there was a trend in favor of the combined TTT with iTA. Patients receiving iTA with TTT had more line gains at six months, than those that did not receive iTA. In addition there was less retreatment in patients in the iTA group, than in the other group. However the anatomical results were clinically and statistically insignificant, in terms of the final size of the CNV, and OCT central foveal thickness.
Although exudative AMD is the leading cause of visual loss in patients above 60 years in western countries, conventional PDT with verteporfin can be given as a treatment in a minority of those with the disease [1]. In addition patients with CNV require multiple retreatments. The proportion requiring retreatments at the first 3-month interval is 90.8% for the TAP study and 68.9% for the VIP study [4-6]. In addition, PDT has minimal cost effectiveness as shown by previous studies [10]. TTT on the other hand is significantly less expensive than PDT and its energy penetrate deep to the choroid and RPE with minimal absorption by neurosensorial retina. The energy is eliminated like heat causing an elevation of local temperature (15–20 degree Celsius) inducing apoptosis with thermal inhibition of angiogenesis and vascular thrombosis [14,15]. TTT in preliminary trials showed benefits in treating both classic and occult subfoveal CNV [14-17].
There are several possible reasons to combine TTT with iTA. Choroidal neovascularization have other constituents such as inflammatory cells, and other signs of inflammation that might not benefit from the short term angiogenic effect of TTT [18-20]. Corticosteroids have a direct antiangiogenic effects [22-25]. And have been shown to be effective in inhibiting neovascularization in animal models [26-28]. Steroids can modulate the production of and reduce the permeability increased by VEGF. These secondary effects would not be expected to occur with TTT alone. Intravitreal TA persists in the vitreous cavity which extends the duration of treatment against the neovascular complex [32,33]. Several clinical studies have shown an apparent beneficial effect where treated patients appeared to have a favorable effect on visual acuity and fundus appearance, although a significant proportion of patients still lost vision [29-31].
In his pilot study, Reichel et al had 16 patients with occult subfoveal CNV treated with TTT with 12 months of follow ups [14]. Visual acuity improved 2 or more lines in 19%, stabilized in 56%, the exudation decreased in 94% of patients, and 19% of cases had to be re-treated. Other authors described improvements of 2 or more lines between 12.4 to 30% of cases, and VA stabilization between 40 to 43%, exudation reduction was around 75% and approximately 25% of patients required re-treatment [15-19]. In our study we obtained 14% and 25% VA improvement of one or more lines for group A and B respectively. However the little number of patients did not permit us to establish a statistical difference between groups, but our results are comparable to those reported in the literature. Also results of stabilization of VA, which was 86% and 66.6% of patients in group A and B respectively, was consistent with previous reports. We believed that these results must to be considered in the context of patients with very low VA, so excessive diminution was difficult, also many patients were considered out of therapeutical limits due to big size of membrane and bad VA, even though we could obtain improvement from baseline visual acuity.
Complications arising from this treatment may be expected to include all of those that could occur from TTT therapy as well as the incremental risks posed by the intravitreal injection of triamcinolone. The additional risks are principally increased IOP, progression of cataract formation, and endophthalmitis. In the present study we have increased IOP in 2 patients in group B, both of which were controlled by medications and resolved completely in 3–4 months after treatment. Progression of cataract and endophthalmitis were not seen in our study group.
This study is limited by the small number of patients in each group with a limited follow ups. The physicians were not blinded during examination of the patients or FAG. The patients also knew they were in an experimental study. Therefore finding from this study should not be used as a justification to treat patients in an uncontrolled fashion. However in the course of development of new treatment strategies, the iteration starts with pilot series data that are used to formulate larger better controlled, but more expensive studies.
Conclusion
The findings from this study suggest that the combination of transpupillary thermotherapy along with intravitreal triamcinolone offers the possibility of better functional results than with TTT alone, meriting additional randomized study.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
RAR, JDR, LTB, TAM, JCL, GPO, RDJ, SMJ, and JFG have made substantial contributions to the conception, design, and acquisition of data together with data analysis and interpretation, and were involved in drafting the article and revising it critically for important intellectual content. HQM has given his final approval for that version to be published.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Figure 1 A 65 year old patient with visual acuity of 1/10 in right eye that developed visual lost three months earlier. Color photograph shows a CNV with a small hemorrhage and sub retinal fluid. Fluorescein angiogram shows A: Early RPE defects above and below the foveal center. Later B: shows late dye leakage indication occult CNV.
Figure 2 Patient in figure 1, four months after TTT and iTA. Color photograph shows absence of sub retinal fluid and hemorrhage. Fluorescein angiography shows in (A) a large area of RPE and choroidal atrophy with (B) late staining of the enlarged chorioretinal scar after treatment with no evidence of dye leakage.
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Chylack LT JrLeske MC Khu P McCarthy D Wu SY Strategies for measuring the rate of age-related cataract formation in vivo Lens eye Toxic Res 1989 6 515 50 2487269
Schindler RH Chandler D Thresher R Machemer R The clearance of intravitreal triamcinolone acetonide Am J Ophthalmol 1982 93 415 7 7072807
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-1621628866510.1186/1471-2164-6-162Research ArticleAssessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria Rey Sébastien [email protected] Jennifer L [email protected] Fiona SL [email protected] Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada V5A 1S62005 17 11 2005 6 162 162 3 8 2005 17 11 2005 Copyright © 2005 Rey et al; licensee BioMed Central Ltd.2005Rey 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
Identification of a bacterial protein's subcellular localization (SCL) is important for genome annotation, function prediction and drug or vaccine target identification. Subcellular fractionation techniques combined with recent proteomics technology permits the identification of large numbers of proteins from distinct bacterial compartments. However, the fractionation of a complex structure like the cell into several subcellular compartments is not a trivial task. Contamination from other compartments may occur, and some proteins may reside in multiple localizations. New computational methods have been reported over the past few years that now permit much more accurate, genome-wide analysis of the SCL of protein sequences deduced from genomes. There is a need to compare such computational methods with laboratory proteomics approaches to identify the most effective current approach for genome-wide localization characterization and annotation.
Results
In this study, ten subcellular proteome analyses of bacterial compartments were reviewed. PSORTb version 2.0 was used to computationally predict the localization of proteins reported in these publications, and these computational predictions were then compared to the localizations determined by the proteomics study. By using a combined approach, we were able to identify a number of contaminants and proteins with dual localizations, and were able to more accurately identify membrane subproteomes. Our results allowed us to estimate the precision level of laboratory subproteome studies and we show here that, on average, recent high-precision computational methods such as PSORTb now have a lower error rate than laboratory methods.
Conclusion
We have performed the first focused comparison of genome-wide proteomic and computational methods for subcellular localization identification, and show that computational methods have now attained a level of precision that is exceeding that of high-throughput laboratory approaches. We note that analysis of all cellular fractions collectively is required to effectively provide localization information from laboratory studies, and we propose an overall approach to genome-wide subcellular localization characterization that capitalizes on the complementary nature of current laboratory and computational methods.
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Background
The identification of a bacterial protein's subcellular localization (SCL) represents an important step in many analyses. Such information may provide clues regarding the function of a protein. It can assist in the design of laboratory experiments to study a particular protein, and in the case of surface-exposed and secreted proteins, it can aid in the identification of potential vaccine candidates, diagnostic agents or antimicrobial targets [1-3]. The rapid and accurate assignment of SCL for a given protein deduced from a genome sequence thus represents an important step in processes ranging from genome annotation to drug discovery.
Several types of laboratory methods are frequently used to identify a protein's localization. Techniques such as immunofluorescence and immunoelectron microscopy [4], PhoA protein fusions [5], fluorescent-protein tagging [6], and the Western/SDS-PAGE [7] analysis of subcellular fractions are often applied to the analysis of either single proteins or a small sets of proteins. While such methods can provide high-quality localization information, they can be costly and/or time-consuming, and the number of proteins for which an SCL can be assigned is relatively low. Recently, proteomics technologies have been developed which are capable of providing SCL information for a much larger number of proteins. Techniques such as two-dimensional gel electrophoresis and mass spectrometry [8-12] have been frequently used to analyze localization for a variety of bacterial genomes, including Pseudomonas aeruginosa [13] and Bacillus sp. [14]. Many of these studies have focused on distinct cellular compartments, through the analysis of samples obtained by subcellular fractionation ("subcellular proteomics") [15-19]. A major disadvantage of subproteome analyses is that the fractionation of a complex structure like the cell into several subcellular compartments is not a trivial task. Contamination from other cellular compartments may occur and some proteins are known to span multiple localization sites [7,20-25]. Despite these limitations, however, genome-scale techniques are rapid, cost-effective, and capable of returning results for hundreds or even thousands of proteins in a single analysis.
Computational methods have also been developed to aid analysis of protein SCL. While some subproteomic studies have used methods like GRAVY [26], SignalP [27], and TMHMM [28] as a complement to their laboratory results [15,17], these programs predict protein features rather than localization sites, and thus are often of limited utility when attempting to confirm a protein's SCL. Prior to 2003, the only localization prediction method available for bacteria that was capable of assigning a protein to one of several different localization sites was PSORT I [29]. Developed in 1991, the program had not undergone any significant updates since its release, and has a measured precision level of only 59% [30]. To meet the need for a comprehensive, updated and precise bacterial localization prediction tool, we therefore developed PSORTb v.1.0 in 2003 [30], releasing an updated version 2 of the program in 2004 [31]. PSORTb uses a series of 10 Gram-positive and 12 Gram-negative analytical modules to examine a query protein. Each module scans the protein for the presence or absence of a particular feature characteristic of a specific localization site, returning as output either a predicted localization site or – if the feature is not detected – a result of "unknown". The modules include: SCL-BLAST for homology-based detection, the HMMTOP transmembrane helix prediction tool, a signal peptide prediction tool, a frequent subsequence-based support vector machine, as well as motif and profile-matching modules. The predicted localization sites outputted by each module are then integrated by a Bayesian network into a final prediction. The program is able to assign a protein to one of five localization sites in Gram-negative bacteria (cytoplasm, cytoplasmic membrane, periplasm, outer membrane, or extracellular) or to one of four sites in Gram-positive bacteria (cytoplasm, cytoplasmic membrane, cell wall, or extracellular). It is also able to generate predictions of multiple localization sites for a protein that spans two cellular compartments, and if not enough information is available to make a confident prediction, it is able to return a prediction of "unknown". This method was designed to emphasize precision, attaining a measured precision level of 96% for both Gram-negative and Gram-positive bacteria, with a recall of 82%. A database of predictions based on all currently available complete genomes is available through PSORTdb [32]. Subsequently, other methods have been developed for computational prediction of bacterial subcellular localization (see psort.org for a list), including methods with comparable accuracy such as Proteome Analyst [33], though PSORTb remains the most precise method to date. The existence of these new computational methods now requires an evaluation of how well laboratory and computational methods identify proteins of different SCL. For genome-wide analysis, do laboratory and computational methods behave equally or are particular localizations better predicted by one or both approaches? How can we best characterize SCL using these methods for future genome-wide studies?
We therefore compared selected bacterial subproteomic studies with PSORTb-based computational SCL predictions. Our study indicates that high-precision computational methods like PSORTb are now exceeding the precision levels associated with high-throughput 2D gel-based laboratory methods for localization identification. We also observe that there is, however, a useful complementary relationship between the laboratory-based and computational methods, with certain localizations being more accurately identified by one method over the other. Our work also illustrates the importance of examining all localizations in concert, preferably using a combination of both methods, to gain a more accurate view of a given protein's localization in the cell.
Results
Comparison of computational and subproteomic-based predictions of SCL for 405 proteins
When computational SCL predictions by PSORTb v.2.0 were compared to the selected subproteomic studies from Gram-negative bacteria (listed in Table 1), 405 proteins were identified which met our selection criteria – the results of the analyses could be matched to specific GenBank records from the organism being studied (see Methods for details). A matrix showing the predicted localization sites for the ten studies is presented in Table 1, together with estimated % agreement and % coverage for each study.
Table 1 PSORTb v.2.0 predicted localization sites for 405 proteins reported in ten subproteome studies.
Laboratory Data PSORTb v.2.0 Predicted Localizationa)
Organism Fractiona) Total C C/CM C/P CM CM/P P P/OM OM OM/EC EC UN Agreementb) Coveragec)
E. coli [64] C 23 19 - - - - 1 - - - - 3 95.0 87.0
Synechocystis [15] CM 63 13 2 - 5 1 6 - 5 1 - 30 24.2 52.4
Synechocystis [46] P 57 2 - 1 - - 8 - 3 - - 43 64.3 24.6
K. pneumoniae [16] OM 3 - - - - - - - 3 - - - 100.0 100.0
S. typhimurium [16] OM 11 2 - - - - - - 6 1 - 2 77.8 81.8
E. coli [17] OM 39 3 - - - 1 3 - 22 1 1 8 74.2 79.5
P. gingivalis [18] OM 6 - - - - - - - 2 - 1 3 66.7 50.0
P. aeruginosa [13] OM 33 4 - - 1 - - 1 22 2 1 2 80.6 93.9
P. aeruginosa [13] EC 150 33 - - 5 1 33 - 9 6 63 6.9 58.0
H. pylori [19] EC 20 3 - - - - 2 - 4 1 1 9 18.2 55.0
a) C = cytoplasmic, CM = cytoplasmic membrane, P = periplasmic, OM = outer membrane, EC = extracellular, and UN = unknown.
b) Percentage of agreement is defined by , where: A represents the number of proteins of the fraction X predicted by PSORTb to be resident at X and X/Y localization sites.
B represents the total number of proteins of the fraction X predicted as not unknown by PSORTb.
c) Percentage of coverage is defined by , where: B represents the total number of proteins of the fraction X predicted as not unknown by PSORTb.
T represents the total number of proteins identified in the fraction X.
Because PSORTb is designed with an emphasis on high precision, the program returns a prediction of "unknown" if not enough information is available to make a confident prediction. 163 of the 405 proteins being compared, or 40.2%, returned a result of unknown and were not considered in the downstream analyses. Of the remaining 242 proteins, the experimentally observed localization site agreed with the computationally predicted localization site in only 104 cases, for a total % agreement of 43.0%. This figure dropped to 25.7% if the unknown proteins were included in the calculation. The figures vary significantly from study to study, with % agreement ranging from a low of 6.9% (4.0% including unknowns) in the largest study to a high of 100% in the smallest study. However, it is clear that among the 405 proteins, there are likely a significant number of false positives and false negatives.
Identification of potential contaminants
Subcellular fractionation is a widely-used method for isolating the proteins resident at a specific cellular compartment [34]. However, a significant limitation of the technique is the problem of cross-contamination, in which small amounts of proteins from neighbouring compartments contaminate the fraction of interest [7,21-23]. This leads to the inclusion of false positives in the resulting datasets.
With the computational and subproteomic localizations differing for as many as 93.1% of the proteins for a particular analysis, we suspected that certain subproteome studies we analyzed were prone to cross-contamination. The two studies examining the extracellular fraction, in particular, had a % agreement with the computational predications of only 6.9% and 18.2%, therefore we suspected that contamination may have been a particular problem for these studies. This may be due in part to autolysis, a process common to many bacterial species which release cellular proteins into the extracellular milieu [35]. It may also be due to cellular lysis during the centrifugation of the cells [19]. If we exclude the study with 100% agreement, which involves only a small (n = 3) number of proteins, we observe that the study with the most agreement between the two methods involved an analysis of the E. coli cytoplasm. The single possible contaminant observed in this E. coli cytoplasmic study suggests that the cytoplasm is the easiest compartment to isolate in a subfractionation analysis.
When a number of subproteome studies of Gram-positive bacteria were analyzed, we observed a similar trend. Of the seven studies we examined [14,36-41], the Corynebacterium glutamicum [36] and Mycobacterium leprae [38] cytoplasmic subproteome experiments displayed the lowest levels of observed/predicted disagreement, at 0% and 8% respectively. However, when two Gram-positive extracellular fractions were analyzed (Staphylococcus aureus [41] and Bacillus sp. [14]), the % disagreement was measured at 53% and 33% – figures which are significantly lower than those observed for Gram-negative bacteria.
We next proceeded to examine the 138 disagreeing cases on an individual basis to identify the source of potential false positive results. While many false positive results appeared simply to be the result of "leaky" subfractionation, we did observe a number of cases in which a protein resident in the fraction of interest was identified along with its interacting partners from neighbouring cellular compartments. For example, Molloy et al. [17] report the presence of the acriflavine resistance protein A (AcrA) in the outer membrane fraction however, AcrA – which is predicted by PSORTb to be a cytoplasmic membrane protein – is known to be dually localized in both the cytoplasmic membrane and the periplasm [42,43]. AcrA interacts with the outer membrane protein TolC to form an export system, thus we suspect that AcrA was found in the outer membrane fraction due to its tight association with TolC. Another instance of "co-fractionation by association" was observed with the PilJ protein isolated from the P. aeruginosa outer membrane fraction. This protein is predicted by PSORTb to be localized to the cytoplasmic membrane and displays significant similarity to the known cytoplasmic membrane protein methyl-accepting chemotaxis protein II from Salmonella typhimurium [44]. PilJ is part of the chemosensory systems of P. aeruginosa [45], and it was likely co-fractionated through its association with another component of the chemosensory system present in the outer membrane.
We also observed several conflicting cases amongst the results when closely related proteins were examined. 85 of the 405 proteins in the analysis can be grouped into 36 groups of proteins which appear multiple times in the results. These 36 groups consist of: 1) a single protein identified more than once in the studies (e.g. OprE, identified in both the P. aeruginosa outer membrane and extracellular fractions [13]); 2) two or more paralogs (e.g. Synechocystis CcmK homolog 1 and CcmK homolog 2, both identified in the cytoplasmic membrane fraction [15]); or 3) two or more orthologs (e.g. Helicobacter pylori carbonic anhydrase, identified in the extracellular fraction [19], and Synechocystis carbonic anhydrase, identified in the periplasmic fraction [46]).
We would expect these groups of closely related proteins to be isolated from the same subcellular fractions, since subcellular localization is highly conserved across diverse taxonomic lineages [47]. However, this is only the case for 18 of the 36 groups, although 33 of the 36 are predicted by PSORTb to reside in the same localization. Fifteen groups contain related proteins isolated from two different fractions. Two groups (the ATP synthase beta chain proteins and the elongation factor family) contain proteins isolated from three fractions, and one group (the GroEL, GroEL2 and GroES chaperonin proteins) was isolated from four different subcellular fractions. These latter three groups illustrate an important trend with respect to contamination – certain abundant, predominantly cytoplasmic, proteins are repeatedly found in the list of potential contaminants, either due to the subfractionation process or their association (even if temporary) with proteins of another localization (for example, the protein folding chaperones). In the majority of these studies, however, they are not noted as potential contaminants/co-purifying proteins.
Our analysis of false positives reveals that the potential for contamination appears to be lowest when the cytoplasm is the subfraction of interest, and highest when the extracellular fraction is analyzed. The data highlights the fact that employing a computational contaminant screening procedure is a valuable addition to a subproteome analysis. It is especially critical for extracellular analyses, as both autolysis and mechanical lysis of cells during subfractionation can release the contents of other cellular compartments into this fraction of interest. The ubiquitous cytoplasmic proteins ATP synthase beta, elongation factors, and the GroEL/ES chaperonins are frequently observed contaminants; however, many of the studies in which these proteins were identified do not address this fact. While these proteins might immediately raise a flag to most proteomics researchers, they are not commonly noted and so may not be appreciated by genomics researchers using SCL data for genome annotation or cell surface drug target identification. Failure to note these proteins as potential contaminants/co-purifying proteins may also have significant consequences for bioinformatics software development. For example, inaccurate subcellular localization assignments could be propagated if the data were used as training data for a machine learning method by researchers unfamiliar with the field.
An estimation of the precision of subproteome 2D gel analyses
An interesting figure results from the analysis of the 44 proteins that were both isolated in a subproteome study and are present in the ePSORTdb database [32] of proteins of known subcellular localization. In 12 of these 44 cases, the fraction from which these proteins were isolated in the subproteomic studies did not match the previously reported experimentally verified localization. If we view these 44 proteins found in ePSORTdb as "100% precise predictions", we arrive at a "true" potential contamination rate of 27.3%. Nine of these conflicting results were found in the extracellular fraction in the subproteomic experiments and may represent by-products of cellular lysis. The remaining three proteins were isolated from the E. coli outer membrane fraction [17], though they were previously shown to be periplasmic proteins. The authors of this subproteome study propose that these proteins were extracted through their association with outer membrane components, rather than improper fractionation technique.
We then carried out a more liberal analysis by investigating the 138 cases where the PSORTb and subproteomic localizations differed. For each of the 138 proteins, we attempted to determine the most probable actual localization site. Localizations for twelve proteins, mentioned above, were found in ePSORTdb. We next looked for a published report of localization in the literature for the remaining 126 proteins. If no published information was available, we then looked for significant (E > 1e-10) similarity to a protein of known localization.
In this fashion, we were able to confirm that the localization predicted by PSORTb was correct in 87 of the 138 proteins. For the remaining 51 proteins, neither published localization information nor similarity to a protein of known localization was observed, and we were unable to determine whether the PSORTb or subproteomic localization site was correct. The results of this analysis are presented in Table 2.
Table 2 Estimation of subproteome study error rate.
Organism Fractiona) Total proteins identified Disagreementsb) Confirmed PSORTb errorsc) Confirmed laboratory errorsd) % Errorse)
E. coli [64] C 23 1 0 0 0.0
Synechocystis [15] CM 63 25 0 4 6.3
Synechocystis [46] P 57 5 0 1 1.8
K. pneumoniae [16] OM 3 0 0 0 0.0
S. typhimurium [16] OM 11 2 0 2 18.2
E. coli [17] OM 39 8 0 6 15.4
P. gingivalis [18] OM 6 1 0 1 16.7
P. aeruginosa [13] OM 33 6 0 3 9.1
P. aeruginosa [13] EC 150 81 2 36 24.0
H. pylori [19] EC 20 9 1 5 25.0
Total 405 138 3 58 14.3
a) C = cytoplasmic, CM = cytoplasmic membrane, P = periplasmic, OM = outer membrane, and EC = extracellular.
b) Disagreement represents the number of proteins of the fraction X predicted by PSORTb not to be resident at X or X/Y localization sites.
c) Confirmed PSORTb error represents the number of disagreeing cases for which the PSORTb predicted localization site was found to be incorrect.
d) Confirmed laboratory error represents the number of disagreeing cases for which the PSORTb predicted localization site was found to be correct.
e) % Errors is calculated as the number of confirmed laboratory errors divided by the total number of proteins identified.
Using this more liberal analysis, we estimated the average error rate of laboratory subproteome experiments to be 14.3%. Estimated error rate values varied considerably between studies, from a low of 0% (K. pneumoniae outer membrane analysis, in which only 3 proteins were investigated) to a high of 25.0% (H. pylori study of the extracellular fraction). Again, we observed that extracellular studies appeared to have the highest error rates due to the strong potential for contamination discussed earlier. On average, though, the subproteomic analysis error rate for all localizations was significantly higher than the error rate of 4% previously determined for PSORTb [31].
Reducing information loss: proteins with dual localization sites
A second disadvantage of subcellular fractionation is the associated information loss. Certain proteins have domains in two or more neighbouring cellular compartments, some may cleave into two products, each residing at a different site [48], and others [20] may be found at different localizations over time, or during different environmental conditions [49]. Because subproteome studies typically address a single cellular compartment, it is quite difficult to identify multiply-localized proteins from the results.
Computational methods can help to reduce the information loss associated with subproteome studies. When a disagreement is observed in cases where the computational and subproteomic localization sites are neighbours, it may indicate a dually localized protein. An example found in the present analysis is the ATP synthase AtpG (beta prime subunit). This protein was extracted from the Synechocystis cytoplasmic membrane fraction but was predicted as a cytoplasmic protein by PSORTb. Inspection of the literature reveals that AtpG contains domains located in both the cytoplasm and cytoplasmic membrane [50-52].
PSORTb also flags proteins predicted to reside in two compartments. Thirteen of the 405 proteins are predicted to reside at dual localization sites, with the bulk of these predicted as outer membrane/extracellular. This particular combination of localization sites suggest an autotransporter – a protein with a beta-barrel transporter domain and extracellular globular domain that is cleaved and released after translocating through the pore formed by the transporter domain. Indeed, many of the 13 proteins flagged by PSORTb are known autotransporters, including esterase and the H. pylori vacuolating cytotoxin.
Although PSORTb can assist in the identification of dually-localized proteins, false negatives are still possible. If the observed site and the single predicted sites are identical, a protein's secondary localization will still go undetected. Though it may not always be feasible, a potential solution to this problem would be to perform 2D gel analyses of all five compartments in one experiment. Not only would this aid in the identification of proteins with multiple localization sites, a comparison of the amounts of protein present in each fraction could be of use when screening for potential contamination.
Comparison of PSORTb with previously reported contaminant screening procedures
Our results illustrate that it is important to screen the results of a subproteome study for potential errors. However, many groups do not perform such a screen, or employ approaches which are limited in their utility.
The authors of two of the subproteomic studies analyzed here performed basic contaminant screens. In the Synechocystis cytoplasmic membrane study [15], the 63 proteins identified were submitted to TMHMM [28]. Seventeen of these proteins were classified as integral membrane proteins based on the presence of one or more helices. The remaining 46 were annotated as peripherally-associated membrane proteins and were then analyzed by SignalP [27]. Proteins with predicted signal peptides were classified as associated to the periplasmic face of the membrane, while those without predicted signal peptides were classified as peripherally associated to the cytoplasmic face.
Using only a single localization predictive method such as TMHMM to identify a feature often results in false positives, particularly in alpha helix detection, where signal peptides are often mistaken for helices. Furthermore, by describing the proteins with no detected helices as peripherally membrane-associated, there is a failure to recognize the fact that these proteins may represent potential contaminants from other fractions. Had PSORTb been used as a screening tool, the authors would have been able to identify 22 potential errors amongst their results with a relatively high degree of confidence.
The authors of the E. coli outer membrane study [17] compared the Swiss-Prot localization site for the proteins they identified to the amounts of those proteins detected on the 2D-gel. They reported that, with the exception of the flagellin protein, only proteins annotated as integral outer membrane proteins were detected in significant levels. They posit that the remaining proteins, detected at lower levels, may exhibit a functional association with proteins in the outer membrane. However, this explanation does not account for several potential cytoplasmic or cytoplasmic membrane contaminants, such as the dihydrolipoamide succinyltransferase SucB [53,54], which were also isolated. A screen such as this also has the potential to produce a high number of false negatives – outer membrane proteins present in low quantities which are mistaken for potential contaminants.
While the authors of the two studies mentioned above do not claim that their approaches identify all contaminants, we found that a robust and comprehensive method such as PSORTb outperforms single methods designed to analyze specific features, such as signal peptides or transmembrane helices. This is not surprising, as it has long been recognized that multi-component approaches to prediction achieve the best performance. Though dually localized proteins likely represent only a small fraction of proteins in the cell, they often represent interesting biological cases, including proteins that play pivotal roles in antimicrobial resistance (i.e. efflux proteins [55]), and virulence (i.e. BrkA [56]) and thus should not be overlooked.
Optimal identification of cytoplasmic membrane proteins requires a combined computational and laboratory approach
Examining the detailed PSORTb results for the proteins reviewed in the present analysis, we observed an interesting trend in the output of the HMMTOP module, which predicts the number of transmembrane alpha-helices in a query protein. Of the 405 proteins analyzed by HMMTOP, only six proteins contained three or more predicted helices. Even more surprising was that only three of these six were identified in the Synechocystis cytoplasmic membrane study. When three cytoplasmic membrane subproteome studies in Gram-positive bacteria were analyzed, the same trend was observed, with only six out of 269, or 2.2%, of proteins predicted to contain three or more transmembrane helices (TMHs). We then analyzed the complete Synechocystis proteome with PSORTb, predicting a total of 540 cytoplasmic membrane proteins, of which 461 contain three or more transmembrane helices.
Our results indicate that 2D gel electrophoresis of the cytoplasmic membrane fraction is only capable of identifying a small proportion of the multi-pass membrane proteins in a given proteome, likely due to the low pI and poor solubility of these proteins [57]. While other techniques can be used to identify these proteins in the laboratory – for example, liquid chromatography coupled with tandem mass spectrometry and affinity labelling [58,59] – PSORTb is a cheaper and faster solution which is capable of identifying these proteins with a high degree of precision.
While PSORTb appears to outperform laboratory subproteomic methods for the identification of proteins with three or more transmembrane helices, the opposite is true for membrane-associated proteins with one or two helices. In their analysis of the Synechocystis cytoplasmic membrane fraction, the authors of the study report 40 membrane-associated proteins. PSORTb, on the other hand, only confidently identifies three such dually localized proteins – two with cytoplasmic domains, and one with a periplasmic domain. In order to maintain a high level of precision, PSORTb requires that one of the following criteria be met to identify a cytoplasmic membrane protein: three or more predicted TMHs, similarity to a known membrane protein, or a positive result from the cytoplasmic membrane SVM module. As a result of these stringent criteria, a large number of cytoplasmic membrane-associated proteins with one or two helices are not identified by PSORTb.
Our observations indicate that the cytoplasmic membrane presents a special case for both laboratory and computational analysis. If a true picture of the membrane proteome is desired, it is necessary to use a combined approach, in which a computational method is used to identify integral cytoplasmic membrane proteins, while a laboratory method is used to identify cytoplasmic membrane-associated proteins.
Discussion
Comparing the precision of laboratory and computational methods
In the present analysis, we compared the localizations predicted by the computational method PSORTb to the localizations of 405 proteins reported in ten subproteome 2D gel electrophoresis studies. The data generated in our analysis indicates that subproteome studies vary greatly in terms of their precision. Certain small studies of particular fractions, such as the analysis of three K. pneumoniae outer membrane proteins or 23 E. coli cytoplasmic proteins, display low or non-existent apparent error rates. Larger studies and those focusing on particular localizations – including the extracellular milieu – can contain significant levels of false positive, or contaminant proteins.
We attempted to estimate the precision associated with subproteome studies using two approaches. In the first, more stringent approach, a comparison of 44 proteins against the ePSORTdb database of proteins of experimentally verified localization yielded a rough estimate of false positives of 27.3%. A second approach, in which we attempted to determine the true localization of 138 proteins using literature and homology-based approaches, yielded an estimate of 14.3%.
While our approximate error rate is by no means a definitive estimate and was not calculated using large samples, it does illustrate the importance of evaluating the results of a subproteome study with a critical eye. While errors associated with each study do vary, on average as many as 1 out of every 4–7 results could be erroneous.
Even more notable is the observation that while our estimated precision of subproteome analysis exceeds that of early predictive tools such as PSORT I [29] (with a reported precision of 59.6% [30]), current high-precision computational methods such as PSORTb (with 96% precision) appear to outperform laboratory subproteome studies, generating fewer false positive results. While it is true that measured precision values calculated from cross-validation studies of test datasets represent a slight overestimation of precision, even a more conservative estimate of 90% precision still exceeds the levels attained by most high-throughput laboratory methods. In other words, PSORTb, first released in 2003, appears to be the first computational method developed that outperforms high-throughput laboratory studies for SCL prediction. Other computational methods have since been developed that also have high accuracy, and slightly more recall (sensitivity) such as Proteome Analyst. However, no method has yet been developed that is as precise as PSORTb.
Limitations of computational methods
While our comparison of the precision achieved by computational and laboratory subproteome analyses indicates that certain predictive tools have surpassed wet-bench methods for localization identification, there are a number of caveats associated with the use of computational tools.
Of the 405 proteins submitted to PSORTb, only 59.8% returned a predicted localization site and in only 43% of these cases did the predicted site match the observed site. The 40.2% "unknown" rate we observed is well below the recall of 82% reported in the paper describing PSORTb. Such a discrepancy between "practical" values and "theoretical" values is frequently observed with machine learning methods, due to the fact that the data used to train and test the method is generally quite well-annotated while "real world" data, on the other hand, contains large numbers of hypothetical proteins.
Unfortunately, until machine learning methods – including PSORTb – are trained on much larger datasets, the gap between recall values is not likely to improve significantly. In the interim, we recommend that users employ additional predictive strategies with higher recall values. Proteome Analyst [33] uses a different approach to PSORTb in generating its predictions – keywords are extracted from Swiss-Prot annotations of proteins homologous to a given query; these keywords are then passed to a machine learning classifier. Proteome Analyst displays excellent precision – the authors report an overall precision of 95.9% for Gram-negative bacteria – and although its coverage when applied to whole genomes is generally comparable to PSORTb, it did provide a much larger number of predictions for the dataset analyzed here – of the 405 proteins submitted, Proteome Analyst returned a predicted localization site or sites for 398.
The performance of a given method can also vary significantly depending on the organism being analyzed. For example, PSORTb was able to generate predictions for only 25% of the proteins identified in the Synechocystis periplasmic fraction (see Table 1). Several factors may explain this low rate of coverage, including particularities of the morphology of Synechocystis sp., the low number of Synechocystis proteins included in PSORTb's training dataset, and the fact that three-quarters of the proteins found in the periplasmic fraction are annotated as hypothetical proteins. This is in contrast to the excellent coverage achieved by PSORTb in the analysis of the E. coli cytoplasmic fraction, which reflects the fact that as a model organism, E. coli proteins occur frequently in PSORTb's training data.
A method's performance also varies between localization sites and, in general, correlates with the amount of training data available for a given localization. PSORTb performs very well when identifying both cytoplasmic and outer membrane proteins, but is not able to make as many predictions for periplasmic and extracellular proteins. Proteins resident at specific localization sites – for example, the periplasm and the extracellular space – can be similar to the point that differentiating the two based on sequence alone can be difficult.
It is also important to note that every predictive method will generate a certain number of false positive results, and that it is critical to keep the measured precision of a given method in mind when carrying out a computational analysis. For example, some computational methods, such as CELLO [60], have a measured precision of only 71.5% [31].
Limitations of laboratory methods
Laboratory analyses also carry with them a number of caveats. We have already shown that one of the major disadvantages of subproteomic studies is the potential for contamination via leaky fractionation or lysis. Growth conditions can also affect the results of a subproteome study. Different growth conditions can alter the expression of a particular protein, thus while a subproteome study can provide valuable data about expression under a given condition, they may not yield a global picture of the proteins expressed by a bacterium. The parameters of the experiment can also play a key role in determining which proteins are identified from a gel.
It is critical to choose an appropriate pH gradient for maximum resolution of total proteins, and even then standard methods may not detect or separate low abundance or hydrophobic proteins. Protein complexes can also be problematic if their subunits are difficult to disassociate [57,61,62].
Proposed method for the optimal characterization of cellular compartments
In the present study, we have shown that computational and laboratory-based analyses of specific cellular compartments complement each other, with each method contributing to improve the accuracy of the other. Although both methods do display certain limitations, each offers a number of significant advantages, which we have summarized in Table 3. In order to capitalize on these advantages, we propose that genome-scale studies aimed at cataloguing the proteins of a particular cellular compartment adopt a complementary approach in which both methods are used.
Table 3 Advantages and disadvantages of computational and subproteomic approaches to localization analysis.
Computational methods Proteomics analysis
Advantages
Rapid predictions for all proteins deduced to be encoded in a given sequence Can be performed under different conditions and provide condition-specific information
Detailed information about specific features of proteins, e.g. signal peptides, TMHs Confirms expression of hypothetical proteins
Identification of potential contaminants in subproteome analyses Large-scale source of data on SCL for hypothetical proteins that cannot be easily predicted computationally
Identification of hydrophobic integral membrane proteins
Disadvantages
Does not perform as well (less predictions) when analyzing an organism that is not similar to well studied/model organisms. Time-consuming
May miss flagging some multiply-localized proteins Low abundance and hydrophobic proteins not readily detected
Poorly predicts particular localizations for which there is little training data, or the proteins are computationally difficult to differentiate between localizations. Difficult to accurately identify all proteins found on the gel
Cannot identify condition-specific data on SCL, particularly proteins that change SCL depending on the condition. One subcellular fraction at once analyzed
Subfractionation often results in contamination
Cannot identify multiply localized proteins
With respect to the subproteomic aspect of such a study, we suggest that rather than analyze a single cellular compartment, a study ought to analyze all available compartments. By determining the relative abundance of a protein in each compartment, a researcher will able to quickly flag potential contaminants and identify proteins with complex localization profiles – dual localizations or localization that varies temporally.
After retrieving the set of protein sequences corresponding to the spots on a 2D gel, the proteins should be submitted to a high-precision localization prediction method for analysis. PSORTb is the most precise localization prediction tool available, and its consensus approach allows the user to acquire detailed information about protein features, such as homology to protein of known localization, or the presence of a signal peptide, transmembrane helices, or specific sequence motifs and patterns. Proteome Analyst is a second high-precision method which complements PSORTb well, through the use of an annotation-based approach.
The computationally predicted and experimentally observed localization sites should then be compared. In cases where the computational and laboratory methods disagree, detailed analysis of the individual protein should be carried out. Through examination of the literature and further computational analysis, very often a confident call regarding the protein's true localization can be made. An excellent model is provided by Elias et al. [63], who employ a multi-faceted approach – including PSORT I, PSORTb, and in-depth examination of individual proteins – to the analysis of their results from a study of Shewanella oneidensis hypothetical proteins.
The combination of 2D gel analysis and PSORTb prediction can provide a remarkably clear and genome-scale picture of protein localization in a given bacterium. Of course, these methods are no replacement for the hypothesis-driven detailed investigation of individual proteins. Instead, they provide an accurate jumping-off point for the in-depth analysis of specific proteins using additional techniques. As both computational and laboratory high-throughput approaches improve in terms of both precision and recall, however, we see an increasingly important role for these methods in the fields of molecular biology and genomics.
Conclusion
We have performed the first focused comparison of genome-wide laboratory/proteomic and computational methods for subcellular localization identification, and show that PSORTb is the first computational method to attain a level of precision exceeding that of high-throughput laboratory approaches. We note that analysis of all cellular fractions collectively is required to effectively provide localization information from laboratory studies, and we propose an overall approach to genome-wide subcellular localization characterization that capitalizes on the complementary nature of current laboratory and computational methods.
Methods
Selection of subproteomic studies
Eight manuscripts describing the 2D gel electrophoresis analysis of ten bacterial subcellular fractions were selected for the present study (Table 1). The studies were chosen to ensure that they represented all five of the possible Gram-negative localization sites over a range of organisms, including: Escherichia coli, Helicobacter pylori, Klebsiella pneumoniae, Porphyromonas gingivalis, Pseudomonas aeruginosa, Salmonella typhimurium, and Synechocystis. In all, eight studies were selected [13,15,16,18,19,46,64] spanning all five localization sites for Gram-negative bacteria. In addition, seven supplementary Gram-positive studies were evaluated to a lesser degree to ensure that the results were generally applicable to all bacteria. A total of 269 proteins from the cytoplasm of C. glutamicum [36,37] and M. leprae [38], from the cytoplasmic membrane of Bacillus anthracis [39], M. leprae [38] and Mycobacterium tuberculosis [40], and from the extracellular fraction of Bacillus sp. [14] and S. aureus [41], were analyzed. The vast majority of the studies used fractionation followed by two-dimensional SDS-PAGE electrophoresis. Proteins were then subjected to peptide mass fingerprinting (PMF) identification. One study [18] used fractionation followed by two successive one-dimensional SDS-PAGE electrophoresis analyses, with subsequent N-terminal amino acid sequence analysis.
Protein selection
For each study, we examined the reported proteins to see if they met two criteria. First, the protein must have been identified through comparison of the spot to the sequence of the bacterial genome under study, and not to another organism. For example, in the S. typhimurium outer membrane study of Molloy et al. [16], only the proteins identified by a PMF search against the S. typhimurium genome were selected, while proteins identified by a PMF search against other organisms were not included. Second, we had to be able to match the protein reported in the study to a GenBank record in order to retrieve the correct amino acid sequence. After these two filtering steps were applied, the final dataset consisted of 405 proteins for the Gram-negative organisms.
Computational analysis
Computational predictions of localization were performed using the standalone version of PSORTb v.2.0 [31]. The complete predictions are available as supplemental material (See Additional file 1: PSORTb complete predictions). Proteins predicted to reside at multiple localization sites were manually identified from the PSORTb results. A protein was annotated with dual localizations if PSORTb returned two sites with scores between 4.50 and 7.49 or if the SCL-BLAST module returned significant similarity to a protein known to have dual localizations. Additional limited computational analyses were performed with Proteome Analyst [33], as described in the text.
Authors' contributions
SR selected the subproteomic studies, carried out the computational predictions and performed their analyses. SR and JLG drafted the manuscript and JLG supplied additional insights regarding the analyses. FSLB coordinated the study and provided further insights when refining the manuscript. All authors read and approved the final manuscript.
Supplementary Material
Additional file 1
PSORTb complete predictions The Excel spreadsheet contains the PSORTb v.2.0 detailed predictions for the 405 proteins reviewed in this study.
Click here for file
Acknowledgements
The authors would like to thank Simon Fraser University colleagues Michael Acab and Matthew R. Laird for their work in the development of PSORTdb and PSORTb, as well as Raymond Lo for his critical reading of the manuscript. SR is a Swiss National Science Foundation Scholar. JLG and FSLB are a Michael Smith Foundation for Health Research Trainee and Scholar, respectively, as well as a Canada Graduate Scholarship holder and Canadian Institutes of Health Research New Investigator, respectively. This work was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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Elias DA Monroe ME Marshall MJ Romine MF Belieav AS Fredrickson JK Anderson GA Smith RD Lipton MS Global detection and characterization of hypothetical proteins in Shewanella oneidensis MR-1 using LC-MS based proteomics Proteomics 2005 5 3120 3130 16038018 10.1002/pmic.200401140
Dukan S Turlin E Biville F Bolbach G Touati D Tabet JC Blais JC Coupling 2D SDS-PAGE with CNBr cleavage and MALDI-TOFMS: a strategy applied to the identification of proteins induced by a hypochlorous acid stress in Escherichia coli Anal Chem 1998 70 4433 4440 9796426 10.1021/ac980132z
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-5-1071630768810.1186/1471-2334-5-107Case ReportCatheter-associated bacteremia by Mycobacterium senegalense in Korea Oh Won Sup [email protected] Kwan Soo [email protected] Jae-Hoon [email protected] Mi Young [email protected] Seong Yeol [email protected] Sangtaek [email protected] Ki Tae [email protected] Jang-Ho [email protected] Kyong Ran [email protected] Nam Yong [email protected] Division of Infectious Diseases, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea2 Department of Laboratory Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea3 Asian-Pacific Research Foundation for Infectious Diseases (ARFID), Seoul, Korea2005 25 11 2005 5 107 107 17 8 2005 25 11 2005 Copyright © 2005 Oh et al; licensee BioMed Central Ltd.2005Oh 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
Rapidly growing mycobacteria is recognized as one of the causative agents of catheter-related infections, especially in immunocompromised hosts. To date, however, Mycobacterium senegalense, which was known as the principal pathogen of bovine farcy, has not been reported in human infection.
Case presentation
We describe the first case of human infection by M. senegalense, which has caused catheter-related bloodstream infection in a cancer patient in Korea. The microorganism was identified by the 16S rRNA gene, rpoB, and 16S-23S rRNA gene internal transcribed spacer (ITS) sequence analyses.
Conclusion
Our first report of catheter-associated bacteremia caused by M. senegalense suggests the zoonotic nature of this species and indicates the expansion of mycobacterial species relating to human infection. M. senegalense should be considered as one of the causes of human infections in the clinical practice.
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Background
Mycobacterium senegalense was originally described by Chamoiseau in 1973 as a subspecies of Mycobacterium farcinogenes [1]. However, it was later recognized as a distinct species closely related to M. fortuitum [2]. M. senegalense is known as the principal pathogen of bovine farcy, which is a chronic disease of skin and superficial lymphatics of cattle in East and Central Africa [3]. Unlike other rapidly growing mycobacteria, human infection by M. senegalense has not been reported to date. Here we report the first case of central venous catheter (CVC) infection caused by M. senegalense.
Case presentation
A 49-year-old woman with non-Hodgkin's lymphoma was admitted to the hospital because of fever for several hours. The patient had been treated for lymphoma since 3 months ago. Five days before admission, the patient was treated with the third cycle of CHOP plus rituximab (R-CHOP). She had no history of travel or contact with animals including cows or their products. Physical examination revealed high fever of 39.8°C. The patient had a subclavian cuffed-CVC(Hickman catheter) on the right side with no evidence of the inflammation at the exit site. Laboratory data and chest radiograph were within normal limits. Three sets of blood samples for cultures were drawn through CVC lines (2 sets) and a peripheral vein (1 set), respectively. The patient was treated empirically with vancomycin (1 g every 12 h intravenously). On the second hospital day, all 3 sets of blood cultures grew gram-positive, acid-fast bacilli. The cultures from the CVC became positive more than 2 hours earlier than that from a peripheral vein. Non-pigmented and pinpoint-shaped colonies were observed on blood or chocolate agar plate after 3 days of incubation at 37°C. It did not grow well on McConkey agar plate. As it grows, the color of the colonies becomes pale-yellow. Vancomycin was replaced by imipenem/cilastatin (500 mg every 6 h intravenously) and amikacin (375 mg every 12 h intravenously). On the sixth hospital day, the CVC was removed because of persistent fever. After removal of CVC, the patient became afebrile and the repeated blood cultures became negative. In vitro susceptibility test was performed by broth microdilution test as described by the National Committee for Clinical Laboratory Standards (NCCLS) guidelines [4]. The result of in vitro susceptibility test was shown in Table 1. The isolates were susceptible to most antimicrobial agents tested except vancomycin. The patient was further treated with oral ciprofloxacin (500 mg every 12 hours) and doxycycline (100 mg every 12 hours) for 4 weeks. She had been doing well with no evidence of recurrence for the next 3 months.
Table 1 Antibiotic susceptibility testing using broth microdilution for strain SMC-7485.
Antibiotic agents MIC (μg/mL) Susceptibility a
Amikacin 0.5 S
Cefoxitin 8 S
Ciprofloxacin 0.25 S
Clarithromycin 0.25 S
Doxycycline 0.12 S
Imipenem 4 S
Tobramycin 4 S
Amoxicillin-clavulanic acid 16/8 -
Moxifloxacin 0.12 S
Trimethoprim-sulfamethoxazole 4/76 -
Vancomycin 16 I
aS, susceptible; I, intermediate. MIC interpretative breakpoints of amoxicillin-clavulanic acid, moxifloxacin, trimethoprim-sulfamethoxazole, and vancomycin are not shown by NCCLS [4] for rapidly growing mycobacteria. Those of moxifloxacin and vancomycin are those recommended for aerobic organisms.
Molecular identification
Conventional automated methods in the clinical microbiology laboratory such as VITEK 2 system (bioMérieux, Hazelwood, Mo.) failed to identify this isolate to a given species. Thus, this isolate ("SMC-7485"), was subjected to the 16S rRNA gene, rpoB, and 16S-23S rRNA gene internal transcribe spacer (ITS) sequence analyses for bacterial identification. Genomic DNA was extracted by using the G-Spin Genomic DNA Extraction Kit (iNtRON, Seoul, Korea). DNA amplification of 16S rRNA gene, rpoB, and ITS were performed by using primer sets 16S-F3 (5'-CAG GCC TAA CAC ATG CAA GT-3')/16S-R3 (5'-GGG CGG WGT GTA CAA GGC-3'), MF (5'-CGA CCA CTT CGG CAA CCG-3')/MR (5'-TCG ATC GGG CAC ATC CGG-3'), and ITS-F (TTG TAC ACA CCG CCC GTC A-3')/ITS-R (5'-TCT CGA TGC CCG GCA TCC ACC-3') [5-7], respectively. Template DNA (ca. 50 ng) and 20 pmol of each primer were added to a PCR mixture tube (AccuPower PCR PreMix; Bioneer, Daejeon, Korea) containing 1 unit of Taq DNA polymerase, each deoxynucleoside triphosphate at a concentration of 250 μM, 10 mM Tric-HCl (pH 8.3), 10 mM KCl, 1.5 mM MgCl2, and gel loading dye [8]. The reaction mixture was then subjected to 35 cycles for amplification. Each cycle consisted of 30 sec at 95°C for denaturation, 30 sec at 60°C, and 1 min at 72°C for extension, followed by final extension at 72°C for 5 min. Amplified PCR product was purified for sequencing using PCR purification kit (CoreOne, Seoul, Korea). The purified PCR product was sequenced directly using the same primers of PCR amplification at both directions. Sequence editing and analyses were performed with the EditSeq and MegAlign programs in DNASTAR (Windows version 3.12e; Madison, Wis.). Determined sequence was compared with public database, GenBank, with the BLASTn program , and sequences showing high similarity were retrieved for further analysis.
16S rRNA gene sequence (1,393 bp) of the strain SMC-7485 showed 100% similarities with those of Mycobacterium senegalense ATCC 35796 [GenBank: AF480596] and Mycobacterium farcinogenes ATCC 35753 [GenBank: AF055333]. It also showed very high similarities (99.5% – 99.7%) with those of Mycobacterium porcinum, Mycobacterium housetonense, Mycobacterium neworleansense, Mycobacterium boenickei, Mycobacterium septicum, and Mycobacterium fortuitum. 16S rRNA gene sequence analysis suggested that the strain SMC-7485 belong to M. senegalense or M. farcinogenes of M. fortuitum group. However, more decisive identification could not be available due to no divergence of 16S rRNA gene sequence between two species [9].
To clarify identification of the strain SMC-7485, we analyzed rpoB and ITS sequences. rpoB gene sequence (301 bp) of SMC-7485 showed that it was the closest to M. senegalense ATCC 35796 [GenBank: AF057483], i.e. 99.7% similarity. The species showing the next highest similarities were M. porcinum, Mycobacterium wolinskyi, Mycobacterium goodii, and M. septicum (96.9% – 99.0%). On the other hand, rpoB gene sequence of M. farcinogenes DSM 43637 [GenBank: AY544910] was diverged from that of SMC-7485 (94.7% similarity). ITS sequence (327 bp) analysis also suggested that the strain SMC-7485 belonged to M. senegalense. The strain SMC-7485 showed an identical ITS sequence with S. senegalense MF-417 [GenBank: AY684051]. In addition, ITS sequence of SMC-7485 showed the similarities of more than 90% with those of several M. senegalense strains deposited in GenBank database. Although ITS sequences of some M. senegalense strains such as ATCC 35796 and NCTC 10956 showed low similarities (80.9%) with that of SMC-7485, they were clearly differentiated from M. farcinogenes and other Mycobacterium species (Fig. 1). Based on 16S rRNA gene, rpoB gene, and ITS sequences, we could identify SMC-7485 as M. senegalense.
Figure 1 Phylogenetic relationships of SMC-7485 and other Mycobacterium species based on ITS sequences, which were retrieved from GenBank database. This tree was generated by the neighbor-joining method. Mycobacterium vaccae DSM 43292 was used as an outlier. Numbers at branching nodes are percentages of 1,000 bootstrap replications. Only values greater than 50% are shown. In this tree, M. senegalense strains are separated into two subgroups.
The nucleotide sequences of the 16S rRNA gene, rpoB, and ITS of the strain SMC-7485 have been deposited in the GenBank database under accession numbers DQ145802 to DQ145804.
Discussion
Rapidly growing mycobacteria such as the M. fortuitum group, the M. chelonae/abscessus group, and the M. smegmatis group are capable of thriving in even the most hostile environments [10]. Due to their ubiquitous capability, human infections by the rapidly growing mycobacteria have been identified with increasing frequency worldwide. Especially, rapidly growing mycobacteria are being recognized as one of the significant pathogens of catheter-related infections in immunocompromised hosts. Among rapidly growing mycobacteria, the M. fortuitum group is the most common mycobacterial pathogen for this clinical condition [10,11].
The M. fortuitum group included M. fortuitum, M. peregrinum, M. mucogenicum, M. senegalense, M. mageritense, and several newly described species such as M. septicum, M. houstonense, M. boenickei, M. neworleansense, and M. brisbanense [12]. Of these, M. senegalense was originally described by Chamoiseau in 1973 as a subspecies of M. farcinogenes. Although M. farcinogenes and M. senegalense have identical 16S rRNA gene sequences, M. senegalense could be identified as a different species based on differences in growth rate, chemical activity and DNA homology [1,6,13,14]. While most species of M. fortuitum group have been reported to be responsible for various human diseases, human infection by M. senegalense has not been described to date [3,10,12]. Instead, it causes the chronic infectious disease of zebu cattle known as bovine farcy, endemic to East and Central Africa [3,9]. Moreover, M. senegalense, which was originally found in Africa, has never been described elsewhere [3,10].
In this report, we have first documented the CVC infection caused by M. senegalense. Because conventional automated methods failed to identify it at the species level, we tried to sequence 16S rRNA gene, rpoB gene, and ITS region. By 16S rRNA gene, rpoB gene, and ITS sequence analyses, we concluded that an agent of CVC infection in our patient was M. senegalense. rpoB gene and ITS sequences could differentiate M. senegalense from M. farcinogenes clearly as in previous reports [6,14]. Moreover, ITS sequence analysis indicated that M. senegalense might consist of at least two heterogeneous groups (Fig. 1). There is possibility that M. senegalense isolate related to human infection has been misidentified as different species because it is difficult to identify nontuberculous mycobacteria (NTM) at the species level [9,16]. However, the strain SMC-7485 is the first described M. senegalense isolate, which is associated with human infection and is found outside Africa, to our knowledge.
In most cases with mycobacterial infection of CVC, the line should be removed for successful control of infection [17]. In this study, the patient failed to respond to an initial regimen of imipenem and amikacin, to which the isolate was susceptible. Persistent infection was controlled after removal of catheter, which emphasized the importance of catheter removal. Because of differences in susceptibilities among species and even within species, rapid identification and subsequent susceptibility testing are essential for selection of appropriate antibiotic agent(s) against rapidly growing mycobacteria [18]. In this study, the strain SMC-7485 was susceptible to amikacin, cefoxitin, ciprofloxacin, clarithromycin, doxycycline, and imipenem. Because of the high frequency of relapse and resistance, combination therapy with multiple antibiotics is usually recommended for serious infections by rapidly growing mycobacteria. However, the optimal antibiotic regimen has yet to be defined for catheter-related infection by these mycobacteria. In our experience, oral antibiotic therapy subsequent to a short course of intravenous antibiotics seemed to be effective and safe. Although it was not possible to determine the optimal duration of antibiotic therapy in our case, this episode was successfully treated with short-term (approximately 5 weeks) antibiotic therapy. Further studies will be required to confirm these findings.
Conclusion
In this paper, we firstly reported catheter-associated bacteremia by M. senegalense. This case suggested that M. senegalense can cause human infections. This pathogen should be included in the list of nontuberculous mycobacteria causing human infections.
List of abbreviations
CVC – central venous catheter
ITS – internal transcribed spacer
MIC – minimum inhibitory concentration
NCCLS – National Committee for Clinical Laboratory Standards
NTM – nontuberculous mycobacteria
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
WSO, SYR, KTK, and STH followed up the patient and obtained consent from the patient for this case report. WSO, KRP, and NYL provided clinical details. KSK performed the molecular identification and phylogenetic analysis, and KSK, MYL, and JHL executed antimicrobial susceptibility testing. WSO, KSK, and JHS drafted 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 thank Dr. Won-Jung Koh (Samsung Medical Center, Sungkyunkwan University School of Medicine) for his helpful comment on this study. This work was partly supported by the ARFID (Asian-Pacific Research Foundation for Infectious Diseases).
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Chamoiseau G Etiology of farcy in African bovines: nomenclature of the causal organism Mycobacterium farcinogenes Chamoiseau and Mycobacterium senegalense (Chamoiseau) comb. nov Int J Syst Microbiol 1979 29 407 410
Ridell M Goodfellow M Numerical classification of Mycobacterium farcinogenes, Mycobacterium senegalense and related taxa J Gen Microbiol 1983 129 599 611 6875513
Pfyffer GE Brown-Elliott BA Wallace RJ Jr Murray PR, Baron EJ, Jorgensen JH, Pfaller MA, Yolken RH Mycobacterium: general characteristics, isolation, and staining procedures Mannual of Clinical Microbiology 2003 8 American Society for Microbiology, Washington, DC 532 59
NCCLS Susceptibility testing of Mycobacteria, Nocardiae, and other aerobic actinomycetes: approved standard. NCCLS document M24-A 2003 Wayne, Pennsylvania
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Ko KS Lee HK Park MY Park MS Lee KH Woo SY Yun YJ Kook YH Population genetic structure of Legionella pneumophila inferred from RNA polymerase gene (rpoB) and DotA gene (dotA) sequences J Bacteriol 2002 184 2123 2130 11914343 10.1128/JB.184.8.2123-2130.2002
Hamid ME Roth A Landt O Kroppenstedt RM Goodfellow M Mauch H Differentiation between Mycobacterium farcinogenes and Mycobacterium senegalense strains based on 16S-23S ribosomal DNA internal transcribed spacer sequences J Clin Microbiol 2002 40 707 711 11826003 10.1128/JCM.40.2.707-711.2002
Brown-Elliott BA Wallace RJ Jr Clinical and taxonomic status of pathogenic nonpigmented or late-pigmented rapidly growing mycobacteria Clin Microbiol Rev 2002 15 716 746 12364376 10.1128/CMR.15.4.716-746.2002
Levendoglu-Tugal O Munoz J Brudnicki A Ozkaynak MF Sandoval C Jayabose S Infections due to nontuberculous mycobacteria in children with leukaemia Clin Infect Dis 1998 27 1227 1230 9827274
Schinsky MF Morey RE Steigerwalt AG Douglas MP Wilson RW Floyd MM Butler WR Daneshvar MI Brown-Elliott BA Wallace RJ JrNcNeil MM Brenner DJ Brown JM Taxonomic variation in the Mycobacterium fortuitum third biovariant complex: description of Mycobacterium boenickei sp. nov., Mycobacterium houstonense sp. nov., Mycobacterium neworleansense sp. nov., and Mycobacterium brisbanense sp. nov. and recognition of Mycobacterium procinum from human clinical isolates Int J Syst Evol Microbiol 2004 54 1653 1667 15388725 10.1099/ijs.0.02743-0
Ridell M Goodfellow M Minnikin DE Minnikin SM Hutchinson IG Classification of Mycobacterium farcinogenes and Mycobacterium senegalense by immunodiffusion and thin-layer chromatography of long-chain components J Gen Microbiol 1982 128 1299 1307 6811691
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Schinsky MF McNeil MM Whitney AM Steigerwalt AG Lasker BA Floyd MM Mycobacterium septicum sp. nov., a new rapidly growing species associated with catheter-related bacteremia Int J Syst Evol Microbiol 2000 50 575 581 10758863
Short WR Emery C Bhandary M O'Donnell JA Misidentification of Mycobacterium peregrinum, the causal organism of a case of bacteremia and automatic implantable cardioverter defibrillator-associated infection, due to its unusual acid-fast staining characteristics J Clin Microbiol 2005 43 2015 2017 15815048 10.1128/JCM.43.4.2015-2017.2005
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-6-671630955010.1186/1471-2202-6-67Research ArticleCallosal connections of dorsal versus ventral premotor areas in the macaque monkey: a multiple retrograde tracing study Boussaoud Driss [email protected]é-Gariépy Judith [email protected] Thierry [email protected] Eric M [email protected] Institut de Neurosciences Cognitives de la Méditerranée, INCM, UMR 6193, CNRS, Université de la Méditerranée, 31 Ch. Joseph Aiguier, 13402 Marseille Cedex 20, France2 Unit of Physiology and Program in Neurosciences, Department of Medicine, University of Fribourg, Rue du Musée 5, CH-1700 Fribourg, Switzerland3 Brain Research Institute, Dept. Neuromorphology, University and ETH Zurich, Winterthurerstr. 190, CH-8057 Zürich, Switzerland2005 25 11 2005 6 67 67 2 2 2005 25 11 2005 Copyright © 2005 Boussaoud et al; licensee BioMed Central Ltd.2005Boussaoud 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 lateral premotor cortex plays a crucial role in visually guided limb movements. It is divided into two main regions, the dorsal (PMd) and ventral (PMv) areas, which are in turn subdivided into functionally and anatomically distinct rostral (PMd-r and PMv-r) and caudal (PMd-c and PMv-c) sub-regions. We analyzed the callosal inputs to these premotor subdivisions following 23 injections of retrograde tracers in eight macaque monkeys. In each monkey, 2–4 distinct tracers were injected in different areas allowing direct comparisons of callosal connectivity in the same brain.
Results
Based on large injections covering the entire extent of the corresponding PM area, we found that each area is strongly connected with its counterpart in the opposite hemisphere. Callosal connectivity with the other premotor areas, the primary motor cortex, prefrontal cortex and somatosensory cortex varied from one area to another. The most extensive callosal inputs terminate in PMd-r and PMd-c, with PMd-r strongly connected with prefrontal cortex. Callosal inputs to PMv-c are more extensive than those to PMv-r, whose connections are restricted to its counterpart area. Quantitative analysis of labelled cells confirms these general findings, and allows an assessment of the relative strength of callosal inputs.
Conclusion
PMd-r and PMv-r receive their strongest callosal inputs from their respective counterpart areas, whereas PMd-c and PMv-c receive strong inputs from heterotopic areas as well (namely from PMd-r and PMv-r, respectively). Finally, PMd-r stands out as the lateral premotor area with the strongest inputs from the prefrontal cortex, and only the PMd-c and PMv-c receive weak callosal inputs from M1.
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Background
The motor cortex of macaques is divided into four main regions: the primary motor cortex (M1), the premotor cortex (PM), the supplementary motor area (SMA) and the cingulate motor area (CMA). These regions have been subdivided further into distinct areas on the basis of anatomical and functional criteria. In the PM region, the dorsal (PMd) and ventral (PMv) areas have been distinguished on anatomical, histochemical and neurophysiological ground [1-6]. More recently, PMd and PMv have been proposed to contain distinct functional areas along the rostro-caudal axis, referred to as PMd-r, PMd-c, PMv-r and PMv-c [5,7-9]. They correspond roughly to areas F7, F2, F5 and F4, respectively, in the nomenclature of Matelli and his co-workers [10-14]. Similarly, SMA has been subdivided into a rostral part (pre-SMA) and a caudal part (SMA-proper) [15], also referred to as F6 and F3, respectively [10,11,13,14,16]. Finally, three areas have been identified within the CMA on the basis of corticospinal projections [17]: a rostral area (CMA-r) and two caudal areas, one dorsal (CMA-d) and one ventral (CMA-v). These multiple subdivisions are illustrated in Figure 1.
Figure 1 Premotor areas represented on a two-dimensional map of the cortex. On the left, surface view of the anterior part of the right hemisphere. The rectangle indicates the cortical region flattened and shown on the right. On the 2-D map, sulci are represented by shaded zones, the dashed lines indicate the fundus of the sluci. The premotor subdivisions are defined on the basis of SMI-32 staining (see text). Abbreviations: Ar, arcuate sulcus; Ce, central sulcus; CgG, cingulate gyrus; Ci, cingulate sulcus; CMA-d, r and v, dorsal rostral and ventral parts of the cingulate motor area, respectively; P = sulcus principalis; M1, primary motor cortex; PMd-c, r, caudal and rostral parts of the dorsal premotor cortex; PMv-c, r, caudal and rostral parts of the ventral premotor cortex; pre-SMA, rostral part of the SMA; SMA-proper, caudal part of the SMA.
The ipsilateral connections of these motor cortical areas with the other cortical areas have been extensively studied since many years, with renewed interest in recent years especially with respect to the posterior parietal cortex [1,7,10-12,18-60]. By contrast, callosal connections of most premotor areas have attracted less attention, despite their importance for understanding inter-hemispheric exchange of information necessary for coordinated actions of the two sides of the body [61]. It is thus of interest to know how each premotor area connects with the opposite hemisphere in terms of topography and strength of the connections. Previous studies have described the callosal connectivity of M1 and SMA-proper [31,61,62]. They have shown that the hand area of M1 receives a minor callosal input from its counterpart in the other hemisphere, whereas the hand area in SMA-proper is more densely interconnected with the other hemisphere. More recently, Liu et al. [63] have contrasted the callosal connections of SMA-proper and pre-SMA and found that the two areas share common callosal inputs but the strength of the connections differs, with pre-SMA more heavily connected with the opposite hemisphere.
Callosal connectivity of the other premotor areas has been less investigated. Only one recent study [64] has described the callosal connections of the rostral and caudal dorsal premotor areas (PMd-r and PMd-c, corresponding to the areas F7 and F2, respectively), whereas those of ventral premotor and cingulate motor areas are still lacking. We performed an extensive multiple tracing investigation of callosal connections of the lateral premotor areas, with emphasis on the distinction between dorsal (PMd) and ventral (PMv) sectors as well as the comparison between their rostral and caudal divisions. We compared data obtained from two groups of animals. In the first group, large injections of 3–4 tracers were performed in each animal (n = 3) filling in most, if not the whole, extent of the PM sub-areas. In a second group of animals (n = 5), we performed smaller injections in the PM sub-areas for comparison with other studies. The first group of animals was used to describe a fairly exhaustive picture of the origin of the callosal projection to the four sub-areas of PM, including the issue of overlap/segregation of the different projections, whereas more precise topographic aspects are described based on the second group of animals.
Results
Injection sites
The locations of the injection sites were confirmed on histological criteria. Figure 2 shows the reconstruction of each injection site on surface views of the brain hemisphere. Each monkey received 2–4 distinct tracers injected in different PM areas. As the figure shows, the injection sites varied in size and location within each PM sub-area and, sometimes, encroached on an adjacent area (see also Table I). A particular protocol was conducted in Mks 1–3 in order to obtain large injection sites covering most of the injected PM sub-area. Examples of such large injection sites are shown on photomicrographs (Fig. 4), following injections of BDA, DY, FB and CB. The injections were performed in such a way (usually at 2 depths along each penetration) to form a cylinder covering all cortical layers, from the surface down to the limit between the grey and the white matter.
Figure 2 Reconstruction of the injection sites on a lateral view of the left hemisphere of the 8 monkeys included in the present study. Tracers: biotinylated dextran amine (BDA), diamidino yellow (DY), fast blue (FB), fluoro ruby (FR), cholera-toxin B subunit (CB). For other abbreviations, see Fig. 1.
Figure 3 Photomicrographs showing SMI-32 staining observed in Mk2 or Mk3 illustrating transition zones between the prefrontal cortex (Pfc) and PMv-r (panel A), between PMd-c and M1 (panel B), between PMv-r and ProM (panel C) and PMv-c and SomC (panel D). See list of abbreviations. Scale bar = 1 mm.
Table 1 List of tracers injected in PM in Monkeys Mk1 – Mk8, with indications on the total volume injected for each tracer, the number of penetrations and sites of infusions.
PMd-r PMd-c PMv-r PMv-c
Mk1 CB FB BDA DY
x (2.7 μl,5,9) x (7 μl,7,7) x (9 μl,6,9) x (16 μl,8,10)
Mk2 BDA DY CB FB
x (20 μl,10,20) x (12 μl,6,12) x (3.9 μl,7,13) x (12 μl,6,12)
Mk3 CB FB BDA
x (2.5 μl,3,5) x (9 μl,7,9) x (6 μl,6,6)
Mk4 DY CB FB
x (0.4 μl,2,4) x (7.5 μl,2,4) x (0.8 μl,2,4)
Mk5 FB DY
W, E (0.4 μl,2,4) D, W (0.6 μl,2,4)
70–80 μA 50 μA
Mk6 DY FR FB (M1)
NE (0.4 μl,2,4) NE (1 μl,2,4) F (0.8 μl,2,4)
Mk7 DY FB
E, S (0.6 μl,2,4) NE (8 μl,2,4)
75 μA
Mk8 FB DY
Eyes (0.8 μl,4,8) F (0.8 μl,4,4)
10–20 μA 12–80 μA
x = No ICMS performed. NE: non-excitable site with ICMS.
Conventions for ICMS: D = Digits; E = Elbow; F = Face; S = Shoulder; W = wrist.
Tracers: biotinylated dextran amine (BDA), diamidino yellow (DY), fast blue (FB), fluoro ruby (FR), cholera-toxin B subunit (CB). Arrows means that the injection site encroaches the adjacent area pointed by the arrow. (Ml) indicates that the injection site in PMd-c or PMv-c encroaches Ml.
Below each tracer, the three numbers between parentheses give the total volume injected, the number of syringe penetrations and the total number of sites where the tracer was infused.
Figure 4 Photomicrographs of typical injection sites for BDA (left column) and for DY, FB and CB (from top to bottom in the right column). Scale bar = 1 mm.
In the following sections, we will describe the callosal labelling based on representative examples of the results obtained in MK1 and MK2 (Fig. 5 and 6). Individual variability is illustrated in figure 8 for 11 cases, and additional tables [Additional file 1] and a figure [Additional file 2] are presented as Supplementary material. In Figures 5 and 6, we have chosen to superimpose the labelling from 4 different tracers for comparison reasons.
Figure 5 Frontal sections of the right hemisphere of Mk1, arranged from rostral to caudal with increasing ID# (10 to 54), showing the distribution of retrogradely labelled neurons as a result of tracers injections in the opposite PMd-r (red dots), PMv-r (grey dots), PMd-c (blue dots) and PMv-c (green dots). The tracers used are indicated in the bottom right. Tracers: biotinylated dextran amine (BDA), diamidino yellow (DY), fast blue (FB), cholera-toxin B subunit (CB). See list of abbreviations.
Figure 6 Frontal sections of the right hemisphere of Mk2, showing the distribution of retrogradely labelled neurons as a result of tracers injections in the opposite PMd-r (red dots), PMv-r (grey dots), PMd-c (blue dots) and PMv-c (green dots). Same conventions as in Figure 5.
Injections in PMd-r
Five injections were made into PMd-r (Table I). Figures 5 and 6 (red dots) illustrate the distribution of retrogradely labelled cells in the hemisphere contralateral to the injection site following large injections into PMd-r of two monkeys. As the figures show, PMd-r receives it main callosal projections from the premotor areas anterior to the level of the genu of the arcuate sulcus, and from prefrontal cortex. Within this region, labelling was assigned to PMd-r, Pfc dorsal to the principal sulcus extending to the cingulate sulcus, pre-SMA and rostral cingulate cortex (CMA-r). In the cortex located laterally or caudally to the level of the genu of the arcuate sulcus, labelling was sparse or limited to small patches (Pfc ventral to the principal sulcus, PMv-r, PMd-c, CMA-v). This general pattern of transcallosal labelling was consistent with the data derived from a smaller injection of DY in PMd-r in Mk 6 (see Fig. 7, green patches), although the labelling was less extensive.
Figure 7 Distribution of labelling after 3 injections in Mk6 illustrated on a 2-D map of the frontal cortex. Same abbreviations and conventions as in Fig. 1.
Figure 8 A: quantitative data giving the percent distribution of callosal neurons observed in different cortical areas as a result of tracer injections made in PMd-r (top left plot), PMd-c (top right plot), PMv-r (bottom left plot) and PMv-c (bottom right plot). The percent values are given by different symbols for each of the three individual monkeys included in the quantitative analysis (Mk1, Mk2 and Mk3). For a given monkey, the sum of the percent values is 100%. B: for comparison, same data, but for the distribution of callosal neurons projecting to pre-SMA and SMA-proper for other monkeys (taken from [63]).
Injections in PMd-c
Seven injections were made in PMd-c in 7 animals (Table I). Figures 5 and 6 illustrate the distribution of labelling on coronal sections (blue dots), and figure 7 shows the data for monkey Mk6 on a 2-D map of the cortex. As after injections in PMd-r, injections in PMd-c yielded extensive labelling in the dorso-medial frontal cortex of the contralateral hemisphere. The main difference is that here, the labelling was relatively more caudal than following injections in PMd-r (compare red and blue dots). Analysis of the distribution of labelled cells in relation with areal borders shows that the strongest labelling was located in PMd-c, PMd-r and pre-SMA. Moderate or weak labelling was also found in the cingulate motor areas (CMA-r, CMA-v and CMA-d), SMA-proper and M1. The general pattern of labelling was the same in Mk3 where large injections were made, except that the labelling was more predominant in PMd-c than in PMd-r (see Fig. 8A and supplementary material). The results following a small injection in PMd-c are shown in Fig. 7, and they confirm the main observations made on the basis of large injection. The main difference is that the labelling was less extensive in rostral PMd-r following a small injection.
Injections in PMv-r
Six injections were made in PMv-r of 6 different animals (Table I). The key finding is that following these injections, most labelled cells in the contralateral hemisphere were found in the cortex located just behind the inferior arcuate sulcus, anterior to the level of the genu (sections 14–22 in Fig. 5 and 6, grey dots), which corresponds to the counterpart area PMv-r. As one moves anteriorly or posteriorly, the dense labelling in PMv-r moves ventrally, forming a long stripe within the bank of the lateral sulcus (Fig. 5 and 6; see also supplementary figure). At its caudal aspect, this labelling is probably in area S2. Additional labelling was found in pre-SMA, CMA-r and ventral Pfc (Fig. 6). Note that labelling was observed in dorsal premotor areas (Fig. 6, section 22), but this projection was not confirmed in the other cases with similar injections. Finally, there was no labelling in PMv-c, i.e. behind the genu.
Injections in PMv-c
Five injections were made in PMv-c (Table I). These injections gave rise to strong labelling in the contralateral frontal areas, with the core of labelling in PMv-c and PMv-r in all cases. Figures 5 and 6 illustrate two representative examples (green dots). As in the cases with injections in PMv-r, callosal labelling following injections in PMv-c is located mainly lateral to the genu of the arcuate sulcus and in mesial cortex. The most extensive labelling was found in the ventral premotor region (including both PMv-r and PMv-c), where it spanned the cortex caudal and anterior to the level of the genu of the arcuate sulcus (sections 18–38 in Fig. 5 and 6). Weak labelling was found consistently in pre-SMA, CMA-r and M1, and in some cases in PMd-r, SMA-proper, CMA-v and CMA-d and PMd-c (see Fig. 8).
Comparison between PMd and PMv
The present study allowed a direct comparison between the callosal connections of the four premotor areas investigated. Comparison can be made directly on coronal sections in figures 5 and 6 (two monkeys with large injections of 4 tracers each) and on a 2D map of the cortex in figure 7, in monkey Mk6 where we made small injections of 3 tracers (see also Fig. 8).
It appears that, at a gross level, callosal projections to dorsal and ventral premotor sectors are organized along both the rostro-caudal and the medio-lateral axes (Fig. 5 and 6). Along the rostro-caudal axis, injections in rostral sectors (PMd-r and PMv-r) tend to yield stronger labelling in rostral frontal areas of the opposite hemisphere, i.e. anterior to the level of the genu of the arcuate sulcus (see for example Fig. 6; red and grey dots). Similarly, large injections in the caudal sectors (PMd-c and PMv-c) resulted in strong callosal labelling in caudal frontal areas (blue and green dots), with however, important labelling in rostral regions overlapping with the projections to rostral sectors. This might be due to the large size of the injection sites, as small injections into PMd-c and PMv-c (Fig. 7) led to less overlap. A similar pattern of labelling is also observed following injections into PMv-c (green dots in Fig. 5 and 6). Figure 8A shows the percentage of cells in different areas, organized a rostral and a caudal group.
Superimposed to this trend, the organisation of callosal projections along the medio-lateral axis is even more striking. Figures 5 and 6 show that callosal inputs to PMd arise almost exclusively from the dorso-medial regions (red and blue dots), whereas those to PMv originate predominantly from lateral regions (green and grey dots), with small zones of overlap in pre-SMA, the cingulate motor areas, the border between PMd-c and PMv-c, and the most medial part of PMd-r. Figure 7 illustrates more clearly the topography of callosal labelling after three small injections in monkey Mk6.
Quantitative analysis
A quantitative analysis was conducted on data from 11 injections in 3 monkeys (Mks 1–3) following the procedure described in the Methods section. This procedure provided a numerical estimate of the contribution of each area to the overall callosal afferent connectivity of PMd-r, PMd-c, PMv-r and PMv-c. The results of this analysis are represented graphically for each monkey in Figure 8A (see also [Additional file 1
]).
Figure 8A shows the variability across the animals with large injections of tracers in the four premotor sectors (Mks 1–3). The variability appeared most prominently for the percentage of the homotopic callosal projections, which ranged from 29 to 40% for PMd-r (in 2 monkeys), from 43 to 81% for PMv-r, from 16 to 23% for PMd-c and from 14 to 19% for PMv-c. For the two latter divisions, the variability was larger for the percentage of the inputs coming from the heterotopic contralateral PMv-r (Fig. 8A). Despite this variability, it appears that PMd-r and PMv-r receive most of their callosal inputs from rostral frontal areas, especially for PMd-r; Inputs from caudal regions are weak or absent. By contrast, PMd-c and, to a lesser extent, PMv-c receive inputs from both rostral and caudal areas.
Figure 8B compares the results of the present study with those previously reported for pre-SMA and SMA-proper [45], using the same analysis of percent contribution of contralateral frontal areas. It appears that the variability observed here is in the same order of magnitude as that observed among 4 monkeys with injections in pre-SMA and SMA-proper.
Discussion
We found that the four premotor areas receive homotopic callosal connections and have distinct patterns of callosal inputs from heterotopic areas. Based on both qualitative and quantitative analyses, the overall results (schematically illustrated in Fig. 9) can be summarized as follows: (1) Callosal inputs to PMd and PMv are organized along a medio-lateral axis; (2) Ventral premotor sectors receive callosal afferents from a limited number of contralateral frontal areas, whereas the dorsal sectors receive inputs from a larger set of areas; (3) The strongest callosal inputs to rostral sectors (PMd-r and PMv-r) were always found to originate from homotopic regions, irrespective of the size of the injection sites and the tracer injected. However, the results following injections into PMv-c and PMd-c varied depending on the size of injections. Small injections yielded preferential labelling in homotopic areas, whereas large injections tended to results in strong labelling in rostral sectors as well; (4) PMd-r stands out as the lateral premotor area with the strongest inputs from the prefrontal cortex, extending from the principal sulcus to cingulate sulcus; (5) caudal sectors (PMd-c and PMv-c) receive weak callosal projections from M1, which does not project to the rostral sectors. We will discuss these findings in relation with previous studies and their functional implication, and address two issues that might affect our interpretations and conclusions, the size of the injection sites and the definition of the borders between areas.
Figure 9 Summary diagram of callosal projections from premotor areas and prefrontal cortex to dorsal and ventral premotor sectors. Thin lines depict strong to moderate projections, dotted lines represent weak projections.
Relation to previous studies
Ipsilateral connections of premotor and motor cortex gained a tremendous interest in recent years, but callosal connectivity has received less attention, with the exception of M1 and the SMA which were extensively studied. It was found that callosal afferents to M1 and SMA depend on the somatotopic organization, namely with the hand area of M1 receiving much less callosal projections than proximal territories [62]. With the identification of finer subdivisions within the non primary premotor cortex, it is important to examine callosal connectivity of each discrete area in order to advance our understanding of their respective function. Of particular interest is the comparative approach in the same animal, where the spatial distribution of the callosal projecting neurons and their respective contribution to the projection can be directly compared. Two recent studies have adopted such an approach by making injections of two distinct tracers, one in each area, in the same brain. One has compared the callosal afferents of pre-SMA and SMA-proper [63], the other compared those of PMd-r and PMd-c [64]. Both studies reported that each of these premotor areas receives callosal inputs primarily from its counterpart area in the opposite hemisphere and, additionally, from other areas of the frontal cortex.
In the present study, we made comparisons along two axes within the lateral premotor cortex, the rostro-caudal axis (rostral versus caudal regions) and the medio-lateral axis (dorsal versus ventral areas). We found that the general pattern of callosal connectivity described previously holds true, with however some surprising observations which we discuss later in this section. Indeed, we found that the strongest callosal projections to PMd-r and PMv-r arise from their counterpart areas, as was reported for PMd-r [64], pre-SMA and SMA-proper [63], irrespective of the size of the injections. However, unexpectedly this was not systematically the case for PMd-c and PMv-c (see Fig. 8A), which were found to receive their strongest callosal inputs from the rostral sub-regions, i.e. from PMd-r and PMv-r, respectively. This result contrasts with those of Marconi et al. [64] regarding PMd-c, which they reported to receive most of its inputs from its contralateral counterpart. Whether this discrepancy is due to technical differences or the location of the injection sites in the two studies is not clear. One likely cause may be the size of the injections, which were much bigger in our Mks 1–3 than in the 2 monkeys in the study of Marconi et al. [64]. This interpretation is supported by our data in cases with small injections in PMd-c and PMv-c (e.g. Mk6), showing a majority of labelled neurons in the counterpart area on the opposite hemisphere.
As in the case of pre-SMA and SMA-proper [61,63], additional callosal projections to the four subdivisions of PM were found to arise from a number of heterotopic areas. Interestingly, the strength and topography of callosal connections were found to vary along the antero-posterior axis. Our findings indicate that, if all projections are taken into account (see Fig. 9 and supplementary material), caudal areas (PMd-c and PMv-c) receive inputs from a larger set of areas than rostral ones (PMd-r and PMv-r), paralleled by a larger number of projecting cells. A similar result was reported by Marconi et al. [64] for PMd-r and PMd-c. Furthermore, the caudal divisions tend to be connected with caudal premotor areas of the opposite hemisphere including SMA-proper, dorsal and cingulate motor areas (CMA-v, CMA-d). Callosal projections from M1 were rarely observed, and were weak. By contrast, the rostral divisions receive inputs from rostral premotor areas, such as pre-SMA and CMA-r, and from prefrontal cortex but do not receive projections from M1. This general principle was also described for pre-SMA versus SMA-proper [63] and for PMd-r versus PMd-c [64]. In particular, our observations regarding inputs from M1 are compatible with the findings of previous studies showing that contralateral M1 projects weakly to SMA-proper and PMd-c, but does not project to pre-SMA nor PMd-r [61,63,64,74,75]. Likewise, the present findings are in agreement with previous reports regarding the callosal inputs from prefrontal cortex to other premotor areas. Thus, it was found that pre-SMA and PMd-r receive inputs from contralateral prefrontal cortex, but not SMA-proper or PMd-c [63,64].
Finally, callosal projections to each area examined in the current study were found to arise from largely segregated populations of cells, but this segregation was much more striking between cells projecting to dorsal versus ventral sectors (see Figs. 6 and 7), than between rostral versus caudal sectors. In fact, following injections of different tracers, one in either PMd-r or PMd-c the other in PMv-c or PMv-r, large cortical regions contained neurons that were labelled with only one tracer. The zones of co-existence of cells labelled with one or the other tracer were limited to medial premotor areas. Despite this co-existence, a fine examination indicates that the two cell populations were organised in separate patches. The situation is somewhat different for the comparisons between PMd-c and PMd-r on one hand, and between PMv-c and PMv-r on the other hand. Callosal cells projecting to PMd-c and those projecting to PMd-r co-exist within several areas with the strongest overlap in PMd-r, pre-SMA and CMA-r. Cells projecting to PMv-c and those projecting to PMv-r co-exist within PMv-r, CMA-r and to a limited extent in pre-SMA. These findings suggest that dorsal and ventral premotor areas belong to separate inter-hemispheric circuits, but their respective subdivisions belong to partly overlapping anatomical systems.
Callosal and ipsilateral connectivity of dorsal and ventral premotor areas: a gradient between prefrontal cortex and motor cortex
It is important to examine callosal and ipsilateral connectivity of the lateral premotor areas before speculating on possible functional implications of the present results. Ipsilateral cortical inputs to PMd and PMv have been the focus of recent anatomical studies (see Introduction for references). Despite slight discrepancies between the findings of these studies, there is a general agreement that projections that arise from parietal cortex and prefrontal cortex are organized along the two axes examined in the present study: the rostro-caudal and medio-lateral axes. Along the medio-lateral axis, it was found that parietal and prefrontal areas located dorsally and medially project to PMd-c and PMd-r, those located laterally project to PMv-r and PMv-c. Hence, PMd receives inputs from the dorsal aspect of dorsolateral prefrontal (DLPf) cortex [25,28,37,76] and from the posterior parietal cortex [29,41,45,47,53,54,75,77,78]. By contrast, PMv is connected with the ventral aspect of DLPf cortex and the inferior parietal lobule [12,47,54]. Along the rostro-caudal axis, it was shown, in particular, that areas located more caudally in the superior parietal lobule and the parieto-occipital sulcus project predominantly to rostral PMd, those located more anteriorly project mostly to caudal PMd. Some of these parietal areas that project to PMd-r are directly connected with extrastriate visual cortex and are involved in early visuo-motor transformations [78-80]; those that project to PMd-c are involved in somatosensory processing, and/or sensori-motor transformations [41,42,45,47,54]. On the other hand, PMd-c (but not PMd-r) projects to ipsilateral M1 and to the spinal cord [81,82]. The situation is less clear for PMv-c versus PMv-r in this respect. However, it is interesting to note that the general scheme where ipsilateral and callosal inputs converge remains valid. For example, inputs from the parietal lobe come from the second somatosensory area (S2), among other areas [41,54]. In the present study, we found callosal inputs to PMv-c and PMv-r from S2 (and to a much less extent from S1). Functionally, S2 and PMv may share sensorimotor signals involved in grasping objects [54].
Conclusion
In summary, lateral premotor areas that receive prefrontal inputs also receive projections from areas involved in early visuo-motor transformations; those that do not receive prefrontal inputs project to M1 and the spinal cord and receive projections from parietal areas involved in high order sensorimotor processing. It is known that rostral and caudal divisions are interconnected, supposedly allowing a functional gradient linking prefrontal cortex with motor cortex. This organization seems to hold true for callosal connectivity. Rostral divisions of lateral premotor cortex, especially PMd, receive callosal inputs from prefrontal cortex, but not from M1, and from their homotopic areas. Caudal divisions, by contrast, do receive inputs from M1, although weak, but have little inputs from prefrontal cortex. Furthermore, PMd-c and PMv-c receive strong callosal inputs from PMd-r and PMv-r, respectively.
Taken together, the anatomical data reviewed above suggest that the general principle of ipsilateral and callosal connectivity of premotor areas remains similar. This seems to argue that callosal connectivity provides similar but complementary information necessary for sensori-motor transformations and bimanual coordination. It is widely accepted that inter-hemispheric connections of motor areas are necessary for the execution of complex motor behavior that requires coordination of both limbs. As reviewed above and elsewhere [e.g. [8,84]], visuomotor information derived from the posterior parietal cortex may reach rostral premotor regions via ipsilateral projections, or indirectly through ipsilateral prefrontal cortex or through callosal fibers (present study; [75,83]). These regions play a key role in high order motor planning, and have projections to caudally adjacent areas, which in turn have direct input to M1 and the spinal cord and their neuronal activity correlates with the kinematics of limb movements. Their callosal connectivity might allow selection of which arm to use, as well as temporal and spatial coordination of bimanual movements. Weak callosal connections of the M1 hand area could reflect the high degree of lateralisation of its neuronal activity during movement execution [84]. By contrast, callosal interactions between premotor areas may convey high order information independent from body representation. In fact, the rostral PMd is involved in spatial attention (see [85] for review) and eye movements [67]. Interestingly, callosal projections to PMd-r sites where eye movements are represented do not differ from those that result from injections at other PMd-r sites. Furthermore, we noted that our injection at an eye movement-related site (Mk8, Table 1; not illustrated) did not lead to any callosal labelling in frontal regions where oculomotor areas would be expected to be located based on sulcal landmarks (e.g., the frontal eye fields on the anterior bank of the arcuate sulcus). This suggests that callosal connections of premotor areas investigated in this study mediate high order information necessary for action planning, independent of the motor effectors.
Methods
The data reported in this paper are based on 23 tracer injections made in eight macaque monkeys (3 Macaca fascicularis and 5 Macaca mulatta). Table I summarizes the location of these injections, the nature and amount of the tracers injected. Figure 2 shows their reconstructions on lateral views of the brain. The injections made in monkeys (Mks) 4–8 have been used to determine ipsilateral connections of premotor cortex [54], and those in Mks 1–3 for assessing the degree of overlap/segregation of thalamocortical projections to PM [65]. In addition, BDA injections in cases Mks 1–3 served for studying the corticothalamic projections of PM [66]. Twelve injections were made in PMd (5 in PMd-r and 7 in PMd-c); eleven injections were in PMv (6 in PMv-r and 5 in PMv-c). We used fluorescent tracers Fast Blue (FB), Diamidino-Yellow (DY) and Fluoro-ruby (FR), and the non fluorescent tracers Biotinylated Dextran Amine (BDA) and Choleratoxin B subunit (CB). Experimental procedures have been performed in accordance with the Guide for the Care and Use of Laboratory Animals (ISBN 0-309-05377-3; 1996) and approved by national veterinary authorities (Switzerland and France).
Surgery
The monkeys (aged 4–10 years and weighing 4–10 kg) were pre-anesthetized with ketamine (5 mg/kg, i.m.) and later deeply anesthetized with propofol (3 ml/kg/h; i.v.). The animals were then placed in a stereotaxic frame. Surgery was performed under aseptic conditions, and body temperature, heart and respiration rates, O2 blood saturation and expired CO2 were monitored during surgery. The skull was opened on one side in order to expose the premotor cortex and visualize the arcuate and central sulci. In Mks 1–4, injections in the PM subdivisions were guided visually based on sulcal landmarks (arcuate and central sulcus), taking the genu of the arcuate sulcus as the rostrocaudal limit between PMd-r and PMd-c as well as between PMv-r and PMv-c, as described earlier [63,65,67,68]. In Mks 5–8, the locations of the injections were in addition guided using intracortical microstimulation, as described earlier for the same animals [54]. Injections were made with a Hamilton syringe (5 or 10 μl) which was inserted perpendicularly to the cortical surface. At the end of the injections, the dura mater, muscles, and skin were sutured and the animals were treated for several days with analgesics (Vetalgin; 100 mg/kg, i.m. or Rymadil; 4 mg/kg, s.c.), and with an antibiotic (Ampiciline 10%; 30 mg/kg, i.m.). The animals survived for 2–3 weeks and were then deeply anesthetized with ketamine, followed by a lethal dose of sodium pentobarbital (Vetanarcol; 90 mg/kg, i.p.). Transcardiac perfusion with 500 ml of saline (0.9%) was followed by 3 litters of a solution of paraformaldehyde (4% in phosphate buffer 0.1 M, pH 7.6) and 2 litters of a solution of paraformaldehyde (4% in a 10% sucrose solution in phosphate buffer). The perfusion was then continued with 20 and 30% solutions of sucrose in phosphate buffer (2 and 1 litters, respectively). The brain was dissected into blocs, stored during 2–4 days in a solution of 30% sucrose, frozen, and cut in the frontal plane. Sections (50 μm thick) were collected in eight series. Two series of sections were immediately mounted on slides (without cover slip) and stored in the refrigerator for fluorescent microscopy analysis. The histological processing to visualize CB and BDA was described in detail in previous reports [66,69,70]. In Mks 4–8, DY, FB and FR labelled neurons were plotted on sections taken at 0.8 mm intervals, using the MicroBrightField Neurolucida System (Colchester, USA). In Mks 1–3, labelled neurons were plotted using a home made motorized microscope stage, as previously reported [66,69,70]. The DY, FB and FR labelled neurons were plotted on the same sections, whereas the non-fluorescent tracers BDA and CB were each plotted on two sections adjacent to the one analyzed for DY, FB and FR. For each tracer, plots were made every 1.6 mm for reconstruction and illustration purposes, but observation of the labelling was done at 0.8 mm intervals. When necessary, intermediate slides were used for a finer assessment of changes in labelling. Drawings with plots of labelled cells were then exported in the form of computer files formatted for later processing using the software CorelDraw 9. In Mks 1–3, the plots with CB and BDA were aligned and superimposed to the plots with FB and DY, allowing direct comparison of the 4 tracers on the same section (Figs. 5 and 6).
Definition of areal borders
In Mks 1–3, adjacent sections were processed for SMI-32 (Sternberger Monoclonal Inc., MD, USA), an antibody directed against a non-phosphorylated neurofilament protein labelling pyramidal cells in the cerebral cortex, according to the following protocol. Briefly, free-floating sections were first pre-incubated for 10 min in 1.5% H2O2 in phosphate-buffered saline (PBS; pH = 7.2) to remove endogenous peroxidase activity. Sections were rinsed several times in PBS, and then incubated overnight at 4°C in SMI-32 monoclonal antibody (dilution 1:3000), 2% normal horse serum and 0.2% triton-X-100. After several rinses, sections were incubated 30–60 minutes at room temperature in biotinylated secondary antibody (1:200, Vector Laboratories, Burlingame, CA) and stained with the avidin-biotin complex (ABC) immunoperoxidase method (Vectastain Elite kits, Vector Laboratories). The reaction was visualized with 3,3'-diaminobenzidine tetrahydrochloride (DAB) as the chromogen, diluted 0.05% in Tris-saline with 0.001% H2O2. Sections were then washed thoroughly and immediately mounted on gelatin-coated slides, dehydrated, and cover slipped. As a control, the primary antibody was omitted from the processing of some sections while the rest of the procedure remained the same. Another series of sections was stained for Nissl.
SMI-32 immunoreactivity provided reliable criteria to set the limit between PMd-c and PMv-c as shown in previous studies [9,47], and the limit between PM and prefrontal cortex or M1 [63,71]. Photomicrographs illustrating criteria based on SMI-32 were shown in a recent report [63] for the borders PMd-c/PMv-c, PMd-r/Pfc, SMA-proper/PMd-c, pre-SMA/PMd-r, SMA-proper/CMA-d and pre-SMA/CMA-r. Further examples of SMI-32 stained sections are shown in Figure 3 to illustrate the following limits based on SMI-32 immunoreactivity: Pfc/PMv-r, PMd-c/M1, lateral border of PMv-r with the promotor area (ProM, as defined by Paxinos et al. [72]) and the lateral border of PMv-c with the somatosensory cortex. Other borders were based on previously published work. For example, the limit between CMA-d and CMA-v corresponds to the fundus of the cingulate sulcus, based on the distribution of corticospinal neurons [17,73]. Nissl and SMI-32 criteria were also used to define the border between CMA and the region CgG of the cingulate gyrus. Nissl and SMI-32 reconstructions were digitized and aligned to the sections containing the plots of labelled neurons.
Quantitative analysis
The distribution of labeled neurons was analyzed quantitatively for 11 out of the 23 injections (in Mks 1–3). For each tracer, the labeled neurons were counted on all reconstructed sections. Then, the percentage of labeled neurons in each cortical area was calculated as the ratio of the number of labelled cells in that area to the total number of callosal labelled neurons for a given tracer injection. This procedure provided a numerical estimate of the contribution of each area to the overall callosal afferent connectivity of PMd-r, PMd-c, PMv-r and PMv-c (Fig. 8A; see also supplementary material).
List of abbreviations used
Ar = arcuate sulcus
BDA = biotinylated dextran amine
C = cerebral cortex
CB = cholera toxin B subunit
CC = corpus callosum
Cd = caudate nucleus
CE = central sulcus
CgG = cingulate gyrus
CIN = cingulate sulcus
Cl = claustrum
CMA-d = dorsal part of the cingulate motor area
CMA-r = rostral part of the cingulate motor area
CMA-v = ventral part of the cingulate motor area
DLPF = dorsolateral prefrontal cortex
DY = diamidino-yellow
FB = fast-blue
FR = fluoro-ruby
GP = globus pallidus
ICMS = intracortical microstimulation
P = sulcus principalis
M1 = primary motor cortex
Pfc = prefrontal cortex
PM = premotor cortex
PMd-c = caudal part of the dorsal premotor cortex
PMd-r = rostral part of the dorsal premotor cortex
PMv-c = caudal part of the ventral premotor cortex
PMv-r = rostral part of the ventral premotor cortex
pre-SMA = rostral part of the SMA
ProM = promotor area
Put = putamen
SMA = supplementary motor area
SMA-proper = caudal part of the SMA
SMI-32 = antibody directed against a nonphosphorylated neurofilament protein that labels pyramidal cells
SomC = somatosensory cortex
S1 = primary somatosensory cortex
S2 = secondary somatosensory cortex
Thal = thalamus
WGA = wheat germ agglutinin
WM = white matter
Authors' contributions
DB contributed to injections, histological processing, data collection and analysis of monkeys Mk4–8. He also drafted and revised the paper. JTG contributed to injections, histological processing, data collection and analysis in monkeys 4–8. TW contributed to injections, histological processing, data collection and analysis in Mks 1–3. EMR contributed to injections, histological processing, data collection and analysis of all 8 monkeys. He also contributed to drafting and revising the paper.
Supplementary Material
Additional File 1
Tables 2A, 2B and 2C. Quantitative analysis in Mk1, Mk2 and Mk3 respectively.
Click here for file
Additional File 2
Distribution of callosal labelling in monkey Mk3.
Click here for file
Acknowledgements
The authors wish to thank the technical assistance of Véronique Moret, Françoise Tinguely, Christine Roulin and Noelle Boyer-Zeller (histology), Josef Corpataux and Bernard Morandi (animal house keeping), André Gaillard (mechanics), Bernard Aebischer (electronics), Laurent Monney (informatics).
Funding: Swiss National Science foundation, grants No 31-43422.95, 4038-43918, 31-61857.00 (EMR) and the National Center of Competence in Research (NCCR) on "Neural plasticity and repair" (EMR). The European Union grant Biomed 2/n-BMH4-CT95-0789 for DB. JTG was supported by the French Ministry of Research and Education and by the Fondation Bettencourt Schuller.
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BMC PediatrBMC Pediatrics1471-2431BioMed Central London 1471-2431-5-441631645810.1186/1471-2431-5-44Research ArticleHow do caregivers know when to take their child for immunizations? Shaw Kate M [email protected] Lawrence E [email protected] National Immunization Program Centers for Disease Control and Prevention Atlanta, Georgia2005 29 11 2005 5 44 44 7 6 2005 29 11 2005 Copyright © 2005 Shaw and Barker; licensee BioMed Central Ltd.2005Shaw and Barker; 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
Childhood vaccinations help reduce and eliminate many causes of morbidity and mortality among children. The objective of this study was to compare 4:3:1:3:3 (4+ doses of diphtheria and tetanus toxoids and pertussis vaccine, 3+ doses of poliovirus vaccine, 1+ doses of measles-containing vaccine, 3+ doses of Haemophilus influenzae type b vaccine, and 3+ doses of hepatitis B vaccine) coverage among children whose caregivers learned by different methods when their child's most recent immunization was needed.
Methods
Between July 2001 and December 2002, a portion of households receiving the National Immunization Survey were asked how they knew when to take the child in for his/her most recent immunization. Responses were post-coded into several categories: 'Doctor/nurse reminder at previous immunization visit', 'Shot card/record', 'Reminder/recall', and 'Other'. Respondents could give more than one answer. Children who did not receive any vaccines, had ≤ 1 visits for vaccinations, or whose caregiver did not provide an answer to the question were excluded from analyses. Chi-square analyses were used to compare 4:3:1:3:3 coverage among 19–35 month old children.
Results
Children whose caregivers indicated that a doctor/nurse told them at a previous immunization visit when to return for the next immunization had significantly greater 4:3:1:3:3 coverage than those who did not choose the response (77.2% vs. 70.1%, p < 0.01). However, no significant difference in coverage was found between households that did/did not indicate that reminder/recalls (71.0% vs. 75.5%, p = 0.24) helped them remember when to take their child for their most recent immunization visit; only borderline significance was found between those that did/did not choose shot cards (70.6% vs. 76.2%, p = 0.07).
Conclusion
A doctor or nurse's reminder during an immunization visit of the next scheduled immunization visit effectively encourages caregivers to bring children in for immunizations, providing an inexpensive and easy way to effectively increase immunization coverage.
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Background
Caregivers learn when their children need immunizations in a variety of ways. For example, providers can remind caregivers, during an immunization visit, of the next scheduled immunization visit, or use reminder/recall systems. Less formally, caregivers can learn of needed immunization visits through relatives or friends, through daycare requirements, or other methods. To assess how the way caregivers know when their child needs immunizations impacts 4:3:1:3:3 (4 or more doses of diphtheria and tetanus toxoids and pertussis vaccine, 3 or more doses of poliovirus vaccine, 1 or more doses of measles-containing vaccine, 3 or more doses of Haemophilus influenzae type b vaccine, and 3 or more doses of hepatitis B vaccine) coverage, we examined data from a question administered to a random subsample of the households in the National Immunization Survey (NIS).
Methods
The NIS is a random-digit-dialing survey conducted annually by the Centers for Disease Control and Prevention to obtain vaccination coverage for the U.S. non-institutionalized population of children aged 19 to 35 months. To obtain vaccination information, a follow-up survey is mailed to all of the eligible children's immunization health providers [1]. The data were weighted to represent the sampling design, number of land-line telephones per house, provider response propensity, and a number of other factors. This makes the results nationally representative. Details about the design and weighting have been previously published [2,3].
Between July 2001 and December 2002, a random sample of 9,908 (from the 49,385 NIS respondents) were asked the open-ended question, "How did you know when to take your child for his/her most recent immunization?". We post-coded responses into four categories: (1) doctor/nurse told me at a previous immunization visit when to come back for the next shot, (2) shot card/record had schedule, (3) reminder/recalls (which included responses like: outreach worker called/came to house to tell me; health department called me/sent me reminder; and physician's office called me/sent me reminder), and (4) other (which included responses like: relative/friend told me; found out during visit to doctor or other health care provider; day care/headstart requirement; WIC nurse told me; government program requirement; and other infrequent responses). Respondents could give more than one answer. Children who did not receive any vaccines, had ≤ 1 visits for vaccinations, or whose caregiver did not provide an answer to the question were excluded from analyses. Sufficient data from providers was obtained from 7,810 (78.8%) of the respondents. Of those 7,687 (98.4%) were used in the analyses, including determination of 4:3:1:3:3 immunization status.
We used chi-square analyses to test for associations between methods of learning when the child needs immunizations (both overall and stratified by demographics) and immunization status. We conducted a stepwise logistic regression analysis to identify associations between the responses chosen and immunization, controlling for demographics. Candidate factors for the stepwise regression were: did/did not rely on each of the methods listed to determine when the child's last immunization was needed and child's race/ethnicity and birth order and maternal marital status, education level, age, poverty status, residence in a Metropolitan Statistical Area (MSA), time from last immunization to interview, and child's age at interview. Time from last immunization to interview and child's age at interview were added to the model to control for the effect of recall bias.
All estimates and standard errors were calculated using SAS release 8.02 (SAS, Cary, NC, 1999) and SAS-callable SUDAAN release 8.0.0 (RTI, RTP, NC, 2001), a software package designed to analyze complex survey data [4]. We conducted all statistical tests with two-tailed alternatives, α = 0.05.
Results
Results for the question "How did you know when to take your child for his/her most recent immunization?" appear in Table 1. Since respondents could give multiple answers, percents add to more than 100. Table 1 summarizes frequencies of responses and 4:3:1:3:3 coverage for those who did/did not choose each response and difference of these coverages. Coverage for those who responded 'doctor/nurse told at previous immunization visit' was significantly greater than that for those who did not so respond (77.2% vs. 70.1%, p < 0.01). No other significant differences were found (shot card, p = 0.07; reminder/recall, p = 0.24; other p = 0.86).
Table 1 How Caregivers Know When to Take Their Child for Immunizations and UTD 4:3:1:3:3a Coverage, United States, National Immunization Survey, Parental Knowledge and Experiences Questionnaire, July 2001 – December 2002.b
How did you know when to take your child for his/her most recent immunization? ChoseResponsec % (95% CI)d UTD4:3:1:3:3 Chose Response % (95% CI) UTD4:3:1:3:3 Did Not Chose Response % (95% CI) UTD 4:3:1:3:3 Difference % points (95% CI)
Doctor/Nurse at Previous Immunization Visit e 70.3 (68.3, 72.3) 77.2 (74.9, 79.5) 70.1 (66.0, 74.1) 7.1 (2.5, 11.8)
Shot Card/Record 19.5 (17.6, 21.3) 70.6 (65.2, 76.0) 76.2 (74.0, 78.3) -5.6 (-11.3, 0.2)
Reminder/Recallf 8.6 (7.4, 9.8) 71.0 (63.8, 78.1) 75.5 (73.4, 77.6) -4.5 (-11.9, 2.9)
Otherg 18.3 (16.7, 19.9) 75.4 (71.4, 79.5) 75.0 (72.7, 77.3) 0.4 (-4.3, 5.1)
a 4 or more doses of diphtheria and tetanus toxoids and pertussis vaccine, 3 or more doses of poliovirus vaccine, 1 or more doses of measles-containing vaccine, 3 or more doses of Haemophilus influenzae type b vaccine, and 3 or more doses of hepatitis B vaccine at 19–35 months of age
b Children in the Q3/2001–Q4/2002 National Immunization Survey were born between August 1998 and June 2001.
c Respondents could choose more than one response. Thus, total % of those who chose response will not add to 100%.
d Percents are weighted estimates.
e p-value < 0.01 for chi-square test comparing % UTD 4:3:1:3:3 who chose the reason and % UTD 4:3:1:3:3 who did not choose the reason
f Outreach worker called/came to house to tell me, health department called me/sent me reminder, or physician's office called me/sent me reminder
g Relative/friend told me, found out during visit to doctor or other healthcare provider, day care/headstart requirement, WIC (Women, Infants, Children) nurse told me, government program requirement, or other
The stepwise logistic regression (Table 2) retained the factor 'doctor/nurse at previous immunization visit' (adjusted odds ratio (AOR): 1.4, 95% CI: 1.0–1.8) and the demographics: child's birth order, maternal age, poverty status, MSA, time from last immunization to interview, and child's age at interview. Other ways of learning when to take a child for the most recent immunization visit were forced to remain in the model, but were not found to be significantly associated with immunization (shot card: AOR: 0.9, 95% CI: 0.7–1.2; reminder/recall: AOR: 1.1, 95% CI: 0.7–1.7; other: AOR: 1.2, 95% CI: 0.8–1.6). Because time from last immunization to interview and age at interview were significant factors, there appears to be some recall bias. However, since 'doctor/nurse at previous immunization visit' remained significantly associated with immunization after controlling from time and age we can conclude that recall bias and age are not the driving factors.
Table 2 How Caregivers Know When to Take Their Child for Immunizations. Adjusted Odds Ratios Predicting Up-to-date Status for 4:3:1:3:3 a, United States, National Immunization Survey, Parental Knowledge and Experiences Questionnaire, July 2001 – December 2002.b
Variable Adjusted Odds Ratio 95% CI
How did you know when to take your child for his/her most recent immunization? Doctor/Nurse at Previous Immunization Visit
Chosen c 1.4 (1.0, 1.8) d
Not Chosen 1.0 Referent
Shot Card/Record
Chosen 0.9 (0.7, 1.2)
Not Chosen 1.0 Referent
Reminder/Recall e
Chosen 1.1 (0.7, 1.7)
Not Chosen 1.0 Referent
Other f
Chosen 1.2 (0.8, 1.6)
Not Chosen 1.0 Referent
First Born Status
No 1.0 Referent
Yes 1.5 (1.2, 1.8)
Age of Mother
≤ 19 Years 1.0 Referent
20–29 Years 0.8 (0.4, 1.4)
≥ 30 Years 1.2 (0.6, 2.2)
Poverty Status g
Above, > $75 K 2.0 (1.4, 2.9)
Above, ≤ $75 K 1.6 (1.2, 2.1)
Below 1.0 Referent
Unknown 2.2 (1.5, 3.4)
Metropolitan Statistical Area (MSA) Status
MSA Central City 1.0 Referent
MSA Non-Central City 1.4 (1.1, 1.7)
Non-MSA 1.2 (0.9, 1.7)
Time from Last Immunization to Interview
≤ 6 months 2.8 (1.9, 4.1)
7 – 12 months 2.0 (1.5, 2.7)
≥ 13 months 1.0 Referent
Age of Child at Interview
19 – 24 months 0.3 (0.2, 0.4)
25 – 29 months 0.6 (0.5, 0.8)
30 – 35 months 1.0 Referent
a 4 or more doses of diphtheria and tetanus toxoids and pertussis vaccine, 3 or more doses of poliovirus vaccine, 1 or more doses of measles-containing vaccine, 3 or more doses of Haemophilus influenzae type b vaccine, and 3 or more doses of hepatitis B vaccine at 19–35 months of age
b Children in the Q3/2001–Q4/2002 National Immunization Survey were born between August 1998 and June 2001.
c Respondents could choose more than one response
d The lower bound was 1.01; the confidence interval (1.01, 1.8) does not contain 1.0.
e Outreach worker called/came to house to tell me, health department called me/sent me reminder, or physician's office called me/sent me reminder
f Relative/friend told me, found out during visit to doctor or other healthcare provider, day care/headstart requirement, WIC (Women, Infants, and Children) nurse told me, government program requirement, or other
g Poverty level depends on household income, year data was collected, and number of people living in household and is determined by the US Bureau of Census poverty threshold.
Table 3 displays relationships between demographics and method of learning when a child needed the most recent immunization. Several characteristics were significantly associated with responses. Among respondents with a household income below the federal poverty level, 12.0% (95% CI: 9.0–15.1%) chose 'reminder/recall', while 8.3% or fewer of the respondents in the other income categories did (income >$75 K: 6.4%, 95% CI: 4.2–8.6 %; above poverty level, income ≤ $75 K: 8.3%, 95% CI: 6.6–9.9%; unknown income: 6.8%, 95% CI: 3.4–10.2%). Of respondents who had a MSA status of 'non-MSA', 14.0% (95% CI: 10.9–17.1 %) chose 'reminder/recall', while 7.5% or fewer respondents in the other MSA status categories did (MSA central city: 7.1%, 95% CI: 5.6–8.6%; MSA non-central city: 7.5%, 95% CI: 5.6–9.4%). Mothers of age over 30 years (73.0%, 95%CI: 70.3–75.6%) and married mothers (72.5%, 95% CI:70.2–74.8%) were more likely to rely on a doctor/nurse at a previous immunization visit. We found no significant associations between response and child's race/ethnicity, time from last immunization to interview, and child's age at interview.
Table 3 How Caregivers Know When to Take Their Child for Immunizations and Demographic Characteristics of Respondents, United States, National Immunization Survey, Parental Knowledge and Experiences Questionnaire, July 2001 – December 2002.a
Characteristic (%) b Doctor/Nurse c % (95% CI) d Shot Card/Record % (95% CI) Reminder/Recall e % (95% CI) Other f % (95% CI)
Child's Race/Ethnicity
P-value 0.08 0.16 0.38 0.46
Hispanic (23.8) 69.4 (65.0, 73.8) 17.5 (13.9, 21.1) 6.9 (4.5, 9.2) 20.5 (16.4, 24.6)
Non-Hispanic, White (56.7) 71.7 (69.2, 74.3) 19.8 (17.3, 22.3) 8.9 (7.3, 10.5) 17.8 (15.9, 19.7)
Non-Hispanic, Black (14.4) 64.6 (58.8, 70.4) 22.7 (17.7, 27.7) 10.1 (6.8, 13.4) 17.8 (13.4, 22.2)
Non-Hispanic, Other (5.1) 74.8 (68.6, 80.9) 15.4 (10.5, 20.2) 9.0 (5.4, 12.6) 15.1 (9.6, 20.5)
Marital Status of Mother
P-value 0.01 0.06 0.50 0.07
Widowed/Divorced/Separated/Deceased (8.4) 64.9 (57.7, 72.1) 14.5 (10.5, 18.6) 7.7 (3.9, 11.4) 26.0 (19.0, 33.0)
Never Married (20.4) 65.0 (60.2, 69.7) 21.7 (17.1, 26.4) 9.8 (7.5, 12.2) 18.7 (14.9, 22.5)
Married (71.1) 72.5 (70.2, 74.8) 19.4 (17.2, 21.5) 8.3 (6.9, 9.8) 17.3 (15.5, 19.0)
Education Level of Mother
P-value 0.01 0.05 <0.01 0.08
<12 Years (16.3) 71.2 (66.1, 76.3) 20.1 (14.8, 25.4) 7.5 (5.1, 9.8) 14.1 (10.1, 18.1)
12 Years (36.0) 66.2 (62.4, 70.1) 20.6 (17.1, 24.1) 11.9 (9.3, 14.5) 17.9 (15.1, 20.8)
>12 Years, Non College Graduate (14.3) 69.4 (64.6, 74.2) 22.7 (18.3, 27.1) 7.1 (5.0, 9.1) 21.3 (17.0, 25.6)
College Graduate (33.4) 74.7 (71.9, 77.4) 16.5 (14.2, 18.8) 6.2 (4.6, 7.8) 19.4 (16.9, 22.0)
First Born Status
P-value <0.01 0.05 0.71 0.14
No (62.8) 67.3 (64.6, 70.0) 20.8 (18.4, 23.3) 8.7 (7.2, 10.3) 19.2 (17.1, 21.3)
Yes (37.2) 75.4 (72.7, 78.1) 17.1 (14.4, 19.9) 8.3 (6.6, 10.0) 16.8 (14.3, 19.2)
Age of Mother
P-value 0.03 0.39 <0.01 0.34
≤ 19 Years (3.5) 68.3 (57.7, 79.0) 25.3 (15.1, 35.6) 13.0 (5.5, 20.5) 12.4 (4.2, 20.6)
20–29 Years (45.6) 67.5 (64.3, 70.7) 20.0 (16.9, 23.0) 10.8 (8.8, 12.8) 18.1 (15.7, 20.6)
≥ 30 Years (50.9) 73.0 (70.3, 75.6) 18.6 (16.3, 20.9) 6.3 (4.9, 7.7) 18.8 (16.6, 21.1)
Poverty Status
P-value <0.01 0.02 0.03 0.65
Above, > $75 K (16.7) 74.6 (70.7, 78.4) 14.7 (11.9, 17.5) 6.4 (4.2, 8.6) 19.7 (16.2, 23.2)
Above, ≤ $75 K (50.7) 71.7 (69.0, 74.4) 20.5 (17.9, 23.1) 8.3 (6.6, 9.9) 18.2 (16.1, 20.4)
Below (21.2) 61.8 (56.7, 67.0) 20.1 (15.9, 24.2) 12.0 (9.0, 15.1) 18.7 (14.8, 22.7)
Unknown (11.5) 73.7 (67.4, 79.9) 20.6 (13.7, 27.4) 6.8 (3.4, 10.2) 15.6 (10.3, 20.9)
MSA Status
P-value <0.01 0.19 <0.01 0.22
MSA Central City (34.4) 69.1 (65.8, 72.4) 20.1 (17.3, 22.9) 7.1 (5.6, 8.6) 19.6 (16.7, 22.6)
MSA Non-Central City (47.2) 75.2 (72.4, 77.9) 17.8 (15.0, 20.5) 7.5 (5.6, 9.4) 16.8 (14.4, 19.1)
Non-MSA (18.5) 60.1 (55.1, 65.1) 22.7 (17.9, 27.5) 14.0 (10.9, 17.1) 19.6 (16.2, 23.0)
Time from Last Immunization to Interview
P-value 0.38 0.25 0.80 0.51
19–24 months (39.4) 70.6 (67.4, 73.7) 17.7 (15.1, 20.2) 8.6 (6.8, 10.4) 18.9 (16.3, 21.5)
25–29 months (34.0) 71.8 (68.6, 75.0) 20.0 (16.8, 23.1) 8.1 (6.1, 10.1) 17.0 (14.4, 19.6)
30–35 months (26.6) 68.0 (63.8, 72.3) 21.5 (17.4, 25.5) 9.2 (6.7, 11.7) 19.0 (15.8, 22.3)
Age of Child at Interview
P-value 0.69 0.86 0.28 0.37
19–24 months (36.4) 71.1 (67.8, 74.3) 18.8 (16.1, 21.6) 9.1 (7.1, 11.1) 17.0 (14.4, 19.5)
25–29 months (29.4) 69.1 (65.6, 72.5) 19.6 (16.5, 22.7) 9.5 (7.2, 11.7) 18.2 (15.3, 21.1)
30–35 months (34.3) 70.6 (67.0, 74.1) 20.0 (16.4, 23.6) 7.3 (5.3, 9.3) 19.8 (16.8, 22.7)
a Children in the Q3/2001–Q4/2002 National Immunization Survey were born between August 1998 and June 2001.
b Weight frequencies of respondents.
c Respondents could choose more than one response. Thus, total % of those who chose response will not add to 100%.
d Percents are weighted estimates.
e Outreach worker called/came to house to tell me, health department called me/sent me reminder, or physician's office called me/sent me reminder
f Relative/friend told me, found out during visit to doctor or other healthcare provider, day care/headstart requirement, WIC (Women, Infants, Children) nurse told me government program requirement, or other
Discussion
We found no difference in immunization coverage among those who did/did not indicate that 'reminder/recall' told respondents when to take children in for the most recent immunization. Similarly, we found no difference among those who said a shot card did/did not indicate when a child needed immunizations (although, at the α = 0.10 level, we would have found a negative association between relying on shot cards and immunization status). In comparison, households indicating that a doctor or nurse told them on a previous visit when the child needed immunization had significantly greater coverage.
Demographics differed among those who indicated various methods of knowing when to take a child in for the most recent immunization visit. However, when we controlled for demographics through a logistic regression model, 'doctor/nurse at previous immunization visit' remained significant. The other ways of knowing when to take the child for the most recent immunization visit were not significant.
The relative paucity of respondents indicating 'reminder/recall' (8.5%) could indicate that providers are not widely using reminder/recall, that caregivers do not rely on it for knowing when children need immunization, or that caregivers tend to forget reminder/recalls. The data do not allow us to choose among these explanations. However, other studies have shown that reminder/recalls are not widely used by physicians. In 1992, of the 1175 pediatricians and family physicians surveyed by Szilagyi et al., only 13% used computer or reminder files to identify undervaccinated children [5]. Nine years later, similar results were found by Tierney et al. Among 433 pediatricians surveyed, only 16% were currently using routine reminder or recall messages [6].
Despite the low use of reminder/recalls, they have long featured in ways to increase immunization coverage. In 1993, the National Vaccine Advisory Committee (NVAC) recommended that all public and private health-care providers operate a tracking system which generates reminders and recalls, and issued another such recommendation in 1999 [7,8] Again in 1998, the Advisory Committee on Immunization Practices, the American Academy of Pediatrics, and the American Academy of Family Physicians jointly recommended the use of a reminder and/or recall system by vaccination providers [9]. Many published studies have found that reminder/recall increase vaccination coverage [10-15].
Conclusion
Although reminder/recalls are an effective method of increasing immunization coverage, our results suggest that taking time at each immunization visit to remind caregivers about the next immunization visit might also be powerful. Verbal reminders given while caregivers are present are cheap, easy, and possibly quite effective. Our study did not find a positive association between reminder/recalls and immunization coverage. However, several other studies have shown that provider recommendation is significantly associated with increase in immunization coverage [16-21]. Our data do not provide sufficient detail to explain this discrepancy.
This study has several limitations. First, methods of determining when the child needed the most recent immunization were determined from caregivers' memories. This possibly introduced recall bias, particularly if ability to recall use of a particular method and immunization are both associated with some unobserved factor. Second, all children in the survey were at least 19 months of age, and the question was only asked about the 'most recent' immunization. The bulk of immunizations are scheduled for the first year of life. It is possible that the 'most recent' immunization is not typical of earlier immunizations. Third, NIS relies on provider-verified immunization history; coverage could be underestimated due to incomplete records and reporting. This study is also subject to other limitations such as the inability to determine the sole impact of the provider on immunization coverage. Doctors/nurses who effectively use verbal reminders may also be more effective at immunizing their patients, and so, higher immunization coverage may not result solely from the verbal reminder. Finally, the dichotomization of immunization coverage does not take into account the varying levels of completeness for the 4:3:1:3:3 series.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
KS carried out the analysis and interpretation of the data and the drafting of the manuscript. LB participated in revisions of the manuscript and provided statistical expertise and supervision. Both authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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Zell ER Ezzati-Rice T Battaglia MP National Immunization Survey: the methodology of a vaccination surveillance system Public Health Rep 2000 115 65 77 10968587 10.1093/phr/115.1.65
SUDAAN User's Manual, Release 80 2001 Research Triangle Park, NC: Research Triangle Institute
Szilagyi PG Rodewald LE Humiston SG Immunization practices of pediatricians and family physicians in the United States Pediatrics 1994 94 517 523 7936863
Tierney CD Yusuf H McMahon SR Adoption of reminder and recall messages for immunization by pediatricians and public health clinics Pediatrics 2003 112 1076 1082 14595049 10.1542/peds.112.5.1076
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Rodewald LE Szilagyi PG Humiston SG Barth R Kraus R Raubertas RF A randomized study of tracking with outreach and provider prompting to improve immunization coverage and primary care Pediatrics 1999 103 31 38 9917436 10.1542/peds.103.1.31
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Freeman VA Freed GL Parental knowledge, attitudes, and demand regarding a vaccine to prevent Varicella Am J Prev Med 1999 17 153 155 10490061 10.1016/S0749-3797(99)00063-X
Nichol KL Mac Donald R Hauge M Factors associated with influenza and pneumococcal vaccination behavior among high-risk adults J Gen Intern Med 1996 11 673 677 9120653
Bardenheier B González IM Washington ML Bell BP Averhoff F Massoudi MS Parental knowledge, attitudes, and practices associated with not receiving Hepatitis A vaccine in a demonstration project in Butte County, California Pediatrics 2003 112 14523210
Lieu TA Glauber JH Fuentes-Afflick E Lo B Effects of vaccine information pamphlets on parents' attitudes Arch Pediatr Adolesc Med 1994 148 921 925 8075734
Ashby-Hughes B Nickerson N Provider endorsement: the strongest cue in prompting high-risk adults to receive Influenza and Pneumococcal immunizations Clin Excell Nurse Pract 1999 3 97 104 10646398
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BMC Mol BiolBMC Molecular Biology1471-2199BioMed Central London 1471-2199-6-211629319210.1186/1471-2199-6-21Research ArticleEvaluation of potential reference genes in real-time RT-PCR studies of Atlantic salmon Olsvik Pål A [email protected] Kai K [email protected] Ann-Elise O [email protected] Tom O [email protected] Ivar [email protected] National Institute of Nutrition and Seafood Research, Nordnesboder 2, N-5005 Bergen, Norway2 Department of Biology, University of Bergen, Thormøhlensgate 55, N-5020 Bergen, Norway2005 17 11 2005 6 21 21 22 7 2005 17 11 2005 Copyright © 2005 Olsvik et al; licensee BioMed Central Ltd.2005Olsvik 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
Salmonid fishes are among the most widely studied model fish species but reports on systematic evaluation of reference genes in qRT-PCR studies is lacking.
Results
The stability of six potential reference genes was examined in eight tissues of Atlantic salmon (Salmo salar), to determine the most suitable genes to be used in quantitative real-time RT-PCR analyses. The relative transcription levels of genes encoding 18S rRNA, S20 ribosomal protein, β-actin, glyceraldehyde-3P-dehydrogenase (GAPDH), and two paralog genes encoding elongation factor 1A (EF1AA and EF1AB) were quantified in gills, liver, head kidney, spleen, thymus, brain, muscle, and posterior intestine in six untreated adult fish, in addition to a group of individuals that went through smoltification. Based on calculations performed with the geNorm VBA applet, which determines the most stable genes from a set of tested genes in a given cDNA sample, the ranking of the examined genes in adult Atlantic salmon was EF1AB>EF1AA>β-actin>18S rRNA>S20>GAPDH. When the same calculations were done on a total of 24 individuals from four stages in the smoltification process (presmolt, smolt, smoltified seawater and desmoltified freshwater), the gene ranking was EF1AB>EF1AA>S20>β-actin>18S rRNA>GAPDH.
Conclusion
Overall, this work suggests that the EF1AA and EF1AB genes can be useful as reference genes in qRT-PCR examination of gene expression in the Atlantic salmon.
==== Body
Background
In real-time RT-PCR, the expression levels of the target genes of interest are estimated on the basis of endogenous controls. Various housekeeping genes, ribosomal RNA (rRNA) and total RNA are most commonly used as references in gene expression analysis today. The purpose of these controls is to remove or reduce differences due to sampling, i.e. differences in RNA quantity and quality. The ideal endogenous control should be expressed at a constant level among different tissues of an organism, at all stages of development and should be unaffected by the experimental treatment. It should also be expressed at roughly the same level as the RNA under study [1]. However, data normalization in real-time RT-PCR remains a real problem, especially for absolute quantification [1]. Numerous studies have revealed that no single universal gene has a constant expression level under all developmental or experimental situations. The best choice of reference gene to use as an endogenous control varies, depending on the tissues of interest in the experiment. A large number of genes have for this reason been selected for normalization of mRNA expression data [2,3]. If the selected reference gene fluctuates randomly between samples, small differences in expression between the genes of interest will be missed. Gene expression coefficient of variation (CV) between different groups of individuals should ideally be as low as possible [4]. In general, the stability of several potential reference genes should be tested in every examined tissue or cell, and under different experimental design [5,6]. An increasing number of papers are discussing the selection of reference genes in real-time RT-PCR analyses [3,7].
Two of the most commonly used reference genes are those encoding glyceraldehyde-3P-dehydrogenase (GAPDH) and β-actin. Recently, the use of these two genes as endogenous controls has been scrutinized, and several studies have documented that the GAPDH and β-actin genes should be used with caution as controls [2,8,9]. GAPDH in mammals is known to play a role in a broad range of cellular mechanisms (for review see Sirover [10]), including being a key enzyme in glycolysis. Overall, GAPDH mRNA levels might be regulated under a large number of physiological states, and its use as a reference is inappropriate for most experimental conditions. Actin is a major component of the protein scaffold that supports the cell and determines its shape, and is the most abundant intracellular protein in eukaryotic cells. Even though commonly used as a reference, the application of the β-actin gene has recently been characterized as a historical carryover from northern blots and conventional RT-PCR (for a general discussion on the use of 'classic' reference genes like GAPDH and β-actin, see Huggett et al. [7]). Eukaryotic elongation factor 1A (eEF1A, formerly elongation factor 1 alpha) plays an important role in translation by catalyzing GTP-dependent binding of aminoacyl-tRNA to the acceptor site of the ribosome. However, the protein is involved in a broad diversity of functions and constitutes 1–3% of the total cytoplasmic protein content of the cell. In human, cDNAs of two actively transcribed isoforms have been cloned (eEF1A-1 and eEF1A-2) (for review see Thornton et al. [11]). Two paralog EF1A genes (A and B) have recently been applied as references in real-time qRT-PCR of Atlantic salmon [12]. It is plausible to assume that the presence of these highly similar genes is a result of a tetraploidization event that occurred in a salmonid ancestor in the comparatively recent past [13,14].
Previously, the 18S rRNA gene was considered to be an ideal internal control in qRT-PCR analysis (Ambion [15]). Ribosomal RNA constitutes up to 80–90% of total cellular RNA, and several studies have shown that rRNA varies less under conditions that affect the expression of mRNAs (discussed in Bustin & Nolan [16]). However, questions have been raised against the use of ribosomal RNA genes as references. Vandesompele et al. [5] have stressed the fact that there sometimes might be imbalances in rRNA and mRNA fractions between different samples, making genes encoding ribosomal RNAs unsuitable as references. To meet these challenges of accurate interpretation of real-time qRT-PCR data, the authors suggested that an index of the most stable housekeeping genes should be used for normalization, and developed the geNorm VBA applet for Microsoft Excel in this regard [5]. A similar software tool, the BestKeeper, has been developed by Pfaffl et al. [6]. These tools can be used to find the most stable reference genes under different experimental conditions. We used the geNorm software which determines the individual stability of a gene within a pool of genes [5]. The stability is calculated according to the similarity of their expression profile by a pair-wise comparison, using their geometric mean as a normalizing factor. The gene with the highest M, i.e. the least stable gene, is then suggested excluded in a stepwise fashion until the most stable genes are determined, and an index suggested, based on the best genes. geNorm has been used to select the most stable reference genes in several recent studies (e.g. [4,17,18]).
The aim of this work was to evaluate the usefulness of six potential reference genes in the Atlantic salmon. Salmonid fish are among the most widely studied model fish species in general, and extensive basic information on many different aspects of their biology has been collected [19]. Large-scale DNA-sequencing projects on salmon have been initiated in several laboratories ; ; ; . In this work we selected the two 'classic' reference genes encoding GAPDH and β-actin, two genes encoding 18S rRNA and S20 ribosomal protein and two paralog genes encoding elongation factor 1A (EF1AA and EF1AB). To evaluate their usefulness as reference genes, RNA from eight tissues of six adult salmon were subjected to real time PCR. The relative transcription levels of the genes were also estimated in four phases of young salmon going through smoltification, in order to check their stabilities under physiological stressful conditions.
Results and discussion
Ranking of six potential reference genes in Atlantic salmon
The ranking of the six examined genes analyzed by geNorm is shown in Table 3. In six tissues (muscle, liver, gills, head kidney, spleen and thymus), the EF1AB gene emerged as the most stable, whereas the EF1AA gene was ranked number one in brain and the β-actin gene was ranked number one in intestine. The 18S rRNA and S20 genes were ranked among the worse genes in all tissues. Not surprisingly, the GAPDH gene was ranked worse in five tissues (liver, head kidney, spleen, brain and thymus), confirming the general skepticism against the use of this gene as reference [7,16,20]. Combined, the total ranking reads EF1AB>EF1AA>β-actin>18S rRNA>S20>GAPDH. We did not analyze our data with the Bestkeeper software. Analyzing reference genes in virus infected cells, Radonic et al. [4] concluded that the Bestkeeper tool gave results that slightly deviated from, but nevertheless corresponded to, those obtained using geNorm.
To be able to evaluate gene stability under stressful conditions, mRNA expression of the selected genes was examined in gills of salmon going through smoltification. Prior to seawater entry, juvenile anadromous salmon undergo a parr-smolt transformation, characterized by behavioral, morphological and physiological changes, known to be challenging for the fish. Physiological alterations include increased seawater tolerance, olfactory sensitivity, metabolic rate, scope for growth and changed hemoglobin and visual pigments [21]. We selected to examine the gills during smoltification, because this tissue plays a major role in ionic and osmotic regulation during adaptation to hyperosmotic seawater. Figure 1 shows the raw Ct values of the studied genes in gills before, during and after smoltification (smoltified in seawater and desmoltified in freshwater). In Figure 2 the same data are presented, but now normalized against an index calculated by geNorm of the three most stable genes (β-actin, EF1AA and EF1AB). Based on the M values, geNorm ranks the stability of the six genes from 24 fish going through smoltification in the following order: EF1AB>EF1AA>β-actin>S20>18S rRNA>GAPDH (Figure 3). In Figure 1 it can be seen that the 18S gene had the lowest individual raw Ct variation. Most individual raw Ct variation of the studied genes is seen in the presmolt and smolt groups. A characteristic drop in expression can be seen for all genes in the smolt group, compared to the presmolt group. After transfer to seawater, the individual raw Ct variation decreased for all genes. Overall, the raw Ct data suggest that the physiological challenging smoltification process affected the expression of all six genes. When the same data were normalized against an index of the three most stable genes, β-actin, EF1AA and EF1AB, the relative expression levels were altered for all genes. Now the ribosomal 18S gene emerges as the second worse, whereas the two paralog EF1A genes became the most stable. This might have to do with the fact that geNorm will top-rank co-expressed genes [22], a weakness that has to be considered when evaluating paralog genes likely to be co-regulated. Even though the eEF1A-2 gene has been identified as an important oncogene and has been shown to be differently expressed in human tissues [11], Hamalainen et al. [23] found the eEF1A-1 gene to be a good reference gene in real-time RT-PCR examinations. A similar finding was reported by Frost and Nilsen [24] in salmon louse, where they showed that the eEF1A and S20 genes were valid candidate references, whereas the 18S rRNA and GAPDH genes were unsuitable. The current findings based on geNorm evaluation question the recommended application of ribosomal genes as references (as suggested for example by Ambion (see reference [15]), and are in line with earlier warnings against the use of rRNA genes as references [5,6]. To avoid the normalization of the genes for β-actin, EF1AA and EF1AB against an index partly based on their own expression, the S20 gene was included in the index instead, and the mean normalized expression for these three genes calculated with the new index. The patterns of expression, however, were approximately the same for the three genes as seen in Figure 2, suggesting that the gene-stability measure M can be used to find the most appropriate reference genes.
PCR poisoning
We see a correlation between the A260/230 absorbance on the NanoDrop and the PCR efficiency (data not shown). We tend to get PCR efficiencies that are too high in some samples with low A260/230 ratios. When the samples are treated with DNase solution, the A260/230 ratio usually drops. After DNase treatment, the A260/280 ratio increased from 1.8 to 2.1 (n = 45 samples). At the same time, the A260/230 ratio dropped from 2.4 to 2.1. The DNase treatment therefore adds substances to the RNA solution that increases the absorbance at 230 nm more than it decrease the 260 nm absorbance. The added substance (salt or some other component) may inhibit the RT reaction or the PCR reaction, sometimes called PCR poisoning. We have seen that the A260/230 ratios are quite low in samples that give inadequate PCR efficiency slopes, especially with RNA from head kidney, thymus and intestine tissues, in which the gradient of the standard curve is less than -3.3 (Table 2). The reason one obtain better amplification rate efficiencies with the more diluted samples is because the inhibitor has been diluted below its effective level. The obvious way around this problem is to dilute the amount of cDNA put into the PCR reaction. Alternatively, cleanup columns can be used to purify and concentrate the RNA.
Transcription levels of the examined genes and the coefficient of variance (CV) in different tissues varied considerably. mRNA levels in tissues are regulated by numerous endogenous and exogenous stimuli [16]. Transcription rates in metabolic active tissues might be up-regulated compared to those of less active tissues, whereas inter-tissue variation in degradation rates of mRNAs, for example, might affect mRNA stability [25]. The results revealed that muscle had the lowest CVs of the studied genes, compared to higher CVs in more active tissues like thymus, head kidney and spleen. In thymus, intestine, head kidney, gills, brain, liver and spleen, the 18S and S20 genes had the lowest CVs, based on raw Ct values. In all tissues, except intestine, the GAPDH gene had the highest CV. Except for thymus, the two elongation factor genes had relatively similar expression in all eight examined tissues. Their expression are most likely co-expressed in the examined tissues, and therefore favored in geNorm calculations [22]. The results also demonstrated that assays optimized for one tissue of an organism do not necessarily work equally well in other tissues. Of the tissues studied in this work, intestine, head kidney and spleen were the most troublesome.
Conclusion
Our data, based on geNorm calculations, suggest that the Atlantic salmon EF1A genes that have been tested in the present study may be good candidate reference genes. The GAPDH gene seems unsuitable as a reference in quantitative real-time RT-PCR. With regard to the 18S rRNA gene, this must be applied with caution. Tools like the geNorm applet for Microsoft Excel can be useful to help select the most stable genes in various experiments.
Methods
Fish handling and experimental design
Tissues from 15 individuals were collected (852 ± 702 g, ranging from 254 to 1898 g). These individuals were not separated based on sex, size or sampling time, but treated as one heterogeneous group to examine the width of mRNA expression of the studied genes in eight different organs. This group of fish was handled and fed according to normal aquacultural management, and none of these individuals were exposed to any particular treatment. To examine if physiological stress may alter the gene expression in the gills, a total of 24 individuals were collected before (termed presmolt, 18.3 ± 0.9 g), during (termed smolt, 28.7 ± 3.7 g) and after smoltification. After smoltification, one group was kept and desmoltified in freshwater (termed desmolt FW, 30.0 ± 3.8 g), while the other group was transferred to seawater (termed smoltified SW, 30.2 ± 4.3 g) (n = 6 in each group). The Atlantic salmon examined during smoltification were from the anadromous population "Vosso" of the river Vosso in Southwestern Norway (see Nilsen et al. [27] for details on how these fish were treated). All fish were treated and euthanized according to Norwegian national legislation for laboratory animals.
Tissue sampling
Samples from eight organs, i.e. gills, liver, brain, head kidney, spleen, thymus, white muscle and posterior intestine, were dissected out and immediately frozen in cryo tubes in liquid N and stored at -80°C before RNA extraction. The RNA extracted from three spleen and four head kidney tissue samples were of low quality, and we had to redo the sampling from four individuals, These tissue samples were stored on RNA later (Ambion) at -20°C before further processing.
RNA extraction
RNA was isolated with phenol-chloroform extraction as described by Chomczynski and Sacchi [28], and stored in 100 μl RNase-free MilliQ H2O. Total RNA was extracted using Trizol reagent (Invitrogen, Life Technologies), according to the manufacturer's instructions. Genomic DNA was eliminated from the samples by DNase treatment according to the manufacturer's description (Ambion). The RNA was then stored at -80°C before further processing. The quality of the RNA was assessed with the NanoDrop® ND-1000 UV-Vis Spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) and the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). A 260/280 nm absorbance ratio of 1.8 – 2.0 indicates a pure RNA sample. The RNA 6000 Nano LabChip® kit (Agilent Technologies, Palo Alto, CA, USA) was used to evaluate the integrity of the RNA. We used the RNeasy MinElute Cleanup kit from Qiagen to purify our most troublesome samples. With this kit the A260/230 ratio increased on average by 5 % (n = 10).
Design of PCR primers and TaqMan probes
The PCR primer and TaqMan MGB probe sequences used for quantification of the genes encoding 18S rRNA, S20 ribosomal protein, β-actin, GAPDH, EF1AA and EF1AB, are shown in Table 1. Four of these genes, 18S, β-actin, EF1AA and EF1AB, have also been used as references in real-time RT-PCR analyses of Atlantic salmon in other recent studies [12,28]. The primers amplify PCR products between 57–98 basepairs (bp) long, which is within the range of 50–150 bp as suggested by Applied Biosystems for their TaqMan assays. qPCR assays were designed using Primer Express 2.0 software (Applied Biosystems, Foster City, CA, USA) to select appropriate primer and probe sequences from known Atlantic salmon genes. The mRNA sequences encoding S20 ribosomal protein and GAPDH were obtained from GenBank accession numbers BG936672 and BU693999, respectively (exon-exon borders were not considered). The EF1AA assay was based on a cDNA clone that we reported to the GenBank previously (AF321836), whereas the EF1AB assay was based on the EST BG933853. An alignment with zebrafish indicated the exon-exon borders [29]. The chosen primers were subsequently used to confirm that the salmon genes contained an intron between the same sites as deduced from the alignment with zebrafish. The PCR products containing the introns were cloned into TOPO vector (Invitrogen) and sequenced (sequences can be provided upon request). PCR primers for β-actin were based on Atlantic salmon BG933897 and designed to span exon-exon borders of this gene, as deduced from corresponding genes in human and zebrafish (NW633959). For 18S rRNA the PCR primers and probe were designed from the Atlantic salmon sequence AJ427629, and placed in a conserved region of the gene based on comparison with the human gene. RNA samples were subjected to DNase treatment to avoid genomic DNA contamination. Amplified PCR products of all actual cDNAs were sequenced to ensure that the correct mRNA sequences were quantified. The fragments were sequenced with BigDye version 3.1 fluorescent chemistry (Applied Biosystems) and run on an ABI PRISM® 377 DNA apparatus at the University of Bergen Sequencing Facility.
Real-time quantitative RT-PCR
A two-step real-time RT-PCR protocol was developed to measure the mRNA levels of the studied genes in eight tissues of Atlantic salmon. The RT reactions were run in triplicate on 96-well reaction plates with the GeneAmp PCR 9700 machine (Applied Biosystems, Foster City, CA, USA) using TaqMan Reverse Transcription Reagent containing Multiscribe Reverse Transcriptase (50 U/μl). Two-fold serial dilutions of total RNA were made for efficiency calculations. Five or six serial dilutions (500 – 15,63 ng) were analyzed by qRT-PCR in separate sample wells and the resulting Cts recorded. Input total RNA concentration was 500 ng in each reaction for β-actin, GAPDH, EF1AA and EF1AB, and 0.5 ng for 18S rRNA and S20 ribosomal protein. Controls for no template (ntc) and controls for no amplification (nac) were run for each master mix, but not for every single sample. Reverse transcription was performed at 48°C for 60 min by using oligo dT primers (2.5 μM) for β-actin, GAPDH, EF1AA and EF1AB and random hexamer primers (2.5 μM) for 18S and S20 in 30 μl total volume. cDNA for evaluation of smoltification effects (24 samples) were primed entirely with random nonamer primers. In this material, total RNA were treated with RQ1 RNase-free DNase (Promega) and cDNA reversely transcribed using 500 ng total RNA and random nonamers in conjunction with the Reverse Transcription Core kit (EuroGenTech, RT-RTCK-05) following the manufacturer's instructions. Input total RNA concentration was 500 ng in each reaction. All 24 samples for each gene were run on the same plate together with six serial dilutions. The final concentration of the other chemicals in each RT reaction was: MgCl2 (5.5 μM), dNTP (500 μM of each), 10× TaqMan RT buffer (1×), RNase inhibitor (0.4 U/μl) and Multiscribe Reverse Transcriptase (1.67 U/μl).
2.5 μl of 10-fold diluted cDNA for β-actin, GAPDH, EF1AA and EF1AB, and 1000-fold diluted cDNA for 18S rRNA and S20 ribosomal protein from each RT reaction was transferred to a new 96-well reaction plate, and the real-time PCR run on the ABI Prism 7000 Sequence Detection System from AB. Real-time PCR was performed by using TaqMan Universal PCR Master Mix, which contains AmpliTaq Gold® DNA polymerase, and gene specific primers (900 nM). Fluorescence marked TaqMan MGB probes (200 nM) were used for data collection during the log linear phase of the reaction. PCR was achieved with a 10 min activation and denaturation step at 95°C, followed by 50 cycles of 15 s at 95°C and 60 s at 60°C. Baseline and threshold for Ct calculation were set automatically with the ABI Prism 7000 SDS software version 1.1, or set manually whenever necessary.
For evaluation of the potential reference genes, raw Ct values are presented. The geNorm VBA applet for Microsoft Excel was used to determine the most stable genes from the set of tested genes [5]. The Ct values were transformed to quantities using standard curves, according to the geNorm manual. The gene expression stability (M) was calculated with the geNorm applet, and the genes were ranked from best to worst, based on the M value.
Statistics
The GraphPad Prism 4.0 software (GraphPad Software, Inc.) was used for the statistical analyses in this work. Linear regression was used to determine PCR efficiency based on dilution curves. Non-parametric Kruskal-Wallis test was used to compare differences among four groups of salmon going through smoltification.
Authors' contributions
PAO was responsible for the experiment, data analysis and drafted the manuscript. KKL conducted the real-time RT-PCR analysis, and contributed throughout the experimental process. AEOJ constructed the qPCR assays for two of the genes. TON provided the cDNA from the smoltification experiment. IH participated as a supervisor in the study design, analyses and writing.
Acknowledgements
We gratefully acknowledge Chris Glover for checking the language. This work was financed by the National Institute of Nutrition and Seafood Research, Bergen, Norway. IH was funded by a grant for functional genomics (FUGE) from the Norwegian Research Council.
Figures and Tables
Figure 1 qRT-PCR analysis of six genes in gills of six Atlantic salmon going through smoltification; presmolt (before smoltification), smolt (during smoltification), smoltified (finished smoltified in seawater) and desmolt (desmoltification in freshwater). Numbers indicate raw Ct values.
Figure 2 qRT-PCR analysis of six genes in gills of six smoltifying Atlantic salmon. The same data as in Figure 1, but now normalized against an index of the three best genes (β-actin, EF1AA and EF1AB) calculated with the geNorm software. The four groups were analyzed with Kruskal-Wallis test, and if significant, the overall p-value is given in the graphs. For β-actin, there were significant differences between the presmolt and the smoltified group (p < 0.05), the presmolt and the desmolt groups (n<0.01) and between the smolt and desmolt groups (p < 0.05). For EF1AA, there was a significant difference between the presmolt and the desmolt groups (p < 0.01). For EF1AB, there was a significant difference between the smolt and the smoltified groups (p < 0.05). An asterisk denotes significant differences between the groups.
Figure 3 Stability of six genes in gills of Atlantic salmon during smoltification calculated with the geNorm software. geNorm calculates the gene expression stability measure M for a control gene as the average pairwise variation V for that gene with all other tested control genes.
Table 1 TaqMan assays used to evaluate potential reference genes in Atlantic salmon.
Gene Forward primer Reverse primer TaqMan MGB probe Amplicon size (bp)
18S rRNA CCCCGTAATTGGAATGAGTACACTTT ACGCTATTGGAGCTGGAATTACC CACCAGACTTGCCCTCC 98
S20 GCAGACCTTATCCGTGGAGCTA TGGTGATGCGCAGAGTCTTG CCTCAAGGTGAAGGGA 85
β-actin CCAAAGCCAACAGGGAGAAG AGGGACAACACTGCCTGGAT TGACCCAGATCATGTTT 91
GAPDH AAGTGAAGCAGGAGGGTGGAA CAGCCTCACCCCATTTGATG CTGATCATTGGAAACCT 96
EF1AA CCCCTCCAGGACGTTTACAAA CACACGGCCCACAGGTACA ATCGGTGGTATTGGAAC 57
EF1AB TGCCCCTCCAGGATGTCTAC CACGGCCCACAGGTACTG CCAATACCGCCGATTTT 59
Table 2 Standard curve evaluation of potential reference genes in Atlantic salmon.
Thymus Muscle Intestine Head kidney
Slope R2 Slope R2 Slope R2 Slope R2
18S rRNA -3.26 0.996 -3.40 0.993 -3.23 0.992 -3.27 0.998
S20 -3.28 0.996 -3.21 0.991 -3.29 0.990 -3.07 0.993
β-actin -2.73 0.994 -3.36 0.997 -3.11 0.997 -3.05 0.996
GAPDH -3.38 0.997 -3.21 0.995 -3.37 0.996 -3.13 0.996
EF1AA -3.06 0.996 -3.05 0.993 -3.05 0.994 -3.06 0.996
EF1AB -3.11 0.998 -3.13 0.998 -3.20 0.993 -3.31 0.997
Gills Brain Liver Spleen
Slope R2 Slope R2 Slope R2 Slope R2
18S rRNA -3.41 0.999 -3.32 0.992 -3.42 0.998 -3.20 0.995
S20 -3.29 0.990 -3.22 0.991 -3.39 0.991 -3.28 0.995
β-actin -3.10 0.999 -3.21 0.997 -3.23 0.990 -3.44 0.994
GAPDH -3.57 0.997 -3.48 0.902 -3.21 0.995 -2.83 0.714
EF1AA -3.38 0.998 -2.86 0.994 -3.11 0.994 -3.15 0.992
EF1AB -3.32 0.991 -3.14 0.991 -3.24 0.991 -3.52 0.996
Table 3 Evaluation of the usefulness of six potential reference genes in eight tissues of Atlantic salmon ranked by the geNorm software. 1 = best, 6 = worst. Six individuals were analyzed for six genes in eight tissues.
Tissue Muscle Liver Gills Head kidney Spleen Brain Intestine Thymus Total ranking
18S rRNA 3 5 6 2 3 5 6 4 4
S20 6 4 5 5 2 3 5 5 5
β-actin 2 3 4 4 4 4 1 2 3
GAPDH 5 6 3 6 6 6 3 6 6
EF1AA 4 2 2 3 5 1 3 3 2
EF1AB 1 1 1 1 1 2 2 1 1
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-1601628865110.1186/1471-2164-6-160Research ArticleLeveraging human genomic information to identify nonhuman primate sequences for expression array development Spindel Eliot R [email protected] Mark A [email protected] Yibing [email protected] Courtney [email protected] Shaun L [email protected] Nicholas F [email protected] Sergio R [email protected] Robert B [email protected] Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR 97006, USA2 College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE, 68182 USA3 Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA2005 15 11 2005 6 160 160 26 8 2005 15 11 2005 Copyright © 2005 Spindel et al; licensee BioMed Central Ltd.2005Spindel 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
Nonhuman primates (NHPs) are essential for biomedical research due to their similarities to humans. The utility of NHPs will be greatly increased by the application of genomics-based approaches such as gene expression profiling. Sequence information from the 3' end of genes is the key resource needed to create oligonucleotide expression arrays.
Results
We have developed the algorithms and procedures necessary to quickly acquire sequence information from the 3' end of nonhuman primate orthologs of human genes. To accomplish this, we identified terminal exons of over 15,000 human genes by aligning mRNA sequences with genomic sequence. We found the mean length of complete last exons to be approximately 1,400 bp, significantly longer than previous estimates. We designed primers to amplify genomic DNA, which included at least 300 bp of the terminal exon. We cloned and sequenced the PCR products representing over 5,500 Macaca mulatta (rhesus monkey) orthologs of human genes. This sequence information has been used to select probes for rhesus gene expression profiling. We have also tested 10 sets of primers with genomic DNA from Macaca fascicularis (Cynomolgus monkey), Papio hamadryas (Baboon), and Chlorocebus aethiops (African green monkey, vervet). The results indicate that the primers developed for this study will be useful for acquiring sequence from the 3' end of genes for other nonhuman primate species.
Conclusion
This study demonstrates that human genomic DNA sequence can be leveraged to obtain sequence from the 3' end of NHP orthologs and that this sequence can then be used to generate NHP oligonucleotide microarrays. Affymetrix and Agilent used sequences obtained with this approach in the design of their rhesus macaque oligonucleotide microarrays.
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Background
Gene expression profiling is expected to rapidly increase the information yield from experiments using nonhuman primates (NHPs). This is important because NHPs are required for the study of AIDS, stem cell biology, reproduction and neuroscience, but are expensive and in short supply [1-14].
One question which must be addressed is: given the close evolutionary relationship between rhesus macaque and humans, why not use available human oligonucleotide microarrays with rhesus macaque samples? Human oligonucleotide microarrays have been used with chimpanzee samples to obtain useful information [15-17]. Cross-species hybridization experiments utilizing rhesus samples with human oligonucleotide microarrays have also been attempted [15,18,19]. Although useful information has been obtained, there are serious limitations to this approach. Cross-species comparisons introduce mismatches between a probe and a transcript that are not related to gene expression. Thus, it is impossible to know if a weak or absent signal is due to low levels of expression or to a mismatch. Approximately 40% of rhesus genes are not detected with a human GeneChip [18,19]. For genes that are scored present, the abundance of some transcripts may be underestimated due to mismatches between some of the human probes and rhesus targets. Longer probes might be expected to be more forgiving of mismatches, but even cDNA microarrays have a significant false negative rate when human microarrays are used with rhesus samples [20]. Thus, the use of human micorarrays with rhesus macaque samples results in a high rate of false negatives and does not allow for the acquisition of quantitative information. Clearly, a rhesus macaque specific expression array is needed.
Construction of oligonucleotide-based microarrays requires sequence from the 3' end of a transcript. There are two reasons for this. First, most sample labeling protocols are 3' biased [21,22]. As a result, probes chosen from sequence more than 1 kb from the 3' end of a gene may not detect a transcript. Second, it is important to choose probes from the 3' untranslated region (UTR) because such probes are most likely to be able to distinguish between gene family members. This is because coding sequences are much more highly conserved than 3' UTR [23].
We report a fast and efficient approach to obtaining high quality sequence of the 3' end NHP orthologs of human genes. The terminal exons of 15,401 well-annotated human genes were identified. Primer3 [24] was used to design primers that amplified at least 300 bp of 3' sequence. PCR was performed with these primers using rhesus macaque genomic DNA as the template. PCR products were cloned and sequenced. Over 5,300 rhesus macaque gene sequences have been deposited in GenBank. These sequences were used in the creation of rhesus macaque oligonucleotide microrarrays by two major companies, Affymetrix and Agilent. In addition, ten of the primer pairs designed in the course of this project were used with DNA obtained from three additional NHPs: cynomolgus monkey (Macaca fascicularis), baboon (Papio hamadryas), and African green monkey (Cercopithecus aethiops).
Results
We found that at least 88% of human last exons were greater than 300 bp in length (Fig. 1A). The mean length for all last exons, including those that may be incomplete on the 3' end was 1,398 bp (N = 15,401). The median length was 1,003 bp. The shortest last exon in this group was 24 bp; the longest was 18,174 bp. Because the 3' end has not been determined for all transcripts, we also examined the lengths of complete last exons (N = 11,584; Fig. 1B). For these genes, the mean and median lengths were 1,414 bp and 1,046 bp, respectively. The shortest and longest last exons in this group were 27 bp and 18,174 bp, respectively. The last exon sequences we determined are available as Additional files 1, 2, 3.
Figure 1 Last exon lengths. A) Lengths of human last exons (including incomplete exons). N = 15,401; mean = 1397.6; median = 1003; standard deviation = 1257.3. The values on the abscissa are the upper lengths of the bins; e.g., the bar at 600 bp includes last exons with lengths > 300 bp and ≤ 600 bp. The last bin – 8100 and marked with an asterisk (*) – contains last exons > 7800 bp in length. B) Lengths of human last exons (complete exons only). N = 11,502; mean = 1414.9; median = 1048; standard deviation = 1255.3. Only last exons obtained from mRNA that was complete on the 3' end are included (see discussion in the text for an explanation of how this was determined). The last bin – 8100 and marked with an asterisk (*) – contains all last exons with lengths > 7800 bp.
PCR was used to amplify rhesus macaque genomic DNA using human primers. The PCR success rate (defined as yielding a correct-sized band with, at most, a few minor bands) with the first set of primers designed was 74%. If the first set of primers failed to amplify, a second pair of PCR primers was designed. The PCR success rate with the second set of primers was 59%. Thus, with no more than 2 sets of human primers, 90% of the rhesus macaque genes could be amplified. Only 2% of the PCR products proved difficult to clone. We have deposited sequence information for over 5,300 rhesus genes in the Sequence Tagged Site database (dbSTS) at NCBI. These sequences were used in the design of rhesus macaque oligonucleotide microarrays by Affymetrix and Agilent.
We chose ten primer sets that had worked successfully with rhesus macaque DNA to determine whether the same primer sets could be used with the genomic DNA of other NHPs. These ten genes were chosen to represent a range of identities between rhesus and human sequence – 91.21 to 98.57% identity (Table 1). The mean similarity between these 10 rhesus and human sequences was 94.42%. All PCRs performed with the 10 primers sets worked with both cynomolgus monkey and baboon DNA. Nine of the ten primer sets worked with vervet DNA. New primers were designed for the one gene (IFNG) that did not work with vervet DNA. The redesigned primers were successful with vervet DNA. The mean similarity to human sequence was about 94% for rhesus, cynomolgus monkey, baboon and vervet sequences. We also compared cynomolgus monkey, baboon and vervet sequences with rhesus sequences. Cynomolgus sequences were highly similar to rhesus sequences: mean 99.76% identical, range 99.48 to 100% identical. Most baboon sequences were also highly similar to rhesus sequences: mean 99.07% identical, range 97.85 to 99.64% identical. The vervet sequences were the least similar to rhesus sequences: mean 98.60% identical, range 97.26 to 99.74% identical.
Table 1 Percent identity of 10 sequences obtained using the primers developed for this project with genomic DNA from rhesus macaques, cynomologus monkeys, baboons and vervets.
% identity with rhesus % identity with human
Gene Cyno Baboon Vervet Rhesus Cyno Baboon Vervet
IGF1 100 99.61 99.74 98.57 98.57 98.12 98.57
ESR1 99.75 99.63 99.63 98.01 98.14 98.38 98.01
IFNG 99.48 99.13 98.62 96.52 96 96.52 96.17
DGKI 99.63 99.63 99.26 95.36 95.14 95.38 94.87
RNF2 99.72 99.02 99.02 94.44 94.16 94.44 94.85
ADRBK2 100 99.64 97.57 93.55 93.55 93.43 92.47
IL15RA 99.63 98.14 96.66 92.36 92.36 92.12 90.15
TNF 100 98.94 98.82 92.17 92.17 92.72 92.72
IL16 99.42 99.13 99.42 92.03 91.46 91.75 92.03
TYK2 100 97.85 97.26 91.21 91.21 91.41 91.6
Mean 99.76 99.07 98.6 94.42 94.28 94.43 94.14
Discussion
Last exons
Our use of genomic DNA as a template for targeted PCR is critically dependent on last exon length. At least 300 bp of sequence is preferred for the design of oligonucleotide probes present in oligonucleotide microarrays available from Affymetrix [25]. Because at least 88% of all human genes have last exons greater than 300 bp, for most genes, genomic DNA can be used as the PCR template for obtaining 3' sequence.
Our calculated mean and median lengths for all human last exons, 1,398 and 1,003, respectively are considerably longer than previous reports of last exon length [26-28], the longest mean of which was 811 bp. The procedures used to determine the lengths of last exons in these studies are not clear though most likely were based on aligning mRNA sequence with genomic sequence as done here. One possible reason for the longer last exons observed in the current study is that the sequence data necessary to determine the true 3'-end of many transcripts has become available only recently. Our results are based on the use of RefSeq Release 11 and GenBank Build 147, both obtained from NCBI on 23 May 2005; other reports are based on earlier versions of the genome when less 3' information was available. Although many of the transcripts in this release of RefSeq are annotated as being complete on the 3' end, we increased the number of complete transcripts by extending them as far as possible in the 3' direction by using additional mRNA sequences. An increase in the number of complete transcripts would obviously result in an increase in the mean last exon length. This is supported by the fact that when we considered only genes for which a 3' end was present, the mean and median values increased.
Use of STS primers for other NHP species
Our results suggest that the primers developed for this project will be useful for obtaining sequence information from the 3' end of genes of other NHP species. Further, given that the rhesus and cynomolgus sequences were very similar, we predict that oligonucleotide microarrays designed based on rhesus sequence will work very well with cynomolgus samples. Baboon samples should also work well with rhesus oligonucleotide microarrays for most genes. Vervet sequences for some genes are more divergent from rhesus than cynomolgus monkey or baboon, as would be expected based on evolutionary relationships. Thus, the false negative detection rate observed with vervet samples on a rhesus oligonucleotide microarray may be significant and a targeted approach to obtaining 3' gene sequence from the vervet using primers developed for this project may be justified.
Conclusion
This study demonstrates that human genomic DNA sequence can be leveraged to obtain sequence from the 3' end of NHP orthologs and that this sequence can then be used to generate NHP oligonucleotide microarrays. Affymetrix and Agilent used sequences obtained with this approach in the design of their rhesus macaque oligonucleotide microarrays.
Methods
Figure 2 illustrates the project flow for generating PCR primers. A more complete overview is presented in Additional file 4.
Figure 2 Flowchart demonstrating the process used to obtain primer pairs for the amplification of NHP orthologs of human genes.
Determination of last exons
Human mRNA reference sequences (RefSeq; Release 11) were obtained from NCBI [29]. Sequences corresponding to non-protein coding, withdrawn, hypothetical or pseudogenes in the LocusLink template database [30] were removed. To create a non-redundant dataset of mRNAs, only the longest RefSeq per gene was retained. After these operations, our dataset contained a total of 15,663 unique transcripts; accession numbers for these sequences are available in Additional file 5.
Because 3' sequence is used to select probes, we aligned (BLAST) RefSeqs with all human mRNA sequences from the GenBank Primate (PRI) and high-throughput cDNA sequencing (HTC) division to extend the 3' end of the transcripts as far as possible. A given RefSeq was extended with the mRNA sequence that: 1. had at least 300 bp of alignment with the 3' end of the RefSeq; 2. had at least 98% identity with the RefSeq in the region of alignment; and 3. extended the RefSeq the furthest in the 3' direction.
The extended mRNA sequences obtained above were aligned with genomic DNA sequences from the phase 2 and 3 Human Genome Project sequence databases [31]. The approximate boundaries of exons can be determined by examining High Scoring Pairs (HSP) from the alignment. Because HSPs only deviate from true exons by a few nucleotides (due to ambiguity in the alignment at the splice site), we defined the last exon as the 3'-most HSP. Both last exons and flanking sequences were recorded. We were unable to determine the last exon for 367 genes.
Determination of completeness
The 3' end has not been determined for all transcripts. To calculate accurate statistics regarding last exon length, it is important to work with a dataset that contains complete last exons; this requires knowledge of which transcripts are complete on the 3' end. To determine whether transcripts were complete on the 3' end, we used two strategies. First, we examined the complete GenBank FlatFile records of the RefSeqs. Transcripts were considered complete on the 3' end if: 1. the Comment field contained the phrase: "COMPLETENESS: full length" or "COMPLETENESS: complete on the 3' end"; or 2. the Feature table contained the keys "polyA_signal" or "polyA_site". Second, because not all complete transcripts have been annotated as such in GenBank, we also aligned the 3' end of the extended mRNA sequence with genomic sequence (hs_phase3). If three or more "A"s were found on the 3' end of the transcript that were not present in the corresponding genomic sequence, we assumed that this indicated a poly-A tail was present in the extended mRNA sequence and that therefore the transcript was complete on the 3' end. We defined full length last exons as those derived from mRNA sequences that were complete on the 3' end.
Primer selection procedure
Affymetrix has identified Probe Selection Regions in the 3' ends of transcripts which are frequently contained within the last exon of genes. We aligned (BLAST) the last exons with the Probe Selection Regions. If there was at least 300 bp of alignment between the Probe Selection Region and the last exon, Primer3 [24] was used to select primers that flanked the Probe Selection Region (Figure 3). If not, Primer3 was used to amplify at least 300 bp of sequence from the last exon. The Human Mispriming library was selected; when this option is chosen, Primer3 screens out interspersed repeats from sequences which can be used for primers.
Figure 3 Diagram illustrating the strategy for designing primers to amplify 3' sequence from NHP genes. Exons are indicated by boxes. Solid lines represent introns. The dashed line indicates sequence 3' to the 3' end of the gene. The poly-A signal is indicated at the 3' end of the last exon. fp = forward primer; rp = reverse primer; PSR = probe selection region.
Bioinformatics
Automation of the Determination of Last Exons and Primer Selection procedures was accomplished with software written in Python and Java and employed packages from the site. An archived collection of Java methods used in determining last exons and generating primer pairs is included as Additional file 6. A complete description of the procedures used to determine last exons and design primers can be found in Additional file 7. A detailed description of the quality control procedures used to verify the results obtained for last exon determination and primer design can be found in Additional file 8. Data was stored and organized in PostgreSQL and Filemaker Pro databases. All primer sequences, PCR conditions and sequences generated as a result of this project have been deposited in GenBank (see Additional file 9 for accession numbers).
PCR
Genomic DNA was isolated from the liver of a one year old male rhesus macaque at the Oregon National Primate Research Center. Primers were synthesized by Sigma-Genosys (The Woodlands, TX) or IDT (Coralville, IA). Primers were resuspended in RNAse/DNAase free water to 50 picomoles/μl. Primers were then aliquoted into 96-well daughter plates with a Biomek 2000 robot (Beckman-Coulter). 10 μl of RNAse/DNAse free water, 1 μl of forward and reverse primers at 50 picomoles/μl and 2 μl of genomic DNA at 100 ng/μl were dispensed by the robot into each well of a 96-well PCR plate (MJ Research). A mastermix was prepared that included PCR buffer, dNTPs and water and was dispensed into each PCR well such that the final concentration was 1× PCR buffer and 200 μM dNTPs. 0.5 μl (2.5 units) of Fast Start High Fidelity Polymerase (Roche) was then added to each well. The PCR plate was placed in a MJ Research PTC-100 thermocycler and the following program used: Step 1. 95°C for 2 minutes; Step 2. 95°C for 30 sec, 51°C for 30 sec, 72°C for 1 min, 35 cycles; Step 3. 72°C for 7 minutes. For the primers that failed the first PCR, PCR conditions were altered. If the first PCR resulted in no band, the annealing temperature was decreased to 48°C. If the first PCR resulted in multiple bands, the annealing temperature was increased to 53°C. All PCR cleanups were done using the QIAquick 96-Multiwell PCR Purification System (Qiagen).
Cloning and DNA purification
Most PCR products were cloned into pGEM-T Easy (Promega). Some PCR products were cloned into pCR-TOPO XL (Invitrogen). After transformation, cells were incubated in SOC medium for 2 hours at 37°C at 180 RPM. 50 μl of the cell suspension was added to 35 mm LB-agar plates and incubated overnight at 37°C. Clones were picked and grown in 2xTY growth media and incubated overnight at 37°C at 300 RPM. Plasmid DNA was purified using the QIAprep 96 Turbo Miniprep Kit (Qiagen).
Sequencing and Genbank deposits
All clones were sequenced in both directions on an ABI3130 Genetic Analyzer using m13 forward and reverse primers. Sequences were aligned, edited in Sequencher (Gene Codes Corporation, Ann Arbor, MI) and BLASTed to check identity and percent homology with the targeted human homolog. The human primers were deleted from the edited sequence and the edited sequence deposited in GenBank following the standard STS format. STS files were generated from a Filemaker Pro database using the Troi File Plug-in 32. A list of accession numbers is provided as Additional file 9.
Authors' contributions
RBN proposed the project, developed the algorithms and wrote the manuscript. MAP designed and implemented the software used in primer selection and determination of last exon, participated in the analysis of the exon lengths and contributed to the writing of the manuscript. ERS assisted with development of the project, designed the strategies for and supervised the sequencing, sequence analysis, annotation and deposition and contributed to the writing of the manuscript. YJ and CG assisted with sequencing, sequence analysis and annotation. ST and NB assisted with the PCR, cloning, DNA preparation and data organization. SRO assisted with the development of the project.
Note
Website Reference
; Rhesus GeneChip Information
Supplementary Material
Additional File 1
Last exon sequences (Part 1). FASTA-formatted files containing the last exons of 15,401 unique human genes. XML-styles tags in the header are used to denote the LocusLink ID (now GeneID) and symbol of the corresponding gene. The tags <baseAccNo> and <extAccNo> denote the accession number of the base reference sequence and mRNA sequence, respectively, used to generate the extended mRNA sequence from which the last exon was derived.
Click here for file
Additional File 2
Last exon sequences (Parts 2). FASTA-formatted files containing the last exons of 15,401 unique human genes. XML-styles tags in the header are used to denote the LocusLink ID (now GeneID) and symbol of the corresponding gene. The tags <baseAccNo> and <extAccNo> denote the accession number of the base reference sequence and mRNA sequence, respectively, used to generate the extended mRNA sequence from which the last exon was derived.
Click here for file
Additional File 3
Last exon sequences (Parts 3). FASTA-formatted files containing the last exons of 15,401 unique human genes. XML-styles tags in the header are used to denote the LocusLink ID (now GeneID) and symbol of the corresponding gene. The tags <baseAccNo> and <extAccNo> denote the accession number of the base reference sequence and mRNA sequence, respectively, used to generate the extended mRNA sequence from which the last exon was derived.
Click here for file
Additional File 4
Procedure Flowchart. Provides a detailed overview of the procedure used to obtain primer pairs for the amplification of NHP orthologs of human genes.
Click here for file
Additional File 5
Accession numbers of Unique Reference Sequences (with LocusLink ID, now GeneID, and Gene Symbol). List of accession numbers of the 15,633 unique human mRNA Reference Sequences used to generate the human last exons in this study; includes the associated LocusLink ID (2nd column) and gene symbol (3rd column) for each sequence.
Click here for file
Additional File 6
Java methods. An archived collection of Java methods used in determining last exons and generating primer pairs. Used in conjuction with bioJava 1.30 (also included). See the file BlastObjectParser.java for a brief explanation on usage of the various processing methods.
Click here for file
Additional File 7
Detailed Methods. Contains the complete procedures used to determine last exons and generate primer pairs. Details left out for brevity in the main text are provided here.
Click here for file
Additional File 8
Quality control. Provides a detailed description of the quality control measures employed when generating last exons and primer pairs.
Click here for file
Additional File 9
List of STS Accession Numbers. List of GenBank accession numbers of the STS sequences generated in this study.
Click here for file
Acknowledgements
We are grateful to Dr. Jeff Rogers of the Southwestern National Primate Research Center for supplying us with baboon genomic DNA. RBN thanks Michael Lankhorst and Shaoai Jiang for their help with the PCR, cloning and DNA preparations. RBN also thanks Allan Williams, Christopher Davies, and Gangwu Mei of Affymetrix for Probe Selection Region Information. MAP thanks Daniel Quest and Alexander Churbanov for help with the BLAST parser. ERS thanks Stacey Lupo for her help with sequencing. This project was supported by a grant from NIH (RR017444) to RBN.
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-1661630575210.1186/1471-2164-6-166Methodology ArticleCharacterization of 954 bovine full-CDS cDNA sequences Harhay Gregory P [email protected] Tad S [email protected] John W [email protected] Michael P [email protected] Michael L [email protected] Warren M [email protected] Ralph T [email protected] Tassell Curt P [email protected] Timothy PL [email protected] USDA-ARS-U.S. Meat Animal Research Center, Clay Center, NE 68901, USA2 USDA-ARS-Beltsville Area Research Center, Beltsville, MD, USA2005 23 11 2005 6 166 166 4 7 2005 23 11 2005 Copyright © 2005 Harhay et al; licensee BioMed Central Ltd.2005Harhay 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
Genome assemblies rely on the existence of transcript sequence to stitch together contigs, verify assembly of whole genome shotgun reads, and annotate genes. Functional genomics studies also rely on transcript sequence to create expression microarrays or interpret digital tag data produced by methods such as Serial Analysis of Gene Expression (SAGE). Transcript sequence can be predicted based on reconstruction from overlapping expressed sequence tags (EST) that are obtained by single-pass sequencing of random cDNA clones, but these reconstructions are prone to errors caused by alternative splice forms, transcripts from gene families with related sequences, and expressed pseudogenes. These errors confound genome assembly and annotation. The most useful transcript sequences are derived by complete insert sequencing of clones containing the entire length, or at least the full protein coding sequence (CDS) portion, of the source mRNA. While the bovine genome sequencing initiative is nearing completion, there is currently a paucity of bovine full-CDS mRNA and protein sequence data to support bovine genome assembly and functional genomics studies. Consequently, the production of high-quality bovine full-CDS cDNA sequences will enhance the bovine genome assembly and functional studies of bovine genes and gene products. The goal of this investigation was to identify and characterize the full-CDS sequences of bovine transcripts from clones identified in non-full-length enriched cDNA libraries. In contrast to several recent full-length cDNA investigations, these full-CDS cDNAs were selected, sequenced, and annotated without the benefit of the target organism's genomic sequence, by using comparison of bovine EST sequence to existing human mRNA to identify likely full-CDS clones for full-length insert cDNA (FLIC) sequencing.
Results
The predicted bovine protein lengths, 5' UTR lengths, and Kozak consensus sequences from 954 bovine FLIC sequences (bFLICs; average length 1713 nt, representing 762 distinct loci) are all consistent with previously sequenced mammalian full-length transcripts.
Conclusion
In most cases, the bFLICs span the entire CDS of the genes, providing the basis for creating predicted bovine protein sequences to support proteomics and comparative evolutionary research as well as functional genomics and genome annotation. The results demonstrate the utility of the comparative approach in obtaining predicted protein sequences in other species.
==== Body
Background
Numerous whole genome sequence projects have been completed or are in progress, spanning a wide range of species among different orders. The genome sequences are providing novel insights into evolution and gene regulation that would have been impossible without these large-scale sequencing efforts. While a variety of sequencing strategies have been applied, the most common currently in use and the strategy chosen for the bovine genome relies mainly on whole genome shotgun (WGS) sequencing and assembly of the sequencing reads based on sequence similarity overlap. The bovine assembly will be supplemented by a much lower coverage of sequence from large-insert clones (Bacterial Artificial Chromosome, BAC) to provide connections between non-overlapping sequence contigs that represent chromosomal locations in close proximity to one another. A more comprehensive build of the genome sequence adds information from physical and genetic maps to WGS and BAC sequence to order contigs on a larger scale. An intermediate level of resolution and a critical check on the accuracy of the other methods can be provided by determining if the proper orientation, order, and spacing of exons in known expressed genes are maintained in the build. This approach requires knowledge of expressed transcript sequence to compare to the genome build.
Another use of transcript sequence is in annotation, a key to the utility of whole genome sequencing. Previous full-length cDNA sequencing projects have established the importance of experimentally derived mRNA sequences to produce gene models that establish accurate exon-intron boundaries [1-5]. These projects provided vital information about alternate splice forms of gene products that generate variation in form and function thought to be a key contributor to diversity in expression and phenotype. FLIC sequences also assisted in discriminating between alternative splicing and gene duplication or pseudogenes, a procedure that is difficult and error prone if based solely on clustered EST sequences.
The other main use of FLIC sequences has been generation of predicted protein sequence, providing a resource to support proteomic approaches and comparative analysis to reveal details of protein function. This goal requires accurate reconstruction of CDS portions of the bona fide transcripts expressed in the target tissues, which may be problematic with clustered EST as mentioned above.
The present effort was undertaken to support all of the potential uses of bFLIC data. The International Bovine Genome Sequencing Consortium [6] led by Baylor College of Medicine recently released the second, 6-fold coverage genome assembly (Worley, K. personal communication). Refinement of the assembly will be facilitated by incorporating bFLICs in the gene modeling and assembly process, similar to their utility in the assembly of genomes of other organisms. The bFLICs will also support efforts at NCBI and ENSEMBL to derive accurate gene models, and derive predicted protein sequence databases. In this sense, the present study is similar to previous full-length cDNA projects carried out for humans [1], mice [3], and other species [5,7]. However, a different approach was used to generate the data than in previously described efforts, as the first step of this project employed sequencing of pooled-tissue, normalized libraries [8,9] that had not been constructed by procedures to enrich for full-length clones, since such procedures could potentially introduce bias that would decrease the diversity of observed mRNA. Moreover, a primary goal of the project was to develop a method to consistently select full-CDS clones from these libraries based on comparison of the single-pass, 5' end sequences to the human Reference Sequence [10] (RefSeq) mRNA database.
This report characterizes the sequences of bovine full-CDS clones selected with a method using 5' end EST sequence data as input. This method efficiently identified apparent bovine homologs of human RefSeq mRNA sequences, collected the full insert sequence, and annotated the resulting bFLICs with GeneIDs, product, repetitive elements, and predicted protein sequences. The method described should be particularly useful for generating full-CDS and predicted protein sequences for organisms with mature databases of sequence from other species in the order (e.g. other mammals) but not included in complete genome sequence projects. The success of the method was characterized by comparison of the bFLIC sequences to human Refseq mRNA and mammalian UTRdb, [11]. Because the investigation was initiated prior to release of the assembled bovine genome, direct comparison between bovine genomic and bFLIC sequence was problematic.
Without available genomic or full-CDS cDNA sequence, it is common practice to rely on gene clusters such as Unigene [12] or TIGR Gene Indices [8,9,13,14] for transcript predictions. These computational derived consensus assemblies containing open reading frames (ORFs) are generated from single pass reads through cDNA libraries. These clusters provide a very important resource for putative gene models and products. The TIGR Bos taurus Gene Index (BtGI) was compared to bovine full-CDS sequences to confirm the existence of experimentally determined transcripts in the computed clusters. This characterization of gene clusters to full-CDS sequences may assist investigators to interpret the significance of their searches against gene cluster databases.
Results and Discussion
Strategy for bovine full-CDS selection and sequencing
The overall strategy for bFLIC processing is depicted in Figure 1 and is similar to an approach recently described for chicken bursal lymphocytes[15]. Single pass 5' reads from bovine clones from five pooled-tissue, normalized EST libraries [8,9] were compared to human RefSeq transcripts to identify potential full-CDS clones so each selected clone was associated with a human RefSeq and GeneID. These EST libraries were chosen because they were generated by the USDA labs collaborating on this project, so the clones were readily obtainable, and they represented over 70% of the total EST sequences in GenBank at the time the project was initiated.
Figure 1 Scheme for bovine full-length insert cDNA (bFLIC) sequence production. Overall scheme for selecting, sequencing, and annotating bFLIC clones. If the largest ORF of the bFLIC spans the CDS of a human RefSeq transcript, then this clone is designated a full-CDS bFLIC with "complete cds" in the definition line in the GenBank submission.
The majority of clones were selected to represent unique loci as defined by human GeneID, and in cases where multiple EST clones were available for a given GeneID the clone with the longest predicted clone length was chosen. Additional criteria were also used relative to the predicted length of insert based on human cDNA length, in order to avoid clones of relatively short insert length. Specifically, clones were selected in size categories between 1,000 and 5,000 bp. A minority of clones were then chosen that were redundant to previously targeted GeneID to ascertain the impact of alternative splicing on EST cluster-based sequence databases. This clone selection yielded full-CDS bFLICs cDNAs with 80% efficiency, which was limited in part by the method of library construction that incorporated a digestion with restriction enzyme NotI following second-strand cDNA synthesis to generate a compatible cloning site on the 3' end of the cDNA [8,9]. Of the 20% failures, 45% are due to NotI sites within the transcript sequence that caused premature termination of the cDNA representations of the transcripts. This is a much higher rate than anticipated based on the average occurrence of NotI sites in genomic DNA and probably reflects a higher percentage of cytidine (C) and guanosine (G) in mRNA sequence (the recognition site for NotI is GCGGCCGC). Hopefully, recent advances in cDNA library production that avoid this type of difficulty will reduce the incidence of truncated clones in future efforts.
Putative full-CDS FLICs selected were sequenced with a "primer walking" procedure in which each sequence read was used to design a primer to extend sequence in the 3' direction. The reads were assembled into contigs, screened for polyA tail and vector, and compared to the human RefSeq transcripts after every walk. Once the 3' end of the insert was encountered (polyA tail or vector), the contig was manually checked for low quality base calls; 5' and 3' finishing primers were used to improve these low quality regions before they subjected to annotation. For each bFLIC, the translated longest ORF (putative protein coding sequence) of the bFLIC was compared to the RefSeq protein database using BLASTP. The bovine protein-human protein comparison served as consistency check with respect to the annotators' association of the bFLIC to human RefSeq. The bFLIC nucleotide sequence comparison to human RefSeq protein sequence (BLASTX) exposed potentially artificial frameshifts/insertion/deletions if present. Only when there was agreement between the annotators' annotation and the computational comparisons were the bFLICs submitted to GenBank.
Summary and length distributions of the bFLICs
Figure 2 shows the distribution of bFLIC clones with mean 1713 nt (s.d. = 557) with values ranging from 605 to 3767 nt. This multi-modal distribution reflects the non-random selection criteria employed. Predicted clone lengths targeted were 1000 +/- 200, 1500 +/- 200, 2500 +/- 200, and 3000 – 4000 nt. This histogram shows that bFLICs larger and smaller than 2000 nt can be successfully sequenced. The bFLIC data is summarized in Table 1. The bFLICs have been used as the source sequence for 411 bovine RefSeqs for annotating and assembling the NCBI build of the bovine genome.
Figure 2 Distribution of bFLIC lengths. Clone lengths targeted were 1000 +/- 200, 1500 +/- 200, 2500 +/- 200, and 3000 – 4000 nucleotides (nt).
Table 1 Summary of full-CDS bFLICs
Number bFLICs submitted 954
Number unique loci 762
Average length (nt) 1713
Success rate (number full-CDS Sequence/number clones sequenced) 80%
Number Bt full-CDS bFLICs used as source clones for GenBank Bt gene models (Entrez Gene) 411
Comparison of bFLICs to human RefSeq mRNA and protein
The protein sequence lengths translated from the full-CDS bFLIC CDSs range in length from 68 to 937 amino acids (aa) (Figure 3). In general, the bovine proteins lengths are similar to that of their human homologs. The relationship between homologous bovine and human proteins is demonstrated in Figure 4, where the distribution of bovine protein lengths is plotted versus their fractional difference from human protein homolog lengths. Figure 4 shows that the most common occurrence is when the bovine and human protein homolog lengths are the same, this occurs with 44% of bovine full-CDS clones. Seventy-five percent of the bovine full-CDS clones code for proteins within +/- 7% their human homolog protein lengths. Bovine proteins that are shorter than their human homologs constitute 34 % of our submission, while those that are longer constitute 22 %. These results show that while 75% of the bFLICs code for proteins identical or nearly identical in length to their human homologs, the remaining bFLICs tend to be shorter than their human homologs rather than longer. Comparison of the "short" bFLICs to the human genome and message sequence show reveals no obvious preference for internal vs. 3' terminus exon excision/change in the "short" bFLICs. The tendency towards shorter bFLICs may be due to a cloning bias towards shorter inserts resulting in the selection and sequencing of shorter bFLIC isoforms. Alternatively, this tendency may reflect fundamental differences in gene structures between human and cattle orthologs and/or paralogs. The possibility that some of these short bFLICs are associated with pseudogenes cannot be eliminated.
Figure 3 Predicted bovine protein length vs. human protein length. Comparison of the predicted proteins from 954 full-CDS bFLICs vs. their human RefSeq protein homologs, unit is amino acid (aa).
Figure 4 Distribution of predicted bovine protein lengths vs. fractional difference from human protein homologs. The frequency of occurrence of bovine protein lengths vs. the fractional difference of protein length from human homolog, (human - bovine)/human.
Comparison of bFLICs 5' UTR to mammalian 5' UTR – verifying CDS start statistics
The differences in bovine protein length relative to their human homologs could be an indication of systematic errors in the clone picking algorithm, sequencing, or annotation procedures. Since the bovine clones were selected to have a high degree of homology within the region of the human message surrounding its initiation codon ATG, differences in clone length should be attributable to truncation/extension of the CDS and differences in the 3' and 5' untranslated region (UTR). The incorrect determination of CDS start in the clone selection step, sequencing errors generating frameshifts and/or insertions/deletions, and misidentifying CDS start in the annotation process could all contribute to the misidentification of the extent of CDS, and by inference, the 5' UTR. Comparisons between full-CDS bFLIC and mammalian 5' UTR length distributions would show a bias towards larger or smaller bovine 5' UTR if the bovine CDS start was systematically chosen too far upstream or downstream of its actual position. Figure 5 shows that bovine and mammal 5' UTR length distributions are very similar throughout the range of 5'UTR lengths. Because only 954 sequences were sequenced, relatively few bovine full-CDS clones were found with 5' UTR lengths > 300 nt. This comparison indicates that start methionines weren't systematically misidentified skewing the 5' UTR lengths, but rather, is consistent with previously annotated 5' UTR mammalian sequence.
Figure 5 Comparison of the 5' UTR lengths distribution between mammalian transcripts and bFLICs. Distribution in 5'UTR lengths (nucleotide) from the 6262 5' UTRs in UTRdb (release 16) and 954 bFLIC 5' UTRs. Density is calculated so that the integrated area of all of the bins for each distribution is equal to 1.
Comparison of bFLICs to mammalian Kozak consensus sequences
The vertebrate initiation codon context is (A/G)CCATGG [16,17], with the initiation ATG codon underlined. The consensus sequence in Figure 6 shows that the most highly conserved position is 3 nucleotides upstream from the start codon. This consensus sequence exhibits the expected behavior, with the most highly conserved position, being an A, 3 nucleotides upstream from the start codon at position -3. The comparison of bovine consensus start logo to the human consensus start logo in Iacono et al. [18] reveals a high degree of similarity. This comparison shows that although there is less conservation at positions -3 and -2 in cattle, there is roughly equal conservation at positions -1 and +4 in cattle and human. Moreover, the relative preference for every nucleotide base from positions -3 to +4 is identical between cattle and human. This high degree of similarity may be surprising, especially since the Kozak sequence is not strictly conserved in eukaryotic mRNAs [19]. Bovine clones were selected for sequencing based on their close homology to human near the CDS start, so it shouldn't be the surprising that sequences were obtained that were similar to human near the CDS start. The conservation of the bovine Kozak consensus sequence suggests that, as with the 5' UTR analysis, start methionines weren't systematically misidentified, but rather, is consistent with previously annotated human transcripts.
Figure 6 Kozak consensus sequence surrounding bFLIC start methionine. Kozak consensus sequence surrounding bovine start methonine using WebLogo [29].
Alternative splicing
Multiple clones were selected for 92 loci, ranging from 12 clones for a single locus (COMMD4 GeneID:54939) to 2 clones for 51 loci. Comparison of full-CDS bFLICs to human message, protein, and genomic suggests alternative bovine transcripts exist for five loci, PSMD4 (GeneID:5710), BCL2L14 (GeneID:79370), NME7 (GeneID:29922), ZDHHC16 (GeneID:84287), HYAL1 (GeneID:3373). Figure 7 shows the comparison of the 3BOV112D22 (BT021708) and 2BOV3D19 (BT021853) to human RefSeq NM_032327. This shows a gap in the coverage of 3BOV112D22 on the human RefSeq CDS while 2BOV3D19 completely covers the human CDS. In Figure 8, where the full-CDS bFLICs are compared to human genomic, it is observed that an exon is present in 2BOV3D19 that is absent in 3BOV112D22. Alternate splicing has been observed for these five loci in humans.
Figure 7 Putative alternative splicing in bFLICs – bovine transcripts vs. human transcript. Alignment of 3BOV112D22 (BT021708) and 2BOV3D19 (BT021853) full-CDS bFLICs to the human RefSeq transcript NM_032327 of the ZDHHC16 gene.
Comparison of bFLICs to TIGR BtGI
The sequences from all EST libraries used for this study have been previously incorporated into the TIGR BtGI. This presented an opportunity to verify the TCs (Tentative Consensus sequences) constructed with single pass reads of source clones by comparing them to contigs built from multi-pass full-length sequencing of the same source clones.
The TCs of TIGR BtGI (Release 11, September 28, 2004) were compared to the full-CDS bFLICs using BLAT[20]. A threshold of 300 or more identities, 1/2 the size of our shortest bFLIC, was chosen to minimize short matches. After the identities threshold was applied, a total of 1346 distinct TCs were found to be similar to 933 of the original 954 bFLICs. If only bFLICs that are members of TCs were considered, 1250 TCs were found to be similar to 855 distinct bFLICs. If there was a further constraint that only matches between a (query) TC and it's (subject) member source clones be considered, then 740 distinct TCs were found to be similar to their source member bFLICs. In the latter analysis, 1 TC can match multiple bFLICs, but not vice versa. This number is quite close to the 762, the number of distinct loci associated with our 954 bFLICs generated in the annotation pipeline. 92 full-CDS bFLICs are not members of a TC.
The analysis of the BLAT similarities between the TIGR BtGI and bFLICS is complicated by the fact that because multiple TCs can represent a single locus by virtue of alternative splice forms, mis-assembly, or other aspects of shared gene structure, a single bFLIC may be similar to multiple TCs besides its parent TC. Accordingly, the BLAT analysis was segregated into two groups. The first group (A) was the comparison of the bFLICs to all 40,810 TCs, where in general, and given our BLAT threshold, a bFLIC will be similar to more than 1 TC. This comparison results in BLAT hits to 1346 TCs. The second group (B) was a comparison of 855 full-CDS bFLICs to only those TCs that the bFLICs are members of, a smaller set (740) of TCs than the first group. Group B TCs represent the minimum number of TCs that span the "transcription potential" of 855 bFLICs.
Table 2 show that complete or nearly complete fractional coverage (> = .95) of a bFLIC by a single TC was observed for nearly 1/2 of the bFLICs relative to groups A and B. Relaxing the fractional coverage requirement to determine the number of bFLICs that have .95 or less TC fractional coverage, exposes a significant difference between group A and B. In group A, 155 bFLICs (out of 933) are shared between the > = .95 and < .95 fractional coverage levels accounting for the distribution of bFLICs between the two levels (672 + 414 - 153 = 933). This is not unexpected as a TC that exhibits > = .95 fractional coverage from one bFLIC, may also cover at < .95 for another bFLIC. In group B, only 2 bFLICs (out of 855) are shared between the > = .95 and < .95 fractional coverage levels, accounting for the distribution of each bFLIC (401 + 456 - 2 = 855) between these two levels. Group B provide a less redundant description of the transcript space of the bFLICs. Unfortunately, when analyzing the sequence of non-TC member transcripts the analysis would typically be conducted in a group A manner. As the bFLIC fractional coverage level is reduced down from < .95 through < .15 level, the number of bFLIC meeting this requirement decreases. The process of decreasing the fractional coverage level amounts to finding bFLICs that aren't well represented by the TCs in the TIGR BtGI. As the fractional coverage level decreases, the bFLICs expectedly tend to lengthen. The data in Table 2 shows that about 1/2 the bFLICs are represented well by single TCs in the TIGR BtGI using strict similarity criteria, but using less strict similarity criteria will result in a more ambiguous representation of the bFLIC using TCs because of the similarity between TCs. As average bFLIC lengths increase from about 1500 nucleotides, the probability decreases that a single TC matches with high fidelity an entire bFLIC.
Table 2 BLAT Results: TIGR BtGI TCs vs. full-CDS bFLICs with identities > = 300
A B
No bFLIC TC membership requirement bFLIC required to be member in query TC
933 total bFLICs in all alignments 855 total bFLICs in all alignments
Fractional coverage of bFLICs by any single TC Number of bFLICS Average Contig Length Number of bFLICS Average Contig Length
> = .95 414 1470 401 1474
< .95 672 1782 456 1865
< .90 639 1797 409 1909
< .80 615 1810 360 1964
< .50 531 1846 264 2081
< .25 389 2139 163 2484
< .20 97 2430 29 2664
< .15 29 2781 8 2920
In order to quantify how well the TCs cover the bFLICs, the number of bFLICs matched by TCs at different fractional coverage levels was determined. Fractional coverage of a bFLIC by a TC = number of identities in the BLAT match/bFLIC length.
Single pass 5' and 3' reads for 169 full-CDS bFLICs were previously incorporated into the TIGR BtGI. The 5' and 3' single pass reads for 94 (56%) were assembled into the same TCs, while 75 (44%) single pass end reads were placed in different TCs. Using the admittedly limited dataset of 169 bFLICS, it is observed that about 1/2 of the TCs were self-consistently constituted from their source clone sequences. It is likely that the TCs not self-consistently constituted were assembled without adequate data linking the two ends from the single source clone.
Conclusion
The bovine transcript sequences described here presently represent the largest publicly accessible resource of annotated full-CDS bFLICs. [Note added during review: since this manuscript's submission, 1710 bovine full-length insert cDNA sequences have been submitted to the Mammalian Gene Collection at NCBI by the Bovine Genome Sequencing Program, Genome Sequence Centre, BC Cancer Agency, Vancouver, BC, Canada] The comparative genomics approach employed for clone selection and the database driven sequencing and analysis pipeline provides a mechanism to target and produce full-CDS bFLICs for specific loci that are represented in available cDNA libraries. The full-CDS bFLICs are being incorporated in the NCBI build of the bovine genome. The approach described here should be adaptable for producing full-CDS FLICs for other organisms, and is particularly appropriate for those organisms without available FLIC sequences but with 5' end EST sequences. Analysis of the bFLICs with respect to TIGR BtGI shows that about 1/2 the bFLIC sequences are well represented by the TCs, while a smaller fraction of the remaining bFLIC aren't well represented by any single TC. Smaller bFLICs are more easily represented by a single TC in the TIGR BtGI than longer bFLICs. As TCs grow larger than 1500 nucleotides, they become increasingly dissimilar from their FLIC counterparts, and therefore become increasingly less suitable as evidence for basing an accurate gene model on. The full-CDS bFLIC Kozak consensus sequence and 5' UTR length distribution is consistent with prior human and mammal transcript data. The genome complexity exhibited by the alternatively spliced bFLICs correspond to human loci that also exhibit alternatively spliced human transcripts. These results only hint at the bovine genome's inherent complexity. These bFLICs and their annotations provide a significant starting point to investigate the bovine genome and gene expression.
Methods
Clone selection
A total of 195,443 5' end sequence reads from the 1BOV, 2BOV, 3BOV, 4BOV, and 5 BOV [8,9] cDNA libraries were masked for repeats with RepeatMasker[21] and compared to human RefSeq mRNA using BLAST [22] yielding 146,741 distinct bovine clones with BLAST hits, 116,911 of which had 300 or more bases with phred quality score greater than or equal to 20. The bovine cDNAs were associated with the human RefSeq mRNAs with the highest bit score, and through the RefSeqs, the bovine cDNAs were associated with human GeneIDs. Based on the clone sequence similarity to the beginning of CDS of human RefSeq mRNAs, 9,989 potential full-CDS clones were identified, associated with 3,482 distinct human loci. Predicted clone length was defined by the sum of the length of the bovine clone upstream of the beginning of the BLAST match plus the length of the human RefSeq sequence downstream from the beginning of the BLAST match. The putative full-CDS clones were grouped by loci (human GeneID) and predicted clone length.
Clones were loaded in 384 well plates and sequenced via primer walking, using a combination of automatically generated primers from autofinish [23], or manually generated primers from consed [24]
Primer walking
The clones were sequenced, assembled, and annotated in a semi-automated pipeline involving a database that stored and provided sequencing, primer, and annotation information for every clone. Perl scripts were used to process reads, place them in the appropriate directories, instantiate phredPhrap for contig assembly, automate the detection of polyA, vector, pick walking primers and update the database. bFLIC clones were sequenced 5' -> 3' until polyA or vector was encountered. In regions of low quality sequence, reverse read primers were manually picked. Perl scripts were also use to automate BLAT[20] comparisons of bFLICs with human RefSeq mRNAs.
Annotation
Human annotators assigned bFLICs to human RefSeq mRNA homologs and determined whether or not the bFLIC was a full-CDS clone. Gbrowse [25] was used to display bovine/human alignments and Artemis [26,27] was used for manual annotation of sequence when required. A clone was deemed to be full-CDS if the BLAT query bFLIC region encompassed the entire CDS of its human homolog, and/or the BLAT query region encompassed the beginning of the human homolog's start methionine and exhibited a polyA stretch of at least 13 adenosines on the 3' end. Subsequently, each masked bFLIC was processed through the quality check/assurance portion of the pipeline where the largest translated ORF was compared with BLASTP [22] to human RefSeq proteins and the entire nucleotide sequence of the bFLIC was compared to RefSeq proteins with BLASTX [28]. A bFLIC was flagged for GenBank submission only if the highest scoring BLASTX and BLASTP hits originated from the same RefSeq mRNA and was identical to the transcript assigned through human review.
RepeatMasker parameters
-species cow -xsmall. Repeats are masked with lower case letters using the cattle specific repeat library.
BLASTX parameters
-U -F "m S" -I T -f 14 -e 1e-20 -a 2 -b 15. Use RepeatMasker output as input. Allow for extension through repetitive regions, but alignment isn't seeded in repetitive region (soft masking).
BLASTP parameters
-v 1 -b 1 -f 14 -e 1e-20 -a 2
The GenBank Accessions for the bFLIC clones are: [GenBank:BT020623 GenBank:BT021084, GenBank:BT021145 ... GenBank:BT021203, GenBank:BT021479 ... GenBank:BT021911].
Kozak consensus sequence
The sequence spanning from 6 nucleotides upstream to 3 nucleotides downstream of the adenosine of the start ATG was extracted from each bFLIC and aligned with clustalw. The alignment file was used as input to WebLogo.
Authors' contributions
GPH designed the full-CDS database, developed and implemented algorithms for full-CDS FLIC detection and scripts for the sequencing, analysis, annotation and submission pipelines. TSS performed FLIC sequencing and annotation. MPH and MLC assisted in the annotation of the bFLICs as well as suggesting improvements in the pipeline. WS assisted in running sequence comparisons on a compute cluster, and perl script development. RW assisted in masking repeats. KV assisted in FLIC sequencing. JWK assisted in the design of the full-CDS database and FLIC pipelines. TPLS contributed to all phases of algorithm, database and pipeline development as well as overseeing FLIC sequencing.
Figure 8 Putative alternative splicing in bFLICs – bovine transcripts vs. human genome. Alignment of 3BOV112D22 (BT021708) and 2BOV3D19 (BT021853) full-CDS bFLICs against human genomic (and splice forms) where an extra exon in the CDS is present in 2BOV3D19 that is absent from 3BOV112D22.
Acknowledgements
Steve Simcox performed sequencing reaction setup for the majority of sequences and made critical improvements to quality control procedures. Renee Godtel provided expert annotation assistance. Tina Sphon assisted in sequence reaction setup.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7452ehp0113-00165916330343Commentaries & ReviewsThe Tobacco Industry and Pesticide Regulations: Case Studies from Tobacco Industry Archives McDaniel Patricia A. 1Solomon Gina 23Malone Ruth E. 41 Center for Tobacco Control Research and Education, University of California, San Francisco, California, USA2 Natural Resources Defense Council, San Francisco, California, USA3 Division of Occupational and Environmental Medicine, Department of Medicine, University of California, San Francisco, California, USA4 Department of Social and Behavioral Sciences and School of Nursing, University of California, San Francisco, California, USAAddress correspondence to P. A. McDaniel, Center for Tobacco Control Research and Education, University of California-San Francisco, 530 Parnassus Avenue, Suite 366, San Francisco, CA 94143-1390, USA. Telephone: (415) 514-9342. Fax: (415) 514-9345. E-mail:
[email protected] authors declare they have no competing financial interests. G.S. is employed by an environmental nonprofit organization with an interest in ensuring that regulations of toxic chemicals are as health protective as feasible.
12 2005 8 8 2005 113 12 1659 1665 27 7 2004 8 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Tobacco is a heavily pesticide-dependent crop. Because pesticides involve human safety and health issues, they are regulated nationally and internationally; however, little is known about how tobacco companies respond to regulatory pressures regarding pesticides. In this study we analyzed internal tobacco industry documents to describe industry activities aimed at influencing pesticide regulations. We used a case study approach based on examination of approximately 2,000 internal company documents and 3,885 pages of U.S. Environmental Protection Agency documents obtained through Freedom of Information Act requests. The cases involved methoprene, the ethylene bisdithiocarbamates, and phosphine. We show how the tobacco industry successfully altered the outcome in two cases by hiring ex-agency scientists to write reports favorable to industry positions regarding pesticide regulations for national (U.S. Environmental Protection Agency) and international (World Health Organization) regulatory bodies. We also show how the industry worked to forestall tobacco pesticide regulation by attempting to self-regulate in Europe, and how Philip Morris encouraged a pesticide manufacturer to apply for higher tolerance levels in Malaysia and Europe while keeping tobacco industry interest a secret from government regulators. This study suggests that the tobacco industry is able to exert considerable influence over the pesticide regulatory process and that increased scrutiny of this process and protection of the public interest in pesticide regulation may be warranted.
ethylene bisdithiocarbamatesEnvironmental Protection Agencymethoprenepesticide regulationphosphinetobacco industryWorld Health Organization
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Tobacco is a pesticide-intensive crop. With nearly 27 million pounds of pesticides (including insecticides, herbicides, fungicides, and suckercides) applied to the U.S.-grown crop from 1994 to 1998, it ranks sixth in terms of the amount of pesticides applied per acre [U.S. Government Accounting Office (GAO) 2003]. The tobacco industry regards pesticides as essential to tobacco production, stating that “the crop could not be produced economically without them” (Davis 1989; Philip Morris 1990b). According to industry documents, government-imposed limitations on pesticide use “may present a serious impediment” to the international tobacco trade (Hill 1989).
Internal tobacco industry documents provide a window into the tobacco industry’s activities regarding pesticide regulations. These case studies drawn from industry documents describe the tobacco industry’s responses to pesticide regulatory action. The documents also provide insight into the relationships between the tobacco industry and pesticide regulatory agencies and tensions between business and public health interests.
The Tobacco Industry Documents
Litigation against the tobacco industry has resulted in the release of nearly 7 million previously secret tobacco industry documents (Bero 2003; Malone and Balbach 2000). Scanned PDF versions of original handwritten, typed, or printed documents have been archived at the University of California, San Francisco, library in electronic repositories, searchable using basic keywords (http://legacy.library.ucsf.edu). Between July 2003 and February 2004, we searched the archives using a “snowball” sampling strategy, beginning with broad search terms (“pesticide” and “crop protection agent”) and using retrieved documents to identify more specific search terms (such as names of specific pesticides, people, and regulatory agencies). Table 1 provides examples of keyword searches and the number of documents yielded. This process produced nearly 300,000 documents relating to many different pesticides. The first author reviewed these documents’ index entries and excluded duplicates and documents unrelated to pesticide regulatory issues. The final sample size was approximately 2,000 documents, spanning 1974–2001.
We also filed Freedom of Information Act (FOIA) requests with the U.S. Environmental Protection Agency (EPA) on pesticide issues raised by information in the industry documents, resulting in 3,885 pages of government documents. Finally, we reviewed public health agency reports based on industry documents (Zeltner et al. 2000). We analyzed the industry, government, and public health agency documents by assembling chronologically constructed case studies, a method common to sociology, political science, and anthropology (e.g., analyses of a corporation’s organizational structure, a social movement, or a tribe) (Hill 1993; Yin 1994) (Table 2). The pesticides chosen for inclusion [methoprene, the ethylene bisdithiocarbamates (EBDCs), and phosphine] were those for which sufficient information related to regulatory activities was available in the archives to construct a case study.
Pesticides and Tobacco
Pesticides used on tobacco are also used regularly on food crops. As with food crops, trace amounts of pesticides remain on tobacco leaves after treatment; typically, residue levels decline during the drying and manufacturing process, although additional pesticides may be applied to the finished product (U.S. GAO 2003). Although pesticides increase production of tobacco and food crops, pesticide exposure may harm humans; thus, regulatory agencies such as the U.S. EPA may set limits on the amount of pesticide residue permitted in or on food and tobacco and establish standards for workers handling pesticides. Because tobacco is burned and the smoke inhaled, active and passive smokers are exposed to pyrolyzed pesticide residues (U.S. GAO 2003). The U.S. EPA has concluded that this exposure poses no short-term risk, but little is known about the long-term health effects (U.S. GAO 2003).
Methoprene
In 1974, Philip Morris formed a partnership with the chemical company Zoecon to market a new insecticide (Manzelli 1975). The insecticide’s active ingredient, methoprene, acted as an endocrine disruptor in cigarette beetles and tobacco moths, preventing their larvae from maturing into adult insects (Manzelli 1975). Philip Morris anticipated that methoprene would replace phosphine, a common warehouse fumigant (Philip Morris 1988) and pledged to assist Zoecon in introducing methoprene “in as many countries as we can” (Seligman 1982).
Some countries have regulations that require the establishment of maximum residue limits (MRLs) for pesticides on crops; however, Philip Morris determined that MRLs were not required in all countries, especially for pesticides on nonfood crops such as tobacco (Ryan 1991). Philip Morris asked Zoecon “to not force this issue and submit for MRLs when not required” (Lindahl 1992b). In April 1991 Zoecon alerted Philip Morris’s director of research that the Malaysian pesticide board had recently set an MRL of 1.0 ppm for methoprene on tobacco (Hutney 1991). Zoecon considered 1.0 ppm too low to enable the effective use of methoprene; the level supported by the labeled application rate was 10 ppm (Ryan 1992). Philip Morris requested that Zoecon ask for an even higher MRL of 15 ppm to allow for application errors (Greenberg and Transon 1992; McCuen 1992). Zoecon representatives met with government authorities and requested a change to 15 ppm (Hutney 1991).
A Zoecon representative informed Philip Morris that “in order to avoid surprises of this nature in the future,” he had directed Zoecon’s pharmaceutical group to obtain information from health authorities in other countries regarding the commodities for which methoprene tolerances were assigned (which could include foods such as rice and mushrooms as well as tobacco) (Hutney 1991). Assigning this task to the pharmaceutical group instead of the pesticide group, the Zoecon representative wrote, “will not arouse the curiosity of the health directorates and will allow us to keep our promise to the tobacco industry, namely, that we won’t initiate queries that may cause the health authorities to direct attention to tobacco” (Hutney 1991).
In April 1992, George Lindahl of Zoecon faxed a letter to Bob McCuen, head of Philip Morris’s biochemical research, outlining some of his concerns about Philip Morris’s approach to establishing MRLs for methoprene on tobacco (Lindahl 1992b). In regard to Zoecon’s effort to establish an MRL of 15 ppm in Malaysia, Lindahl explained that
I know we simply argued this case without any data to support our request. In more advanced countries, this tactic will not succeed. … All our data demonstrate the need for a 10 ppm MRL. If a higher value is desired then we will require data from real field operations showing that a worse [sic] case scenario for faulty application will result in a 15 ppm residue, and hence the need for this value. (Lindahl 1992b)
In a fax following this one, Lindahl asked Philip Morris to provide such data; a handwritten comment from a Philip Morris employee who reviewed the fax noted that “data doesn’t [sic] exist” (Lindahl 1992a). Initially, the Malaysian authorities agreed to increase methoprene’s MRL to 10 ppm (Lindahl 1992c); subsequently, it was raised to 15 ppm (Mueller and Ward 1998). Philip Morris continued to advocate (through Zoecon) for MRLs of 15 ppm in Italy and Germany (Greenberg and Transon 1992).
In the meantime, anticipating the creation of a single European market with uniform pesticide regulations, Philip Morris had asked the longtime tobacco industry law firm, Shook, Hardy, and Bacon, to prepare a document with MRL recommendations for possible submission to the European Community (Kemna 1991). Philip Morris first provided a draft of recommended MRLs to the Scientific Working Group of the Confederation of European Community Cigarette Manufacturers (CECCM) (Philip Morris 1991c). At their June 1991 meeting, members of this group (including representatives of Philip Morris, British American Tobacco, R.J. Reynolds, Gallaher, and Rothmans) recommended that the document be rewritten as a voluntary code of practice “to be used pre-emptively … in advance of any EC [European Community] initiative” to impose formal regulations on pesticide residue limits on tobacco (Philip Morris 1991a). A meeting participant reported, “It is hoped that, by implementing this Code, the EC Commission would not any longer see the need to develop a formal EC regulation on pesticide residues in tobacco (products)” (Mueller 1991). Manuel Bourlas, Philip Morris’s director of research and development, was appointed chair of a subgroup of tobacco company representatives who were to assist in preparing the code (Philip Morris 1991a).
This voluntary code underwent numerous revisions throughout 1991 and 1992 (CECCM 1991, 1992a, 1992b, 1992c, 1992d, 1992e; Philip Morris 1991b). Although 236 regulated and unregulated tobacco pesticides were in use at the time (Mitchell 1991b), the voluntary code proposed MRLs for only 25–27 pesticides [including chlordane, dichlorodiphenyltrichloroethane (DDT), lindane, dithiocarbamates, methoprene, and maleic hydrazide]. According to British American Tobacco’s Terry Mitchell, “many of the substances in the list are no longer recommended for tobacco production” (e.g., DDT) (Mitchell 1991a). Moreover, this list did not impose “any constraint automatically on non-specified substances” (Mitchell 1991a). Mitchell noted that this lack of limits was “highly desirable” (Mitchell 1991a).
In December 1992, Walter Russell, a legal assistant, reported that the code “has undergone two more revisions (by SHB) [Shook, Hardy, and Bacon] and it [is] currently watered down, but still causing much agitation” (Philip Morris 1992). Russell pointed out that the code set MRLs that Philip Morris “might have trouble complying with” if they were to become international standards (Philip Morris 1992). In addition, “failure to comply with tolerances written by the tobacco industry which might come up during litigation would put the tobacco industry at great disadvantage” (Philip Morris 1992). He indicated that Philip Morris had decided to withdraw its support from the voluntary code (Philip Morris 1992). In 1993, the tobacco companies suspended work on the document due to “principle disagreements both within and between participating companies” (R.J. Reynolds 1993). Throughout the 1990s, the tobacco industry continued to anticipate European Union harmonization of tobacco pesticide MRLs (Philip Morris 1995); as of April 2004, the European Union had established community-level MRLS for 150 pesticides, but none specifically applied to tobacco (European Union 2004).
EBDC Fungicides
In 1987, the U.S. EPA initiated a review of EBDC fungicides, prompted by the agency’s determination that a breakdown product of EBDCs, ethylene thiourea (ETU), was a probable human carcinogen (U.S. EPA 1987). Anticipating the U.S. EPA’s cancellation of many EBDC uses, U.S. manufacturers voluntarily withdrew EBDC registrations for all but 13 food crops in 1989, including wheat and corn (U.S. EPA 1989). At least one company continued to hold registrations for EBDCs on tobacco, but only for seed bed use, not plants (Arce 1989).
In internal documents, the tobacco industry expressed concern that the U.S. EPA’s action could result in the “imposition of potentially crippling product residue tolerances” in Europe [Centre de Coopération pour les Recherches Scientifiques Relatives au Tabac (CORESTA) 1989b; Mitchell 1990]. EBDCs were regarded as vital to control blue mold outbreaks in Europe (Philip Morris 1990a). In October 1989, members of CORESTA, an international tobacco research organization with members drawn largely from the tobacco industry, established a subcommittee to “provide regulatory agencies with a sound basis for the development of tobacco agro-chemical regulations” (CORESTA 1989a, 1989b).
As discussed in a larger World Health Organization (WHO) report on tobacco industry influence at that agency, the subcommittee hired a consultant, Gaston Vettorazzi, to provide advice on influencing regulation (CORESTA 1990b; Zeltner et al. 2000). Vettorazzi was a former WHO toxicologist and former technical secretary of the Joint Food and Agriculture Organization/WHO Meeting on Pesticide Residues (JMPR), an international meeting of scientists whose decisions often formed the basis of international law (Zeltner et al. 2000). Selected partly for his “old boys’ contacts” (Reif 1991b), Vettorazzi’s initial duties were to provide a review and analysis of toxicologic data on EBDCs and ETU (CORESTA 1990a).
Some CORESTA members were concerned that Vettorazzi’s review might conclude that EBDCs were unsafe (Beuchat 1990). However, according to one member’s notes, at his first meeting with the subcommittee in April 1990, Vettorazzi stated that “someone has to lay the red carpet for [me], otherwise [I] can spoil more than help” (Reif 1990).
Vettorazzi’s initial review concluded that ETU was neither carcinogenic nor genotoxic (Vettorazzi 1991a). Some of the tobacco industry scientists commented that this statement was “too strong in light of the NTP feeding studies”—a reference to the U.S. National Toxicology Program’s conclusion that animal studies showed clear evidence of ETU’s carcinogenicity (Reif 1991a). Vettorazzi subsequently revised his conclusions, stating that ETU’s “toxicity, including carcinogenicity, can be explained by the known mechanisms of action characteristic of thyroid-function inhibiting agents” (Vettorazzi 1991b). Thus, he stated, a threshold could be set below which ETU did not cause thyroid tumors (Vettorazzi 1991b).
CORESTA authorized the distribution of Vettorazzi’s revised report to his former colleagues at WHO, once all references to tobacco and CORESTA were removed (CORESTA 1992). WHO’s JMPR was scheduled to review EBDCs/ETU in 1993; if this review were favorable, the tobacco industry would be assured continued access to EBDCs in Europe (Zeltner et al. 2000).
With CORESTA funding ($100,000 a year) and approval, Vettorazzi offered to assist J. Herrman, of the JMPR WHO Secretariat, with JMPR toxicologic reviews, without disclosing his tobacco industry ties (Herrman 1991; Vettorazzi 1991c, 1992a). Vettorazzi wrote and reviewed several working papers on compounds to be discussed at the 1992 JMPR, including the EBDC thiram (Herrman 1992; Vettorazzi 1992b). One outcome of that meeting was the reestablishment, at a higher level, of the previously cancelled Acceptable Daily Intake (ADI) for thiram (Vettorazzi 1992b).
Vettorazzi continued his work with WHO in 1993, supplying his CORESTA-funded reviews to the adviser responsible for drafting the working paper that would form the basis of the September JMPR on EBDCs/ETU without revealing their sponsor (Zeltner et al. 2000). Vettorazzi also attended the September meeting as an invited “temporary adviser” (Zeltner et al. 2000). The meeting’s outcome reflected Vettorazzi’s conclusions. In contrast to the U.S. EPA, JMPR determined that ETU was not genotoxic, and thus raised the ADI level from 0.002 to 0.004 mg/kg body weight (Black 1993). CORESTA considered this “a very positive result for the industry,” since it “clearly indicates that the ‘carcinogenicity’ of [ETU] is not really a burning issue any longer” (CORESTA 1994; Mueller 1993). JMPR’s safety standard became part of international trade law, preserving tobacco industry access to EBDCs (Zeltner et al. 2000). Soon after the JMPR meeting, CORESTA extended Vettorazzi’s contract for 18 months, listing one of his duties as providing “information about the activities of pesticide action groups” (CORESTA 1993). He was to be paid another $100,000 (CORESTA 1993). Vettorazzi continued working for CORESTA until at least 2001, when the organization paid him $30,000 to monitor international activities related to tobacco pesticide residues and registrations (CORESTA 2001).
Phosphine
Phosphine is a fumigant used on stored commodities, including nuts, seeds, grains, coffee, tobacco, and finished cigarettes to kill insects. Because of the risks it poses, applicators are advised to wear respirators and protective clothing, and warehouses must be sealed to prevent leaks that contribute to air pollution and endanger nearby residents (U.S. EPA 1998b). By the early 1990s, several case reports had been published noting sometimes fatal phosphine poisoning among workers and community members (Garry et al. 1989, 1993; Heyndrickx et al. 1976; Schoonbroodt et al. 1992; Wilson et al. 1980).
In December 1998, the U.S. EPA proposed a series of 15 risk mitigation measures (RMMs) for phosphine. The U.S. EPA’s primary concern was the risk that phosphine posed to applicators and community residents (U.S. EPA 1998b). Thus, the RMMs included a threshold limit value of 0.03 ppm of phosphine during fumigation (reduced from the existing 0.3-ppm standard), the establishment of a 500-foot buffer zone around all fumigated structures, and prior notification of all residents living within 750 feet of a fumigated structure (U.S. EPA 1998a).
The Tobacco Association of the United States, in a letter to the U.S. EPA, stated that the economic burdens imposed by the RMMs would “make it virtually impossible for our industry to continue to fumigate stored tobacco” (Ward 1999). The Tobacco Association, R.J. Reynolds, Philip Morris, and > 150 other organizations with a stake in the continued use of phosphine formed a lobbying group, the Commodity Industry Coalition for Phosphine Fumigation (Harrell 1999).
R.J. Reynolds, represented primarily by toxicologist Joel Seckar, took an active role in the Commodity Industry Coalition (Seckar 1999c). The company calculated that complying with the U.S. EPA’s buffer zone requirement would cost approximately $50 million in new land and warehouse purchases (R.J. Reynolds 1999a). Increasing the time required to aerate warehouses before employee reentry to comply with the worker exposure limit of 0.03 ppm would increase costs, as would the possibility of liability suits brought by nearby residents notified of phosphine use (Degesch America 1998; R.J. Reynolds 1999d).
Coalition members lobbied Congress, released media statements, worked closely with the U.S. Department of Agriculture, and attended U.S. EPA-sponsored stakeholder meetings (Goldman 1998; Lyon 1999; R.J. Reynolds 1999b, 1999c). Their message was that the proposed RMMs were overly conservative, based on “anecdotal information and hypothetical risk” rather than on “sound science” (Lyon 1999; Ong and Glantz 2001). To challenge the scientific basis of the U.S. EPA’s proposals, the coalition decided to hire an expert whose research would support existing standards (Seckar 1999h). They chose Sciences International, a consulting firm specializing in health and environmental risk assessment. It was headed by Elizabeth Anderson, a former director of the Carcinogen Assessment Group and the Office of Health and Environmental Assessment at the U.S. EPA (Sciences International 2005). She was also an experienced expert defense witness, having served in that capacity in a number of environmental lawsuits brought against corporations (Anderson 1999c).
To support the Commodity Industry Coalition’s assertion that the proposed exposure level of 0.03 ppm was too conservative, Sciences International focused on the inter-species uncertainty factor. The U.S. EPA had first determined from a published subchronic toxicity study of rats that there were no observed effects attributable to inhaled phosphine at 3 ppm (Seckar 1999a). To extrapolate to humans, the U.S. EPA had then used two 10-fold uncertainty factors, one for intraspecies variability and one for interspecies variability, to arrive at a maximum exposure level of 0.03 ppm (Sciences International 1999c). Documents indicate that Sciences International’s strategy was to convince the U.S. EPA that the interspecies uncertainty factor was unnecessary, showing that because a number of animal species reacted in the same manner to phosphine, humans were similar enough that the interspecies uncertainty factor could be removed (Seckar 1999a, 1999b). This would leave only the intraspecies factor of 10, which would result in a maximum exposure level for humans of 0.3 ppm, the existing standard.
In April 1999, the U.S. EPA representatives met with a small group of Commodity Industry Coalition members, including R.J. Reynolds’s Seckar and Sciences International’s Anderson (Seckar 1999a). Anderson questioned the U.S. EPA’s interspecies uncertainty factor, citing several animal studies and an epidemiologic study to suggest that the U.S. EPA’s calculations were too conservative (Seckar 1999a). In an e-mail, Seckar noted that Anderson’s presentation was very effective, as evidenced by the fact that U.S. EPA representatives were now informing coalition members that the 0.03 ppm standard “was not ‘set in stone,’” a direct contradiction of earlier statements to the U.S. Department of Agriculture (Bair 1999; Seckar 1999d). (Despite Freedom of Information Act requests, we were unable to obtain U.S. EPA documents related to its meetings with the coalition.)
Soon after, Sciences International asked the Commodity Industry Coalition for additional funding to turn its phosphine report into a peer-reviewed journal article (Turim 1999). In a memo to Seckar, Anderson (1999b) explained that
My experience is that consultant reports funded by those being regulated, and written expressly for the EPA, are easily and frequently ignored or dismissed by the Agency, no matter how scholarly. However, a paper or article that is peer-reviewed and published, or in the peer review process for publication, in an accepted scientific journal can neither be ignored nor dismissed.
Anderson suggested that since she was editor-in-chief of Risk Analysis, “perhaps the peer review process could be expedited if we decide that it is the journal of choice” (Anderson 1999b). R.J. Reynolds, Brown and Williamson, and several other tobacco companies agreed to fund most of the cost of this work (Seckar 1999e). The paper was published in Risk Analysis in 2004, with the acknowledgment that “This work was supported by the Phosphine/Metal Phosphide Coalition, consisting of the producers and users of phosphine and metal phosphides for the control of insects in stored commodities” (Pepelko et al. 2004).
Coalition members also pursued other strategies. At a meeting with U.S. EPA representatives in March 1999, the Commodity Industry Coalition proposed that the U.S. EPA participate in a series of small, coalition-sponsored focus groups to “educate [EPA] on the issues involved with … fumigations” (Seckar 1999g). One such group met in May 1999, when tobacco companies demonstrated a tobacco warehouse fumigation (Ward and Cowan 1999). The following month, several companies conducted additional emissions tests to show that the proposed 500-foot buffer was unnecessary (Bridges 1995). However, an e-mail message from a Philip Morris employee indicated that Philip Morris’s test coordinator had “some reservations regarding the quality of the test design/data generation” and that he himself believed that “the test plan and methods will provide, literally, no information, so it won’t hurt us to do it” (Bridges 1995).
In June 1999, Sciences International submitted a first draft of its phosphine toxicity review to some coalition members (Sciences International 1999a). A reviewer from the coalition’s lobbying firm pointed out that the animal studies cited did little to support the idea that the interspecies uncertainty factor should be eliminated “since most [of the animals] appear to be rat or mouse strains with similar breathing characteristics” (Wilkinson 1999). Instead, the studies cited by Sciences International seemed to support the idea that phosphine called for a conservative standard, as they indicated that “phosphine is a very toxic material to most species tested” (Wilkinson 1999). Another reviewer noted that the uncertain and tentative tone of the report “will trigger concerns by EPA and they will say ‘if [an] expert in the field states that there remains great uncertainty, maybe we are on solid ground by being very conservative’” (Barolo 1999a). Sciences International staff revised the report, removing tentative statements and asserting that their work to date supported reducing the interspecies uncertainty factor to 1 (effectively eliminating it), thus preserving the existing exposure standard of 0.3 ppm (Sciences International 1999b). They submitted this revised interim report to the U.S. EPA in July 1999 (Sciences International 1999b). At a Commodity Industry Coalition meeting that same month, coalition consultant Dan Barolo, former director of the U.S. EPA’s Office of Pesticide Programs (OPP), reportedly urged members to speed their efforts because
phosphine is quite hazardous when used improperly. The more the Coalition slows the process, the greater the chance for an accident with possible fatalities, which would send EPA back into conservative mode and make it far more difficult for them to publish reasonable RMMs. (Seckar 1999f)
In August, John Whalan, a toxicologist at the U.S. EPA’s Health Effects Division, summarized in a memo his analysis of Sciences International’s interim report (Whalan 1999). He noted that
there is no precedent for using an [interspecies uncertainty factor] of 1 when establishing … an inhalation regulatory value in the Health Effects Division. The only time an interspecies [uncertainty factor] is not applicable is when human data are used. The available data do not support deviating from Agency policy, and the Coalition did not provide any new data. (Whalan 1999)
He also pointed out that Sciences International’s review of animal studies, intended to show that phosphine toxicity was relatively constant across species, was largely “irrelevant” because it did not include a comparison of toxicity for a small versus large mammal.
In September 1999, phosphine registrants and several coalition members again met with U.S. EPA officials to discuss alternative RMMs proposed by the coalition (Seckar 1999i). Instead of a 500-foot buffer and a 750-foot neighbor notification requirement, the coalition recommended a “site management plan” that required companies to develop emergency preparedness measures. The U.S. EPA asked the Commodity Industry Coalition to reword its proposals to specify how and when workers and bystanders would be informed of danger (Seckar 1999i). On the exposure limit for workers, the U.S. EPA now proposed a 0.1-ppm standard (reflecting a reduction from 10 to 3 in the interspecies uncertainty factor) based upon Sciences International’s interim report (despite the weaknesses noted by Whalan) (Seckar 1999i). (The U.S. EPA failed to provide memos or notes regarding this decision.)
In several fall 1999 memos to Seckar, Sciences International staff explained that they thought it would be difficult to convince the U.S. EPA to drop the interspecies uncertainty factor without human exposure studies (Anderson 1999a; Gray 1999). Commodity Industry Coalition members expressed reluctance to commit to human studies without confirmation that this would convince the U.S. EPA to “give up” the uncertainty factor (Barolo 1999b). Barolo commented to Seckar, “I do not believe it will be easy for OPP to abandon both safety factors. There are too many unknowns from children to endocrine to reliability of studies to absence of dog/monkey study. … Some day they are going to figure out there is a 0.1 ppm standard in other countries and the door will close” (Barolo 1999c).
Although Sciences International had not yet submitted to the U.S. EPA its full report on phosphine, in December 1999, the U.S. EPA made its final decision (Sharp 1999). (This decision was published in the Federal Register in February 2001 [U.S. EPA 2001]). The U.S. EPA now mandated a “fumigation management plan” like that proposed by the Commodity Industry Coalition (U.S. EPA 2000). The agency also eliminated the inter-species safety factor and left the old 0.3-ppm standard in place, on condition that phosphine registrants conduct additional research if Sciences International’s review was found to be inadequate (U.S. EPA 2000). A coalition member noted that “it is important to point out that this additional work will take years and that the current 0.3 ppm threshold will stay in place during that time” (Sharp 1999). R.J. Reynolds credited its leadership on the scientific issues with saving the company “many millions of dollars” (R.J. Reynolds 2000).
Conclusion
Although others have charged that agencies responsible for protecting human health and the environment are unduly influenced by the industries they regulate (Abraham 2002; Huff 2002), it is rare to be able to study this process from the perspective of the regulated industry. This study provides documentation of the behind-the-scenes activities of an industry as it attempts to influence the regulatory process on matters that have a direct bearing on public health.
Our analysis has limitations. Given the sheer volume and limited indexing of the documents, it is impossible to ensure that we located all potentially relevant documents. Some may have been destroyed or concealed by the tobacco companies (Liberman 2002); others may have never been obtained in the legal discovery process. In addition, we had no access to pesticide company documents, except those in the tobacco documents archives. Finally, despite properly filed Freedom of Information Act requests, we were unable to obtain from the U.S. EPA documentation of its meetings with the industry’s Commodity Industry Coalition. All minutes of meetings with stakeholders should be part of the public record.
Despite these limitations, the case studies discussed here provide insight into tactics that the tobacco industry applies to a regulatory agency when trying to influence the outcome of a decision. These tactics go significantly beyond the usual approaches—such as participation in public comment periods and public meetings—to influence scientific and regulatory decision making. Tobacco industry tactics described in these cases include:
Encouraging a chemical company (Zoecon) to advocate for high MRLs without any supporting data and directing that same company to gather information about international regulatory efforts on methoprene in a manner designed to hide the interest of the tobacco industry in this chemical;
Attempting to forestall regulatory efforts on tobacco pesticides in the European Community by creating voluntary industry MRLs for a subset of chemicals;
Hiring an ex-WHO scientist to participate (without disclosing his funding source) in the WHO regulatory effort on EBDCs;
Hiring several ex-U.S. EPA scientists to influence the U.S. EPA’s regulatory decision making on phosphine;
Hiring scientific consultants with instructions to marshal data to support the tobacco industry’s a priori arguments and funding consultants to publish a report supporting these arguments in a journal over which the consultants had influence;
Staging fumigations for the U.S. EPA with the knowledge that the methodology was flawed and the results would show no emissions problem.
Yet, as the case of European MRLs showed, the tobacco industry does not always work together effectively to influence regulations. Tobacco companies may disagree about regulatory strategies or conclude that inaction is preferable to action that might have unintended consequences. Moreover, the fact that even voluntary, industry-friendly pesticide guidelines posed significant problems for Philip Morris underscores tobacco industry motivation for resisting or influencing more stringent, government-imposed regulations.
This study also raises questions about industry influence over regulatory agencies. In the case of WHO deliberations on EBDCs, the tobacco industry coordinated covert actions, hiding the financial ties and involvement of CORESTA. Rigorous disclosure requirements and oversight might have allowed the WHO’s agencies to judge more accurately the potential for bias related to conflicts of interest. In the case of the U.S. EPA’s review of phosphine, a regulatory agency appears to have been quite willing to cooperate with the industry and its consultants. This is a reminder of why regulatory processes were designed to be transparent and open to the public, and why “closed-door” meetings between regulators and industry have been ruled illegal (Federal Advisory Committee Act 1972; Registration Standards 2004; Special Review Procedures 2002).
Protection of the public interest hinges on an open process and regulatory agencies’ willingness to stand up to pressure from regulated industries. When these are in doubt, public confidence in the fairness and efficacy of regulations may be unwarranted. The resource disparities between powerful industries and public health organizations may also make it difficult to ensure that the public interest is fairly represented, particularly when discussions occur behind closed doors, as apparently occurred at the U.S. EPA. Increased public and media scrutiny of these processes could help ensure that public health considerations are weighed at least as heavily as commercial ones.
Finally, given the deadly epidemic of tobacco-caused disease, which kills an estimated 5 million people annually worldwide (WHO 2004), is it in the public interest for regulatory agencies today to continue facilitating standards that make it easier and less costly to grow, transport, store, and manufacture tobacco products?
We thank E.A. Smith and B. Skinner for critically reviewing drafts of the manuscript.
This research was supported by grants CA90789 and CA095989 from the National Cancer Institute and by American Legacy fellowship funding.
Table 1 Number of documents yielded by searches of tobacco company collections at the Legacy Tobacco Documents Library using selected key words and wildcards(*).
Tobacco company
Key word American Tobacco Brown and Williamson Lorillard Philip Morris R.J. Reynolds
Pesticide(*) 224 232 872 7,632 6,095
Crop protection agent(*) 3 60 66 1,533 193
Kabat/methoprene 65 182 604 5,416 2,336
Dithiocarbamate/EBDC(*) 1 22 130 278 275
Phosphine 28 21 195 247 580
World Health Organization/WHO 909 2,047 6,769 28,902 14,024
Environmental Protection Agency/EPA 1,423 2,082 23,791 155,094 24,961
Agrochemical Advisory Committee 0 37 48 684 383
Table 2 Overview of case studies.
Pesticide Regulatory action Dates Agency Tactics Outcome
Methoprene MRL of 1.0 ppm 1991–1995 Malaysian pesticide board Work through chemical industry partners to avoid raising tobacco issues, request higher MRL with no supporting research MRL raised to 15 ppm
Methoprene, others Industry concern about future MRLs 1991–1993 European Community Attempt to create voluntary MRLs to forestall regulation No voluntary MRLs, no EC regulations
EBDCs/ETU Potential imposition of residue tolerances 1989–1993 UN FAO/WHO JMPR Hire ex-WHO scientist to review EBDCs and ETU, covertly lobby and assist JMPR ETU listed as not genotoxic, higher ADI assigned
Phosphine 15 proposed risk mitigation measures including worker exposure standard of 0.03 ppm 1998–2001 U.S. EPA Hire consultant with EPA ties to challenge scientific basis of proposed exposure standard, write journal article Worker exposure standard increased to 0.3 ppm
UN FAO, United Nations Food and Agricultural Organization.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8016ehp0113-00166616330344Commentaries & ReviewsComparison of the Use of a Physiologically Based Pharmacokinetic Model and a Classical Pharmacokinetic Model for Dioxin Exposure Assessments Emond Claude 12*Michalek Joel E. 3Birnbaum Linda S. 2DeVito Michael J. 21 National Research Council, National Academy of Sciences, Washington, DC, USA2 Pharmacokinetics Branch, Environmental Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA3 Air Force Research Laboratory, Brooks City-Base, Texas, USAAddress correspondence to M. DeVito, Pharmaco-kinetic Branch, MD B143-01, Experimental Toxicology Division, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 USA. Telephone: (919) 541-0061. Fax: (919) 541-4284. E-mail:
[email protected]*Current address: Department of Environmental and Occupational Health, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada.
The authors declare they have no competing financial interests.
12 2005 25 8 2005 113 12 1666 1668 15 2 2005 25 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In epidemiologic studies, exposure assessments of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) assume a fixed elimination rate. Recent data suggest a dose-dependent elimination rate for TCDD. A physiologically based pharmacokinetic (PBPK) model, which uses a body-burden–dependent elimination rate, was developed previously in rodents to describe the pharmacokinetics of TCDD and has been extrapolated to human exposure for this study. Optimizations were performed using data from a random selection of veterans from the Ranch Hand cohort and data from a human volunteer who was exposed to TCDD. Assessment of this PBPK model used additional data from the Ranch Hand cohort and a clinical report of two women exposed to TCDD. This PBPK model suggests that previous exposure assessments may have significantly underestimated peak blood concentrations, resulting in potential exposure misclassifications. Application of a PBPK model that incorporates an inducible elimination of TCDD may improve the exposure assessments in epidemiologic studies of TCDD.
dioxinepidemiologyPBPKpharmacokineticphysiologically based pharmacokinetic modelRanch Handrisk assessment
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Exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is associated with increased risk for cancer, diabetes, and reproductive toxicities in numerous epidemiologic studies (Schecter and Gasiewicz 2003). Several of these studies base exposure estimates on measurements of blood levels years after accidental or occupational exposures. Peak exposures have been estimated in these studies assuming a mono- or biphasic elimination rate for TCDD, with estimates of half-life ranging from 5 to 12 years (Hooiveld et al. 1998; Michalek et al. 2002; Steenland et al. 2001). Recent clinical studies suggest that the elimination rate of TCDD is dose dependent (Michalek et al. 2002). In experimental animals, several studies also demonstrate dose-dependent elimination (Abraham et al. 1988; Diliberto et al. 2001). In both the animal and human data, as the exposure dose increases the apparent half-life decreases, indicating an inducible elimination of TCDD.
We developed a physiologically based pharmacokinetic (PBPK) model that describes the pharmacokinetics of TCDD in rodents (Emond et al. 2004). This approach is a mathematical description of the physiologic, biochemical, and physicochemical processes involved in the pharmacokinetics of TCDD. This model, originally validated in rodents, includes a mathematical description of the aryl hydrocarbon receptor–mediated induction of cytochrome P450 1A2 (CYP1A2). In the model, the elimination rate of TCDD is dose dependent and is a function of CYP1A2 induction. Experimental evidence suggests that CYP1A2 is responsible for hepatic sequestration of TCDD (Diliberto et al. 1997) and is also one of the enzymes responsible for its metabolism (Hakk and Diliberto 2002). Thus, at low exposures, there is minimal induction and the elimination of TCDD is very slow. However, at higher exposures, induction approaches a maximum and the elimination rate is much faster. Human physiologic and biochemical parameters were incorporated into the rodent PBPK model for species extrapolation.
Materials and Methods
In the present study a rodent PBPK model (Emond et al. 2004) was extrapolated to humans. Initial optimization of the human PBPK model used two data sets. The first data set comes from studies of U.S. Air Force veterans from Operation Ranch Hand. Veterans involved in Operation Ranch Hand were responsible for the aerial spraying of Agent Orange and other herbicides contaminated with TCDD during the Vietnam War from 1962 to 1971. We selected a subpopulation involving 343 Ranch Hand veterans and determined TCDD concentrations in blood samples collected every 5 years from 1982 to 1998 for a total of four or five samples from each veteran from this subpopulation (Michalek et al. 2003). Data from 20 randomly selected subjects from the Ranch Hand cohort subpopulation were used to optimize the human PBPK. The second set of data used to optimize the model was from Poiger and Schlatter (1986), in which a single volunteer received a single oral dose of 1.14 ng TCDD/kg and was followed for 40 days. These data were used in the optimization of the absorption and distribution processes occurring during the initial phase of the exposure.
Our assessment of the human PBPK model used an additional 10 randomly selected subjects from the Ranch Hand cohort and showed a good correlation (r2 = 0.995) between predicted blood concentrations in 1982 and measured blood concentrations in 1982 (Table 1). We also assessed the human PBPK model with a second data set. In the fall of 1997, two women presented clinical signs of TCDD intoxication (Geusau et al. 2002). After presentation of chloracne, between the spring of 1998 through 2001, 25 and 20 blood samples were collected from patients 1 and 2, respectively (Geusau et al. 2002). These women are among those with the highest TCDD blood concentrations ever measured in adults.
Results
In the veterans of Operation Ranch Hand, TCDD blood concentrations were first determined starting in 1982 (Michalek et al. 1996, 2002). The exposure occurred between 1962 and 1971, with a typical tour of duty lasting only a year. Peak blood concentrations were assumed to occur at the time of discharge from Vietnam. We documented the time of discharge for each veteran in the Ranch Hand cohort, and used these individual data in the back calculation for this study. TCDD blood concentrations were determined at four or five time points for each Veteran starting in 1982. For each TCDD measurement we used data on body weight and height for each individual to estimate the body mass index for each veteran. We used the body mass index to estimate size of the adipose tissue compartment at the time of TCDD measurement for each individual based on the approach of Deurenberg et al. (1991). We estimated peak TCDD blood concentrations for each individual with the PBPK model using their individual data on blood concentrations, adipose tissue mass, and the time of discharge from Vietnam. We also estimated peak blood concentrations using a classical one compartment pharmacokinetic model with a first-order elimination. The classical model assumed a TCDD half-life of 8.7 years and used the TCDD blood concentrations at 1982 (Michalek et al. 1996) and the time of discharge as inputs into the model to estimate peak blood concentrations.
In 1982, the range of blood concentrations from 10 randomly chosen subjects, shown in Table 1, was approximately 16-fold, from 12.7 to 209 ppt. We used a classical pharmaco-kinetic approach; peak blood concentrations ranged approximately 12-fold, from 53 to 640 ppt (Table 1). Minor differences in the ranking and range of TCDD blood concentrations occur when comparing estimated peak concentrations using the one compartment classical pharmacokinetic model to blood concentrations measured in 1982. When using the PBPK model to estimate peak blood concentrations, we found a much larger range in exposures and a significant difference in the exposure rankings (Table 1). The PBPK model estimates that peak blood concentrations at the time of discharge range > 250-fold, from 138 to approximately 40,000 ppt. This large difference is due to the inclusion of a dose-dependent elimination rate in the PBPK model. At the lower exposures, the half-life of TCDD is > 10 years, and at the higher exposures the half-life is only weeks. Models fits to these data are presented in Figure 1.
The model predictions show good correlations with the measured blood concentrations in the two highly exposed women (Figure 2). The model predicts a rapid decrease in the blood concentrations during the distribution phase of the first few months of exposure, followed by an elimination that appears first order at these exposures because of maximal induction of TCDD sequestration metabolism. The elimination rates in these women suggest that the overall half-life of TCDD during the first 2 years of exposure is < 3 months. In the first blood samples collected from these women, the concentrations of TCDD were 144,000 and 26,000 ppt (lipid adjusted) in patient 1 and 2, respectively (Geusau et al. 2002). The PBPK model estimates that initial blood concentrations may have been as high as 507,000 ppt and 87,000 ppt (lipid adjusted) in patients 1 and 2, respectively. Based on this model, maximum CYP1A2 induction occurs at blood concentrations of approximately 1,250 ppt (lipid adjusted). Measured levels of TCDD in the women were approximately 20–100 folds higher than the blood concentrations that are predicted to be at maximal induction (Geusau et al. 2002).
Discussion
Studies on the elimination of TCDD have examined cohorts many years after the exposures and suggest that the half-life approaches a decade. However, these studies did not examine the initial elimination of TCDD immediately after high-level exposures. The high concentration predicted with the model during the first 6 months is an extrapolation of what should be the concentration at the time of initial exposure. Limited data are available to validate the model for the initial exposure period. One data set is available from Poiger and Schlatter (1986). Although these data were used in the optimization of the model, the small sample size and only a single dose level do not provide confidence that the data from Poiger and Schlatter (1986) represent the wide range of potential exposures and populations at risk.
A number of pharmacokinetic models have incorporated dose-dependent elimination of TCDD. These models use a variety of approaches to describe the dose dependency. Andersen et al. (1993) use a hyperbolic function related to receptor occupancy to describe the dose-dependent elimination. This function is modified by a species specific “fold” factor that is used to adjust the elimination rate. In rats this factor is 1 and allows for a doubling of the elimination rate; other species would have different adjustment factors. Kohn et al. (2001) also use a Hill equation for the kinetics of the metabolizing enzyme with cytosolic TCDD concentrations as the substrate concentration. TCDD is also hypothesized to be eliminated through biliary pathways after hepatocyte lysis at high exposures in the model of Kohn et al. (2001). In the models of Carrier et al. (1995a, 1995b) and Aylward et al. (2005), the elimination of TCDD is described as a function of total hepatic TCDD concentrations. The elimination of TCDD in these models is dose dependent because there is a dose-dependent sequestration of TCDD in the liver. In the present model we describe the elimination rate as a function of CYP1A2 induction. The different approaches used to describe the dose-dependent induction of TCDD elimination are due to a lack of understanding of the biologic basis of these phenomena. This uncertainty in our understanding of the elimination of TCDD indicates that caution should be used when applying any of these models to human epidemiologic studies. However, the use of dose-dependent elimination of TCDD is an important concept to consider when choosing and applying pharmacokinetic tools in exposure assessments for dioxin.
Recent studies that measured TCDD blood concentrations shortly after high-level exposure indicate that the half-life is dose dependent (Geusau et al. 2002), as do clinical studies of the Ranch Hand cohort (Michalek et al. 2002). The use of first-order elimination of TCDD could significantly underestimate past exposures, resulting in exposure misclassifications in the epidemiologic studies. Using a PBPK model that incorporates a dynamic elimination rate may provide a more accurate assessment of past exposures in the epidemiologic studies. A better understanding of the biologic basis of the dose-dependent elimination of TCDD would allow for the development of more biologically realistic PBPK models. Further validation of this model is required before use in a quantitative exposure assessment. However, a pharmacokinetic model that includes an inducible elimination should be applied when assessing past exposures to TCDD.
This project was funded in part by a cooperative agreement MIPR FQ7624-00-YA085 with the U.S. Air Force and cooperative agreement CR 828790 with the National Research Council, National Academy of Sciences, and performed at the U.S. Environmental Protection Agency (Research Triangle Park, NC, USA).
This document has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for publication. Approval does not signify that the content necessarily reflects the view and policies of the agency, nor does mention of the trade names or commercial products constitute endorsement or recommendation for use.
Figure 1 Model predictions of TCDD blood concentration in 10 veterans (A–J) from the Ranch Hand cohort.
Figure 2 Time course of TCDD in blood (pg/g lipid adjusted) for two highly exposed women (patients 1 and 2). Symbols represent measured concentrations, and lines represent model predictions. These data were used as part of the model evaluation (Geusau et al. 2002).
Table 1 Comparison of initial blood concentration (Cblood) determination by first-order elimination or by PBPK model in 10 Ranch Hand veterans.a
Cblood in 1982
Cblood at the time of discharge from Vietnam
Group Measured (pg/g lipid adjusted) Predicted with PBPK model (pg/g lipid adjusted) Estimated with constant T1/2 of 8.7 years (pg/g lipid adjusted) Estimated with a PBPK model (pg/g lipid adjusted)
Low 12.7 13.7 53 138
16.7 20.1 44 166
23.5 26.9 72 277
24.6 29.5 112 587
25.0 19.4 83 168
High 33.7 37.8 103 492
43.8 25.5 123 197
115.5 132.3 381 6,622
182.3 198.3 602 40,376
209.7 234.6 640 35,412
T1/2, half-life of TCDD in the blood.
a The model provides a good prediction of the measured blood concentrations in 1982 with a coefficient of determination of R2 = 0.995.
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References
Abraham K Krowke R Neubert D 1988 Pharmacokinetics and biological activity of 2,3,7,8-tetrachlorodibenzo-p -dioxin. 1. Dose-dependent tissue distribution and induction of hepatic ethoxyresorufin O -deethylase in rats following a single injection Arch Toxicol 62 359 368 3242446
Andersen ME Mills JJ Gargas ML Kedderis L Birnbaum LS Neubert D 1993 Modeling receptor-mediated processes with dioxin: implications for pharmacokinetics and risk assessment Risk Anal 13 25 36 8383868
Aylward LL Brunet RC Carrier G Hays SM Cushing CA Needham LL 2005 Concentration-dependent TCDD elimination kinetics in humans: toxicokinetic modeling for moderately to highly exposed adults from Seveso, Italy, and Vienna, Austria, and impact on dose estimates for the NIOSH cohort J Expo Anal Environ Epidemiol 15 51 65 15083163
Carrier G Brunet RC Brodeur J 1995a Modeling of the toxico-kinetics of polychlorinated dibenzo-p -dioxins and dibenzofurans in mammalians, including humans Toxicol Appl Pharmacol 131 253 266 7716767
Carrier G Brunet RC Brodeur J 1995b Modeling of the toxico-kinetics of polychlorinated dibenzo-p -dioxins and dibenzofuranes in mammalians, including humans. II. Kinetics of absorption and disposition of PCDDs/PCDFs Toxicol Appl Pharmacol 131 267 276 7716768
Deurenberg P Weststrate JA Seidell JC 1991 Body mass index as a measure of body fatness: age- and sex-specific prediction formulas Br J Nutr 65 105 114 2043597
Diliberto JJ Burgin D Birnbaum LS 1997 Role of CYP1A2 in hepatic sequestration of dioxin: studies using CYP1A2 knock-out mice Biochem Biophys Res Commun 236 431 433 9240455
Diliberto JJ Devito MJ Ross DG Birnbaum LS 2001 Subchronic exposure of [3H]-2,3,7,8-tetrachlorodibenzo-p -dioxin (TCDD) in female B6C3F1 mice: relationship of steady-state levels to disposition and metabolism Toxicol Sci 61 241 255 11353133
Emond C Birnbaum LS DeVito M 2004 Physiologically based pharmacokinetic model for developmental exposures to TCDD in the rat Toxicol Sci 80 115 133 15056810
Geusau A Schmaldienst S Derfler K Papke O Abraham K 2002 Severe 2,3,7,8-tetrachlorodibenzo-p -dioxin (TCDD) intoxication: kinetics and trials to enhance elimination in two patients Arch Toxicol 76 316 325 12107649
Hakk H Diliberto JJ 2002 Comparison of overall metabolism of 2,3,7,8-TCDD in CYP1A2 (−/−) knockout and C57BL/6N parental strains on mice Organohalogen Compounds 55 461 464
Hooiveld M Heederik DJ Kogevinas M Boffetta P Needham LL Patterson DG Jr 1998 Second follow-up of a Dutch cohort occupationally exposed to phenoxy herbicides, chlorophenols, and contaminants Am J Epidemiol 147 891 901 9583720
Kohn MC Walker NJ Kim AH Portier CJ 2001 Physiological modeling of a proposed mechanism of enzyme induction by TCDD Toxicology 162 193 208 11369115
Michalek JE Ketchum NS Tripathi RC 2003 Diabetes mellitus and 2,3,7,8-tetrachlorodibenzo-p -dioxin elimination in veterans of Operation Ranch Hand J Toxicol Environ Health A 66 211 221 12521668
Michalek JE Pirkle JL Caudill SP Tripathi RC Patterson DG Jr Needham LL 1996 Pharmacokinetics of TCDD in veterans of Operation Ranch Hand: 10-year follow-up J Toxicol Environ Health 47 209 220 8604146
Michalek JE Pirkle JL Needham LL Patterson DG Caudill SP Tripathi RC 2002 Pharmacokinetics of 2,3,7,8-tetra-chlorodibenzo-p -dioxin in Seveso adults and veterans of operation Ranch Hand J Expo Anal Environ Epidemiol 12 44 53 11859432
Poiger H Schlatter C 1986 Pharmacokinetics of 2,3,7,8-TCDD in man Chemosphere 15 1489 1494
Schecter A Gasiewicz TA 2003. Dioxins and Health. 2nd ed. Hoboken, NJ:Wiley-Interscience.
Steenland K Calvert G Ketchum N Michalek J 2001 Dioxin and diabetes mellitus: an analysis of the combined NIOSH and Ranch Hand data Occup Environ Med 58 641 648 11555685
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7917ehp0113-00166916330345Commentaries & ReviewsA Critical Review of Biomarkers Used for Monitoring Human Exposure to Lead: Advantages, Limitations, and Future Needs Barbosa Fernando Jr1Tanus-Santos José Eduardo 1Gerlach Raquel Fernanda 2Parsons Patrick J. 31 Department of Pharmacology, Faculty of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil2 Department of Morphology, Estomatology and Physiology, Dental School of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil3 Wadsworth Center, New York State Department of Health, Albany, New York, USAAddress correspondence to F. Barbosa Jr., Department of Pharmacology, Faculty of Medicine of Ribeirão Preto, University of São Paulo–FMRP-USP, Av. Bandeirantes, 3900, Monte Alegre, CEP 14049-900, Ribeirão Preto, SP, Brazil. Telephone: 5516 6023183. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 11 8 2005 113 12 1669 1674 11 1 2005 10 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Lead concentration in whole blood (BPb) is the primary biomarker used to monitor exposure to this metallic element. The U.S. Centers for Disease Control and Prevention and the World Health Organization define a BPb of 10 μg/dL (0.48 μmol/L) as the threshold of concern in young children. However, recent studies have reported the possibility of adverse health effects, including intellectual impairment in young children, at BPb levels < 10 μg/dL, suggesting that there is no safe level of exposure. It appears impossible to differentiate between low-level chronic Pb exposure and a high-level short Pb exposure based on a single BPb measurement; therefore, serial BPb measurements offer a better estimation of possible health outcomes. The difficulty in assessing the exact nature of Pb exposure is dependent not so much on problems with current analytical methodologies, but rather on the complex toxicokinetics of Pb within various body compartments (i.e., cycling of Pb between bone, blood, and soft tissues). If we are to differentiate more effectively between Pb stored in the body for years and Pb from recent exposure, information on other biomarkers of exposure may be needed. None of the current biomarkers of internal Pb dose have yet been accepted by the scientific community as a reliable substitute for a BPb measurement. This review focuses on the limitations of biomarkers of Pb exposure and the need to improve the accuracy of their measurement. We present here only the traditional analytical protocols in current use, and we attempt to assess the influence of confounding variables on BPb levels. Finally, we discuss the interpretation of BPb data with respect to both external and endogenous Pb exposure, past or recent exposure, as well as the significance of Pb determinations in human specimens including hair, nails, saliva, bone, blood (plasma, whole blood), urine, feces, and exfoliated teeth.
biomarkersbiomonitoringbloodbonefeceshairleadplasmasalivateethtoxicokineticsurine
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Over the last two decades, atmospheric concentrations of lead have decreased significantly around the globe as more and more nations have chosen to remove tetraethylead from gasoline (Thomas et al. 1999). However, humans may also be exposed to Pb through contaminated food, water, and house dust and through industrial activities such as metal recycling and the battery industry. In the United States, for example, although the use of Pb in house paint peaked in 1940 and was banned in 1978, 40% of the nation’s housing stock is estimated to still contain Pb-based paint (Wakefield 2002).
After Pb enters the body, it can travel along several pathways depending on its source and, by extension, its bioavailability. The fraction of Pb that is absorbed depends mainly on the physical and chemical form, particularly particle size and the solubility of the specific compound. Other important factors are specific to the exposed subject, such as age, sex, nutritional status and, possibly, genetic background [Agency for Toxic Substances and Disease Registry (ATSDR) 1999; National Research Council 1993]. One of the earliest toxicokinetics studies reported that Pb, once absorbed into the blood compartment, has a mean biological half-life of about 40 days in adult males (Rabinowitz et al. 1976). The half-life in children and in pregnant women was reported to be longer, because of bone remodeling (Gulson et al. 1996; Manton et al. 2000). However, another study was unable to confirm this finding (Succop et al. 1998).
Like many other “bone-seeking” elements, Pb from blood is incorporated into calcified tissues such as bone and teeth, where it can remain for years (Rabinowitz 1991; O’Flaherty 1995). According to Rabinowitz (1991), the half-life of Pb in bone (bone-Pb) ranges from 10 to 30 years. However, the use of the term “half-life” to describe the biological clearance of Pb from bone implicitly makes assumptions about the kinetics of the process by which Pb is released. Some researchers prefer to use the term “residence time” to avoid implying more precision than what can be directly determined (Chettle D, personal communication). From calcified tissue stores, Pb is slowly released, depending on bone turnover rates, which in turn are a function of the type of bone, whether compact (slow turnover) or trabecular (rapid turnover) (O’Flaherty 1995). Brito et al. (2002) reported that the release rate of Pb from bone varies with age and intensity of exposure. Brito et al. (2005) also examined estimates of exchange rates among compartments. The transfer of Pb from blood to other compartments was much more rapid than the 1-month estimate reported previously (Brito et al. 2005), with the overall clearance rate from blood (sum of rates from blood to cortical bone, to trabecular bone and to other tissue), implying a half-life of 10–12 days (Brito et al. 2005). This highlights the difference between the overall clearance viewed from outside, when no allowance can be made for recirculation, and actual transfer rates.
Physiologic differences between children and adults account for much of the increased susceptibility of small children to the deleterious effects of Pb: whereas in adults 94% of Pb body burden is stored in bones and teeth, this proportion is only 70% in children (Barry 1981). In addition, the continuous growth of young children implies constant bone remodeling for skeletal development (O’Flaherty 1995). This contributes to a state in which Pb stored in bone is continually released back into the blood compartment, a process that has been described as “endogenous contamination” (Gulson et al. 1996). This process is particularly significant for pregnant women because pregnancy causes an increase in bone remodeling. The apparently limited success of various Pb hazard control measures in decreasing blood Pb (BPb) levels in exposed children and pregnant women may reflect a constant bone resorption process (Rust et al. 1999). Popovic et al. (2005) recently reported very different long-term Pb kinetics between men and women, with premenopausal women appearing to retain Pb more avidly or release Pb more slowly compared to postmenopausal women and to men.
Biomonitoring Human Exposure to Lead
Biomonitoring for human exposure to Pb reflects an individual’s current body burden, which is a function of recent and/or past exposure. Thus, the appropriate selection and measurement of biomarkers of Pb exposure is of critical importance for health care management purposes, public health decision making, and primary prevention activities.
It is well known that Pb affects several enzymatic processes responsible for heme synthesis. Lead directly inhibits the activity of the cytoplasmic enzyme δ-aminolevulinic acid dehydratase (ALAD), resulting in a negative exponential relationship between ALAD and BPb. Pb depresses coproporphyrinogen oxidase, resulting in increased coproporphyrin activity. Pb also interferes with the normal functioning of the intramitochondrial enzyme ferrochelatase, which is responsible for the chelation of iron by protoporphyrin. Failure to insert Fe into the protoporphyrin ring results in depressed heme formation and an accumulation of protoporphyrin; this in turn chelates zinc in place of Fe, to form zinc protoporphyrin. These effects also result in modifications of some other metabolite concentrations in urine (ALA-U), blood, (ALA-B) and plasma (ALA-P), coproporphyrin in urine (CP). The activities of pyrimidine nucleotidase (P5’N) and nicotinamide adenine dinucleotide synthase (NADS) are also modified in blood after Pb exposure. Levels of these various metabolites in biological fluids have been used in the past to diagnose Pb poisoning when direct Pb levels were difficult to obtain in tissues or body fluids (Leung et al. 1993) or as information complementary to BPb test results. They are more accurately described as biomarkers for toxic effects of Pb. In this review we focus on markers that are more accurately defined as biomarkers of Pb exposure, namely, Pb concentrations in biological tissues and fluids. Biomarkers for the toxic effects of Pb have been reviewed in some detail elsewhere (Sakai 2000).
Throughout the last five decades, whole blood has been the primary biological fluid used for assessment of Pb exposure, both for screening and diagnostic purposes and for biomonitoring purposes in the long term. Although BPb measurements reflect recent exposure, they may also represent past exposures, as a result of Pb mobilization from bone back into blood (Gulson et al. 1996). In those subjects without excessive exposure to Pb, 45–75% of the Pb in blood may have come from bone (Gulson et al. 1995; Smith et al. 1996). In exposed children, however, it has been reported that the bone-Pb contribution to blood can be 90% or more (Gwiazda et al. 2005). Thus, reductions in BPb levels after environmental Pb remediation may be buffered somewhat by contributions from endogenous Pb sources (Lowry et al. 2004; Rust et al. 1999). Remediation efforts typically result in reductions of BPb levels in exposed children of no more than 30%, when evaluated within several months after intervention (U.S. Enviromental Protection Agency 1995). Roberts et al. (2001) reported that in children with BPb levels between 25 and 29 μg/dL who were not treated with chelation drugs, the time required for BPb to decline to < 10 μg/dL is about 2 years. Some researchers have suggested that the efficacy of Pb hazard remediation efforts should be evaluated over extended periods to allow adequate time for mobilization and depletion of accumulated skeletal Pb stores and to allow a reduction in the absolute contribution to BPb levels from these stores (Gwiazda et al. 2005; Lowry et al. 2004). Thus, the mean of serial BPb levels should be a more accurate index of long-term Pb exposure.
Data collected as part of the U.S. National Health and Examination Survey (NHANES) give the 95th percentile for BPb as 7.0 μg/dL for children 1–5 years of age and as 5.20 μg/dL for adults 20 years of age and older [U.S. Centers for Disease Control and Prevention (CDC) 2003]. Although the BPb levels of U.S. populations have dropped markedly compared to 30 years ago, new concerns have been raised regarding possible adverse health effects in children at BPb levels < 10 μg/dL; perhaps there is no safe threshold but, rather, a continuum of toxic effects (Canfield et al. 2003). In light of these concerns, the CDC Advisory Committee on Childhood Lead Poisoning Prevention formed a working group to review the evidence for adverse health effects at BPb levels < 10 μg/dL in children. Although this working group concluded that several studies in the literature had demonstrated a statistically significant association between BPb levels < 10 μg/dL and some adverse health effects in children, the effects were very small and could conceivably have been influenced by residual confounding factors. The working group’s report called for further studies to examine the relationship between lower BPb levels and health outcomes to provide a more complete understanding of this issue (CDC 2004).
Many studies have reported statistically significant associations between BPb levels and various health effect outcomes. Some, however, have been statistically weak, with the magnitude of the effect relatively small. According to Hu et al. (1998), such weaknesses of association may occur because BPb is not a sufficiently sensitive biomarker of exposure or dose at the target organ(s) or because the relationships involved are biologically irrelevant and are only found because of an uncontrolled confounding factor. Furthermore, in view of the kinetics of Pb distribution within the body (cycling among blood, bone, and soft tissues), differentiation of low-level chronic exposure from a short high-level exposure is not possible on the basis of a single BPb measurement (Hu et al. 1998). Consequently, there is renewed interest in alternative biomarkers that may aid diagnosis of the extent of Pb exposure. Such alternatives include Pb determinations in plasma/serum, saliva, bone, teeth, feces, and urine. However, none of these matrices has gained convincing acceptance as an alternative to BPb. This is partly due to data based on erroneous or dubious analytical protocols that do not consider the confounding variables.
Plasma/Serum Lead
Plasma-Pb likely represents a more relevant index of exposure to, distribution of, and health risks associated with Pb than does BPb. Indeed, from a physiologic point of view, we can assume that the toxic effects of Pb are primarily associated with plasma-Pb because this fraction is the most rapidly exchangeable one in the blood compartment. In recent years increased attention has been paid to monitoring the concentration of Pb in plasma (or serum). However, research on associations between plasma-Pb and toxicologic outcomes is still sparse, and a significant gap in knowledge remains.
Plasma/serum Pb levels in nonexposed and exposed individuals reported in older publications range widely, from 0.02 to 14.5 μg/L (Versieck and Cornelis 1988). This is probably due to inappropriate collection methods, analytical instrumentation, and methods for Pb determination. The development and use of more sensitive analytical instrumentation, especially inductively coupled plasma mass spectrometry (ICP-MS), has resulted in determinations of Pb in plasma and serum specimens with much lower detection limits and with better accuracy. More recent data, also based on ICP-MS methods, have shown plasma-Pb levels < 1.0 μg/L in nonexposed individuals (Schutz et al. 1996).
The use of advanced analytical techniques is not the only essential requirement to ensure accurate and reliable plasma-Pb data. Contamination of the specimen may occur at the preanalytical phase, namely, during collection, manipulation, or storage. Use of Class-100 biosafety cabinets and clean rooms for specimen preparation and analysis is mandatory. Moreover, all analytical reagents used must be of the highest purity grade. These conditions are far more rigorous than are typically required for clinical BPb measurements performed in a commercial laboratory. After the blood specimen has been collected, the serum/plasma separation must be performed as soon as possible because there is high potential for Pb to move from the dominant BPb subcompartment repository, namely, the erythrocytes, into the plasma via hemolysis, leading to erroneously high results for plasma-Pb. Plasma hemolysis can be estimated by analyzing hemoglobin levels in the specimen because these levels are likely to become abnormally elevated with hemolysis (Smith et al. 2002). Materials for specimen collection and storage and the anticoagulant must be of the highest quality because these can be another source of Pb contamination.
Commercial evacuated blood tubes, prepared specifically for BPb measurements, are available with < 5 μg/L Pb (Esernio-Jenssen et al. 1999), but it is nevertheless desirable for the analyzing laboratory to characterize the background Pb contamination in each new lot of tubes to ensure that reported concentrations are not compromised by contamination. The choice of anticoagulant is important because EDTA, as a strong metal-chelating agent, may be difficult to obtain without some background contamination and may give misleadingly high plasma-Pb results because of selective extraction of Pb bound to erythrocytes. The use of heparin is problematic because heparinized blood is more prone to form fibrin clots after several hours. These issues were evaluated by Smith et al. (1998) in some detail; they compared commercial Vacutainer-type tubes with ultracleaned collection tubes containing either EDTA or heparin. As there are no commercial blood collection tubes available that are certified for ultra-low Pb measurements, the analyzing laboratory should prepare precleaned polyethylene tubes containing ultra low-Pb anticoagulants.
There are many reports of plasma-Pb measurements where validation data are either weak or absent. For example, some simply cite successful participation of the analyzing laboratory in quality assurance (QA) programs for whole blood Pb operated by the CDC and the College of American Pathologists (Hernandez-Avila et al. 1998), whereas others neglect to cite any kind of QA program (Dombovari et al. 2001). Participation in QA schemes designed specifically for whole BPb, while commendable, does not address the much more challenging analysis for plasma-Pb. This problem is compounded by the lack of certified reference materials for either serum or plasma-Pb (Cake et al. 1996). For these reasons, production of plasma or serum reference materials that have Pb concentrations certified close to current human values is urgently needed to support method validation.
Saliva Lead
Saliva has been proposed as a diagnostic specimen for various purposes, as it is easily collected (Silbergeld 1993). However, in the absence of consistent and dependable saliva Pb measurements, it is not generally accepted as a reliable biomarker of Pb exposure. Saliva shows large variations in its ion content throughout the day, coupled with changes in salivary flow rates before, during, and after meals. Variations also arise depending on the manner in which saliva collection is stimulated (or not) and on the nutritional and hormonal status of the individual.
Some data suggest an association between Pb levels in saliva and those in either plasma or blood (Omokhodion and Crockford 1991; Pan 1981). Moreover, it has been argued that Pb in saliva is the direct excretion of the Pb fraction in diffusible plasma namely, the fraction not bound to proteins) (Omokhodion and Crockford 1991). Despite the associations reported in the literature, the older saliva Pb concentrations are quite high, and the values vary among studies. Recent data suggest much lower saliva Pb levels, in both exposed and unexposed subjects (Koh et al. 2003; Wilhelm et al. 2002). According to Wilhelm et al. (2002), Pb content in the saliva of unexposed children is usually < 0.15 μg/dL.
Uncontrolled variation in salivary flow rates, lack of standard or certified reference materials, and absence of reliable reference values for human populations are major factors that limit the utility of saliva Pb measurements. In addition the very low levels of Pb present in saliva limit the range of suitable analytical techniques, thereby further diminishing the utility and reliability of this biomarker for evaluating Pb exposure.
Hair Lead
Hair is a biological specimen that is easily and noninvasively collected, with minimal cost, and it is easily stored and transported to the laboratory for analysis. These attributes make hair an attractive biomonitoring substrate, at least superficially. Because Pb is excreted in hair, many have suggested it for assessing Pb exposure, particularly in developing countries where specialized laboratory services may be unavailable and resources are limited (Schumacher et al. 1991). However, an extensive debate is ongoing about the limitations of hair as a biomarker of metal exposure generally. Here we limit our discussion to Pb exposure, although many of the issues for Pb, such as preanalytical concerns for contamination control, sampling, and reference ranges, also apply to other metals.
The ability to distinguish between Pb that is endogenous, namely, absorbed into the blood and incorporated into the hair matrix, and Pb that is exogenous, namely, derived from external contamination, is a major problem. During the washing step it is assumed that exogenous Pb is completely removed, whereas endogenous Pb is not. However, no consensus exists about how removal of exogenous Pb is best accomplished. Some publications that describe the use of hair for assessing Pb exposure reference a hair washing method proposed by the International Atomic Energy Agency (IAEA) in 1978. The approach entailed washing hair specimens with acetone/water/acetone (Ryabukin 1978). However, a recent study (Morton et al. 2002) demonstrated that the IAEA method failed to remove exogenous Pb from hair.
Another issue is the significant variation in the Pb concentration profile among various subpopulations according to age, sex, hair color, and smoking status (Wolfsperger et al. 1994). Moreover, geographic, racial/ethnic, and ecologic factors can also affect Pb distribution in hair within a given population. Thus, it is difficult to establish reference ranges because confounding factors impose restrictions on the interpretation of individual results. No consensus exists on the length of the hair specimen to be collected, or the amount, or the position on scalp. Variations in Pb content between single hairs from the same individual can be as high as ± 100%, particularly in the distal region (Renshaw et al. 1976).
Recently, the ATSDR established an expert advisory panel to review current knowledge about the use of hair analysis for trace metals in biomonitoring (ATSDR 2001). The general consensus was that many scientific issues need to be resolved before hair analysis can become a useful tool in understanding environmental exposures. Although hair analysis may be able to answer some specific questions about environmental exposure to a few substances, it often raises more questions than it answers. The scientific community currently does not know the range of Pb contamination levels typically found in human hair. Without reliable data on baseline or background hair contamination levels in the general population, health agencies cannot determine whether results from a given site are unusually high or low (ATSDR 2001).
In addition to the preanalytical issues and the absence of reliable reference ranges, the quality of analytical techniques used for determining Pb, as well as other trace metals, in hair has been questioned. In a recent interlaboratory study of commercial laboratories that specifically market the test for trace metals in hair, interlaboratory agreement was judged very poor, with wide discrepancies observed for Pb as well as for other elements (Seidel et al. 2001).
Urinary and Fecal Lead
The determination of Pb in urine (urine-Pb) is considered to reflect Pb that has diffused from plasma and is excreted through the kidneys. Collection of urine for Pb measurements is noninvasive and is favored for long-term biomonitoring, especially for occupational exposures. However, a spot urine specimen is particularly unreliable because it is subject to large biological variations that necessitate a creatinine excretion correction. Urine-Pb originates from plasma-Pb that is filtered at the glomerular level; thus, according to some authors (Tsaih et al. 1999), urine-Pb levels that are adjusted for glomerular filtration rate may serve as a proxy for plasma-Pb. Hirata et al. (1995) found a better correlation between the concentration of plasma-Pb and urine-Pb than between BPb and urine-Pb for lead workers with low levels of Pb exposure. Manton et al. (2000), using high-precision Pb isotope ratio measurements, found the concentration of urine-Pb to be about 10% of that in whole blood; however, the correlations were not particularly robust. In contrast, correlations with isotopic ratios were excellent. According to Tsaih et al. (1999), cortical bone contributes a mean of 0.43 μg Pb per day excreted in urine, whereas trabecular bone contributes as much as 1.6 μg Pb per day. Cavalleri et al. (1983) observed different Pb kinetics between exposed and nonexposed subjects after the administration of CaNa2EDTA. In unexposed subjects BPb levels remained stable even after 5 hr of CaNa2EDTA administration. However, plasma-Pb levels in the unexposed group decreased by as much as one half, while urine-Pb increased by a factor of 10. In the Pb-exposed group the same amount of chelation therapy resulted in plasma-Pb levels increasing by a factor of 2, while BPb levels decreased by a factor of 2, with a higher urine-Pb excretion. Thus, it seems that in nonexposed subjects a major contribution for urine-Pb is derived from the Pb fraction in soft tissues that is in equilibrium with that in plasma compartment. We could speculate that the larger the amount of erythrocyte-bound Pb, the weaker the binding forces, and that a significant fraction of Pb is released from red blood cell membranes into plasma and is then filtered by the kidney. Because the amount of Pb excreted is very high, the kidneys are unable to remove it rapidly from the blood stream; this may account for the temporal elevation of plasma-Pb levels.
The availability of reliable urine quality-control materials and reference materials certified for Pb content and participation in external quality assessment schemes for urine-Pb are important factors in assuring the accuracy of analytical results. However, the tendency for urate salts to precipitate out of urine during transit and storage can be a complicating factor in the analysis. Moreover, because only a few studies have examined associations between urine-Pb and other biomarkers, the use of urine-Pb measurements is essentially limited to long-term occupational monitoring programs, monitoring patients during chelation-therapy, and, until very recently, to clinical evaluation of potential candidates for chelation therapy.
Measurement of fecal-Pb content over several days is one possible approach to estimating the overall magnitude of childhood Pb intake. According to Gwiazda et al. (2005), fecal-Pb content should give an integrated measure of Pb exposure/intake from all sources, dietary and environmental, inside and outside the home (by isotopic composition). However, a limitation to the use of fecal-Pb is that the collection of complete fecal samples over multiple days may not be feasible. As stated by Gwiazda et al. (2005), fecal-Pb reflects unabsorbed, ingested Pb plus Pb that is eliminated via endogenous fecal (biliary) routes; interindividual variations in these physiologic processes may show up as variation that is wrongly attributable to environmental Pb exposure.
Nail Lead
Like hair, nails have many superficial advantages as a biomarker for Pb exposure, especially because specimen collection is noninvasive and simple and because nail specimens are very stable after collection, not requiring special storage conditions. Nail-Pb is considered to reflect long-term exposure because this compartment remains isolated from other metabolic activities in the body (Takagi et al. 1988). Because toenails are less affected by exogenous environmental contamination than fingernails, they have been preferred for Pb exposure studies. Toenails have a slower growth rate than fingernails (up to 50% slower, especially in winter) and thus may provide a longer integration of Pb exposure.
Lead concentration in nails depends on the subject’s age (Nowak and Chmielnicka 2000), but it seems not to depend on the subject’s sex (Rodushkin and Axelsson 2000).
Gulson (1996a) reported high variability in Pb levels measured in the same fingernails and toenails of various subjects, even after rigorous washing procedures; such lack of reproducibility suggests that nail specimens offer only limited scope in assessing exposure to Pb.
Bone Lead
Because bone accounts for > 94% of the adult body burden of Pb (70% in children) (O’Flaherty 1995), many researchers accept that a cumulative measure of Pb dose may be the most important determinant of some forms of toxicity (cumulative measure means an exposure that is integrated over many years, rather than based on a single BPb measurement) (Landrigan and Todd 1994; Hu et al. 1998). In support of this hypothesis, recent studies have shown that bone-Pb but not BPb is significantly related to declines in hematocrit and hemoglobin among moderately Pb-exposed construction workers and to decreased birth weight and increased odds of clinically relevant hypertension (Gonzalez-Cossio et al. 1997; Hu et al. 1996). According to Hu et al. (1998), other adverse health outcomes likely to be associated with bone-Pb levels include impairment of cognitive performance and growth in children and kidney failure, gout, elevated blood pressure, reproductive toxicity, and adverse cardiovascular events in adults.
As pointed by Hu et al. (1998), two major paradigms relate to skeletal Pb: bone-Pb as an indicator of cumulative Pb exposure (bone-Pb as a repository), and bone-Pb as a source of body burden that can mobilized into the circulation (bone-Pb as a source). Hernandez-Avila et al. (1998) reported a strong association between bone-Pb levels and serum-Pb levels of adults exposed to Pb. That study indicated the potential role of the skeleton as an important source of endogenous, labile Pb that may not be adequately discerned through measurement of BPb levels. The same authors argued that skeletal sources of Pb accumulated from past exposures should be considered along with current sources when exposure pathways are being evaluated. In an attempt to characterize the source of Pb exposure, Gulson et al. (1995) measured the 206Pb/204Pb isotopic ratios in immigrant Australian subjects, Australian-born subjects, and environmental samples. The immigrant population exhibited Pb isotopic ratios from 17.7 to 18.5, distinct from the ratio in Australian-born subjects (~ 17.0). This difference allowed a distinction to be drawn between current exposure acquired from Australian sources and older bone-stored Pb that was not acquired from Australian sources.
Differing bone types have differing bone-Pb mobilization characteristics. For example, the tibia principally consists of cortical bone, whereas the patella is largely trabecular bone. Pb in trabecular bone is more biologically active than Pb in cortical bone, and trabecular bone has a shorter turnover time. The endogenous contribution of Pb from bone stores is an important health consideration. The O’Flaherty kinetic model can be used to indicate the quantity of Pb delivered from bone as a function of bone turnover and Pb exchange (O’Flaherty 1995). A recent revision of this model (Fleming et al. 1999) suggests that a smeltery worker with a tibia Pb concentration of 100 μg/g can expect a continuous endogenous contribution to BPb of 16 μg/dL. A pregnant woman with a tibia Pb concentration of 50 μg/g can end up with a contribution of 8 μg/dL BPb; this figure does not consider the increased rate of bone turnover associated with pregnancy. Individuals not exposed to Pb in the workplace typically display tibia Pb levels up to about 20 μg/g (Roy et al. 1997).
Over the last decade bone-Pb measurements based on noninvasive in vivo X-ray fluorescence (XRF) methods have become increasingly accepted. The technique uses fluorescing photons to remove an inner-shell electron from a Pb atom, leaving it in an excited state. The result is emission of X-ray photons that are characteristic of Pb. Measurements are performed by using one of four kinds of XRF: two involve fluorescence of the K-shell electrons of Pb (K-XRF), and the other two involve fluorescence of the L-shell electrons (L-XRF) (Todd et al. 2002a). Several groups, mainly in North America, have reported the development of in vivo measurement systems; the majority have adopted the K-XRF approach based on excitation with a 109Cd isotope and backscatter geometry because of its advantages: it provides a robust measurement with a better detection limit and a lower effective (radiation) dose (as compared to L-XRF) (Todd and Chettle 1994). The radiation dose is not a limiting factor in using this technique with humans, as demonstrated by Todd and Chettle (1994).
Calibration is usually performed with Pb-doped plaster-of-Paris phantoms (Todd et al. 2002a). Method accuracy has been evaluated through comparison of XRF data from cadaver specimens with electrothermal atomic absorption spectrophotometry data (Todd et al. 2002b). However, XRF sensitivity and precision for Pb still constitute an analytical challenge. In addition to sample-to-sample reproducibility, XRF can also display a certain amount of imprecision associated with each calculated bone-Pb value (Ambrose et al. 2000). This uncertainty, estimated using a goodness-of-fit statistic from the curve fitting of the background, ranged from 3 to 30 μg/g Pb; clearly, this represents a problem for measurements of low-level Pb namely, young children and nonexposed populations. Another problem inherent to the XRF technique is photon scattering due to overlying tissue or subject movement during the measurement period (Ambrose et al. 2000). Normalization of the Pb signal to the calcium backscatter signal appears to solve this problem. Precision depends on the amount of tissue overlying the bone: the greater the thickness of tissue, the poorer the precision. Todd and Chettle (1994), comparing the K-shell with L-shell precisions with 3 and 6 mm of overlying soft tissue, reported that K-XRF precision worsens by only 5%, whereas L-XRF precision worsens by 49% for greater thickness. The precision of the L-XRF method is much more severely affected by the strong attenuation of the Pb L-shell X rays.
Todd et al. (2001) reported contiguous inhomogeneities in the distribution of Pb toward the proximal and distal ends of the tibia bones. They speculated that the region of lower Pb concentration has lower blood flow in the Haversian canals and, consequently, less Pb available for uptake into bone matrix during bone remodeling (Todd et al. 2001). Trabecular bone has a larger surface area and a greater volume of blood delivered per unit of time compared to cortical bone. In addition there are more active osteons per gram in trabecular bone to accomplish resorption and deposition. Hernandez-Avila et al. (1998) reported that, in individuals with no history to occupational Pb exposure, bone-Pb (in particular trabecular Pb) exerts an additional independent influence on plasma-Pb after control for BPb.
Thus, an appropriate selection of the precise bone type to be analyzed for Pb content must be made before commencing. Moreover, further research on the relationship between various bone-Pb subcompartments and other Pb measures is warranted.
Tooth Lead
Like bone, teeth accumulate Pb over the long term. However, there is some evidence that teeth are superior to bone as an indicator of cumulative Pb exposure because the losses from teeth are much slower (Maneakrichten et al. 1991). Moreover, deciduous teeth are relatively easy to collect and analyze; exfoliation generally occurs after the age of 6 years. Teeth are also very stable for preservation purposes.
Chronic Pb exposure from mouthing activity in early childhood may be camouflaged by “dilution” effects during periods of rapid skeletal growth in young children and adolescents and may not be detected by a single BPb measurement. However, most published data on tooth-Pb have been based on whole tooth analysis, with no attempt to distinguish among tooth types (different teeth are formed at different ages) or to differentiate the Pb concentration in enamel from that in dentin (enamel contains much more Pb, by mass, than does dentin). The influence of age and/or sex have also not been considered (Brown et al. 2002). Furthermore, use of deciduous teeth is only possible for children over 6 years in age.
Recently, Gomes et al. (2004) proposed a solution to the limitations mentioned above by using an in vivo enamel biopsy of children. In this approach superficial minerals are leached from teeth and Pb is determined by electrothermal atomic absorption spectrophotometry. One important drawback to this approach is that, because an accumulation gradient for Pb has not yet been established for enamel, only biopsies of a given depth can be compared to one another. Another issue related to tooth-Pb measurements is whether Pb that accumulates in the first few micrometers of the enamel surface was incorporated posteruptively (e.g., from the mouth, saliva, food) rather than during the period when the tooth was mineralized inside the bone.
An interesting and potentially valuable aspect of tooth-Pb measurements is their capacity to elucidate the history of Pb exposure. Teeth are composed of several distinct tissues formed over a period of several years, and different parts of the tooth can bind Pb at different stages of the individual’s life. Therefore, a section of tooth can yield historical information on the individual’s exposure to Pb. For example, the enamel of all primary teeth, and parts of the enamel from some permanent teeth, are formed in utero and thus may provide information on prenatal exposure to Pb. This information could be valuable in improving our understanding of dose–effect relationships for embryonic anomalies, particularly neurotoxic dysfunction. The dentine of the primary teeth provides evidence of exposure during the early childhood years, when hand-to-mouth activity is usually an important contributor to Pb body burden (Gulson 1996b). However, enamel Pb levels may be useful for indirectly estimating the Pb composition of the mother’s bone (Gulson 1996b).
More recently there has been some interest in using laser ablation ICP-MS to examine Pb distribution in tooth profiles. This approach offers spatially resolved measurements of trace element distribution that can be compared to a temporal axis via reference to the neonatal line, enabling researchers to use not only the Pb concentration of the entire tooth but also the specific amount of Pb in each tooth layer, namely, a time line of Pb exposure. Nevertheless, some serious challenges remain before this technique can be fully exploited (Uryu et al. 2003).
Conclusions
Thus far an impressive body of data has been established based on the use of alternative biomarkers for monitoring exposure to Pb. However, it is still unclear to what extent such data are superior to the information obtained from BPb measurements. Clearly, many of the limitations identified in the foregoing sections must be resolved before alternative biomarkers can be accepted as superior indicators of Pb exposure. At this time BPb measurements are still the most reliable indicator of recent Pb exposure, although serial BPb measurements may offer a better assessment of temporal fluctuations in Pb absorption. If reliable and reproducible plasma-Pb measurements can be obtained, these may offer better correlation with toxic effects. However, we do not yet know what a single plasma-Pb value means, in terms of health effects; in the absence of a normal reference range, the clinical utility for individual assessment is problematic. Further research on this issue is needed, especially for children and adults with low to moderate Pb exposure. Further efforts are also warranted in the further development and continued use of well-established analytical protocols, as well as in the estimation of random and systematic errors. Efforts are needed to create regional reference ranges of nonexposed populations for each biomarker, to acquire data related to long-term and short-term exposures, and to evaluate the influence of nutritional status and ethnicity (genetic polymorphisms).
A critical question that might be asked with respect to an individual’s bone-Pb measurement is what does it mean in terms of health risk or, perhaps, clinical management? To answer this question, we may need to distinguish between bone-Pb measurements in children and pregnant women, namely, those with high bone turnover rate compared to (nonpregnant) adults. In children, bone-Pb may have little effect on BPb levels, but it may help us to estimate the extent to which BPb derives from endogenous sources and the possible contribution to the labile plasma-Pb pool.
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Environ Health Perspect. 2005 Dec 11; 113(12):1669-1674
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8072ehp0113-00167516330346ResearchEvidence of Detrimental Effects of Environmental Contaminants on Growth and Reproductive Physiology of White Sturgeon in Impounded Areas of the Columbia River Feist Grant W. 1Webb Molly A.H. 1Gundersen Deke T. 2Foster Eugene P. 3Schreck Carl B. 145Maule Alec G. 6Fitzpatrick Martin S. 31 Department of Fisheries and Wildlife, Oregon State University, Corvallis, Oregon, USA2 Environmental Science Program, Pacific University, Forest Grove, Oregon, USA3 Oregon Department of Environmental Quality, Portland, Oregon, USA4 Oregon Cooperative Fish and Wildlife Research Unit, Oregon State University, Corvallis, Oregon, USA5 Biological Resources Division, U.S. Geological Survey, Corvallis, Oregon, USA6 Biological Resources Division, U.S. Geological Survey, Columbia River Research Laboratory, Cook, Washington, USAAddress correspondence to G.W. Feist, Department of Fisheries and Wildlife, 104 Nash Hall, Oregon State University, Corvallis, OR 97331-3803 USA. Telephone: (541) 737-2463. Fax: (541) 737-3590. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 11 7 2005 113 12 1675 1682 2 3 2005 11 7 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. This study sought to determine whether wild white sturgeon from the Columbia River (Oregon) were exhibiting signs of reproductive endocrine disruption. Fish were sampled in the free-flowing portion of the river (where the population is experiencing reproductive success) and from three reservoirs behind hydroelectric dams (where fish have reduced reproductive success). All of the 18 pesticides and almost all of the 28 polychlorinated biphenyls (PCBs) that were analyzed in livers and gonads were detected in at least some of the tissue samples. Metabolites of p,p′-dichlorodiphenyltrichloroethane (DDT) [p,p′-dichlorodiphenyldichloroethylene (DDE) and p,p′-1,1-dichloro-2,2-bis(4-chlorophenyl)ethane (DDD)] were consistently found at relatively high levels in fish. Some males and immature females showed elevated plasma vitellogenin; however, concentrations were not correlated with any of the pesticides or PCBs analyzed. Negative correlations were found between a number of physiologic parameters and tissue burdens of toxicants. Plasma triglycerides and condition factor were negatively correlated with total DDT (DDD + DDE + DDT), total pesticides (all pesticides detected – total DDT), and PCBs. In males, plasma androgens and gonad size were negatively correlated with total DDT, total pesticides, and PCBs. Fish residing in the reservoir behind the oldest dam had the highest contaminant loads and incidence of gonadal abnormalities, and the lowest triglycerides, condition factor, gonad size, and plasma androgens. These data suggest that endocrine-disrupting chemicals may be accumulating behind dams over time. Overall, results of this study indicate that exposure to environmental contaminants may be affecting both growth and reproductive physiology of sturgeon in some areas of the Columbia River.
endocrine-disrupting chemicalsgrowthPCBspesticidesreproductive physiologysex steroidswhite sturgeon
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The lower Columbia River supports one of the most productive white sturgeon (Acipenser transmontanus) fisheries in North America (DeVore et al. 1995; McCabe and Tracy 1994). Fish trapped behind the dams of the hydroelectric system, however, have reduced reproductive success compared with animals in the free-flowing portion of the river (Beamesderfer etal. 1995). Reduced reproductive fitness of fish in these impounded sections of the river has been attributed to habitat, flow, and temperature, but environmental toxicants could also be playing a role. The long-lived, late-maturing, and benthic lifestyle of sturgeon may make them particularly susceptible to the actions of persistent bio-accumulating pollutants (DeVore etal. 1995).
The Columbia River receives pollution from a variety of sources that include sewage treatment plants, bleached-kraft pulp mills, aluminum smelters, mining operations, and agricultural and urban runoff. Recently, it has been determined that past operation of the hydroelectric facilities has led to contamination of certain areas of the river with polychlorinated biphenyls (PCBs) (URS Corporation 2002). A wide variety of environmental contaminants have been shown to have adverse effects on reproduction in fishes (Kime 1995; Tyler et al. 1998; Van Der Kraak 1998), and many of these bioaccumulating toxicants have been detected in sediments and fish from the Columbia River [Foster et al. 1999, 2001a, 2001b; U.S. Environmental Protection Agency (EPA) 2002].
This study was designed to examine whether environmental pollutants are having an adverse effect on the reproductive physiology of white sturgeon in the wild and to determine whether fish demonstrate evidence of reproductive endocrine disruption that correlates to specific areas within the river where sturgeon are known to have low reproductive success.
Materials and Methods
Fish sampling.
Fish were sampled during the commercial and sport harvest in February through April of 2000 and 2001. Because of state fishing regulations, only fish within a slot limit of 110–137 cm fork length were sampled. This slot limit is set to ensure that mature fish are not removed from the fishery. Fish were sampled from four areas of the Columbia River: the free-flowing portion of the river in the estuary at Astoria, Oregon, and in reservoirs above Bonneville (river mile 191), The Dalles (river mile 216), and John Day (river mile 292) dams (Figure 1). These dams were constructed in 1938, 1960, and 1971, respectively. A total of 174 fish were sampled, representing 42–45 individuals (19–24 males and 21–23 females) for each location. Length and weight were recorded, and condition factor (CF) was determined. Gonads were removed and weighed, and gonadosomatic index (GSI) was determined. Gonads and livers were collected for both histologic and contaminant analysis. Plasma samples were collected for analysis of 17β-estradiol (E2), testosterone (T), 11-keto-testosterone (KT), vitellogenin (Vtg), calcium, and triacylglycerides (TAG). In 2001, pectoral fin spines were collected to determine the age of fish.
All animals were treated in accordance with Oregon State University’s Care of Laboratory Animals guidelines (Oregon State University Institutional Animal Care and Use Committe 2005).
Plasma analyses.
We extracted the steroids T, KT, and E2 from plasma following the method of Fitzpatrick etal. (1986). Extraction efficiencies for all steroids were determined by adding tritiated steroids to tubes containing plasma (n = 4) during each extraction. This resulted in 12 extraction efficiencies for each steroid. The average extraction efficiencies (ranges) for T, KT, and E2 were 92.5 (88.8–94.6), 82.5 (81.6–83.0), and 83.4% (79.8–85.5%), respectively. All steroid assay results were corrected for recovery.
We measured plasma concentrations of T, KT, and E2 by radioimmunoassay (RIA) as described by Sower and Schreck (1982) and modified by Feist et al. (1990). All samples were analyzed in duplicate. The lower limit of detection was 1.25 pg/tube for all assays, except KT (3.12 pg/tube). The intra- and interassay coefficients of variation for all assays were < 5 (n = 12) and 10% (n = 12), respectively. We validated steroid levels determined by RIA by verifying that serial dilutions were parallel to standard curves.
Vtg was measured by enzyme immunoassay following the methodology of Linares-Casenave et al. (1994) and Heppell and Sullivan (1999). Purified white sturgeon Vtg and antibody were a gift from S. Doroshov (University of California–Davis). The lower limit of detection was 3.9 ng/mL, and the assay was validated by verifying that serial dilutions of samples were parallel to the standard curve. The intra- and interassay coefficients of variation were < 5 (n = 72) and 10% (n = 72), respectively. We determined calcium and TAG plasma content using diagnostic kits from Sigma (587-A and 334-A; St. Louis, MO).
Histology.
Gonad and liver tissue was stored in 10% phosphate-buffered formalin, embedded in paraffin, sectioned at 7 μm, and stained by hematoxylin and eosin (Luna 1968). Slides were examined under a compound scope (Motic Instruments, Inc., Richmond, B.C., Canada) using 10× to 100× objectives. We scored germ cells for stage of development according to the protocol of Van Eenennaam and Doroshov (1998). Stage 1 (differentiation of testis and ovary) and stage 2 (proliferation of spermatogonia and endogenous growth of the oocyte) fish were immature, whereas stage 3–6 males (onset of meiosis through spermiation) and stage 3–7 females (early vitellogenesis through ovulation) were classified as maturing. Each slide (liver and gonad tissue) was examined completely for presence or absence of gross lesions or other abnormalities, followed by semiquantification of macrophage aggregates (MA) in gonad and liver tissue and of eosinophils and lymphocytes in hepatic tissue in a randomly chosen field of view (10×). We formulated an index for semiquantification for the fish captured in the fisheries: 0, no MA or lymphocytes; 1, 1–25% of the tissue contained MA or lymphocytes; 2, 26–50% of the tissue contained MA or lymphocytes; 3, 51–75% of the tissue contained MA or lymphocytes; 4, 75–100% of the tissue contained MA or lymphocytes.
Contaminant analysis.
We analyzed a sub-sample of livers (n = 97) and gonads (n = 98) for 18 chlorinated pesticides and 28 PCB congeners (Appendix 1). This represented 11–17 males and 10–14 females from each sampling location.
Extraction and cleanup procedures of sturgeon tissues were based on the methods described by Price etal. (1986) and Gundersen et al. (1998). Liver and gonad samples were homogenized using a Brinkmann Polytron tissue homogenizer (Brinkmann Instruments, Inc., Westbury, NY), and a portion was removed for measurement of moisture content. Subsamples of tissue homogenates (~ 5 g) were combined with sodium sulfate (~ 50 g) and ground to a fine powder using a mortar and pestle. Dried tissues were Soxhlet extracted (10 hr) with 170 mL of 1:1 petroleum ether/hexane (vol/vol spectral grade; Sigma-Aldrich, St. Louis, MO). Extracts were concentrated to < 15 mL with a rotary evaporator and transferred to tared vials, where the remaining solvent was evaporated to dryness using a warm water bath and a stream of pure nitrogen (N2). The amount of lipid in each sample was determined gravimetrically. Lipid extracts were cleaned using 20 g Florisil-packed glass columns (400 × 19 mm), and PCBs and chlorinated pesticides were eluted with 6% ethyl ether/petroleum ether (vol/vol). PCBs and pesticides were fractionated into two eluates using 5 g silica gel-packed glass columns (10.5 × 300 mm). The first fraction [PCBs and p,p′-dichlorodiphenyldichloroethylene (DDE)] was eluted with hexane. The second fraction (chlorinated pesticides) was eluted with benzene.
We analyzed the cleaned fractions using a Varian CP-3800 gas chromatograph (Varian, Inc., Walnut Creek, CA) equipped with a 63Ni electron capture detector, a CP-8200 AutoSampler, a Star Chromatography Workstation (version 5; Varian Inc.), and an SPB-608 fused silica capillary column (30 mm × 0.25 mm × 0.25 μm film thickness; Supelco, Bellefonte, PA). Gas chromatographic parameters used were as follows: carrier gas, helium (1.5 mL/min); makeup gas, nitrogen; detector temperature, 300°C; injector temperature, 290°C; and oven temperature,
Quality assurance measures included the analysis of reagent blanks, duplicates, and matrix spike samples. Percent recoveries of PCB congeners and organochlorine pesticides in matrix spikes were between 90 and 110%; therefore, sample extracts were not corrected for percent recovery. Detection limits for individual PCB congeners and chlorinated pesticides were 0.01 μg/g wet weight. The State of Oregon Environmental Quality Laboratories and Applied Research, Organic Laboratory section (Portland, OR), analyzed two tissue homogenates for chlorinated pesticides (interlaboratory comparison). The relative percent difference of organochlorine pesticide concentrations reported by the two laboratories in the two samples differed by an average of < 17%.
Aging of fish.
Ages of fish sampled in 2001 were determined by pectoral fin spine analysis following the procedures described by Beamesderfer et al. (1989). Two independent determinations were conducted at the Oregon Department of Fish and Wildlife (Clackamas, OR) and at University of California–Davis (Davis, CA). Of the fish, 27% had identical age assignments by the different readers, 45% were aged within 1 year, 22% within 2 years, 2% within 3 years, and 4% > 5 years. We averaged ages of fish that were not in agreement between the two determinations.
Western blot analysis.
Hepatic microsomes were prepared by differential centrifugation according to Carpenter et al. (1990) and stored at −80°C until use. Briefly, livers were minced in ice-cold buffer (0.1 M Tris-acetate, pH 7.4; 0.1 M KCl; 1 mM EDTA; 20 μM butylated hydroxytoluene; and 1 mM phenyl-methylsulfonylfluoride) and homogenized in 4 volumes of the same buffer. The homogenate was centrifuged at 10,000 × g for 30 min, and the resulting supernatant was centrifuged at 100,000 × g for 90 min. The microsomal pellet was resuspended in buffer (0.1 M phosphate buffer, pH 7.25; 20% glycerol; and 1 mM EDTA). Microsomes were stored at −80°C until use.
We measured the putative white sturgeon hepatic cytochrome P450 3A (CYP3A) enzyme in microsomes by Western blotting using a polyclonal antibody generated against rainbow trout LMC5 (3A27). Microsomal CYP3A protein was measured using Western immunoblot techniques according to Towbin et al. (1979) with modifications. Briefly, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was performed using 8% polyacrylamide precast minigels. We prepared membranes according to the manufacturers recommendations, and proteins were transferred to membranes followed by incubation with rabbit anti-trout antibody (a generous gift from D. Buhler). Membranes were rinsed with phosphate-buffered saline–Tween and incubated with horseradish peroxidase–conjugated secondary antibodies (anti-rabbit) for detection of oxidized luminol (Amersham Biosciences, Piscataway, NJ). The chemiluminescent signal was captured on film (Hyperfilm ECL, Amersham Biosciences), and films were scanned for quantification.
Statistics.
We conducted all mean comparisons between physiologic parameters, tissue contaminant load, river location, and sex of fish using a one-way analysis of variance (ANOVA) with a Bonferroni procedure. All correlations between tissue contaminant load and physiologic parameters were conducted using reciprocal-Y regression. We performed all analyses using Statview software (Abacus Concepts, Inc., Berkeley, CA), and the accepted level of significance for all tests was p < 0.05.
Results
All 18 of the chlorinated pesticides examined in tissues from wild fish were detected in at least some of the samples (Table 1). We consistently found relatively high levels of metabolites of p,p′-dichlorodiphenyl-trichloroethane (DDT) [DDE and p,p′-1,1-dichloro-2,2-bis(4-chlorophenyl)ethane (DDD)] in fish. Concentrations of DDE were always greater than those of DDD and DDT in both livers and gonads (Figure 2). We found no differences in toxicant levels between tissues. Of the 28 PCB congeners examined, 26 were detected in at least some of the samples (Table 2).
Total DDT (DDD + DDE + DDT), total pesticides (all pesticides detected – total DDT), and PCBs (total of all detected) were significantly higher in livers and gonads of fish from Bonneville Reservoir compared with other locations (Figure 3). Fish from the Bonneville Reservoir had significantly lower TAG plasma concentrations and GSI than two of the other locations (Figure 4). Fish from Bonneville also had significantly lower calcium plasma concentrations and CF compared with all other locations.
We found a negative correlation between plasma TAG and total DDT, pesticides, and PCBs in livers (Table 3). To varying degrees, this was also true for TAG compared with contaminants in gonads and for CF compared with contaminants in livers and gonads. Although we observed significant relationships, r2 values indicated that a large amount of variation was present within the data.
Plasma concentrations of T were higher in males than in females at all sample locations except Bonneville (Figure 5). Males from the estuary had significantly higher levels of KT than did females, but this was not observed at other locations. Males from the estuary had significantly higher plasma T and KT than did males in the Bonneville and John Day reservoirs. Plasma concentrations of E2 were very low in all fish examined (Table 4). We observed no differences between either sex or location.
Plasma Vtg was at or very near the detection limit of the assay for all fish sampled in the estuary and Bonneville (Figure 5). Some males and immature females from The Dalles and John Day reservoirs had detectable levels of Vtg. Males from John Day had significantly higher concentrations of Vtg than did fish from all other locations. Females from The Dalles had concentrations of Vtg that were nearly significant compared with females from the estuary (p = 0.060) as well as compared with females from Bonneville (p = 0.058). There was no correlation between plasma Vtg and any of the pesticides or PCBs that were monitored.
Gonadal histology revealed a total of 82 females, 73 males, and 3 hermaphrodites from the 2 years of sampling. Sixteen gonad samples contained only adipose tissue and no gonial cells. Of the females, 81 were immature (all stage 2 except for 3 stage 1 females), and 1 was a maturing female (stage 3; early vitellogenesis). Of the males, 66 were immature (all stage 2), 1 was in stage 3 of gonadal development (onset of meiosis), and 6 were in stage 5 of development (spermiation). No maturing fish were captured in Bonneville Reservoir. All of the maturing males had significantly higher levels of plasma androgens (T, 92.2 ± 20.9; KT, 84.0 ± 16.4 ng/mL) than did immature males (T, 5.1 ± 1.1; KT, 4.3 ± 1.0 ng/mL). All 3 of the hermaphroditic fish had predominately female ovotestes. Two of the 3 fish were captured in Bonneville Reservoir, and the other was from the estuary. Several fish showed irregular ovarian plasma membranes and intrusion of muscle into the ovary. MAs were found in both female and male gonadal tissue and were most often found to contain melanin.
Liver histology revealed a high incidence of MA and lymphocytes. However, no pattern was discernible with regard to contaminant level. We found a very high incidence of MA and/or lymphocytes in liver samples from 11 fish; of these, 7 were from the Bonneville Reservoir, 2 were from the estuary, and 1 each were from The Dalles and John Day reservoirs.
We found a negative correlation between plasma T and total DDT, pesticides, and PCBs in livers of male white sturgeon (Figure 6). We also observed these relationships for contaminants in gonads (Figure 7). To varying degrees, this was also true for plasma KT and GSI compared with contaminants in gonads and livers (Table 5).
Spermatogonia proliferation (stage 2) in white sturgeon is associated with increased circulating androgen concentrations regardless of age or size (Feist et al. 2004). In immature wild white sturgeon, T concentrations > 4 ng/mL may be used to differentiate stage 2 males from stage 1 males and immature females (Webb et al. 2002). All 66 immature males in our study were in stage 2 of gonadal development, yet 47 (71.2%) had plasma T concentrations that were < 4 ng/mL. Of the 48 stage 2 males that were analyzed for toxicants, 31 had levels of T < 4 ng/mL. In addition, no males with liver contaminant levels > 9.5 ppm (total DDT), > 5.6 ppm (total pesticides), or > 2.8 ppm (PCBs) had plasma T concentrations > 4 ng/mL (Figure 6). Where this was observed, concentrations of toxicants in gonads were 11.6, 3.7, and 2.5 ppm, for total DDT, total pesticides, and PCBs, respectively (Figure 7).
Age determination of fish by pectoral fin spine analysis in 2001 revealed that sturgeon from Bonneville (18.3 ± 1.0 years; range, 14–27) and John Day (17.4 ± 0.4 years; range, 14–20) were significantly older than those sampled in The Dalles (14.8 ± 0.5 years; range, 10–19). Bonneville fish were also significantly older than estuary fish (14.6 ± 1.0 years; range, 10–17).
To investigate the possibility that DDE reduces plasma androgens by increasing steroid metabolism and excretion via up-regulation of liver cytochrome P450 isozymes, we conducted a preliminary and purely qualitative Western blot analysis to measure the putative CYP3A in microsomes. In trout this enzyme is responsible for hydroxylating steroids as a first step for metabolism and excretion (Lee et al. 2001). A Western blot for this isozyme is shown in Figure 8. Male sturgeon with higher liver content of DDE showed increased immunoreactivity for CYP3A.
Discussion
The life history of white sturgeon may make them particularly susceptible to the actions of persistent bioaccumulating pollutants. These fish are bottom dwellers and feed on benthic prey items that are closely associated with sediments containing hydrophobic pollutants. Sturgeon can live for > 100 years, and females mature between 16 and 35 years of age (DeVore et al. 1995). Thus, toxicants may accumulate and have deleterious effects over a long period of time before the fish reach a stage when they are able to reproduce. A recent study in the Columbia River found that sturgeon contained the highest body burdens of contaminants out of 12 species of fish examined (U.S. EPA 2002). Levels of toxicants seen in the present study were comparable with those found by the U.S. EPA and also comparable with levels previously reported by our laboratory (Foster et al. 2001a, 2001b).
Fish trapped behind the oldest of the dams examined (Bonneville) had the highest contaminant loads and the lowest CF, gonad size, and plasma androgens and triglycerides. These fish also had the highest incidence of gonadal abnormalities. This suggests that endocrine-disrupting chemicals (EDCs) may be accumulating behind dams over time.
It has recently been determined that past operation of the dam at Bonneville has resulted in areas within the reservoir that have very high levels of PCBs (URS Corporation 2002). In our study, Bonneville fish were older than fish from two of the other sampling locations. Fish from this reservoir also grow slower, and females mature at a later age than other locations (Beamesderfer et al. 1995). Thus, these fish may be exposed to higher levels of contaminants and for longer periods of time than comparably sized fish from other areas of the river. Food availability may be the main cause for reduced growth in Bonneville fish, but effects of toxicants cannot be ruled out. The negative correlations found between plasma triglycerides and CF with tissue burdens of pesticides and PCBs add strength to this possibility.
Our laboratory has previously documented a negative correlation between plasma androgens and tissue content of p,p′-DDE for Columbia River sturgeon (Foster et al. 2001b). In the present study, we observed negative correlations between both plasma androgens and GSI of males compared with total DDT, total pesticides, and PCBs. Our sample size for this study was much greater than our previous research, which may explain why these relationships were not seen in the earlier study. p,p′-DDE has also been shown to have demasculinizing effects in the guppy (Poecilia reticulata) (Baatrup and Junge 2001; Bayley et al. 2002). Our data also suggest that DDT and its metabolites may reach threshold levels in liver and gonad above which the fish are incapable of elevating plasma T concentrations. This may result in the inability of males with high body burdens of contaminants to attain sexual maturity.
We have preliminary evidence that the mechanism of action of plasma androgen reduction by p,p′-DDE, or possibly by other pesticides or PCBs, is by increasing steroid metabolism through up-regulation of CYP3A. DDE has been shown to induce this isozyme and increase metabolism of T in mice (Dai et al. 2001). Rainbow trout (Oncorhynchus mykiss) injected with DDE, however, showed a decrease in CYP3A-dependent 6β-hydroxylation of T (Machala et al. 1998). The dose used for the rainbow trout study was much higher (50 mg/kg) than levels seen in wild fish in our study and may not have simulated the effects of chronic exposure to lower concentrations of DDE.
Our finding that plasma androgens were higher in males than females (except in the Bonneville Reservoir) has been previously documented by our laboratory (Foster et al. 2001a, 2001b). We have used differences in plasma steroids between males and females to develop a model for sexing both immature and maturing wild white sturgeon and for determining sex of cultured fish at an early age (Feist et al. 2004; Webb etal. 2002).
Although banned for use in the United States in 1973, DDT and its metabolites are still being detected in sturgeon at relatively high levels. This indicates that this compound is extremely persistent in the environment. Tissue burdens were always DDE > DDD and DDT, indicating that aerobic degradation of DDT (yielding primarily DDE) is the main metabolic pathway as opposed to anaerobic degradation (yielding primarily DDD) (Spencer etal. 1996). This suggests that the most likely source of DDT metabolites is from agricultural runoff of the parent compound as opposed to anaerobic degradation of DDT in sediments.
The type and source of xenoestrogen(s) responsible for elevating plasma Vtg in males and immature females from The Dalles and John Day reservoirs remains uncertain. None of the pesticides or PCBs monitored in this study was correlated with plasma Vtg. Fish exposed in our laboratory to the pesticides (permethrin and pyriproxyfen) or herbicides (atrazine and simazine) that are currently being used in agricultural practices in the Columbia basin did not show increases in plasma Vtg (data not shown). Caged sturgeon, in areas of the river where some wild fish had elevated Vtg, also did not show an increase in this protein (data not shown). This suggests that wild fish either are being exposed to potential EDCs for longer periods of time or are bioaccumulating them through ingestion of prey.
Other candidates for induction of Vtg include the alkylphenols, which have been shown to be weakly estrogenic in fish (Jobling etal. 1996; White etal. 1994). Fish exposed to octylphenol and nonylphenol in our laboratory experienced increased plasma Vtg (data not shown), but we are unable to find a likely source for alkylphenolic compounds in The Dalles and John Day reservoirs. There are many sources of alkylphenols in the estuary and Bonneville Reservoir, yet we found no elevated Vtg in wild sturgeon sampled in this area of the river. The cause of elevated Vtg in wild fish is most likely due to other EDCs or metabolites of toxicants not yet identified, or combinations of compounds.
The overall results of this study indicate that exposure to environmental contaminants may be affecting both growth and reproductive physiology of sturgeon in some areas of the Columbia River. Questions remain, however, as to what effects these contaminants have on the ability of sturgeon to successfully reproduce. It is unknown if lowered energy reserves, GSI, and androgens, and elevated Vtg actually inhibit or decrease the ability of sturgeon to mature and spawn. Because of the slot-size limit (fish that are 110–137 cm in fork length), most wild fish sampled in this study were immature. Larger sturgeon that have reached a sufficient size and age to mature must be examined to determine possible deleterious effects of contaminants on reproduction. Different year classes of sturgeon also need to be investigated to determine if toxicants are bioaccumulating as the fish age. Finally, prey items need to be examined for the presence of EDCs to determine if sturgeon are acquiring these compounds from their diet or other sources.
The poor reproductive success of sturgeon in impounded areas of the Columbia River is most likely due to a wide variety of stressors, including food availability, poor spawning habitat, and changes in flow and temperature. Exposure to environmental contaminants may be an additional stressor that is contributing to this reduced reproductive fitness.
We thank C. Anthony, D. Buhler, R. Chitwood, B. Siddens, and A. Schwindt, Oregon State University; T. Rien, Oregon Department of Fish and Wildlife; J. Linares-Casenavae, S. Doroshov, and J. Van Eenennaam, University California–Davis; and W. Gale, U.S. Geological Survey Columbia River Research Laboratory.
This research was funded by the U.S. Geological Survey (99HQAG0152).
Figure 1 Sample sites for white sturgeon from the Columbia River in the estuary near Astoria, Oregon (EST), and the reservoirs behind Bonneville (B), The Dalles (TD), and John Day (JD) dams. Abbreviations: B.C., British Columbia; ID, Idaho; OR, Oregon; WA, Washington State.
Figure 2 Mean concentrations (± SE) of DDT and its metabolites in livers (n = 97) and gonads (n = 98) of white sturgeon from all sample areas combined. Means with different letters indicate a significant difference within a tissue (ANOVA, p < 0.05).
Figure 3 Concentrations (mean ± SE) of total DDT (A; DDD + DDE + DDT), total pesticides (B; all pesticides detected – total DDT), and PCBs (C; total of all detected) in livers and gonads of white sturgeon from four locations on the Columbia River. Each bar represents a sample size of 22–28.
*Statistically different from other locations (ANOVA, p < 0.05).
Figure 4 Mean plasma concentrations (± SE) of TAG (A), CF (B), calcium (C), and GSI (D) in white sturgeon from four locations on the Columbia River. Each bar represents a sample size of 42–45. Means with different letters indicate a significant difference between locations (ANOVA, p < 0.05).
Figure 5 Mean plasma concentrations (± SE) of T (A), KT (B), and Vtg (C) and individual Vtg concentrations (D) in male and immature female white sturgeon from four locations on the Columbia River. Each bar represents a sample size of 19–24 (A–C). Means with different letters or numbers indicate a significant difference between locations or between sexes within a location, respectively (ANOVA, p < 0.05).
Figure 6 Individual plasma T versus total DDT (A), total pesticides (B), or total PCB (C) concentrations in livers of male white sturgeon. Reciprocal-Y regression: p < 0.001 and r2 = 0.79 for DDT, p < 0.001 and r2 = 0.56 for pesticides, and p < 0.001 and r2 = 0.80 for PCBs. All males with toxicant levels higher than those denoted by the vertical dashed line have < 4 ng/mL T.
Figure 7 Individual plasma T versus total DDT (A), total pesticides (B), or total PCB (C) concentrations in gonads of male white sturgeon. Reciprocal-Y regression: p < 0.001 and r2 = 0.85 for DDT, p < 0.001 and r2 = 0.31 for pesticides, and p < 0.001 and r2 = 0.82 for PCBs. All males with toxicant levels higher than those denoted by the vertical dashed line have < 4 ng/mL T.
Figure 8 Western blot of CYP3A protein in individual livers of male white sturgeon with varying levels of liver DDE.
Table 1 Concentration (mean ± SE) of chlorinated pesticides in livers (n = 97) and gonads (n = 98) of white sturgeon from the Columbia River.
Liver
Gonad
Pesticide D Lipid (μg/g) D Lipid (μg/g)
Aldrin 2 0.002 ± 0.002 5 0.011 ± 0.006
α-BHC 19 0.039 ± 0.009 26 0.023 ± 0.005
β-BHC 14 0.115 ± 0.046 11 0.023 ± 0.005
γ -BHC 8 0.024 ± 0.011 21 0.047 ± 0.014
δ -BHC 9 0.019 ± 0.007 15 0.154 ± 0.127
p,p′-DDD 86 1.863 ± 0.544 93 1.619 ± 0.400
p,p′-DDE 97 18.40 ± 7.313 98 10.60 ± 2.086
p,p′-DDT 28 0.274 ± 0.103 41 0.259 ± 0.073
Dieldrin 16 0.134 ± 0.045 15 0.031 ± 0.009
Endrin 10 0.114 ± 0.060 11 0.022 ± 0.007
Endrin aldehyde 16 0.108 ± 0.062 13 0.064 ± 0.032
Endrine ketone 8 0.038 ± 0.165 2 0.010 ± 0.007
Endosulfan I 34 0.161 ± 0.044 45 0.133 ± 0.025
Endosulfan II 9 0.108 ± 0.051 14 0.087 ± 0.047
Endosulfan sulfate 3 0.005 ± 0.003 8 0.008 ± 0.003
Heptachlor 8 0.018 ± 0.008 13 0.037 ± 0.019
Heptachlor epoxide 15 0.081 ± 0.031 25 0.074 ± 0.024
p,p′-Methoxychlor 14 0.112 ± 0.044 5 0.027 ± 0.017
Abbreviations: BHC, benzene hexachloride; D, number of detections.
Table 2 Concentration (mean ± SE) of PCBs in livers (n = 97) and gonads (n = 98) of white sturgeon from the Columbia River.
Liver
Gonad
Pesticide (IUPAC no.) D Lipid (μg/g) D Lipid (μg/g)
28 3 0.020 ± 0.011 0
44 6 0.055 ± 0.042 4 0.004 ± 0.002
52 3 0.038 ± 0.024 3 0.024 ± 0.105
60 19 0.125 ± 0.033 11 0.163 ± 0.129
66 8 0.131 ± 0.066 2 0.025 ± 0.020
74 2 0.008 ± 0.006 4 0.037 ± 0.022
87 1 0.006 ± 0.006 2 0.008 ± 0.006
99 12 0.101 ± 0.036 12 0.077 ± 0.041
101 28 0.238 ± 0.088 24 0.217 ± 0.131
105 14 0.135 ± 0.051 9 0.033 ± 0.016
110/77 12 0.060 ± 0.019 17 0.128 ± 0.050
118 9 0.054 ± 0.020 10 0.152 ± 0.085
126 6 0.035 ± 0.016 5 0.024 ± 0.018
128 1 0.007 ± 0.007 6 0.043 ± 0.031
138 28 0.258 ± 0.071 28 0.233 ± 0.072
151 4 0.025 ± 0.015 7 0.032 ± 0.014
153 18 0.264 ± 0.101 20 0.157 ± 0.062
156 6 0.035 ± 0.018 7 0.013 ± 0.006
169 2 0.007 ± 0.005 0
170 3 0.006 ± 0.003 3 0.003 ± 0.001
180 3 0.030 ± 0.026 3 0.001 ± 0.001
183 9 0.042 ± 0.015 13 0.029 ± 0.010
187 20 0.163 ± 0.047 21 0.113 ± 0.032
194 4 0.018 ± 0.009 1 0.001 ± 0.001
199 10 0.022 ± 0.007 10 0.065 ± 0.030
203/170 10 0.043 ± 0.017 10 0.016 ± 0.008
Abbreviations: D, number of detections; IUPAC, International Union of Pure and Applied Chemistry.
Table 3 Regression analyses of TAG and CF versus various contaminants in livers and gonads of Columbia River white sturgeon.
Liver
Gonad
TAG
CF
TAG
CF
Contaminant r2 p-Value r2 p-Value r2 p-Value r2 p-Value
Total DDT 0.60 < 0.001 0.08 < 0.005 0.20 < 0.001 0.11 < 0.001
Total pesticides 0.48 < 0.001 0.15 < 0.001 0.04 < 0.050 0.18 < 0.001
Total PCBs 0.60 < 0.001 0.11 < 0.002 0.10 < 0.002 0.07 < 0.008
Table 4 Concentration (mean ± SE) of plasma E2 (ng/mL) in male (n = 19–24) and female (n = 21–23) white sturgeon at four locations from the Columbia River.
Estuary Bonneville The Dalles John Day
Female 0.09 ± 0.02 0.11 ± 0.03 0.13 ± 0.02 0.28 ± 0.05
Male 0.16 ± 0.03 0.07 ± 0.01 0.14 ± 0.03 0.38 ± 0.10
Table 5 Regression analyses of KT and GSI versus various contaminants in livers and gonads of male Columbia River white sturgeon.
Liver
Gonad
KT
GSI
KT
GSI
Contaminant r2 p-Value r2 p-Value r2 p-Value r2 p-Value
Total DDT 0.08 < 0.050 0.24 < 0.001 0.11 < 0.020 0.21 < 0.001
Total pesticides NS NS 0.15 < 0.006 NS NS 0.22 < 0.001
Total PCBs 0.16 < 0.004 NS NS NS NS 0.10 < 0.030
NS, not significant.
Appendix 1 Chlorinated pesticides and PCBs measured in Columbia River white sturgeon livers and gonads.
Chlorinated pesticide PCB (IUPAC no.)
Aldrin 2,2′,5-Trichlorobiphenyl (18)
α-BHC 2,4,4′-Trichlorobiphenyl (28)
β-BHC 2,2′,3,5′-Tetrachlorobiphenyl (44)
γ-BHC 2,2′,5,5′-Tetrachlorobiphenyl (52)
δ-BHC 2,3,4,4′-Tetrachlorobiphenyl (60)
p,p′-DDD 2,3′,4,4′-Tetrachlorobiphenyl (66)
p,p′-DDE 2,4,4′,5-Tetrachlorobiphenyl (74)
p,p′-DDT 3,3′,4,4′-Tetrachlorobiphenyl (77)
Dieldrin 2,2′,3,4,5′-Pentachlorobiphenyl (87)
Endrin 2,2′,4,4′,5-Pentachlorobiphenyl (99)
Endrin aldehyde 2,2′,4,5,5′-Pentachlorobiphenyl (101)
Endrine ketone 2,3,3′,4,4′-Pentachlorobiphenyl (105)
Endosulfan I 2,3,3′,4′,6-Pentachlorobiphenyl (110)
Endosulfan II 2,3′,4,4′,5-Pentachlorobiphenyl (118)
Endosulfan sulfate 3,3′,4,4′,5-Pentachlorobiphenyl (126)
Heptachlor 2,2′,3,3′,4,4′-Hexachlorobiphenyl (128)
Heptachlor epoxide 2,2′,3,4,4′,5′-Hexachlorobiphenyl (138)
p,p′-Methoxychlor 2,2′,3,5,5′,6-Hexachlorobiphenyl (151)
2,2′,4,4′,5,5′-Hexachlorobiphenyl (153)
2,3,3′,4,4′,5-Hexachlorobiphenyl (156)
3,3′,4,4′,5,5′-Hexachlorobiphenyl (169)
2,2′,3,3′,4,4′,5-Heptachlorobiphenyl (170)
2,2′,3,4,4′,5,5′-Heptachlorobiphenyl (180)
2,2′,3,4,4′,5′,6-Heptachlorobiphenyl (183)
2,2′,3,4′,5,5′,6-Heptachlorobiphenyl (187)
2,2′,3,3′,4,4′,5,5′-Octachlorobiphenyl (194)
2,2′,3,3′,4,5,5′,6′-Octachlorobiphenyl (199)
2,2′,3,4,4′,5,5′,6-Octachlorobiphenyl (203)
Abbreviations: BHC, benzene hexachloride; IUPAC, International Union of Pure and Applied Chemistry.
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Van Der Kraak G 1998 Observations of endocrine effects in wildlife with evidence of their causation Pure Appl Chem 70 1785 1794
Van Eenennaam JP Doroshov SI 1998 Effects of age and body size on gonadal development of Atlantic sturgeon J Fish Biol 53 624 637
Webb MAH Feist GW Foster EP Schreck CB Fitzpatrick MS 2002 Potential classification of sex and stage of gonadal maturity of wild white sturgeon using blood plasma indicators Trans Am Fish Soc 131 132 142
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8084ehp0113-00168316330347ResearchFolate, Homocysteine, and Arsenic Metabolism in Arsenic-Exposed Individuals in Bangladesh Gamble Mary V. 1Liu Xinhua 2Ahsan Habibul 3Pilsner J. Richard 1Ilievski Vesna 1Slavkovich Vesna 1Parvez Faruque 1Levy Diane 2Factor-Litvak Pam 3Graziano Joseph H. 141 Department of Environmental Health Sciences,2 Department of Biostatistics, and3 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA4 Department of Pharmacology, College of Physicians and Surgeons, Columbia University, New York, NY, USAAddress correspondence to M.V. Gamble, Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 60 Haven Ave., B1, New York, NY 10032, USA. Telephone: (212) 305-7949. Fax: (212) 305-3857. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 21 7 2005 113 12 1683 1688 3 3 2005 21 7 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Chronic exposure to arsenic is occurring throughout South and East Asia due to groundwater contamination of well water. Variability in susceptibility to arsenic toxicity may be related to nutritional status. Arsenic is methylated to monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) via one-carbon metabolism, a biochemical pathway that is dependent on folate. The majority of one-carbon metabolism methylation reactions are devoted to biosynthesis of creatine, the precursor of creatinine. Our objectives of this cross-sectional study were to characterize the relationships among folate, cobalamin, homocysteine, and arsenic metabolism in Bangladeshi adults. Water arsenic, urinary arsenic, urinary creatinine, plasma folate, cobalamin, and homocysteine were assessed in 1,650 adults; urinary arsenic metabolites were analyzed for a subset of 300 individuals. The percentage of DMA in urine was positively associated with plasma folate (r = 0.14, p = 0.02) and negatively associated with total homocysteine (tHcys; r = −0.14, p = 0.01). Conversely, percent MMA was negatively associated with folate (r = −0.12, p = 0.04) and positively associated with tHcys (r = 0.21, p = 0.0002); percent inorganic arsenic (InAs) was negatively associated with folate (r = −0.12, p = 0.03). Urinary creatinine was positively correlated with percent DMA (r = 0.40 for males, p < 0.0001; 0.25 for females, p = 0.001), and with percent InAs (r = −0.45 for males, p < 0.0001; −0.20 for females, p = 0.01). Collectively, these data suggest that folate, tHcys, and other factors involved in one-carbon metabolism influence arsenic methylation. This may be particularly relevant in Bangladesh, where the prevalence of hyperhomocysteinemia is extremely high.
arsenicarsenicosisBangladeshcreatininedimethylarsinic acidfolatehomocysteinehyperhomocysteinemiamicronutrient deficiencymonomethylarsonic acidone-carbon metabolismS-adenosylmethioninevitamin B12well water
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Arsenic that is naturally present in soil can be mobilized and transported, leading to increased concentrations of As in aquifers that are sources of drinking water (Harvey et al. 2002). The largest contemporary known mass exposure to As is occurring due to the consumption of tube-well water throughout the Ganges-Brahmaputra Delta in Bangladesh and India. In Bangladesh alone, this exposure is affecting approximately 25–30 million residents. Our survey of roughly 6,000 contiguous wells in Araihazar, Bangladesh, the region of interest in the current study, reported well-water As concentrations ranging from < 5 to 860 μg/L (van Geen et al. 2002). This range greatly exceeds the maximum contaminant level of 10 μg/L promulgated by the U.S. Environmental Protection Agency (EPA 2001) and the World Health Organization (WHO 2004), as well as the Bangladesh standard of 50 μg/L.
Individuals chronically exposed to As are at increased risk for various cancers, including cancers of the skin, bladder, lung, and liver. Chronic As exposure is also a risk factor for stroke (Chiou et al. 1997), ischemic heart disease (Hsueh et al. 1997), and neurologic consequences in adults and children (Wasserman et al. 2004). In addition, inorganic arsenic (InAs) has long been considered to be a teratogen in multiple mammalian species (Shalat et al. 1996; Wlodarczyk et al. 2001).
The biomethylation of InAs generates mono- and dimethyl arsenic species [mono-methylarsonic acid (MMA) and dimethylarsinic acid (DMA), respectively; Figure 1] which are more readily excreted than InAs (Vahter and Marafante 1987). Individuals whose urine contains relatively lower proportions of DMA have been reported to be at increased risk for skin and bladder cancers (Chen et al. 2003a, 2003b; Hsueh et al. 1997; Yu et al. 2000). Thus, methylation of InAs has traditionally been considered to be a detoxification pathway. However, a growing literature of experimental studies indicates that the trivalent methylated arsenic intermediates (MMAIII and DMAIII) may be more toxic than InAsIII or any of the pentavalent intermediates (Ahmad et al. 1999; Lee et al. 1988; Nesnow et al. 2002; Styblo et al. 2002).
In humans, there is considerable inter-individual variability in the proportions of InAs, MMA, and DMA excreted in urine. InAs and MMA are enzymatically methylated via one-carbon metabolism, a biochemical pathway dependent on folate for recruitment of methyl groups. One-carbon metabolism also requires vitamins B12 (cobalamin) and B6 as cofactors (Figure 2). Not surprisingly, animal studies have suggested that folate nutritional status may influence As metabolism. For example, dietary folate deficiency (Spiegelstein et al. 2005) and/or dietary methyl donor deficiency significantly decreased total urinary As excretion, mainly due to lower DMA excretion; these diets also gave rise to increased retention of As in tissues, particularly in the liver and lungs (Tice et al. 1997; Vahter and Marafante 1987). These studies provided experimental evidence that the well-characterized nutritional regulation of one-carbon metabolism can influence As methylation and excretion.
The evidence for nutritional regulation of As methylation and excretion in humans is limited. In a study of 11 families in Chile, Chung et al. (2002) reported intrafamily associations in arsenic methylation. Although the correlation for InAs/(MMA+DMA) between father and mother was low (r = 0.18), adjustment for folate or homocysteine increased the correlation substantially (r = 0.33 and 0.55, respectively). The authors did not conclude that there was a significant effect of nutritional factors on methylation, but the data were highly suggestive (Chung et al. 2002). A case–control study in West Bengal, India, using dietary assessment, found a modest increase in risk for arsenicosis skin lesions for individuals within the lowest quintiles for dietary intake of animal protein, calcium, fiber, and folate (Mitra et al. 2004).
In the current study we tested the hypothesis that nutritional regulation of one-carbon metabolism, specifically folate nutritional status, contributes to the interindividual variability observed in InAs methylation. We conducted a cross-sectional study to assess the relationships among plasma total homocysteine (tHcys), total cysteine (Cys), folate, and cobalamin concentrations and As metabolism in adults residing in Araihazar, Bangladesh.
Methods
The data presented are from the Nutritional Influences on Arsenic Toxicity (NIAT) study, an ongoing study on nutritional influences on arsenic metabolism. The NIAT study works in collaboration with a larger multidisciplinary program by health, earth, and social scientists from Columbia University and Bangladesh (the Columbia University Superfund Basic Research Program), the National Institute of Preventive and Social Medicine, and Dhaka University. The centerpiece of the public health research is a prospective cohort study, Health Effects of Arsenic Longitudinal Study (HEALS), of 12,000 adults exposed to a wide range of water As concentrations, from which the current sample is derived.
Study region.
Bangladesh is a nation of roughly 124 million people inhabiting an area of 145,000 km2. It is divided into 64 districts, each of which is divided into 10–50 administrative units or Thanas. The study site is in Araihazar, one of 464 Thanas in Bangladesh, and is a 25-km2 region approximately 30 km east of Dhaka. The study site was chosen because a) it is known to have a wide range of As concentrations in the drinking water, permitting dose–response analyses and b) it is within a reasonable commuting distance from Dhaka. Socioeconomic data indicate that this region is not particularly poor by Bangladeshi standards.
Eligibility criteria/participant recruitment/ethics.
The HEALS cohort study includes a random sample of 11,746 married men and women between 20 and 65 years of age who were recruited between September 2000 and May 2002 and are followed at 2-year intervals (Ahsan et al., in press). This study only included married couples to minimize the likelihood of loss to follow-up due to a change of residence after marriage. Centered around visits of one of the investigators (M.V.G.), a subset of 1,650 of these cohort study participants were consecutively enrolled in the present study. We further selected 300 of these 1,650 participants to measure urinary arsenic metabolites. This subset of participants was selected to be representative of the study population for total urinary As; the subset excluded those identified as being cobalamin deficient (plasma cobalamin concentrations < 185 pmol/L). Oral informed consent was obtained by our Bangladeshi field staff physicians, who read an institutional review board approved assent form to the study participants. Ethics approval was obtained from the Institutional Review Board of Columbia Presbyterian Medical Center and the Bangladesh Medical Research Council, and informed consent was obtained by our Bangladeshi field staff physicians.
Plasma collection and handling.
We obtained plasma samples for tHcys, folate, and total cobalamin by venipuncture after the participant had been sitting for 10–15 min for an interview. Blood was collected into EDTA-containing tubes and immediately placed in IsoRack cool packs (Brinkmann Instruments, Westbury, NY) designed to maintain samples at 0°C for > 6 hr. Within 4 hr, samples were transported in hand-carried coolers containing additional ice packs to our local laboratory situated in our three-story field clinic in Araihazar. Samples were centrifuged at 4°C and plasma separated from the cells. Plasma was then stored in aliquots at −80°C and shipped frozen on dry ice to Columbia University for analysis.
Measures of arsenic exposure.
We analyzed well-water arsenic concentrations as part of a comprehensive well survey before the onset of the cohort study. The actual well-water arsenic concentration was labeled onto each well, with signs indicating safety or danger, and many study participants subsequently switched wells to reduce exposure (van Geen et al. 2002); therefore, well-water arsenic did not always strictly represent current arsenic exposure. However, we do not believe that this led to significant exposure misclassification, as the duration of drinking water from the surveyed well greatly exceeded the duration of drinking water from an alternate well. In the analyses pertaining to folate and tHcys concentrations, any potential exposure misclassification would only bias to the null. Furthermore, we collected urine samples for urinary arsenic measurements on the same visit that the plasma samples were collected.
Well-water and urinary As provide two different estimates of As exposure. Although well-water As provides a direct measure of exposure that is uninfluenced by in vivo metabolism, it does not take into account the amount of water consumed or the accumulated body burden. Urinary As, traditionally considered a marker of recent exposure, may more closely reflect the body burden of As in a given individual because a) it receives contributions from tissue stores and b) it is more strongly associated with skin lesions than is well-water As (Ahsan et al. 2000). Urinary As concentrations are heavily influenced by variations in hydration status, as indicated by the association between total urinary As and urinary creatinine concentrations (r = 0.58, p < 0.0001, N = 1,650); thus, urinary creatinine must be included as a separate variable in the urinary As analyses. Because animal studies suggest that impaired one-carbon metabolism incurred by methyl-deficient diets decreases total urinary arsenic by up to 20–30% (Spiegelstein et al. 2003, 2005; Tice et al. 1997; Vahter and Marafante 1987), both urine As and well water As were included in the analyses.
Total urinary As.
We measured total urinary As concentrations by graphite furnace atomic absorption spectrometry using the Analyst 600 graphite furnace system (Perkin Elmer, Shelton, CT) in the Columbia University Trace Metals Core Lab, essentially as previously described (Nixon et al. 1991). Our laboratory participates in a quality control program coordinated by Philippe Weber at the Quebec Toxicology Center in Quebec, Canada. During the course of this study, intra-class correlation coefficients between our laboratory’s values and samples calibrated at the Quebec laboratory were 0.99. Urinary creatinine concentrations were analyzed by a colorimetric Sigma Diagnostics Kit (Sigma, St. Louis, MO).
Urinary arsenic metabolites.
Urinary arsenic metabolites were speciated for 300 participants using a method adapted from Heitkemper (Vela et al. 2001). This method employs HPLC separation of arsenobetaine (AsB), arsenocholine (AsC), arsenate, arsenite, MMA, and DMA, followed by detection by inductively-coupled mass spectrometry (ICP-MS). We calculated the percentages of InAs, MMA, and DMA after subtracting AsC and AsB from the total. In most cases, AsC and AsB were nondetectable.
Plasma folate and cobalamin.
We analyzed folate and cobalamin in 1648 plasma samples by radioimmunoassay (Quantaphase II, Bio-Rad Laboratories, Richmond CA), as previously described (Gamble et al. 2005).
Plasma homocysteine and cysteine concentrations.
We measured plasma tHcys and Cys concentrations in 1644 plasma samples by HPLC with fluorescence detection according to the method described by Pfeiffer et al. (1999), as described previously (Gamble et al. 2005). The within-day and between-day coefficients of variation for tHcys were 5% and 8%, respectively.
Statistical analyses.
We calculated descriptive statistics for characteristics of the study sample separately by sex. Because we expected the distributions to be non-normal, sex differences in quantitative variables were tested using the Wilcoxon rank-sum test that requires no distribution assumption. We used chi-square tests to test for sex differences in categorical variables.
We used linear regression analyses to examine associations. Because As exposure might potentially influence plasma folate and tHcys concentrations, we explored the associations between these variables in the 1,650 samples. In sequential regression analyses on the subset of 300 participants with urinary arsenic metabolite data, we examined determinants of percentages of InAs (%InAs), MMA(%MMA), and DMA (%DMA).
Because tHcys, folate, and cobalamin in plasma, and water arsenic, urine arsenic, and urine creatinine have skewed distributions, we used log-transformation to achieve approximately symmetric distributions. In preliminary analyses, water arsenic, age, sex, cigarette smoking, and betelnut use were found to be associated with urinary arsenic metabolites and were therefore included in our regression models as potential confounding variables.
Results
Characteristics of the population.
The general characteristics of the NIAT study sample have been previously described in detail (Gamble et al. 2005). Out of 1,650 participants, 973 were women and 677 were men. The mean ages for women and men were 34.6 ± 8.8 and 42.2 ± 9.8 years, and body mass indices were 20.2 ± 3.2 and 19.4 ± 3.0, respectively. Betelnut use was practiced by 30% of women and 40% of men, whereas 6% of women and 76% of men smoked cigarettes.
tHcys, folate, and cobalamin findings.
We recently reported a high prevalence of hyper-homocysteinemia, particularly among males (63% ≥ 11.4 μM) in this study population (Gamble et al. 2005). Folate and cobalamin nutritional status accounted for 15% and 5%, respectively, of the variability in tHcys.
Arsenic exposure.
As is shown in Table 1, urinary As concentrations, expressed in micrograms per liter, did not differ between males and females. However, when adjusted for urinary creatinine, as is routinely done to correct for the effects of hydration, males had significantly lower As concentrations than females. This divergence is attributable to significant sex differences in urinary creatinine, which is related to lean body mass (Schutte et al. 1981). Well-water As concentrations ranged from 0.1 μg/L to 650 μg/L, with 82% of wells having concentrations > 10 μg/L and 63% > 50 μg/L.
Influence of arsenic exposure on folate, tHcys and Cys.
Among the entire sample (n = 1,650), water arsenic concentrations were significantly associated with plasma folate (r = −0.13, p < 0.0001), tHcys (r = 0.05, p = 0.03), and Cys (r = 0.13, p < 0.0001). Although total urinary arsenic (micrograms per gram creatinine) was also negatively associated with plasma folate (r = −0.15, p < 0.0001), it was not associated with tHcys or Cys (r = −0.03, p = 0.29; r = −0.03, p = 0.23 ). Folate deficiency may influence urinary creatinine concentrations because the synthesis of creatine, the precursor of creatinine, accounts for approximately 75% of folate- and S-adeno-sylmethionine (SAM)-dependent transmethylation reactions (Figure 2, reaction 6; Mudd et al. 1975). Thus, we sought to rule out the possibility that the inverse association between plasma folate and urinary As per gram creatinine might be due to an association between plasma folate and urinary creatinine. Indeed, plasma folate concentrations were positively correlated with urinary creatinine for males (r = 0.083, p = 0.0308) (Gamble et al. 2005). Urinary arsenic concentrations (micrograms per liter) were negatively associated with plasma folate (r = −0.13, p < 0.0001), even when not adjusted for urinary creatinine.
tHcys, folate, cobalamin, and urinary arsenic metabolites.
Urinary As metabolites measured in a subset of 300 study participants showed a wide interindividual variability in arsenic methylation capacity. For example, the percentage of total urinary As present as DMA (%DMA) ranged from 20 to 90%, with the majority falling between 50 and 80% (Figure 3). In addition, methylation capacity differed by sex: on average, females had a higher %DMA than males (72.2 ± 10.4 vs. 69.7 ± 7.7 %DMA, respectively, p = 0.0012) and a lower %MMA (11.5 ± 4.8 vs. 15.5 ± 5.2 %MMA, respectively, p < 0.0001). The %InAs did not differ by sex (16.3 ± 10.1 vs. 14.7 ± 5.5, p = 0.60). The sex differences persisted in regression analyses for %DMA and %MMA after adjustment for other covariates including age and water arsenic (data not shown). Although age was not significantly associated with %DMA, it was positively associated with %MMA and negatively associated with %InAs (r = 0.29, p < 0.0001, and r = −0.24, p < 0.0001, respectively). Although only 12 of these 300 participants had arsenic-related skin lesions, the presence of skin lesions was associated with higher %MMA (18.5 ± 5.2 vs. 13.1 ± 5.3, p = 0.0016) and with lower %DMA (66.6 ± 9.1 vs. 71.3 ± 9.3, p = 0.0518). These findings have recently been confirmed in a much larger sample of the HEALS cohort study (data not shown).
As hypothesized, %DMA was positively associated with plasma folate and negatively associated with plasma tHcys (Table 2). Conversely, %MMA was negatively associated with plasma folate and positively associated with tHcys. Although %InAs was also negatively associated with folate, the association with tHcys was not significant. In addition, plasma Cys concentrations were negatively correlated with %InAs and positively correlated with %MMA. Plasma cobalamin concentrations were not correlated with arsenic metabolites.
Due to the clear biochemical link between creatine biosynthesis and SAM-dependent transmethylation reactions (Figure 2, reaction 6), the expression of urinary As per gram creatinine might potentially confound the associations with arsenic metabolites. To explore this possibility, we examined the association between urinary creatinine and urinary arsenic metabolites. We observed that urinary creatinine was strongly and positively associated with %DMA for both males and females and negatively associated with %InAs and with %MMA for females (Table 3). These associations remained highly significant after controlling for other covariates including body weight, age, and water arsenic. Furthermore, the associations between As metabolites and folate or tHcys became nonsignificant when urinary creatinine was included in regression analyses.
We previously reported that cigarette smoking and betelnut use are both negative predictors of plasma folate and positive predictors of tHcys (Gamble et al. 2005). Betelnut use also proved to be a significant predictor of %MMA, even after adjusting for covariates including sex, cigarette smoking, water arsenic, urinary creatinine, and plasma folate (p = 0.03).
Discussion
Our previous observation of a high prevalence of hyperhomocysteinemia in Araihazar, Bangladesh (Gamble et al. 2005) afforded us a suitable setting in which to assess the potential impact of hyperhomocysteinemia and folate deficiency on arsenic metabolism in a human population. Although cobalamin concentrations were not significantly associated with arsenic metabolism, this finding was not unexpected because a) cobalamin deficiency is relatively rare in this region, where vegetarianism is uncommon, and b) cobalamin-deficient participants were excluded from the study on arsenic metabolites. Thus, the results of this study do not rule out the possibility that cobalamin deficiency may influence arsenic methylation. In general, although the effect sizes were relatively small, the associations between As methylation and folate and tHcys were as predicted; however, a few points are of particular interest. First, the observation that folate is negatively (and equally) associated with both %InAs and %MMA implies that adequate folate nutritional status is required for both the first and the second methylation steps. This may be of clinical relevance because a growing body of evidence links increased risk for adverse health outcomes to higher %MMA and reduced risk with higher %DMA in urine (Chen et al. 2003a, 2003b; Hsueh et al. 1997; Yu et al. 2000).
A second finding of interest is our observation that tHcys is not associated with %InAs but is positively associated with %MMA (Table 2). In one-carbon metabolism, all SAM-dependent methylation reactions yield the methylated product and S-adenosylhomocysteine (SAH; Figure 2, reaction 6). SAH is then hydrolyzed to homocysteine in a reaction that is readily reversible. Plasma SAH levels increase linearly with even mild elevations in homocysteine levels (Yi et al. 2000). This is of particular relevance because SAH is a potent product inhibitor of most transmethylation reactions (Yi et al. 2000), including those of arsenic (De Kimpe et al. 1999). SAH binds tightly to methyltransferases and is only removed if the pathway is pulled forward by downstream removal of tHcys, as might be achieved with folate supplementation (Figure 2, reaction 4). SAH is actually more crucial than SAM in regulating methylation reactions (Yi et al. 2000). Thus, it is possible that the positive association between tHcys and %MMA and negative association with %DMA may reflect inhibition of the second methylation step by SAH. It is not entirely clear why tHcys is not also positively associated with %InAs. Perhaps because SAH binds to the arsenic methyltransferase during the first methylation step, it subsequently inhibits the second methylation step. The potential for folate supplementation to reverse hyperhomocysteinemia and thereby facilitate arsenic methylation is currently being tested in a placebo-controlled double-blind folate supplementation trial to folate deficient participants.
The amino acid Cys is produced from Hcys as an intermediate in glutathione biosynthesis (Figure 2, reaction 8). Consequently, Cys concentrations in plasma are positively correlated with both tHcys and glutathione (GSH). Cys has recently been reported to play a role in redox cycling (Jones et al. 2004). Like glutathione, Cys is capable of reducing pentavalent AsV to AsIII
in vitro (Celkova et al. 1996), and this reduction is a prerequisite to methylation. The observation that plasma Cys concentrations are negatively associated with %InAs and positively associated with %MMA suggests that this capacity may be physiologically relevant. However, we cannot rule out the possibility that the observed associations are simply due to a positive association between Cys and plasma GSH. The complete lack of an association with %DMA suggests that Cys (or possibly GSH) does not similarly reduce MMAV to MMAIII to facilitate the second methylation step.
A number of noteworthy observations regarding urinary creatinine arose from this study. The formation of creatine from methylation of guanidinoacetate has been estimated to account for approximately 75% of transmethylation reactions (Mudd and Poole 1975). Creatine is a precursor of creatinine, and both are synthesized and circulate at concentrations proportional to muscle mass, which is generally greater in males than females. Consequently, urinary creatinine is also greater for males than females (Barr et al. 2005; Gamble and Liu 2005). Because creatinine is excreted at a relatively constant rate throughout the course of the day, it is commonly used as a correction factor to adjust for variations in urine concentration. It is important to note, however, that this adjustment introduces an artificial sex difference such that, using total urinary arsenic/g creatinine as the exposure variable, males may artificially appear to have a lower exposure than females. This finding has particular relevance to the field of As toxicology because it has been reported that males may be more susceptible than females to arsenic toxicity (Chen et al. 2003a; Kristiansen et al. 1997) for two reasons. First, creatinine adjustment causes males to appear to have a lower exposure than females (Table 1). Second, although speculative, it is also possible that the increased susceptibility among males is related to the fact that males place higher demands on one-carbon metabolism due to their greater muscle mass and associated demands for creatine formation and resultant higher tHcys (Brattstrom et al. 1994).
The associations between the methylation of As and urinary creatinine are quite remarkable and intriguing. In this study, urinary creatinine was the strongest predictor by far of arsenic methylation. Particularly for males, the effect sizes were more than double those of any of the nutritional variables. There are a number of plausible mechanisms which could explain these associations: a) for reasons described above, urinary creatinine may serve as a proxy for one-carbon “metabolic rate”; b) urinary creatinine is a known proxy for muscle mass, which could have some unknown impact on As methylation; c) urinary creatinine is influenced by dietary protein intake, which influences one-carbon metabolism in-so-far as it provides a source of methionine; and d) urinary creatinine is influenced by renal function, which, for reasons that remain unclear, is a primary determinant of tHcys concentrations (Arnadottir et al. 1996; Wollesen et al. 1999). Any of these mechanisms acting either independently or in concert may play a role in determining what fraction of InAs is methylated.
Like cigarette smoking and betelnut use (Gamble et al. 2005), total urinary As and well-water As were found to be negatively associated with plasma folate. Because folate is highly prone to oxidative degradation, and because there is a substantial basic literature indicating that exposure to As induces oxidative stress, it is possible that this observation is attributable to As-induced oxidative degradation of folate. The positive associations between well-water As and tHcys and Cys likely follow from their metabolic interrelations with folate.
Alternatively, it has been postulated that increased demands on one-carbon metabolism for the methylation of As in those chronically exposed to As-contaminated drinking water may deplete the methyl donor pool (Mass and Wang 1997). We have not measured the hepatic methyl donor pool, nor is it entirely clear from our understanding of folate and one-carbon metabolism what effect depletion of the methyl donor pool would have on plasma folate concentrations. If one were to assume that methyl depletion would increase folate turnover, then plasma folate concentrations would be reduced. This is another potential explanation for the observation that well water As and urinary As were negatively associated with plasma folate, but determination of the mechanism underlying this observation will require additional study.
In conclusion, the results of this cross-sectional study suggest that adequate folate nutritional status facilitates both the first and second methylation steps, resulting in excretion of lower proportions of InAs and MMA and higher DMA. Conversely, individuals with hyperhomocysteinemia have a reduced ability to methylate MMA to generate DMA. As evidence has accrued linking increased risk for various adverse health outcomes to higher %MMA and lower %DMA in urine, it appears that hyperhomocysteinemia may represent a modifiable risk factor for arsenic toxicity. This may be particularly important in Bangladesh, where the incidence of hyperhomocysteinemia is extremely high (Gamble et al. 2005). Elucidation of the mechanism(s) underlying the relationship between urinary creatinine and arsenic methylation deserves further investigation.
We thank S. Alam for overseeing laboratory operations in Araihazar, and our staff, field workers, and study participants in Bangladesh, without whom this work would not have been possible.
This work was supported by grants RO1 ES011601, 5P30ES09089, and 1 P42 ES10349 from the National Institutes of Health.
Figure 1 Arsenic metabolic pathway. Arsenate is reduced to arsenite in a reaction thought to be dependent on GSH or other endogenous reductants. Abbreviations: GSH, glutathione; GSSG, glutathione disulfide; GST, glutathione-S-transferase; SAH, S-adenosylhomocysteine; SAM, S-adenosylmethionine; TR, thioredoxin reductase. Arsenite then undergoes an oxidative methylation, with SAM as the methyl donor, forming MMAV and SAH. MMAV is reduced to MMAIII before a subsequent oxidative methylation step yielding DMAV and SAH. Little is known regarding in vivo reduction of DMAV to DMAIII. Enzymes capable of catalyzing the illustrated reactions include Cyt19 (Lin et al. 2002), arsenite methyltransferase and methy-larsonite methyltransferase (two activities of one enzyme) (Zakharyan et al. 1995), and MMAV reductase (also known as GST-Ω) (Zakharyan et al. 2001).
Figure 2 Overview of one-carbon metabolism. 1. Dietary folates are reduced to dihydrofolate (DHF) and tetrahydrofolate (THF) by dihydrofolate reductase. 2. The β-carbon of serine is transferred to THF by serine hydroxymethyltransferase, forming 5,10-methenyl-THF and glycine. 3. At a major branch point between transmethylation reactions and nucleotide biosynthesis, 5,10-methenyl-THF can be reduced to 5,10-methylene-THF and further reduced to 5-methyl-THF by 5,10-methylene-THF reductase. 4. In a reaction catalyzed by the vitamin B12-containing enzyme, methionine synthetase, the methyl group of 5-methyl-THF is transferred to homocysteine, generating methionine and regenerating THF. 5. Methionine adenosyl-transferase activates methionine to form S-adenosylmethionine (SAM). 6. SAM serves as a universal methyl donor for numerous acceptors, including predominantly guanidinoacetate (GAA), but also DNA, arsenic, and others, in reactions that involve a number of methyltransferases. 7. The by-product of these methylation reactions, S-adenosylhomocysteine (SAH), is hydrolyzed to generate homocysteine. SAH is a potent inhibitor of most SAM-dependent methylations. 8. Homocysteine is either used to regenerate methionine or is directed to the transsulfuration pathway through which it is ultimately catabolized. 9. The transsulfuration pathway is also responsible for glutathione (GSH) biosynthesis.
Figure 3 Frequency distribution for As metabolites. Interindividual variability across 300 participants for arsenic metabolites in urine.
Table 1 Sex differences in urinary creatinine and urinary arsenic when expressed per gram creatinine (mean ± SD).
Females (n = 961) Males (n = 675) p-Value
Urinary arsenic (μg/L) 134 ± 120 133 ± 137 0.26
Urinary creatinine (mg/dL) 57 ± 41 70 ± 53 < 0.0001
Arsenic/g creatinine 284 ± 226 194 ± 179 < 0.0001
Table 2 Spearman correlation coefficients for As metabolites versus plasma folate, tHcys, and cobalamin for 300 participants.
%InAs %MMA %DMA
tHcys (μM) 0.06 0.21# −0.14**
Cysteine (μM) −0.11* 0.16** 0.01
Folate (nM) −0.12* −0.12* 0.14*
Cobalamin (pM) −0.06 −0.002 0.04
* p < 0.05.
** p < 0.01.
# p < 0.001.
Table 3 Spearman correlation coefficients for As metabolites versus urinary creatinine.
%InAs %MMA %DMA
Males (n = 136) −0.45## −0.13 0.40##
Females (n = 164) −0.20**a −0.16* 0.25**
Total (n = 300) −0.32## −0.09 0.30##
a Correlation between urinary creatinine and %InAs differ by sex (p = 0.014).
* p < 0.05.
** p < 0.01.
## p < 0.0001.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8138ehp0113-00168916330348ResearchBody Burdens of Polybrominated Diphenyl Ethers among Urban Anglers Morland Kimberly B. 1Landrigan Philip J. 1Sjödin Andreas 2Gobeille Alayne K. 1Jones Richard S. 2McGahee Ernest E. 2Needham Larry L. 2Patterson Donald G. Jr21 Department of Community and Preventive Medicine, Mount Sinai School of Medicine, New York, New York, USA2 National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USAAddress correspondence to K. Morland, Mount Sinai School of Medicine, Department of Community and Preventive Medicine, One Gustave Levy Place, Box 1057, New York, NY 10029 USA. Telephone: (212) 241-7531. Fax: (212) 996-0407. E-mail:
[email protected] roles of A.S., R.S.J., E.E.M., L.L.N., and D.G.P. in this project were limited to providing the laboratory analyses and interpretation of the laboratory data.
The authors declare they have no competing financial interests.
12 2005 8 8 2005 113 12 1689 1692 21 3 2005 8 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Polybrominated diphenyl ethers (PBDEs) have been widely used in the United States and worldwide as flame retardants. Recent PBDE production figures show that worldwide use has increased. To determine whether fish consumption is a source of PBDE exposure for humans, a cross-sectional epidemiologic study of New York and New Jersey urban anglers was conducted during the summers of 2001–2003. Frequency of local fish consumption was assessed by questionnaire, and blood samples for PBDE analysis were collected from 94 anglers fishing from piers on the lower Hudson River and Newark Bay. We analyzed PBDEs by gas chromatography–isotope dilution–high-resolution mass spectrometry. The congeners found in anglers’ serum at the highest concentrations were, by International Union of Pure and Applied Chemistry numbers, BDE-47, BDE-153, and BDE-99. Anglers reporting consumption of local fish had higher, but nonstatistically significantly different, concentrations of PBDEs than did anglers who did not eat local fish. For some congeners (BDE-100 and BDE-153), we observed moderate dose–response relationships between serum PBDE levels and frequency of reported fish intake. These findings suggest that consumption of locally caught fish is not a major route of human exposure for this study population.
fish consumptionPBDEpolybrominated diphenyl ethers
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Polybrominated diphenyl ethers (PBDEs) are a class of chemicals used extensively as flame retardants to decrease flammability of polymeric materials and textiles (Sjödin etal. 2003). PBDEs possess similar chemical and physical properties to polychlorinated biphenyls (PCBs), that is, high lipophilicity, low vapor pressure, and resistance to environmental degradation [World Health Organization (WHO) 1993, 1994]. Tetra- through hexa-BDEs have been shown to bioaccumulate, but an exception is decabromodiphenyl ether (BDE-209), which has been shown to have a surprisingly short half-life in people industrially exposed to this compound (Sjödin etal. 1999).
Three technical PBDE mixtures are produced and are identified by their average bromine content as pentabromodiphenyl ether (penta-BDE), octabromodiphenyl (octa-BDE), and decabromodiphenyl (deca-BDE). Penta-BDEs are used primarily in polyurethane foam, whereas octa-BDEs are used in acrylonitrile butadiene styrene (ABS) resin. Deca-BDE is used in a variety of polymeric materials (Alaee et al. 2003). PBDEs are additive flame retardants, and unlike reactive flame retardants such as tetrabromobisphenol A, they are not covalently bound to the polymer and therefore are more likely to leach out of the product during its lifetime (Sjödin etal. 2003).
Global production of PBDEs has increased rapidly over the past decade (deWit 2002; Ikonomou etal. 2002). The majority (95%) of the penta-BDEs are used in North America, and approximately 40% of global use of technical octa-BDEs and deca-BDEs occurs in North America (Bromine Science and Environmental Forum 2004). Manufacture and use of penta-BDEs and octa-BDEs have been discontinued in Japan and Europe, and voluntary withdrawal from the U.S. market took effect as of the end of 2004 (European Union 2003; Tullo 2003; Watanabe and Sakai 2001). However, manufacture and use of deca-BDEs are expected to continue indefinitely. Human and environmental biomonitoring will most likely be needed for years to come because of the high persistence and expected long half-lives of other PBDE congeners in environmental media.
PBDEs have been detected in sediments (Hassanin et al. 2004; Sellstrom et al. 1999; Tullo 2003; Watanabe and Tatsukawa 1989; Zegers etal. 2003), as well as in fish and other marine and terrestrial species (Dodder et al. 2002; Hale et al. 2002; Johnson and Olson 2001; Luross et al. 2002; Rice et al. 2002; Sellstrom et al. 1999; Voorspoels et al. 2003). Levels of PBDEs found in fish range from 180 ng/g lipid weight in salmon caught in the Baltic Sea (based on the sum of six tri-hexa-BDE congeners quantified) to 3,000 ng/g lipid weight in trout caught from Lake Michigan (Asplund et al. 1999; Manchester-Neesvig etal. 2001).
Human body burdens of PBDEs have increased markedly over the past several decades. For instance, Petreas et al. (2003) recently documented in samples of maternal serum that concentrations of BDE-47 in California women rose from below the detection limit to 50.6 ng/g lipid, comparing convenient samples of maternal serum collected between 1959 and 1967 with samples collected between 1996 and 1998. Other researchers have observed similar trends over the past three decades (Akutsu et al. 2003; Sjödin etal. 2004a).
The human health consequences of PBDE exposure have not been studied in detail. However, two reported adverse outcomes in laboratory animals dosed with high levels of PBDEs are neurologic deficiencies (Branchi et al. 2003; Eriksson et al. 2001; Viberg et al. 2003) and endocrine disruption (Rind 2002; Zhou etal. 2002).
Routes of human exposure to PBDEs are not fully defined. We hypothesized that a principal route of human exposure to PBDEs is through fish intake. This hypothesis is based on the fact that PBDEs are environmentally persistent and biomagnify in the marine food chain. Other potential routes of exposure such as inhalation may be of quantitatively greater importance than fish consumption, especially in the case of persons with occupational exposure (Sjödin et al. 2001). However, few studies have been conducted to evaluate the relative significance of these various routes of human exposure or to examine exposure source at different ages.
The purpose of this cross-sectional epidemiologic investigation was to determine whether consumption of fish caught in the Hudson River and Newark Bay by urban anglers was associated with increased body burden of PBDEs.
Materials and Methods
Study design and subjects.
During the summers of 2001–2003, a cross-sectional study was conducted among New York and New Jersey urban anglers ≥18 years of age. A total of 191 anglers were recruited with the aim to measure the association between body burdens of PCBs, mercury, and other organochlorines and reported fish intake. Participants recruited from local piers and fishing clubs provided questionnaire data, and 65% provided blood samples. A subsample of this population, 93 anglers with sufficient serum and complete questionnaire data, was selected for this analysis.
Data collection.
After signed consent had been obtained, trained interviewers administered a questionnaire to each participant to obtain information on the frequency, species, and amount of locally caught fish consumed during the current fishing season. Respondents were asked to report the number of meals eaten per month as well as the usual serving size of the following species: American eel, black fish, blue crabs, blue fish, clams or mussels, flounder, fluke, striped bass, tommy cod, weak fish, white catfish, and/or white perch. Along with information regarding fish intake, demographic information and data on knowledge of fish advisories and practices related to cooking and cleaning fish were also obtained.
In addition to the questionnaire, participants were asked to provide a venous blood sample. Using venipuncture, a trained phlebotomist collected blood samples into 10-mL red-top Vacutainer tubes at the piers. Samples were centrifuged on site; serum was then processed and stored (at –20°C) at Mount Sinai until transported to the Centers for Disease Control and Prevention for analysis.
Serum analyses for PBDEs.
The methodology used for the analysis of serum samples for PBDEs has been described in full elsewhere (Sjödin et al. 2004b) and is described briefly below. Samples were fortified with 13C-labeled internal standards, and formic acid was added to the samples as a denaturant; samples were finally diluted with water before solid-phase extraction (SPE). Liquid handling before extraction was automated using the 215 Liquid Handler (Gilson Inc., Middleton, WI), and SPE was automated using the Rapid Trace SPE workstation (Zymark, Hopkinton, MA). Removal of coextracted lipids was performed on a two-layered cleanup cartridge packed in an SPE tube, containing silica (top layer) and a mixture of silica sulfuric acid (bottom layer), using the SPE workstation for automation. Final analytical measurement of the target analytes was performed by gas chromatography–isotope dilution–high-resolution mass spectrometry using an MAT95XP instrument (ThermoFinnigan MAT, Bremen, Germany).
Concentrations of target analytes were calculated as nanograms per gram fresh weight (weight of serum) and nanograms per gram lipid weight (weight of serum lipids). Serum concentrations of total triglycerides and total cholesterol were determined using commercially available test kits (product nos. 011002803-0600 and 011573303-0600, respectively) from Roche Diagnostics Corp. (Indianapolis, IN) and a Hitachi 912 chemistry analyzer (Hitachi, Tokyo, Japan). All concentration data were corrected for analytical background, if detected, in method blank samples processed at the same time as the unknown samples. Blank samples (n = 3) and quality assurance/quality control samples (n = 3) were processed at the same time as the unknown samples. We conducted analyses for the following PBDE congeners (by International Union of Pure and Applied Chemistry numbers): BDE-47, BDE-85, BDE-99, BDE-100, BDE-153, BDE-154, and BDE-183. We could not report data for deca-BDE (BDE-209) because of high background contamination during the processing of the unknown samples. Results for PCB-153 are presented for comparison.
The limit of detection (LOD) when no analytical background was detected in blank samples was defined as a signal-to-noise ratio > 10. When an analytical background was detected in the blanks, the LOD was defined as three times the SD of the blanks.
Data analysis.
Consumption of local fish by respondents was measured based on self-reported intake. Anglers who reported eating none of the fish species were categorized as “eating no locally caught fish”; this group was used as the reference group in all comparisons. Those respondents who reported eating any species at any frequency or amount were categorized in the statistical analyses as “eating locally caught fish.” Lipid-adjusted and non-lipid-adjusted geometric mean (GM) concentrations of the individual PBDE congeners were calculated and stratified by fish consumption category (any vs. none). In addition, total weekly fish consumption was calculated by summing species specific intake, and the following codes were assigned: no meals per month = 0, < 1 month = 0.05, 1 month = 0.25, 2–3 times per month = 0.50, 1 per week = 1, 2–3 times per week = 2.5, 4–5 times per week = 4.5, and ≥6 times per week = 6. Individuals were then categorized based on their total weekly fish consumption as having eaten locally caught fish a) never, b) less than once a week, or c) once or more a week. Associations between PBDE congeners and other covariates were not observed (data not shown). We calculated GMs and estimated differences between means and p-values using generalized linear models. All analyses were conducted using SAS software (version 8.2 for Windows; SAS Institute Inc., Cary, NC). Samples having concentrations < LOD were coded with the LOD.
Results
Characteristics of the urban anglers and their self-reported frequency of fish intake are presented in Table 1. They were predominantly male (84%) and in their 50s, and they were racially diverse. Fifteen percent of the respondents reported no intake of locally caught fish. Anglers who reported not eating their local catch were mostly white and African American. More than 60% of this group reported having attended at least some college, and their annual incomes were evenly distributed. Among Hispanic, African-American, and Asian anglers, a larger proportion of anglers reported eating locally caught fish. The proportion of this group who reported having attending college was 46.3%, and roughly 50% reported annual household incomes < $50,000/year. Those anglers who reported not eating locally caught fish were heavier than those who ate local fish [body mass index (BMI) = 32.3 vs. 29.4]. The most commonly eaten species were fluke (76.3%) and striped bass (73.8%).
Data on serum PBDE concentrations by congener in the entire population studied are presented in Table 2; both unadjusted and lipid-weight-adjusted GMs, along with the minimum and maximum values, are given. The congener found at the highest concentration was 2,2′,4,4′-tetra-BDE (BDE-47; 0.091 ng/g fresh weight; 13.3 ng/g lipid weight). The next highest in concentration were 2,2′,4,4′,5-penta-BDE (BDE-99; 3.2 ng/g lipid weight); 2,2′,4,4′,5,5′-hexa-BDE (BDE-153; 3.2 ng/g lipid weight), and 2,2′,4,4′,6-penta-BDE (BDE-100; 2.7 ng/g lipid weight). For all other congeners (BDE-85, BDE-154, and BDE-183), GM concentrations were ≤1 ng/g lipid weight. BDE-209 was < LOD for all samples analyzed because of laboratory background. For BDE-85, BDE-154, and BDE-183, > 70% of the samples were < LOD, and for BDE-47 and BDE-153, < 10% of the samples were < LOD. Mean concentrations of PCB-153 were substantially higher than concentrations of PBDE congeners.
Geometric mean concentrations of the PBDE congeners stratified by reported local fish intake are shown in Table 3. For all PBDE congeners, GM concentrations were higher for anglers who reported eating locally caught fish than for anglers who reported not eating locally caught fish. However, differences were small and not statistically significant at an α-level of 0.05 for all congeners except BDE-183, for which 70% of samples were measured < LOD. For instance, anglers who reported intake of locally caught fish had a GM BDE-47 concentration of 13.4 ng/g lipid [geometric SD (GSD) = 3.3] compared with anglers who reported no intake (GM concentration, 12.6 ng/g lipid; GSD = 5.4). Concentrations in anglers who reported local fish intake are higher for all congeners; however, the greatest differences were observed for BDE-153, BDE-85, and BDE-183, where 70%, 59%, and 48% greater concentrations, respectively, were observed in anglers who reported local fish intake. The mean concentration of PCB-153 did not increase with reported fish intake.
Geometric mean serum levels for each PBDE congener are presented in Table 4, comparing anglers who reported no local fish consumption, fish intake once or more a week, and fish intake more than once a week. For BDE-100 and BDE-153, we observed a moderate increase in mean concentration across these three strata as reported fish consumption increased; this was also true for BDE-85 and BDE-183, where a greater proportion of the samples were measured < LOD. However, the mean concentrations of all congeners were higher among anglers who reported fish intake more than once a week compared with those who reported no intake. Mean concentration of PCB-153 was lowest among anglers who reported eating local fish once a week or less, with similar mean concentrations for those who reported eating no fish and those who reported eating locally caught fish more than once a week.
Discussion
In the present study we found differences albeit small, in mean concentrations of PBDEs for anglers who reported eating locally caught fish compared with those who eat no local fish. These differences did not reach statistical significance at α= 0.05 for most of the congeners examined. Because congener-specific GMs were consistently higher for anglers who reported eating their catch and because dose–response patterns were observed for some congeners, the possibility cannot be excluded that consumption of locally caught fish may be a route of PBDE exposure. However, these data suggest that consumption of locally caught fish is certainly not a major route of exposure in this population.
Our findings differ from those reported by other investigators who have studied anglers. For example, fish consumption has been shown to be a major exposure route in Swedish fishermen consuming large quantities of Baltic Sea fish (Sjödin et al. 2000). This Swedish study found that people reporting high fish consumption had a median BDE-47 level five times higher [2.2 ng/g lipid weight; 10 and 90% confidence interval (CI), 0.96–5.7] than that of nonconsumers (0.4 ng/g lipid weight; 10 and 90% CI, < 0.1–2.5).
The null effects observed in the present study may be due to a variety of factors. First, our data were limited by large variability in the levels of each congener. For instance, the measured lipid-adjusted concentrations of BDE-47 ranged from 0.71 to 1,389 ng/g lipid weight, with a median of 10.8 ng/g lipid weight. Although the data were normalized in the analyses using GMs, substantial variation remained within fish eaters and noneaters, making the ability to observe differences between groups difficult. This issue of variation may be resolved in future studies by increasing the sample size. In addition, other factors have influenced our ability to detect differences such as the quantity of the samples. Although variation in the chemical measurements remained low (quality assurance/quality control, 4.9–7.0%), substantial LOD variation remained within congeners. This may be improved in future studies with greater and similar serum samples for each participant.
Other unmeasured exposure routes to PBDEs may also have affected our ability to detect associations. For instance, a recent analysis of indoor dust samples collected in the United States and Germany has suggested that significantly higher levels of tetra-BDE and deca-BDE congeners are found in the United States compared with Germany. The median levels of BDE-47, BDE-99, BDE-153, and BDE-209 for the dust samples collected in the United States were 430 ng/g dust (range, 230–3,000 ng/g), 880 ng/g dust (range, 69–3,700 ng/g), 140 ng/g dust (range, 5–650 ng/g), and 2,000 ng/g dust (range, 120–21,000 ng/g), respectively. In Germany, by contrast, < 14 ng/g dust (range, < 14–22 ng/g), 10 ng/g dust (range, < 4–38 ng/g), < 6 ng/g dust (range, < 6–22 ng/g), and 60 ng/g dust (range, < 5–410 ng/g) were found for BDE-47, BDE-99, BDE-153, and BDE-209, respectively (Sjödin et al. 2004c). However, there is no reason to believe that differential indoor dust exposure exists between consumers and nonconsumers of locally caught fish. Nevertheless, exposure to indoor dust may be an exposure route for this population. It is possible, however, that there is a differential bias due to consumption of commercial fish. A recent market basket study has shown that commercial fish and shellfish have high levels of PBDEs (Bocio et al. 2003), although information on store-bought fish was not elicited during the interviews in the present study.
Although we did not observe statistically significant differences by reported fish consumption, serum concentrations of PBDEs in this study are similar to those reported in other populations. In the United States, Mazdai etal. (2003) reported a median concentration of BDE-47 of 28 ng/g lipid in maternal serum; Sjödin et al. (2004a) reported a mean concentration in archived serum pools of 34 ng/g lipid; and Schecter et al. (2003) reported a mean concentration in maternal breast milk of 40.8 ng/g lipid. The GM concentration of BDE-47 in our study was 13.3 ng/g lipid (median = 10.79 ng/g lipid weight), a somewhat lower concentration than findings from other U.S. populations. These levels are higher than concentrations reported in Europe, where, for instance, Lind etal. (2003) reported a mean concentration of BDE-47 of 2.35 ng/g lipid weight in breast milk for Swedish women sampled between 1996 and 1999.
Although the Great Lakes Chemical Company, a primary North American manufacturer of PBDEs, voluntarily agreed to stop producing octa- and penta-BDEs in 2004, the company will continue to produce deca-BDEs (Great Lakes Chemical Corporation 2003). Moreover, the persistent nature of these chemicals, in addition to the lack of understanding of how they degrade, leaves room for concern about human contamination. Future studies that continue to monitor human body burdens of PBDEs as well as define exposure pathways are needed to further our understanding of how these chemicals influence human health.
This study was funded by the Superfund Basic Research Program (grant ES07384-09).
Table 1 Characteristics of urban anglers and reported fish intake (n = 93).
Characteristic No local fish intake (n = 14) Any local fish intake (n = 79)
Male (%) 85.7 83.8
Age, years (mean ± SD) 58.5 ± 11.2 50.1 ± 14.0
Race (%)
White 78.6 60.0
African American 14.3 18.8
Hispanic 7.1 16.3
Asian 0.0 2.5
Other/no response 0.0 2.5
Total yearly household income (%)
< $10,000 7.1 8.8
$10,000–29,999 21.4 20.0
$30,000–49,999 21.4 22.5
$50,000–74,999 21.4 17.5
≥$75,000 21.4 15.0
Not reported 7.1 16.3
Highest level of school completed (%)
< High school graduate 21.4 12.5
Graduated high school 14.3 41.3
≥Some college 64.3 46.3
BMI (mean ± SD) 32.3 ± 6.6 29.4 ± 4.9
Reported intake of any of the following species of locally caught fish (%)
American eel 21.3
Black fish 43.0
Blue crab 42.5
Blue fish 65.0
Clams or mussels 20.3
Flounder 60.0
Fluke 76.3
Striped bass 73.8
Tommy cod 11.3
Weak fish 57.5
White catfish 8.8
White perch 13.8
Table 2 Mean concentration of PBDEs in human serum.
Unadjusted (ng/g fresh weight)a Lipid adjusted (ng/g lipid weight)a < LOD
No.b GM Minimum Maximum GM Minimum Maximum (%)
PBDE congener
47 93 0.091 0.005 12.613 13.288 0.706 1388.649 7
85 92 0.007 0.002 0.685 1.033 0.200 109.096 73
99 93 0.022 0.002 3.318 3.225 0.334 545.541 33
100 93 0.010 0.002 2.548 2.701 0.300 280.615 12
153 93 0.022 0.003 1.500 3.166 0.389 165.162 4
154 89 0.004 0.000 0.224 0.630 0.088 24.711 75
183 93 0.004 0.001 0.017 0.525 0.115 2.015 71
PCB-153 80 0.407 0.038 2.794 60.518 9.720 495.903 0
a Geometric minimum, and maximum.
b Number of participants.
Table 3 GM concentration of PBDEs by local fish intake (ng/g lipid weight).
No local fish intake
Any local fish intake
No.a GM (GSD) No.a GM (GSD) p-Value
PBDE congener
47 14 12.61 (5.42) 79 13.41 (3.30) 0.87
85 14 0.70 (3.56) 78 1.11 (3.54) 0.21
99 14 2.83 (4.69) 79 3.30 (3.24) 0.67
100 14 2.32 (4.66) 79 2.77 (2.94) 0.59
153 14 2.02 (4.13) 79 3.43 (2.88) 0.10
154 12 0.56 (3.74) 77 0.64 (2.09) 0.57
183 14 0.38 (1.99) 79 0.56 (1.65) 0.01
PCB-153 14 65.19 (2.25) 66 59.57 (2.30) 0.71
a Number of participants.
Table 4 GM concentration of PBDEs by frequency of reported local fish intake (ng/g lipid weight).
No local fish intake
Fish intake ≤1/week
Fish intake > 1/week
No.a GM (GSD) No.a GM (GSD) No.a GM (GSD)
PBDE congener
47 14 12.61 (5.42) 25 11.55 (3.07) 54 14.37 (3.41)
85 14 0.70 (3.56) 25 0.89 (3.28) 53 1.23 (3.65)
99 14 2.83 (4.69) 25 2.68 (2.92) 54 3.63 (3.38)
100 14 2.32 (4.66) 25 2.34 (2.63) 54 3.00 (3.08)
153 14 2.02 (4.13) 25 2.58 (3.06) 54 3.91 (2.76)
154 12 0.56 (3.74) 23 0.51 (1.91) 54 0.71 (2.13)
183 14 0.38 (1.99) 25 0.49 (1.70) 54 0.59 (1.62)
PCB-153 14 65.19 (2.25) 20 40.58 (2.06) 46 70.40 (2.29)
a Number of participants.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8145ehp0113-00169316330349ResearchEffects of Particle Size Fractions on Reducing Heart Rate Variability in Cardiac and Hypertensive Patients Chuang Kai-Jen 1Chan Chang-Chuan 1Chen Nan-Ting 1Su Ta-Chen 12Lin Lian-Yu 21 Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Taipei, Taiwan2 Department of Internal Medicine, National Taiwan University Hospital, Taipei, TaiwanAddress correspondence to C.-C. Chan, Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Rm. 1447, 1st Sec., No. 1 Renai Rd., Taipei 100, Taiwan. Telephone/Fax: 886-2-2322-2362. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 8 8 2005 113 12 1693 1697 18 3 2005 8 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. It is still unknown whether the associations between particulate matter (PM) and heart rate variability (HRV) differ by particle sizes with aerodynamic diameters between 0.3 μm and 1.0 μm (PM0.3–1.0), between 1.0 μm and 2.5 μm (PM1.0–2.5), and between 2.5 μm and 10 μm (PM2.5–10). We measured electrocardiographics and PM exposures in 10 patients with coronary heart disease and 16 patients with either prehypertension or hypertension. The outcome variables were standard deviation of all normal-to-normal (NN) intervals (SDNN), the square root of the mean of the sum of the squares of differences between adjacent NN intervals (r-MSSD), low frequency (LF; 0.04–0.15 Hz), high frequency (HF; 0.15–0.40 Hz), and LF:HF ratio for HRV. The pollution variables were mass concentrations of PM0.3–1.0, PM1.0–2.5, and PM2.5–10. We used linear mixed-effects models to examine the association between PM exposures and log10-transformed HRV indices, adjusting for key personal and environmental attributes. We found that PM0.3–1.0 exposures at 1- to 4-hr moving averages were associated with SDNN and r-MSSD in both cardiac and hypertensive patients. For an interquartile increase in PM0.3–1.0, there were 1.49–4.88% decreases in SDNN and 2.73–8.25% decreases in r-MSSD. PM0.3–1.0 exposures were also associated with decreases in LF and HF for hypertensive patients at 1- to 3-hr moving averages except for cardiac patients at moving averages of 2 or 3 hr. By contrast, we found that HRV was not associated with either PM1.0–2.5 or PM2.5–10. HRV reduction in susceptible population was associated with PM0.3–1.0 but was not associated with either PM1.0–2.5 or PM2.5–10.
air pollutionautonomic systemepidemiologyheart rate variabilityparticulate matter
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The association between particulate air pollution and cardiovascular mortality rate and hospitalization has been reported in epidemiologic studies (Koken et al. 2003; Pope et al. 1999, 2004a). In most epidemiologic studies, particulate matter (PM) has been characterized as the mass concentration of coarse particles with aerodynamic diameters < 10 μm (PM10) and fine particles with aerodynamic diameters < 2.5 μm (PM2.5). The association appears to be more evident as particle size gets smaller. Schwartz et al. (1996) reported that the association between PM and daily mortality rates was more evident with exposure to PM2.5 than to PM10. By examining the relationship between air pollution and cardiopulmonary health in elderly subjects with coronary heart disease (CHD), de Hartog et al. (2003) showed that PM2.5 had a greater association with some cardiac symptoms than did PM10. Several panel studies also demonstrated that decreased heart rate variability (HRV) was separately associated with either mass concentrations of PM10 (Gold et al. 2000; Pope et al. 1999) and PM2.5 (Creason et al. 2001; Gold et al. 2000; Holguin et al. 2003; Liao et al. 1999; Magari et al. 2001, 2002; Park et al. 2005; Pope et al. 2004b; Riediker et al. 2004) or number concentrations of submicrometer particles with a size range of 0.02–1.0 μm (Chan et al. 2004). However, it is still unknown whether the association between PM and HRV differs by particle size. To shed light on this question, we used a panel of cardiac and hypertensive patients to study which size fractions had greater effects on HRV reduction among PM with aerodynamic diameters between 0.3 μm and 1.0 μm (PM0.3–1.0), between 1.0 μm and 2.5 μm (PM1.0–2.5), and between 2.5 μm and 10 μm (PM2.5–10).
Materials and Methods
Subjects.
This panel study was designed to monitor changes in PM mass concentrations and HRV indices continuously and simultaneously in our study subjects from November 2002 through March 2003. There were 10 patients with CHD and 16 patients with either prehypertension or hypertension in this study. These patients were recruited from the cardiology section, Department of Internal Medicine, National Taiwan University Hospital, and a community health center in the Taipei metropolitan area (Hsin-Chuang Health Center). All the CHD patients had history of angina pectoris and/or acute myocardial infarction and had cardiac catheterized and percutaneous transluminal coronary angioplasty during the year before our panel study. The prehypertensive/hypertensive patients’ hypertension statuses were identified by their annual health checkup at the health center. Each subject’s sex, age, body mass index (BMI), smoking status, and medical history were collected by a face-to-face interviewed questionnaire. Each subject’s current health status was obtained from medical charts and examinations. Professionally trained nurses performed sitting blood pressure measurements for each patient with mercury sphygmomanometer. The criteria of Chobanian et al. (2003) were used to define eight subjects as hypertensive [systolic blood pressure (SBP) ≥140 mmHg or diastolic blood pressure (DBP) ≥90 mmHg] and another eight subjects as prehypertensive (SBP 120–139 mmHg or DBP 80–89 mmHg). To reduce confounding effects in this study, we excluded the following subjects from our recruitment: current smokers; patients with hyperthyroidism, acute cardiopulmonary failure, or paced cardiac rhythm; and patients with current medications of anticholinergics, beta-blockers, or antiar-rhythmic agents. The ethics committee of the National Taiwan University Hospital approved this study. Written informed consent was obtained from each participant before the study embarked.
Continuous Holter monitoring and tape processing.
We performed continuous ambulatory electrocardiographic (ECG) monitoring for each subject by using a PacerCorder 3-channel device (model 461A; Del Mar Medical Systems LLC, Irvine, CA, USA) with a sampling rate of 250 Hz (4 msec). We sent ECG tapes to National Taiwan University Hospital and used a Delmar Avionics model Strata Scan 563 (Irvine, CA, USA) to do the analysis. The ECG wave complex (QRS) was classified as normal sinus rhythm, arterial or ventricular premature beats, and noise by comparing the adjacent QRS morphologic features. The normal-to-normal (NN) intervals were deduced from the adjacent normal sinus beats. The NN interval time series were then transferred to a personal computer and postprocessed by a program written in Matlab language (version 5.2; MathWorks Inc., Natick, MA, USA). The missing intervals of the raw NN data were linearly interpolated and resampled at 4 Hz by the Ron-Berger method (Berger et al. 1986). Each 5-min segment of NN intervals was taken for HRV analysis. The time domain measurements of HRV were the SD of NN intervals (SDNN) and the square root of the mean of the sum of the squares of differences between adjacent NN intervals (r-MSSD). The frequency-domain measurements of HRV included low frequency (LF; 0.04–0.15 Hz), high frequency (HF; 0.15–0.40 Hz), and LF:HF ratio, which were calculated by Welch’s averaged peri-odogram of the NN intervals (Task Force 1996; Welch 1967). Fast Fourier transformation was performed to estimate power spectral density. To avoid sleep effects on HRV, in our data analysis we used approximately 16-hr Holter measurements when the subjects were awake between 0700 hr and 2300 hr. Each subject provided approximately 192 successful segments of 5-min HRV measurements for further data analysis.
Personal exposure measurements.
Personal exposures to different sizes of PM were measured persistently by using a personal dust monitor (DUST-check portable dust monitor, model 1.108; Grimm Labortechnik Ltd., Ainring, Germany), which measured and recorded 1-min mass concentrations of PM0.3–1.0, PM2.5, PM10, as well as ambient temperature and humidity. The DUST-check portable dust monitor measured number concentrations by particle’s light-scattering property and used a correction factor to derive mass concentrations from reference aerosols with a density of 0.92 and reflective index of 1.45. Collocated Rupprecht and Patashnick 1400a tapered element oscillating micro-balance (TEOM) samplers (Thermo Electron Corporation, East Greenbush, NY, USA) were used to calibrate the mass concentrations of PM10, PM2.5, and PM0.3–1.0 measured by our DUST-check monitor before and after study. Concurrent PM measurements by the TEOM and the DUST-check monitor showed good association between these two monitors for three size fractions: PM10 (r2 = 0.90), PM2.5 (r2 = 0.91), and PM0.3–1.0 (r2 = 0.79). However, in the concurrent PM measurements by two monitors, the DUST-check monitor reported approximately 10, 15, and 30% more PM10, PM2.5, and PM0.3–1.0 mass concentration, respectively, than did the TEOM monitor.
To measure our patients’ personal PM exposures, a technician carrying a DUST-check monitor was asked to accompany each subject from 0700 hr to 2300 hr. The sampling inlet was kept at a distance of approximately 1–2 m away from each study subject, depending on the subject’s activities. The technician also recorded subjects’ time–activity patterns, such as walking, sitting, sleeping, dining, and environmental tobacco smoke exposures during daytime. After sampling, we obtained mass concentrations of PM2.5–10 by subtracting PM2.5 concentrations from PM10 concentrations recorded in our monitors. We obtained mass concentrations of PM1.0–2.5 by subtracting PM0.3–1.0 concentrations from PM2.5 concentrations recorded in our monitors. By summarizing 1-min PM2.5–10, PM1.0–2.5, and PM0.3–1.0 concentrations to 1-hr moving averages between 0700 hr and 2300 hr, we obtained approximately 1,000 segments of PM concentrations for each subject in our data analysis.
Statistical analysis.
We first plotted PM by HRV indices for each subject to determine if there were observed associations between these two variables, and if there were any outliers that heavily influenced such associations. We also used stepwise multiple regressions without PM to determine key HRV-related personal covariates with a p-value < 0.15. The covariates that changed the estimated effect of PM by > 10% were included in our final models with PM measurements. We then applied linear mixed-effects regression models to examine the association between PM and HRV for cardiac and hypertensive patients separately and jointly by running S-PLUS 2000 (MathSoft Inc., Cambridge, MA, USA). In our data analysis, we treated each subject’s sex, age, BMI, and hour of day as time-invariant variables, whereas PM2.5–10, PM1.0–2.5, PM0.3–1.0, temperature, humidity, and HRV were treated as time-varying variables. The outcome variables were SDNN, r-MSSD, LF, HF, and LF:HF ratio, and the exposure variables were 1- to 4-hr moving averages of PM2.5–10, PM1.0–2.5, and PM0.3–1.0. All HRV indices except LF:HF ratio were log10-transformed for further data analysis. In our mixed-effects models, we treated subject’s sex, age, BMI, hour of day, temperature, humidity, and PM as fixed effects and each subject as a random effect. We used smoothing spline in S-PLUS to plot outcome variables against temperature and humidity to determine whether their relation was linear or nonlinear. Linear terms were chosen to control temperature and humidity in our final models because our diagnostic plots showed a linear relation between outcome variables and meteorologic variables. Single-pollutant mixed-effects models were used to determine pollution effects for PM2.5–10, PM1.0–2.5, and PM0.3–1.0 separately. Multipollutant mixed-effects models were used to determine what size fractions had greater pollution effects among PM2.5–10, PM1.0–2.5, and PM0.3–1.0. The first-order auto-regressive model (AR1) was chosen to adjust temporal autocorrelation of HRV measurements because residuals plots showed that AR1 was sufficient to remove the autocorrelation of the observed outcome series. Model selections were based on the criteria of minimizing Akaike’s information criterion (Akaike 1974). Pollution effects are expressed as percent changes in HRV by interquartile changes in PM concentrations.
Results
As shown in Table 1, the ages of our 26 study subjects were 61–72 years among 10 cardiac patients and 52–76 years among 16 prehypertensive/hypertensive patients (the hypertensive group). Their mean BMIs were 25.6 kg/m2 for the cardiac patients and 24.4 kg/m2 for the hypertensive group. Our study subjects’ HRV indices, PM exposures, and meteorologic conditions during the study period are summarized in Table 2. The cardiac patients had significantly higher values of HRV indices than did the hypertensive group. Moreover, the cardiac patients had significantly higher PM2.5–10 exposures but lower PM1.0–2.5 and PM0.3–1.0 exposures than did the hypertensive group. On average, PM0.3–1.0 levels of 26.8 μg/m3 in the cardiac patients and 37.2 μg/m3 in the hypertensive group accounted for 49.5 and 58.3% of PM10 mass concentrations in their respective groups. The interquartile ranges of PM0.3–1.0 exposures spanned 28.3 μg/m3 for the cardiac patients and 27.2 μg/m3 for the hypertensive group. Pearson correlations between any two combinations of PM2.5–10, PM1.0–2.5, and PM0.3–1.0 showed moderate correlations between PM0.3–1.0 and PM1.0–2.5 (r = 0.65) and between PM1.0–2.5 and PM2.5–10 (r = 0.51) only. Hourly temperature varied from 17.6°C to 33.0°C, and hourly relative humidity varied from 28.6 to 80.5% during the study period.
The associations between PM and time-domain HRV indices estimated by mixed-effects models are listed in Table 3. With sex, age, BMI, hour of day, temperature, and humidity being adjusted in our mixed-effects models, PM0.3–1.0 exposures significantly decreased SDNN and r-MSSD for both the cardiac patients and the hypertensive group. By contrast, PM2.5–10 and PM1.0–2.5 exposures were not associated with SDNN or r-MSSD in our study subjects. For cardiac patients, interquartile increases in PM0.3–1.0 with 2- to 4-hr moving-average exposure were associated with 2.87–4.88% decreases in SDNN. Their r-MSSDs were decreased by 4.43–8.25% with 1- to 4-hr moving averages, respectively. For the hypertensive group, interquartile increases in PM0.3–1.0 with 1- to 4-hr moving averages exposure accounted for about 1.49–1.79% decreases in SDNN and 2.73–5.07% decreases in r-MSSD. The greatest decreases in time-domain HRV indices occurred with 3-hr moving averages for the cardiac patients and 4-hr moving averages for the hypertensive group. We examined the time course of PM exposures only up to 4-hr moving averages because available data became substantially decreased for calculating moving averages > 5 hr.
The associations between PM and frequency-domain HRV indices by our mixed-effects models are list in Table 4. For the cardiac patients, interquartile increases in PM0.3–1.0 exposures significantly decreased LF by 3.83% with 3-hr moving averages and HF by 5.28% with 2-hr moving averages. For the hypertensive group, interquartile increases in PM0.3–1.0 exposures decreased LF by 2.32% and 1.86% with 1-hr and 2-hr moving averages, respectively. Their respective HF values decreased by 2.84 and 3.29% by interquartile increases in PM0.3–1.0 exposures with 1- to 3-hr moving averages. By contrast, PM2.5–10 and PM1.0–2.5 exposures were not associated with LF or HF in our study subjects. No association was observed between PM of all three size ranges and the LF:HF ratios in our study subjects.
Because our study subjects are exposed to PM10, PM2.5, and PM0.3–1.0 simultaneously during the panel study, their exposures to three size fractions of PM can be treated as copollutants in our multipollutant models. We found that PM0.3–1.0 effects on HRV reduction in multipollutant models remained as significant as those in the single-pollutant models. By contrast, both PM2.5–10 and PM1.0–2.5 were not associated with HRV reduction in the multipollutant models. Figure 1 lists one exemplary result of our multipollutant models, which shows the percent reduction in HRV by PM2.5–10, PM1.0–2.5, and PM0.3–1.0 using 3-hr moving averages of these three PM fractions and all 26 subjects in this study. As shown in Figure 1, our subjects’ SDNN, r-MSSD, and HF values were decreased by about 3.16, 5.20, and 5.05% for interquartile increases in 3-hr PM0.3–1.0 moving averages, respectively. To further determine whether disease status could modify the association between PM and HRV, we combined the data of cardiac and hypertensive patients together and put them into our multi-pollutant models with and without the disease status as a variable in the models. We found that the addition of disease status did not significantly change the coefficients of PM in our multipollutant models (data not shown).
Discussion
This is the first study to report that PM0.3–1.0 measured in mass concentrations had effects on reducing HRV among cardiac, prehypertensive, and hypertensive patients. This study supports that PM0.3–1.0 had effects on decreasing HRV indices in susceptible populations, as we reported in a previous panel study using number concentrations of submicrometer particles with a size range of 0.02–1.0 μm (Chan et al. 2004). One toxicologic study also reported that PM1.0 induced more production of interleukin-8, lipid peroxidation, and tumor necrosis factor-α in mouse macrophage RAW 264.7 cells than did PM2.5–10 or PM1.0–2.5 (Huang et al. 2003).
The time courses of PM0.3–1.0 on HRV in cardiac and hypertensive patients ranging from 1 to 4 hr are in agreement with the findings of previous studies (Chan et al. 2004; Gold et al. 2000; Magari et al. 2001, 2002). These results indicate that PM0.3–1.0 can have acute effects on cardiac autonomic function. It has been reported that particles can affect both sympathetic and parasympathetic nervous systems directly immediately after exposures (Kodavanti et al. 2000; Lai and Kou 1998). One possible pathway of such a mechanism is the rapid passage of inhaled particles with diameters < 100 nm into the blood circulation (Nemmar et al. 2001, 2002). Under appropriate circumstances, the activation of pulmonary neural reflexes secondary to PM interactions in autonomic tone may contribute to the instability of vascular plaque or initiate cardiac arrhythmias. Such a direct effect of PM represents a plausible explanation for the occurrence of rapid cardiovascular responses in 1-hr moving average of PM0.3–1.0 exposure. Another possible pathophysiologic link between PM and less acute effects of cardiovascular responses is that inhaled particles may exacerbate the autonomic function of the heart via induced inflammation in lung and proinflammatory cytokine expression in cardiac macrophages (Stone and Godleski 1999). Previous studies also reported that ultrafine particles deposited in the alveoli might increase blood coagulation via mechanisms of pulmonary inflammation or direct action on red blood cells (Donaldson et al. 2001; Peters et al. 1997). This subsequently may contribute to a systemic inflammatory state, which may in turn be capable of activating hemostatic pathways, impairing vascular function, and causing atherosclerosis. Accordingly, we believe particle-induced pulmonary inflammation can also indirectly result in HRV changes or autonomic imbalance in the delayed phase after PM0.3–1.0 exposures. This may explain why HRV decrease reached its peak at 3–4 hr after PM0.3–1.0 exposure in our study.
There is a growing recognition that autonomic dysfunction plays an important role in cardiovascular mortality. Autonomic nervous system changes in HRV may increase the likelihood of sudden cardiac death (Task Force 1996). Decrease in HRV is also a strong predictor of cardiac mortality (La Rovere et al. 2003). Because the cardiac autonomic alteration included both time-domain and frequency-domain HRV indices in this study, we believe that cardiovascular diseases may be increased by PM0.3–1.0-induced decreases in autonomic nervous system control or the withdrawal of vagal activity (Bigger et al. 1992; Kleiger et al. 1987). However, it was still unclear whether short-term and small HRV fluctuations caused by PM0.3–1.0 exposures will eventually lead to cardiac deaths. Because cardiac death is a consequence of a complex interaction between the autonomic nervous system, a myocardial substrate altered in the course of disease processes, and myocardial vulnerability leading to arrhythmogenic or ischemic response, the presence of a single cardiac alteration is usually not sufficient to trigger cardiac death (Zareba et al. 2001). Further studies on environmental cardiology are needed to determine whether the PM0.3–1.0-associated HRV fluctuations observed in panel studies have meaningful implications of cardiovascular mortality clinically.
The following limitations of our study design must be considered in explaining our findings of PM0.3–1.0 effects on reducing HRV in this study. First, the lack of information on personal exposure to other air pollutants, such as nitrogen dioxide, carbon monoxide, ozone, and sulfur dioxide may confound the observed associations between PM0.3–1.0 and HRV indices. Because these air pollutants are usually correlated with PM, they can bias our study outcomes toward either positive or null results (Zeger et al. 2000; Zeka and Schwartz 2004). Therefore, we cannot entirely rule out the effects of these air pollutants on reducing HRV in this study. Second, the observed PM0.3–1.0 effects on HRV reduction may be due to differences in particle components rather than particle sizes. The lack of measuring chemical and biologic components in our subjects’ PM exposures prevents us from differentiating particle size from particle components in HRV reduction in our study. Third, it is possible that the DUST-check monitor may have been turned off in high PM environments, such as busy traffic zones, during the monitoring period. More frequent calibrations of the DUST-check monitor during the study could have been more temporally supportive to validate continued accuracy although a comparison with a collocated TEOM sample was made to calibrate DUST-check monitors before and after the study. Fourth, we cannot exclude the confounding effects of respiration on the association between PM0.3–1.0 and HRV because our subjects’ breathing patterns were not measured in our study and the quantity, periodicity, and timing of vagal cardiac outflow are associated with variations of respiratory depth and interval (Yasuma and Hayano 2004). Fifth, the technician’s presence may also alter the subjects’ psychology and autonomic system, and then alter their behaviors, including breathing patterns and heart rates. Sixth, the use of 5-min segments of NN intervals eliminates the opportunity to evaluate HRV frequencies > 5 min and to compare our results against those findings using different averaging times, such as 24-hr SDNN and standard deviation of the averages of NN intervals in all 5-min segments of the entire recording. Therefore, our results did not preclude the findings of previous daily time-series studies on respiratory and cardiovascular mortality, which have generally observed exposure lag structures to be 1–5 or more days, because this study examined time course only within 1 day.
Regardless of these limitations, we believe our data generally support the conclusion that PM0.3–1.0 is an environmental stressor, which may contribute to the fluctuations of HRV indices and trigger a cascade of events by increasing autonomic function imbalance, and may potentially lead to ischemia or fatal arrhythmia in patients with underlying CHD, prehypertension, or hypertension. Cardiac patients together with hypertensive adults are susceptible to PM0.3–1.0 and should be considered a high-risk target population in planning future public health abatement measures against PM0.3–1.0 pollution.
This work was supported by grants (EPA-90-FA11-03-A232 and EPA-91-FA11-03-D036) from the Taiwan Environmental Protection Agency.
Figure 1 Estimated percent changes in HRV by interquartile increase in PM0.3–1.0, PM1.0–2.5, and PM2.5–10 exposures at 3-hr moving averages for 26 study subjects using multipollutant mixed-effects models. Error bars indicate 95% confidence intervals.
Table 1 Basic characteristics of 26 study subjects.
Characteristic Cardiac patients Hypertensive patients
Sex (n)
Female 1 11
Male 9 5
Age (years) 68.1 ± 3.6 (61–72) 68.8 ± 6.6 (52–76)
BMI (kg/m2) 25.6 ± 4.8 (19.5–34.7) 24.4 ± 2.8 (20.6–31.8)
Heart rate (beats/min) 79.6 ± 14.8 (48.7–123.0) 77.4 ± 11.9 (47.9–114.8)
Health status (n)
CHD 10 0
Prehypertensiona 0 8
Hypertensionb 0 8
Values are mean ± SD (range) unless otherwise noted.
a Prehypertension: SBP 120–139 mmHg or DBP 80–89 mmHg.
b Hypertension: SBP ≥ 140 mmHg or DBP ≥90 mmHg.
Table 2 Summary statistics for HRV indices, air pollution levels, and meteorologic variables (mean ± SD).
Variable Cardiac patients Hypertensive patients p-Valuea
Time-domain HRV
Log10 SDNN (msec) 1.53 ± 0.24 1.56 ± 0.21 < 0.0001
Range 0.71–2.01 0.73–2.10
No. 1,527 2,864
Log10 r-MSSD (msec) 0.97 ± 0.29 1.00 ± 0.27 0.0002
Range 0.39–1.83 0.40–1.86
No. 1,527 2,864
Frequency-domain HRV
Log10 LF (msec2) 2.15 ± 0.57 2.21 ± 0.49 0.0006
Range 0.05–3.91 0.38–4.16
No. 1,527 2,864
Log10 HF (msec2) 1.97 ± 0.65 2.07 ± 0.63 < 0.0001
Range 0.44–3.88 0.33–4.03
No. 1,527 2,864
LF:HF ratio 2.75 ± 3.33 2.14 ± 2.14 < 0.0001
Range 0.04–40.12 0.06–17.81
No. 1,527 2,864
Air pollutants
PM2.5–10 1-hr mean (μg/m3) 16.4 ± 10.7 14.0 ± 11.1 < 0.0001
Interquartile range 14.8 11.9
Range 0.7–59.6 0.3–66.5
No. 1,084 2,273
PM1.0–2.5 1-hr mean (μg/m3) 10.9 ± 8.5 12.6 ± 7.8 < 0.0001
Interquartile range 10.8 7.9
Range 0.9–48.8 0.5–62.8
No. 1,084 2,273
PM0.3–1.0 1-hr mean (μg/m3) 26.8 ± 25.9 37.2 ± 25.8 < 0.001
Interquartile range 28.3 27.2
Range 1.4–136.2 1.3–196.4
No. 1,084 2,273
Meteorologic variables
Temperature (°C) 25.0 ± 3.5 26.3 ± 3.6 < 0.0001
Range 18.4–31.4 17.6–33.0
No. 1,248 2,568
Relative humidity (%) 55.4 ± 8.5 57.0 ± 8.2 < 0.0001
Range 28.6–74.2 39.5–80.5
No. 1,248 2,568
a Difference between cardiac and hypertensive patients was tested by t-test.
Table 3 Percent changes (95% confidence interval)a in time-domain HRV for interquartile increase in PM exposures estimated by mixed-effects models.
Cardiac patients
Hypertensive patients
Exposure matrix PM2.5–10 PM1.0–2.5 PM0.3–1.0 PM2.5–10 PM1.0–2.5 PM0.3–1.0
SDNN
1-hr moving −1.73 (−3.53 to 0.08) −1.36 (−3.56 to 0.85) −1.50 (−3.45 to 0.45) −2.64 (−3.93 to 0.55) −2.39 (−5.40 to 0.62) −1.63* (−2.42 to −0.85)
2-hr moving −1.97 (−4.43 to 0.49) −2.40 (−5.13 to 0.32) −2.87* (−5.23 to −0.51) −3.51 (−7.87 to 0.85) −2.47 (−5.19 to 0.26) −1.75* (−2.74 to −0.76)
3-hr moving −1.70 (−4.39 to 0.98) −4.00 (−8.11 to 0.10) −4.88* (−7.79 to −1.97) −2.74 (−6.22 to 0.74) −1.83 (−5.17 to 1.52) −1.49* (−2.62 to −0.36)
4-hr moving −1.75 (−5.42 to 1.92) −4.50 (−9.52 to 0.52) −3.95* (−7.59 to −0.31) −2.49 (−6.13 to 1.15) −2.36 (−5.81 to 1.10) −1.79* (−2.97 to −0.61)
r-MSSD
1-hr moving −4.39 (−9.54 to 0.03) −4.39 (−8.89 to 0.10) −4.43* (−8.10 to −0.77) −2.53 (−5.10 to 0.04) −3.12 (−7.27 to 1.04) −2.73* (−4.39 to −1.08)
2-hr moving −4.36 (−8.99 to 0.27) −5.68 (−11.83 to 0.46) −6.91* (−11.41 to −2.40) −5.42 (−10.92 to 0.09) −4.33 (−9.91 to 1.24) −3.37* (−5.44 to −1.30)
3-hr moving −4.20 (−9.02 to 0.61) −6.30 (−12.73 to 0.14) −8.25* (−13.64 to −2.87) −3.15 (−6.32 to 0.03) −2.59 (−5.37 to 0.18) −3.36* (−5.65 to −1.07)
4-hr moving −2.70 (−9.24 to 3.84) −3.99 (−13.07 to 5.10) −4.94 (−11.60 to 1.72) −4.23 (−8.88, to 0.42) −5.17 (−10.79 to 0.44) −5.07* (−7.55 to −2.59)
a Coefficients are expressed as percent changes for interquartile changes in PM exposures in models adjusting for sex, age, BMI, hour of day, temperature, and humidity.
* p < 0.05.
Table 4 Percent changes (95% confidence interval)a in frequency-domain HRV for interquartile increase in PM exposures estimated by mixed-effects models.
Cardiac patients
Hypertensive patients
Exposure matrix PM2.5–10 PM1.0–2.5 PM0.3–1.0 PM2.5–10 PM1.0–2.5 PM0.3–1.0
LF
1-hr moving −1.85 (−4.33 to 0.62) −1.65 (−4.67 to 1.37) −1.91 (−4.51 to 0.69) −4.38 (−8.78 to 0.03) −3.72 (−7.84 to 0.30) −2.32* (−3.58 to −1.07)
2-hr moving −3.87 (−8.22 to 0.47) −3.10 (−6.84 to 0.64) −2.39 (−5.57 to 0.79) −5.23 (−10.95 to 0.05) −3.23 (−6.71 to 0.26) −1.86* (−3.46 to −0.25)
3-hr moving −2.98 (−6.65 to 0.69) −4.10 (−9.00 to 0.79) −3.83* (−8.29 to −0.36) −3.34 (−1.72 to 0.04) −1.75 (−3.87 to 0.37) −1.11 (−2.89 to 0.66)
4-hr moving −3.11 (−8.22 to 1.99) −4.96 (−11.97 to 2.06) −2.82 (−7.76 to 2.12) −2.96 (−6.63 to 0.71) −2.61 (−5.26 to 0.04) −1.53 (−3.43 to 0.37)
HF
1-hr moving −4.46 (−9.23 to 0.32) −3.66 (−8.25 to 0.93) −3.94 (−8.00 to 0.12) −4.92 (−9.94 to 0.10) −3.97 (−8.37 to 0.43) −3.10* (−4.95 to −1.25)
2-hr moving −4.41 (−9.55 to 0.72) −4.86 (−10.52 to 0.81) −5.28* (−10.20 to −0.36) −6.07 (−12.28 to 0.13) −4.28 (−9.15 to 0.60) −3.29* (−5.61 to −0.96)
3-hr moving −3.80 (−9.12 to 1.53) −3.31 (−10.36 to 3.74) −4.30 (−10.18 to 1.57) −1.94 (−5.44 to 1.55) −1.54 (−4.63 to 1.56) −2.84* (−5.41 to −0.26)
4-hr moving −3.39 (−10.62 to 3.84) −2.15 (−12.03 to 7.73) −2.38 (−9.49 to 4.74) −2.78 (−6.78 to 1.21) −3.55 (−9.04 to 1.94) −3.91 (−8.72 to 0.89)
LF:HF ratio
1-hr moving 8.45 (−3.48 to 20.38) 3.71 (−14.09 to 21.52) 5.75 (−4.06 to 15.56) 5.94 (−3.27 to 15.15) 3.43 (−8.77 to 15.63) 7.54 (−2.45 to 17.54)
2-hr moving 1.66 (−15.22 to 18.55) −6.84 (−29.89 to 16.21) 4.93 (−8.03 to 17.89) 10.70 (−2.19 to 23.59) 7.55 (−6.34 to 21.44) 10.16 (−1.28 to 21.59)
3-hr moving 11.69 (−7.27 to 30.64) −24.06 (−56.35 to 8.24) −9.11 (−27.76 to 9.55) −1.51 (−17.02 to 14.00) −3.32 (−21.22 to 14.57) 14.49 (−1.80 to 30.77)
4-hr moving 8.18 (−17.22 to 33.57) −47.72 (−96.30 to 1.17) −10.38 (−34.89 to 14.12) 3.41 (−16.91 to 23.74) 4.32 (−18.64 to 27.29) 16.58 (−0.75 to 33.91)
a Coefficients are expressed as percent changes for interquartile changes in PM exposures in models adjusting for sex, age, BMI, hour of day, temperature, and humidity.
* p < 0.05.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8231ehp0113-00169816330350ResearchLeukotoxin Diols from Ground Corncob Bedding Disrupt Estrous Cyclicity in Rats and Stimulate MCF-7 Breast Cancer Cell Proliferation Markaverich Barry M. 12Crowley Jan R. 3Alejandro Mary A. 1Shoulars Kevin 1Casajuna Nancy 2Mani Shaila 1Reyna Andrea 1Sharp John 21 Department of Molecular and Cellular Biology, and2 Center for Comparative Medicine, Baylor College of Medicine, Houston, Texas, USA3 Department Mass Spectrometry Facility, Washington University Medical School, St. Louis, Missouri, USAAddress correspondence to B.M. Markaverich, Department of Molecular and Cellular Biology, One Baylor Plaza, Houston, TX 77030 USA. Telephone: (713) 798-6497. Fax: (713) 798-6588. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 8 8 2005 113 12 1698 1704 12 4 2005 8 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Previous studies in our laboratory demonstrated that high-performance liquid chromatography (HPLC) analysis of ground corncob bedding extracts characterized two components (peak I and peak II) that disrupted endocrine function in male and female rats and stimulated breast and prostate cancer cell proliferation in vitro and in vivo. The active substances in peak I were identified as an isomeric mixture of 9,12-oxy-10,13-dihydroxyoctadecanoic acid and 10,13-oxy-9,12-dihydroxyoctadecanoic acid, collectively designated tetrahydrofurandiols (THF-diols). Studies presented here describe the purification and identification of the HPLC peak II component as 9,10-dihydroxy-12-octadecenoic acid (leukotoxin diol; LTX-diol), a well-known leukotoxin. A synthetic mixture of LTX-diol and 12,13-dihydroxy-9-octadecenoic acid (isoleukotoxin diol; i-LTX-diol) isomers was separated by HPLC, and each isomer stimulated (p < 0.001) MCF-7 cell proliferation in an equivalent fashion. The LTX-diol isomers failed to compete for [3H]estradiol binding to the estrogen receptor or nuclear type II sites, even though oral administration of very low doses of these compounds (>> 0.8 mg/kg body weight/day) disrupted estrous cyclicity in female rats. The LTX-diols did not disrupt male sexual behavior, suggesting that sex differences exist in response to these endocrine-disruptive agents.
breast cancercorncob beddingendocrine disruptorestrous cyclesleukotoxin diols
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We recently discovered that housing adult male or female rats on ground corncob bedding blocks male and female sexual behavior and cyclicity (Markaverich et al. 2002b). These results suggested that this bedding material contained endocrine-disruptive substances. We initially postulated that the endocrine-disruptive agent(s) in corncob was likely a phytoestrogen because earlier studies demonstrated that plant isoflavonoids possess estrogenic activity in a variety of experimental systems (Adlercreutz et al. 1992; Bickoff et al. 1958; Markaverich et al. 1995; Martin et al. 1978). On the basis of these observations, we reasoned that the MCF-7 human breast cancer cell proliferation (the E-Screen) assay (Soto et al. 1995) would be a suitable rapid in vitro screen for the endocrine disruptors in extracts of ground corncob bedding. Our initial studies on ground corncob bedding extracts led to the purification of two peaks of mitogenic activity on reverse-phase high-performance liquid chromatography (HPLC). Peak I was purified to homogeneity and identified as an isomeric mixture of 9,12-oxy-10,13-dihydroxyocta-decanoic acid and 10,13-oxy-9,12-dihydroxy-octadecanoic acid [tetrahydrofurandiols (THF-diols)] (Markaverich et al. 2002a). The compounds (Figure 1) were synthesized and found to stimulate MCF-7 human breast cancer proliferation in vitro and block sexual behavior in male rats (Mani et al. 2005) and female rats and ovarian cyclicity (Mani et al. 2005; Markaverich et al. 2002b) at concentrations approximately 200-fold lower than classical phytoestrogens (Markaverich et al. 1995). In addition, the THF-diols are apparently devoid of estrogenic activity and do not bind to the estrogen receptor (ER) or nuclear type II [3H]estradiol binding sites (Markaverich et al. 2002a, 2002b). Thus, the THF-diols were identified as very active endocrine-disruptive agents that block steroid-hormone–dependent pathways through a nonconventional mechanism.
In this article we describe the purification of the peak II component by HPLC and its identification by gas chromatography–mass spectrometry (GC-MS). Synthetic isomeric preparations of the compound stimulated breast cancer cell proliferation and blocked estrous cyclicity in female rats but were devoid of biologic effects on male sexual behavior. Like the THF-diols, this novel endocrine-disruptive agent derived from fatty acid metabolism in plants does not bind to ER or nuclear type II [3H]estradiol binding sites and thus antagonizes estrogenic response through non-classical pathways (Maggiolini et al. 2001; Markaverich et al. 1988). The studies emphasize the importance of considering the effects of the environmental housing conditions on experimental model systems and also indicate that human exposure to corncob mitogens with “endocrine-disruptive potential” could represent a significant human health problem.
Materials and Methods
Animals and treatment.
We used adult (60-day-old) Sprague-Dawley male and female rats (Harlan Laboratories, Madison, WI) for these studies. Animals were housed in suspended stainless steel wire cages and maintained in compliance with federal guidelines for animal care (Human Health Extension Act of 1985, Public Law 99-158) with appropriate institutional animal care and use committee approval and were treated humanely with regard for alleviation of suffering. Rats were maintained under climate-controlled conditions on a 12-hr light/dark cycle (lights on at 0600 hr) with food (Harlan Teklad Global Diet no. 2014 containing no alfalfa, soybean, or phytoestrogen components; Harlan Teklad, Madison, WI) and water provided ad libitum. Male and female rats were acclimated to this environment for at least 3 weeks before the initiation of the studies.
For the cycling studies, daily vaginal smears were collected from eight adult female rats housed under standard conditions and given tap water containing 2% Tween 80 vehicle as a drinking solution for 30 days to establish baseline controls and confirm that the animals were cycling in a normal fashion. This vehicle has no significant effects on ovarian cyclicity in rats or on male or female sexual behavior relative to tap water controls (Mani et al. 2005; Markaverich et al. 1988, 2002a, 2002b). On day 31, the cycling female rats were given a 1:1 mixture of the 9,10-dihy-droxy-12-octadecenoic acid [leukotoxin diol (LTX-diol)] isomers (2 μg/mL) in the tap water–Tween 80 vehicle for an additional 30 days. This dose was chosen on the basis of previous published studies with the THF-diols, which are structurally similar to the LTX-diols (Figure 1) and which we suspected would have similar biologic activities. Daily vaginal smears were collected throughout the 30-day treatment period and evaluated for cyclicity as previously described (Markaverich et al. 2002a, 2002b). In this particular study, the female rats served as their own controls because their ovarian cycles were determined before and after LTX-diol treatment. Previous studies with the THF-diols demonstrated that equivalent results are obtained with separate groups of controls or treated animals or with animals serving as their own controls (Markaverich et al. 2002a, 2002b).
To determine whether the LTX-diols affect male reproductive function, three replicate studies employing six established adult male Sprague-Dawley breeder rats were performed as previously reported for the THF-diols (Mani et al. 2005; Markaverich et al. 1988, 2002a, 2002b). The male breeder rats who had side litters at least three times were housed under reversed lighting conditions in hanging wire cages and were provided a drinking solution containing LTX-diol isomers (2μg/mL of drinking solution) for 1–4 weeks. Sexual behavior was evaluated by examining the number of mounts, intromissions, ejaculations, ejaculation latencies (in seconds), and grooming frequencies in a 30-min test period with each sexually receptive female as per well-established procedures in our laboratories (Hull et al. 1990). Ovariectomized steroid-primed Sprague-Dawley female rats were used as stimulus animals and were brought into behavioral estrus by a subcutaneous priming injection of 2 μg estradiol benzoate in sesame oil 48 hr before receiving 100 μg progesterone (subcutaneously), as previously described (Markaverich et al. 2002b).
Reagents and solvents.
We obtained linoleic acid and m-chloroperoxybenzoic acid (mCPBA) from Sigma Chemical (St. Louis, MO). N,O,-bis(trimethylsilyl)trifluoro-acetamide with 10% trimethyl chlorosilane (BSTFA) was obtained from Pierce Chemical (Rockford, IL). We purchased Sep-Pak C18 cartridges (3 cc) from Waters Corporation (Milford, MA), and C18 minicolumns from Varian (Walnut Creek, CA). All solvents were HPLC grade from Burdick and Jackson (Muskegon, MI).
Purification of peak II from corncob bedding.
We prepared the ethyl acetate extract of ground corncob bedding as previously described (Markaverich et al. 2002a, 2002b). The dried extract was redissolved in approximately 20 mL HPLC-grade methanol, and 2 mL aliquots were analyzed on a Beckman Gradient HPLC System (Beckman Coulter, Fullerton, CA) equipped with a diode array detector and a Dynamax Ultrasphere-Octyl HPLC column (Varian). The column was equilibrated in acetonitrile (CH3CN):water (30:70) containing 0.1% acetic acid at a flow rate of 4 mL/min. A linear gradient to 40% CH3CN was initiated 5 min after injection and completed by 90 min. Fractions (1 min) were collected, and triplicate aliquots were assayed for mitogenic activity in MCF-7 breast cancer cells.
Mitogenic effects of HPLC fractions and synthetic LTX-diol on MCF-7 human breast cancer cells.
For the assessment of mitogenic activities of the various preparations on MCF-7 cells, we followed previously described methods (Markaverich et al. 2002a, 2002b). Briefly, we added the aliquots of the HPLC fractions (1–5 μL) or HPLC-purified synthetic LTX-diol isomers in redistilled ethanol to cultured MCF-7 cells grown in phenol red–free Dulbecco’s modified Eagle’s medium (DMEM) containing 5% charcoal-stripped, sulfatase-treated fetal calf serum. Cell number was determined 7 days after treatment by hemocytometer counts or by the MTT (methyl thiazoyl tetrazolium) absorbance assay (Markaverich et al. 2002a). HPLC fractions containing peak II of mitogenic activity (Figure 2) were collected on C18 mini-columns (Varian, Lake Forest, CA) and analyzed by GC-MS. The mass spectra of the TMS (trimethylsilylether) derivatives of the two isomers were in agreement with those reported in the literature (Halankar et al. 1989). Synthetic LTX-diol and 12,13-dihy-droxy-9-octadecenoic acid (iso-LTX-diol) were redissolved in 100% redistilled ethanol and added to the cultured cells.
GC-MS studies.
For GC-MS analysis, the HPLC peak components or authentic compounds were derivatized by a number of procedures. We prepared the trimethylsilyl ethers by adding a 1:4 mixture of BSTFA:CH3CN to the dried samples, which were vortexed and heated at 70°C for 30 min. Methyl esters of the carboxylic acids in the HPLC fractions or synthetic compounds were prepared by redissolving the dried samples in methanol:1 N HCl (1:3) and heating at 70°C for 60 min. The samples were taken to dryness under nitrogen and redissolved in methanol before analysis. We performed catalytic hydrogenation to reduce double bonds in the unknowns or synthetic compounds by redissolving the dried samples in 1 mL methanol in the presence of a small amount of platinum while bubbling H2 gas slowly into the sample to evaporate the methanol. The dried material was redissolved in methanol and centrifuged to remove the platinum pellet. The sample was transferred to a clean vial and taken to dryness under nitrogen, and the TMS derivative was prepared as described above.
The derivatized samples were analyzed on a Varian 3400 gas chromatograph interfaced with a Finnigan SSQ 7000 mass spectrometer (ThermoFinnigan, San Jose, CA) equipped with a DB-1 column (12.5 m, 0.2 mm inner diameter, 0.0.33 μm film coating; P.J. Cobert, St. Louis, MO). The initial temperature of 120°C was held for 1 min after sample injection, increased linearly to 270°C at 10°C/min, and held at 270°C for 5 min. For electron ionization, the source temperature, electron energy, and emission current were 200°C, 100 eV, and 300 μA, respectively. For chemical ionization, the source temperature, electron energy, and emission current were 140°C, 240 eV, and 300 μA, respectively. Methane was used as carrier gas for chemical ionization. The injector and transfer line temperatures were 250°C.
Synthesis of 9,10-epoxy-12-octadecenoic and 12,13-epoxy-9-octadecenoic acids.
The epoxy acids were synthesized from linoleic acid as previously described (Zheng et al. 2001), with slight modifications. Linoleic acid (10 mg) was dissolved in 3 mL methylene chloride. mCPBA (70 mg) was added to the sample, and the mixture was stirred at 22°C for 5 hr. The reaction mixture was washed sequentially with 1 mL portions of 25 mM NH4HCO3, saturated saline solution, and H2O. The washed extract was evaporated to dryness under N2, and the waxy white solid was stored at –20°C.
Synthesis and purification of LTX-diol and iso-LTX-diol.
Approximately 20 mg of the waxy white solid described above was dissolved in THF:H2O:5% perchloric acid (5:1:1) and stirred at 22°C for 1 hr. The products were extracted into ethyl acetate and evaporated to dryness under vacuum. LTX-diol and iso-LTX-diol were separated from linoleic acid and THF-diols in these synthetic mixtures by chromatography on C18 reverse-phase mini-columns. To accomplish the separation, the dry synthetic mixture was redissolved in approximately 300 μL CH3CN. This solution was applied to a C18 minicolumn pre-equilibrated with 2 mL CH3CN and 4 mL 0.5% acetic acid. The column was washed with 40% CH3CN in 0.5% acetic acid to elute residual by-products of the mCPBA. The column was then eluted with 50% CH3CN in 0.5% acetic acid to obtain the THF-diols. Elution with 60% CH3CN/0.5% acetic acid facilitated the collection of an isomeric mixture of LTX-diol and iso-LTX-diol. Linoleic acid elutes from the C18 minicolumns with higher concentrations (> 70%) of CH3CN and therefore was removed from these LTX-diol preparations. The identity and purity of the eluents and contaminants in each of the above fractions were confirmed by GC-MS.
HPLC separation of LTX-diol and iso-LTX-diol.
To compare the biologic activities of the two isomers, separation of LTX-diol and iso-LTX-diol was performed on a Beckman HPLC system equipped with an Altex Ultrasphere-ODS semipreparative column (10 mm × 25 cm) (Beckman Coulter) eluted isocratically with 45% CH3CN containing 0.1% acetic acid at a flow rate of 2.0 mL/min. Compounds were detected at 203 nm with a Beckman diode array detector. The peaks at 32 and 36 min were confirmed by GC-MS to be pure iso-LTX-diol and LTX-diol, respectively.
Effects of LTX-diol and iso-LTX-diol on MCF-7 breast cancer cell proliferation.
The LTX-diol isomers isolated by HPLC were taken to dryness under nitrogen at 50°C, weighed, and redissolved at known concentrations in 100% ethanol for the cell proliferation assays as described above. Briefly, MCF-7 cells grown as described above were treated with a range of LTX-diol or iso-LTX-diol concentrations (0.1–10 μg/mL), and cell number was determined 7 days after treatment, as described previously (Markaverich et al. 2002a, 2002b). The isomers had equivalent biologic activity in the MCF-7 cell proliferation assay; therefore, the mixture was used for the remaining studies.
LTX-diol competition for [3H]estradiol binding to ER and type II sites in rat uterine nuclear fractions.
Earlier studies on partially purified ground corncob extracts containing the mitogenic activity revealed that these preparations did not compete for [3H]estradiol binding to ER or nuclear type II [3H]estradiol binding sites. This was later confirmed with pure preparations of synthetic THF-diol (Markaverich et al. 2002a, 2002b). Thus, we suspected that the biologic effects of the LTX-diols were probably not mediated via direct interactions with either ER or the nuclear type II sites because the LTX-diols are structurally related to the THF-diols. To directly evaluate these possibilities, we assessed LTX-diol competition for [3H]estradiol binding to ER or type II sites (Markaverich et al. 2002a, 2002b). Briefly, adult female Sprague-Dawley rats were ovariectomized and implanted with 20-μg estradiol-containing beeswax pellets to promote nuclear ER retention and stimulate nuclear type II sites (Markaverich et al. 2002b). Seven days after treatment, the uteri were removed from these animals, stripped of extraneous tissues, weighed, and chilled in ice-cold saline before analysis. For ER competition studies, rat uterine tissue was homogenized in 10 mM Tris, 1.5 mM EDTA, and 0.1 mM dithiothreitol, pH 7.4 at 22°C, and uterine nuclear suspensions were incubated at 37°C for 30 min in the presence of 10 nM [3H]estradiol ± 0.01–10 μM LTX-diol or diethylstilbestrol (DES) under conditions that measure only [3H]estradiol binding to ERs (Markaverich et al. 1981). For type II site competition studies, uterine tissue was homogenized in TE buffer (10 mM Tris, 1.5 mM EDTA) and nuclear suspensions were incubated at 4°C × 60 min in the presence of 30 nM [3H]estradiol ± 0.00015–30 μM LTX-diol or luteolin (a competitive inhibitor of type II sites but not ERs) under conditions that measure [3H]estradiol binding to type II sites but not occupied ERs. After incubation, the nuclear suspensions for ER or nuclear type II site assays were washed 3 times by resuspension and centrifugation (800 × g × 7 min) in 1 mL TE buffer, and the final washed pellets were extracted with 1 mL 100% ethanol. Radioactivity in the ethanol extract was determined by liquid scintillation spectrometry (Markaverich et al. 1981). [3H]Estradiol binding in the absence of competitor (controls) was approximately 10,000 cpm for ERs and 30,000 cpm for type II sites. Results were expressed as the percentage of [3H]estradiol bound in the presence of the indicated concentrations of LTX-diol, DES, or luteolin relative to the vehicle control (100%).
Statistical analyses.
We analyzed data from cell proliferation assays and animal cycling studies (body weights, fluid consumption) statistically by analysis of variance (ANOVA) and Tukey’s test on the treatment means using InStat (GraphPad Software Inc., San Diego, CA). The data recorded from the behavioral tests for male sexual behavior (data not shown) were compared using Kruskal-Wallis ANOVA followed by Dunn’s method for post hoc comparison using Graph Pad Prism software, version 4 (Graph Pad Software Inc.).
Results
Purification of mitogenic agents in ground corncob extract.
HPLC analysis of an ethyl acetate extract of ground corncob animal bedding separated two major peaks of mitogenic activity (peaks I and II), as shown in Figure 2. Peak I was previously identified as a mixture of the THF-diol isomers (Figure 1) shown to disrupt male and female endocrine function in vivo (Mani et al. 2005; Markaverich et al. 2002a, 2002b). We purified and identified peak II in the present study. Preliminary studies revealed that the peak II component did not contain compounds sufficiently volatile for GC-MS without derivatization. Therefore, the material was derivatized with BSTFA and analyzed by GC-MS. Figure 3A represents the total ion chromatogram obtained by GC-MS analysis showing two major peaks that eluted from the column. When run in the electron ionization mode, the first peak at 14.53 min generated the spectra shown in Figure 3B. Positive chemical ionization data (not shown) determined that the molecular weight of the BSTFA-unknown derivative was 530 amu. Catalytic hydrogenation indicated the presence of one carbon–carbon double bond. Reaction with methanolic HCl before derivatization with BSTFA produced a new peak with a molecular weight consistent with the replacement of one TMS group with a methyl group. This observation indicated the presence of a COOH group in the unknown because carboxylic acids are readily esterified in either methanolic HCl or BSTFA. Further inspection of the spectral data suggested that the compound was a 9,10-diol derived from linoleic acid. This compound would have a molecular weight of 530 amu when derivatized with BSTFA and would be expected to fragment between the 9 and 10 positions (Murphy 1993), leading to fragments at m/z 317 and 213, and between carbons 10 and 11, producing m/z 419. Loss of 90 amu [–Si(CH3)3OH)] from the m/z 419 accounts for the m/z 329 result. The unknown in Figure 3B was tentatively identified as LTX-diol. This was confirmed with a match of spectra in the literature, and the compound has a molecular weight of 314 amu. (Draper and Hammock 2000; Sugiyama et al. 1987).
Synthesis and purification of LTX-diol and iso-LTX-diol.
For confirmation of structure and biologic activity, we synthesized an isomeric LTX-diol mixture as described in “Materials and Methods.” This procedure involved forming the epoxides from linoleic acid and then opening the epoxide ring to generate the vicinal diols. A diepoxide is also formed that will cyclize and generate the THF-diol that we identified from corncob bedding (Mani et al. 2005; Markaverich et al. 2002a, 2002b). Unreacted linoleic acid and by-products of mCPBA not removed by extraction were all present in the reaction mixture containing LTX-diol and iso-LTX-diol (Figure 4A; peaks at 13.99 and 14.07 min). Solid-phase extraction on reverse-phase C18 cartridges (Waters Corp.) removed the straight-chain hydroxy fatty acids and separated LTX-diol and iso-LTX-diol from the other components (Figure 4B). Thus, we were able to purify an LTX-diol and iso-LTX-diol isomer mixture to near homogeneity by this procedure. The LTX-diol and iso-LTX-diol isomers were separated by HPLC (Figure 5), and the individual isomers were used for the cell proliferation assays.
Effects of synthetic LTX-diol and iso-LTX-diol on MCF-7 cell proliferation.
LTX-diol and iso-LTX-diol purified by HPLC (Figure 5) were added to tissue culture medium in 2 μL ethanol such that the final concentration varied from 0.1 to 10 μg/mol (0.32–1.6 μM; Figure 6). Both isomers stimulated MCF-7 human breast cancer cell proliferation to equivalent degrees at concentrations ranging from 0.32 to 1.6 μM. These concentrations of the LTX-diols are > 400-fold lower than those doses generally characterized as toxic in insect cell cultures in vitro (Hayakawa et al. 1986; Sisemore et al. 2001). Thus, at these lower concentrations, the LTX-diols are mitogenic, and we anticipate that the compounds will stimulate the proliferation of hormone-dependent and hormone-independent breast and prostate cancer cells if their mechanism of action is similar to that of the THF-diols (Mani et al. 2005; Markaverich et al. 2002a, 2002b). Although LTX-diol identified as the peak II component isolated from ground corn-cob in this study is an auto-oxidation product of linoleic acid in animals, a cytosolic epoxide hydrolase can metabolize the epoxy fatty esters to their vicinol diols (Halankar et al. 1989). This same enzyme exists in plants, as well (Blee and Shuber 1992), and therefore, it is feasible that epoxy fatty acids, known to be abundant in the plant kingdom, may be metabolized to vicinol diols.
LTX-diol competition for ER and nuclear type II sites.
Data in Figure 7 show that the LTX-diol mixture failed to compete for [3H]estradiol binding to rat uterine nuclear ER (Figure 7A) or nuclear type II sites (Figure 7B). These data are consistent with results obtained with partially purified ground corncob extracts containing both the THF-diols and LTX-diols (Markaverich et al. 2002a, 2002b). Thus, it is unlikely that the effects of these compounds on cellular proliferation or biologic response in vivo in male or female rats involve binding interactions with either ER or type II sites.
Effects of LTX-diols on the estrous cycle of female rats and male sexual behavior.
Adult female rats displayed normal 4.6 ± 0.25 day estrous cycles for 30 days when maintained on tap water/5.0% Tween 80 vehicle (Figure 8). Administration of LTX-diols to these animals at a daily dose level of >> 0.8 mg/kg body weight (2 μg/mL drinking solution) for 30 days disrupted the estrous cycle in 100% of the animals (Figure 8); this disruption was evident within the first 3 or 4 days after treatment and was sustained for the 30-day treatment period. These animals displayed vaginal smears resembling atypical sustained metestrus as described for THF-diols (Markaverich et al. 2002a, 2002b). Consequently, these atypical smears were not appropriate for differential cell count analyses to define a potential mechanism of action of these compounds; we are currently performing detailed mechanistic studies with these compounds in vitro. We found no significant treatment effects on body weights (controls = 265 ± 8.7 g; LTX-diol = 243 ± 4.8 g) or fluid consumption (controls = 38.85 mL/day/rat; LTX-diol = 38.75 mL/day/rat). Based on fluid consumption, the LTX-diol–treated female rats consumed >> 0.7 ± 0.072 mg LTX-diol/kg body weight per day. This higher dose level than that used for the THF-diols (>> 0.3 mg/kg body weight per day) was due to the difference in fluid volume consumption by the animals in the two studies (Markaverich et al. 2002a, 2002b).
Treatment of established breeder males with the LTX-diol drinking solution for 1–4 weeks had no significant effect on mounting, intromission, ejaculation, ejaculation latency, or grooming behavior (data not shown). These results are in sharp contrast to studies with similar doses of THF-diols, which completely blocked male sexual behavior (Mani et al. 2005; Markaverich et al. 2002a, 2002b). These findings suggest that sex differences may exist in responses to the THF-diols and the LTX-diols, or that higher doses of the LTX-diols are required to block male sexual behavior. More definitive dose–response and time studies to address these possibilities are in progress. Although we have not directly quantified the levels of LTX-diol or THF-diol in corncob bedding by GC-MS, it is clear from HPLC analysis of a number of corncob extracts that the concentration of these disruptor/mitogenic agents in different lots or varieties of corncob, and genetically engineered corn, vary significantly (Markaverich BM, Alejandro MA, Crowley JR, unpublished data). Thus, these differences in the relative levels of these compounds in ground corncob may serve to limit or enhance exposure and potential toxicity.
Discussion
Previous studies from our laboratories described the presence of an endocrine disruptor in ground corncob bedding that blocked male and female sexual behavior and cyclicity (Mani et al. 2005; Markaverich et al. 2002a, 2002b) in rats. The endocrine-disruptive activity copurified on HPLC with two peaks of mitogenic activity, leading us to believe that the mitogenic agents and endocrine disruptors were the same compounds. We identified the first mitogenic HPLC peak component(s) as the THF-diols (Figure 1). These compounds were synthesized and shown to stimulate MCF-7 breast cancer cell proliferation in vitro and block reproductive behavior and cyclicity in rats at doses in the range of 0.35–0.7 mg/kg body weight (Mani et al. 2005; Markaverich et al. 2002a, 2002b). In the present study we identified a second pair of mitogenic agents (LTX-diols) in HPLC peak II (Figure 2) from corncob bedding that also disrupt the estrous cycle of rats (Figure 8). The LTX-diols maximally stimulate MCF-7 human breast cancer cell proliferation at doses (0.1 μg/mL) that are 50-fold lower than those of the THF-diols (5 μg/mL), suggesting that the compounds have significantly different mitogenic activities (Markaverich et al. 2002a). The LTX-diols were approximately equivalent to THF-diols [0.3 mg/kg/day (Markaverich et al. 2002a)] in terms of endocrine-disruptive activities. When female rats were given the vehicle for 30 days, they displayed normal estrous cycles (4.6 ± 0.25 days); however, consumption of >> 0.7 mg/kg LTX-diol and iso-LTX-diol disrupted cyclicity within 3 or 4 days, and this suppression was sustained throughout the 30-day treatment period. Vaginal smears taken from the LTX-diol–treated animals indicated that a prolonged atypical metestrous state was induced, as previously observed for the THF-diols (Mani et al. 2005; Markaverich et al. 2002a, 2002b). No significant treatment effects on fluid consumption or body weights of the animals were observed, suggesting that systemic toxicity was not a problem.
The doses of LTX-diols required to block estrus in the present studies were equivalent to the dose level of THF-diols administered to adult male and female rats (0.35–0.75 mg/kg body weight) to block sexual behavior and cyclicity (Mani et al. 2005; Markaverich et al. 2002a, 2002b). We suspect the LTX-diols and THF-diols are acting through similar mechanisms. The actual concentration of THF-diols and LTX-diols administered in all of these studies was 2 μg/mL of the Tween 80 drinking solution. The difference in daily doses delivered to the male and female rats in the various studies is attributed to differences in body weights and fluid consumption volumes. Because we observed a virtual 100% block of estrous cyclicity by LTX-diols in the present studies, and of male and female sexual behavior and cyclicity in response to the THF-diols in previous studies (Mani et al. 2005; Markaverich et al. 2002a, 2002b), the actual concentration of these compounds required to block biologic response may be lower than that used here. Once we have sufficient quantities of the compounds on hand, extensive dose studies will be completed with the individual compounds alone and combined to determine whether synergistic interactions exist.
Surprisingly, the LTX-diol isomers did not block male sexual behavior in the present studies. This is in marked contrast to data obtained with the THF-diols (Mani et al. 2005). At present, we have no explanation for this discrepancy other than that there may be dose or sex differences with respect to the response to the LTX-diols and/or differences in the biologic fate of these compounds. It is clear that both the LTX-diols (Figure 8) and THF-diols (Markaverich et al. 2002a) block estrous cyclicity. Housing adult female rats on corncob bedding blocked female sexual behavior (lordosis), but we have not evaluated the effects of the LTX-diols on this parameter. Studies are under way to delineate effects of the LTX-diols and THF-diols on male and female sexual behavior and to define the biologic fate and mechanism of action of these compounds in both sexes. The THF-diols are very closely related to the LTX-diols (both likely evolving from linoleic acid pathways), and we suspect that these compounds are modulating gonadotrophic-hormone–releasing hormone (GHRH) release through nitrous oxide (NO)–dependent pathways. Leukotoxins stimulate NO release from mammalian cells (Ishizaki et al. 1995), and NO mediates male and female sexual behavior (Hull et al. 1994; Ishizaki et al. 1995) via stimulating GHRH release from hypothalamic neurons (Moses 1999). Therefore, it is possible that the THF-diols and LTX-diols disrupt endocrine function via hypothalamic pathways involving GHRH release. We are currently exploring these possibilities.
Similarly, we do not yet understand the mechanisms underlying the mitogenic activities of the THF-diols or LTX-diols in human cancer cells in vitro or in vivo. However, in vivo pathways may involve the modulation of hypo-thalamic–pituitary–gonadal axes and lipid metabolism as well. If the THF-diols or LTX-diols modulate luteinizing-hormone–releasing hormone release via pathways related to NO synthase, phospholipase A (PLA), cyclooxygenase (COX), or lipoxygenase (LOX), the compounds may directly or indirectly control breast or prostate cancer growth and proliferation via modulation of gonadotropin release and/or ovarian or testicular steroidogenesis. Effects on male and female sexual behavior and cyclicity are certainly consistent with this notion (Mani et al. 2005; Markaverich et al. 2002a, 2002b). In addition, although the THF-diols and LTX-diols on ground corncob extracts stimulate estrogen-dependent (MCF-7 cells) cell proliferation, the compounds also stimulate cell proliferation in estrogen-independent breast cancer (MDA-MD-231 cells) and prostate cancer (LNCap vs. PC-3 cells) cell lines (Markaverich et al. 2002a, 2002b) in vitro. These findings imply direct effects on nonestrogenic cellular pathways controlling cell proliferation, as well. An association also exists between COX activity and prostaglandin E2 induction of aromatase in MCF-7 breast cancer cells. Thus, the potential exists for generating estrogens required for cell proliferation (Brueggemeir et al. 2001; Richards et al. 2002). Because THF-diols and/or LTX-diols may affect COX pathways involved in prostaglandin synthesis, it is also possible that these compounds modulate aromatase. Tetradecanoyl phorbol acetate (TPA) induction of COX activity in MDA-MB-231 cells results in enhanced proliferation (Brueggemeir et al. 2001; Richards et al. 2002), and the THF-diols and /or LTX-diols may also control the proliferation of ER-negative cells by modulating COX activity. The dose–response data in Figure 5 are certainly consistent with the regulation of enzymatic pathways, where lower doses might be expected to stimulate enzymatic activity and higher concentrations may inhibit enzymatic activity via substrate inhibition. Alternatively, there may be intracellular receptors for the THF-diols and LTX-diols that would explain their mitogenic activities at lower concentrations and inhibitory properties at higher concentrations (Markaverich et al. 2002a, 2002b). Classical bell-shaped dose–response curves are typically seen for compounds such as estradiol that mediate their effects via ER binding mechanisms (Clark and Peck 1979). Thus, it is certainly possible that intracellular receptors exist for the THF-diols and/or LTX-diols, and we will be evaluating these possibilities.
Epidermal growth factor (EGF) stimulation of cell proliferation involves membrane-associated PLA-mediated release of arachidonic acid and linoleic acids from the cell membrane. The conversion of these fatty acids to prostaglandins (Nolan et al. 1988) or linoleic acid metabolites [9-hydroxyoctadecadienoic acid (9-HODE), 12-HODE, and 13-HODE] mediates EGF stimulation of [3H]thymidine incorporation into DNA (Glasgow and Eling 1990, 1994), cell cycle transition, and apoptosis (Durgam and Fernandes 1997; Kachhap et al. 2000; Tong et al. 2002). Breast cancer specimens contain higher concentrations of PLA than do benign breast tissues, and low PLA activity is associated with longer disease-free interval and survival even though no relationship was noted between PLA and ER or progesterone receptor status (J. Yamashita et al. 1995; S. Yamashita et al. 1993, 1994). In MCF-7, MCF-10, and MDA-MB-231 human breast cancer cells, LOX, but not COX, inhibitors block EGF/transforming growth factor α stimulation of 12-HODE, and 13-HODE production and cellular proliferation (Natajaran et al. 1997; Reddy et al. 1997). A number of LOX inhibitors including nordihydroguaiaretic acid, baicalein, and Rev-5901 inhibit MCF-7 and MDA-MB-231 cell proliferation and induce apoptosis. LOX products [5-eicosatrienoic acid (5-HETE), 12-HETE] reverse these effects (Natajaran et al. 1997). EGF stimulation of MCF-7 cell proliferation causes a dose-dependent increase in the formation of LOX products, including 12-HETE (Tong et al. 2002). Thus, LOX products (HODEs, HETEs) stimulate proliferation of these cells. THF-diols and LTX-diols are derived from linoleic acid pathways that generate HODEs and HETEs. It is possible that the THF-diols and LTX-diols modulate cellular proliferation by controlling the synthesis of these linoleic acid metabolites and/or by mimicking these compounds as mitogenic agents.
In addition to their effects on endocrine-regulated pathways and tumor cell proliferation (Mani et al. 2005; Markaverich et al. 2002a, 2002b), exposure to THF-diols and LTX-diols may cause additional health problems. Hydroxy fatty acids are substrates for liver glucuronosyltransferases (Jude et al. 2001; Street et al. 1996). Although this pathway is a probable means of detoxification of dihydroxy fatty acids, compounds such as LTX-diol are subject to inhalation and may be trapped in the lung as the free hydroxy fatty acids. Farmers are at high risk for respiratory problems and account for 30% of adults suffering from respiratory illness. Risk factors are believed to include exposure to fungus, molds, pesticides, organic dust from a variety of areas including animal bedding, and silicosis (Kansas State University Cooperative Extension Service 1981; North Carolina Cooperative Extension Service 1995). Uncharacterized chemical agents in these materials may include the LTX-diols and THF-diols. In addition to being used as bedding for small animals, ground or milled corncob is also used as adsorbent for chemical spills, a polishing agent for metals, and a pesticide carrier for insects such as spider mites and fire ants. This product is also used as cat litter. Thus, exposure of the general public to toxic agents in ground corncob is likely. Clearly, these fatty acid diols stimulate breast cancer cell proliferation in vitro and disrupt reproductive function in rats at relatively low concentrations. Sustained exposure to such compounds may represent a significant health hazard.
This work was supported by grants to B.M.M. from the National Institute of Environmental Health Sciences (ES09964), the Office of Research on Women’s Health, the National Cancer Institute (CA-35480), the American Institute of Cancer Research (98A077), and the National Council of Research Resources of the National Institutes of Health (P41-RR-00954).
Figure 1 Structures of LTX-diols and THF-diols.
Figure 2 Reverse-phase HPLC of mitogenic activity from ground corncob bedding. An aliquot (2 mL) of an ethyl acetate extract of ground corncob bedding was injected onto a Dynamax C8 column eluted as described in “Materials and Methods.” and 1-minute fractions were collected. Aliquots of the fractions were dried and assayed for mitogenic activity in cultured MCF-7 human breast cancer cells. The peak of mitogenic activity at approximately 45 min (peak I) was previously identified as THF-diols, and the peak of activity at approximately 70 min (peak II) was collected and analyzed by GC-MS.
Figure 3 GC-MS analysis of HPLC peak II component isolated from ground corncob. An aliquot of the pooled peak II fractions (Figure 1) was derivatized with BSTFA to generate the trimethylsilyl ether and chromatographed on a DB-1 column as described in “Materials and Methods.“ (A) Total ion chromatogram for this sample. (B) Electron ionization spectrum for peak at 14.53 min.
Figure 4 GC-MS analysis of synthetic LTX-diol. The LTX-diol mixture, synthesized as described in “Materials and Methods,” was derivatized and analyzed as described in Figure 2. (A) Total ion chromatogram of the sample before cleanup on C18 reverse-phase resin. (B) Total ion chromatogram after cleanup on the C18 reverse-phase resin.
Figure 5 Separation of LTX-diol isomers by reverse-phase HPLC. The synthetic mixture of LTX-diol and iso-LTX-diol shown in Figure 3 was separated and detected as described in “Materials and Methods.”
Figure 6 Effects of the LTX-diol isomers on MCF-7 human breast cancer cells grown for 7 days in the absence or presence of of LTX-diol or iso-LTX-diol. Cell number was determined by hemocytometer counts.
*Significantly different from controls or other LTX-diol dose levels (p < 0.001). Data represent the mean ± SEM for at least 12 determinations.
Figure 7 Synthetic LTX-diol competition for ER (A) and nuclear type II sites (B), as described in “Materials and Methods.”
Figure 8 Effects of LTX-diols on the estrous cycle of adult female rats (n = 8). See “Materials and Methods” for details.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8315ehp0113-00170516330351ResearchHousehold Disposal of Pharmaceuticals as a Pathway for Aquatic Contamination in the United Kingdom Bound Jonathan P. Voulvoulis Nikolaos Centre for Environmental Policy, Imperial College London, London, United KingdomAddress correspondence to N. Voulvoulis, Department of Environmental Science and Technology, Imperial College London, London, SW7 2BP UK. Telephone: 44-207-594-7359. Fax: 44-0-2075810245. E-mail:
[email protected]. received support from the Holly Hill Trust.
The authors declare they have no competing financial interests.
12 2005 9 8 2005 113 12 1705 1711 13 5 2005 9 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Pharmaceuticals are produced and used in increasingly large volumes every year. With this growth comes concern about the fate and effects of these compounds in the environment. The discovery of pharmaceuticals in the aquatic environment has stimulated research in the last decade. A wide range of pharmaceuticals has been found in fresh and marine waters, and it has recently been shown that even in small quantities, some of these compounds have the potential to cause harm to aquatic life. The primary pathway into the environment is the use and disposal of medicines; although much of the research in the area currently focuses on the removal of pharmaceuticals during sewage treatment processes, disposal via household waste might be a significant pathway requiring further research. To investigate the household disposal of unused and expired pharmaceuticals as a source of pharmaceutical compounds in the environment, we carried out a survey and interviewed members of 400 households, predominantly from southeastern England. We used the information on when and how they disposed of unfinished pharmaceuticals to construct a conceptual model to assess the pathways of human pharmaceuticals into the environment. The model demonstrated that disposal of unused pharmaceuticals, either by household waste or via the sink or toilet, may be a prominent route that requires greater attention.
drugsprescriptionsrisk assessmentsurveywastewater treatment
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The presence and potential adverse effects of pharmaceuticals in the aquatic environment have begun to receive increasing attention in the popular and scientific press in recent years. This increase is largely a result of a number of scientific papers published in the 1990s that reported trace levels of pharmaceuticals detected in environmental samples, including sewage effluent, surface water, groundwater, and even drinking water, mainly in European countries (Buser et al. 1998; Daughton and Ternes 1999; Halling-Sorensen et al. 1998; Heberer 2002; Jones et al. 2001; Kolpin et al. 2002; Ternes 1998; Ternes et al. 1999). The existence of pharmaceuticals in the U.K. aquatic environment has been established, but the extent of their distribution and the possible ecotoxicologic consequences associated with their presence are less clear. Pharmaceuticals are produced and used in very large volumes, and their use and diversity are increasing every year. Estimates based on the number of prescriptions issued suggest that around 100 tons of drugs were prescribed in Germany in 1995 (Ternes 1998). In the United Kingdom in 2000, use exceeded 10 tons/year for each of the top 25 compounds, and the amount of the top three compounds prescribed [acetaminophen (paracetamol), metformin hydrochloride, and ibuprofen] was > 100 tons/year each (Jones et al. 2002). Recent research has shown that these compounds could have a negative effect on the aquatic environment (Jones et al. 2003). Observed environmental effects are limited mainly to the feminizing activity of endocrine-disrupting compounds such as the synthetic hormone 17α-ethinyl estradiol on fish near wastewater treatment works (WWTW) outfalls (Jobling et al. 1998; Länge et al. 2001; Routledge et al. 1998). Other concerns include the development of antibacterial resistance either in or near WWTWs (Schwartz et al. 2002) or in the environment as a consequence of veterinary drug use (Petersen et al. 2002). Detection of these negative effects in the environment is difficult; although in vivo and in vitro laboratory tests generally show that the toxic effects of these compounds are not seen at the low levels currently detected in the environment, the possibility of variations in sensitivity, chronic exposure, and mixture effects such as concentration addition and synergism (Altenburger et al. 2004; Cleuvers 2004; Richards et al. 2004) mean that other negative effects cannot be ruled out. As a result, risk assessment guidance has been developed to predict the environmental impact caused by new pharmaceuticals [Bound and Voulvoulis 2004; European Agency for the Evaluation of Medicinal Products (EMEA) 2005; U.S. Food and Drug Administration (FDA) 1998].
There are two main routes for pharmaceuticals to enter the environment. The first is via the effluent from WWTWs after excretion from the body (Figure 1). After administration, a significant proportion of a pharmaceutical may pass through the body unmetabolized. The degree to which a compound is changed in the body depends on its structure and mechanism of action. The β-blocker nadolol may pass through the human body completely unmodified (RxList 2005a). In contrast, only 3% of the parent form of the antiepileptic carbamazepine is excreted unchanged in the urine (RxList 2005b); the rest may be conjugated or hydroxylated and also released in the feces. Release via this pathway is governed by the pharmacology of the drug and the efficiency of the WWTW. Excretion rates of many pharmaceuticals, such as those shown in Table 1, can be found in both medical (Martindale 1993) and environmental literature (Calamari et al. 2003; Jjemba, in press). The exact rates also depend on the dosage and the physiology of the individual.
Data on WWTW removal efficiencies are sparse and are largely dependent on the facilities at individual WWTWs and on variables such as local rainfall and temperature (Table 2). For example, only 9% of diclofenac was found to be removed by biologic filtration, whereas 75% was removed by activated sludge treatment (Stumpf etal. 1999). Because these data are required by the draft European Union guidelines on risk assessment (EMEA 2005), there will presumably be an increase in research in this area.
The second route by which pharmaceuticals can enter the environment is by the disposal of out-of-date or unwanted medicines, which may occur via the sink/toilet or in household waste that is then taken to landfill sites (Figure 1). Entry into the environment by this route is dependent on the habits of the patient and the efficiency of prescription practices leading to fewer unfinished prescriptions. Discarded pharmaceuticals are defined in the United Kingdom by the Controlled Waste Regulations 1992 [Her Majesty’s Stationery Office (HMSO) 1992] as clinical waste and as such are controlled by the Special Waste Regulations 1996 (HMSO 1996). According to this legislation, such waste may be disposed of in landfill sites designed to accommodate hazardous waste, or it may be incinerated. However, once dispensed to or purchased by a member of the public, any unwanted pharmaceutical products are classified as household waste, and their disposal is not subject to any controls. Manufacturer packaging usually recommends disposal by returning to the pharmacist; however, disposal via the sink/toilet or in normal household waste is common. Pharmaceuticals in landfill sites are subject to biologic degradation processes, but some may persist and even leach into surrounding groundwater and rivers (Ahel etal. 1998; Holm etal. 1995; Schwarzbauer etal. 2002).
An investigation into the disposal habits of the American public found that only 1.4% of the people they surveyed returned unused medication to the pharmacy, whereas 54% threw them away and 35.4% disposed of them in the sink/toilet (Kuspis and Krenzelok 1996). These methods of disposal result from U.S. Drug Enforcement Administration regulations that strictly control the transfer of drugs and controlled substances. It is possible for some institutions to return unwanted drugs via organizations affiliated with the Returns Industry Association, a group of licensed “reverse distributors” that offer a return and disposal service (Daughton 2003b). Although regulations vary among U.S. states, most pharmacies cannot accept returns from patients. Measures to allow the return of unused medication from long-term care facilities have been passed or are being considered in some states. Developments in legislation are listed on the National Conference of State Legislatures website (NCSL 2005). Some states will also allow the redistribution of drugs within their expiration date, although they do not permit the return of drugs by private individuals. This service is therefore limited largely to medicines that never leave pharmacies and care facilities.
A source of concern is that, at the pharmacies questioned, 68% of unreturnable medicines were disposed of in nonhazardous waste or via the drain. Traditionally, disposal advice to consumers has been limited to flushing down the toilet or, in some cases, burning or grinding and discarding in household waste (Pray and Pray 2004), but emerging environmental concerns mean that this is sometimes modified (Daughton 2003b). People are first advised to check whether local pharmacies or doctors are able to receive returns or whether hazardous waste facilities exist in the area. As a last resort, disposal in household waste is deemed to be less harmful than disposal via the sewage system (Boehringer 2004). A study by Braybrook et al. (1999), designed to examine ways to streamline the prescription process in order to reduce costs, looked at some of the reasons people gave for returning unused pharmaceuticals to the pharmacy. The most common reason was a change of medication. Most items (80%) were returned within a year of their prescription date, but some people returned the medicines only after the infrequent removal of unwanted items that have built up over time, with some products being returned 13 years after they were dispensed.
The aim of the present study was to identify and assess the significance of the different pathways of pharmaceuticals from the household to the environment. Knowledge of the motivation behind different disposal methods is useful in the management of the release of pharmaceuticals in the environment and in the assessment of the associated risk. This project aimed to demonstrate the possible importance of household disposal of unused medicines as a pathway into the aquatic environment.
Materials and Methods
Pharmaceuticals are a large and varied class of compounds with diverse properties and applications. To facilitate their study, they are often grouped by their therapeutic action. We targeted eight therapeutic groups in this study. We used various criteria, including volume of prescription, toxicity, and evidence of presence in the environment, in the selection process. Table 3 presents a summary of the factors that cause concern (risk indicators), with examples of pharmaceuticals within the groups.
A survey was devised to investigate disposal patterns of the eight selected groups of pharmaceuticals. This survey was part of a study into the disposal of household hazardous waste supported by the Environment Agency of England and Wales (Slack et al. 2005b). Respondents were asked whether they ever had any of the types of medicines and when and how they disposed of them. Information about the age, sex, education, profession, and postal code of the respondent in order to assess socioeconomic status was collected. Respondents gave their written informed consent to this information being used anonymously in our study. Only closed-ended questions were used, with the questioner specifying possible answers. These questions have the advantages of being quick to administer, easy to answer, and easier to analyze and interpret than are open-ended questions (Petersen 2000). Where list questions were employed, no limit was placed on the number of answers that could be given, so that respondents were not required to choose a single answer when it did not wholly represent their attitude or behavior.
Using Equation 1 and the method of McCall (1982), we calculated that the number of respondents required to obtain a representative sample, n, was 384. We divided the total population into four groups: those who lived in population centers of ≥250,000 (cities), 249,999–50,000 (very large towns), 49,999–10,000 (mid-sized towns), and < 9,999 (small towns/villages). The numbers of people estimated to live in each type of area were extrapolated down and applied to the sample size in order to achieve a representative spread:
where n is the estimated sample size required for desired precision and confidence, 384; πis the preliminary estimate of proportion opposed to this initiative within the population, 0.5; z is the two-tailed value of the standardized normal deviation associated with the desired level of confidence; and e is the desired precision, half the maximum acceptable confidence interval, here 0.05.
We used a model based on the flow diagram in Figure 1 to quantify the amount of pharmaceuticals that reach the environment by the various pathways shown. The division between the use and disposal of drugs is based on responses from subjects who said they finished the prescription and therefore had nothing to dispose of and those who said they disposed of drugs at another time (e.g., when the drug expired). Because it was difficult to collect information on the proportion of these medicines that were used before disposal, a number of assumptions had to be made. The main assumption was that subjects who said that they had some medicine to dispose of first consumed 50% of the prescription, disposing of the remaining 50%. We also assumed that all prescriptions of each individual drug contained the same quantity and strength of drugs. These assumptions limit the accuracy of the present model. The most reliable way to establish the proportion of prescriptions that remain unfinished and the method of disposal chosen would be to collect unwanted medicines directly from households.
As with any survey, the quality of the results depends on on the truthfulness of the responses. Forgetfulness and embarrassment about socially stigmatized medication, for example, may lead to misreporting and incorrect estimates. People may feel pressured to give the answers that they think are the “right” ones, those that are more socially acceptable, or those that they believe the questioner wants to hear. This was minimized by the passive questioning style, with as little prompting as possible. In a review of the accuracy of patient self-reporting, Evans and Crawford (1999) found mixed results: patient recall, as one might expect, was more reliable over short time periods and less so in elderly patients.
We did not request specific data about types and amounts of medication, so the reported data should be less affected by these problems. If such information was required, a patient diary for recording all incidences of medication would be the most effective way of obtaining it. The answers relating to behavior are less dependent on recall and more dependent on opinion and personal preference. However, a patient diary would also be useful in charting the methods of disposal and the exact volumes concerned. This project has served as a pilot study, establishing the need for more specific data, and a more detailed drug collection study is now under way to provide accurate information to supplement the proposed model.
The model uses data that describe the percentage of the parent compound that passes through the body unchanged. It is possible that conjugates could be hydrolyzed back to the parent compound in the environment. It is also possible that metabolic products could be more toxic in the environment than the parent compound. Where such data are available, they could be incorporated. They were not, however, incorporated in the model presented here.
Results
The survey was carried out in southeastern England during the summer of 2003. The minimum sample size was exceeded with 392 people interviewed (54.8% female, 45.2% male), closely reflecting the actual distribution in the United Kingdom (51.3% female, 48.7% male). The subjects were also spread evenly across age ranges and family types. Almost everyone (98%) had some type of pharmaceutical in their house; most (60.2%) had a mixture of over-the-counter (OTC) and prescription medicines, whereas 30.7% had only OTC medicines and 9.1% had only prescription medicines. Responses indicate that just more than half (52.8%) finish their medication and hence have none to dispose of. Around a third (30.7%) keep them until the expiration date, and 12.2% dispose of them when the treatment has been completed. Figure 2 describes the disposal of unwanted pharmaceuticals. Two-thirds (63.2%) discard them in household waste, with the remainder returning them to a pharmacist (21.8%) or emptying them into the sink or toilet (11.5%). A small number took them to municipal waste sites that sometimes have special waste facilities.
The data can be broken down into the eight selected pharmaceutical groups to show how behavior varies with respect to drug type (Table 4). Nearly 80% of people consume all of the painkillers that they buy or are prescribed, whereas the figure for antibiotics (18%) is worryingly low. Household waste was the most popular disposal method for all types of drugs. Although the average rate of sink/toilet disposal for all drug types is 11.5%, none of the 90 people who had hormone treatments admitted to flushing them down the toilet, with the number returning them to the pharmacy increasing accordingly.
The information on the disposal of two different types of pharmaceuticals, metoprolol and ibuprofen, along with figures on the elimination of the compound in the human body and WWTW removal efficiencies, was used to model the relative importance of the pathways into the environment. Metoprolol succinate is a β-blocker, mainly used in the treatment of high blood pressure. It is available only by prescription in the United Kingdom. Figure 3 is a mass balance flow chart showing the fate of 100 units of the parent compound. Only 46.8% of respondents who had been prescribed β-blockers said that they finished the prescription. Assuming, as previously stated, that those people took half of the medication, then 26.6 units are disposed of and 73.4 units of the active ingredient are consumed. Because 90% of the medication taken is modified by the body, this leaves 7.3 units of active ingredient that are introduced to the wastewater system (Huschek et al. 2004). When combined with the 4.4 units (16.7%) of drugs that are put down the drain, this results in a total of 11.7 units entering WWTWs. Here, 83% is removed (Ternes 1998), leaving 2 units to be discharged into surface water. Of the 26.6 units that are unused, 4.4 are returned to the pharmacy whereas 17.7 units, nearly 10 times as much as is released into the environment from WWTWs, are put into household waste that is subsequently taken to a landfill. Once there, some will be removed by biologic and chemical degradation within the landfill, some will be collected at leachate treatment plants and subjected to similar processes as in the WWTWs and then released into surface water, and some may leach directly into the surrounding groundwater and possibly rivers.
In the case of metoprolol, household disposal might be a significant pathway into the environment. This is because the drug is not removed or modified by the body, nor is it modified by WWTW processes. The literature currently reflects a bias toward research of WWTW treatment rather than landfill leachate that may not fully address the risks of pharmaceuticals to the environment. It is also important to note that the sludge generated during WWTW treatment may be itself land-filled or spread on agricultural land—the risk of pharmaceuticals is not necessarily removed, just moved. Millions of tons of sewage sludge are generated in the European Union every year. The proportion of the pharmaceutical load contained within the solid waste products of WWTWs depends largely on the properties of the drug, especially the octanol–water coefficient (KOW), which is an indicator of the likelihood that the compound will be partitioned into the solid phase. Other important interactions are the sorption to organic matter, surface adsorption to mineral constituents, ion exchange, complex formation with metal ions such as Ca2+, Mg2+, Fe3+, or Al3+, and hydrogen bonding (Diaz-Cruz et al. 2003). Once these “biosolids” have been spread on agricultural land or landfilled, degradation may continue, but there is also the potential for soil and groundwater contamination, runoff, and even adverse effects on plants or animals reared on the land (Jjemba 2002; Xia etal. 2005).
The same model applied to ibuprofen (Figure 4) shows that usage is a more prominent pathway than it is for metoprolol. Results of the survey showed that fewer people (20.8%) had any painkillers to dispose of. Assuming they consumed half of these, only 10.4 units require disposal. Therefore, even though from the model the rates of elimination in the body and WWTWs are comparable with those of metoprolol, the ratio of the active ingredient entering landfill sites compared with that entering surface water from WWTWs is 5.5:1 for ibuprofen (the ratio for metoprolol is 8.9:1). This demonstrates that both human behavior and pharmacologic properties of the active ingredient are important in assessing the significance of the different pathways into the environment.
Discussion
Despite advice on pharmaceutical packaging that recommends the return of unused medicines to pharmacies, or occasionally to flush them down the toilet, the predominant method of disposal in the United Kingdom was found to be via household waste. Although this result is similar to that found in the United States by Kuspis and Krenzelok (1996), the figures for those returning their unused medication to the pharmacy (21.8% in the United Kingdom compared with 1.4% in the United States) and those who disposed of the medicines down the toilet (11.5% in the United Kingdom and 35.4% in the United States) may reflect the disparity between regulations and advice in the two regions. The answers given to the survey conducted in the present study suggest that there may be a significant quantity of pharmaceuticals entering the household waste stream in the United Kingdom. This is of potential concern because medicines deposited in their original form in landfill sites bypass the human body and WWTWs. It is therefore possible that even though comparatively small quantities may travel by this pathway, it could have increased significance because of this avoidance of removal mechanisms. The variation in these removal rates makes it difficult to generalize the relevance of the different pathways into the environment for all medicines.
The model described in Figures 3 and 4 is intended to show that the household disposal of medicines is worthy of consideration in the risk assessment and management process. In its current form, this model is not capable of predicting the precise amounts of pharmaceuticals entering the environment by each pathway. However, with the limited information available, it does show that, under the conditions proposed, the disposal pathway is a potential cause for concern and should figure more prominently in the investigations into the presence of pharmaceuticals in the aquatic environment. The model also shows how different drugs will favor different pathways. More than twice the percentage of people questioned said they disposed of β-blockers compared with painkillers. This could be due to changing prescriptions or the fact that people foresee a future use for painkillers. It could also be related to the patient’s perception of risk about the relative dangers of the two types of drugs (Bound et al., in press).
With other compounds, the dominant factor could be the metabolism or the stability within the WWTWs. The model gives figures for the proportion of the parent compound that passes through the body and the WWTWs unchanged. Some of the other products of these processes may also have ecotoxicologic properties. It may be possible to modify the model where further knowledge about the pharmaceutical is available. In the case of metoprolol, Huschek et al. (2004) found that some of the other metabolic products also showed pharmaceutical activity. If these are included in the calculation, a total of 34% of the drug was excreted in active forms. Where this information is known, it could also be included in the calculation. However, “active” refers to the pharmacologic properties that may not necessarily coincide with environmental toxicology.
The most straightforward way to eliminate the risk posed by the disposal pathway would be to reduce the quantity of drugs being improperly discarded. One possibility is to increase the prominence of product labeling and the provision of stronger advice on how to dispose of any remaining drugs. The results of the survey showed that, although half of people finished their prescriptions, reasons for disposal included expiration (30.7%) and completion of treatment before finishing the prescription (12.2%). This is understandable for OTC medications. However, in the case of prescription medication, this indicates that the instructions that accompany the prescription have not been adhered to, because completion of the treatment and the end of the prescription should coincide if normal practice is followed. This level of noncompliance (patient not following completely the instructions from their physician) is similar to estimates elsewhere (Donovan and Blake 1992).
Patients may deviate from recommendations for many reasons: they may be avoiding unpleasant side effects; they may believe that, because symptoms have been alleviated, there is no need to continue medication; or it could simply be forgetfulness. A possible solution would be to increase the information given to patients by doctors and pharmacists about the need to complete courses of medication and the importance of safe disposal when medicines remain unused. However, if up to 50% of patients do not follow advice that could have important impacts on their own health, will they be prepared to alter their behavior based on environmental concerns? A Canadian survey reported that, although > 50% of people said that they read the labels of OTC medications, only 2% said that they read product packaging to discover appropriate disposal methods. However, when directly prompted about disposal information, 57% stated that they did (COMPAS Inc. 2002). These factors would seem to undermine the efficacy of product labeling as a means of reducing improper disposal.
Investigations into environmental contamination via landfill leachate (Ahel et al. 1998; Eckel et al. 1993; Holm et al. 1995; Schwarzbauer et al. 2002; Slack et al. 2005a) are far less common than similar studies into pollution from WWTWs. They are also often concerned with sites that have received large quantities of pharmaceuticals in bulk as part of industrial disposal rather than just household waste. Modern landfill sites are usually equipped with linings capable of preventing a high proportion of leachate from escaping into the surrounding groundwater. Where this is the case, leachate treatment plants are often employed to reduce or remove harmful contaminants before their release into surface waters. These facilities are potentially capable of intensive waste management systems partly because of the low volumes involved compared with WWTWs or drinking-water plants. Such processes include ozonation, nanofiltration, and activated carbon adsorption (Wintgens et al. 2003; Wu et al. 2004). Advanced facilities such as these are not currently widespread, but they could be introduced to reduce the release of pharmaceuticals, pesticides, and endocrine disruptors into the aquatic environment. Older sites and those in developing countries are unlikely to have modern membrane liners to prevent leaching, although some may rely on natural geologic features to minimize groundwater contamination. Further studies on the concentrations of pharmaceutical compounds within landfill sites and in leachate would be informative, and if necessary, those sites not equipped with the necessary treatment facilities could be upgraded.
Current and proposed risk assessment guidelines in the European Union (EMEA 2005) and the United States (FDA 1998) do not consider the disposal pathway when calculating the predicted environmental concentrations of medicines. Applicants for new licenses could use studies such as the one presented here to predict the proportion of medicine that will be disposed of in general waste using figures for local prescription practices and public behavior. This approach may be considered too time-consuming when compared, for example, to earlier “worst-case scenario” approaches (EMEA 2001) that assume that all of the prescribed drug will end up in the surface water. But the more recent revisions account for removal in the body and WWTWs. This optimization of the process means that some compounds that might have been recommended for assessment under the earlier system will now be shown to be sufficiently safe because a high proportion of the compound is degraded to a less toxic form. However, if a significant proportion of the drug is not undergoing the transformation within the patient and WWTWs, there is a possibility that enough of the active ingredient would reach the environment to trigger further investigation. We believe that a complete risk assessment framework should give some consideration to the disposal pathway.
Daughton (2003a) advocated the development of a database to catalogue the distribution of prescription and OTC drugs (information on the latter in particular is difficult to obtain at the present). Regional variations in the supply of pharmaceuticals could be coupled with data on the metabolic breakdown and WWTW degradation (this could be locally optimized to include the type of treatment processes in use in a specific region, e.g., primary, secondary, activated sludge) to more accurately predict the release of a pharmaceutical in the environment. Furthermore, this information could be combined with data on the disposal of unused medicines, as proposed in this study. Where facilities exist, information on returns from pharmacies and hospitals could also be incorporated to provide a more effective method for the prediction of environmental concentrations. Knowledge about the presence of drugs in household waste could benefit the management of risks to the environment. Minimizing the disposal pathway could be more effective and less costly than extensive WWTW modifications or other remediation steps.
Figure 1 Pathways of drug fate from domestic households to the environment.
Figure 2 Subjects’ usual disposal methods for pharmaceuticals.
Figure 3 The fate of metoprolol by units used.
Figure 4 The fate of ibuprofen by units used.
Table 1 Urinary excretion rates of unchanged active ingredient for selected pharmaceuticals.
Drug Therapeutic class Parent compound excreted (%) Reference
Ibuprofen Painkiller 10 Dollery 1991
Paracetamol Painkiller 4 Huschek et al. 2004
Amoxycillin Antibacterial 60 Martindale 1993
Erythromycin Antibacterial 25 Huschek et al. 2004
Sulfamethoxazole Antibacterial 15 Hirsch et al. 1999
Atenolol β -Blocker 90 Dollery 1991
Metoprolol β -Blocker 10 Huschek et al. 2004
Carbamazepine Antiepileptic 3 Huschek et al. 2004
Felbamate Antiepileptic 40–50 RxList 2005c
Cetirizine Antihistamine 50 RxList 2005d
Bezafibrate Lipid regulator 50 Ternes 1998
Table 2 Removal of selected pharmaceuticals in WWTWs.
Drug Percent WWTW removal Treatment process Reference
Bezafibrate 99.5 Activated sludge Kreuzinger et al. 2004
83 Activated sludge Ternes 1998
50 Activated sludge Stumpf et al. 1999
27 Biologic filter Stumpf et al. 1999
Carbamazepine 10 Activated sludge Kreuzinger et al. 2004
7 Activated sludge Ternes 1998
Diclofenac 75 Activated sludge Stumpf et al. 1999
69 Activated sludge Ternes 1998
9 Biologic filter Stumpf et al. 1999
0 Average of 7 WWTWs Lee et al. 2003
17α -Ethinyl estradiol 78 Activated sludge Ternes et al. 1999
64 Biologic filter Ternes et al. 1999
Gemfibrozil 69 Activated sludge Ternes 1998
46 Activated sludge Stumpf et al. 1999
16 Biologic filter Stumpf et al. 1999
5 Average of 7 WWTWs Lee et al. 2003
Ibuprofen 99 Activated sludge Kreuzinger et al. 2004
90 Activated sludge Ternes 1998
87 Average of 7 WWTWs Lee et al. 2003
80–100 Activated sludge Kanda et al. 2003
75 Activated sludge Stumpf et al. 1999
60–70 Activated sludge Carballa et al. 2004
65 Biologic filter Rodriguez et al. 2003
22 Biologic filter Stumpf et al. 1999
14–44 Biologic filter Kanda et al. 2003
Indomethacin 40 Average of 7 WWTWs Lee et al. 2003
Ketoprofen 69 Activated sludge Stumpf et al. 1999
48 Biologic filter Stumpf et al. 1999
18 Average of 7 WWTWs Lee et al. 2003
Metoprolol 83 Activated sludge Ternes 1998
Naproxen 78 Activated sludge Stumpf et al. 1999
70 Average of 7 WWTWs Lee et al. 2003
66 Activated sludge Ternes 1998
40–55 Activated sludge Carballa et al. 2004
45 Biologic filter Rodriguez et al. 2003
15 Biologic filter Stumpf et al. 1999
Propranolol 96 Activated sludge Ternes 1998
Sulfamethoxazole 67 Activated sludge Carballa et al. 2004
Table 3 Selected pharmaceutical groups and their environmental risk indicators.
Drug Examples Risk indicator References
Painkillers NSAIDS (e.g., ibuprofen), other analgesics (e.g., acetaminophen) Very high prescription and OTC volumes; detected in the environment Buser et al. 1999
Antibiotics Penicillins, sulfamethoxazole High volumes; detected in the environment; concerns over toxicity and antibacterial resistance Berger et al. 1986
Hirsch et al. 1999
Leff et al. 1993
Wollenberger et al. 2000
β -Blockers Propranolol, metoprolol High volumes; detected in the environment Calamari et al. 2003
Ternes 1998
Antiepileptics Carbamazepine, phenobarbital High volumes; long-term prescriptions; persistent Andreozzi et al. 2002
Lipid regulators Statins (e.g., atorvastatin), clofibrate Long-term prescriptions; commonly detected Buser et al. 1998
Heberer et al. 1997
Antidepressants Fluoxetins, risperidone Subject of toxicity testing Brooks et al. 2003
Hormone treatments Contraceptive pills, 17α -ethinyl estradiol, hormone replacement Most extensively studied toxicologic properties; widely detected Arcand-Hoy et al. 1998
Länge et al. 2001
Purdom et al. 1994
Rodgers-Gray et al. 2000
Antihistamines Loratadine, cetirizine Commonly held nonprescription medicine
Abbreviations: NSAIDS, nonsteroidal anti-inflammatory drugs; OTC, over-the-counter.
Table 4 Disposal characteristics (%) based on drug type.
When
How
Drug Present Empty Expired Treatment finished Other Trash bin Sink/toilet Pharmacy Other
Painkiller 94.1 79.2 18.4 2 0.4 69.6 10.9 18.5 1
Antihistamine 45.9 61.4 33 3.7 1.9 75.3 9.1 14.3 1.3
Antibiotic 56.4 17.6 10.5 69.9 2.1 71.4 3.6 14.3 10.7
Antiepileptic 2 66.7 22.2 11.1 0 100 0 0 0
β -Blocker 11.2 46.8 12.8 38.3 2.1 66.7 16.7 16.7 0
Hormone 23.2 68.1 4.3 26.6 1.1 75 0 25 0
Lipid regulator 6.9 41.4 6.9 51.7 0 66.7 0 0 33.3
Antidepressant 9.7 53.7 14.6 29.3 2.4 66.7 0 33.3 0
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8154ehp0113-00171216330352ResearchInfluence of Geographic Location in Modeling Blood Pesticide Levels in a Community Surrounding a U.S. Environmental Protection Agency Superfund Site Gaffney Shannon H. 12Curriero Frank C. 3Strickland Paul T. 1Glass Gregory E. 4Helzlsouer Kathy J. 5Breysse Patrick N. 11 Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA2 ChemRisk, Inc., San Francisco, California, USA3 Department of Biostatistics,4 Department of Molecular Microbiology and Immunology, and5 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USAAddress correspondence to S.H. Gaffney, 25 Jessie St., Suite 1800, San Francisco, CA 94611 USA. Telephone: (415) 618-3223. Fax: (415) 896-2444. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 17 8 2005 113 12 1712 1716 30 3 2005 17 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In this study we evaluated residential location as a potential determinant for exposure to organochlorine compounds. We investigated the geographic distribution characteristics of organochlorine levels in approximately 1,374 blood samples collected in 1974 from residents of a community with a potential organochlorine source. Street addresses of Washington County, Maryland, residents were obtained and geocoded in a geographic information system. We used multivariate linear regression models to characterize the blood organochlorine levels of these residents that had been analyzed as part of previous studies using both environmental- and individual-level covariates. This was done to evaluate if the geographic distribution of blood levels in participants was related to the environmental source in the community. Model inference was based on generalized least squares to account for residual spatial variation. A significant inverse relationship was found between blood dieldrin levels and residential distance from the potential source. For every mile of distance from the source, blood dieldrin levels decreased 1.6 ng/g in study participants (p-value = 0.042), adjusting for age, sex, education level, smoking status, and drinking water source. 1,1-Dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE) levels in the blood did not change significantly based on residential distance from the source, taking the same covariates into account. However, these results are limited by the inability to account for several potential confounders. This study demonstrates that spatially distributed covariates may play an important role in individual exposure patterns. Spatial information may enable researchers to detect a potential exposure pattern that may not be revealed with only nonspatial variables.
biomarkersDDEDDTdieldringeostatisticsorganochlorinesspatial statisticsSuperfund
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Spatial information has long been used to study the environmental contamination patterns of persistent organochlorine pollutants. These environmental data are often used as surrogates for exposure experienced by the studied community. However, organochlorine levels may also be measured in serum, providing a more accurate account of exposure. Because we can also link spatial information, such as location of residence, to blood donors, spatially evaluating biomarkers of exposure is a logical extension to investigating spatial patterns in environmental media.
In the early 1930s, a large chemical company built a 19-acre facility in the city of Hagerstown in Washington County, Maryland, for the production of fertilizers and formulation of pesticides, including 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane (DDT) and dieldrin. Site investigations since the 1970s have indicated the presence and migration of organochlorine pesticides, such as DDT and dieldrin, as well as other toxicants to off-site areas, and the U.S. Environmental Protection Agency (EPA) placed the site on the National Priority List for cleanup in 1997 as a Superfund site [Maryland Department of the Environment (MDE) 1993, 1994, 1995, 1996; Roy F. Weston Inc. 1997]. A more detailed description of the site can be found elsewhere (Henshaw 2004; Henshaw et al. 2004).
In this study we investigated the relationship between the location of the homes of Washington County residents, their proximity to the Superfund site, and the levels of organochlorine compounds in the blood of the residents. Spatial and other known covariates are evaluated in multivariate linear regression models of blood organochlorine levels. Residual spatial variation from these regressions that is not accounted for by the model is further evaluated using generalized least squares (GLS) with a spatial correlation structure so as to provide proper estimation of effect standard errors and corresponding tests of significance.
Materials and Methods
Data.
More than 20,000 adult Washington County residents (roughly one-third of the county population) signed written consent forms to donate blood for research purposes as part of the Campaign Against Cancer and Stroke (CLUE I) in the fall of 1974 (Comstock et al. 1991). A subset of these samples (n = 1,391) was analyzed for organochlorine compounds to examine the association between concentrations of these compounds and subsequent cancer (Cantor et al. 2003; Helzlsouer et al. 1999; Rothman et al. 1997). All samples were assayed for DDT, 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE), and polychlorinated biphenyls (PCBs). About half of these samples were also assayed for additional organochlorines such as dieldrin. Details concerning the blood collection, storage, and analytical methods have been published elsewhere (Cantor et al. 2003; Helzlsouer et al. 1999; Rothman et al. 1997). This study was approved by the Johns Hopkins Committee on Human Research.
We used 1,391 blood samples assayed for organochlorine concentrations. Blood-sample organochlorine concentrations from four subjects whose blood was assayed twice were averaged for analysis purposes. Thirteen subjects were found to reside outside of Washington County at the time of blood draw and were removed from the sample pool. In addition, one sample had reported DDE values > 2.25 times higher than the next highest reported value, and was therefore considered a reporting error and removed from analysis. Ultimately, a total of 1,374 samples were considered valid for this study.
Street addresses and ZIP codes of the study participants were collected as part of the CLUE campaign. ArcGIS 8.2 (Environmental Systems Research Institute, Inc. 2002) software was used to geocode the addresses, providing a corresponding set of longitude and latitude coordinates. The geocoding process employed several base maps (Boscoe et al. 2002; McElroy et al. 2003). StreetMap 2000 (Environmental Systems Research Institute 2000), the U.S. Census Bureau 2000 Topologically Integrated Geographic Encoding and Referencing (TIGER) street maps (U.S. Census Bureau 2002), and the Delorme Street Atlas software (Delorme 2003) were all used to maximize geocoding results. In situations where several study participants had the same residential address, such as in an apartment building, the geocoded coordinates were altered slightly by adding 1-foot increments to each of the longitude and latitude coordinates, ensuring that all participants had a unique location and that no two participants were grouped as one when mapping. The Superfund site address was also geocoded. Details regarding this geocoding process are reported elsewhere (Henshaw 2004).
In addition to geocoded address information, we obtained information on participant demographics, such as age, race, sex, education level, marital status, and district average socioeconomic status; variables that have been shown to be predictive of blood organochlorine levels, such as smoking status (current smoker at the time of blood draw); and drinking-water source (municipal/well/spring) (Acquavella et al. 1986; Fitzgerald et al. 1999; Glynn et al. 2003; James et al. 2002; Moysich et al. 2002; Rivero-Rodriguez et al. 1997; Soliman et al. 2003; Sweeney et al. 2001; Wariishi et al. 1986). These data were abstracted from CLUE-based questionnaires and a private Washington County census. The spatial variables of direction and distance from the Superfund site to the residence as well as urban/rural location of residence were created and added to the list of potential covariates. The distance-to-site variable was created to represent a proxy for unknown and unmeasured unique contributions from the source. Urban residence is defined in this study as living within 1.5 miles of the center of Hagerstown. A direction variable was created as a series of indicator variables denoting 24 directional bins (every 15 degrees) around the site. This direction variable was then considered a potential effect as an interaction term, with distance thus allowing a separate distance effect for each direction.
Because DDT breaks down to DDE in the blood, DDE levels were chosen to represent DDT exposure [Centers for Disease Control and Prevention(CDC) 2003]. Total PCBs used for analysis in this study refers to the sum of PCB congeners 105, 118, 146, 153, 156, 170, and 180. Dieldrin was also chosen for analysis to represent an additional compound that was used at the Superfund site.
Blood samples were nonfasting. All compounds were analyzed both unadjusted for lipid content and lipid-adjusted using the method of Phillips et al. (1989). The analytical labs reported levels below the official limits of detection (LODs); reported levels were used because they are considered more valid than an interpolated value (Cantor et al. 2003; Rothman et al. 1997). The LOD divided by the square root of 2 was used for the unreported values below detection (< 5% of the samples were below the LOD) (Hornung and Reed 1990).
Statistical analysis.
Levels of DDE, PCBs, and dieldrin in participants’ blood were mapped using their geocoded coordinates to study the spatial distribution of the levels of these organochlorines and their possible relationship to the Superfund site. Spatial structure in the levels of organochlorines was further explored using estimated semivariograms (Cressie 1991). Semivariograms were also estimated for regression residuals as a diagnostic check on the independence assumption inherent in ordinary least squares (OLS) inference.
Multivariate linear regression was used to develop models that best describe the blood levels of each organochlorine, both lipid adjusted and unadjusted. These models are of the form
where s denotes spatial coordinates, Y(s) represents blood organochlorine levels of participants residing at location s, X1(s) . . . Xn(s) are covariates (including possible interactions) indexed by location s, β1 . . . βn values are their associated effects, and β0 is the baseline intercept. The residual error term ɛ(s) was assumed to be normally distributed with a zero mean and constant variance. To further account for possible residual spatial variation, residuals were allowed to be spatially dependent by parameterizing their correlation as a decreasing function of the distance between their locations. In the geostatistical literature, model 1, with these specifications, is known as a universal kriging model commonly used for spatial prediction at unobserved or unmeasured locations (Cressie 1991).
We began to select models for blood levels of each organochlorine by running all possible models derived from each combination of covariates considered as regression main effects as well as investigating univariate relationships between each covariate and the outcome variable. All covariates were checked for colinearity, and those found to be correlated with one another were evaluated separately in the models to determine which were the best predictors. The fraction of variance explained by the model adjusted for the number of explanatory variables (adjusted R2) was used to rank model performance. The top-performing portion of models was then investigated further for significant interactions among the included covariates. The final models were chosen based on model parsimony and scientifically meaningful interpretations. All exposure determinants, geographic or not, were considered on an equal setting before developing the regression models.
All regression inference at this step was based on OLS regression assuming uncorrelated or independent residuals, which is possibly not a valid assumption. If residual spatial variation exists (an assumption that can be evaluated), then OLS estimates and corresponding tests of significance can lead to invalid results (Diggle et al. 1998a). We used OLS because the objective was first to arrive at a manageable set of plausible final models and then to investigate and correct for residual dependence. OLS methods for estimating regression parameters in models with dependent residuals could lead to spurious significant inclusion of covariates, which would then be reevaluated adjusting for residual spatial dependence (Diggle 2000; Diggle et al. 1998a).
The final models were adjusted for possible residual spatial variation (Carroll et al. 1988; Cressie 1991). Semivariograms were estimated from the OLS model residuals as a diagnostic check for residual spatial variation (Cressie 1991). This was affirmed in all models considered, and we selected the exponential spatial correlation function, routinely applied in spatial statistics, as the function best characterizing the residual spatial variation (Cressie 1991). We also examined directional dependence in the residual spatial variation (anisotropy) by estimating directional dependent semivariograms. The results consistent across all models suggested that the assumption of spatial isotropy better characterized the residual variation. Fitting individual variograms to each contaminant also allows for differences in spatial dispersion of the contaminant that could result from differing chemical properties and uses in production.
We then jointly reestimated the parameters of interest quantifying the covariate effects, β0, . . . βn, with the exponential spatial correlation parameters (range, sill, and nugget in geostatistical terminology) using maximum likelihood, yielding GLS estimates for covariate effects (Diggle et al. 1998b). The GLS estimated standard errors were then used to update tests of significance. We also analyzed transformed blood organochlorine outcomes using the Box-Cox family of transformations, g(Y) = (Yγ − 1)/γ, with Y representing an organochlorine compound and γ a parameter of the likelihood to be estimated (Christensen et al. 2001). Two possible transformations to note are γ = 1, no transformation, and γ = 0, natural log transformation. Throughout the analyses, the maximum likelihood estimate for γ was consistently close to one. All regressions were therefore analyzed on the original scale.
All statistical analyses were performed using the R statistical computing environment with the contributed package, geoR, for geostatistical operations (R Development Core Team 2003; Ribeiro and Diggle 2001).
Results
Demographic information regarding the study population is given in Table 1. The mean age of the participants was 53 years. The participants were 58% male, 19% were current smokers, 98% were white (2% African American), and the mean education level was between 11 and 12 years. Because there were so few African Americans, the results are limited to only the white population (n = 1,350).
Table 2 summarizes the blood DDE, total PCB, and dieldrin levels in the participants of the study. The mean levels of DDE, total PCBs, and dieldrin in the blood of Washington County residents adjusted for lipid content were 3023.5, 771.5, and 113.07 ng/g, respectively.
Approximately 96% of the addresses were geocoded successfully. Aside from clustering of residences in accordance with population density, spatial patterns were not apparent. The 50 addresses that were not geocoded, and hence removed from the analysis, consisted mainly of rural routes and post office boxes that the base map was unable to locate (Hurley et al. 2003; McElroy et al. 2003). They showed no distinct patterns with respect to covariates such as age, education, sex, smoking status, or blood organochlorine levels and were from various ZIP codes, uniformly dispersed across the county. Their exclusion from the analysis, therefore, is not expected to introduce bias.
From the exhaustive search of all covariates, we chose plausible regression models for each organochlorine based on their ability to predict model variability. Spatial dependence was found in the residuals of all organochlorines in this step, as diagnosed by their estimated residual semivariograms. Parameter estimates and tests of significance were adjusted for this residual spatial dependence using the GLS-based approach outlined in “Materials and Methods.”
Table 3 gives the results of the adjusted (multivariate) models used to describe the blood levels of DDE, total PCBs, and dieldrin in the Washington County study population. The impact each covariate had on the blood organochlorine level alone was also measured using univariate GLS regression (results listed in Henshaw 2004). Although all covariates mentioned in “Materials and Methods” were evaluated in the regression analysis, only those covariates that were part of the most predictive model of blood organochlorines are presented in Table 3.
Age, sex, smoking status, education, drinking water source, and distance to the Superfund site improved the overall fit of the model of blood DDE levels. Women, non-smokers, and city water drinkers had statistically significantly less DDE in their blood than did men, smokers, and those who drink spring or well water, respectively, when all other covariates were controlled. DDE levels also increased significantly with age. No statistically significant association was found between the level of DDE in the blood and distance of the residence from the Superfund site.
After adjusting for age, sex, smoking status, education, and drinking water source, a statistically significant negative association was found between dieldrin levels in blood and the residential distance from the Superfund site. The only significant predictors of blood dieldrin levels were smoking status and drinking spring water versus city water. Furthermore, smokers tended to have significantly less dieldrin in their blood than did nonsmokers. Nonetheless, the results of this dieldrin model suggest that those who lived closer to the site had higher levels of dieldrin in their blood than did those who lived farther away. If the trend were assumed to be linear, there would be a 1.6 ng/g decrease in blood dieldrin levels for every mile a residence was located away from the site. Follow-up analysis using half-mile increments for distance suggests that linearity in the effect of distance to the site is supported more at distances near the site. However, the linear relationship appears to be weak because it held true only within the first half-mile increment. When the distance variable was broken into mile increments, a linear relationship was not seen.
No relationships between distance to the Superfund site and blood levels of total PCBs were found (Table 3). The spatial covariate, urban versus rural residence, was marginally predictive of lipid-adjusted total PCB levels in blood when adjusting for age, sex, education, smoking status, and drinking water source (p < 0.1). Blood levels of total PCBs in participants living within 1.5 miles of the center of Hagerstown were lower than in those living outside of Hagerstown, holding age, sex, education, smoking status, and drinking water source constant. The association was not significant for lipid-unadjusted blood total PCB levels. In addition, while adjusting for other explanatory variables, men, smokers, and well-water drinkers had marginally higher blood PCB levels than did women, nonsmokers, and those who drink city water, respectively. However, only the association with sex was statistically significant. Finally, a positive association with age and a negative association with years of education and blood PCBs were found, although neither of these relationships is statistically significant.
After correcting for spatially dependent residuals, most model parameter estimates were not changed significantly. However, those covariates that bordered on statistical significance (i.e., p-values ~ 0.05) were sensitive to correcting for spatially dependent residuals. For example, a statistically significant urban/rural residence relationship with lipid-adjusted total PCBs was found in OLS regression but became statistically insignificant after correcting for spatial dependence in the residuals.
Discussion
In this study we investigated the importance of evaluating spatial covariates and taking into account residual spatial dependence in regression models attempting to explain levels of contaminants in humans. Spatial information is more commonly used in evaluating environmental contamination but is often overlooked in studies modeling the same contaminants in humans, despite the fact that biomarkers are indicators of exposure. Results of this study indicate that models for blood organochlorine levels can benefit by including spatial information.
Results suggest that residential location may be a potential exposure determinant of organochlorine levels in human blood as biomarkers of exposure to persistent organochlorine compounds in Washington County, Maryland. A significant association is present between blood dieldrin levels and residential distance from the Superfund site. However, an association between residential location and the Superfund site in the county was not found with blood DDE levels. In fact, DDE levels in blood increased with distance from the site instead of decreasing, as anticipated. One possible reason for this pattern may be that DDE is a widespread compound that can be found in the blood of > 90% of the U.S. population, whereas dieldrin was not as commonly found in the environment and in human blood (CDC 2003; Longnecker et al. 1997). In addition, DDT was most likely used often and all over the county before the 1970s, in agricultural occupations. Therefore, there may have been multiple nonpoint sources of exposure to DDT in the study population. It is important to note that the mean levels of DDE in this population are > 10 times the Second National Health and Nutrition Examination Survey (NHANES II) reported national background level of 297 ng/g in 1999–2000 (CDC 2003). The results presented in this article suggest that this widespread use of DDT may be a much larger contributor to internal dose than any increase in body burden associated with living close to the Superfund site. Dieldrin, on the other hand, had more limited use for termite control. Therefore, the site may have been the primary source of dieldrin exposure, resulting in higher blood levels for those individuals living closer to the site.
Further research is needed to determine the validity of the association between blood dieldrin levels and the Superfund site. Not only is the statistical significance of this association marginal, but also the model is based on a sample less than half the size of that for DDE. Furthermore, the model found smoking to be negatively associated with blood dieldrin levels. No other studies in the literature have suggested such an association with smoking, and therefore more research into this finding is warranted. Overall, the results are inconclusive as to whether there is a direct relationship between residential distance to the Superfund site and levels of organochlorines in the blood of the participants.
For the most part, the covariates found to be associated with blood organochlorines in this study are consistent with the literature. For instance, other studies have found that blood organochlorine concentrations were positively associated with age or current smoking status (Fitzgerald et al. 1999; Glynn et al. 2003; Pereg et al. 2002; Sala et al. 1999). Glynn et al. (2003) and Sala et al. (1999) also report that place of residence influences blood levels of organochlorines. The positive age association can be explained by the fact that older people have had a longer duration of exposure, which is reflected in their body burden of organochlorines. Smokers may have higher exposure to blood organochlorines due to the constant hand-to-mouth activity. It also makes sense that those people who rely on wells or springs for drinking water have higher levels of organochlorines in their blood, because the site has been shown to have polluted the surrounding waterways, and the geology of the area is such that surface water can easily contaminate the groundwater (MDE 1993, 1994, 1995, 1996).
The literature regarding certain covariates evaluated in this study is somewhat inconsistent. For instance, we found that men have higher levels of blood organochlorines than women do, perhaps because lactation may lower the organochlorine body burden in women. Some studies have reported similar findings (Stehr-Green 1989; Wolff and Anderson 1999), whereas others observed that women had higher levels than men or that there was no association with sex (Bertram et al. 1986; Sala et al. 1999; Wariishi et al. 1986). A potential reason for this inconsistency could be that sex does not always account for differences in occupation and body mass index (BMI), because these are both potential confounders in this relationship.
An additional limitation is that information on possible confounders or effect modifiers is not complete. For example, the CLUE questionnaire did not obtain height and weight measurements, and therefore BMI could not be considered even though it is a predictor of blood organochlorine levels (Glynn et al. 2003; James et al. 2002; Pelletier et al. 2002; Sala et al. 1999; Schildkraut et al. 1999). Other variables such as breast-feeding history, weight loss, and occupational and home exposure to pesticides have also been significant predictors of blood organochlorine levels (Glynn et al. 2003; Hernandez-Valero et al. 2001; Sala et al. 1999; Soliman et al. 2003; Wariishi et al. 1986). Although this information was available for many of the participants in this study, it was collected > 5 years after the blood drawings. Because these data may not have been representative of the behavior at the time of blood draw, they were omitted from the analysis. Therefore, future studies should collect information on all possible risk factors at the time of blood collection.
High residual error and low explained levels of variation in regression models are common when dealing with human populations because of human variability, and they indicate that there is still unexplained uncertainty in these models. Results of this study demonstrate that spatial dependence in these residuals accounts for some of this error. However, residual spatial variation was recognized in all regression models, suggesting that further investigation of spatial information not considered in this study may improve these models. It is therefore important to collect information not only on potential individual-level risk factors but also on all spatial risk factors when designing future studies. Additional potential risk factors that may have been helpful in this study would have included: BMI, occupation, household and occupational exposure to organochlorines, consumption of local and fatty fish, consumption of homegrown vegetables, recreational swimming in local surface waters, land use, and drinking water well location and/or source aquifer.
Besides accounting for all potential risk factors, future research in this area would benefit from the addition of environmental exposure models. For example, air dispersion or groundwater modeling results could be coupled with biomarkers in assessing the impact of residing near a potential source. These models would take into account wind and groundwater patterns that have the potential to greatly affect contamination at a specific location. Not enough information on the Superfund site studied here was available for such models to be incorporated into our results. This limitation may greatly affect the results of this study because much of the contamination may have been via groundwater and surface water, thereby obscuring the relationship between the site and residence and introducing exposure measurement error.
The study described in this article relies on two assumptions related to participant address information. First, it assumes that participants’ addresses at the time of the blood draw represent their residential location during the time they were most exposed to organochlorines. If there were changes of addresses before blood sampling, and if this exposure measurement error was random, the results may be biased toward the null. It is also possible that they may have had more exposure at their place of employment or recreation than at their residence. Furthermore, we assumed that the locations of the residences were geocoded accurately. However, this assumption is not always valid because there exists positional inaccuracy associated with geocoding using a geographic information system (Bonner et al. 2003; Boscoe et al. 2002; Cayo and Talbot 2003; McElroy et al. 2003). Although this positional inaccuracy was not found to be significant in a study by Bonner et al. (2003), the sensitivity to this bias of the models presented in this article is evaluated elsewhere (Henshaw 2004).
In summary, > 1,200 Superfund sites across the country are contaminated with substances that adversely affect human health (U.S. EPA 2003), and these sites are often located in urban areas surrounded by residences. We presented an analytical approach to investigating the relationship between residential location and possible organochlorine exposure. This approach included spatial information that allowed for the consideration of possible geographic determinants of exposure and regression inference that accounted for possible residual spatial variation.
This study was supported by National Institute for Environmental Health Sciences (NIEHS) training grant ES 07141, the Johns Hopkins NIEHS Center in Urban Environmental Health (P30 ES 03819), National Institute for Occupational Safety and Health Education and Research Center training grant T42-CCT310419, and U.S. Department of Health and Human Services grant U01-CA86308.
Table 1 Summary of demographic information for study population.
Characteristic Value
No. of participants 1,374
Age [years (range)] 53.1 ± 0.30 (15–90)
Whites (%) 98.3
Education [years (range)] 11.24 ± 0.08 (1–29)
Current smokers (%) 25.9
Males (%) 57.8
City water drinkers (%) 66.7
Spring water drinkers (%) 0.7
Well water drinkers (%) 11.8
Urban residents (%) 35.8
Distance to site [miles (range)] 3.97 ± 0.12 (0.26–28.33)
Values are percent or mean ± SD.
Table 2 Summary of blood organochlorine data for study population.
Organochlorine No. of samples Range Mean ± SD
p,p-DDE
Lipid adjusted (ng/g) 1,274 36.4–25,205 3023.5 ± 66.3
Unadjusted (ng/mL) 1,374 0.19–185.9 20.0 ± 0.45
Total of PCB congeners 105, 118, 146, 153, 156, 170, 180
Lipid adjusted (ng/g) 1,046 129.4–9,355 771.5 ± 22.0
Unadjusted (ng/mL) 1,049 0.73–63.9 5.0 ± 0.15
Dieldrin
Lipid adjusted (ng/g) 685 1.7–825 113.07 ± 3.05
Unadjusted (ng/mL) 688 0.01–5.6 0.72 ± 0.02
Table 3 Results of GLS regression determining the effects of covariates on blood organochlorine levels: parameter coefficients (95% confidence intervals).
Compounds used at the site
DDE
Compounds not used at the site: total PCBs
Covariate Lipid unadjusted (ng/mL) Lipid adjusted (ng/g) Dieldrin Lipid adjusted (ng/g) Lipid unadjusted (ng/mL) Lipid adjusted (ng/g)
Distance from the site to residence (miles) 0.12 (−0.21 to 0.44) 9.1 (−31.8 to 50.0) −1.6** (−3.1 to −0.1) NA NA
Urban vs. rural residence (rural vs. Hagerstown) NA NA NA 0.37 (−0.13 to 0.87) 66.9* (−16.1 to 149.9)
Age (years) 0.12*** (0.05 to 0.20) 10.7* (−0.89 to 22.3) −0.19 (−0.75 to 0.37) 0.01 (−0.009 to 0.87) −0.43 (−3.55 to 2.68)
Sex (male vs. female) 10.8*** (9.2 to 12.3) 1,695*** (1,453 to 1,937) 14.6* (−2.5 to 31.7) 4.2*** (3.8 to 4.7) 635*** (567 to 701)
Education (years) −0.12 (−0.40 to 0.15) 3.8 (−38.7 to 46.3) −1.8 (−4.2 to 0.6) −0.06 (−0.13 to 0.02) −5.3 (−17.4 to 6.8)
Smoking status (nonsmoker vs. smoker) −2.4** (−4.2 to −0.5) −296.4** (−580.6 to −12.3) 21.8*** (7.1 to 36.5) −0.25 (−0.74 to 0.25) −36.7 (−114.9 to 41.5)
Drinking water source (spring vs. municipal) 15.4*** (6.9 to 23.9) 1918.8*** (600.8 to 3236.8) 221.2*** (152.6 to 289.7) −0.17 (−2.4 to 2.1) −83.4 (−437.0 to 270.2)
Drinking water source (well vs. municipal) 2.5* (−0.2 to 5.2) 362.6* (−43.6 to 768.7) 9.4 (−12.1 to 30.9) 0.27 (−0.40 to 0.94) 48.9 (−57.7 to 155.5)
Adjusted R2 0.209 0.243 0.3376 0.428 0.36
NA, variable not chosen for final model.
* p-value < 0.10
** p-value < 0.05
*** p-value < 0.01.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7413ehp0113-00171716330353ResearchAcute Ozone-Induced Differential Gene Expression Profiles in Rat Lung Nadadur Srikanth S. 1Costa Daniel L. 1Slade Ralph 1Silbjoris Robert 2Hatch Gary E. 11 Pulmonary Toxicology Branch, Experimental Toxicology Division, and2 Clinical Research Branch, Human Studies Division, National Health Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USAAddress correspondence to S.S. Nadadur, National Center for Environmental Assessment, U.S. EPA, Mail Drop B243-01, Research Triangle Park, NC 27711 USA. Telephone: (919) 541-0672. Fax: (919) 541-2985. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 23 6 2005 113 12 1717 1722 13 7 2004 23 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Ozone (O3) is an oxidant gas that can directly induce lung injury. Knowledge of the initial molecular events of the acute O3 response would be useful in developing biomarkers of exposure or response. Toward this goal, we exposed rats to toxic concentrations of O3 (2 and 5 ppm) for 2 hr and the molecular changes were assessed in lung tissue 2 hr postexposure using a rat cDNA expression array containing 588 characterized genes. Gene array analysis indicated differential expression in almost equal numbers of genes for the two exposure groups: 62 at 2 ppm and 57 at 5 ppm. Most of these genes were common to both exposure groups, suggesting common roles in the initial toxicity response. However, we also identified the induction of nine genes specific to 2-ppm (thyroid hormone-β receptor c-erb-A-β and glutathione reductase) or 5-ppm exposure groups (c-jun, induced nitric oxide synthase, macrophage inflammatory protein-2, and heat shock protein 27). Injury markers in bronchoalveolar lavage fluid (BALF) were used to assess immediate toxicity and inflammation in rats similarly exposed. At 2 ppm, injury was marked by significant increases in BALF total protein, N-acetylglucosaminidase, and lavageable ciliated cells. Because infiltration of neutrophils was observed only at the higher 5 ppm concentration, the distinctive genes suggested a potential amplification role for inflammation in the gene profile. Although the specific gene interactions remain unclear, this is the first report indicating a dose-dependent direct and immediate induction of gene expression that may be separate from those genes involved in inflammation after acute O3 exposure.
acute exposuregene expression profileslungmicroarrayozonerat
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The photochemical oxidant ozone (O3) is the air pollutant in smog thought to be of greatest concern with regard to acute health effects [U.S. Environmental Protection Agency (EPA) 1996]. Although considerable progress has been made in improving U.S. air quality since air pollution standards were established in 1970, about 50% of the U.S. population currently lives where O3 levels exceed the National Ambient Air Quality Standard (NAAQS) (U.S. EPA 1993). Of the six NAAQS pollutants, O3 has been the most problematic pollutant to control because it is formed from intermediates originating from many different sources. Hence, concerns about adverse health impacts remain. It is known that acute exposure to this gas at ambient levels results in acute lung injury and inflammation in humans (Devlin et al. 1991). Airway epithelial cells are damaged and lung function is impaired in both humans and laboratory animals (Hatch et al. 1994; Koren et al. 1989). Additionally, because O3 reaches the deep lung and damages distal airway and proximal alveolar structures (including the surface epithelia and connective tissues), there is a potential for permanent damage with repeated exposure and injury to the deep lung (Costa et al. 1985). Recent epidemiological studies have associated increased morbidity, particularly in children with asthma, during periods of high O3 pollution (Tolbert et al. 2000; White et al. 1994).
O3 appears to induce initial damage to the respiratory epithelium because of an oxidative cascade after its initial reaction with lipids and proteins at the air–liquid interface (Pryor 1992). Injury to the epithelium results in sloughing of ciliated cells into bronchoalveolar lavage fluid (BALF). Increased protein concentration and N-acetylglucosaminidase (NAG) activity in the BALF also occur because of leakage of proteins from blood plasma or intracellular spaces (Dye et al. 1999; Hu et al. 1982; Vincent et al. 1996). The release of inflammatory cytokines and chemokines from injured cells initiates the infiltration of neutrophils, which are also increased in the BALF (Devlin et al. 1991) and at least in the short run are thought to contribute to injury. Despite the evidence that this overt process wanes when repeated over time, it appears that the injury and inflammation cascade promotes cellular hypertrophy and the deposition of interstitial matrix materials and generalized remodeling of the fine structures of the deep lung (Chang et al. 1992; U.S. EPA 1993).
O3 is also hypothesized to initiate intra-cellular oxidative stress through ozonide and hydroperoxide formation (Pryor 1992). These intracellular oxidants are likely to activate gene transcription through redox-mediated signaling pathways that govern the cascade of injury, repair, and other cellular responses associated with the oxidant burden. For example, the inflammatory cytokines and chemokines inter-leukin (IL-8), macrophage inflammatory protein-2 (MIP-2), and cytokine-induced neutrophil chemoattractant (CINC), which are found in the BALF and lung tissues of rodents exposed to O3 (Michelec et al. 2002; Zhao et al. 1998), can initiate differential transcriptional activation of genes. Because gene expression is mediated by various transcription factors, which can ultimately determine the outcomes of the challenge, we hypothesized that gene expression profiles derived using gene arrays could aid in identifying exposure-specific gene regulation for O3 that might then lead to the identification of potential gene markers for acute lung injury. Although the inflammatory response to O3 has been well documented, the earliest signaling pathways associated with this process are not known.
The acute O3 lung injury model has been widely used to explore injury and repair processes (Bassett et al. 1988; Kleeberger et al. 1997; Prows et al. 1999). It provides a well-documented and reproducible tool to study the fundamental events associated with acute lung injury induced by oxidant overload. It was felt that oxidant-based profiles arising from this study might aid in our understanding of various biochemical pathways involved in lung injury, inflammation, and repair processes. It may also be possible to identify acute markers associated with long-term outcomes that serve to guide hypotheses generation to explore further understanding of acute lung injury.
Commercially available microarray technologies can facilitate efforts at global gene expression profiling. However, the rat genome is not yet completely sequenced, and the global approach with microarrays containing numerous expressed sequence tags may not be able to provide the needed information on possible candidate genes that can be further explored at this time. We therefore used the nylon micro-array with a limited and targeted number of well-characterized rat genes to identify gene expression profiles involved in the acute response to toxic doses of O3.
Materials and Methods
Animals.
Fischer 344 rats (male, 90 days of age) were obtained from Charles River Laboratories (Raleigh, NC) and kept in temperature- and humidity-controlled rooms with a 12/12-hr light/dark cycle. Standard rat chow (ProLab, Brentwood, MO) and water were provided ad libitum. The animal facility is Association for Accreditation of Laboratory Animal Care approved, and all procedures were reviewed and implemented through the Institutional Animal Care and Use Committee process of the U.S. EPA National Health and Environmental Effects Research Laboratory.
Inhalation exposures.
Rats (six animals per group) were placed in individual stainless-steel wire-mesh cages inside a 135-L exposure chamber and exposed to either 2.0-ppm O3 or 5.0-ppm O3 for 2 hr. Control animals were exposed to filtered room air. Chamber O3 concentration was monitored with a Dasibi model 1003AH O3 monitor (Dasibi Environmental Corp., Glendale, CA).
Lung removal.
Two hours postexposure, rats were anesthetized by an ip injection of (50 mg/kg body weight) pentobarbital (Abbott Laboratories, North Chicago, IL) and exsanguinated by severing the dorsal aorta. The chest cavity was opened, and the lungs were removed en bloc. Individual lobes were separated, quick frozen in liquid nitrogen, and stored at −80°C until used for RNA extraction.
Bronchoalveolar lavage.
Rats exposed identically to those used for gene expression analysis were also anesthetized and bled. A tracheal cannula was inserted to about 0.5 cm above the carina, and the whole lung was lavaged three times with the same volume of isotonic 0.85% NaCl (Ca2+ and Mg2+ free) that had been warmed to 38°C. A volume equal to 30 mL/kg of body weight was injected and reinjected 3 times in succession. This saline was then withdrawn and placed on ice. Cells were separated by centrifugation at 1,100 × g for 15 min at 4°C. Aliquots of the supernatant were taken for protein and enzyme assays. The cell pellet was resuspended in saline and separated into two fractions. One fraction was stained with 0.6% crystal violet in 4% acetic acid and counted in a hemocytometer to obtain the total cell count. The other fraction was cytocentrifuged (Shandon, Inc., Pittsburgh, PA) onto a microscope slide and stained for differential cell counting using Diff-Quik stain (Fisher Scientific, Pittsburgh, PA). Total protein in the bronchoalveolar lavage (BAL) supernatant was assayed using the method of Bradford (1976), with bovine serum albumin as standard. NAG was measured from the hydrolysis of p-nitrophenyl-N-acetyl-β-d-glucosamine, using p-nitrophenol as standard (Vincent et al. 1996). Lysozyme was measured by the Micrococcus lysis method (Konstan et al. 1982).
RNA extraction.
Rats exposed exclusively for the gene expression studies did not undergo BAL to avoid confounding of the gene expression that might be associated with the physical stress of lavage or the loss of desquamated cells. Total RNA was extracted from lung lobes dissected free of the trachea, using Trizol reagent (Invitrogen, Carlsbad, CA). RNA was treated with DNAse (Invitrogen) to remove any contaminating DNA and purified after phenol:chloroform extraction. Quantity and quality of RNA was checked by ultraviolet spectrophotometer and formaldehyde gel analysis (Sambrook and Russell 2001). To ensure adequate RNA sample size and to minimize variability between samples in this exploratory study, we implemented a system of sample pooling. From the six rats of each exposure group, three pooled samples of two rats were created randomly. A fourth sample was generated by pooling RNA from all six animals at a ratio equal to a normalized group sample. This method was modified from similar pooling procedures followed in gene array studies (Liu et al. 2003; Noh et al. 2004).
Atlas cDNA array analysis.
Rat cDNA expression array containing 588 cDNAs (spotted in duplicate) on a nylon membrane was purchased from Clontech (Palo Alto, CA) and used in this study. GenBank accession numbers for these genes provided by Clontech were derived from the National Center for Biotechnology Information (NCBI) UniGene database (http://www.ncbi.nlm.nih.gov). Total RNA (15 μg) was converted to 32P-labeled cDNA in a reverse transcriptase reaction following the manufacturer-suggested protocol, with a slight modification. The reaction was extended for 15 min after the addition of cold 40 μM dATP to improve the quality of the probe (Nadadur and Kodavanti 2002). 32P-labeled cDNA probes were separated from unincorporated nucleotides using a spin column (Nucleospin extraction kit, Clontech), and the efficiency of 32P incorporated into cDNA was measured by scintillation counting. The rat Atlas cDNA array was hybridized with 32P-labeled cDNA probes overnight at 60°C. The microarrays were washed to highest stringency condition (two 20-min washes in 0.1 × saline–sodium citrate and 0.1% sodium docecyl sulfate). The nylon membranes were exposed to a phosphor screen for 4 hr, and array blot images were scanned using a Phosphorimager (Molecular Dynamics, Piscataway, NJ). Four array hybridizations were performed for each group.
Microarray data analysis: quality control and quality assurance measures.
The scanned images were aligned using AtlasImage software (version 2.7; Clontech). The spot intensities (gene expression) were globally normalized and corrected for background with the median setting following the protocols defined in the AtlasImage software, version 2.7. Spot density values for all the genes were imported to GeneSpring software (version 6.0; Silicon Genetics, Redwood City, CA) and subjected to quality control (QC) measures to identify the total number of genes that showed hybridization signals above the background in all 12 arrays (four arrays per group). The QC gene list generated was analyzed to identify altered genes using a filter of 2-fold change.
Statistical analysis.
Gene lists generated (for genes either induced or suppressed by 2-fold) were subjected to statistical analysis using the GeneSpring preprogrammed statistical package. Genes whose expressions were altered by 2-fold were subjected to one-way analysis of variance (ANOVA) setting p-values of < 0.05. The comparison is performed for each gene in all the groups, and the genes with the set cutoff (p-values of < 0.05) are returned. The genes selected by one-way ANOVA were also corrected for false rate discovery following the Benjamini and Hochberg (1995) method. Gene lists (induced/suppressed) generated in this way were used in Venn diagram analysis to identify the genes that were common or unique to each exposure group (2 or 5 ppm) and were listed.
Real-time reverse transcriptase PCR.
Relative gene expression was quantified using real-time reverse transcriptase (RT) quantitative PCR on selected genes to verify the microarray data. Total RNA (5 μg) was reverse transcribed to generate first-strand cDNA using Moloney murine leukemia virus reverse transcriptase (Invitrogen) and random primer mix (Invitrogen). Taqman predeveloped assay reagents (Applied Biosystems, Foster City, CA) were used for amplification of induced nitric oxide synthase (Nos2), Jun, and glyceraldehyde-3-phosphate dehydroge-nase (GAPDH). Oligonucleotide primer pairs for thyroid hormone-β receptor (Thrb) glutathione reductase (Gsr) were designed using a primer design program (Primer Express, Applied Biosystems) and obtained from Integrated DNA Technologies (Coralville, IA). Quantitative fluorogenic amplification of cDNA was performed using the ABI Prism 7700 Sequence Detection System (Applied Biosystems). The relative abundance of mRNA levels was determined from standard curves generated from a serially diluted standard pool of cDNA prepared from human bronchial epithelial cells. The relative abundance of GAPDH mRNA was used to normalize levels of the mRNAs of interest.
Results
Bronchoalveolar lavage fluid analysis.
The indicators for lung injury and inflammation measured in BALF 2 hr after the 2-hr exposure to air or 2 or 5 ppm O3 are presented in Table 1. BALF protein concentrations were increased significantly by 20-fold in the 5-ppm group but were changed only about 1.5-fold in the 2-ppm group. NAG was increased 7.5-fold in the 5-ppm group and 1.5-fold in the 2-ppm group. Lysozyme was not significantly affected in either exposure group. Total cell counts appeared to be decreased by about 20% after both the 2- and 5-ppm exposures. This decrease is common to O3-exposed animals immediately after exposure because it is thought that macrophages become activated and are not available to BAL. Neutrophil and ciliated cell percentages in the BALF (which are normally close to zero) increased significantly in both the 2- and 5-ppm groups in a concentration-dependent manner. However, this increment at 2 ppm, although significant, was in the range of BALF neutrophils considered “normal” for control rats. Had BAL been conducted 12–15 hr postexposure, as is more typical (Hatch et al. 1986), it is likely that these values would have been considerably higher. Notably, however, in the 5-ppm group, the neutrophils and ciliated cells were substantially increased to 23 and 40%, respectively, of total cells, indicative of concomitant immediate airway and alveolar damage and inflammation.
Microarray analysis.
Analysis of the expression of 588 genes spotted on the rat cDNA nylon array showed that 540 genes were expressed constitutively in the lung of all the treatment groups including controls. With exposure to O3, statistically significant augmentation (with 2-fold set as a minimal induction threshold in the statistical analysis) of expression was found in 62 genes at 2 ppm and 57 genes at 5 ppm O3. Of these genes, 26 were induced commonly in both 2- and 5-ppm exposure groups, and a total of 36 genes in the 2-ppm group and 31 genes in the 5-ppm group were suppressed (Table 2). Despite the difference in the exposure concentration, the immediate toxic response appeared to be mediated by the transcriptional regulation of many common genes: induction of 17 and suppression of 25 genes in both exposure groups. Further analysis indicated concentration-specific induction and/or suppression of unique genes (Table 2), suggesting their possible roles in initiating different downstream signaling networks. The up-regulated genes that were common to both 2 and 5 ppm O3 treatment are listed in Table 3; the common down-regulated genes are listed in Table 4. Induced genes unique to both the 2- and 5-ppm exposure groups are listed in Table 5. Similarly, suppressed genes that are unique to the 2- and 5-ppm exposure groups are listed in Table 6.
Of 13 functional groups represented on this microarray, O3-altered gene expression profiles were distributed predominantly into four broad functional groups: a) metabolism (lipid, protein), b) intracellular transducers/stress response (modulators, oncogenes), c) growth factors/receptors (kinases, activators/inhibitors), and d) cell surface receptors (adhesion proteins and ligands). Among these groups, stress-response proteins, oncogenes, and cell cycle–related genes were up-regulated, whereas cell surface receptors were down-regulated. Lipid metabolism genes were differentially expressed in response to O3 inhalation. The altered expression in lipid metabolism and the transcription factors nuclear factor κB (Nfkb1), ras oncogenes, and insulin-like growth factor (IGF) binding protein-2 (Igfbp2) and the concentration-specific differential expression of stress-response proteins such as Jun, Gsr, and calcium-dependent signal mediators, observed in the present study for the first time, will shed new light on their possible roles in acute O3 toxicity. Further analysis of the altered expression of genes unique to 2 or 5 ppm (Tables 5, 6) will be more useful in identifying exposure-specific immediate lung injury.
To validate the altered gene expressions observed in the microarray assessment, real-time RT-PCR was performed on five selected genes (four of which were not known to be associated with O3 toxicity, and one known gene was found altered in rat lung tissue on exposure to O3). As shown in Table 7, the expression of these five genes is in good agreement with the microarray analysis.
Discussion
The studies we report here represent part of our ongoing effort to characterize the immediate biologic responses of rat lung tissue to a toxic dose of O3 and to use this information to develop biomarkers for its toxicity (Hatch et al. 1986, 1994). This effort was to generate gene expression profiles for rat lung tissue using high-throughput microarray technologies to distinguish levels of injury based on the differential expression of specific groups of genes thought to be involved in this process. The gene expression profiles derived at 2 hr after O3 inhalation represent toxicant-induced transcriptional activation/inactivation that is not likely confounded by other physiologic factors as might occur after established inflammation. To the best of our knowledge, our present study is the first to be published on the near-immediate impact of acute O3 exposure on gene expression response profiles in rat lung tissue. Two related reports on O3-altered gene expression profiles have appeared in the literature. One involved mice (Gohil et al. 2003) assayed after repeated O3 exposures (1 ppm; 8 hr/day) for 3 days, with analysis performed immediately after the third exposure. Another investigation was carried out in rats exposed to 1 ppm O3 for 3 hr (Bhalla et al. 2002) and evaluated for the expression of inflammatory marker genes at a relatively late time point (10–12 hr postexposure). In both studies it is likely that significant inflammation and repair processes were involved. In contrast, gene expression profiles derived in the present study represent the near-immediate transcriptional alterations in response to a single exposure to a toxic dose of O3 and, not surprisingly, present a profile different from these other studies.
In the present study we exposed rats to 2 and 5 ppm of O3 for 2 hr. The 2-ppm exposure was selected to represent a possible human exposure during vigorous human exercise at a high exposure concentration of approximately 0.4 ppm of O3 (Hatch et al. 1994), whereas the higher level (5 ppm) might represent a more severe oxidant challenge that may initiate acute respiratory distress syndrome involving concomitant oxidant injury and inflammation. Using 18O-labeled O3, we (Hatch et al. 1994) have shown that the impact of acute exposure to O3 at 0.4 ppm with intermittent heavy exercise in humans resulted in lung tissue dosimetry approximately equal to that of the rat exposed sedentary to 2 ppm for the same 2-hr period.
The initial interaction of O3 with the unsaturated fatty acids in the epithelial lining fluid is thought to generate lipid ozonation products that drive various signaling cascades that result in the biochemical events characteristic of O3 pulmonary toxicity. As such, the immediate molecular changes leading to gene induction at this step may be identifiable using high-throughput technologies leading to candidate biomarkers for O3 exposure and toxicity. Thus, induced genes may ultimately lead to the development of markers that can be screened using noninvasive approaches (Krishna et al. 1998; Liu et al. 1999).
The airway epithelium is the first line of defense against inhaled toxicants and also is the primary site of O3-induced injury (Koren et al. 1991). Acute exposure to O3 leads to immediate epithelial injury, pulmonary neutrophilic inflammation subsequent to permeability changes, and the leakage of serum proteins into the air spaces of the lung. The increase in BALF protein content, NAG activity, and recoverable neutrophils are collectively indicative of airway and alveolar epithelial necrosis. This pattern of markers and inflammatory cellular response is typically observed at later time points (12–18-hr postexposure) as markers of exposure and injury (Bhalla and Gupta 2000; Hatch et al. 1994; van Bree et al. 2001). The earliest cellular and molecular events are generally not studied because of lack of sensitive tools.
The statistically significant differences in the expression of 119 genes in the two exposure groups together suggest that immediate transcriptional regulation of these genes may be involved in the tissue injury and/or regenerative responses. The gene expression data derived in the present study suggest that the O3-induced injury is mediated by differential activation of genes predominantly distributed in two groups: fatty acid metabolism and cell proliferation. In contrast, genes representing signal mediators, receptors, or second messengers were suppressed. Interestingly, the altered gene expression profiles of the two exposure groups (2 and 5 ppm) indicated that most genes affected were common (Tables 3, 4). It remains to be seen if the response generalizes to other oxidants.
The 3.5-fold induction in the expression of the adhesion molecule L-selectin observed 2 hr after exposure to 2 and 5 ppm O3 suggests its role in the migration and increased accumulation of neutrophils observed at this early time point. Induction of other adhesion molecules, including P-selectin, has been observed in human BALF cells on acute exposure to 0.12 ppm of O3 (Blomberg et al. 1999; Krishna and Holgate 1999). Increased expression of apurinic and apyrimidinic (AP) endonuclease (~ 5-fold) indicates possible activation of DNA repair processes (He et al. 2001). Simultaneous induction of β-arrestin-1 and β-arrestin-2, along with cyclins, clearly suggests the initiation of epithelial cell DNA repair and subsequent cell proliferation. Besides, β-arrestin proteins, which belong to the G-protein–coupled receptor family, are also known to act as scaffold proteins that mediate the activation of MAP kinase cascades (Luterrel et al. 2001; Sun et al. 2002).
The differential activation of lipid metabolism genes (induction of fatty acid amide hydrolase, phopholipase A2–activating protein) agrees with the long-known biochemical evidence of lipid ozonation products generated from the phospholipid pools of the pulmonary surfactant or the epithelial cell membranes (Kafoury et al. 1999). In vitro O3 exposure also has been shown to activate phospholipase A2, C, and D in cultured epithelial cells (Wright et al. 1994). The consequences of altered expression of phospholipases and the generation of lipid signal transduction network elements in response to lipid ozonation products are complex (Kafoury et al. 1999). Lipid signal transduction networks involve cross-talk among various isoforms (Liscovitch 1992). The altered expression of genes involved in lipid metabolism suggests their possible involvement in initiating a cascade of biochemical events that can lead to cellular responses characteristic of O3 toxicity in the lung.
The present study also indicated dose-specific unique gene expression profiles. The high dose of 5 ppm induced the expression of various stress-response genes such as the transcription factor Jun, Nos2, MIP-2 (Cxcl2), and heat shock protein 27 (Hspb1). This is the first observation of such an immediate induction of these genes. Although the induced expression of heat-shock proteins MIP-2 and Nos2 has been reported at later time points such as 4–8 hr after exposure to 2 ppm O3 (Driscoll et al. 1993; Johnston et al. 2001; Zhao et al. 1998), the induction observed here occurred within 2 hr after 2 hr of 5 ppm but not 2 ppm. The induction of MIP-2 and Nos2 only in the rat lungs exposed to 5 ppm O3 suggests their participation in or the result of the rapid and immediate influx of neutrophils observed in this group. Induction of Jun and Hspb1 in rat lungs exposed to 5 ppm O3 suggests a role in downstream signaling of stress-response cascade(s). Understanding the relationships and roles of these genes provides novel insight as to the mechanisms of oxidant toxicity and subsequent adaptive responses. Conversely, Thrb and Gsr were induced exclusively in 2-ppm–exposed animals compared with 5 ppm, suggesting a toxic response specific to the lower dose of O3.
The role of hormonal factors, particularly thyroid hormone, in O3 toxicity has been recognized previously (Fairchild and Graham 1963). Recent studies by Huffman et al. (2001) showed that a 2-fold increase in circulating thyroid hormone levels appeared to enhance pulmonary toxicity to short-term inhalation to 2 ppm O3 in rats, suggesting a role for this hormonal reflex. Thyroid hormone has been shown to regulate its own receptor, and the protooncogene c-erbA has also been identified as a thyroid hormone receptor. Three of the four c-erbA gene products—erbA-α1, erbA-β1, and erbA-β2—encode biologically active thyroid hormone receptors (Teboul and Torresani 1993). Hyperthyroidism in rats produces organ hypertrophy and an increase in circulating levels of IGF and its binding proteins (IGFBP) (Rosato et al. 2002). IGF-1 is the major mediator of growth hormone effects (Iglesias et al. 2001). It has also been observed that expression of IGF and IGFBP may mediate the number and density of thyroid hormone receptors (Pellizas et al. 1998). The 5-fold induction in the expression of thyroid hormone receptor Thrb and 5- to 15-fold suppression in IGF-binding protein are the first observations of O3-induced alterations in thyroid hormone receptor expression and regulation of Igfbp2. These observations suggest the possible role of Thrb and Igfbp2 in the increased O3 toxicity observed in hyperthyroid rats (Huffman et al. 2001).
Immediately altered gene expression pro-files derived for the rat lung upon exposure to toxic doses of O3 indicated altered expression of an array of genes common to both the concentrations studied (2 and 5 ppm), whereas some were unique to each dose. These gene profiles represent a spectrum of initiating events and recovery responses. The induced genes involved fatty acid metabolism, cell proliferation, and stress response, and the suppressed genes involved signal mediators, second messenger systems, and G-protein–coupled receptors. The observation of differential expression of Igfbp2 and Thrb provides the first biochemical clue for their involvement in O3 toxicity and its exacerbation in hyperthyroid conditions. Increased expression of genes involved in cell proliferation, DNA damage repair, and the stress response, such as Nos2, Gsr, and transcription factors c-jun and NF-κb, suggests the initiation of injury recovery response pathways. Further detailed analysis of these genes and their downstream signaling pathways may shed light on their roles, and they may serve as potential biomarkers for monitoring O3 toxicity. The gene expression profiles presented here were derived from total lung tissue, which could have in part masked or diluted the injury response in airway epithelium. Alternatively, marginated or infiltrating inflammatory cells could have also confounded the gene expression profiles as observed. Gene expression profiles obtained from in vitro studies using airway and bronchial epithelial cells and from BALF cells might expand our understanding of cell specificity in O3 pulmonary toxicity, although the interactions of the various cell types might be lost.
The gene expression profiles derived in the present study provide insights into potential markers of the early O3 response. These markers must now to be evaluated at lower levels of O3 to establish a context within a dose–response model. The goal will be to use these profile maps to relate to mechanisms in human exposure scenarios.
We thank J. Richards for protein and NAG analyses and J. McKee for engineering assistance with ozone exposures. We also thank K. Dreher, M. Madden, and L. Birnbaum for critical review of the manuscript.
This article has been reviewed by the National Health and Environmental Effects Research Laboratory, U.S. EPA, and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.
Table 1 Changes in BAL indicators in rats 2 hr after exposure to O3.a
Parameter Air 2.0 ppm 5.0 ppm
Protein, μg/mL 96.5 ± 3.94 159.0 ± 8.91* 2,001.0 ± 348.0*
N-Acetylglucosaminidase 2.4 ± 0.28 3.82 ± 0.34* 18.0 ± 1.14*
Lysozyme, μg/mL 85.2 ± 1.71 79.5 ± 1.91* 71.4 ± 4.31*
Total cells, × 1,000/mL 37.2 ± 5.49 28.2 ± 2.36 30.9 ± 2.89
Neutrophils, % 0.60 ± 0.09 2.33 ± 0.87* 22.8 ± 4.47*
Ciliated cells, % 0.23 ± 0.16 6.07 ± 1.61* 40.4 ± 7.93*
a Results presented here are mean ± SE for six rats/group.
* Significantly different (p ≤ 0.05) by Student’s t-test.
Table 2 The number of differentially expressed (>2-fold) genes observed in rat lung tissue after 2-hr exposure to O3.a
Exposure concentration No. of genes altered Up-regulated Down-regulated
2 ppm Common 17 25
Unique 9 11
Total 26 36
5 ppm Common 17 25
Unique 9 6
Total 26 31
a Results presented here show the number of genes that were altered (by ≥ 2-fold) and that were statistically significant by one-way ANOVA (p < 0.05). Genes that were common to both treatment groups and unique to each exposure group were derived by the Venn diagram approach in GeneSpring software, version 6.0, as detailed in ”Materials and Methods.”
Table 3 List of common genes induced (> 2-fold) in rat lung after 2-hr exposure to 2 and 5 ppm O3.a
Accession no.b Gene symbolc Gene namec Fold changed
U72497 Faah fatty acid amide hydrolase 14.17
M92848 Ceacam1 ecto-ATPase precursor (Cell-CAM105) 10.00
U17901 Plaa phospholipase A-2 activating protein (PLAP) 7.96
U09793 Kras2 K-RAS 2B protooncogene 7.43
D14015 Ccne1 G1/S specific cyclin (cyclin E1) 5.57
L07736 Cpt1a mitochondrial carnitine O-palmityltransferase 5.43
D10728 Cd5 T-cell surface glycoprotein (lymphocyte antigen CD5) 4.89
D44495 Apex1 apurinic/apyrimidinic endonuclease 4.86
X13722 Ldlr low-density lipoprotein receptor 4.61
AF007789 Plaur urokinase receptor 4.45
AF017437 Cd47 integrin-associated protein form 4 3.93
M91589 Arrb1 beta-arrestin 1 3.80
D10831 Sell L-selectin precursor 3.50
X98490 Rpa2 replication protein A 3.38
M91590 Arrb2 beta-arrestin 2 2.41
L26267 Nfkb1 NF-kappa B transcription factor p105 subunit 2.38
X70871 Ccng1 G2/M specific cyclin G (cyclin G1) 2.11
a Genes that were induced and common to both 2- and 5-ppm–exposed rat lung are listed here.
b Accession numbers derived from the NCBI Unigene database (http://www.ncbi.nlm.nih.gov/).
c Gene symbols and names derived from the Duke Integrated Genomics Database (https://dig.cgt.duke.edu/try_query.php).
d Fold induction in gene expression. Fold changes in expression of these genes were statistically significant by one-way ANOVA (p < 0.05).
Table 4 List of common genes suppressed (> 2-fold) in rat lung after 2 hr exposure to 2 and 5 ppm O3.a
Accession no.b Gene symbolc Gene namec Fold changed
U87306 Unc5b transmembrane receptor UNC5H2 −33.3
J04486 Igfbp2 insulin like growth factor binding protein-2 (IGFBP-2) −15.5 (2 ppm)
−5.0 (5 ppm)
D26439 Cd1d1 rat CD1 antigen precursor −10.78
M63334 Cam4k calcium-calmodulin dependent protein kinase IV −10.40
M31838 Tacr2 substance K receptor −6.42
L27057 Pde4a cAMP phosphodiesterase 4A −5.14
V01217 Actb cytoplasmic beta-actin −4.58
X06890 Rab4a ras-related protein RAB4A −4.28
U87305 Unc5a transmembrane receptor UNC5H1 −3.97
M64092 Pkib PKI-beta cAMP protein kinase inhibitor −3.73
M94056 Dpep1 dipeptidase −3.64
L34067 Gpc1 glypican-1 precursor −3.33
X13817 Calm3 calmodulin −3.21
Z22867 Pde3b cAMP-dependent phsophodiesterase −3.21
AB004454 Psen2 presenilin2 −3.10
M59859 Marcks miristoylated alanine-rich C-kinase substrate −2.93
J05155 Plcg2 phospholipase C gamma 2 −2.93
J03754 Atp2b2 PMCA, calcium-transporting ATPase plasma membrane form −2.92
X06889 Rab3a ras-related protein RAB3A −2.60
J03806 Plcg1 phospholipase C gamma 1 −2.57
U69278 Epha3 Eph-related receptor tyrosine kinase (Rek4) −2.54
M32748 Lif leukemia inhibitory/cholinergic neuronal differentiation factor −2.44
M60525 Vgf VGF nerve growth factor, inducible −2.40
U34841 Gprk5 G-protein-coupled receptor kinase 5 −2.31
U06069 Stxbp1 Sec1; syntaxin binding protein 1 −2.11
M94043 Rab38 RAB-related GTP-binding protein −2.02
a The genes that were found down-regulated/suppressed and common to both 2- and 5-ppm–exposed rat lung are listed here.
b Accession numbers derived from the NCBI Unigene database (http://www.ncbi.nlm.nih.gov/).
c Gene symbols derived from the Duke Integrated Genomics Database (https://dig.cgt.duke.edu/try_query.php).
d Fold suppression of gene expression. Fold changes in the expression of these genes were statistically significant by one-way ANOVA (p < 0.05).
Table 5 List of induced (> 2-fold) genes that are unique to 2 or 5 ppm O3.a
Accession no.b Gene symbolc Gene namec Fold changed
2 ppm O3
J03933 Thrb thyroid hormone receptor beta, c-erbA-β 5.32
U73174 Gsr glutathione reductase 5.21
L08447 Cd3z T-cell receptor CD3 zeta subunit 4.37
L46791 Ces3 liver carboxylase precursor 10 (carboxylesterase 3) 3.95
J02650 Rpl19 60S ribosomal protein L19 3.51
X96394 Abcc1 multidrug resistance protein 2.70
D29766 Bcar1 FAK substrate p130 2.53
U49062 Cd24 signal transducer CD24 2.39
D16554 Ubb polyubiquitin 2.25
5 ppm O3
X17163 Jun c-jun AP1 5.26
M84203 Kcnc2 potassium channel protein (KshIII A) 5.20
D10862 Id1 inhibitor of DNA binding 1 4.33
M81855 Abcb1 multidrug resistance protein 1 2.74
D14051 Nos2 inducible nitric oxide synthase 2.61
U45965 Cxcl2 Mip-2 chemokine ligand 2 2.57
M86389 Hspb1 heat shock 27 kDa protein 1 2.55
L29232 Igf1r IGF-1 receptor 2.50
D16237 Cdc25b M-phase inducer phosphatase 2 2.48
a Genes that were induced and unique to either 2- or 5-ppm–exposed rat lung are listed here. Accession numbers derived from the NCBI Unigene database (http://www.ncbi.nlm.nih.gov/).
c Gene symbols and names derived from the Duke Integrated Genomics Database (https://dig.cgt.duke.edu/try_query.php)
d Fold induction in gene expression. Fold changes in expression of these genes were statistically significant by one-way ANOVA (p < 0.05).
Table 6 List of suppressed (> 2-fold) genes that are unique to 2 or 5 ppm O3.a
Accession no.b Gene symbolc Gene namec Fold changed
2 ppm O3
J02999 Rab2 ras-related protein RAB2 3.50
L19698 Rala GTP binding protein (Ral A) 3.11
X07287 Pkrcg protein kinase C-γ 2.86
J03552 Mug1 plasma proteinase inhibitor 2.81
D85760 Gna12 guanine nucleotide-binding protein α-12 2.55
M99567 Plcb3 phospholipase C β-3 2.45
U00620 Cfs2 GM-CSF 2.45
M59980 Kcnd2 voltage-gated K+ channel protein 2.18
M83666 Hck Hck tyrosine protein kinase, p56 2.15
AF020777 Ptk2 focal adhesion kinase 2.04
AF000300 Lyn lyn A tyrosine kinase 2.03
5 ppm O3
U46034 Mmp11 matrix metalloproteinase 11 3.61
D55627 Rbl2 retinoblastoma-like 2 3.49
M95738 Slc6a11 Na+/K+ dependent GABA transporter 2.95
M28647 Atp1a1 Na+/K+ ATPase α1 subunit 2.42
U93306 Kdr VEGFR-2 2.16
M20637 Plcd1 phospholipase C delta 1 2.07
a The genes that are found suppressed/down-regulated and unique to either 2- or 5-ppm–exposed rat lung are listed here.
b Accession numbers from derived the NCBI Unigene database (http://www.ncbi.nlm.nih.gov/).
c Gene symbols and name s derived from the Duke Integrated Genomics Database (https://dig.cgt.duke.edu/try_query.php).
d Fold induction in gene expression. Fold changes in expression of these genes were statistically significant by one-way ANOVA (p < 0.05).
Table 7 Confirmation of gene array expression by real time RT-PCR for a select list of genes.a
2 ppm
5 ppm
Gene symbolb Gene nameb Gene array RT-PCR Gene array RT-PCR
c-erb thyroid hormone receptor 5.0c 3.0 NC NC
c-jun transcription factor AP1 NC NC 5.0 3.0
Nos2 inducible nitric oxide synthase NC NC 2.0 1.8
Gsr glutathione reductase 5.0 5.2 NC NC
Igfbp2 insulin-like growth factor binding protein 2 − < 15 −20.0 − < 5.0 −5.5
NC, no change in expression.
a Log numbers derived from real-time PCR analysis were normalized to the expression of the housekeeping gene GAPDH , which was unaltered by O3 exposure in rat lung tissue.
b Gene symbols and names derived from the Duke Integrated Genomics Database (https://dig.cgt.duke.edu/try_query.php).
c Fold change in expression compared with air-exposed control rat lung tissue.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8190ehp0113-00172316330354ResearchThe Association between Fatal Coronary Heart Disease and Ambient Particulate Air Pollution: Are Females at Greater Risk? Chen Lie Hong Knutsen Synnove F. Shavlik David Beeson W. Lawrence Petersen Floyd Ghamsary Mark Abbey David Department of Epidemiology and Biostatistics, Loma Linda University, Loma Linda, California, USAAddress correspondence to S. Knutsen, Loma Linda University, School of Public Health, Health Research, Evans Hall, Room 215, Loma Linda, CA 92350 USA. Telephone: (909) 558-4988. Fax: (909) 558-0268. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 2 8 2005 113 12 1723 1729 8 4 2005 1 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The purpose of this study was to assess the effect of long-term ambient particulate matter (PM) on risk of fatal coronary heart disease (CHD). A cohort of 3,239 nonsmoking, non-Hispanic white adults was followed for 22 years. Monthly concentrations of ambient air pollutants were obtained from monitoring stations [PM < 10 μm in aerodynamic diameter (PM10), ozone, sulfur dioxide, nitrogen dioxide] or airport visibility data [PM < 2.5 μm in aerodynamic diameter (PM2.5)] and interpolated to ZIP code centroids of work and residence locations. All participants had completed a detailed lifestyle questionnaire at baseline (1976), and follow-up information on environmental tobacco smoke and other personal sources of air pollution were available from four subsequent questionnaires from 1977 through 2000. Persons with prevalent CHD, stroke, or diabetes at baseline (1976) were excluded, and analyses were controlled for a number of potential confounders, including lifestyle. In females, the relative risk (RR) for fatal CHD with each 10-μg/m3 increase in PM2.5 was 1.42 [95% confidence interval (CI), 1.06–1.90] in the single-pollutant model and 2.00 (95% CI, 1.51–2.64) in the two-pollutant model with O3. Corresponding RRs for a 10-μg/m3 increase in PM10-2.5 and PM10 were 1.62 and 1.45, respectively, in all females and 1.85 and 1.52 in postmenopausal females. No associations were found in males. A positive association with fatal CHD was found with all three PM fractions in females but not in males. The risk estimates were strengthened when adjusting for gaseous pollutants, especially O3, and were highest for PM2.5. These findings could have great implications for policy regulations.
air pollutioncoronary diseaseischemic heart diseaselong-term exposuremortalityparticulate matter
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Since the early reports of increased deaths from cardiopulmonary disease (CPD) after serious air pollution episodes (Firket 1931; Logan 1953), studies both within the United States and abroad have found similar short-term effects of air pollution (Dominici et al. 2003; Samet et al. 2000; Zanobetti et al. 2003).
Studies have also found increased risk of CPD, noncancer respiratory, and respiratory cancer deaths with chronic exposure to ambient particulate matter (PM) (Abbey et al. 1999; Dockery et al. 1993; McDonnell et al. 2000; Pope et al. 1995, 2002, 2004a), black smoke (NOx) (Hoek et al. 2002), and nitrogen oxides (Hoek et al. 2002; Nafstad et al. 2004). Four main prospective studies have been conducted in the United States to assess long-term health effects of ambient air pollution in adults [the Six Cities Study, the American Cancer Society (ACS) study, the Adventist Health Study on the Health Effects of Smog (AHSMOG), and the national cohort of male U.S. veterans]. Associations with fine particulates [PM < 2.5 μm in aero-dynamic diameter (PM2.5)] have been found for all-cause mortality, CPD mortality, and respiratory/lung cancer mortality in the ACS, Six Cities, and AHSMOG studies and with mortality attributable to ischemic heart disease (IHD), dysrhythmias, heart failure, and cardiac arrest in the ACS study. AHSMOG (Abbey et al. 1999) has also shown positive associations, although not always significant, between PM < 10 μm in aerodynamic diameter (PM10) and all-natural-cause mortality and CPD mortality in males but not in females. For fatal lung cancer and any mention of non-malignant respiratory disease, a positive association was found with PM10 in both sexes. The national cohort of male U.S. veterans, where all subjects were hypertensive at baseline, found no increased mortality with increasing levels of fine particulates (Lipfert et al. 2000). From Europe, Hoek et al. (2002) reported increased risk of CPD mortality and all-cause mortality with increased concentrations of black smoke and nitrogen dioxide, and Nafstad et al. (2004) found increased risk of noncancer respiratory mortality and CPD mortality with increasing levels of NOx.
Several studies on short-term effects have found that ambient PM increases cardiac arrhythmia (Peters et al. 2000), decreases heart rate variability (Pope et al. 2004b), increases the inflammatory response measured by C-reactive protein (CRP) (Riediker et al. 2004), and increases blood viscosity (Peters et al. 1997) as well as other blood markers (e.g., hemoglobin, fibrinogen, platelet counts, white cell counts) (Riediker et al. 2004). These observed effects would provide a mechanism by which chronic exposure to ambient air pollution is associated with risk of coronary heart disease (CHD).
This study reports on the risk of fatal CHD associated with long-term ambient air pollution in AHSMOG.
Materials and Methods
Study population.
AHSMOG began in April 1977 by enrolling 6,338 participants from the Adventist Health Study (AHS) (n = 34,198), a large cohort study of the relationship between lifestyle and risk of chronic disease (Beeson et al. 1989). To be included in AHSMOG, subjects must be nonsmoking, non-Hispanic whites ≥25 years of age at baseline and must have lived ≥10 years within 5 miles of their 1976 neighborhood. All subjects satisfying these criteria were selected from three large metropolitan areas in California: San Francisco, South Coast (i.e., Los Angeles and eastward), and San Diego air basins. In addition, a 13% random sample of 862 AHS subjects was selected from the rest of California assuring large variation and wide ranges in concentrations of different ambient air pollutants.
As part of their enrollment in the AHS in 1976, all participants completed a comprehensive questionnaire that included questions on education, anthropometric data, smoking history, dietary habits, exercise patterns, and previous physician-diagnosed chronic diseases (Beeson et al. 1989). Monthly residence and work location histories were obtained for each subject for the period January 1966 through December 1998, or until date of death or date of last contact, by using mailed questionnaires (1977, 1987, 1992, 2000), tracing by telephone, and interviewing of surrogates (for deceased subjects). Only 29 (< 0.01%) persons were lost to follow-up with respect to vital status, and these were censored at date of last contact for inclusion in risk sets. The follow-up questionnaires contained standardized questions on respiratory symptoms (American Thoracic Society 1995) and questions to ascertain lifestyle and housing characteristics pertinent to relative exposure to ambient air pollutants, as well as occupational exposures to dust and fumes and indoor sources of air pollution, including environmental tobacco smoke (ETS).
Several air pollutants were estimated for study participants using the statewide network of monitoring stations maintained by the California Air Resource Board (CARB) (Abbey et al. 1991). Because estimated PM2.5 measures were not available on a statewide basis during follow-up, only the 3,769 (2,422 females and 1,347 males) belonging to the airport subcohort (those who lived within an airshed adjacent to one of nine California airports with available visibility measures: Alameda, Bakersfield, Fresno, Long Beach, Los Angeles, Ontario, Sacramento, San Jose, and San Diego) were included in this study. Of these, 530 (n = 332 females, n = 198 males) were excluded because of a history of CHD, stroke, or diabetes at baseline, leaving 3,239 subjects for analyses.
Estimation of ambient air pollution concentrations. Estimates of monthly ambient
concentrations of PM10, ozone, sulfur dioxide, and NO2 were formed for study participants for 1973–1998 using fixed-site monitoring stations maintained by CARB. The detailed methods for estimating ambient air pollutants for study participants are described elsewhere (Abbey et al. 1991, 1995a). Briefly, monthly indices of ambient air pollutant concentrations at 348 monitoring stations throughout California were interpolated to geographic ZIP code centroids according to home and work location histories of study participants. These were cumulated and then averaged over time. Interpolations were restricted to ZIP code centroids within 50 km of a monitoring station and were not allowed to cross barriers to airflow or other topographic obstructions > 250 m above the surrounding terrain. Concentrations of PM10 before 1987 were estimated using site- and season-specific regressions based on total suspended particles (TSPs) (Abbey et al. 1995a). Since 1987, directly monitored PM10 has been used.
Daily estimates of ambient PM2.5 concentration were obtained for 11 airsheds from daily measures of visibility collected at the nine California airports for the years 1973–1998 using regression equations relating PM2.5 and visibility. Because of wind patterns, Ontario provided three separate airsheds (East, West, Central). Detailed methods for PM2.5 estimation have been described previously (Abbey et al. 1995b). Individual monthly average PM2.5 concentrations were calculated as the mean of the daily ambient PM2.5 estimates for the airshed in which the participant resided. Any month with PM2.5 estimates for > 75% of the days was considered to have valid data.
Ascertainment of deaths.
Fatal CHD, defined by codes 410–414 of the International Classification of Diseases, 9th Revision (ICD-9) (World Health Organization 1977) as either “definite fatal myocardial infarction” or “other definite fatal CHD,” as underlying or immediate cause of death was used to assess fatal CHD.
Deaths were ascertained through 1998 using record linkage with both the California death certificate files and the National Death Index (Centers for Disease Control and Prevention, National Center for Health Statistics, Atlanta, GA, USA). In addition, our tracing procedures, which included church records, were used (Beeson et al. 1989). Thus, among the airport subcohort free of CHD, stroke, and diabetes at baseline, we identified 1,054 total deaths during follow-up. Death certificates were obtained, and a state-certified nosologist, blinded to the exposure status, coded each death certificate according to the ICD-9 codes.
Statistical analysis.
Sex-specific comparisons of baseline descriptive information between CHD mortality cases and noncases were made using the Student t-test or chi-square test.
Time-dependent Cox proportional-hazards regression modeling was used to study associations between pollutants (PM2.5, PM10–2.5, PM10, O3, SO2, and NO2) and CHD mortality with attained age as the time variable (Greenland 1989). This was further augmented by adding the sandwich variance estimate (Lin 1994) to adjust for correlated observations within each airshed. All 11 air-sheds around the nine airports were included in the model. We also included the airports as dummy variables stratified with the Cox model. Rate ratios were calculated for an increment of 10 μg/m3 for each of the particulate pollutants and 10 ppb for each of gaseous pollutants, except SO2, which was calculated for an increment of 1 ppb. Because measures for most of the pollutants were available only from 1973, we had 4-year monthly averages for these pollutants at baseline in 1977. To standardize the exposure window preceding events, we therefore selected 4-year average as our moving time period of exposure, but excluded the last month before the event to avoid measuring short-term effects. Participants who did not die were censored at end of follow-up, or at time of last contact if they were lost to follow-up (394 females, 166 males). The different pollutants were entered into the model as continuous variables.
The basic multivariable model included past cigarette smoking, body mass index (BMI), years of education, and frequency of meat consumption. We added an interaction term between sex and pollutant to this basic model that was significant, and therefore, all analyses were sex specific. Additional candidate variables for inclusion in the final model were ETS (years lived or worked with a smoker), total physical activity at baseline, history of hypertension at baseline, exposure to dust/fumes at work, frequency of eating nuts (Fraser et al. 1992), number of glasses of water per day (Chan et al. 2002), time spent outdoors, and hormone replacement therapy (HRT) (female models). In addition, we found that the levels of PM pollutants used in this study have declined from 1973 to 1998 (Figure 1), and we therefore included calendar time as a candidate variable to adjust for possible changes in PM composition over time. All candidate variables were entered into the basic multivariable model one at a time to assess their impact on the main effect. Only calendar year changed the relative risks (RRs) > 10% (actually 16%) and was retained in the final model (Greenland 1989).
The proportional hazards assumption was checked by examining log [−log(survival)] curves versus the time (attained age) as well as the product term of each respective variable in the final model with the log of the time variable (Greenland 1989). Each of these interaction terms produced a p-value > 0.05 based on the Wald statistic, indicating that the proportional hazards assumptions were not seriously violated. This was supported further by visual inspection.
The same sex-specific, time-dependent multivariable Cox proportional-hazards regression models with and without the sandwich variance estimate, airport dummy variables, and stratified analysis were further used to study associations in two-pollutant models for particulates (PM2.5, PM10–2.5, or PM10) with each of the gases (O3, SO2, and NO2) and CHD mortality. We evaluated the interactions between two individual pollutants for inclusion in the final model based on whether they changed the RRs > 10%. None of the terms met this criterion (Greenland 1989). All analyses were repeated for postmenopausal females separately.
In addition, we repeated sex-specific analyses using cumulative monthly averages of each particulate pollutant from 1973 to censoring and also for each of the PM fractions using three levels of exposure (≤25, > 25–38, > 38 μg/m3) rather than as a continuous variable. We used the SAS statistical package (version 9.1; SAS institute, Cary, NC) for all analyses.
Results
During 22-year follow-up (1977–1998), there were 155 CHD deaths in females and 95 among males, 23.7% of all deaths in this group.
Those who died of CHD were older at baseline, had fewer years of education, and were more likely to have hypertension; a larger proportion of the females were postmenopausal, and of these, fewer had used HRT (Table 1). A higher proportion of female noncases had lived or worked with a smoker (ETS), and noncases tended to drink more water than did cases. The mean concentrations and correlations of pollutants for this airport subcohort from 1973 through the month of censoring are provided in Table 2. Frequency histograms of the individual mean ambient concentrations of each of the PM fractions from 1973 to censoring month are given in Figure 2. Those in the lowest distribution of PM2.5 lived in the airsheds represented by the San Diego, San Jose, Sacramento, and Alameda airports; medium levels were found in Fresno, Los Angeles International, Bakersfield, Long Beach, Ontario West, and Ontario Central; and the highest distribution represents Ontario East. Figure 1 shows the secular trends in PM10, PM2.5, and O3 during the study for the Ontario East and San Diego air basins and for the study population as a whole.
Risk of fatal CHD.
All results presented are from the time-dependent Cox model without and with the inclusion of the sandwich variance estimate. For females, in age-adjusted single-pollutant models, a positive but nonsignificant relationship was found between each of the three PM fractions and risk of fatal CHD (Table 3). This association became stronger in multivariate analyses, with PM2.5 having the highest RR of 1.42 [95% confidence interval (CI), 1.11–1.81] for each increment of 10 μg/m3.
In two-pollutant models with O3 (Table 4), the associations with each of the PM fractions became stronger and statistically significant both in age-adjusted and in multivariable-adjusted models, with the strongest relationship for PM2.5 (RR = 1.99; 95% CI, 1.37–2.88). NO2 did not change the associations between PM and fatal CHD, whereas SO2 strengthened the association some, but not to the same degree as did O3. Point estimates remained virtually unchanged both in single-pollutant and in multipollutant models when including the sandwich variance estimate. When airports were included as dummy variables or in stratified analyses, the risk estimates either remained the same or were strengthened. Limiting the analyses to postmenopausal females resulted in small increases in risk estimates.
Using cumulative monthly averages from 1973 to censoring instead of the 4-year moving average gave similar but somewhat weaker associations. Using PM2.5 estimates as tertiles (Figure 3 for females) showed that those exposed to levels > 38 μg/m3 were 2.3 times more likely to die of CHD than were those living in areas where concentrations were ≤ 25 μg/m3 (p-value for trend = 0.007). After adjusting for O3 in two-pollutant models, the risk estimates for PM2.5 increased to 2.03 and 5.35 in the medium and highest tertiles, respectively (p-value for trend = 0.006).
No significant associations were found between any of the gaseous pollutants and fatal CHD in either the age-adjusted or multivariable-adjusted analyses in single-pollutant or in two-pollutant models with PM. However, the association with NO2 was elevated for both males and females in single-pollutant models (Table 3). In males, no association was found between particulate pollutants and fatal CHD either as continuous or as categorical (tertiles) variables in single- or two-pollutant models (Tables 3, 4).
Discussion
Most studies of the association between ambient particulate air pollution and cardiovascular disease (CVD) have been limited to effects of short-term increases in PM on hospital admissions for CVD (Zanobetti et al. 2000) and total mortality (Dominici et al. 2003; Samet et al. 2000). Of the particulate pollutants, PM2.5 seems to show the strongest association with CVD outcomes (Pope et al. 2002, 2004a).
The Six Cities and the ACS studies have reported a positive association between CPD and cardiovascular deaths and long-term exposure to ambient PM. The association was strongest for fine particles, with RRs varying between 1.06 for CPD deaths (Pope et al. 2002) and 1.12 for cardiovascular deaths (Pope et al. 2004a) for each increment of 10 μg/m3 after adjusting for age, sex, diet, and other demographic covariates. When comparing most-polluted with least-polluted areas, the RR for CPD death was 1.31 for a difference of 24.5 μg/m3 in the ACS study (Pope et al. 1995) and 1.37 for a difference of 18.6 μg/m3 in the Six Cities Study (Dockery et al. 1993). Pope et al. (2004a) reported a somewhat higher risk estimate for mortality from IHD, with an RR of 1.18 for an increment of 10 μg/m3, and concluded that “predominant PM mortality associations” were with IHD. The effect of fine particles on CPD mortality has not been reported from AHSMOG to date. For PM10 and CPD mortality, no significant relationships were found, but males had higher estimates than did females (Abbey et al. 1999).
Two European cohort studies have both looked at traffic-related pollution (Hoek et al. 2002; Nafstad et al. 2004). Hoek et al. (2002) found that persons living near a major road had a 1.95 greater risk of CPD death than did others and, that for each increase of 10 μg/m3 in black smoke, the RR increased by 34%. Among Norwegian men, Nafstad et al. (2004) found that for each increase of 10 μg/m3 in nitrogen oxides (markers of traffic pollution), the risk increased by 8% for fatal IHD and by 16% for respiratory deaths.
We found significant relationships between ambient PM and fatal CHD only in females. To our knowledge, no other cohort study on the health effects of ambient air pollution has reported sex-specific risks for CHD mortality. Therefore, we cannot readily compare our findings with others. However, the ACS study did find a slightly higher, although not significant, risk of CPD mortality among never-smoking females versus males in the most-polluted cities compared with the least polluted (RR = 1.57 in females vs. 1.24 in males) (Pope et al. 1995). As far as we have been able to assess, neither the Six Cities Study nor the Dutch study (Hoek et al. 2002) has reported sex-specific findings on CPD mortality. The Norwegian cohort included only males (Nafstad et al. 2004), as did the male U.S. veterans cohort mortality study (Lipfert et al. 2000). In a study of short-term effects, Peters et al. (1997) reported a stronger effect of TSPs on blood viscosity in females than males during episodes of high air pollution in Augsburg, Germany.
Several experimental studies of pulmonary deposition of inhaled particles in healthy adults showed that particle deposition characteristics differ between males and females under controlled breathing conditions. Kim and Hu (1998) found that deposition in females is greater than that in males and that the deposition was more localized within the lung in females. The authors suggest that regional deposition enhancement in women may lead to a greater health risk in females than in males. This is consistent with the hypothesized mechanism in which the deposition of particles in the lung could elicit inflammatory responses resulting in a systemic signal (Seaton et al. 1995).
An experimental study of 50 persons (Sorensen et al. 2003) showed significant positive associations between personal PM2.5 exposure and oxidation products [e.g., plasma malondialdehyde, red blood cells (RBCs), and hemoglobin concentrations] in females but not in males. The authors suggest that females possibly are more sensitive to airborne pollution than are males because they have fewer RBCs and thus may be more sensitive to toxicologic influences of air pollutants.
A recent study supporting our sex-differential findings assessed the relationship between ambient levels of PM2.5 at place of residence and degree of intima media thickness as measured by ultrasound (Künzli et al. 2005). Cross-sectional analyses of baseline data from two clinical trials in Los Angeles showed that the association was statistically significant among women but not among men. Also, the associations were stronger among older persons who had never smoked or who reported using lipid-lowering treatment at baseline. The strongest association, however, was found among older women (≥60 years of age). These findings corroborate with our findings from AHSMOG, which is also an older population, with mean age at fatal CHD of 67.6 years in men and 72.3 years in women.
Our findings and those of other studies show that particulate air pollution seems to have a stronger effect on fatal CHD than on other fatal CPD end points. The ACS study found a somewhat higher RR associated with an increase in PM2.5 of 10 μg/m3 for fatal IHD (RR = 1.18; 95% CI, 1.14–1.23) (Pope et al. 2004a) than what they had previously found for CPD mortality (RR = 1.09; 95% CI, 1.03–1.16) (Pope et al. 2002). In females, our findings for fatal CHD and PM are stronger than those we have previously reported for CPD mortality in the total AHSMOG cohort (Abbey et al. 1999) and in the airport cohort (McDonnell et al. 2000). Also, in a previous report we found positive associations with CPD mortality only in males (Abbey et al. 1999). In extended follow-up of CPD mortality in the total AHSMOG cohort through 1998 using the same models as previously, we continue to find a slightly stronger association in males than in females (unpublished data). However, when we exclude baseline CHD, stroke, and diabetes, these sex differences disappear, and when we limit our analyses to the airport cohort, CPD mortality is actually significantly increased in females but not in males (RR = 1.14 vs. 1.02 in males). These findings warrant further study of the effect of PM in sensitive subgroups and in densely populated areas (e.g., airport cohort) versus less densely populated areas. It also suggests that health effects of air pollution are different in males and females.
Even though we found the strongest association with PM2.5, the coarse fraction was also associated with significant risk. One possible explanation for the higher risk estimates for all three PM fractions in our study could be more precise estimates of ambient air pollution and thus less exposure misclassification. AHSMOG is the only study with monthly estimates of ambient air pollution for each subject throughout the entire follow-up period. Other reasons could be the homogeneity of the population (see “Strengths and limitations,” below).
Because different components of air pollution frequently occur together and are highly correlated (Table 2), the U.S. Environmental Protection Agency (EPA) has suggested that the association observed with PM could instead be due to gaseous pollutants (U.S. EPA 1989). We found no significant association between fatal CHD and gaseous pollutants in single- or two-pollutant models. However, in two-pollutant models, both O3 and SO2 strengthened the relationship between PM and fatal CHD, whereas NO2 had no effect. The modifying effect of O 3 can possibly be explained by findings indicating that lung epithelial permeability increases with exposure to O3 (Blomberg et al. 2003), thus making the body more susceptible to intrusion of particulate matter. The proposed mechanisms for the observed cardiovascular effects of particulates have been discussed in detail in a statement from the American Heart Association (Brook et al. 2004). Several pathways may be involved, but initiation of pulmonary and systemic oxidative stress and inflammation by components of the different PM particles seems to be the most accepted. The resulting cascades of physiologic responses are believed to be able to jointly initiate processes that ultimately lead to a CHD event. Elevated ambient PM2.5 levels have been shown to be associated with cardiac autonomic function (Peters et al. 2000), heart rate and heart rate variability (Pope et al. 2004b), CRP levels (Riediker et al. 2004), and changes in blood viscosity favoring coagulation (Peters et al. 1997; Seaton et al. 1995). Several authors have suggested that risk of CVD may be mediated, at least partly, through increased concentrations of plasma fibrinogen, possibly due to an inflammatory reaction caused by air pollution (Koenig et al. 1998). Fibrinogen is an important determinant of plasma viscosity and an independent risk factor for CHD (Koenig et al. 1998). Numerous animal models corroborate the findings in humans of an effect of PM on heart rate (Chang et al. 2004), blood viscosity (Coates and Richardson 1978), and pulmonary inflammation (Wichers et al. 2004).
These pathways are very similar to those suggested for the effect of cigarette smoking on risk of CHD, such as elevated inflammatory markers, especially CRP levels (Panagiotakos et al. 2004), fibrinogen and white cell counts (Panagiotakos et al. 2004), blood viscosity (Frohlich et al. 2003), heart rate (Bolinder and de Faire 1998), and oxidative stress (Guthikonda et al. 2004). Smoking also has been found to trigger acute vasoconstriction and thus the enhanced development of atherosclerosis in the systemic vasculature (Kiechl et al. 2002). Finally, in studies of the effect of smoking and ETS, Diez-Roux et al. (1995) and Howard et al. (1994) have reported clear effects on intima media thickness progression over time and on arterial wall stiffness (Mack et al. 2003).
Strengths and limitations.
Because all subjects in AHSMOG are nonsmokers, our results are free from the confounding of active cigarette smoking. We had detailed information about ETS and have been able to adjust for this effect. Any modifying effect of alcohol is also eliminated because virtually everyone abstains from alcohol. Because AHSMOG has extensive information on lifestyle, we were able to adjust for the effects of a number of such factors, including dietary factors, found to be associated with CHD in this cohort. This adjustment actually strengthened the associations between PM and fatal CHD in females but not in males.
Although we have shown cardiovascular effects of particulate air pollution in this study, we have unknown amounts of measurement error in both the estimated long-term ambient concentrations of pollutants and other covariates. One source of measurement error derives from interpolating ambient concentrations (PM10, O3, NO2, SO2) from fixed-site monitoring stations to ZIP code centroids of work and home locations of study participants (Abbey et al. 1991, 1995a). Another source of measurement error is that ambient PM2.5 concentration was not measured directly for the duration of this study, but estimated from airport visibility, temperature, and humidity (Abbey et al. 1995b). The precision of the PM10–2.5 is unknown because it is calculated as the difference between PM10 and PM2.5. Use of ambient concentrations rather than measures of personal exposure could be one limitation in this study, but it is unlikely that we have selective bias in the females only. Further, we cannot rule out the possibility that the observed sex difference in effect could be due to measurement error. Males, more than females, reported working > 5 miles from their residence and thus may have spent more time in heavy traffic (more commutes and longer commuter distances). We have not been able to take this into consideration when estimating each subject’s ambient air pollution levels.
Conclusions
In summary, in this study we found an elevated risk of fatal CHD associated with ambient levels of PM10, PM10–2.5, and PM2.5 in females but not in males. The risk estimates were strengthened when adjusting for gaseous pollutants and were highest for PM2.5. Our findings are in line with findings by others of an effect of PM on CPD mortality, but are of greater magnitude, possibly because the outcome was limited to fatal CHD with better control of confounding factors such as alcohol and tobacco.
Further studies are needed from larger cohorts and/or with longer follow-up to support our findings of a sex-differential effect of PM on risk of fatal CHD. Developing more accurate ways to assess an individual’s exposure to ambient levels of PM will improve precision of risk estimates. Further, it is important to study whether the effects of air pollution are reversible in a manner similar to that found when smokers stop smoking. The effect of different exceedance frequencies should also be explored as well as the effect of different chemical compositions of PM.
Correction
Some of the values in Table 3 published originally online were incorrect; they have been corrected here.
Financial support is provided by U.S. Environmental Protection Agency (EPA) grant CR-83054701. Although the research described in this article has been funded by the U.S. EPA, it has not been subjected to agency review and does not necessarily reflect the view of the agency.
Figure 1 Mean concentration over time, 1973–1998: (A) PM2.5; (B) PM10; and (C) O3. (A and B) Sexes combined: AHSMOG cohort (solid line), Ontario East air basin (dashed line), and San Diego air basin (dotted line). (C) AHSMOG cohort (solid line), mountain areas (dashed line), and coastal areas (dotted line). The y-axis scales differ among the three panels.
Figure 2 Frequency distribution of mean ambient concentration of (A) PM10, (B) PM2.5 , and (C) PM10–2.5, 1973 to censoring month; n = 3,239. Note that the x-axis scales differ among the three panels.
Figure 3 RR of fatal CHD and tertiles of PM2.5 mean concentration in single- and two-pollutant models (PM2.5 + O3); all females.
Table 1 Selected characteristics of study population at baseline.
Male (n = 1,149)
Female (n = 2,090)
Characteristic Cases (n = 95) Noncases (n = 1,054) Cases (n = 155) Noncases (n = 1,935)
Age [years (mean ± SD)] 67.6 ± 11.5 55.8 ± 12.9** 72.3 ± 8.9 56.6 ± 13.4**
Years of education (mean ± SD) 13.5 ± 3.5 14.6 ± 3.2* 12.6 ± 2.8 13.4 ± 2.6**
Never smokers 51 (53.7) 717 (68.0)* 133 (85.8) 1,655 (85.5)
BMI at or above median 46 (48.4) 477 (45.3) 76 (49.0) 875 (45.2)
Meat consumptiona,b
< 1 week 40 (42.1) 496 (47.1) 88 (56.8) 913 (47.2)
1 week 50 (52.6) 516 (49.0) 57 (36.8) 917 (47.4)
Total exercise
Low 25 (26.3) 344 (32.6) 67 (43.2) 937 (48.4)
Moderate and high 70 (73.7) 709 (67.3) 83 (53.5) 990 (51.2)
History of hypertension 32 (33.7) 171 (16.2)** 70 (45.2) 444 (22.9)**
ETS 57 (60.0) 619 (58.7) 77 (49.7) 1,208 (62.5)*
Nutsa
≤2/month 29 (30.5) 331 (31.4) 60 (38.7) 684 (35.3)
1–4/week 37 (38.9) 428 (40.6) 51 (32.9) 736 (38.0)
≥5/week 23 (24.2) 255 (24.2) 33 (21.3) 397 (20.5)
Watera,c
≤2 glasses 6 (6.3) 119 (11.3) 26 (16.8) 351 (18.1)
3–4 glasses 44 (46.3) 369 (35.0) 49 (31.6) 708 (36.6)
≥5 glasses 42 (44.2) 546 (51.8) 79 (51.0) 833 (43.0)
Postmenopausal 138 (89.0) 1,323 (68.4)**
HRT in postmenopausal females 20 (14.5) 431 (32.6)**
Values are presented as no. (%) or mean ± SD.
a Some columns do not add to 100% because of missing data.
b Significant at p < 0.01 for females only.
c Significant at p < 0.05 for males only.
* p < 0.01,
** p < 0.001.
Table 2 Descriptive statistics and correlations between long-term averages of pollutants estimated for study participants, 1973 through month of censoring, females and males combined (n = 3,239).
PM10 (μg/m3) PM2.5 (μg/m3) PM10–2.5 (μg/m3) O3 (ppb) NO2 (ppb) SO2 (ppb)
Mean ± SD 52.6 ± 16.9 29.0 ± 9.8 25.4 ± 8.5 26.2 ± 7.3 34.9 ± 9.7 4.5 ± 2.7
PM10 1.00 0.83* 0.91* 0.79* 0.50* 0.36*
PM2.5 1.00 0.59* 0.60* 0.25* 0.30*
PM10–2.5 1.00 0.75 0.51* 0.35*
O3 1.00 0.22* 0.11*
NO2 1.00 0.70*
SO2 1.00
* p < 0.01.
Table 3 Age-adjusted and multivariable-adjusted RRs of fatal CHD for specific PM components: single-pollutant models.
Age adjusted
Multivariable adjusteda Multivariable adjustedb Postmenopausal females, multivariable adjustedb
Pollutant Increment Cases RR (95% CI) Cases RR (95% CI) Cases RR (95% CI) Cases RR (95% CI)
Females PM10 10 μg/m3 92 1.11 (0.98–1.26) 92 1.22 (1.06–1.40) 92 1.22 (1.01–1.47) 80 1.30 (1.08–1.57)
PM2.5 10 μg/m3 92 1.19 (0.96–1.47) 92 1.42 (1.11–1.81) 92 1.42 (1.06–1.90) 80 1.49 (1.17–1.89)
PM10–2.5 10 μg/m3 92 1.20 (0.95–1.53) 92 1.38 (1.07–1.77) 92 1.38 (0.97–1.95) 80 1.61 (1.12–2.33)
O3 10 ppb 92 0.89 (0.67–1.18) 92 0.97 (0.71–1.32) 92 0.97 (0.68–1.38) 80 1.07 (0.73–1.59)
NO2 10 ppb 92 1.09 (0.88–1.35) 92 1.17 (0.92–1.49) 92 1.17 (0.98–1.40) 80 1.20 (1.01–1.44)
SO2 1 ppb 87 0.93 (0.87–1.01) 87 0.94 (0.85–1.04) 87 0.94 (0.81–1.08) 77 0.94 (0.80–1.11)
Males PM10 10 μg/m3 53 0.95 (0.81–1.11) 53 0.94 (0.80–1.11) 53 0.94 (0.82–1.08)
PM2.5 10 μg/m3 53 0.89 (0.69–1.17) 53 0.90 (0.67–1.19) 53 0.90 (0.76–1.05)
PM10–2.5 10 μg/m3 53 0.93 (0.68–1.29) 53 0.92 (0.67–1.28) 53 0.92 (0.66–1.29)
O3 10 ppb 53 0.87 (0.58–1.29) 53 0.89 (0.59–1.33) 53 0.89 (0.60–1.30)
NO2 10 ppb 53 1.24 (0.94–1.64) 53 1.16 (0.86–1.56) 53 1.16 (0.89–1.51)
SO2 1 ppb 51 1.06 (0.98–1.14) 51 1.02 (0.92–1.13) 51 1.02 (0.94–1.11)
a Adjusted for smoking status (past vs. never), years of education, BMI (below vs. at or above median), meat consumption (< 1/week vs. ≥1/week), calendar time.
b Model “b” with sandwich variance estimate.
Table 4 Age-adjusted and multivariable-adjusted RRs of fatal CHD for specific PM components: two-pollutant models.
Age adjusteda Multivariable adjustedb Multivariable adjustedc Postmenopausal females, multivariable adjustedc
Pollutant PM Gas Cases RRd (95% CI) Cases RRd (95% CI) Cases RRd (95% CI) Cases RRd (95% CI)
Females PM10 + O3 92 1.33 (1.12–1.59) 92 1.45 (1.21–1.74) 92 1.45 (1.31–1.61) 80 1.52 (1.37–1.69)
NO2 92 1.11 (0.97–1.26) 92 1.21 (1.05–1.40) 92 1.21 (1.00–1.46) 80 1.29 (1.06–1.57)
SO2 87 1.15 (1.02–1.31) 87 1.27 (1.10–1.47) 87 1.27 (1.08–1.50) 77 1.33 (1.11–1.59)
PM2.5 + O3 92 1.61 (1.17–2.22) 92 1.99 (1.37–2.88) 92 2.00 (1.51–2.64) 80 1.95 (1.52–2.50)
NO2 92 1.18 (0.95–1.47) 92 1.39 (1.08–1.80) 92 1.40 (1.04–1.87) 80 1.46 (1.13–1.89)
SO2 87 1.36 (1.05–1.74) 87 1.50 (1.15–1.97) 87 1.51 (1.17–1.95) 77 1.51 (1.19–1.92)
PM10–2.5 + O3 92 1.47 (1.10–1.96) 92 1.62 (1.21–2.17) 92 1.62 (1.31–2.01) 80 1.85 (1.50–2.29)
NO2 92 1.19 (0.92–1.54) 92 1.35 (1.03–1.76) 92 1.34 (0.94–1.94) 80 1.59 (1.07–2.36)
SO2 87 1.31 (1.03–1.68) 87 1.49 (1.15–1.93) 87 1.49 (1.12–1.99) 77 1.68 (1.20–2.35)
Males PM10 + O3 53 0.97 (0.78–1.20) 53 0.96 (0.77–1.19) 53 0.96 (0.87–1.05)
NO2 53 0.90 (0.76–1.07) 53 0.91 (0.76–1.09) 53 0.91 (0.78–1.07)
SO2 51 0.92 (0.78–1.09) 51 0.93 (0.78–1.11) 51 0.93 (0.78–1.11)
PM2.5 + O3 53 0.92 (0.65–1.29) 53 0.91 (0.64–1.30) 53 0.91 (0.78–1.06)
NO2 53 0.82 (0.61–1.10) 53 0.85 (0.63–1.15) 53 0.85 (0.70–1.04)
SO2 51 0.86 (0.65–1.14) 51 0.88 (0.65–1.19) 51 0.88 (0.73–1.07)
PM10–2.5 + O3 53 1.01 (0.67–1.51) 53 0.97 (0.64–1.46) 53 0.97 (0.74–1.26)
NO2 53 0.86 (0.62–1.20) 53 0.87 (0.62–1.23) 53 0.87 (0.60–1.26)
SO2 51 0.90 (0.64–1.27) 51 0.89 (0.63–1.27) 51 0.85 (0.55–1.32)
a Age adjusted with sandwich variance estimate.
b Adjusted for smoking status (past vs. never), years of education, BMI (below vs. at or above median), meat consumption (< 1/week vs. ≥1/week), calendar time.
c Model “b” with sandwich variance estimate.
d RR was calculated for an increase of 10 μg/m3 in concentration of the specific PM components.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8106ehp0113-00173016330355ResearchEffect of Lead Exposure and Ergonomic Stressors on Peripheral Nerve Function Bleecker Margit L. Ford D. Patrick Vaughan Christopher G. Lindgren Karen N. Tiburzi Michael J. Walsh Karin Scheetz Center for Occupational and Environmental Neurology, Baltimore, Maryland, USAAddress correspondence to M.L. Bleecker, Center for Occupational and Environmental Neurology, 3901 Greenspring Ave., Suite 101, Baltimore, MD 21211 USA. Telephone (410) 669-1101. Fax: (410) 669-1103. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 8 8 2005 113 12 1730 1734 10 3 2005 8 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In this study we investigated the effect of recent and chronic lead exposure, and its interaction with ergonomic stressors, on peripheral nerve function. In a cross-sectional design, we used retrospective exposure data on 74 primary lead smelter workers. We measured blood and bone lead levels and, from historical records, calculated lead dose metrics reflecting cumulative lead exposure: working-lifetime integrated blood lead (IBL) and working-lifetime weighted-average blood lead (TWA). We additionally created five metrics related to IBL that cumulated exposure only above increasing blood lead levels ranging from 20 to 60 μg/dL (IBL20–IBL60). Current perception threshold (CPT) assessed large myelinated (CPT2000), small myelinated (CPT250), and unmyelinated (CPT5) sensory nerve fibers. Using multiple linear regression, we modeled CPT on the different measures of lead dose after adjusting for relevant covariates. CPT had a curvilinear relationship with TWA, with a minimum at a TWA of 28 μg/dL. Both TWA and IBL accounted for a significant percentage of the variance of CPT2000 (ΔR2 = 8.7% and 3.9%, respectively). As the criterion blood lead level increased from IBL20 through IBL60, so did the percentage of CPT2000 variance explained, with ΔR2 ranging from 5.8% (p < 0.03) for IBL20 to 23.3% (p < 0.00) for IBL60. IBL60 also significantly contributed to the explanation of variance of CPT250 and significantly interacted with ergonomic stressors. Measures of chronic blood lead exposure are associated with impairment of large and small myelinated sensory nerve fibers. This effect is enhanced at the highest doses by ergonomic stressors.
bone leadcumulative lead doseergonomic stressorslead dose thresholds for peripheral nerveperipheral nerve fiber size
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The classic description of lead neuropathy is that of a motor neuropathy that typically presents as wrist drop. More recently, investigators demonstrated that, in the development of lead neuropathy, sensory nerve fibers are affected earlier than motor nerve fibers (Chuang et al. 2000; Kovala et al. 1997; Rubens et al. 2001; Schwartz et al. 2001; Singer et al. 1983), and nerve conduction studies showed mild slowing of both sensory and motor conduction velocities as well as diminished amplitude of the sensory potential (Araki et al. 1986; Ashby 1980; Baker et al. 1984; Bordo et al. 1982; Buchthal and Behse 1979; Catton et al. 1970; Chen et al. 1985; Chia et al. 1996a, 1996b; Jeyaratnam et al. 1985; Kovala et al. 1997; Pasternak et al. 1989; Rubens et al. 2001; Seppäläinen and Hernberg 1980; Seppäläinen et al. 1979; Singer et al. 1983; Yeh et al. 1995). After reviewing the lead neuropathy literature from 1974 to 1984, Ehle (1986) concluded that sensory nerve conduction is more likely to be affected than is motor nerve conduction, that the upper extremities are more likely to be involved than the lower extremities, and that these effects usually occur after a year of lead exposure, with a continuous linear relationship between blood lead and nerve conduction velocity only when blood lead exceeded 70 μg/dL.
In evaluating peripheral nerve function, electrodiagnostic testing examines the integrity of only large myelinated nerve fibers with the fastest conduction velocities. Current perception threshold (CPT), a neuroselective test, measures sensory nerve conduction threshold in three nerve fiber populations—large myelinated (Aβ), small myelinated (Aδ), and unmyelinated (C) nerve fibers. In peripheral neuropathies associated with a variety of medical conditions, CPT abnormalities have demonstrated good agreement with nerve conduction studies (Katims et al. 1989; Rendell et al. 1989; Weseley et al. 1989). Additionally, pathology in the small myelinated and unmyelinated nerve fibers shown with CPT but not detected by routine nerve conduction studies occur in Fabry’s disease (Ro et al. 1999), diabetic and alcoholic C-fiber neuropathies (Oishi et al. 2002), arsenic exposure (Tseng 2003), and leprosy (Katims J, personnel communication). Capsaicin, a topical drug for pain relief that affects small nerve fibers, was found to elevate CPT thresholds for small myelinated and unmyelinated nerve fibers but not for large myelinated nerve fibers (Kiso et al. 2001).
In the past, the usual biomarker used to study lead neuropathy was PbB, a blood lead measure of recent exposure (Bordo et al. 1982; Davis and Svendsgaard 1990, Pasternak et al. 1989; Rubens et al. 2001). More recently, studies have shown an association between several biomarkers of chronic lead exposure—working lifetime-weighted average blood lead (TWA), working lifetime-integrated blood lead (IBL), and bone lead (PbBn)—and impairment of peripheral nerve function at a time when concurrent PbB was not elevated (Chia et al. 1996a; Chuang et al. 2000; Kovala et al. 1997; Schwartz et al. 2001; Triebig et al. 1984; Yeh et al. 1995). Which of these is the best metric for modeling chronic lead effects on the peripheral nerve remains to be demonstrated.
In the older literature, lead poisoning presented as muscle paralysis, typically occurring in the muscles most used (Aub et al. 1925). In fact, patterns of weakness differed by occupation but did not necessarily follow the distribution of a specific nerve (Cantarow and Trumper 1944). Although it is established that lead impairs peripheral nerve function, not studied to date is the effect of the interaction between lead exposure and chronic repetitive muscle use on that function.
We report here on the use of CPT to examine different nerve fiber populations in the upper extremities of a group of current lead workers with substantial chronic lead exposure and a broad range of ergonomic stressors (ESs).
Materials and Methods
Subjects.
A screening neuropsychological battery had been administered to 468 current and retired smelter workers by testers blinded to the degree of lead exposure of the worker. If performance on two or more tests in any functional domain was < 1.5 SDs compared with age-matched norms, the worker was invited for a complete clinical evaluation. Eighty current workers were identified by this criterion. Bleecker et al. (1995, 1997, 2002, 2003) and Lindgren et al. (1996) have described other aspects of these samples in previous publications. All participants volunteered for the study and signed an informed consent form approved by a combined provincial management–labor oversight committee. The Human Subjects Committee at the University of Maryland, Baltimore, approved the PbBn protocol.
Exposure.
As employees of a primary smelter (located in New Brunswick, Canada), participants were routinely exposed to several sources of inorganic lead dust and, to a lesser extent, lead fumes. Since the smelter began operations in 1966, PbB levels of all employees have been checked at least quarterly. The frequency of PbB measurements depended on the relative degree of lead exposure of any given job and whether the employee had been relocated because of lead exposure. PbB levels precipitating relocation dropped from 90 μg/dL in 1966 to 75 μg/dL in 1974, 65 μg/dL in 1987, and 50 μg/dL in 1990. In general, the smelter workers in this study had chronic inorganic lead exposure that had been high in the distant past but lower in the more proximate past, with relatively low PbB levels at the time of this study.
Blood samples for lead testing had been collected preshift by the facility nursing staff in the infirmary, a building physically distinct from the smelter, using standard techniques to minimize the likelihood of lead contamination of the samples. A local off-site laboratory using the dithizone method initially performed sample analysis. By the early 1970s, these analyses were conducted by a regional contract laboratory using graphite-furnace atomic-absorption spectrophotometry; this laboratory subsequently became a participant in the interlaboratory blood lead proficiency testing program of the then–U.S. Centers for Disease Control. Results of this proficiency testing showed good agreement. For the purpose of this study, blood lead results from the two different laboratories were considered equivalent.
We calculated the lead levels used to determine IBL, a measure of cumulative blood lead, as the sum—over each participant’s working lifetime—of the products of each PbB level and one-half the time interval from the preceding blood lead to the following blood lead measure. TWA, the measure of average intensity of lead exposure over the period of employment, was created by dividing IBL by total years of employment at the smelter. To examine the effect of the amount of time a subject’s blood lead concentration was above a criterion level, we also created a series of metrics—IBL20, IBL30, IBL40, IBL50, IBL60—calculated in the same manner as IBL but including only areas under the time–blood lead curve that were above increasingly higher criterion blood lead levels; for example, IBL20 μg/dL was calculated by cumulating only that part of the area under the curve > 20 μg/dL (Figure 1). PbB was obtained on the day of testing. PbBn analysis, previously described (Bleecker et al. 1995), used the methods of Chettle et al. (1991). Measurements made at the mid-tibia with K-shell X-ray fluorescence were performed at the University of Maryland Toxicology Program laboratories.
Working-lifetime weighted-average ES.
An ES rating was created with the assistance of the smelter safety committee, who reviewed all jobs ever worked by the participants and stratified them on a three-tiered ordinal scale. Using the method of Moore and Garg (1995), we converted the ordinal scale to interval with the following weights: 1, light; 6, medium; and 18, heavy. We then cumulated over each participant’s employment history the products of duration of time worked in a given job and the job’s assigned ES weight. From this, we calculated a time-weighted average ES.
Current perception threshold.
CPT measures the minimum transcutaneous current intensity needed to produce a sensation (Neurometer, Neurotron Inc., Baltimore, MD). Because it uses a constant alternating current, there is no change in current intensity with variations in skin impedance. The sinusoidal waveform of the alternating current excites different subpopulations of nerve fibers as a function of frequency: 2,000 Hz, large myelinated fibers; 250 Hz, small myelinated fibers; and 5 Hz, small unmyelinated fibers.
Electrodes were attached to the dorsolateral aspect of the fourth digit of the nondominant hand. CPT was initially approximated by the “method of limits,” where the current was increased until the worker reported a sensation (i.e., buzzing). To more precisely ascertain threshold, the current was decremented and reincremented until a range was reached where a stimulus was correctly identified at one intensity and not at a slightly lower one for three consecutive trials. During this part of the testing, the stimulus presentation used a “forced choice method” paradigm with the presentation of a real and placebo stimuli. The procedure was repeated for all three frequencies at each site and are referred to in this article as CPT2000, CPT250, and CPT5.
Data analyses.
Before the analyses, we examined age, current alcohol use, current smoking, ES, and the lead exposure metrics using univariate descriptive statistics to check for accuracy of data entry, missing values, and assumptions underlying multivariate analysis. Four individuals had values > 2.5 SDs above the mean of the CPT score and considered univariate outliers; one individual was identified through Mahalanobis distance as a multivariable outlier with p < 0.001. One individual was missing ergonomic data, leaving 74 individuals for analysis. Those removed were not significantly different from the remaining sample on the independent variables or the covariates.
SPSS-PC (version 12.0.1; SPSS Inc., Chicago, IL) was used for data analyses. The determination of covariates was based on risk factors associated with the development of a peripheral neuropathy. These included age, dichotomous current smoking, dichotomous current alcohol use, and working-lifetime weighted-average ES. Other medical conditions commonly associated with peripheral neuropathy were not present. The three CPT measures were modeled using multiple linear regression with the measures of lead dose, PbB, TWA, IBL, IBL20, IBL30, IBL40, IBL50, IBL60, and PbBn after adjusting for the covariates. Additionally, on the basis of a priori considerations (Hopkins and Morgan-Hughes 1969; Jacobs and Le Quesne 1984), we modeled the interaction between ES and each of the exposure variables in these regressions.
Results
Demographic data for the 74 workers included in the analyses are presented in Table 1, along with mean values for the four measures of lead exposure and the outcome measures of CPT by frequency. As expected, thresholds by fiber population decreased from large myelinated nerve fibers to small myelinated nerve fibers and more so for small unmyelinated nerve fibers.
Table 2 presents the results of the unique variance contributed to CPT by the measure of lead dose after adjustment for the covariates. Of the simple exposure variables, IBL, TWA, PbB, and PbBn, only the two based on cumulative blood lead levels—IBL and TWA—were significantly related to CPT, and in both cases only to CPT2000, after adjusting for the covariates. IBL explained 3.9% of the variation in CPT2000 (p < 0.08). Regression diagnostics revealed nonlinearity in the relationship between TWA and CPT2000, which was addressed by including a quadratic term in the model. Combined, the TWA and TWA2 terms accounted for 8.7% of the variation in CPT2000 (p < 0.03). The calculated minimum for the quadratic relationship for TWA and CPT2000 was 28 μg/dL (Figure 2).
To examine the contribution to CPT by exposure above different blood lead levels, we stratified IBL by the cumulative time a subject’s PbB was above different criterion levels—IBL above a PbB level of 20 μg/dL (n = 74), 30 μg/dL (n = 73), 40 μg/dL (n = 70), 50 μg/dL (n = 68), and 60 μg/dL (n = 61). The different sample sizes at each level reflect workers who did not have PbB that reached the required level. In Table 3, separate linear regressions revealed the unique variance that IBL20, IBL30, IBL40, IBL50, and IBL60 each contributed to the three frequencies of CPT, after adjusting for age, smoking, alcohol use, and ES. As the criterion PbB level increased from IBL20 through IBL60, so did the percentage of CPT2000 variance explained, with ΔR2 ranging from 5.8% (p < 0.03) for IBL20 to 23.3% (p < 0.00) for IBL60. Only IBL60 accounted for a significant amount of variance of CPT250, reflecting increased nerve damage with time spent at PbB > 60 μg/dL. Despite diminished power with IBL60 due to a smaller sample size, the dose effect remained significant.
To address the interaction of motor activity and lead toxicity on the peripheral nerves, we tested interaction terms created by multiplying the IBL variables based on the increased criterion blood lead levels × ES with multiple linear regression, controlling for the covariates and base terms. The strength of association of the interaction term with CPT2000 increased from IBL20 × ES (R2 = 1.1%, p = not significant) to IBL60 × ES (R2 = 6.1%, p < 0.02). The interaction is shown in Figure 3 as heterogeneity of regression slopes in the two groups stratified by high and low ES, suggesting that in the presence of high ES there is an enhanced lead effect on the peripheral nerve.
Discussion
In this group of lead-exposed workers, IBL and TWA, two measures of chronic lead exposure, were significantly related to decrements in peripheral nerve function as measured by CPT, whereas PbBn and PbB were not. PbBn, with a half-life of 17–25 years, is a measure of lead stored in the bone compartment and is not a consistent biomarker of lead effect in the nervous system (Bleecker et al. 1997; Hanninen et al. 1998; Kovala et al. 1997). Also, PbB, with a half-life of 30 days, is a weak measure of lead exposure for the peripheral nervous system as demonstrated in a meta-analysis of 32 electrodiagnostic studies of lead neuropathy (Davis and Svendsgaard 1990). With ongoing exposure, lead accumulates in the nervous system and is retained there even as PbB falls. This accounts for the lack of a consistent relationship between lead content in the nervous system and PbB (Cantarow and Trumper 1944; Feldman et al. 1977; Goldstein et al. 1974). Because lead neuropathy requires exposure for months to years, it is not surprising that PbB, a biomarker reflecting recent exposure, has an inconsistent association with this outcome. Other studies have found measures of chronic lead exposure associated with changes in nerve conduction velocity at a time when PbB was not (Chia et al. 1996a, 1996b). However, measures of chronic lead exposure associated with vibration thresholds or nerve conduction studies continue to vary among the published studies, from PbBn (Schwartz et al. 2001) to TWA (Chuang et al. 2000; Seppäläinen et al. 1979; Triebig et al. 1984) to IBL (Chia et al. 1996a, 1996b; Kovala et al. 1997; Yeh et al. 1995). A Finnish study (Kovala et al. 1997) demonstrated that IBL had a stronger relationship than did PbBn with nerve conduction studies, a finding similar to that of this study.
The strength of IBL as a measure of cumulative exposure improved when the amount of time at lower blood lead levels was not included in the exposure term. This resulted in an increased strength of the linear model from ΔR2 for IBL = 3.9% (p < 0.08) to ΔR2 for IBL20 = 5.8% (p < 0.03). One possible explanation is that blood lead levels less relevant to the outcome were removed. This would result in improved precision of measurement due to a decreased nondifferential exposure misclassification.
IBL is a term composed of duration and intensity of exposure; however, the mean duration of lead exposure in the literature reporting significant association between IBL and peripheral nerve conduction parameters varies from 2.5 years (Yeh et al. 1995), to 5.3 years (Chia et al. 1996a, 1996b), to 16 years (Kovala et al. 1997), to 20 years in the present study with CPT. Despite decreased duration spent at the increasing criterion blood lead level, the variance accounted for by the exposure term increased, suggesting that average intensity may be more critical than duration of exposure for neurotoxicity in the peripheral nerves. In the present study, the absence of a significant relationship between years employed and CPT2000 is consistent with this hypothesis.
As reported by Ehle (1986), an association of nerve conduction studies with lead exposure occurred when PbB exceeded 70 μg/dL; however, an increasing number of studies are finding this association at much lower PbB levels. Chuang et al. (2000) found a threshold curve for vibration perception at a mean PbB level of 31 μg/dL. Chia et al. (1996a, 1996b) and Chen et al. (1985) suggested that the threshold effect for changes in nerve conduction studies occurs at a PbB level of 40 μg/dL, whereas Seppäläinen et al. (1983) showed that it was closer to 30 μg/dL. Yeh et al. (1995) found electromyographic abnormalities beginning at PbB levels of 17 μg/dL. In this study, there was no association of CPT and PbB; however, the curve minimum of TWA was at 28 μg/dL. This association of low PbB with nerve function may be caused by the attention given sensory nerve fibers that are affected earlier in the development of lead neuropathy (Ehle 1986; Rubens et al. 2001; Singer et al. 1983).
CPT for large myelinated fibers showed that these were the primary nerve fibers affected by lead exposure. Vibration perception thresholds also carried by large myelinated fibers is associated with chronic lead exposure (Chuang et al. 2000; Kovala et al. 1997; Schwartz et al. 2001). These findings agree with neuropathology of a biopsy of human lead neuropathy that found loss of the large myelinated nerve fibers in a sensory nerve (Buchthal and Behse 1979). CPT provided neuroselective stimuli that allowed for detection of expanded pathology at IBL60 with involvement of large (CPT2000) and small (CPT250) myelinated nerve fibers, a biologically plausible finding.
Lead affects the upper extremities more frequently than the lower extremities (Chuang et al. 2000; Ehle 1986; Pasternak et al. 1989; Schwartz et al. 2001; Yeh et al. 1995). Dermal absorption of inorganic lead is minimal compared with inhalation and oral absorption. However, direct cutaneous exposure in the upper extremities may occur through skin absorption, as reported in humans with limited exposure in an experimental setting (Moore et al. 1980; Stauber et al. 1994; Sun et al. 2002). This may contribute to the increased prevalence of upper-extremity involvement, because lower extremities are usually protected from cutaneous exposure. The upper-extremity involvement is unusual because toxic neuropathies classically begin in the largest and longest axons in the feet. Earlier literature of lead neuropathy reported different patterns of weakness in the upper extremities based on occupation, which some believed was due to a myopathy (Aub et al. 1925; Cantarow and Trumper 1944; Hamilton 1925). The conclusion reached was that motor activity increased the effects of lead toxicity (Jacobs and Le Quesne 1984). In the present study, exposure to ES, used as a surrogate for active motor units, did interact with lead exposure but was significant only at IBL60. This is not unexpected because the earlier literature usually reported motor involvement presenting as weakness or paralysis only at PbB levels > 60 μg/dL.
Another possible explanation for the interaction of lead and active motor units is that nerves affected by lead are more susceptible to traction or mechanical compression, as would occur in the carpal tunnel of workers with exposure to ESs such as heavy lifting and shoveling. This interaction between a peripheral neuropathy and a focal entrapment neuropathy exists in patients with diabetes (Gilliatt and Willison 1962), Guillain-Barré syndrome (Lambert and Mulder 1964), and familial neuropathy (Earl et al. 1964). This paradigm examined in animal models revealed that the onset of compression neuropathy in healthy animals took several months versus a few weeks in animals with an underlying neuropathy; this latter compression lesion was more severe (Hopkins and Morgan-Hughes 1969). Serial electrodiagnostic studies on the upper extremities of lead-exposed workers showed that the median nerve was more susceptible to the effects of lead than was the ulnar nerve (Chia et al. 1996b). This finding may again reflect the interaction with ES. The principle of increased susceptibility of a compromised peripheral nerve to a second insult is well known in oncology, where patients with preexisting neuropathy may develop incapacitating toxic neuropathies after the administration of safe doses of chemotherapeutic agents (Chaudhry et al. 2003).
The ability to infer a causal relationship between lead exposure and peripheral nerve function is limited in a cross-sectional study. IBL and TWA were based on blood lead levels obtained over the working lifetime of the participants, thus increasing the likelihood of any causal inferences made.
In this population of lead smelter workers, nerve function as measured by CPT is associated with impairment in large and small myelinated sensory nerve fibers with a threshold effect at a TWA of 28 μg/dL. Peripheral nerve impairment is associated with markers of chronic lead exposure, TWA and IBL, but not PbBn, and may be present when recent PbB is at an acceptable concentration. Even with chronic lead exposure, intensity is more important than duration of exposure. At higher levels of lead exposure, nerve fibers affected by lead are more susceptible to the presence of more active motor units as reflected by ESs.
We thank F. McNeill for performing the bone lead measurements.
This work was supported by the New Brunswick Occupational and Safety Commission.
Figure 1 IBL above increasing criterion PbB levels, as shown by the shaded area under the curve. (A) IBL; (B) IBL20; (C) IBL30; (D) IBL40; (E) IBL50; (F) IBL60.
Figure 2 Predicted curvilinear relationship between TWA and CPT2000. CB, confidence bound.
Figure 3 IBL60 and ES interaction.
Table 1 Descriptive statistics on demographics, exposure, and finger CPT for 74 current smelter workers.
Variable Mean ± SD Minimum–maximum
Age (years) 44 ± 8.4 24 to 64
Education (years) 8 ± 2.8 0 to 13
Years employed 20 ± 5.3 4 to 26
Current alcohol users (%) 60 —
Current smokers (%) 14 —
PbB (μg Pb/dL) 26 ± 7.1 13 to 43
IBL (μg-year/dL) 891 ± 298.8 81 to 1,376
TWA (μg Pb/dL) 42 ± 8.4 17 to 57
PbBn (μg Pb/g bone mineral) 40 ± 23.8 −12 to 90
CPT2000 Hz (mA) 330 ± 72.4 180 to 512
CPT250 Hz (mA) 134 ± 50.5 32 to 278
CPT5 Hz (mA) 83 ± 37.9 16 to 190
Values are mean ± SD except where noted.
Table 2 Unique variance (%) of CPT in the finger explained by measures of lead dose.
Variable CPT2000 CPT250 CPT5
IBL (μg-year/dL) 3.9* 0.4 0.0
TWA (μg Pb/dL) — 0.8 0.3
TWA + TWA2 (μg Pb/dL) 8.7** — —
PbBn (μg Pb/g bone mineral) 1.8 1.3 0.8
PbB (μg Pb/dL) 0.2 1.8 0.3
ΔR2 for exposure only. Analyses controlled for age, alcohol, smoking, and ESs.
* p < 0.08
** p < 0.03.
Table 3 Unique variance (%) of CPT in the finger explained by IBL metrics with increasing criterion blood lead levels.
Variable CPT2000 CPT250 CPT5
IBL 3.9* 0.4 0.0
IBL20 5.8** 1.0 0.1
IBL30 7.8# 1.8 0.2
IBL40 10.8## 2.7 0.5
IBL50 14.4## 3.7 0.6
IBL60 23.3## 10.1# 1.7
ΔR2 for exposure only. Analyses controlled for age, alcohol, smoking, and ESs.
* p < 0.08
** p < 0.03
# p < 0.02
## p < 0.005.
==== Refs
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Sun CC Wong TT Hwang YH Chao KY Jee SH Wang JD 2002 Percutaneous absorption of inorganic lead compounds AIHA J (Fairfax, Va) 63 641 646
Triebig G Weltle D Valentin H 1984 Investigations on neurotoxicity of chemical substances at the workplace Int Arch Occup Environ Health 53 189 204 6323322
Tseng CH 2003 Abnormal current perception thresholds measured by neurometer among residents in blackfoot disease-hyperendemic villages in Taiwan Toxicol Lett 146 27 36 14615065
Weseley S Liebowitz B Katims J 1989 Neuropathy of uremia: evaluation by nerve conduction velocity versus neurospecific current perception threshold Nephron 52 317 322 2770947
Yeh JH Chang YC Wang JD 1995 Combined electroneurographic and electromyographic studies in lead workers Occup Environ Med 52 415 419 7627320
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8178ehp0113-00173516330356ResearchEvaluation of Exposure to Arsenic in Residential Soil Tsuji Joyce S. 1Van Kerkhove Maria D. 2Kaetzel Rhonda S. 1Scrafford Carolyn G. 3Mink Pamela J. 3Barraj Leila M. 3Crecelius Eric A. 4Goodman Michael 51 Exponent, Bellevue, Washington, USA2 Exponent, New York, New York, USA3 Exponent, Washington, DC, USA4 Battelle Marine Sciences Laboratory, Sequim, Washington, USA5 Emory University, Atlanta, Georgia, USAAddress correspondence to J. Tsuji, Exponent, 15375 SE 30th Place, Suite 250, Bellevue, WA 98007 USA. Telephone: (425) 519-8700. Fax: (425) 519-8799. E-mail:
[email protected] expressed are those of the authors and not those of their individual employers.
The authors received funding from the FMC Corporation.
12 2005 17 8 2005 113 12 1735 1740 5 4 2005 17 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In response to concerns regarding arsenic in soil from a pesticide manufacturing plant, we conducted a biomonitoring study on children younger than 7 years of age, the age category of children most exposed to soil. Urine samples from 77 children (47% participation rate) were analyzed for total arsenic and arsenic species related to ingestion of inorganic arsenic. Older individuals also provided urine (n = 362) and toenail (n = 67) samples. Speciated urinary arsenic levels were similar between children (geometric mean, geometric SD, and range: 4.0, 2.2, and 0.89–17.7 μg/L, respectively) and older participants (3.8, 1.9, 0.91–19.9 μg/L) and consistent with unexposed populations. Toenail samples were < 1 mg/kg. Correlations between speciated urinary arsenic and arsenic in soil (r = 0.137, p = 0.39; n = 41) or house dust (r = 0.049, p = 0.73; n = 52) were not significant for children. Similarly, questionnaire responses indicating soil exposure were not associated with increased urinary arsenic levels. Relatively low soil arsenic exposure likely precluded quantification of arsenic exposure above background.
arsenicbiomonitoringexposuresoilurine
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Arsenic occurs naturally in the environment, and inorganic forms are of greatest health concern [Agency for Toxic Substances and Disease Registry (ATSDR) 2000]. Arsenic in soil has been the focus of regulatory action at sites in the United States for which health risk assessments are used to guide decisions on soil cleanup. Communication of risk assessment results, however, may lead people to believe that their cancer risk is substantial and to desire medical tests. Biomonitoring is typically offered to indicate whether exposures, and presumably risks, are above background (ATSDR 2000).
In a small U.S. community in New York State (Middleport), historical pesticide manufacture was associated with arsenic in soil [U.S. Environmental Protection Agency (U.S. EPA) 2002b]. Soil sampling and remediation initially focused on the plant site (FMC Corporation), adjacent school property, and drainage ditches and creeks that received surface water flow from the plant. Several residential properties near the plant site or along drainages also had arsenic soil levels in excess of the state cleanup level of 20 mg/kg.
Health risks from soil were of concern to the local regulatory agency and the community. At the request of community representatives, we conducted an exposure study that focused on young children for arsenic biomonitoring and primary analyses of soil arsenic exposure. Preschool-age children are considered the most exposed age group for chemicals in soil (Polissar et al. 1990; U.S. EPA 2002a).
Materials and Methods
In the summer and fall of 2003, Middleport residents were offered sampling of urine, toe-nails, soil, house dust, and homegrown produce, but not drinking water because the community is supplied by a water district [< 5 μg/L arsenic; Niagara County Water District (NCWD) 2004; U.S. EPA 2002b].
Study population.
Recruitment focused on young children (i.e., < 7 years of age), although residents of all ages were informed of the study and allowed to participate. In addition to community meetings, notices, and mailings, all houses in the study area were systematically visited for census and recruitment. Repeated attempts were made as needed, especially for houses with evidence of children (according to neighbors, town clerk, presence of toys, etc.). Participation required review of study information, written consent, and completion of a questionnaire on demographic, socioeconomic, and behavioral information and housing characteristics. At the time of urine collection, participants (or parents) completed a questionnaire of dietary habits, activities, conditions, and behaviors potentially related to arsenic exposure.
Biomarkers.
Urine.
Participants provided two first-morning-void urine samples on consecutive days between 1 August and 13 September. Participants were asked not to eat seafood for 3 days before sampling. Participants received urine collection kits (including pediatric urine bags for nontoilet-trained children) the day before collection. After collection, urine samples were stored on frozen gel packs or refrigerated before delivery to Lockport Memorial Hospital laboratory (Lockport, NY) for measurement of creatinine analysis by colorimetric method (values for the two first-morning-void samples were averaged). Samples were then shipped on frozen gel packs by courier to Battelle Marine Sciences Laboratory (Sequim, WA) and frozen until arsenic analysis. Quality assurance procedures were followed for all phases of data collection for urine and other samples.
Battelle Marine Sciences Laboratory analyzed composite urine samples (10 mL from each daily sample) for total arsenic by inductively coupled plasma–mass spectrometry (ICP-MS) (U.S. EPA 1996a) with a method detection limit of 0.2 μg/L. After further acidification of diluted samples with hydrochloric acid to pH < 2 and reduction with sodium borohydride, arsenic species (i.e., those related to ingestion and metabolism of inorganic arsenic) were trapped on a chromatography column and analyzed with hydride generation atomic absorption spectroscopy (U.S. EPA 1996b). Target method detection limits for arsenic species—inorganic arsenic, mono-methylarsonic acid (MMA), and dimethylarsinic acid (DMA)—were 0.06, 0.4, and 0.08 μg/L, respectively, with some estimated MMA and DMA values below these limits. In statistical analyses, undetected arsenic species in urine were conservatively assigned a level of 0.25 μg/L (half the method reporting limit).
In addition to analysis of standard quality control samples, 1 in 20 samples was analyzed by the U.S. Centers for Disease Control and Prevention (CDC) Inorganic Toxicology Laboratory (Atlanta, GA) for total arsenic, inorganic arsenic, MMA, DMA, arsenobetaine (AsB), trimethylarsine oxide, and arsenocholine (detection limits of 1.2, 1.0, 0.9, 1.7, 0.4, 1.0, and 0.6 μg/L, respectively). The latter two arsenic forms were not detected. AsB, an organic arsenic form in foods such as fish, was detected by the CDC in most of the 24 split samples. The results of both laboratories were highly correlated for total arsenic (R2 = 0.99; CDC = Battelle × 1.1 − 5.58) and reasonably correlated for speciated arsenic (R
2 = 0.67; CDC = Battelle × 0.68 + 2.96), given the differences in analytical techniques and detection limits.
Toenails.
Participants were informed that a condition of toenail sampling was wearing shoes outdoors for a month before collection. Those requesting sampling were given collection materials. Because of the time required to collect a sufficient sample (0.5 g requested), toenail samples were received from late August through October. Samples were scored for visible dirt/discoloration from 1 (clean) to 4 (all clippings dirty/discolored). Of the 84 samples submitted, 67 (none from young children) had sufficient mass for analysis (0.05 g). Toenail samples prepared according to Karagas et al. (2000) (nail polish removed with acetone, if necessary; sonicated in deionized water for 10 min; rinsed in deionized water) were acid digested and analyzed by ICP-MS using Method 6020 (method detection limit = 0.02 mg/kg; U.S. EPA 1986).
Environmental samples.
Soil.
Geomatrix Inc. (Amherst, NY) collected composite soil samples for yard, play area, and garden areas within properties using an approach similar to that of Hwang et al. (1997a). Yard soil composites included subsamples from randomly selected locations (at least 3 m apart) within each of a minimum of four representative sectors and two to six additional composite samples for yard areas > 1,000 m2. Low areas near drainages were sampled as a separate composite. Play area composites included a minimum of four subsamples with an additional sub-sample for every 59 m2 in excess of 230 m2. Yard and play area soil was sampled at a 0–7.6 cm (0–3 in.) depth below any vegetative cover. Vegetable garden soil was collected as separate composites at 0–15 cm (0–6 in.) and 0–30 cm (0–12 in.) depths within each vegetable garden, with additional locations sampled for every 2.3 m2 of area.
Soil samples and standard field control samples were analyzed by H2M Laboratories (Melville, NY) for total arsenic using trace ICP-atomic emission spectroscopy using Methods 3050B/6010B (U.S. EPA 1986), with a targeted quantitation limit of 1 mg/kg. Field and laboratory quality control samples were within standard accuracy and precision limit goals.
In addition to the 77 families who consented to soil sampling, soil data from discrete sample locations were available for eight additional properties (none with children < age 7) sampled during the site remedial investigation. Discrete samples within a property were averaged.
Soil arsenic data within properties were evaluated as arithmetic mean arsenic level of 0–6 in. depth in the garden, play area, and yard samples and maximum arsenic level among these areas.
House dust.
Sandler Occupational Medicine Associates Inc. (Gaithersburg, MD) sampled house dust between 3 September and 11 December. Residents were instructed not to sweep or vacuum the week before sampling. Although approximately half did not comply with this request, lack of compliance did not affect house dust results.
Based on methods of Hwang et al. (1997a) and Que Hee et al. (1985), dust samples were collected with a vacuum pump through tubing into a cassette with a 0.8 μm filter at 2.5 L/min. A composite sample (0.5 g minimum) was obtained using a 625 cm2 template in at least three locations: the most used entrance, most frequently occupied room (living room, kitchen, or family room), and child’s bedroom. H2M Laboratories analyzed filters for arsenic (same methods as for soil).
Produce.
Homegrown produce was sampled in August and early September as a service to residents, not as a comprehensive survey. Battelle Marine Sciences Laboratory freeze-dried and ball-milled samples, digested approximately 0.5 g in 2 M sodium hydroxide at 80°C for about 16 hr, and analyzed for total arsenic (ICP-MS; target method detection limit = 0.062 mg/kg, dry weight).
Data analysis.
The outcome measure of primary interest (dependent variable) was speciated arsenic in urine (i.e., sum of inorganic arsenic, MMA, and DMA). Exposure measures of primary interest (independent variables) were soil and house dust arsenic data. Other potential sources of arsenic exposure (e.g., diet), mediators of soil exposure (e.g., mouthing behaviors), and other covariates were ascertained through the questionnaire responses. Data were analyzed using the statistical software SPSS for Windows (version 7.0; SPSS, Chicago, IL) and Microsoft Excel (Microsoft Corporation, Redmond, WA).
Variables with little variation were excluded from the inferential analyses (except for those of interest, e.g., playing in creeks), and some categories were collapsed because of sparse numbers. Environmental and bio-marker data were log transformed based on their distribution (Hwang et al. 1997a). The log-transformed distributions were not significantly different from a normal distribution, except for speciated urinary arsenic, for which log transformation improved the fit with respect to normality (change in p-value from 0.007 to 0.013; Kolmogorov-Smirnov test of normality).
We estimated simple bivariate Pearson correlation coefficients among the dependent variable, exposure variables of primary interest, and continuous variables derived from the questionnaires. Analysis of variance and t-tests were conducted, where applicable, to evaluate associations between the primary outcome and independent variables and other variables derived from the questionnaires.
Linearity of relationships was examined visually before conducting regression analyses. Age-adjusted regression models that included speciated arsenic in urine with each of the environmental variables (i.e., soil and house dust arsenic levels) were run to identify a “base” model from which to build multiple regression models (including dependent and independent variables that appeared to best characterize the exposure–outcome association). To be conservative, variables with p < 0.15 in the age-adjusted models were included.
To evaluate possible nonindependence of subjects within families, analyses were also conducted using one randomly selected subject per family. Because both analyses yielded similar results, all subject samples were treated as independent samples regardless of household.
Results
Community and participant demographics.
Of the 826 households in the study area, 39 were vacant, and 55 could not be contacted but had no evidence of children. These 55 homes (mostly apartments) were assumed to have one adult resident of unknown age (average of vacant or one or two persons).
Census results and the study population were generally similar to 2000 U.S. Census data (U.S. Census Bureau 2000) for Middleport (Table 1). Although the study area included outlying areas, the study area population outside Middleport was low. Forty-seven percent of children younger than 7 years of age, 48% of children younger than 13 years, and 23% of all ages of the study area population participated in urine sampling (Table 1). Soil and house dust samples were collected for 58 and 73%, respectively, of participating children younger than 7 years of age. Sampling for urinary arsenic, house dust, and soil was reasonably representative across ages (data for children shown in Figure 1). House dust and soil arsenic values were also relatively evenly distributed over age with no apparent interactions.
Urine.
Speciated arsenic levels in the urine were < 20 μg/L and not significantly different between young children and older participants (Table 2). The geographic distribution of the speciated urine data showed little relation to the FMC Corporation facility or historical drainage from the plant (Figure 2A). By comparison, higher soil arsenic concentrations tended to be located near the plant site and along drainages to the east and north (Figure 2B).
Toenails.
Toenail samples were < 1 mg/kg [geometric mean (GM) = 0.13 mg/kg; geometric SD (GSD) = 2.53 mg/kg; range = 0.02–0.97 mg/kg], despite evidence of surface contamination. Toenail arsenic levels increased about 75% per unit increase in discoloration score (R2 = 0.205, p = 0.0001) and were not correlated with speciated arsenic in urine.
Soil and house dust.
Arsenic levels in soil averaged (GM) approximately 20 mg/kg and were < 100 mg/kg except for a few discrete samples from the properties sampled during the remedial investigation (Table 3). The highest maximum (1,124 mg/kg) and average (340 mg/kg) sample values were from the same property. The second highest maximum and average sample values were 103 mg/kg and 69 mg/kg, respectively.
Of the 111 households consenting to house dust sampling, 70 also had soil samples taken. The contribution of arsenic in soil to arsenic in house dust appears to be low and could not be quantified. Arsenic concentrations in house dust were generally lower than in soil (Table 3). Arsenic concentration or surface loading in house dust was not correlated with average or maximum soil concentration for properties with children younger than 7 years of age or for all properties sampled.
Produce.
Twenty-five types of produce from 42 gardens had arsenic concentrations < 0.6 mg/kg (wet weight). Tomatoes, the most prevalent crop (37 gardens), had arsenic concentrations near or below the limit of detection (≤ 0.010 mg/kg). Small sample sizes of other types of vegetables and low tomato results precluded analysis of correlations of arsenic levels in vegetables with soil or biomarkers.
Biomarker and environmental arsenic correlations.
Speciated arsenic in urine was not correlated with arsenic in soil or house dust for children younger than 7 years of age (Table 4). When corrected for creatinine, speciated arsenic in urine was correlated with arsenic in house dust (p = 0.030). Age (p = 0.003) and body weight (p = 0.029) showed a significant positive association with speciated urinary arsenic levels but were negatively associated with speciated arsenic levels corrected for creatinine (Table 4). The only significant associations between urinary arsenic and categorical exposure variables were visiting a local orchard (p = 0.002) or a home undergoing renovation (p = 0.027) within a week of sampling (Table 5).
Age-adjusted regression models failed to indicate an association between arsenic in urine and the environmental variables. Increasing the considered age range to younger than 13 years of age (n = 76 for soil; n = 88 for house dust concentration) or to all ages (n = 249 for soil; n = 278 for house dust concentration) did not result in a significant association between speciated urinary arsenic and the environmental variables in age-adjusted regression models. Because a “base” model could not be established, further multiple regression models were not run.
Results for children younger than 13 years of age were generally similar to those in children younger than 7 years: for example, highest correlation between speciated urinary arsenic levels and mean soil arsenic level (r = 0.201, p = 0.081) and significant association of speciated urinary arsenic with age (r = 0.294, p = 0.001). Creatinine-corrected speciated urinary arsenic, however, was not significantly correlated with arsenic in house dust, and urinary arsenic associations with body weight or visiting a house with renovations were not significant.
For all participants, speciated urinary arsenic levels had the highest correlation with arsenic concentration in house dust (r = 0.110, p = 0.068), were negatively correlated with eating homegrown produce (r = −0.097, p = 0.043), and were higher for those who ate rice or rice products [GM (n) = 4.5 μg/L (127) vs. 3.7 μg/L (308); p = 0.003]. Age was negatively correlated with speciated (r = −0.158, p < 0.001) urinary arsenic levels, and males had slightly higher speciated urinary arsenic levels (GM = 4.17 μg/L vs. 3.63 μg/L; p = 0.029).
Discussion
Comparison with other sites.
The ATSDR (2000) reported a reference level of 50 μg/L for total arsenic in urine, but not for speciated arsenic, the better measure of exposure to inorganic arsenic. Toenail arsenic levels were below the reported reference level of 1 mg/kg (ATSDR 2000).
Speciated urinary arsenic levels of young children (i.e., < 7 years of age) in Middleport were low compared with levels reported for children at other sites with higher soil arsenic levels (Table 6). Results of Polissar et al. (1987) reflected high levels of arsenic emitted from a recently operating smelter. Urinary arsenic levels for children were also much higher than for adults, unlike what we found at Middleport. After smelter closure, urinary results were considerably lower [Tacoma-Pierce County Health Department (TPCHD) 1988; Table 6].
Middleport urinary arsenic levels for all ages combined were also consistent with results reported for “control” populations including all ages (Hinwood et al. 2003b, 2004; Polissar et al. 1987, 1990).
Biomarker-based measures of arsenic exposure.
Because inorganic arsenic also occurs naturally in food and water (ATSDR 2000; Schoof et al. 1999; Yost et al. 2004), low levels of speciated arsenic are expected in urine. Although organic arsenic in seafood and some terrestrial organisms (Irgolic et al. 1999) primarily affects total rather than speciated arsenic in urine, other forms of arsenic in seafood (e.g., arsenosugars in bivalves and seaweed) can contribute to methylated arsenic species in urine (Le et al. 1999; Polissar et al. 1990).
Arsenic in urine is considered the most reliable biomarker of recent arsenic exposure (e.g., a few days to a week; ATSDR 2000). Biomonitoring of communities typically uses first-morning-void samples because 24-hr urine collection particularly from children is inconvenient and missed samples are likely (Hwang et al. 1997b). Hwang et al. (1997a, 1997b) analyzed two consecutive, first-morning-void urine samples for approximately 300 children and 24-hr urine in a subset of 25 children, but used the first-morning-void samples in the exposure analysis, and reported no differences in study results between using the average or highest of the two first-morning-void samples.
Toenail and hair samples reflect longer term exposure but are not easily related to a daily dose and are confounded by external arsenic contamination that is not easily removed (Harkins and Susten 2003; Hindmarsh et al. 1999; Hinwood et al. 2003a).
Sources and factors potentially affecting arsenic exposure.
Several elements of the study increased the likelihood of detecting exposures from arsenic in soil: a) the study focused on the age group with greatest soil exposure; b) approximately half of young children in the community participated; c) biomonitoring occurred during summer when soil exposures would be highest; d) urinary samples were analyzed for the specific forms of arsenic related to inorganic arsenic exposure; and e) the study design evaluated the statistical relationship between environmental samples and individual urinary arsenic levels, including evaluation of other factors affecting exposure, rather than simply comparing mean urinary arsenic levels with another community.
As also noted by a study in Bingham Creek, Utah, [University of Cincinnati Department of Environmental Health (UCDEH) 1997], increased awareness had little effect on exposure. Few parents attempted to limit their children’s exposure to soil (5 of 76 for < 7 years of age; 8 of 135 for < 13 years of age), and urinary arsenic levels were not significantly lower.
Correlations between environmental arsenic and urinary arsenic levels.
Lack of correlation between urinary arsenic and environmental measures may be the result of low arsenic levels in Middleport or limited sample size (participation in soil sampling was likely limited by the site agreement that data be shared with the state environmental agency) relative to the weakness of the correlations. Based on the highest estimated correlation coefficient between speciated urinary arsenic and soil, the sample size of children would have to be larger (≥ 203) than the estimated population of young children (164) to detect a significant correlation at α = 0.05. Speciated urinary arsenic, however, was not correlated with arsenic in soil in Bingham Creek, which involved 696 children (UCDEH 1997).
Reported correlations between speciated or inorganic urinary arsenic and measures of arsenic in soil are weak (r = 0.12–0.25, Hwang et al. 1997a, 1997b; Spearman r = 0.39, Hinwood et al. 2004). An increase in soil arsenic from 10 to 100 mg/kg would increase the GM of speciated urinary arsenic in young children in Middleport by only 1.2 times, according to Hwang et al. (1997a). Lower bioavailability and ingestion rates of arsenic in soil relative to food and water, combined with relatively low soil arsenic concentrations, are likely factors in the low soil arsenic exposure in this community.
Creatinine adjustment of urinary arsenic did not improve correlations between urine and soil arsenic levels, although a correlation with house dust became significant. Larger studies reported similar findings, except that urinary arsenic was not correlated with house dust at one study location (Anaconda, MT; Hwang et al. 1997a, 1997b) and only weakly correlated (r = 0.08; p < 0.05) in Bingham Creek (UCDEH 1997). Because creatinine excretion levels vary with muscle mass, sex, age, diet, genetic factors, diseases, and time, creatinine is not an accurate measure of sample dilution (Barr et al. 2005). Collection at a standard time (first morning void) and using 2-day composite samples likely reduced sample dilution variation in our study.
Although quantifying environmental exposure for individuals is uncertain, young children are more likely to be exposed to their immediate home environment, and composite soil samples are more representative of exposure over a yard than are a few discrete point samples (Hwang et al. 1997a; UCDEH 1997).
For several participants, collection of house dust samples, particularly for measures of concentration, was delayed by scheduling difficulties. Such delays are not expected to affect the arsenic concentration in house dust as much as for arsenic loading, unless a source of arsenic has increased (e.g., burning treated wood). Arsenic loading thus may be less representative of conditions at the time of urinary sampling.
Indirect indicators of potential arsenic exposure.
Unlike the direct correlations with soil data, these indirect analyses (survey responses, geographic distribution of urine data) included data from nearly all 77 young children. Higher urinary arsenic levels in the few children who visited orchards may reflect exposure from historical use of arsenic-containing pesticides. Consumption of garden vegetables has not been associated with increased urinary arsenic levels at other sites, as well (Hwang et al. 1997a; Polissar et al. 1987; UCDEH 1997).
Rice consumption may increase arsenic exposure, as observed in the total study population, because compared with other foods, a large percentage of arsenic in rice is in the inorganic form (Schoof et al. 1999). Thus, although we were not able to detect increased exposure from arsenic in soil, we may have been able to detect small contributions from dietary inorganic arsenic, a primary source of inorganic arsenic exposure (Meacher et al. 2002).
Conclusions
The results of this study are consistent with studies involving larger populations and higher soil arsenic concentrations. Although our results may seem inconsistent with those of risk assessment, biomonitoring and risk assessment differ in their focus. Speciated arsenic in urine includes all sources of inorganic arsenic (e.g., diet and water). Consequently, measurement of increased soil exposure is limited by the magnitude of this exposure relative to background sources of inorganic arsenic. Risk assessments of soil incorporate health-protective policy to avoid underestimation of soil exposure, regardless of whether background exposures from other sources are higher. Explaining these differences to the community is important for communicating risks.
We thank the citizens of Middleport, New York, for their assistance and J. Mandel and members of the scientific advisory panel (D. Barr, R. Bornschein, F. Frost Jr., D. Gute, P. Kostecki, H. Pastides, and P. Succop) for insightful guidance.
The study was funded by the FMC Corporation.
Figure 1 Speciated urinary arsenic levels of children younger than 7 years of age according to age. Soil and house dust sampling for individuals is noted.
Figure 2 Geographic distribution of (A) average value of speciated arsenic in urine per family, including all participants (distribution for children is similar), and (B) average yard soil concentration data.
Table 1 Demographic characteristics of the study area and study participants [n (%)].
Study area 2000 U.S. Censusa Study participants
Total persons 1,930 1,917 439
Population by sex
Male 874 (45) 908 (47) 206 (47)
Female 981 (51) 1,009 (53) 233 (53)
Unknown 75 (4) — —
Population by age (years)
< 5 104 (5) 141 (7) 43 (10)
< 7 (i.e., younger than 84 months) 164 (8) — 77 (18)
5–9 116 (6) 129 (7) 70 (16)
10–14 105 (5) 172 (9) 42 (10)
15–19 128 (7) 155 (8) 28 (6)
≥ 20 997 (52) 1,320 (69) 256 (58)
Unknown 465 (25) — —
Individuals by race (%)
White — 1,867 (97) 402 (92)
African American — 16 (< 1) 9 (2)
Native American — 5 (< 1) 13 (3)
Asian — 9 (< 1) 0
Other — 20 (1) 8 (< 2)
Unknown — 15 (< 1) 7 (< 2)
Total households 826 757 167
With children younger than 7 years 106 (13) — 55 (33)
With children younger than 13 years 161 (19) — 75 (47)
With individuals younger than 18 years 227 (27) 286 (38) 90 (54)
Income ≤ $40,000/year — 358 (47)b 72 (43)
Income > $40,000/year — 399 (53)b 82 (49)
Unknown — — 13 (8)
a Within Middleport village boundaries.
b 2000 U.S. Census income categories (U.S. Census Bureau 2000) were less than or greater than $35,000.
Table 2 Summary of arsenic concentration (μg/L) in urine.
Individual arsenic species
Total arsenic Speciated arsenic Inorganic arsenic MMA DMA
Children < 7 years (n = 77)
GM (GSD) 15.1 (1.8) 4.0 (2.2) 0.81 (1.5) 0.54 (1.9) 2.5 (2.9)
Range 2.1–59.6 0.89–17.7 0.31–2.1 0.12–2.1 0.27–13.8
Children < 13 years (n = 142)
GM (GSD) 15.7 (1.7) 4.6 (2.1) 0.83 (1.4) 0.55 (1.8) 3.0 (2.6)
Range 2.1–59.9 0.89–19.9 0.31–2.7 0.11–2.4 0.27–17.1
Children ≥ 7 years/adults (n = 362)
GM (GSD) 15.8 (2.1) 3.8 (1.9) 0.78 (1.4) 0.44 (1.8) 2.5 (2.3)
Range 3.9–773 0.91–19.9 0.31–2.7 0.024–2.4 0.17–17.1
All participants (n = 439)
GM (GSD) 15.7 (2.0) 3.9 (1.9) 0.78 (1.4) 0.46 (1.8) 2.5 (2.4)
Range 2.1–773 0.89–19.9 0.31–2.7 0.024–2.4 0.17–17.1
Table 3 Summary of arsenic concentration in soil and house dust.
Soil (mg/kg)
House dust
Property average Property maximum Arsenic concentration (mg/kg dust) Surface loading of arsenic (μg/100 cm2)
All households
No. of homes sampled 85 85 96 111
GM (GSD) 20.6 (2.0) 24.7 (2.2) 10.8 (3.0) 0.071 (4.4)
Range 4.6–340 6.2–1,124 1.0–172 0.004–2.97
Households with children younger than 7 years
No. of homes sampled 29 29 36 37
GM (GSD) 19.9 (1.6) 23.8 (1.7) 11.2 (3.1) 0.058 (4.0)
Range 10.4–46.4 10.4–58.8 1.7–172 0.004–0.77
Table 4 Correlation of urinary arsenic levels with environmental arsenic levels and numerical exposure factors for children younger than 7 years of age.
Correlation with urinary arsenic (μg/L)
Exposure factor No. Mean ± SD Median Range Speciated arsenic Creatinine- corrected speciated arsenic
Soil arsenic average (mg/kg) 41 18.8 (1.6)a 15.6 10.4–46.4 0.137 −0.019
Soil arsenic maximum (mg/kg) 41 22.9 (1.7)a 22.6 10.4–58.8 0.045 −0.132
House dust arsenic concentration (mg/kg) 52 10.6 (2.9)a 9.5 1.7–172 0.049 0.301*
House dust surface loading (μg As/100 cm2) 53 0.058 (4.1)a 0.056 0.004–0.77 0.090 0.232
Age of child (years) 77 4.3 ± 2 4.7 0.1–7 0.331** −0.263*
Weight (kg) 75 18.3 ± 6.4 18 5–35 0.253* −0.317**
Time playing in outdoor area (days/week) 70 5.2 ± 1.7 5 1–7 −0.150 0.003
Washed hands (times/day) 77 4.4 ± 3.1 3 0–20 −0.052 −0.275*
Playing near creeks (days/week) 10 4.0 ± 2.5 4 1–7 0.160 0.152
Playing in orchards (days/week) 3 1.7 ± 0.6 2 1–2 −0.484 −0.868
Urinary and environmental arsenic variables were log transformed before analysis. Other numerical survey variables not shown did not have significant correlations: body mass index, number in household, and frequency of bathing, taking food/drink outdoors, drinking tap water, and eating homegrown produce, seafood, and rice products.
a GM (GSD).
* p < 0.05.
** p < 0.01.
Table 5 Summary of categorical questionnaire variables and associated urinary arsenic levels (μg/L) for children younger than 7 years of age.
Response No. Speciated arsenic GM (GSD)
Sex Female 40 3.80 (2.46)
Male 37 4.25 (2.00)
Visited a house/building with ongoing renovations? Yes 6 7.93 (1.62)*
No 68 3.76 (2.21)
Don’t know 1 5.75 (—)
Limit child’s exposure to soil or dust? Yes 5 2.18 (2.76)
No 71 4.12 (2.17)
Play near creeks? Yes 10 4.23 (2.46)
No 67 3.98 (2.22)
Spent time at local orchard or produce farm? Yes 3 5.43 (1.04)*
No 73 3.96 (2.28)
* Significant difference in urinary arsenic levels between “yes” and “no” responses (t-test; p < 0.05). Other categorical responses with no significant differences: type of ground play surface, playing with outdoor pet, age of house, frequency of sucking fingers, frequency of putting objects in mouth, family income, exposure to smoking, daycare attendance, race, pacifier use, herbal medicine use, exposure to treated wood, street paved, eaten homegrown produce, eaten seafood, eaten rice/rice products, large digging or moving soil projects in last year. No significant results for creatinine-corrected speciated arsenic.
Table 6 Speciated urinary arsenic and soil arsenic levels for young children at various sites.
Speciated urinary arsenic concentration (μg/L)
Soil arsenic concentration (mg/kg)
n GM (GSD) Range n GM (GSD) Range
Middleport, NY, 2003 77 4.0 (2.2)
5.3 ± 3.0a 0.89–17.7 29 19.9 (1.6)
22.5 ± 11.7a 10.4–58.8
Bingham Creek, UT (UCDEH 1997)
Residences near Bingham Creek channel 696 5.86 (1.96) ND–35 1,045 27 (1.8) 4–623
Ruston/North Tacoma, WA, 1985–1986 (Polissar et al. 1987)
< 0.5 miles from smelter 118 52.1 (42.5)b NR 45 352 (410)b 12–2,069
0.5–1.2 miles from smelter 97 22.5 (29.3)b NR 40 125 (109)b 9–1,322
1.5–8.5 miles from smelter 49 13.7 (10.3)b NR 34 29.6 (49)b 2–290
Reference site (Bellingham, WA) 4 13.3 (3.3)b NR 10 6.6 (2.7)b 2–10
> 100 miles from smelter
Ruston/North Tacoma, WA, 1987 (TPCHD 1988)
< 0.5 miles from smelter 88 16.2 (16) NR NR NR NR
Anaconda, MT (Hwang et al. 1997a, 1997b)
Close to smelter 177 9.5 (1.7) NR–16.4 876 286c NR
Intermediate 62 7.5 (1.5) NR–19.0 405 150c NR
Remote 42 7.1 (1.8) NR–12.1 302 90c NR
Abbreviations: ND, not detected; NR, not reported.
a Arithmetic average ± SD.
b Arithmetic averages were reported for urine and soil. Urine values are the weighted arithmetic average from separate results for male and female.
c Average yard soil arsenic concentrations for Anaconda are the GM calculated as the weighted average of all soil samples.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8153ehp0113-00174116330357ResearchAssociations between Health Effects and Particulate Matter and Black Carbon in Subjects with Respiratory Disease Jansen Karen L. 1Larson Timothy V. 1Koenig Jane Q. 1Mar Therese F. 1Fields Carrie 1Stewart Jim 1Lippmann Morton 21 University of Washington, Seattle, Washington, USA2 New York University School of Medicine, Tuxedo, New York, USAAddress correspondence to J. Q. Koenig, Department of Environmental and Occupational Health Sciences, School of Public Health and Community Medicine, University of Washington, Box 357234, Seattle, WA 98195 USA. Telephone: (206) 543-2026. Fax: (206) 685-3990. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 25 8 2005 113 12 1741 1746 29 3 2005 25 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. We measured fractional exhaled nitric oxide (FENO), spirometry, blood pressure, oxygen saturation of the blood (SaO2), and pulse rate in 16 older subjects with asthma or chronic obstructive pulmonary disease (COPD) in Seattle, Washington. Data were collected daily for 12 days. We simultaneously collected PM10 and PM2.5 (particulate matter ≤10 μm or ≤2.5 μm, respectively) filter samples at a central outdoor site, as well as outside and inside the subjects’ homes. Personal PM10 filter samples were also collected. All filters were analyzed for mass and light absorbance. We analyzed within-subject associations between health outcomes and air pollution metrics using a linear mixed-effects model with random intercept, controlling for age, ambient relative humidity, and ambient temperature. For the 7 subjects with asthma, a 10 μg/m3 increase in 24-hr average outdoor PM10 and PM2.5 was associated with a 5.9 [95% confidence interval (CI), 2.9–8.9] and 4.2 ppb (95% CI, 1.3–7.1) increase in FENO, respectively. A 1 μg/m3 increase in outdoor, indoor, and personal black carbon (BC) was associated with increases in FENO of 2.3 ppb (95% CI, 1.1–3.6), 4.0 ppb (95% CI, 2.0–5.9), and 1.2 ppb (95% CI, 0.2–2.2), respectively. No significant association was found between PM or BC measures and changes in spirometry, blood pressure, pulse rate, or SaO2 in these subjects. Results from this study indicate that FENO may be a more sensitive marker of PM exposure than traditional health outcomes and that particle-associated BC is useful for examining associations between primary combustion constituents of PM and health outcomes.
asthmablack carbonchronic obstructive pulmonary diseasefractional exhaled nitric oxidepanel studyparticulate matter
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Interest in particulate matter (PM) air pollution has been driven by epidemiologic studies reporting adverse cardiac and respiratory health effects [Bascom et al. 1996; Dockery 2001; U.S. Environmental Protection Agency (EPA) 2004]. To further investigate the basis for these epidemiologic findings, it is important to assess individual exposures to PM and their related health effects. Panel studies that include indoor, outdoor, personal, and fixed-site PM monitoring can provide an important link between the effects observed in a population and the effects at the individual subject level.
Panel studies often report gravimetric measures of PM. However, current research is focusing on the constituents of PM (Brunekreef et al. 2005). Elemental carbon (EC) is one component of PM that has been associated with respiratory health effects in children. In a 10-year study of 1,759 children, Gauderman et al. (2004) found a strong association between reduced annual growth in forced expiratory volume in 1 sec (FEV1) in children and exposure to EC, nitrogen dioxide, and acid vapor. EC, measured on quartz filters by thermal desorption, is strongly associated with, but not identical to, “black carbon” (BC), as measured by diffuse transmittance through or reflectance from a Teflon filter. In a recent study, Kim et al. (2004) reported that concentrations of traffic-related pollutants (PM, BC, total nitrogen oxides, and NO2) were associated with respiratory symptoms in children.
EC and BC have also been associated with cardiovascular health effects. In a study of defibrillator discharge interventions among 100 adult patients, Peters et al. (2000) found that patients with ≥10 interventions experienced increased arrhythmias in association with short-term variations in BC, NO2, carbon monoxide, and fine particulate mass (PM2.5). In a study of 269 elderly Boston, Massachusetts, residents equipped with Holter monitors, an elevated BC level was associated with a –0.1 mm ST-segment depression; this BC level predicted increased risk of ST-segment depression among those with at least one episode of that level of ST-segment depression (Gold et al. 2005). Furthermore, in elderly subjects in Boston, BC increases were associated with a decrease in flow-mediated vascular reactivity (–12.6%; O’Neill et al. 2005). These studies implicate particles whose predominant source is traffic as a risk factor for adverse health effects.
Accumulated data suggest that PM exposure may lead to pulmonary inflammation (Gong et al. 2003; Li et al. 1996; Salvi et al. 1999). Chronic inflammation is a hallmark of lung diseases such as asthma and chronic obstructive pulmonary disease (COPD) (Gan et al. 2004) and may be aggravated in susceptible groups by PM pollution. A noninvasive method of estimating airway inflammation among sensitive groups is fractional exhaled nitric oxide (FENO). Over the last decade, FENO has been shown to be reproducible, inexpensive, and easy to measure serially. FENO concentrations are also highly correlated with other markers of airway inflammation, such as sputum eosinophils and bronchial hyperresponsiveness in subjects with asthma (Jones et al. 2002). Studies have reported positive associations between FENO and ambient PM2.5 exposures to air pollutants in community-based studies (Adamkiewicz et al. 2004; (Koenig et al. 2003).
Spirometry has historically been used as a method of measuring health effects of exposure to PM air pollution. Numerous panel studies have examined the effects of short-term ambient PM exposure on daily lung function [FEV1, forced vital capacity (FVC), and peak expiratory flow rate (PEF)] (U.S. EPA 2004). Subjects with asthma tended to show small PEF decrements for increases in PM10 and PM2.5 concentrations, as seen in several studies (Gielen et al. 1997; Pekkanen et al. 1997; Peters et al. 1997; Romieu et al. 1996).
Another measure of respiratory health, oxygen saturation of the arterial blood (SaO2), has been collected in panel studies. In a study of 90 elderly subjects, Pope et al. (1999a) found that SaO2 decreased in association with PM10 in the Utah Valley; however, the association was not statistically significant and may have been confounded by atmospheric pressure (Pope et al. 1999b). Linn et al. (1999) found no association of SaO2 and PM10 in a panel study of 30 subjects in Los Angeles, but DeMeo et al. (2004) found a reduction in oxygen saturation associated with PM2.5 in a 12-week repeated-measures study of 28 elderly Boston residents.
Changes in cardiac measures such as blood pressure and pulse rate, which are possible risk factors for cardiovascular morbidity and mortality, have been the focus of several PM panel studies. A study in Germany showed a consistent significant increase in blood pressure in adults in association with increased concentrations of total suspended particulates (TSP) at a central site (Ibald-Mulli et al. 2001). Other studies also have shown increases in blood pressure with PM (Linn et al. 1999; Mar et al., 2005). Pope et al. (1999a) reported an association between PM10 and pulse rate; a 10 μg/m3 increase in the previous 1–5 day average PM10 was associated with an average increase of 0.8 beats per minute. Peters et al. (1999) found increases in pulse rate during an air pollution episode in Europe in January 1985. However, Mar et al. (2005) found decreases in heart rate associated with indoor and outdoor PM2.5 and PM10.
Therefore, based on the literature, there is some suggestion of associations between PM and changes in FENO, spirometry, SaO2, blood pressure, and pulse rate. To determine whether changes in these health endpoints were associated with residential and personal PM and BC exposures, we conducted a panel study in Seattle, Washington, of 16 older subjects with COPD and/or asthma. This research was part of a multicity panel study designed to evaluate geographical differences in PM and cardiorespiratory health effects due to PM exposure. The study was conducted in New York City and Seattle. Seattle was chosen because it is known to have elevated wood smoke levels in winter. Our primary hypothesis was that airway inflammation in individuals with asthma and/or COPD would be associated with PM air pollution and BC, a measure shown to represent elemental carbon.
Materials and Methods
PM exposures and health effects were measured in this panel study of susceptible subjects in Seattle during the winter of 2002–2003. The study included 16 individuals with physician-diagnosed asthma, COPD, or asthma and COPD. Those individuals diagnosed with both asthma and COPD were grouped under COPD. A seventeenth subject (#2) did not participate in the full study period and was not included in the analyses. The health outcomes measured during the study were FENO, spirometry, exhaled breath condensate, pulse oximetry, heart rate, blood pressure, symptoms, and medication use. Exhaled breath condensate and symptoms are not reported here. We collected PM2.5 and PM10 Harvard Impactor (HI; Air Diagnostics and Engineering, Inc., Naples, ME) 24-hr filter samples simultaneously at a central outdoor site, as well as outside and inside the subject’s home. Marple Personal Environmental Monitors for PM10 (MPEM10; MSP Corporation, Shoreview, MN) were worn to record personal exposure. We subsequently analyzed the filters for mass, light absorbance to estimate BC, and trace elemental compositions via X-ray fluorescence. Only mass and BC are reported here.
Study subjects.
The participants were recruited from a community in north Seattle, ranged from 60–86 years of age, and were nonsmokers living alone or with other non-smokers. Each subject in the panel was asked to participate for a 12-day monitoring session. Approximately 75% of the subjects were prescribed inhaled corticosteroid therapy, and two were prescribed a leukotriene receptor antagonist (montelukast). Both of these anti-inflammation medications have been shown to prevent increases in FENO in atopic subjects with asthma (Jones et al. 2002; Piacentini et al. 2002). The remaining subjects were prescribed only inhaled albuterol as needed. Subjects filled out a questionnaire to describe their medical, residential, and occupational history before enrollment in the study. A second questionnaire was administered daily during the study period to record typical physical activity, time spent outdoors, home behavior, travel, and daily medication use. All subjects read and signed a consent form approved by the University of Washington Human Subjects Office.
Offline FENO.
Exhaled breath was collected according to American Thoracic Society recommendations for offline measurement (Slutsky et al. 1999); however, we collected only one sample per subject visit during the late morning of each day. Previous replicate measures with the same collection devices showed good agreement. The sample was collected daily in the subjects’ homes for up to 12 consecutive days. We collected exhaled breath before taking lung function measurements because deep inspirations may affect NO concentration (Deykin et al. 1998), and subjects were asked not to eat 1 hr before collection. The subjects were instructed to inhale to nearly total lung capacity and exhale through an offline collection device (Model 280i; Sievers Ionics, Boulder, CO). The subjects repeated this inhalation–exhalation cycle twice, and the third breath was collected into a nonreactive, self-sealing Mylar-like balloon. Subjects maintained a constant flow rate (0.35 mL/sec), inhaled NO-free air during the entire procedure, and exhaled with sufficient pressure (13 cm H2O) to close the epiglottis and prevent contamination of the airway NO sample by nasal NO. We collected samples at the same time of day (late morning) at their residences. NO was measured within 24 hr of collection using a chemiluminescent nitrogen oxide (NOx) monitor (model 280i; Sievers Ionics). Multiple NO concentrations from Mylar-like bags varied by < 2 ppb over a 24-hr period, consistent with that found by Jobsis et al. (1999). The monitor was calibrated daily using zero air and 45 ppm NO.
Lung function and SaO2.
Spirometry was performed according to American Thoracic Society recommendations (Crapo et al. 1995). The subjects performed the spirometry maneuvers during the technician visit. We measured FEV1, FVC, FEV1/FVC, PEF, and MEF (mid-expiratory flow). We recorded maximum forced expiratory maneuvers using diaphragm spirometers (SMI III Spirometer; Spirometrics Inc., Gray, ME). Subjects performed the maneuvers while sitting. Each subject was asked to perform three satisfactory blows, defined as FVC and FEV1, agreeing within 5% and a forced expiratory time exceeding 6 sec. No more than five blows were attempted. Height, weight, age, sex, and ethnicity were determined from subject’s questionnaire responses. Spirometers were kept at the subject’s home and calibrated just before the test session using 3-L calibration syringes (Ohio Medical Products; Airco, Inc., Madison, WI). The use of respiratory medications was recorded daily. Three times daily (morning, mid-day, and evening) the subjects sat at rest and placed the sensor of a pulse oximeter (Nellcor Model N-20P; Nellcor, Pleasanton, CA) on the left index finger. Date, SaO2, and pulse rate were recorded.
Cardiac measurements.
Blood pressure was recorded, using the left arm while at rest, during the technician visits. The blood pressure cuffs (AND UA-767; A&D Medical, Milpitas, CA) were calibrated before and after the study period. Any cardiac medications used were recorded daily.
PM mass monitoring.
We collected 24-hr PM2.5 and PM10 measurements during each 12-day session inside and outside the subjects’ residences and at a central agency site (Lynnwood) using HIs. Radiance Research (Seattle, WA) nephelometers provided continuous data on fine particles, comparable to PM1 (Liu et al. 2002). The indoor and outdoor PM concentrations were measured with single-stage inertial HIs and 37-mm Teflon filters for PM10 and PM2.5. One HI2.5–HI10 pair was located inside each home in the main activity room and connected to a pump (SP 280, Air Diagnostics Inc.). Another HI2.5–HI10 pair was located outside each home and connected to a pump (SP 280). The on and off flow rates were calibrated and recorded daily with a rotameter (150-nm Tube 604; Cole-Parmer Instrument Co., Vernon Hills, IL). All HI sampling periods were for 24 hr (approximately 1100 hr to 1100 hr) at a flow rate of 10 L/min. Our research group has previously evaluated the performance of continuous PM monitors (nephelometers) and HIs used in the context of a panel study (Liu et al. 2003).
Simultaneous data also were collected with a MPEM10 during the study period (24 hr for 12 consecutive session days). The MPEM10 was connected to a personal pump (400S: BGI, Inc., Waltham, MA) with a mass flow controller operated at 4 L/min. Each subject carried an MPEM10 in the breathing zone for 24 hr, except while sleeping or showering. The monitor was attached to the shoulder strap of either a backpack or a fanny pack that contained the air pump. When the monitor was not worn, it was placed at an elevation of 3–5 ft (e.g., on a table) close to the subjects. Field technicians visited the subjects daily to calibrate the pumps with a rotameter and to record on and off flow rates and change samplers.
We weighed the filters before and after sample collection for particle mass concentration. All filter weights were measured in either duplicate or triplicate using an electronic ultra-microbalance (UMT2: Mettler Toledo, Greifensee, Switzerland). The filters were equilibrated for at least 24 hr before weighing. We performed both equilibration and weighing inside a controlled environmental chamber with constant relative humidity (34.7°C ± 2.5%) and temperature (22.4 °C ± 1.9%) (Allen et al. 2001). Standard protocols included the use of field blanks, filter-lot blanks, laboratory blanks, and externally certified standard weights for all gravimetric analyses for quality assurance and quality control purposes. Relative humidity, outdoor temperature, NO, and NO2 concentrations were monitored continuously at the Beacon Hill central site by the Washington State Department of Ecology.
Black carbon measurements.
We estimated BC, a measure shown to represent EC from motor vehicles and woodstoves in Seattle (Larson et al. 2004), using an integrated plate reader (Lin et al. 1973). It is generally agreed that the major contributor to light absorption by airborne particles is BC, and levels of BC can easily be measured by this nondestructive optical technique. The method derives absorption from the change in light transmission through a Teflon filter on which particles have been collected. We analyzed the filters from the HIs for BC (wavelength of 525 nm) after the mass measurements. The integrated plate reader was re-zeroed with a blank filter between measurements. The light absorption coefficient, bap, was computed using the amount of light transmitted through this exposed filter, the amount transmitted through the same filter before sampling, and the volume of air that passed through the filter. We used a previously derived association between bap and EC in Seattle to quantify the BC concentrations (Larson et al. 2004).
Statistical analysis.
We hypothesized that increases in PM2.5 and BC are associated with increases in FENO. We analyzed within-subject, within-session associations between FENO and air pollution metrics using a linear mixed effects model with random intercept, controlling for age, relative humidity, and temperature. Subjects were stratified by health status in the FENO, spirometry, and SaO2 analyses. We put use of cardiac medications into the model as an interaction term for the blood pressure and pulse rate analyses. The model included terms for within-subject, within-session (12-day monitoring period) effects; within-subject, between-session effects; the confounding variable of temperature; and relative humidity. Our primary interest was the within-subject, within-session effects of PM2.5 and BC on FENO levels. Our numerous exploratory analyses, the within-subject, within-session effects of PM2.5, PM10, and BC on spirometry, SaO2, blood pressure, and pulse rate required use of the Bonferroni test for multiple comparisons. The Bonferroni test indicated a value of p < 0.0001 was significant. Therefore, for these analyses we chose p < 0.0001 as our criteria for statistical significance. We used STATA software (Stata Corp., College Station, TX). The model used was as follows:
where Xid is the PM2.5 reading for individual i on day d; χ̄i is the mean PM2.5 reading for a subject; and medi is an indicator for medication use (constant for each subject ).
Results
Subject characteristics.
Characteristics of the 16 subjects are given in Table 1. On average, the subjects spent 88% of their time indoors at home, 3% of their time in transit, and 9% of their time indoors away from home. Four subjects reported having received both a doctor’s diagnosis of asthma and of COPD.
Airborne concentration measurements.
The measured concentrations and interquartile ranges of PM10, PM2.5, and BC are presented in Table 2 for all the subjects, for the 7 subjects with asthma alone, and for the 9 subjects with COPD. At the fixed-site monitor, the overall 24-hr average PM2.5 was 14.0 μg/m3, the 24-hr minimum was 1.3 μg/m3, and the 24-hr maximum was 44 μg/m3. At the same site the overall 24-hr average PM10 was 18.0 μg/m3, the 24-hr minimum was 2.5 μg/m3, and the 24-hr maximum was 51 μg/m3. The overall 24-hr average BC was 7.2 μg/m3, the 24-hr minimum was below detection limits, and the 24-hr maximum was 2.6 μg/m3.
Exhaled NO.
A total of 179 midday breath samples were collected during the 12-day monitoring periods. Average FENO levels are shown in Table 3. The mean FENO levels were higher for those with COPD (25.4 ppb) than for those with asthma (19.2 ppb) or COPD and asthma (16.5 ppb). In those subjects with asthma, a 10 μg/m3 increase in outdoor PM2.5 and PM10, relative to each subject session average, was associated with a 4.2 ppb [95% confidence interval (CI), 1.3–7.1; p = 0.004) and 5.9 ppb (95% CI, 2.9–8.9; p = 0.000) increase in FENO, respectively. There was no association between FENO and the 24-hr measures of indoor PM2.5 or PM10. A 1 μg/m3 increase in outdoor, indoor, and personal BC, relative to each subject session average, was associated with a 2.3 ppb increase in FENO (95% CI, 1.08–3.57; p = 0.000), a 4.0 ppb increase in FENO (95% CI, 2.02–5.91; p = 0.000), and a 1.2 ppb increase in FENO (95% CI, 0.17–2.22; p = 0.02), respectively (Table 3). No significant association was found between PM or BC and changes in FENO in subjects with COPD. The effect levels and confidence intervals are given in Table 3.
SaO2, blood pressure, and pulse rate.
No associations were observed between air pollution and SaO2, blood pressure, or pulse rate in this study.
Discussion
This study showed an association between FENO in elderly subjects with asthma and indoor and outdoor BC. Increases in FENO also were associated with outdoor PM10 and PM2.5 in these same subjects. Results of this study are consistent with our earlier study of children with asthma who were not on corti-costeroid therapy (Koenig et al. 2003). That study showed an increase of approximately 4 ppb FENO associated with a 10 μg/m3 increases in indoor, outdoor, personal, and central-site PM2.5 in Seattle. Finding a similar magnitude of response in the two different groups (children and elderly with asthma) strengthens the importance of this finding. Results of the present study also are consistent with other earlier studies in Seattle showing that hospitalizations for asthma (Sheppard et al. 1999) as well as increases in asthma symptoms and increased use of rescue medications (Yu et al. 2000; Slaughter et al. 2004) are associated with fine particles in Seattle.
Our data suggest that exposure to PM10 may play an important role in asthma exacerbation. This significant association between FENO and PM10 was not surprising, especially for subjects with asthma that have narrowed airways, as the thoracic coarse particles deposit preferentially in the larger bronchial airways and these airways may be the ones with the greatest inflammation potential (U.S. EPA 2004). The observed association is supported by studies that have linked PM10 to pulmonary inflammation in animal models (Li et al. 1996) and the induction of inducible nitric oxide synthase in human bronchial epithelial cells (Martin et al. 1997).
Other studies (Steerenberg et al. 1999; Tunnicliffe et al. 2003; van Amsterdam et al. 1999) have also reported positive associations between FENO and ambient exposures to air pollutants in community-based studies. Adamkiewicz et al. (2004) reported that an increase in the 24-hr average PM2.5 concentration of 17.7 μg/m3 was associated with a 1.45 ppb increase in FENO in elderly subjects with asthma and COPD in a panel study in Steubenville, Ohio. Fischer et al. (2002) reported a 1-day and 2-day lag association between FENO and PM10, black smoke, and NO. In contrast, no increase in FENO was seen in adult subjects with asthma after exposure to concentrated coarse particles (Gong et al. 2003) or ultrafine particles (Pietropaoli et al. 2004). Several controlled ozone exposure studies have assessed FENO in atopic subjects with asthma (Newson et al. 2000; Nightingale et al. 1999) and healthy subjects (Olin et al. 2001), but none has found an association.
We found that FENO was associated with PM air pollution in study participants with asthma but not those with COPD. It is interesting that five of the seven subjects with asthma were using inhaled corticosteroids, which has been associated with mitigation of eNO in air pollution studies (Koenig et al. 2003) and clinical settings (Deykin et al. 1998). This finding contrasts with that of a study of elderly subjects by Adamkiewicz et al. (2004) that found a PM2.5 response in subjects with COPD but not asthma, although there was some overlap in the study population and medications were not recorded. In our study, levels of FENO, on average, were higher in COPD than asthma subjects. Exhaled NO in stable COPD has been found to be lower than in nonsmoking asthmatics (Kharitonov et al. 1995), but patients with unstable COPD have higher NO levels than ex-smokers with COPD (Maziak et al. 1998).
BC may more closely identify the sources of PM than standard measures of mass concentration. The contribution of BC to total PM varies geographically and temporally due to the distribution of the combustion sources that produce BC. Although BC is a major component of diesel exhaust, it is also a major component of particles produced by burning vegetation (Conny and Slater 2002; Hobbs et al. 2003; Mayol-Bracero et al. 2002; Posfai et al. 2004). Recent source apportionment studies in Seattle found that burning vegetation and mobile sources are major contributors to PM2.5 (Maykut et al. 2003) and that burning vegetation is the dominant contributor to variations in the day-to-day BC in the winter (Larson et al. 2004). Burning vegetation, and to a lesser extent, mobile sources, may therefore be responsible for the observed increases in FENO associated with BC.
It is somewhat surprising that we did not find an association between standard spirometry measures and association with PM2.5, PM10, and BC. An earlier study completed in Seattle during the wood-burning season (Koenig et al. 1993) showed that spirometry, specifically FVC and FEV1, decreased in association with increases in particulate matter air pollution in children with asthma. Another study, in Vancouver, British Columbia Canada, showed a slight but not statistically significant decrease in daily FEV1 change in subjects with COPD was associated with increase in PM2.5 (Brauer et al. 2001). In three separate longitudinal diary studies, decreases in PEF were shown to be associated with increased levels of PM2.5 (Schwartz and Neas 2000).
Our exploratory hypotheses were that increases in PM2.5 and BC are associated with decreases in spirometry (FEV1, MEF) and SaO2 and with increases in blood pressure and pulse rate. In our study, no significant associations were seen between these health measures and PM2.5, PM10, or BC (indoor, outdoor, personal). Some studies have found that PM10 and PM2.5 both appear to affect lung function in asthmatics (U.S. EPA 2004); however, many of the studies experienced higher mean PM concentrations (in the range of 50 μg/m3) than were experienced by subjects in this study. The lack of significant associations between SaO2 and PM has also been observed in other studies (Linn et al. 1999). In addition, no significant associations were observed between blood pressure and pulse rate and PM2.5, PM10, and BC in this study. This is in contrast to studies that have reported increases in blood pressure (Ibald-Mulli et al. 2001; Linn et al. 1999; Mar et al. 2005) and pulse rate (Peters et al. 1999; Pope et al. 1999a) with exposure to PM. Our study results are consistent with those of a larger panel study in Seattle (Mar et al. 2005), but that study did see minor decreases in pulse rate in healthy subjects. Yet another study did find some changes in ectopic beats in subjects with COPD (Brauer et al. 2001). To our knowledge the present study is the first air pollution study simultaneously exploring FENO, spirometry, and cardiac outcomes. It appears that FENO is more sensitive to changes in PM2.5, PM10, and BC than the other outcomes. This finding emphasizes the importance of including noninvasive, sensitive measures of health outcomes in panel studies.
The intensive monitoring of health effects and PM metrics in this study of susceptible individuals provides better estimates of actual exposures than epidemiologic data based on PM2.5 at a central site. There are, however, several limitations to this study. Relatively small numbers of subjects in each patient group (asthma, COPD, and those diagnosed with asthma and COPD) were monitored due to time constraints and technician availability. The same constraints also limited our ability to collect replicate NO measurements at a single time point. Subjects with COPD have difficulty performing spirometry. Also, relatively low ambient PM concentrations were experienced during the study period. That there were weaker associations between FENO and personal PM or BC may be explained by small sample air volumes, especially for the 4 L/minute personal PM10 samples, and the higher relative measurement error for these samples.
In conclusion, these data implicate combustion-derived PM, as measured by light absorption coefficient primarily from wood burning, as being associated with airway inflammation in adult subjects with asthma. Further, these data support the fact that FENO is a relatively simple, noninvasive measure to explore the mechanisms responsible for respiratory effects in air pollution epidemiologic field studies. Further research on susceptible populations is needed to understand the association between combustion-derived PM and airway inflammation.
We thank our subjects for their enthusiastic participation. We also thank D. Lennington and R. Murashige for technical assistance and the Washington State Department of Ecology for atmospheric data.
This research was supported by grant R 827355 from the U.S. Environmental Protection Agency (EPA), grant PO ES 07033 from the National Institutes of Health, and a subcontract from New York University under U.S. EPA Cooperative Agreement CR 827164.
This study has not been subjected to the U.S. EPA’s required peer and policy review. It does not necessarily reflect the views of the U.S. EPA, and no official endorsement should be inferred.
Table 1 Subject characteristics of the 16 study participants.
Health Subject Age Sex FEV1 Percent predicted FEV1 Mean FENO (ppb) Group mean FENO (ppb) Medication use
Asthma 1 83 F 1.35 82 8.1 ± 3.1 19.2 CS,B
5 85 F 1.24 79 9.7 ± 5.6 CS,I,M
6 75 M 2.38 72 26.8 ± 10.9 CS,B
9 62 F 2.07 82 19.4 ± 2.1 CS,B
14 71 F 2.7 117 26.4 ± 6.9
15 86 M 1.46 66 32.9 ± 8.4 CS
17 60 F 1.99 85 11.3 ± 3.1
COPD/asthma 3 73 M 0.85 42 10.8 ± 4.8 16.5 I,B
4 79 M 1.17 37 10.5 ± 4.4 CS,B
8 77 F 1.95 52 10 ± 4.1 CS,B
11 75 M 1.6 61 33.3 ± 14.7 CS,B,I
12 76 F 0.74 39 11.2 ± 6.1 CS,M
COPD 7 76 M 1.95 56 24 ± 10 25.4
10 76 F 0.78 43 14.4 ± 8.3 CS,B
13 78 M 2.41 83 24.4 ± 8.9 B
16 74 F 0.57 27 54.3 ± 28.6 CS
Mean 75 1.6 64 20.5
Abbreviations: B, beta-agonist; CS, corticosteroid; I, ipratropium bromide; M, montelukast.
Table 2 Mean (interquartile range) daily residential airborne concentration measurements (μg/m3) for all subjects during the study period.
Pollution Monitoring location All subjects Asthma (n = 7) COPD (n = 9)
PM2.5 Indoor 7.29 (4.05) 7.25 (5.72) 7.33 (3.18)
Outdoor 10.47 (8.87) 8.99 (7.55) 11.66 (6.71)
PM10 Indoor 11.93 (6.93) 12.54 (10.19) 11.45 (4.56)
Outdoor 13.47 (9.53) 11.86 (8.77) 14.76 (6.14)
Personal (Marple PEM) 23.34 (20.72) 26.88 (20.08) 19.91 (19.94)
BC Indoor 1.34 (1.12) 1.21 (1.12) 1.45 (1.11)
Outdoor 2.01 (1.68) 1.83 (2.22) 2.15 (1.31)
Personal (Marple PEM) 1.64 (2.05) 1.59 (2.38) 1.69 (1.78)
Interquartile range (75th percentile – 25th percentile). Values for PM2.5 and PM10 are given as change per 10 μg/m3; values for BC are given as change per 1 μg/m3.
Table 3 Associations between FENO (ppb) and 24-hr average PM2.5 and PM10 (μg/m3) in subjects with asthma and COPD.
Asthma (n = 7)
COPD (n = 9)
Pollution Location B p-Value 95% CI B p-Value 95% CI
PM2.5 Indoor 3.69 0.10 −0.74 to 8.12 −0.35 0.92 −7.45 to 6.75
Outdoor 4.23 0.004* 1.33 to 7.13 3.83 0.19 −1.84 to 9.49
PM10 Indoor 3.81 0.11 −0.86 to 8.50 2.19 0.45 −3.48 to 7.87
Outdoor 5.87 0.000* 2.87 to 8.88 4.45 0.12 −1.11 to 10.01
Personal 0.66 0.29 −0.56 to 1.88 0.17 0.85 −1.61 to 1.96
BC Indoor 3.97 0.000* 2.02 to 5.91 1.16 0.32 −1.14 to 3.45
Outdoor 2.32 0.000* 1.08 to 3.57 1.81 0.21 −1.00 to 4.61
Personal 1.20 0.02* 0.17 to 2.22 0.62 0.33 −0.62 to 1.86
Values for PM2.5 and PM10 are given as change per 10 μg/m3; values for BC = are given as change per 1 μg/m3.
* Statistically significant.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7947ehp0113-00174716330358ResearchDiscrimination of Vanadium from Zinc Using Gene Profiling in Human Bronchial Epithelial Cells Li Zhuowei 1Stonehuerner Jackie 2Devlin Robert B. 2Huang Yuh-Chin T. 21 Center for Environmental Medicine and Lung Biology, University of North Carolina, Chapel Hill, North Carolina, USA2 National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USAAddress correspondence to Y.-C.T. Huang, Human Study Facilities, U.S. EPA, CB 7315, 104 Mason Farm Rd., Chapel Hill, NC 27599 USA. Telephone: (919) 843-9504. Fax: (919) 966-6271. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 21 6 2005 113 12 1747 1754 19 1 2005 21 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. We hypothesized that gene expression profiling may discriminate vanadium from zinc in human bronchial epithelial cells (HBECs). RNA from HBECs exposed to vehicle, V (50 μM), or Zn (50 μM) for 4 hr (n = 4 paired experiments) was hybridized to Affymetrix Hu133A chips. Using one-class t-test with p < 0.01, we identified 140 and 76 genes with treatment:control ratios ≥ 2.0 or ≤ 0.5 for V and Zn, respectively. We then categorized these genes into functional pathways and compared the number of genes in each pathway between V and Zn using Fisher’s exact test. Three pathways regulating gene transcription, inflammatory response, and cell proliferation distinguished V from Zn. When genes in these three pathways were matched with the 163 genes flagged by the same statistical filtration for V:Zn ratios, 12 genes were identified. The hierarchical clustering analysis showed that these 12 genes discriminated V from Zn and consisted of two clusters. Cluster 1 genes (ZBTB1, PML, ZNF44, SIX1, BCL6, ZNF450) were down-regulated by V and involved in gene transcription, whereas cluster 2 genes (IL8, IL1A, PTGS2, DTR, TNFAIP3, CXCL3) were up-regulated and linked to inflammatory response and cell proliferation. Also, metallothionein 1 genes (MT1F, MT1G, MT1K) were up-regulated by Zn only. Thus, using microarray analysis, we identified a small set of genes that may be used as biomarkers for discriminating V from Zn. The novel genes and pathways identified by the microarray may help us understand the pathogenesis of health effects caused by environmental V and Zn exposure.
cell proliferationinflammationinterleukin-1interleukin-8metalmicroarraytranscription
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The advancement of microarray technology has allowed investigators to examine simultaneously changes in thousands of genes induced by environmental toxins. McDowell et al. (2000), using gene array with more than 8,000 cDNAs, found patterns of gene expression consistent with acute lung injury in nickel-treated mice. Sato et al. (1999) showed changes in genes related to cell growth and possibly carcinogenesis in rat lungs treated with diesel particles. More recently, Andrew et al. (2003) demonstrated distinct expression patterns in human lung cells exposed to low and high doses of arsenic. The capability of microarrays to provide a snapshot view of expression of a large number of genes may help us generate mechanistic hypotheses as well as identify biomarkers of exposure specific to environmental toxins. The availability of such specific genomic biomarkers may be important in determining the nature of environmental exposures.
Vanadium is present in several environmental settings, for example, during overhauling of oil-fired boilers and burning of heavy fuel in power plants. Exposures to high levels of V-rich particles produce upper and lower respiratory symptoms (Levy et al. 1984; Woodin et al. 1999, 2000). Intratracheal administration of vanadyl sulfate (VOSO4) and a V-rich pollutant dust, residual oil fly ash (ROFA), increased pulmonary artery pressure acutely in buffer-perfused rabbit lungs (Huang et al. 2002) and constricted isolated rat aortic rings (Cadene et al. 1997). Particulate air V concentration correlated with increases in heart rate variability index in boilermakers (Magari et al. 2002). V or ROFA altered the expression of many genes and their protein products related to acute stress (Carter et al. 1997; Gavett et al. 1997, 1999; Nadadur et al. 2000; Samet et al. 1998) and cell survival and tissue growth in cultured cells (Chen et al. 2001; Huang et al. 2000; Zhang et al. 2001).
Zinc is ubiquitous in the natural environment, including ambient air (Walsh et al. 1994). Exposure to excessive Zn (via metal fumes) is a potential hazard for industrial workers who perform welding and smelting operations. Inhalation of high concentrations of zinc oxide or zinc chloride produce respiratory epithelial cell damage, inflammation, and acute injury (Doig and Challen 1964; Evans 1945; Kuschner et al. 1995; Matarese and Matthews 1986; Nemery 1990; Pare and Sandler 1954). Treatment of lung epithelial cells in vitro with Zn compounds enhanced inflammatory signaling and produced cyto-toxicity and cell death (Riley et al. 2003; Samet et al. 1998, 1999).
Although V and Zn belong to different elemental classes in the periodic table, they share many biologic properties. For example, both metals are potent enhancers for phosphorylation of signaling proteins, including mitogen-activated protein kinase (Samet et al. 1998) and epidermal growth factor receptors (Wu et al. 1999), and both increase Ras activity (Wu et al. 2002) and interleukin-8 (IL8) release (Samet et al. 1998). Many of these effects may be attributed to the capability of these metals to inhibit protein tyrosine phosphatase activity (Samet et al. 1999). Both V and Zn also inhibit metabolic activity of the cells (Riley et al. 2003). V and Zn may coexist in the ambient environment after being released from different emission sources (Nriagu and Pacyna 1988). The development of a biomarker that discriminates these metals thus may help define the sources and nature of exposures. In this study we hypothesized that gene profiling may be used to discriminate V from Zn in human bronchial epithelial cells (HBECs). We sought to identify a small group of genes that may serve as biomarkers of exposure.
Materials and Methods
Cell culture.
Two bronchoscopists obtained bronchial epithelial cells from normal volunteers through bronchoscopic bronchial brushings following the same operational guidelines (Ghio et al. 2000; Huang et al. 2003). Subjects were informed of the procedures and potential risks, and each gave written informed consent. The protocol was approved by the University of North Carolina School of Medicine Committee on Protection of the Rights of Human Subjects and by the U.S. Environmental Protection Agency. A single experienced technician processed all brushings by following the established standard of procedures in our laboratory. The cells (passage 2 or 3) were maintained in bronchial epithelial growth medium (BEGM) (Clonetics, San Diego, CA), supplemented with bovine pituitary extract, insulin 5 μg/mL, hydrocortisone 0.5 μg/mL, gentamicin 50 μg/mL, retinoic acid 0.1 ng/mL, transferrin 10 μg/mL, triiodothyrodine 6.5 ng/mL, epinephrine 0.5 μg/mL, and human epidermal growth factor 0.5 ng/mL. Cells were judged to be 95–100% confluent at the time of metal treatment.
Metal treatment.
Stock solutions of metals were prepared in sterile water (Baxter Healthcare Corp., Deerfield, IL) and were diluted with BEGM before experiments. Cells were grown in 100-mm diameter petri dishes and exposed to 5.5 mL of BEGM with or without 50 μM VOSO4 or zinc sulfate (ZnSO4) (Johnson Matthey Corp., Ward Hill, MA) for 4 hr.
Purification and hybridization of RNA.
Total cellular RNA was extracted from HBECs with Trizol reagent (GIBCO BRL Life Technologies, Gaithersburg, MD) and further purified with phenol/chloroform. The RNA integrity was assessed with an Agilent 2100 bioanalyzer (Agilent Technologies, Inc., Palo Alto, CA). The 260:280-nm ratios for all RNAs were > 1.9. The RNA hybridization to the U133A GeneChip oligonucleotide microarray (Affymetrix, Inc., Santa Clara CA) was performed by Expression Analysis Inc. (Durham, NC). Affymetrix Hu133A 2.0 gene chips were used for the study. The chip contained probes for 14,500 human genes. Target was prepared and hybridized according to the Affymetrix technical manual (Affymetrix, Inc. 2004a). Total RNA (10 μg) was converted into cDNA using reverse transcriptase (Invitrogen Corp., Carlsbad, CA) and a modified oligo(dT)24 primer that contains T7 promoter sequences (GenSet Corp., San Diego, CA). After first-strand synthesis, residual RNA was degraded by the addition of RNaseH and a double-stranded cDNA molecule was generated using DNA polymerase I and DNA ligase. The cDNA was then purified and concentrated using a phenol:chloroform extraction followed by ethanol precipitation. The cDNA products were incubated with T7 RNA polymerase, and biotinylated ribonucleotides using an in vitro transcription kit (Enzo Diagnostics Inc., New York, NY). Half the cRNA products were purified using an RNeasy column (Qiagen Inc., Valencia, CA) and quantified with a spectrophotometer. The cRNA target (20 μg) was incubated at 94°C for 35 min in fragmentation buffer (Tris, magnesium acetate, potassium acetate). The fragmented cRNA was diluted in hybridization buffer (2-morpholinoethanesulfonic acid, NaCl, EDTA, Tween 20, herring sperm DNA, acetylated bovine serum albumin) containing biotin-labeled oligoB2 and eukaryotic hybridization controls (Affymetrix). The hybridization cocktail was denatured at 99°C for 5 min, incubated at 45°C for 5 min, and then injected into a GeneChip cartridge. The GeneChip array was incubated at 42°C for at least 16 hr in a rotating oven at 60 rpm. GeneChips were washed with a series of non-stringent (25°C) and stringent (50°C) solutions containing variable amounts of 2-morpholinoethanesulfonic acid, Tween 20, and SSPE (3 M NaCl, 0.2 M, NaH2PO4, 0.02 M EDTA). The microarrays were then stained with streptavidin phycoerythrin, and the fluorescent signal was amplified using a biotinylated antibody solution. Fluorescent images were detected in a GeneChip Scanner 3000 (Affymetrix), and expression data were extracted using the default settings in the MicroArray Suite 5.0 software (Affymetrix). All GeneChips were scaled to a median intensity setting of 500. Four independent sets of experiments were performed on HBECs obtained from four different individuals. Each set consisted of control (vehicle), VOSO4, and ZnSO4.
Quantitative polymerase chain reaction.
Quantitative polymerase chain reaction (Q-PCR) was performed for selected genes to validate microarray results. HBECs were lysed in guanidine isothiocyanate (GITC) buffer [4 M GITC (Boehringer Mannheim, Indianapolis, IN), 25 mM sodium citrate (pH 7.0), 0.5% sarkosyl, and 0.1 M DTT], and RNA was pelleted at 80,000 rpm through a cesium chloride gradient for 2 hr at 15°C. cDNAs were synthesized from 0.4 μg of total RNA in 100 μL of a buffer containing 5 μM random hexaoligonucleotide primers (Pharmacia, Piscataway, NJ), 10 U/μL Moloney murine leukemia virus reverse transcriptase (GIBCO BRL Life Technologies), 1 U/μL RNase inhibitor (RNasin; Promega, Madison, WI), 0.5 mM dNTP (Pharmacia), 50 mM KCl, 3 mM MgCl2, and 10 mM Tris-HCl (pH 9.3). After 1 hr of incubation at 39°C, the reverse transcriptase was heat inactivated at 94°C for 4 min.
Q-PCR of specimen cDNA and standard cDNA was performed using TaqMan master mix (Perkin Elmer, Foster City, CA), 1.25 μM probe, 3 μM forward primer, and 3 μM reverse primer in a 50-μL volume. The probe, which contains both a fluorescence reporter dye at the 5′-end (6-carboxyfluorescein, 6-FAM: maximum emission wavelength = 518 nm) and a quencher dye at the 3′-end (6-carboxytetra-methyl rhodamine, TAMRA: maximum emission wavelength = 582 nm), is degraded by the 5′–3′ exonuclease activity of the Taq DNA polymerase, and the resulting fluorescence is detected by a laser in the sequence detector (TaqMan ABI Prism 7700 Sequence Detector System; PerkinElmer). The relative abundance of mRNA levels was determined from standard curves generated from a serially diluted standard pool of cDNA prepared from BEAS-2B cells. The relative abundance of glyceral-dehyde-3-phosphate dehydrogenase (GAPDH) mRNA was used to normalize levels of the mRNAs of interest. Six additional sets of Q-PCR experiments consisting of control (vehicle), VOSO4, and ZnSO4 were performed using HBECs from six different individuals.
Microarray data analysis.
The microarray data were deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/; accession number GSE2111). Gene expression values were background corrected and normalized globally using the default setting of the Affymetrix Microarray Suite 5.0 software, and log2-transformed according to the Affymetrix Statistical Algorithm Reference Guide (Affymetrix, Inc. 2004b). The log2 ratios of treatment (V or Zn) over control and V over Zn for all probe sets were analyzed using the one-class t-test against the null hypothesis of 0 (ratio = 1) using the Multiexperiment Viewer (version 3.0; The Institute of Genomic Research, Rockville, MD). A p-value of < 0.01 was considered statistically significant. If more than one probe set for the same gene were flagged, their ratios were averaged.
Functional classification of genes.
Biologic processes represented by the differentially expressed genes were compiled using the GOCharts in the Database for Annotation, Visualization and Integrated Discovery (DAVID) (http://apps1.niaid.nih.gov/david/) with the coverage and specificity set at level 5 (high) and the hits threshold at 1; with the classification of the Gene Ontology Consortium (http://www.geneontology.org); and with the human gene resources from NCBI (http://www.ncbi.nlm.nih.gov). Comparison of the probe sets in the biologic processes between V and Zn was determined by the Fisher’s exact test (p < 0.05) (StatView 4.0; SAS Inc., Cary, NC).
Results
Differentially expressed genes associated with V treatment.
Incubation of HBECs with VOSO4 at 50 μM for 4 hr showed no cytotoxicity as supported by the lack of lactate dehydrogenase (LDH) release (data not shown). There were 140 differentially expressed genes with known protein products. Seventy-six genes were up-regulated with a treatment:control ratio ≥ 2.0 (Table 1), and 64 genes were down-regulated with a treatment:control ratio ≤ 0.5 (Table 2). The expression of five up-regulated genes (IL8), prostaglandin-endoperoxide synthase 2 (PTGS2), intercellular adhesion molecule 2 (ICAM2), diphtheria toxin receptor (heparin-binding epidermal growth factor-like growth factor) (DTR), and dual specificity phosphatase 1 (DUSP1) was confirmed by Q-PCR in additional experiments (Figure 1). The 140 genes could be further classified functionally into 28 biologic processes containing at least three gene hits.
Differentially expressed genes associated with Zn treatment.
Incubation of HBECs with ZnSO4 at 50 μM for 4 hr also showed no LDH release (data not shown). There were 76 differentially expressed genes with known protein products. Forty-three genes were up-regulated with a treatment:control ratio ≥ 2.0 (Table 3), and 33 genes were down-regulated with a treatment:control ratio ≤ 0.5 (Table 4). The up-regulation of metallothionein 1F (MT1F) and heme oxygenase 1 (HMOX1) was confirmed by Q-PCR (Figure 1). The 76 genes could be further classified into 14 biologic processes containing at least three gene hits.
Identification of genes differentiating V from Zn.
To identify genes that would discriminate V from Zn, we first analyzed V:Zn ratios using the same statistical filtration method. A total of 163 genes were identified. The results of the hierarchical clustering analysis using these genes are shown in Figure 2. We next compared biologic processes associated with V with those associated with Zn. We found that four biologic processes, regulation of transcription (24 genes), DNA-dependent transcription (22 genes), inflammatory responses (11 genes), and regulation of cell proliferation (10 genes), contained a disproportionately greater number of V-induced genes. Because all genes involved in the DNA-dependent transcription pathway were also flagged in the regulation of transcription pathway, these two processes were combined into one, designated “gene transcription.” The number of probe sets in the three biologic pathways associated with V and Zn treatment was compared using the Fisher’s exact test. The p-values for these three pathways, gene transcription, inflammatory response, and regulation of cell proliferation, are 0.004, 0.037, and 0.013, respectively.
We next matched genes in these three pathways with the 163 genes and identified 12 candidate genes: B-cell CLL/lymphoma 6 (BCL6), IL1α (IL1A), IL8, PTGS2, DTR, chemokine (C-X-C motif) ligand 3 (CXCL3), promyelocytic leukemia (PML), sine oculis homeobox homolog 1 (Drosophila) (SIX1), tumor necrosis factor (TNF), α -induced protein 3 (TNFAIP3), Zn finger and BTB domain containing 1 (ZBTB1), Zn finger protein 44 (KOX 7) (ZNF44), and Zn finger protein 450 (ZNF450). The hierarchical cluster analysis showed that these 12 genes clearly discriminated the V group from the Zn group (Figure 2) and could be separated into two clusters (Figure 2). Cluster 1 contained ZBTB1, PML, ZNF44, SIX1, BCL6, and ZNF450 that were down-regulated by V and involved in gene transcription. Cluster 2 contained IL8, IL1A, PTGS2, DTR, TNFAIP3, and CXCL3 that were up-regulated and linked primarily to inflammatory response and cell proliferation. We also noted metallothionein 1 genes were up-regulated by Zn but not by V. Zn treatment increased the expression of MT1F by 4.6-fold, MT1G by 29-fold, and MT1K by 81-fold. These metallothionein 1 genes constituted the third group of candidate biomarker genes.
Discussion
In the present study we first determined the differential gene expression patterns in HBECs exposed to 50 μM of V and Zn and found 140 and 76 genes altered by V and Zn, respectively, compared with control. These genes could be classified into 28 and 14 biologic pathways, respectively, that each had at least three gene hits. Seven differentially expressed genes were validated prospectively in six additional experiments using HBECs from six different individuals. When the numbers of genes in the pathways were compared between V and Zn, three biologic processes (gene transcription, inflammatory response, and regulation of cell proliferation) contained a disproportionately greater number of V-induced genes. We then matched the genes in these three pathways with the 163 genes that differentiated V from Zn, and identified 12 candidate genes.
These 12 genes clearly discriminated the V group from the Zn group based on the hierarchical clustering analysis and could be separated into two clusters. The first cluster consisted of 6 genes (ZBTB1, PML, ZNF44, SIX1, BCL6, ZNF436) that were down-regulated by V but mildly up-regulated by Zn. All 6 genes were involved in gene transcription, and BCL6 was also linked to inflammatory response and regulation of cell proliferation. The inhibitory effects of V on the expression of these genes have not been reported. Five of these genes encode Zn finger proteins (ZBTB1, ZNF44, BCL6, ZNF436) or proteins containing Zn-binding domains (PML) that play a role in DNA binding (Bray et al. 1991; Zhong et al. 2000). SIX1 encodes a protein characterized by a divergent DNA-binding homeo-domain and an upstream SIX domain, which may be involved in determining DNA-binding specificity and protein–protein interactions. Mice lacking the SIX1 gene have impaired organogenesis of skeletal muscle and kidney during embryo development (Laclef et al. 2003; Xu et al. 2003). Multiple adult tissues, including the lung, also express SIX1 (Boucher et al. 1996), but its function is unclear. The BCL6 gene encodes a Zn finger transcription repressor frequently associated with B-lymphocytes. Translocation and hypermutation of this gene have been detected in B-cell lymphoma (Ohno 2004). BCL6 is also expressed in the epithelial lining of upper airways (Bajalica-Lagercrantz et al. 1998). Based on our results, BCL6 might be involved in gene transcription, inflammatory response, and cell proliferation in airway epithelial cells. The PML gene encodes a Zn-binding protein in the tripartite motif (TRIM) family and is often involved in the translocation with the retinoic acid receptor-α gene associated with acute promyelocytic leukemia. High levels of PML protein are expressed in human vascular endothelial cells, epithelial cells, and macrophages (Flenghi et al. 1995).
Cluster 2 contained six genes that were up-regulated by V but down-regulated or unchanged by Zn. Four (IL8, IL1A, PTGS2, CXCL3) were related to inflammatory response, three (IL8, IL1A, DTR) related to regulation of cell proliferation, and two (DTR, TNFAIP3) related to gene transcription. Vanadium is known to induce IL8 in cultured bronchial epithelial cells (Carter et al. 1997; Mukherjee et al. 2004) and in the nasal fluid of workers exposed to V-rich pollutant particles (Woodin et al. 1998). Exposure to pollutant particles with high concentrations of V and Ni increased expression of PTSG2 (COX2) in nasal epithelial cells of dogs (Calderon-Garciduenas et al. 2003). Vanadium also increased the expression of DTR [heparin-binding epidermal growth factor-like growth factor (HB-EGF)] in HBECs and fibroblasts (Ingram et al. 2003; Zhang et al. 2001). The stimulatory effects of V on IL1A and TNFAIP3 gene expression, however, have not been reported. IL1A is one of the nine genes in the IL1 gene family and is involved in various immune responses, inflammatory processes, and hematopoiesis (Arend 2002). TNFAIP3 (A20) is a Zn finger protein that is rapidly induced by TNF. It inhibits NF-κ B activation as well as TNF-mediated apoptosis (Gon et al. 2004; He and Ting 2002; Wertz et al. 2004). The CXCL3 (GRO-γ ) gene is a member of a gene superfamily encoding a set of related cytokines with inflammatory and growth regulatory properties (Haskill et al. 1990). Constitutive expression of CXCL3 has been identified in infiltrating leukocytes, bronchial epithelial cells, alveolar type II cells, and alveolar macrophages (Becker et al. 1994; Johnson et al. 1996). Several inflammatory stimuli, including IL1, TNF, lipopolysaccharide, and silica, induce the expression of CXCL3 (Becker et al. 1994; Haskill et al. 1990; Johnson et al. 1996; Rangnekar et al. 1991). Note that chemokine (C-X-C motif) ligand 1 (CXCL1) was also up-regulated by V (Table 1). Thus, it appears that the signaling pathways involving IL1, TNF, and chemokines activation may be novel targets for V and may play an important role in V-induced acute respiratory syndrome in boil-ermakers and power plant workers (Levy et al. 1984; Woodin et al. 2000). Up-regulation of IL1A and other growth-related genes (e.g., DTR, FOS, CXCL1, and EDN1) also indicates that the IL1A pathway may be also involved in clinical conditions associated with cell growth, such as fibrosis (Bonner et al. 1998, 2000).
Although not selected because they were not matched to any known pathways, several metallothionein 1 genes (MT1F, MT1G, MT1K) were significantly up-regulated by Zn. Metallothioneins (MT) are low-molecular-weight metal- and sulfur-rich proteins widely distributed in the organs, including the lung (Courtade et al. 1998). These intracellular proteins are thought to be involved in heavy metal detoxification and the homeostasis of essential trace metals, such as Zn and copper (Kagi 1993; Karin 1985). Exposure to zinc oxide fume increased mRNA of MTs in rat lungs (Cosma et al. 1992). Systemic administration of Zn enhanced MT levels in the liver (Conrad et al. 1997). Mice lacking MTs were more sensitive to Zn toxicity compared with wild-type mice (Park et al. 2001). In our study, in addition to increases in MT1F (4.6-fold), MT1G (29-fold), and MT1K (81-fold), other MTs, although not identified by our statistical filtration, also had elevated ratios: 1.36 for metallothionein 1X (MT1X), 1.17 for metal-lothionein 1H (MT1H) and 1.21 for metal-lothionein 2A (MT2A). These results confirm that up-regulation of the MTs may represent early cellular defense against Zn (Irato et al. 2001; Park et al. 2001) and may be used to distinguish Zn and other heavy metals from V.
In our study, we used the one-class t-test with a p-value of < 0.01 and a ratio cutoff of ≥ 2.0 or ≤ 0.5 to identify differentially expressed genes. This statistical algorithm selected 140 genes (1.0%) from V-treated cells, 76 genes (0.5%) from Zn-treated cells, and 163 genes (1.1%) that differentiated V from Zn out of 14,500 genes in the Affymetrix Hu133A 2.0 gene chip. We are not aware of other large-scale genomic studies on V and Zn. One study reported 65 differentially expressed genes out of 1,200 genes (5.4%) associated with 4-hr 50 μM arsenic treatment in BEAS-2B cells, using a ratio cutoff of 2.0 and signal difference of 13 (Andrew et al. 2003). It is difficult to compare across the different studies, but the smaller percentage of recovery of significant genes in our study may indicate in part a more stringent filtration method. Also, the cells in our study were exposed to 50 μM VOSO4 and ZnSO4, or 14 and 18 μg of elemental V and Zn, respectively. These doses would be equivalent to working 3 hr in the environments of boilermakers and welders with the ambient V and Zn concentrations of 8 and 10 μg/m3, respectively (Marquart et al. 1989; Woodin et al. 2000), assuming ventilation of 10 L/min.
Conclusion
It has been estimated that there are approximately 25,000 boilermakers and 300,000 welders nationwide. These workers can be exposed to high concentrations of V and Zn, respectively, at their workplaces. Our study compared gene expression profiles induced by V and Zn in HBECs and identified a group of 12 genes and several metallothionein 1 genes that may be used as a biomarker for V and Zn exposure, respectively. Determining the applicability of these candidate genes as biomarkers will require exposure studies enrolling a large number of subjects. The gene expression profiles provided by our study also identified potentially novel genes and pathways involved in the pathogenesis of health effects caused by environmental V and Zn exposure.
We thank A. Ghio and L. Dailey of the Human Studies Division of the U.S. Environmental Protection Agency (U.S. EPA) for performing bronchoscopic brushings and cell culture, respectively.
The research described in this article has been reviewed by the U.S. EPA Health Effects and Environmental Research Laboratory and has been approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the U.S. EPA, nor does mention of the trade names or commercial products constitute endorsement or recommendation for use.
Figure 1 Gene expression ratios measured by Q-PCR. The expression of a gene associated with V or Zn treatment, relative to the control; n = 6 independent experiments in cells from six different individuals for Q-PCR. Dashed line denotes an expression ratio of 1 (no change). Data are mean ± SE.
Figure 2 The hierarchical clustering analysis for the 163 genes that discriminated V from Zn (A) and the 12 genes from this list identified by additional filtration algorithms described in the text (B). Each row represents one single gene, and each column represents one experiment. Red areas are up-regulation, and green areas are down-regulation, relative to control. The 12 genes clearly discriminate between the V group and the Zn group. The analysis also divided the genes into two clusters. Gene names are from NCBI (http://www.ncbi.nlm.nih.gov/).
Table 1 Genes up-regulated by VOSO4.
Gene accession no.a Fold changeb Gene symbola Gene namea
Hs.624 8.04 IL8 interleukin 8
Hs.290873 6.67 PPEF2 protein phosphatase, EF hand calcium-binding domain 2
Hs.518417 5.52 STX6 syntaxin 6
Hs.233389 5.36 CPVL carboxypeptidase, vitellogenic-like
Hs.196384 4.67 PTGS2 prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase)
Hs.248189 4.46 KRTHA6 keratin, hair, acidic, 6
Hs.211600 4.33 TNFAIP3 tumor necrosis factor, alpha-induced protein 3
Hs.477070 4.30 CSNK1D casein kinase 1, delta
Hs.431460 4.24 ICAM2 intercellular adhesion molecule 2
Hs.44385 4.24 SBLF stoned B-like factor
Hs.799 4.21 DTR diphtheria toxin receptor (heparin-binding epidermal growth factor-like growth factor)
Hs.418167 4.16 ALB albumin
Hs.246310 4.11 JAM2 junctional adhesion molecule 2
Hs.406990 4.06 PDE4DIP phosphodiesterase 4D interacting protein (myomegalin)
Hs.992 4.04 PLA2G1B phospholipase A2, group IB (pancreas)
Hs.496222 3.97 ANGPTL1 angiopoietin-like 1
Hs.65758 3.78 ITPR3 inositol 1,4,5-triphosphate receptor, type 3
Hs.66713 3.70 DIPA hepatitis delta antigen-interacting protein A
Hs.519884 3.65 GCNT2 glucosaminyl (N-acetyl) transferase 2, I-branching enzyme
Hs.157259 3.64 SIPA1L3 signal-induced proliferation-associated 1-like 3
Hs.436023 3.56 PRDM1 PR domain containing 1, with ZNF domain
Hs.303980 3.51 CYP11A1 cytochrome P450, family 11, subfamily A, polypeptide 1
Hs.236646 3.49 HOXD9 homeo box D9
Hs.171695 3.46 DUSP1 dual specificity phosphatase 1
Hs.197693 3.44 CACNG2 calcium channel, voltage-dependent, gamma subunit 2
Hs.485910 3.34 RARSL arginyl-tRNA synthetase-like
Hs.211238 3.30 IL1F9 interleukin 1 family, member 9
Hs.520319 3.30 SLC22A16 solute carrier family 22 (organic cation transporter), member 16
Hs.445555 3.22 SERPINI2 serine (or cysteine) proteinase inhibitor, clade I (neuroserpin), member 2
Hs.256667 3.20 PDK2 pyruvate dehydrogenase kinase, isoenzyme 2
Hs.248122 3.10 GPR24 G-protein-coupled receptor 24
Hs.511899 3.02 EDN1 endothelin 1
Hs.523506 2.99 WARS2 tryptophanyl tRNA synthetase 2 (mitochondrial)
Hs.333175 2.86 PLA2G12B phospholipase A2, group XIIB
Hs.410817 2.78 RPL13 ribosomal protein L13
Hs.520942 2.77 CLDN4 claudin 4
Hs.50823 2.74 PDCD6 programmed cell death 6
Hs.550498 2.72 RCE1 RCE1 homolog, prenyl protein protease (S. cerevisiae)
Hs.436023 2.67 PRDM1 PR domain containing 1, with ZNF domain
Hs.421724 2.66 CTSG cathepsin G
Hs.2250 2.63 LIF leukemia inhibitory factor (cholinergic differentiation factor)
Hs.282387 2.58 RPC32 polymerase (RNA) III (DNA directed) (32 kDa)
Hs.525389 2.56 ARHJ ras homolog gene family, member J
Hs.106019 2.54 PPP1R10 protein phosphatase 1, regulatory subunit 10
Hs.250281 2.52 SLC13A3 solute carrier family 13 (sodium-dependent dicarboxylate transporter), member 3
Hs.2128 2.48 DUSP5 dual-specificity phosphatase 5
Hs.89690 2.45 CXCL3 chemokine (C-X-C motif) ligand 3
Hs.11169 2.45 MIG-6 mitogen-inducible gene 6
Hs.789 2.41 CXCL1 chemokine (C-X-C motif) ligand 1 (melanoma growth- stimulating activity, alpha)
Hs.485004 2.37 ZNF306 zinc finger protein 306
Hs.534478 2.36 DUSP21 dual-specificity phosphatase 21
Hs.441972 2.34 IFNT1 interferon tau-1
Hs.503598 2.33 JMJD2D jumonji domain containing 2D
Hs.546252 2.25 EDG3 endothelial differentiation, sphingolipid G-protein-coupled receptor, 3
Hs.85862 2.23 PDLIM3 PDZ and LIM domain 3
Hs.445489 2.22 PLEKHB1 pleckstrin homology domain containing, family B (evectins), member 1
Hs.1722 2.21 IL1A interleukin 1, alpha
Hs.466871 2.21 PLAUR plasminogen activator, urokinase receptor
Hs.159291 2.20 DRP2 dystrophin-related protein 2
Hs.303649 2.19 CCL2 chemokine (C-C motif) ligand 2
Hs.111944 2.19 CYP3A7 cytochrome P450, family 3, subfamily A, polypeptide 7
Hs.533683 2.19 FGFR2 fibroblast growth factor receptor 2
Hs.50550 2.19 KBTBD10 kelch repeat and BTB (POZ) domain containing 10
Hs.78944 2.19 RGS2 regulator of G-protein signaling 2, 24 kDa
Hs.190783 2.17 HAL histidine ammonialyase
Hs.463059 2.17 STAT3 signal transducer and activator of transcription 3 (acute- phase response factor)
Hs.25647 2.16 FOS v-fos FBJ murine osteosarcoma viral oncogene homolog
Hs.127022 2.14 PTPRE protein tyrosine phosphatase, receptor type, E
Hs.447899 2.13 SIGLEC8 sialic acid-binding Ig-like lectin 8
Hs.344812 2.13 TREX1 three prime repair exonuclease 1
Hs.528670 2.12 MMP25 matrix metalloproteinase 25
Hs.514913 2.11 SERPINB2 serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 2
Hs.506381 2.07 FGD6 FYVE, RhoGEF and PH domain containing 6
Hs.278658 2.06 KRTHB6 keratin, hair, basic, 6 (monilethrix)
Hs.439060 2.08 CLDN1 claudin 1
Hs.507348 2.05 HS3ST1 heparan sulfate (glucosamine) 3-O-sulfotransferase 1
Only genes with known protein products are shown.
a Gene annotations are from NCBI (http://www.ncbi.nlm.nih.gov).
b Fold changes are the average of four individuals.
Table 2 Genes down-regulated by VOSO4.
Gene accession no.a Fold changeb Gene symbola Gene namea
Hs.441975 −11.75 HSXIAPAF1 XIAP-associated factor-1
Hs.370503 −8.11 FYB FYN-binding protein (FYB-120/130)
Hs.76884 −7.48 ID3 inhibitor of DNA binding 3, dominant negative helix-loop- helix protein
Hs.520506 −7.37 FBXO5 F-box only protein 5
Hs.22393 −6.90 DENR density-regulated protein
Hs.433060 −6.86 ACPP acid phosphatase, prostate
Hs.37045 −6.77 PTH parathyroid hormone
Hs.282410 −6.69 CALM1 calmodulin 1 (phosphorylase kinase, delta)
Hs.474251 −6.60 SCARF2 scavenger receptor class F, member 2
Hs.534101 −5.89 MYO3B myosin IIIB
Hs.442578 −5.53 LHX9 LIM homeobox 9
Hs.292356 −5.29 CYBB cytochrome b-245, beta polypeptide (chronic granulomatous disease)
Hs.1973 −5.21 CCNF cyclin F
Hs.24684 −5.12 ARRDC3 arrestin domain containing 3
Hs.350756 −4.44 STAU2 staufen, RNA-binding protein, homolog 2 (Drosophila)
Hs.133892 −4.41 TPM1 tropomyosin 1 (alpha)
Hs.24120 −4.23 ZNF44 zinc finger protein 44 (KOX 7)
Hs.275243 −3.89 S100A6 S100 calcium-binding protein A6 (calcyclin)
Hs.526464 −3.71 PML promyelocytic leukemia
Hs.522090 −3.55 GALT galactose-1-phosphate uridylyltransferase
Hs.432424 −3.51 TPP2 tripeptidyl peptidase II
Hs.485233 −3.47 MAPK14 mitogen-activated protein kinase 14
Hs.434924 −3.44 RIMS3 regulating synaptic membrane exocytosis 3
Hs.75294 −3.38 CRH corticotropin-releasing hormone
Hs.173984 −3.16 TBX1 T-box 1
Hs.444106 −3.11 TOR2A torsin family 2, member A
Hs.254042 −3.02 BAT1 HLA-B associated transcript 1
Hs.75862 −2.96 MADH4 MAD, mothers against decapentaplegic homolog 4 (Drosophila)
Hs.498292 −2.89 SDCCAG8 serologically defined colon cancer antigen 8
Hs.1650 −2.78 SLC26A3 solute carrier family 26, member 3
Hs.293798 −2.69 ZNF436 zinc finger protein 436
Hs.397073 −2.66 PMS2L5 postmeiotic segregation increased 2-like 5
Hs.54416 −2.63 SIX1 sine oculis homeobox homolog 1 (Drosophila)
Hs.118513 −2.59 MGC21621 G-protein-coupled receptor MrgF
Hs.129634 −2.57 CINP cyclin-dependent kinase 2-interacting protein
Hs.21388 −2.55 ZDHHC21 zinc finger, DHHC domain containing 21
Hs.131846 −2.51 TAF6L TAF6-like RNA polymerase II, p300/CBP-associated factor (PCAF)-associated factor, 65 kDa
Hs.116622 −2.46 ZFP30 likely ortholog of mouse zinc finger protein 30
Hs.478588 −2.41 BCL6 B-cell CLL/lymphoma 6 (zinc finger protein 51)
Hs.47712 −2.41 ZNF555 zinc finger protein 555
Hs.487774 −2.41 HNRPA2B1 heterogeneous nuclear ribonucleoprotein A2/B1
Hs.339 −2.37 P2RY2 purinergic receptor P2Y, G-protein coupled, 2
Hs.501309 −2.35 CIRBP cold-inducible RNA-binding protein
Hs.534040 −2.33 KBTBD6 kelch repeat and BTB (POZ) domain containing 6
Hs.6093 −2.27 ARRDC4 arrestin domain containing 4
Hs.400802 −2.27 ZBTB1 zinc finger and BTB domain containing 1
Hs.474799 −2.25 PDXP pyridoxal (pyridoxine, vitamin B6) phosphatase
Hs.13982 −2.23 KCTD6 potassium channel tetramerisation domain containing 6
Hs.487561 −2.22 ICA1 islet cell autoantigen 1, 69 kDa
Hs.48297 −2.17 ZNF363 zinc finger protein 363
Hs.424926 −2.14 TM7SF1 transmembrane 7 superfamily member 1 (up-regulated in kidney)
Hs.101937 −2.14 SIX2 sine oculis homeobox homolog 2 (Drosophila)
Hs.409876 −2.12 ZNF450 zinc finger protein 450
Hs.63335 −2.12 TERF2 telomeric repeat binding factor 2
Hs.105633 −2.12 WINS1 WINS1 protein with Drosophila Lines (Lin) homologous domain
Hs.142167 −2.11 HSZFP36 ZFP-36 for a zinc finger protein
Hs.186424 −2.09 BCOR BCL6 co-repressor
Hs.518438 −2.08 SOX2 SRY (sex determining region Y)-box 2
Hs.195710 −2.08 ZNF503 zinc finger protein 503
Hs.535499 −2.02 RARA retinoic acid receptor, alpha
Hs.310640 −2.02 T2BP TRAF2-binding protein
Hs.513645 −2.02 PAK6 p21(CDKN1A)-activated kinase 6
Hs.131494 −2.00 ARNT aryl hydrocarbon receptor nuclear translocator
Only genes with known protein products are shown.
a Genes annotations are from NCBI (http://www.ncbi.nlm.nih.gov).
b Fold changes are the average of four individuals.
Table 3 Genes up-regulated by ZnSO4
Gene accession no.a Fold changeb Gene symbola Gene namea
Hs.188518 81.01 MT1K metallothionein 1K
Hs.433391 28.87 MT1G metallothionein 1G
Hs.283678 8.40 PCDHB14 protocadherin beta 14
Hs.412196 8.09 ESRRBL1 estrogen-related receptor beta-like 1
Hs.502182 5.46 BDNF brain-derived neurotrophic factor
Hs.517581 4.78 HMOX1 heme oxygenase (decycling) 1
Hs.165736 4.67 SCAND2 SCAN domain containing 2
Hs.519469 4.65 SLC30A1 solute carrier family 30 (zinc transporter), member 1
Hs.513626 4.58 MT1F metallothionein 1F (functional)
Hs.154296 4.58 TLL2 tolloid-like 2
Hs.303090 3.94 PPP1R3C protein phosphatase 1, regulatory (inhibitor) subunit 3C
Hs.118354 3.66 PRR3 proline-rich polypeptide 3
Hs.466891 3.55 ZNF233 zinc finger protein 233
Hs.59889 3.47 HMGCS2 3-hydroxy-3-methylglutaryl-coenzyme A synthase 2 (mitochondrial)
Hs.278973 3.33 ANGPT4 angiopoietin 4
Hs.73962 3.31 EPHA7 EphA7
Hs.445835 3.22 SERTAD4 SERTA domain containing 4
Hs.352241 3.09 TAS2R40 taste receptor, type 2, member 40
Hs.78036 3.08 SLC6A2 solute carrier family 6 (neurotransmitter transporter, noradrenalin), member 2
Hs.89714 3.05 CXCL5 chemokine (C-X-C motif) ligand 5
Hs.195471 3.02 PFKFB3 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3
Hs.460260 3.02 AKR1C2 aldoketo reductase family 1, member C2
Hs.16064 2.98 MAGI1 membrane-associated guanylate kinase interacting protein-like 1
Hs.143036 2.81 CABP4 calcium-binding protein 4
Hs.488671 2.67 BAZ1B bromodomain adjacent to zinc finger domain, 1B
Hs.444450 2.62 EGLN1 egl nine homolog 1 (C. elegans)
Hs.465642 2.59 SEMA6B sema domain, transmembrane domain (TM), and cyto plasmic domain, (semaphorin) 6B
Hs.32374 2.57 DTX3 deltex 3 homolog (Drosophila)
Hs.405667 2.49 CD8B1 CD8 antigen, beta polypeptide 1 (p37)
Hs.516664 2.48 EFNA1 ephrin-A1
Hs.487188 2.46 MLLT4 myeloid/lymphoid or mixed-lineage leukemia (trithorax homolog, Drosophila); translocated to, 4
Hs.6638 2.33 MYEF2 myelin expression factor 2
Hs.150136 2.25 MAPK7 mitogen-activated protein kinase 7
Hs.372000 2.24 NSMAF neutral sphingomyelinase (N-SMase) activation associated factor
Hs.194721 2.21 NCR2 natural cytotoxicity triggering receptor 2
Hs.508720 2.19 RAB20 RAB20, member RAS oncogene family
Hs.522610 2.18 LOC119180 lysozyme 2
Hs.75535 2.16 FOXN4 forkhead box N4
Hs.485572 2.11 SOCS2 suppressor of cytokine signaling 2
Hs.521171 2.09 HIG2 hypoxia-inducible protein 2
Hs.80288 2.05 HSPA1L heat-shock 70 kDa protein 1-like
Hs.123450 2.03 JPH3 junctophilin 3
Hs.441047 2.01 ADM adrenomedullin
Only genes with known protein products are shown.
a Gene annotations are from NCBI (http://www.ncbi.nlm.nih.gov).
b Fold changes are the average of four individuals.
Table 4 Genes down-regulated by ZnSO4.
Gene accession no.a Fold changeb Gene symbola Gene namea
Hs.376873 −6.25 ZNF390 zinc finger protein 390
Hs.106513 −6.09 TLL1 tolloid-like 1
Hs.200929 −5.87 IL23R interleukin-23 receptor
Hs.268581 −5.47 LPIN2 lipin 2
Hs.112218 −5.36 CAPN10 calpain 10
Hs.532082 −5.23 IL6ST interleukin 6 signal transducer (gp130, oncostatin M receptor)
Hs.483136 −4.53 COMMD10 COMM domain containing 10
Hs.141308 −4.39 MOG myelin oligodendrocyte glycoprotein
Hs.7138 −4.10 CHRM3 cholinergic receptor, muscarinic 3
Hs.120633 −4.08 SESN3 sestrin 3
Hs.512587 −3.58 MST1 macrophage stimulating 1 (hepatocyte growth factor-like)
Hs.370510 −3.23 IGSF4 immunoglobulin superfamily, member 4
Hs.533040 −3.21 PDLIM7 PDZ and LIM domain 7 (enigma)
Hs.552578 −3.03 TCF1 transcription factor 1, hepatic; LF-B1, hepatic nuclear factor (HNF1), albumin proximal factor
Hs.472558 −2.92 SDBCAG84 serologically defined breast cancer antigen 84
Hs.506394 −2.77 ubiquitin specific protease 44
Hs.438994 −2.69 ZNF544 zinc finger protein 544
Hs.32721 −2.61 SAG S-antigen; retina and pineal gland (arrestin)
Hs.74082 −2.48 KLRC3 killer cell lectin-like receptor subfamily C, member 3
Hs.382683 −2.47 PRG-3 plasticity-related gene 3
Hs.522291 −2.42 PRKWNK2 protein kinase, lysine deficient 2
Hs.493275 −2.34 TRIM31 tripartite motif-containing 31
Hs.129895 −2.29 TBX3 T-box 3 (ulnar mammary syndrome)
Hs.546263 −2.29 KIR3DL2 killer cell immunoglobulin-like receptor, three domains, long cytoplasmic tail, 2
Hs.546354 −2.21 RRP4 homolog of yeast RRP4 (ribosomal RNA processing 4), 3′-5′-exoribonuclease
Hs.19385 −2.17 ABHD5 abhydrolase domain containing 5
Hs.344400 −2.19 MPHOSPH6 M-phase phosphoprotein 6
Hs.411311 −2.17 IL24 interleukin 24
Hs.492236 −2.17 H326 H326
Hs.255432 −2.06 CIB3 calcium and integrin binding family member 3
Hs.476052 −2.02 SNRK SNF-1 related kinase
Hs.432898 −2.01 MAP3K13 mitogen-activated protein kinase kinase kinase 13
Only genes with known protein products are shown.
a Gene annotations are from NCBI (http://www.ncbi.nlm.nih.gov).
b Fold changes are the average of four individuals.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7998ehp0113-00176316330360ResearchAflatoxin Contamination of Commercial Maize Products during an Outbreak of Acute Aflatoxicosis in Eastern and Central Kenya Lewis Lauren 1Onsongo Mary 2Njapau Henry 3Schurz-Rogers Helen 1Luber George 1Kieszak Stephanie 1Nyamongo Jack 4Backer Lorraine 1Dahiye Abdikher Mohamud 5Misore Ambrose 6DeCock Kevin 7Rubin Carol 1the Kenya Aflatoxicosis Investigation Group *1 National Center for Environmental Health, Centers for Disease Control and Prevention, Chamblee, Georgia, USA2 Foreign Agricultural Service, U.S. Department of Agriculture, Nairobi, Kenya3 Office of Plant and Dairy Foods, Center for Food Safety and Applied Nutrition, Food and Drug Administration, College Park, Maryland, USA4 Kenya National Public Health Laboratory, Nairobi, Kenya5 Kenya Field Epidemiology and Laboratory Training Program, and6 Preventive and Promotive Health, Kenya Ministry of Health, Nairobi, Kenya7 Centers for Disease Control and Prevention, Kenya Office, Nairobi, KenyaAddress correspondence to L. Lewis, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, MS F-46, Chamblee, GA 30341 USA. Telephone: (770) 488-3428. Fax: (770) 488-3450. E-mail:
[email protected]* Members of the Kenya Aflatoxicosis Investigation Group are J. Nyikal, C. Njuguna, A. Langat, I.K. Kilei, C. Tetteh, S. Likimani (Kenya Ministry of Health); J. Oduor (Famine Early Warning and Food Information System, Ministry of Agriculture); D. Nzioki (Makindu, Makueni District); B. Wanjiku Kamau (District Farm Inputs, Machakos District); J. Onsongo (World Health Organization Kenya Country Office); L. Slutsker, C. Mutura [U.S. Centers for Disease Control and Prevention (CDC), Kenya Office]; P. Mensah (World Health Organization Regional Office for Africa); F. Kessel (Foreign Agricultural Service, U.S. Department of Agriculture); D.L. Park, S. Trujillo (U.S. Food and Drug Administration); A. Funk, K.E. Gieseker, E. Azziz-Baumgartner, N. Gupta, (CDC).
The authors declare they have no competing financial interests.
12 2005 10 8 2005 113 12 1763 1767 4 2 2005 10 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In April 2004, one of the largest aflatoxicosis outbreaks occurred in rural Kenya, resulting in 317 cases and 125 deaths. Aflatoxin-contaminated homegrown maize was the source of the outbreak, but the extent of regional contamination and status of maize in commercial markets (market maize) were unknown. We conducted a cross-sectional survey to assess the extent of market maize contamination and evaluate the relationship between market maize aflatoxin and the aflatoxicosis outbreak. We surveyed 65 markets and 243 maize vendors and collected 350 maize products in the most affected districts. Fifty-five percent of maize products had aflatoxin levels greater than the Kenyan regulatory limit of 20 ppb, 35% had levels > 100 ppb, and 7% had levels > 1,000 ppb. Makueni, the district with the most aflatoxicosis case-patients, had significantly higher market maize aflatoxin than did Thika, the study district with fewest case-patients (geometric mean aflatoxin = 52.91 ppb vs. 7.52 ppb, p = 0.0004). Maize obtained from local farms in the affected area was significantly more likely to have aflatoxin levels > 20 ppb compared with maize bought from other regions of Kenya or other countries (odds ratio = 2.71; 95% confidence interval, 1.12–6.59). Contaminated homegrown maize bought from local farms in the affected area entered the distribution system, resulting in widespread aflatoxin contamination of market maize. Contaminated market maize, purchased by farmers after their homegrown supplies are exhausted, may represent a source of continued exposure to aflatoxin. Efforts to successfully interrupt exposure to aflatoxin during an outbreak must consider the potential role of the market system in sustaining exposure.
aflatoxicosisaflatoxincornfood safetyKenyamaizemoldmycotoxin
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Mycotoxins are fungal metabolites that can contaminate agricultural products and threaten food safety. The Food and Agriculture Organization estimates that mycotoxins contaminate 25% of agricultural crops worldwide (Smith et al. 1994). Aflatoxins, a group of mycotoxins mainly produced by Aspergillus flavus and Aspergillus parasiticus, are of particular public health importance because of their effects on human health. Aflatoxins have both carcinogenic and hepatotoxic actions, depending on the duration and level of exposure. Chronic dietary exposure to aflatoxins is a major risk factor for hepatocellular carcinoma, particularly in areas where hepatitis B virus infection is endemic. Ingestion of higher doses of aflatoxin can result in acute aflatoxicosis, which manifests as hepatotoxicity or, in severe cases, fulminant liver failure (Fung and Clark 2004). Contamination of food supplies by these and other naturally occurring toxins is of particular concern in rural communities of developing countries (Bhat et al. 1997).
Outbreaks of acute aflatoxicosis from highly contaminated food have been documented in Kenya, India, and Thailand [Council for Agriculture Science and Technology (CAST) 2003]. In April 2004, an outbreak of acute hepatotoxicity was identified among people living in Kenya’s eastern and central provinces. Epidemiologic investigations determined that the outbreak was the result of aflatoxin poisoning from ingestion of contaminated maize (corn). As of 20 July 2004, 317 cases and 125 deaths had occurred, making this one of the largest and most severe outbreaks of acute aflatoxicosis documented worldwide [Centers for Disease Control and Prevention (CDC) 2004]. Because of the high number of cases and large geographic area involved, health officials were concerned that aflatoxin-contaminated maize might be circulating in the regional maize distribution system. To assess the potential exposure to aflatoxin through consumption of commercial maize products (market maize), we conducted a cross-sectional assessment of market maize contamination. The primary objectives of this study were to characterize the extent of aflatoxin contamination within the maize market system and to assess the relationship between market maize aflatoxin levels and the outbreak of aflatoxicosis. In addition, we sought to identify factors contributing to aflatoxin contamination of market maize.
The outbreak covered more than seven districts encompassing an area approximately 40,149 km2 (15,502 mi2). Of the 317 case-patients, 89% resided in four districts (Makueni, Kitui, Machakos, and Thika). The estimated total population of these four districts is 2.8 million (Central Bureau of Statistics 1999). Of the four districts, Makueni and Kitui were most heavily affected (representing 47% and 32% of case-patients, respectively), followed by Machakos (6% of cases) and Thika (4% of cases) (CDC 2004).
Overall, the area has a rural population that is primarily from the Akamba ethnic group. Most of the local population engages in small-scale, mixed farming that includes some livestock. Maize is the primary dietary staple and the main crop produced. At harvest, farmers store most of their maize for household consumption and sell the rest to meet other household needs. When household maize stores are exhausted, farmers purchase maize back from market vendors.
Maize is distributed through a network of rural markets. Small lots of maize from local farmers are pooled and may be combined with imported maize and redistributed. No formal records of maize sources or trade are available at this level of distribution (Oduor J, personal communication). The markets are a mixture of small, family-owned shops providing consumer goods and services, and traditional open-air markets where migrant vendors bring products to sell or trade.
The market maize assessment presented in this article is one of three complementary, epidemiologic investigations conducted in response to the aflatoxicosis outbreak. First, a descriptive epidemiologic investigation was performed. Based on hypotheses generated by the descriptive investigation, two concurrent, complementary investigations were conducted: a case–control investigation of the outbreak and the assessment of market maize. An abbreviated description of all three studies and preliminary findings were reported in the Morbidity and Mortality Weekly Report in September 2004 (CDC 2004).
Materials and Methods
This study was conducted, beginning 4 June 2004, over a 3-week period during the peak of the outbreak. We collected maize samples from markets located in the four districts where 87% of the aflatoxicosis case-patients resided (Makueni, Kitui, Machakos, and Thika). We interviewed vendors and collected maize products in major agricultural markets in the districts most affected by aflatoxicosis. Markets in 5 of the 31 divisions within the four study districts were not sampled for logistical reasons.
Market selection.
Individual agricultural markets in each district were selected for inclusion on the basis of information obtained from interviews with the district agriculture officer of each district. We created a sample of major agricultural markets that represented potential exposure to aflatoxin among all market maize consumers within the study area. Markets were selected for inclusion based on the following criteria: a) geographic location of the population served by the market, b) having an increased number of maize vendors, c) having a variety of maize vendor types, and d) holding an important position in the maize distribution system for the district.
Large government grain warehouses operated by Kenya’s National Cereals and Produce Board (NCPB) also were included in this study. The NCPB is involved in grain marketing and acts as a strategic grain reserve for food supply functions of the country, including famine relief.
Vendor selection.
At each market surveyed, vendors were selected to create a sample that included all types of maize vendors represented within each marketplace. Vendor types were store merchant, wholesale maize distributor, small-scale miller, or open market vendor (i.e., migrant vendor who brings products to sell in an open air market). The variability of maize sold determined the number of vendors interviewed at each market. More interviews were conducted at markets with maize from a variety of sources. Maize variability was assessed based on a) the size of each market, b) variety of vendor types present, and c) relationship of the market to major distribution routes within the district. Vendors were systematically selected based on location within the market in order to obtain geographic distribution within the marketplace. Selected maize vendors were administered a face-to-face interview and requested to provide samples of each of their maize products for aflatoxin analysis.
Survey instrument.
Face-to-face interviews were conducted with maize vendors at the marketplace in Kiswahili, Kikamba, or English. All vendors were administered a standard survey questionnaire. Information was collected on market location, vendor type, vendor trade practices, maize history (as could be recalled by the vendor), and vendor’s assessment of the quality (at the time of purchase) of maize products sampled.
Maize sample collection.
Maize products were sampled from every vendor interviewed. A 1-kg sample was taken from every maize product offered by the vendor. Maize products were dried maize kernels, maize flour (commercial or locally milled), and muthokoi (kernels with the outer hull removed). If the vendor offered the same product from different sources (i.e., maize kernels purchased from local farmers and maize kernels from a distributor), then a 1-kg sample was taken from each.
Most of the samples were collected from 90-kg bags of maize. Multiple samples were taken from different parts of one bag or several bags belonging to one vendor and combined to produce a 1-kg sample for analysis. The maize samples were collected using the respective vendor’s sampling tools (i.e., spikes and scoops). Samples were transported and stored in paper bags. Prepackaged 1- or 2-kg bags of commercial maize flour were also collected for analysis.
Maize sample analysis.
The samples were analyzed for total aflatoxin using a slightly modified immunoaffinity method based on Association of Official Analytic Chemists (AOAC) method 991.3 (Trucksess et al. 1991). Briefly, the whole sample was ground to pass a No. 20 sieve, and a 50-g subsample was removed for analysis. Methanol:water (80:20) solvent (100 mL) and 5 g NaCl were added to the 50-g subsample, and the mixture was blended at high speed for 1 min. The mixture was then filtered through a fluted filter paper (Whatman 2V, Whatman plc, Middlesex, UK), and the filtrate was diluted (1:4) with water and refiltered through a glass-fiber filter paper. Two milliliters of the glass-fiber filtrate was placed on an Aflatest P immunoaffinity column (VICAM, Watertown, MA, USA) and allowed to elute at 1–2 drops/sec. The column was washed two times with 5 mL water, and aflatoxin was eluted from the column with 1 mL high performance liquid chromatography (HPLC)-grade methanol. A bromine developer (1 mL) was added to the methanol extract, and the total aflatoxin concentration was read in a precalibrated VICAMSeries-4 fluorometer set at 360 nm excitation and 450 nm emission. Samples containing > 250 ppb were repeated using a 1:49 water:sample dilution ratio. The modified fluorometry method had ≥85% recovery and a 1 ppb limit of detection.
Data analysis.
Vendor type variables.
Participants were classified as one or more vendor type(s) on the basis of the type of business and maize trade in which they were engaged.
Geographic location variables.
District-and division-level administrative boundaries for each market were used to create geographic location variables. The four districts included in this study are divided into 31 divisions: Makueni (7 divisions), Kitui (8 divisions), Machakos (10 divisions), and Thika (6 divisions). A variable was created that dichotomized divisions into those in which one or more cases of aflatoxicosis had occurred and those with no aflatoxicosis case-patients. Data on the location of aflatoxicosis case-patients were obtained from the descriptive study of the outbreak. Data collection methods for the descriptive study have been published elsewhere (CDC 2004).
Maize history and vendor trade variables.
Vendors were asked where the maize was grown. Maize from within the same district as the market was considered local maize; maize from outside the district where the market was located was classified as outside maize. Outside maize was further categorized as being from Loitokitok (a major import route for Tanzanian maize), Busia (a major import route for Ugandan maize), and other districts in Kenya. Participants were asked who had sold them the maize product [i.e., local farmers, a merchant, or a lorry vendor (a migrant vendor who buys and sells from a truck)]. These variables relate to the specific maize product sampled. For mixed maize from more than one source, vendors indicated all that applied. Vendors were also asked about selling practices, including who purchases their maize products (e.g., local residents, small-scale millers, or other merchants).
Maize type and quality variables.
The type of maize product was indicated for each sample collected. Vendors were asked whether, in their opinion, the maize had appeared completely dry at the time of purchase. Interviewers did not inquire about methods used to assess extent of dryness.
Maize aflatoxin concentration.
The continuous aflatoxin concentration variable represents the individual aflatoxin concentrations for each maize product collected. A dichotomous aflatoxin variable was also created using the U.S. Food and Drug Administration (FDA) and Kenya Bureau of Standards regulatory limit for aflatoxin in products for human consumption, 20 ppb (FDA 1997; Kenya Bureau of Standards 1988). Samples were dichotomized based on whether or not the aflatoxin levels were > 20 ppb.
Data analysis and analytic methods.
Data were analyzed using SAS computer software (SAS Institute Inc., 2001). We used mixed linear models to investigate the association between the natural log of aflatoxin concentrations in maize samples and questionnaire variables. Nested random effects (i.e., divisions within districts, markets within divisions, and vendors within markets) were added to account for potential correlation among samples. We calculated least-squares means for the fixed effects specified in the models.
Results
Descriptive results.
We surveyed 65 markets within the four study districts. Within those markets, we interviewed 243 vendors and collected 350 maize products (Table 1). All but two vendors we approached agreed to be interviewed and provide samples.
Most (65%) vendors were store merchants, followed by open market vendors (19%), wholesale distributors (10%), and small-scale millers (3%). The most common maize products sold in the market place were maize kernels (69%), followed by muthokoi (18%) and maize flour (12%). Most vendors (89%) reported that their maize products were dry at the time of purchase.
During the study period (June 2004), the maize trade was primarily local. The majority (88%) of maize was locally grown, sold to vendors by local farmers (70%), and bought by local residents (88%). Of the 45 samples representing maize products from outside the local area, 30 (67%) were from Loitokitok and or Busia, and 15 (33%) were from other Kenyan districts.
Aflatoxin levels in market maize indicate widespread aflatoxin contamination. Of the 350 market maize samples collected, 192 (55%) had levels greater than the regulatory limit of 20 ppb. One hundred twenty-one (35%) of the maize samples had aflatoxin levels > 100 ppb (five times the regulatory limit), and 24 (7%) had levels > 1,000 ppb. Aflatoxin levels ranged from 1 ppb (the lower limit of detection) to values as high as 46,400 ppb. Each of the four study districts had a substantial proportion of market maize with aflatoxin levels > 20 ppb (Table 2). Makueni and Kitui districts had the highest proportions of samples, with aflatoxin levels > 20 ppb (65% and 62%, respectively), followed by 51% of maize from Machakos markets and 34% from Thika (Table 2).
Fourteen samples were collected from NCPB warehouses in Makueni, Kitui, and Machakos districts. Of the 14, 8 (57%) had levels > 20 ppb, and 6 (43%) had levels ≥100 ppb. Among NCPB warehouses, samples from the Makueni facility contained the highest levels of aflatoxin, including two that were > 1,000 ppb.
Analytic results.
Significant differences were found in the geometric mean (GM) of market maize aflatoxin levels between districts. These differences were consistent with the geographic distribution of aflatoxicosis cases. Makueni and Kitui, the districts with the highest number of aflatoxicosis cases, also had the highest market maize aflatoxin levels. Maize from markets in Makueni had a GM aflatoxin level greater than 2.5 times the upper acceptable regulatory limit [GM = 52.91 ppb; 95% confidence interval (CI), 27.19–103.21 ppb]. Kitui had the second highest GM aflatoxin level, followed by Machakos and Thika (Table 3, Figure 1). When aflatoxin contamination data from Makueni were compared with data from Machakos and Thika, the differences in GM aflatoxin levels were statistically significant (p = 0.0249 and p = 0.0004, respectively).
At the division level, those divisions with one or more aflatoxicosis case-patients had significantly higher aflatoxin levels in market maize than did market maize from divisions with no aflatoxicosis case-patients (GM = 27.70 ppb vs. 6.14 ppb, p = 0.0022).
The aflatoxin GM in locally grown market maize from within the affected area was higher than levels in market maize grown outside the local area. The difference was not, however, statistically significant (GM = 19.84 ppb vs. 9.64 ppb, p = 0.1748). The low aflatoxin level in maize from outside the local area primarily reflects maize from Loitokitok and Busia, major import routes from neighboring African countries. The GM aflatoxin for maize from Loitokitok and Busia was 9.14 ppb (95% CI, 3.32–25.13 ppb).
No significant differences were observed among GM aflatoxin levels of market maize based on whether or not maize was from a store merchant, open market vendor, wholesale distributor, small-scale miller, or other type of maize vendor. Aflatoxin levels did not vary significantly among the types of market maize products (i.e., maize kernels, flour, or muthokoi). No significant differences were seen in aflatoxin levels based on vendor selling practices or whether the maize was wet at the time of purchase.
The dichotomous aflatoxin concentration variable was analyzed to compare the odds of exposure to aflatoxin at levels > 20 ppb by market location, maize history, and vendor type. Significant differences were seen among all four study districts. The odds of exposure to aflatoxin levels > 20 ppb were more than four times higher in samples from Makueni than in samples from Thika [odds ratio (OR) = 4.29; 95% CI, 1.71–10.80]. At the division level, maize samples from markets located in divisions with aflatoxicosis case-patients were three times more likely to have maize aflatoxin levels > 20 ppb compared with samples from markets in divisions not affected by aflatoxicosis (OR = 3.13; 95% CI, 1.69–5.88). Locally grown maize from the affected area was significantly more likely to have aflatoxin levels > 20 ppb compared with maize from other regions of Kenya or imported from other countries (OR = 2.71; 95% CI, 1.12–6.59). Aflatoxin levels by type of vendor did not differ significantly.
Discussion
Maize is the primary dietary staple in the region affected by the aflatoxicosis outbreak. Aflatoxin contamination of market maize, therefore, is an important public health concern. Our findings demonstrate widespread aflatoxin contamination of maize within the regional market distribution system. A high proportion (55%) of maize samples from markets in all four study districts had aflatoxin levels greater than the regulatory standard of 20 ppb. Twenty-four samples (7%) had exceedingly high levels (i.e., > 1,000 ppb). Thus, consumers of market maize in this area of Kenya have been at significant risk for exposure to high levels of aflatoxin.
Aflatoxin levels in market maize mirror the geographic distribution of aflatoxicosis cases associated with the outbreak. Data from this study indicate a statistically significant association between the locations of aflatoxin-contaminated market maize and cases of aflatoxicosis. However, the specific nature of this relationship cannot be inferred by findings from this study alone. We can further our understanding of how aflatoxin in market maize relates to the outbreak of aflatoxicosis by looking at findings from the complementary, case–control investigation of the outbreak (CDC 2004), in conjunction with findings from this assessment of market maize.
The case–control investigation was conducted concurrently with the market maize assessment and was limited to cases and village-matched controls in the two most affected districts (Makueni and Kitui). The case–control study showed that aflatoxicosis in the affected area was associated with eating homegrown maize and storing homegrown maize under damp conditions. The maize implicated in this outbreak was harvested in February during unseasonable, early rains. As a result, maize was stored wet under conditions conducive to mold growth. This probably led to aflatoxin contamination of farm household maize (CDC 2004).
It is likely that the contaminated, home-grown maize implicated in the outbreak entered the market distribution system when local farmers sold a portion of their farm household stores to market vendors. This information is consistent with both known trade practices in the region and reports from maize vendors and district agricultural officers during the market maize study. The vendors and agricultural officers informed us that the maize sold in the market during our study was purchased in March through May, was obtained from local farmers in the affected area, and was from the February 2004 harvest. These reports are also consistent with our findings in the market maize study that show that 88% of market maize was locally grown, and maize bought from local sources had higher aflatoxin levels than did maize bought from sources outside the affected area.
The case–control investigation also demonstrated that eating market maize was not significantly associated with aflatoxicosis in the outbreak (CDC 2004). Contaminated market maize may, however, represent a significant source of continued exposure to aflatoxin after the homegrown maize implicated in the outbreak had been consumed or discarded. Known trade practices indicate that once household stores have been depleted, local farm families are likely to buy back essentially the same contaminated maize they sold to vendors, thus continuing exposure. During the market maize study, district agricultural officers stated that local consumer demand for market maize in Makueni and Kitui was expected to increase because of depletion of farm household stores and the anticipated failure of the upcoming harvest (Oduor J, personal communication). As a result, consumer dependence on market maize was expected to be particularly high in the two districts with the highest market maize aflatoxin levels (Makueni and Kitui), thus amplifying the cycle of reexposure to aflatoxin in this population.
Our findings should be interpreted in light of some limitations. Vendors may have been reluctant to report buying and storing wet maize and following other practices known to favor fungal growth. Also, the association between the aflatoxicosis cases and market maize aflatoxin levels is ecologic and subject to ecologic fallacy. We do not, however, make causal inference based solely on ecologic data from this study. Finally, we used aflatoxin levels in maize as a surrogate for potential exposure to aflatoxins rather than measuring actual exposure using human biomarkers. Maize is, however, the dietary staple in this population, and aflatoxin levels in maize are therefore likely to provide a good indication of aflatoxin exposure (Moss 1998).
The conditions implicated in triggering this outbreak are consistent with previous reports of aflatoxicosis outbreaks. In 1981, an outbreak of aflatoxicosis from contaminated maize occurred in this same region of Kenya—the Makueni District. In both 1981 and 2004, drought and food shortages were followed by unseasonable rains during harvest, which probably favored the growth of aflatoxigenic aspergilli in household maize (Ngindu et al. 1982). The largest documented outbreak of aflatoxicosis took place in western India in 1975. This event also occurred in the context of unseasonable rains during harvest, which led to contamination of homegrown maize stored under damp conditions (Krishnamachari et al. 1975). Investigations of these previous outbreaks document the importance of unseasonable rains and improper storage of homegrown maize in aflatoxicosis outbreaks. However, they do not include documentation of potential exposure through market maize products. This study represents the only published assessment of market stores during an aflatoxicosis outbreak and the only reported investigation to explore the role of the regional market distribution system in exposure to aflatoxin.
Our assessment demonstrates that market maize represents a significant source of continued exposure to aflatoxin, long after contaminated household stores have been consumed or discarded. These data suggest that public health efforts to interrupt aflatoxin exposure during an aflatoxicosis event must include both an assessment of aflatoxin contamination within the regional market distribution system and replacement of contaminated market products.
This outbreak occurred in the context of critical regional and national food shortages resulting from prolonged drought and crop failure. Immediate response efforts have focused primarily on food replacement and relief. Some inspections of local and imported commercial products are also being conducted. Products suspected of mold contamination are being seized and replaced (Integrated Regional Information Networks 2004). To effectively prevent future outbreaks of aflatoxicosis, establishment of long-term interventions such as a comprehensive food safety program must be implemented. These interventions must target both market vendors and local farmers in order to prevent or minimize future aflatoxicosis outbreaks and reduce long-term exposure to aflatoxins.
Figure 1 Aflatoxicosis rate and market maize aflatoxin by division in each of the four study districts. Each dot represents the rate of aflatoxicosis by division, and dots are in the center of each division (divisions are not shown).
Table 1 Description of study sample [n (%)] by district.
Study district Divisions Agricultural markets Maize vendors Maize products
Makueni 7 16 (25) 67 (26) 96 (27)
Kitui 8 11 (17) 50 (21) 73 (21)
Machakos 10 20 (31) 66 (27) 105 (30)
Thika 6 18 (28) 60 (25) 76 (22)
Total 31 65 243 350
Values shown are the total number of markets, vendors, maize products included in the study by district and the percentage of total within the district.
Table 2 Distribution of aflatoxin levels in maize products collected from agricultural markets in the study districts.
Maize aflatoxin > 20 ppbb [n (%)]
Study district No. of maize productsa Maize aflatoxin ≤20 ppbb [n (%)] 21–99 ppb 100–1,000 ppb > 1,000 ppb
Makueni 91 32 (35) 12 (13) 36 (40) 11 (12)
Kitui 73 28 (38) 15 (21) 23 (32) 7 (10)
Machakos 102 50 (49) 26 (25) 23 (23) 3 (3)
Thika 76 50 (66) 13 (17) 10 (13) 3 (4)
Total 342 160 (47) 66 (19) 92 (27) 24 (7)
Values shown are the number of maize product samples with aflatoxin and the percentage of total samples within the district.
a Number of maize product samples analyzed for aflatoxin, which do not include eight samples collected but not analyzed for aflatoxin concentration.
b Acceptable upper limit for aflatoxin in grains is 20 ppb (FDA 1997; Kenya Bureau of Standards 1988).
Table 3 Geographic distribution by district, January through June 2004.
Market maize aflatoxin level (ppb)
District No. of aflatoxicosis casesa Aflatoxicosis incidence rateb GM (95% CI) Range
Makueni 129 16.7 52.91 (27.19–103.21) 1–5,400c
Kitui 88 17.1 35.27 (17.32–72.77) 1–25,000
Machakos 19 2.1 17.84 (9.79–32.54) 1–3,800
Thika 12 1.9 7.52 (3.83–14.78) 1–46,400
Total 233 8.2 20.53 (13.42–31.39) 1–46,400
a Total number of aflatoxin cases per district (CDC 2004).
b Incidence per 100,000 population; denominator is based on Kenya 1999 census data (Central Bureau of Statistics 1999).
c Lower limit of detection is 1 ppb.
==== Refs
References
Bhat RV Shetty PH Amruth RP Sudersham RV 1997 A food-borne disease outbreak due to consumption of moldy sorghum and maize containing fumonisin mycotoxins J Toxicol Clin Toxicol 35 249 255 9140318
CAST 2003. Mycotoxins: Risks in Plant, Animal, and Human Systems. Task Force Report No. 139. Ames, IA:Council for Agriculture Science and Technology.
CDC (Centers for Disease Control and Prevention) 2004. Outbreak of aflatoxin poisoning—eastern and central provinces, Kenya, January–July, 2004. MMWR Morb Mortal Wkly Rep 53:790–792. Available: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5334a4.htm [accessed 20 October 2005].
Central Bureau of Statistics 1999. Kenya: 1999 Population Census. Nairobi, Kenya:Central Bureau of Statistics, Ministry of Planning and National Development.
FDA (U.S. Food and Drug Administration) 1997. Adulterated Food. Federal Food Drug and Cosmetic Act, Chapter IV: Definitions and Standards for Food, Sec 402(a)(1). Available: http://www.fda.gov/opacom/laws/fdcact/fdcact4.htm [accessed 20 October 2005].
Fung F Clark RF 2004 Health effects of mycotoxins: a toxicological overview J Toxicol Clin Toxicol 42 217 234 15214629
Integrated Regional Information Networks, United Nations, Office for the Coordination of Humanitarian Affairs 2004. Kenya: Food Distribution Stepped Up in Two Districts after Contaminated Grain Kills 81. Available: http://www.irinnews.org/report.asp?ReportID=41581&SelectRegion=East_Africa&SelectCountry=KENYA [accessed 10 December 2004].
Kenya Bureau of Standards 1988. Dry-Milled Maize Products. Kenyan Standard No. 05158. Nairobi:Kenya Bureau of Standards.
Krishnamachari KA Nagarajan V Ramesh VB Tilak TBG 1975 Hepatitis due to aflatoxicosis: an outbreak in western India Lancet 1 7915 1061 1063 48730
Moss MO 1998 Recent studies of mycotoxins J Appl Microbiol 84 62S 76S
Ngindu A Johnson BK Kenya PR Ngira JA Ocheng DM Nandwa H 1982 Outbreak of acute hepatitis caused by aflatoxin poisoning in Kenya Lancet 1 8285 1346 1348 6123648
SAS Institute Inc 2001. SAS/STAT Guide for Personal Computers. Version 8. Cary, NC:SAS Institute Inc.
Smith JE Solomons GL Lewis CW Anderson JG 1994. Mycotoxins in Human Nutrition and Health. Brussels: European Commission CG XII.
Trucksess MW Stack ME Nesheim S Page SW Albert RH Hansen TJ 1991 Immunoaffinity column coupled with solution fluorometry or liquid chromatography post-column derivatization for determination of aflatoxins in corn, peanuts, peanut butter: collaborative study Assoc Off Anal Chem 74 1 81 88
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7989ehp0113-00176816330361ResearchWorkgroup Report: Workshop on Source Apportionment of Particulate Matter Health Effects—Intercomparison of Results and Implications Thurston George D. 1Ito Kazuhiko 1Mar Therese 2Christensen William F. 3Eatough Delbert J. 4Henry Ronald C. 5Kim Eugene 6Laden Francine 7Lall Ramona 1Larson Timothy V. 8Liu Hao 9Neas Lucas 10Pinto Joseph 11Stölzel Matthias 12Suh Helen 7Hopke Philip K. 61 Nelson Institute of Environmental Medicine, New York University School of Medicine, Tuxedo Park, New York, USA2 Department of Environmental Health, University of Washington, Seattle, Washington, USA3 Department of Statistics, and4 Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah, USA5 Department of Civil and Environmental Engineering, Southern California University, Los Angeles, California, USA6 Center for Air Resources Engineering and Science, Clarkson University, Potsdam, New York, USA7 Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts USA8 Department of Civil and Environmental Engineering, and9 Department of Biostatistics, University of Washington, Seattle, Washington, USA10 National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Chapel Hill, North Carolina, USA11 National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA12 Institute of Epidemiology, Focus Network Aerosols and Health, National Research Center for Environment and Health (GSF), Neuherberg, GermanyAddress correspondence to G.D. Thurston, New York University School of Medicine, Nelson Institute of Environmental Medicine, 57 Old Forge Rd., Tuxedo Park, NY 10987 USA. Telephone: (845) 731-3564. Fax: (845) 351-5472. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 1 9 2005 113 12 1768 1774 31 1 2005 1 9 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Although the association between exposure to ambient fine particulate matter with aerodynamic diameter < 2.5 μm (PM2.5) and human mortality is well established, the most responsible particle types/sources are not yet certain. In May 2003, the U.S. Environmental Protection Agency’s Particulate Matter Centers Program sponsored the Workshop on the Source Apportionment of PM Health Effects. The goal was to evaluate the consistency of the various source apportionment methods in assessing source contributions to daily PM2.5 mass–mortality associations. Seven research institutions, using varying methods, participated in the estimation of source apportionments of PM2.5 mass samples collected in Washington, DC, and Phoenix, Arizona, USA. Apportionments were evaluated for their respective associations with mortality using Poisson regressions, allowing a comparative assessment of the extent to which variations in the apportionments contributed to variability in the source-specific mortality results. The various research groups generally identified the same major source types, each with similar elemental makeups. Intergroup correlation analyses indicated that soil-, sulfate-, residual oil-, and salt-associated mass were most unambiguously identified by various methods, whereas vegetative burning and traffic were less consistent. Aggregate source-specific mortality relative risk (RR) estimate confidence intervals overlapped each other, but the sulfate-related PM2.5 component was most consistently significant across analyses in these cities. Analyses indicated that source types were a significant predictor of RR, whereas apportionment group differences were not. Variations in the source apportionments added only some 15% to the mortality regression uncertainties. These results provide supportive evidence that existing PM2.5 source apportionment methods can be used to derive reliable insights into the source components that contribute to PM2.5 health effects.
fine particleshealth effectsmortalityparticulate mattersource apportionmentsulfatetime-seriesuncertainty
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Airborne particulate matter (PM) air pollution is presently regulated by the National Ambient Air Quality Standards (NAAQS) using gravimetric mass as the particle metric to assess air quality. However, an enormous number of different chemical species are associated with the various types of ambient particles, depending upon their source origins (e.g., Cooper and Watson 1980). For example, primary particles emitted from coal combustion are characteristically highly enriched with arsenic and selenium, whereas residual oil combustion particles are more enriched in nickel and vanadium, and soil particles are especially enriched in the crustal elements (e.g., silicon, aluminum). In addition, secondary components of particles (e.g., sulfates, nitrates, and organic compounds) are formed in the atmosphere from gaseous pollutant emissions. These secondary components can either condense on primary particles or form secondary particles that can then collide and coagulate with primary particles. Individual particles in an urban airshed can contain both primary and secondary components, and the composition of ambient aerosols have been found to reflect source PM emission characteristics differences over space (e.g., between cities) and time (e.g., across seasons) (e.g., Spengler and Thurston 1983). Because the composition of particle types varies greatly, it is probable that some types of particles are more toxic than others. Thus, treating all particles that contribute to the mass concentration equally in the regulatory process may lead to inefficient protection of public health. A potentially more effective regulatory approach would be to address the individual types of particles independently, focusing control efforts on the most toxic categories. However, because toxicities of individual source components are not yet certain, and because virtually all published PM health effects studies to date have used PM mass (in various size categories) as the particle pollution index, the current NAAQS for airborne PM use airborne particle mass as the indicator for making air quality compliance determinations. Equal treatment of all particles that contribute to mass, irrespective of composition, may be leading to less-optimal control strategies to avoid the adverse human health effects of PM, potentially causing the present PM ambient standard to be less protective of health in some areas of the nation than in others. There is a need for epidemiologic and toxicologic evaluation of the extent to which the toxicity of ambient PM mass varies by particle type and source.
Because source composition and/or physical properties of particles vary between different source categories, the mass can be statistically apportioned into contributions from various source categories, opening the possibility of evaluating PM component effects using epidemiologic methods presently used on the PM mass. As discussed by Hopke et al. (in press), this area of research, called receptor modeling, has been active for over 3 decades. A number of accepted methods are being used to apportion the total mass into source categories, and these source apportionment methods can now be used as inputs to epidemiologic models of the human health effects of air pollution. However, to date only a small number of published efforts have related source-apportioned PM impacts to human health effects (e.g., Laden et al. 2000; Mar et al. 2000; Ozkaynak and Thurston 1987). The effect of the imputation of these apportionments on the ability of epidemiologic methods to evaluate the health effects associated with various PM components is uncertain. Because a number of methods are used to determine source contributions to PM mass impacts, and application of these methods varies among researchers, their application, although providing new insights, can also be expected to introduce added uncertainty into the derivation of estimates of PM toxicity [e.g., to the estimation of mortality relative risks (RRs) per amount of mass of fine particulate matter < 2.5 μm in aerodynamic diameter (PM2.5)]. The scientific and regulatory community is uncertain whether meaningful and reliable source apportionments of PM2.5 health effects are possible with today’s data and methods. A workshop was therefore organized by a consortium of U.S. Environmental Protection Agency (EPA) PM centers to assess the extent to which variations in current source apportionment methods and their application may affect the ability of epidemiologic studies to discern PM health effects on a source-specific basis.
On 29–30 May 2003, the U.S. EPA PM centers sponsored the Workshop on the Source Apportionment of PM Health Effects, hosted by the New York University (NYU) PM Research Center. The specific goal of this workshop was to evaluate the variability of the various PM source apportionment approaches in assessing PM source contributions to ambient PM2.5 concentrations in real-world data sets and to then assess the influence of this variability on the ability of statistical time-series analyses to discern which source categories contribute significantly to daily PM2.5 mass–mortality associations. No new health or environmental data were generated by participants during this effort. Instead, the same pre-existing reference PM mass and constituent data sets from two cities (Washington, DC, and Phoenix, AZ) were sent to various leading source apportionment research groups in advance of the workshop (in December 2002), and each group individually analyzed the same data sets for daily source PM2.5 contributions. These various daily PM2.5 mass source apportionments were then independently submitted before the workshop (in April 2003), and each was individually evaluated for their respective associations with daily mortality in each city in a consistent manner across the various apportionment research groups/methods. The PM–mortality health effects time-series modeling evaluations were conducted for the Washington and Phoenix data sets by researchers at the NYU and University of Washington U.S. EPA PM Research Centers, respectively. Washington and Phoenix were selected for this workshop analysis because the PM data available from these cities in past years were collected and analyzed for trace constituents in a manner similar to that used by the U.S. EPA in the nationwide Speciation Trend Network (STN), so as to come to workshop conclusions relevant to that developing STN data set. In addition, the consideration of these two very different cities with differing sources and weather provided a broader test of the consistency of these methods than would a single city or two cities from the same region of the country. Keeping the health effects model consistent across the various source apportionment researchers and methods allowed a separate discernment of the extent to which variability in the source apportionment step contributed to variability in the ultimate health effects analyses results.
The goals of the workshop were to bring together key researchers to assess the reliability of source apportionment–health effects methods by analyzing daily mortality with existing PM2.5 data sets similar to those now being collected by the U.S. EPA Speciation Network and to identify key future research needs for source apportionment health effects evaluation. As noted in Table 1, research groups from seven institutions, using various source apportionment approaches, participated in this workshop. Most of the groups were affiliated with one of the five U.S. EPA PM centers.
Materials and Methods
Particulate matter data sets.
The two PM2.5 mass and composition data sets employed in the source apportionments were selected based on their ready availability for analysis, the similar availability of a compatible daily mortality record for health effects analysis, and the fact that their PM2.5 composition analyses were similar in many ways to those characteristics available to researchers from the new U.S. EPA PM2.5 STN. In this way, analyses of existing data sets could be accomplished quickly and would provide information relevant to future analyses that might be conducted with the rapidly expanding U.S. EPA STN database. Brief descriptions of these databases are provided below, and more detailed descriptions are provided in the companion workshop papers (Hopke et al., in press; Ito et al., in press; Mar et al., in press).
In Phoenix, daily integrated 24-hr samples were collected with a dual fine-particle sequential sampler (URG Corp., Chapel Hill, NC, USA) on 37-mm diameter Teflon and quartz filter media for fine particle mass and species measurements. A total of 981 samples were collected from March 1995 through June 1998. Each sample was characterized by the measured concentrations of the following 46 chemical elements: sodium, magnesium, aluminum, silicon, phosphorus, sulfur, chlorine potassium, calcium, scandium, titanium, vanadium, chromium, manganese, iron, cobalt, nickel copper, zinc, gallium, germanium, arsenic, selenium, bromine, rubidium, strontium, yttrium, zirconium, molybdenum, rhodium, palladium, silver, cadmium, tin, antimony, tellurium, iodine, cesium, barium, lanthanum, tungsten, gold, mercury, lead, organic carbon, and elemental carbon (EC). The analytical uncertainty estimates associated with each measured concentration and the detection limits for both instruments were also included.
In Washington the PM2.5 samples were collected on Wednesdays and Saturdays at the Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring site located in downtown Washington. A total of 718 samples were collected between 31 August 1988 and 31 December 1997. Integrated 24-hr PM2.5 samples were collected on Teflon, nylon, and quartz filters. The Teflon filters were used for mass concentrations and analyzed via particle-induced X-ray emission for the elements Na, Mg, Al, Si, P, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn; via X-ray fluorescence for elements Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr, Y, Zr, Mo, Rh, Pd, Ag, Cd, Sn, Sb, Te, I, Cs, Ba, La, W, Au, Hg, and Pb; and via proton elastic scattering analysis for elemental hydrogen concentration. The nylon filter was analyzed by ion chromatography for sulfate, nitrate, and chloride. The quartz filters were analyzed by the IMPROVE method for temperature-resolved organic and EC fractions (IMPROVE/TOR).
Daily mortality data sets.
Washington death records were extracted from the National Center for Health Statistics database for the period from 31 August 1988 to 31 December 1997, and daily counts were aggregated for the District of Columbia and surrounding six areas: Montgomery County, Maryland; Prince George’s County, Maryland; Fairfax County, Virginia; and Alexandria, Fairfax, and Falls Church, Virginia. Three categories of deaths were analyzed: total nonaccidental; cardiovascular; and cardiovascular plus respiratory.
Phoenix mortality data from 1995 to 1997 were obtained from the Arizona Center for Health Statistics. In this analysis, we included only mortality counts for residents ≥65 years of age from ZIP code regions thought to be most represented by the U.S. EPA monitoring platform (Mar et al. 2000). We evaluated total nonaccidental mortality [International Classification of Diseases, 9th Revision (ICD-9) codes < 800.00; World Health Organization 1978] and cardiovascular mortality (ICD-9 codes 390.00–448.99) from 9 February 1995 to 31 December 1997. From 1995 to 1997 there were a total of 9,081 nonaccidental deaths and 4,109 cardiovascular deaths.
Source apportionment modeling.
The above-described PM2.5 mass and composition data sets were provided to each participating research group in December 2002 for independent analysis, using each group’s preferred source apportionment technique(s). To allow a consistent intercomparison of results across research groups, participants were requested to submit results in a standardized format and with a list of items describing the details of source apportionment analysis (e.g., type and extent of rotation, treatment of outliers, criteria used to include species in the analysis). Of the 11 potential participants to whom the data were sent, eight participant/teams from seven institutions submitted source apportionment results by the required deadline (April, 2003).
As described in more detail in the companion paper by Hopke and collaborators (in press), the fundamental principle of source apportionment (receptor) modeling is that mass conservation can be assumed, and a mass balance analysis can be used to identify and apportion sources of airborne PM in the atmosphere. If the number and nature of the sources affecting the air-monitoring station are known, then the only unknown is the mass contribution of each source to each sample, sjk. These values can be estimated using regression. This approach was first independently suggested by Winchester and Nifong (1971) and Miller et al. (1972) and is now called the chemical mass balance (CMB) model (Chow and Watson 2002, Cooper and Watson 1980; Cooper et al. 1984). In general, CMB models assume that the recorded aerosol mass (Mk ) in micrograms per cubic meter is due to the sum of impacts by individual sources (Sjk ):
where k = 1,2, ....m days; j = 1,2, ....p sources, and the total concentration of aerosol property Cik (i.e., element i ’s ambient concentration on day k at a site) is
where fij = the mass fraction of property i in emissions from source j.
Thus, if the source profiles (fij) are known, the source contributions (Sjk) can be determined from the linear regression of the Cik on the fij.
However, if (as is more usually the case), the source emission “signatures” are not known exactly, but only qualitatively (e.g., that vanadium is enriched in residual oil combustion particles, but the exact percentage is not known), then factor analysis (FA) methods are applied to identify and quantify the sources and their impacts. The FA approach to source apportionment assumes that the total concentration of each observable (element) is made up of the sum of contributions from each of p pollution source components:
where
(the standardized z-score of element i ’s kth observation), and Pjk = the jth factor component’s value on the kth day; Wik = the scoring coefficient matrix of the components; and si = the standard deviation of element i.
With respect to CMB models, the Pjk are equivalent to the Sjk source impacts, and the Wij are equivalent to the Fij source profiles. However, the Pjk and Wij are derived by the FA from the correlation matrix and are outputs of the FA (instead of inputs, as is the case for CMB). Such FA approaches generally have a major advantage, in that they can identify and quantify nontraditional aerosols such as secondary aerosols (formed in the atmosphere) and can incorporate non-PM tracers such as the gaseous pollutants. Such FA and principal components analysis (PCA) models attempt to simplify the description of a system by determining a minimum set of basis vectors that span the data space to be interpreted. In other words, a new set of variables is found as linear combinations of the measured variables so that the observed variations in the system can be reproduced by a smaller number of these causal factors. This approach has been widely used in studies of airborne PM composition data (Gao et al. 1994; Hopke et al. 1976; Roscoe et al. 1982).
Traditional FA and PCA are useful for identifying source components contributing to the PM mass but do not directly provide an apportionment in the form presented above. However, the solutions can be manipulated to provide such a quantitative solution. One approach is specific rotation FA (Koutrakis and Spengler 1987), which uses a targeted Procrustes factor rotation. An alternative approach, absolute principal-component analysis (APCA) (Thurston and Spengler 1985), has also been used to produce quantitative apportionments. Two more-recent approaches are Unmix (Henry and Kim 1999; Kim and Henry 1999, 2000a, 2000b) and positive matrix factorization (PMF) (Paatero 1997; 1999; Paatero et al. 2002). These and similar multivariate techniques, described and documented in more detail in Hopke et al. (in press), have been applied by the different research groups to achieve source apportionments of the Washington and Phoenix PM2.5 data sets (Table 2).
After all the estimated source-specific impact assessments were submitted by workshop participants, the agreement across source apportionment analyses was evaluated. This was first evaluated by an intercomparison of the various analyses’ respective mean estimates of source-specific mass impacts in each city. In addition, as the various source apportionment results were to be employed as inputs into a daily time-series mortality analyses, the time-series intercorrelations of their respective daily estimates of source impacts were also evaluated and intercompared across source categories in each city.
Health effects modeling.
After the source apportionments were submitted, all Washington and Phoenix daily source apportionments were provided to K. Ito of the NYU PM Center and T. Mar of the University of Washington PM Center, respectively, for inclusion in time-series mortality models to assess the resulting variations in their source-specific health effects estimates (RRs). The city-specific mortality models employed are described below.
The model-building steps of the Washington time-series mortality model development used in these analyses (Ito et al., unpublished data) were designed to be similar to those used in past studies of PM2.5 mass, as follows:
We first developed the base mortality model as a function of season and other temporal trends in Poisson generalized linear models (GLMs) (McCullagh and Nelder 1989). Using natural splines, we fit a smooth function of time to mortality to adjust the model for seasonal trends and unmeasured seasonal confounders, such as influenza epidemics. The inclusion of this term also reduces undesirable residual autocorrelation and overdispersion in the mortality regression, so the choice of the spline degrees of freedom (df) for smoothing of time (df = 38, or 4 per year) was based both on the fit to the mortality series and minimization of autocorrelation of the model residuals.
Weather variables and a day-of-week variable were then also incorporated into the base model, consistent with past general practice in PM2.5 modeling, including a) natural splines of the same-day temperature with 4 df to fit “hot” temperature effects; b) natural splines of the average of lags 1 through 3 of daily temperature (i.e., up to 3 days before the date of death) to fit “cold” temperature effects; and c) an indicator for “hot” (daily mean temperature > 80°F) and “humid” (daily relative humidity > 70%) days to fit the interaction. The end result of this step was a base model to which air pollutant variables could be added and evaluated.
To the base model, each of the alternative source components was individually added (for each research group/method) to separately test the individual associations of each source category with mortality, after controlling for the variables considered in the base model. The RR associated with both an interquartile (25th to 75th percentile) and a 5th- to 95th-percentile increase in the source estimate was computed for lag days 0 to 5 for each of the source apportionment analyses. This approach provided directly comparable mortality effect estimates for each source category and for apportionment modeling results of participating groups.
The basic steps of time-series model development used in the Phoenix analyses (Mar et al., in press) were the same as for Washington. Similarly, associations between source contributions and cardiovascular and total nonaccidental mortality were analyzed using Poisson GLMs in S-PLUS 2000 (Insightful Inc., Seattle, WA, USA). The same Phoenix base mortality model was applied to source apportionment analyses of all groups to provide a consistent basis for comparison across source components and groups (i.e., to eliminate model specification variability from the analysis). The base model controlled for extreme temperatures using an indicator variable, mean temperature, relative humidity, day of week, and time trends. Natural spline smoothers were used for time trends, temperature, and relative humidity. We applied 12 df for the smoothing of time trend (i.e., 4 df per year). The degrees of freedom for the natural splines for time trends were selected to minimize autocorrelation in the residuals and the Akaike Information Criterion (AIC) (Akaike 1974). For the analysis of cardiovascular mortality, 5 spline df and 2 days lag for temperature were incorporated, based on past experience with models of PM2.5 and mortality in this city. For the total mortality analysis, 5 spline df and 1 day lag for temperature were employed, and 2 df for the smoothing of relative humidity with 0 days lag for both the cardiovascular and total mortality analyses. The degrees of freedom and the lags were chosen to minimize the AIC. As in the case for the Washington analyses, the respective estimated source contributions of the various research groups were added to this base model, in turn, as the particle pollution variable. The RR associated with both an interquartile (25th to 75th percentile) and a 5th- to 95th-percentile increase in the source estimate was computed for lag days 0 to 5 for each of the source apportionment analyses. Again, this consistent mortality analysis approach across source apportionments allowed a direct comparison of the daily mortality effect estimates across the various source apportionment analyses in each city.
Finally, we evaluated the size and significance of the additional variability introduced to the PM–mortality, time-series analysis by variations in the source apportionment process across groups and methods, consistent with the primary goal of this workshop. To this end, the various source apportionments’ resulting mean mass contributions and estimated percent excess deaths per 5th- to 95th-percentile increment increase by source-apportioned PM2.5 were intercompared and then analyzed (within each city) by analysis of variance (ANOVA) and a GLM. This allowed us to compare variations in model estimates that were due to “between-source” versus “within-source” (i.e., variation due to different analyses).
Results
Source apportionment intercomparisons.
As described in Hopke et al. (in press), the various source apportionment analyses from each of the participating research groups were inter-compared in two ways: a) by comparing the mass contributions attributed to each of the sources; and b) by calculating the correlation coefficients between the source contributions from PM2.5 from the various groups within source groups. The solutions of the various groups were compared with each other on an equal basis, because an accepted “gold standard” method does not exist at this time for source apportionment. Table 2 notes the source apportionment analyses performed on these data sets. Figures 1 and 2 present the means and distributions of the resulting PM2.5 mass source apportionment for each source category in Washington and Phoenix, respectively. Most groups were able to identify the same major sources in their source apportionment analyses of the trace constituent data. However, not all sources were identified by all researchers; some groups did not provide impacts for all possible source categories. Two researchers from BYU contributed separate analyses: Eatough (BYU1) and Christensen (BYU2). In this plot, when researchers broke out the source impacts differently from other researchers (e.g., when secondary sulfates were broken into sulfates 1 and sulfates 2, or traffic was subdivided into categories, such as diesel vs. gasoline fueled motor vehicles), the results have been grouped to provide more directly comparable totals. The mass apportionment uncertainties included in Figures 1 and 2 visually indicate an overall consistency in impacts by source category, as they provide confidence intervals (CIs) that overlap across analyses of the various groups, especially for the larger mass contributors. To be more quantitative, we conducted an ANOVA F-test of the within-source versus between-source variations for each of the major source categories in Figures 1 and 2. The results indicated significantly greater variability (p < 0.001) across source categories than across investigators/methods (i.e., investigator/method variations were small compared with source-to-source variations). Overall, these plots and statistical analyses indicate that, although the estimated mass impact results vary across analyses and not all sources were identified by all investigators (especially in the case of the smaller mass impact sources), there is both qualitative and quantitative consistency in the major PM2.5 contributing sources identified and their mass impacts across the independent analyses of these data by the various research groups and apportionment methods.
Because these apportionment results were to be applied in time-series analyses, another evaluation of the consistency of the source apportionments across research groups and apportionment methods was conducted. Variability was examined in the paired correlations of estimated daily source apportionment mass contributions in the various analyses over time and within each city. As shown in Figure 3A for Washington and Figure 3B for Phoenix, the sulfate-containing, crustal, and nitrate components exhibited among the highest mean intercorrelations across the various research groups in these cities. Among the chief PM2.5 mass contributors (Figures 1 and 2), the weakest cross-analyses correlations in Figure 3 were usually found for the sources with the greatest uncertainty in their composition (i.e., lacking unique constituents for unique identification), notably, traffic and wood burning in Washington, and wood burning and metals in Phoenix.
Time-series mortality effect estimate intercomparisons.
The source apportionment results for each group were combined with the mortality data in Washington and Phoenix, and time-series mortality regressions were then run (Ito et al., in press; Mar et al., in press). Figure 4 displays the resulting mean RR estimates and 95% CIs of cardiovascular (CV) and total daily mortality for each major source category identified in Washington and Phoenix for the overall workshop estimate, with source apportionment interanalysis variation excluded and interanalysis variation included. Results were derived using the lag of maximum association in each analysis. It is clear from the comparisons that the variability introduced by the uncertainty of the across-source apportionment groups and analyses is small, relative to the overall uncertainty of these estimates. In quantitative terms, the percent increase in the uncertainty (i.e., in the CI) for the mortality RR of each displayed source category in Washington added by the interanalysis variability was as follows: soil (23% for CV, 18% for total); traffic (12% for CV, 16% for total); and sulfate (25% for CV, 26% for total). In the Phoenix mortality analyses, the percent increase in the uncertainty (i.e., in the CI) for the mortality RR of each displayed source category added by the interanalysis variability was as follows: soil (4% for CV, 7% for total); traffic (6% for CV, 33% for total); and sulfate (7% for CV, 5% for total). Thus, while the uncertainty added by the differences in source apportionments varies from source to source in these cases, the overall average increase is about 15%, which suggests that the error added by variability in source apportionment approach is quite small relative to the baseline uncertainty inherently associated with making these time-series pollution RR estimates.
The between-source variation in these daily mortality RRs was also compared with within-source variations (variation due to different analyses). As shown in Table 3, significantly larger variation was found between sources than between research groups in reported RRs (p < 0.001) using an ANOVA (in a GLM) of the individual investigator estimates and variances (for each death category in each city) (Ito et al., in press; Mar et al., in press). In the GLM, between-group variation was a nonsignificant predictor for both death categories in both cities (with p-values ranging from 0.38 to 0.65 for between-group differences), whereas the between-source variation was a statistically significant predictor of RR in both cities and death categories (p < 0.001). Overall, these results indicate that a) variations in choice of research group or source apportionment method have only a small effect on variations in the RR estimates for identified sources, relative to the variations in RR caused by different source components and the mortality regression process, and b) researcher variations in source apportionment applications should not be a barrier to comparing the source-specific PM2.5 RRs.
The size of the source-specific RR estimates from these analyses can also be compared with other published source-category effect estimates, although very few are available currently. The most consistently significant category was secondary sulfates, which have been widely examined before in the published literature. In this case, the total mortality RR estimates for the secondary sulfate component were 5.2% change per 10 μg/m3 in Phoenix and 3.8% per 10 μg/m3 in Washington. This is somewhat larger than the sulfate-dominated coal component reported by Laden et al. (2000), but much smaller than that derived from Ozkaynak and Thurston (1987). Their research indicated 8% per 10 μg/m3 for this component, but that study was of annual mortality associated with long-term exposures, rather than the daily mortality considered here. It is interesting, however, that the Washington component estimate from this work (3.8% per 10 μg/m3 for the sulfate component) is very close to the sulfate-related coal component value derived by Laden and colleagues for Boston, Massachusetts (2.8%). Motor vehicles, another component that approached significance in this work, yielded 0.9% per 10 μg/m3 RR in Phoenix, and 4.2% in Washington. These results are similar to the 3.4% per 10 μg/m3 found by Laden and colleagues (2000), and the 2% per 10 μg/m3 derived from the work of Ozkaynak and Thurston (1987). Thus, these source-specific estimates appear reasonable when compared with the limited source-specific mortality analyses done in the past, but much more work of this type must be done before broad-based comparisons with the RR results from this workshop are possible.
Discussion and Conclusions
With regard to the PM2.5 mass apportionments, the findings of this intercomparison among results from some of the leading source apportionment research groups indicate that the same major source types (those that contribute most of the PM2.5 mass at each site), with similar elemental makeups (i.e., key tracers), are consistently identified by different groups in each city. Methods generally yielded the most consistent results (i.e., the highest correlations across groups over time) for sources with the most definable (unique) tracers or combinations of tracers in each city. In Washington, soil, secondary sulfate and nitrate, oil burning, and incineration were most unambiguously identified by various methods; wood burning, salt, and traffic were less well correlated across analyses. In Phoenix, soil, traffic, secondary sulfate, and sea spray were most highly correlated across analyses; wood and vegetative burning, metals industry particles, and coal fly ash were less well correlated. Based on the relative sizes of these intergroup intercorrelations for each of the source types in these two cities, the soil-, sulfate-, residual oil-, and salt-associated mass components were generally seen to be most unambiguously identified by the various source apportionment methods, while vegetative burning and traffic were less well correlated across groups. However, the source mass impacts predicted for the various source categories were generally not significantly different from one another across the research groups, indicating consistency in the source apportionment results. The addition of further tracers/analyses may be required to improve the consistency of the less well-discriminated sources. For example, the measurement of low-volatility organic compounds has been suggested as one way to better discern traffic-related PM components (Schauer et al. 1996; Schauer and Cass 2000). Overall, however, although there are no gold standard correct answers for the source identification and apportionments in the real-world data sets considered in this workshop, the apportionment consistency in the largest PM2.5 source contributors across researchers in these cities, often using differing statistical methods, indicates reliability in the source apportionment approach.
With regard to the health effects apportionments to the different source components of PM2.5, the between-source variation in daily mortality RR was significantly larger than the between-research group variation in reported RRs. Thus, analysis-to-analysis variability in the source apportionments was small compared with the overall uncertainty in the mortality RR estimates. In addition, between-group variation in RR estimates was nonsignificant, whereas the between-source type variation was statistically significant. This result indicates that variations in choice of research group or source apportionment method have only a small effect on variations in the RR estimates, relative to the variations in RR caused by different source components. Indeed, in mortality categories where significant PM2.5 mass–daily mortality associations were detected in these cities (e.g., for cardiovascular deaths in both cities), most source categories were non-significant contributors. However, the most strongly associated source categories showed statistically significant contributions. Across these two cities, the most consistently associated PM2.5 source category was sulfate-associated mass. The source RR estimates generally had overlapping confidence bands, indicating that larger numbers of observations will be required in each of these cities to have enough power to significantly differentiate the impacts of the various source impacts. The overall source-specific RR estimates derived in this work appeared reasonable when compared with the limited source-specific mortality analyses published in the past, but many more source apportionment–mortality analyses of this type must be done before broad-based comparisons with the source-specific RR results from this workshop are possible.
Overall, the results of this intercomparison of the health effects apportionments found that variations in PM source apportionment research group or method introduced relatively little uncertainty into the evaluation of differences in PM toxicity on a source-specific basis, adding an average of only approximately 15% to the overall source-specific mortality RR uncertainties. Variations in these apportionment modeling choices do not prevent the consistent discernment of variations in the relative strengths of source-specific PM2.5 mortality associations. However, the uncertainty added by the source apportionment estimation suggests that longer data records may be required for significant effects to be detectable in source-specific analyses than for PM2.5. The conduct of daily speciation sampling (rather than every third day) in major U.S. cities would be one way to rapidly improve the power of future source apportioned PM time-series health effects analyses. Daily sampling would also better clarify the potentially differing distributed-lag natures of the various source-specific impacts identified in this workshop. Although further research and the possible addition of more key tracers to the speciation of PM2.5 are needed to better characterize ambient tracer profiles for sources with less well-defined compositional characteristics (e.g., for vegetative burning and traffic), the results of this workshop indicate that present-day PM2.5 source apportionment methods can provide valuable insights into the source components that contribute most to PM2.5–health effects associations.
We thank the individual researchers who participated in this workshop, often on their own time and resources. We also thank Columbia University’s Arden House Conference Center in Harriman, New York, for hosting the May 2003 workshop that led to this manuscript.
The workshop was organized under the auspices of the participating U.S. Environmental Protection Agency (EPA) PM Health Effects Research Centers (grant R827351 at New York University, R827351 at the University of Washington, R827353 at Harvard University, and R927354 at the University of Rochester). Support for the organization and administration of the workshop was also provided by the New York State Energy Research and Development Authority (grant 375-34215). Additional support was provided by the New York University–National Institute of Environmental Health Sciences Center grant (ES00260).
The information in this document has been subjected to review by the U.S. EPA National Health and Environmental Effects Research Laboratory and approved for publication. Approval does not signify that the contents reflect the views of the agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.
Figure 1 Mean, interquartile range (box), and range (maximum–minimum) of mass impacts predicted by each research group’s source apportionment analysis of the Washington PM2.5 data set. MR, multiple regression. (A) Soil; (B) nitrates; (C) traffic; (D) wood burning; (E) secondary SO4; (F) residual oil; (G) sea salt; (H) incinerator.
Figure 2 Mean, interquartile range (box), and range (maximum–minimum) of mass impacts predicted by each research group’s source apportionment analysis of the Phoenix PM2.5 data. (A) Soil; (B) secondary SO4; (C) traffic; (D) metals/industry/smelter; (E) vegetation/wood burning; (F) sea salt.
Figure 3 Box and whisker plots of the distributions of temporal correlation coefficients (r) between all possible pairs of similar source contributions resolved for (A) Washington and (B) Phoenix.
Figure 4 Mean RR estimates and 95% CIs for each major source category in Washington (A) cardiovascular and (C) total nonaccidental mortality, and Phoenix (B) cardiovascular and (D) total nonaccidental mortality for the overall workshop estimate, with source apportionment interanalysis variation excluded and with the interanalysis variation included.
Table 1 Summary of workshop goals and participating research institutions.
Workshop goals Participating research institutions
To bring together key researchers to assess the reliability of source apportionment–health effects methods by analyzing daily mortality with existing PM2.5 data sets similar to those now being collected by the U.S. EPA Specialization Network. Brigham Young University (BYU)
Clarkson University (CU)
Harvard University (HU)
New York University (NYU)
University of Rochester and GSF (UR/GSF)
To identify key future research needs for source apportionment– health effects evaluation. University of Southern California (USC)
University of Washington (UW)
GSF, German National Research Center for Environment and Health.
Table 2 Summary of the source apportionment analyses performed by each participating group.
Research institutions Phoenix, AZ Washington, DC
BYU Unmix Unmix, iterated, confirmatory FA
CU PMF2 and expanded model (ME) PMF2
HU Target rotated PCA Target rotated PCA
NYU PMF, APCA PMF, APCA, single-elemental multiple regression
UR/GSF APCA
USC Unmix Unmix
UW PMF
Table 3 ANOVA analysis of source-specific mortality RR estimates.
Mortality category ANOVA p-value Source category variance (%) Research group variance (%)
Washington CV < 0.001 47.5 9.5
Washington total < 0.001 80.0 2.6
Phoenix CV < 0.001 76.3 4.5
Phoenix total < 0.001 64.8 6.3
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Henry RC Kim BM 1999 Extension of self-modeling curve resolution to mixtures of more than three components. Part 1. Finding the basic feasible region Chemom Intell Lab Syst 8 205 216
Hopke PK Gladney ES Gordon GE Zoller WH Jones AG 1976 The use of multivariate analysis to identify sources of selected elements in the Boston urban aerosol Atmos Environ 10 1015 1025 1008915
Hopke PK Ito K Mar T Christensen W Eatough DJ Henry RC In press. PM source apportionment and health effects. I. Intercomparison of source apportionment results. J Expo Anal Environ Epidemiol.
Ito K Christensen W Eatough DJ Henry RC Kim E Laden F In press. PM source apportionment and health effects. II. An investigation of inter-method variability in associations between source-apportioned fine particle mass and daily mortality in Washington, DC. J Expo Anal Environ Epidemiol.
Kim BM Henry RC 1999 Extension of self-modeling curve resolution to mixtures of more than three components. II. Finding the complete solution Chemom Intell Lab Syst 49 67 77
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Kim BM Henry RC 2000b Application of SAFER model to the Los Angeles PM10 data Atmos Environ 34 1747 1759
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Mar TF Koenig JQ Larson TV Christensen W Eatough DJ Henry RC In press. PM source apportionment and health effects. III. Investigation of inter-method variations in associations between estimated source contributions of PM2.5 and daily mortality in Phoenix, AZ. J Expo Anal Environ Epidemiol.
Mar TF Norris GA Koenig JQ Larson TV 2000 Associations between air pollution and mortality in Phoenix, 1995–1997 Environ Health Perspect 108 347 353 10753094
McCullagh P Nelder JA 1989. Generalized Linear Models. London:Chapman and Hall.
Miller MS Friedlander SK Hidy GM 1972 A chemical element balance for the Pasadena aerosol J Colloid Interface Sci 39 165 176
Ozkaynak H Thurston GD 1987 Associations between 1980 U.S. mortality rates and alternative measures of airborne particle concentration Risk Anal 7 449 460 3444932
Paatero P 1997 Least squares formulation of robust, non-negative factor analysis Chemom Intell Lab Syst 37 23 35
Paatero P 1999 The multilinear engine—a table-driven least squares program for solving multilinear problems, including the n-way parallel factor analysis model J Comput Graph Stat 8 854 888
Paatero P Hopke PK Song XH Ramadan Z 2002 Understanding and controlling rotations in factor analytic models Chemom Intell Lab Syst 60 253 264
Roscoe BA Hopke PK Dattner SL Jenks JM 1982 The use of principal components factor analysis to interpret particulate compositional data sets J Air Pollut Control Assoc 32 637 642
Schauer JJ Rogge WF Hildemann LM Mazurek MS Cass GR Simoneit BRT 1996 Source apportionment of airborne particulate matter using organic compounds as tracers Atmos Environ 30 22 3837 3855
Schauer JJ Cass GR 2000 Source apportionment of wintertime gas-phase and particle-phase air pollutants using organic compounds as tracers Environ Sci Technol 34 1821 1832
Spengler JD Thurston GD 1983 Mass and elemental composition of fine and coarse particles in six U.S. cities J Air Pollut Control Assoc 22 12 1162 1171
Thurston GD Spengler JD 1985 A quantitative assessment of source contributions to inhalable particulate matter pollution in metropolitan Boston Atmos Environ 19 9 26
Winchester JW Nifong GD 1971 Water pollution in Lake Michigan by trace elements from pollution aerosol fallout Water Air Soil Pollut 1 50 64
WHO. 1978. International Classification of Diseases, 9th Revision. Geneva:World Health Organization.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8193ehp0113-00177516330362ResearchEnvironmental MedicineAcute Effects of a Fungal Volatile Compound Wålinder Robert 1Ernstgård Lena 2Johanson Gunnar 2Norbäck Dan 1Venge Per 3Wieslander Gunilla 11 Department of Medical Sciences/Occupational and Environmental Medicine, University Hospital, Uppsala, Sweden2 Division of Work Environment Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden3 Department of Medical Sciences/Clinical Chemistry and Asthma Research Center, University Hospital, Uppsala, SwedenAddress correspondence to R. Wålinder, Department of Occupational and Environmental Medicine, University Hospital, 751 85 Uppsala, Sweden. Telephone: 46-18-6113641. Fax: 46-19-519978. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 9 8 2005 113 12 1775 1778 11 4 2005 9 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Objective: 3-Methylfuran (3-MF) is a common fungal volatile product with active biologic properties, and previous studies have indicated a contribution to airway disease. The aim of the present study was to assess the acute health effects of this compound in humans.
Design: Acute effects were assessed via chamber exposure to (1 mg/m3) 3-MF.
Participants and measurements: Twenty-nine volunteers provided symptom reports, ocular electromyograms, measurement of eye tear film break-up time, vital staining of the eye, nasal lavage, acoustic rhinometry, transfer tests, and dynamic spirometry.
Results: No subjective ratings were significantly increased during exposure. Blinking frequency and the lavage biomarkers myeloperoxidase and lysozyme were significantly increased, and forced vital capacity was significantly decreased during exposure to 3-MF compared with air control.
Conclusions and relevance to clinical practice: Acute effects in the eyes, nose, and airways were detected and might be the result of the biologically active properties of 3-MF. Thus, 3-MF may contribute to building-related illness.
3-methylfuranairway physiologybiomarkerbuilding-related illnessfungihypersensitivity pneumonitislungmicrobial volatile organic compound (MVOC)mold
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Controlled human exposure studies have shown acute dose–effect relations for exposure to volatile organic compounds (VOCs) with respect to odor and irritative symptoms (Mölhave et al. 1986, 1991). Also, histamine release from human bronchoalveolar cells has been shown after exposure to microbial VOCs (MVOCs) from indoor mold (Larsen et al. 1998). Up to 300 different compounds, including 3-methylfuran (3-MF), can be detected in indoor air (Berglund and Johansson 1996).
3-MF is formed by a broad spectrum of fungi (Börjesson et al. 1992) and can be used as a marker for the active growth of microorganisms in water-damaged buildings (Wessen and Schoeps 1996). The substance has a characteristic fungal smell. It is biologically active and binds covalently to tissue macromolecules after metabolic oxidation. In one study, increased indoor levels of 3-MF were significantly related to symptoms of airway obstruction (Smedje et al. 1996). Thus, 3-MF may be suspected to contribute to the exacerbation of pulmonary diseases (Boyd et al. 1978).
The aim of the present study was to assess the acute effects of 3-MF on the eyes, nose, and airways via a battery of physiologic and biochemical tests (Ernstgård et al. 2002). The choice of 3-MF was based on its chemical properties and previous epidemiologic associations with respiratory symptoms (Smedje et al. 1996).
Materials and Methods
Subjects and chamber exposures.
The study group consisted of 30 healthy volunteers (14 females) 20–54 years of age (mean ± SD, 33 ± 9 years) that were medically examined before the first exposure. Atopy was tested by laboratory verified IgE antibodies to common Swedish allergens: cat, dog, horse, birch pollen, timothy, mugwort, Cladosporium herbarum, Dermatophagoides pteronyssinus, and Dermatophagoides farinae (Phadiatop test; Pharmacia Diagnostics, Uppsala, Sweden); 43% of the volunteers had laboratory verified atopy. The volunteers were informed orally and in writing about the design of the study, possible hazards, and their freedom to discontinue participation at any time. The study was approved by the Regional Ethical Committee at the Karolinska Institute, Solna, Sweden, and written consent was obtained from the participants.
The subjects were exposed to clean air and 3-MF (1 mg/m3) in random order. Each exposure session lasted for 2 hr. Exposures were conducted during resting conditions with the subjects seated. Up to five subjects at a time were exposed. Exposures were performed from December through February to minimize possible interference with pollen exposure, with a minimum period of 2 weeks between the two exposure conditions. The exposures were carried out in a 20-m3 dynamic exposure chamber with 18–20 air changes per hour. The temperature and the humidity in the chamber were set to 24°C and 30%, respectively. Temperature and humidity were continuously recorded (Vaisala HMP 36, Vaisala, Helsinki, Finland) and logged (Squirrel Meter Logger 1200 Series, Grant Instruments, Cambridge, UK). 3-MF vapor was generated by injecting liquid solvent into inlet air by means of a high-pressure piston pump (Gilson 302, Gilson, Villiers-le-bel, France). The inlet air was dispersed throughout the entire chamber ceiling.
Air was sampled from the upper central part of the exposure chamber to monitor the concentration of the compound during exposures. The air samples were transferred through a Teflon-coated tube to a gas chromatograph by means of a pump (DDA-P101-BN, Gast, Benton Harbor, MI, USA). The gas chromatograph (Auto system; Perkin Elmer, Buckinghamshire, UK) was equipped with a wide-bore capillary column (CP-sil 8, 10 m, 0.53 mm inner diameter, 2 μm; Chrompack, Middleburg, the Netherlands) and a flame ionization detector. Helium was used as a carrier gas; the temperatures of the oven and the detector were 55°C and 250°C, respectively.
Symptom questionnaire.
At six different times, subjects were asked to fill out a questionnaire with 10 questions related to smell, irritative symptoms (of the eyes, nose, and throat), dyspnea, headache, fatigue, dizziness, nausea, and intoxication. Answers were given by marking along a 100-mm visual analogue scale graded from “not at all” (0 mm) to “almost unbearable” (100 mm). The questionnaire was elaborated for vapor exposure and has been used in several inhalation studies (Ernstgård et al. 2002; Falk et al. 1991; Iregren et al. 1993; Nihlen et al. 1998).
Blinking frequency.
Blinking of the left eye was recorded by electromyography (EMG) using three skin electrodes, two on the orbicularis oculi muscle and one reference electrode on the cheekbone. The EMG signal was amplified and transferred via telemetry to a personal computer. We used a software program in C++ to identify the characteristic EMG signal patterns. We identified blinks by comparison against nine conditions related to the size, shape, and appearance of the pattern (Ernstgård et al. 2002).
Tear film break-up time.
Precorneal tear film stability was assessed by measuring the tear film break-up time by scanning the precorneal tear film with a biomicroscope (Topcon SL1E; Topcon, Tokyo, Japan). The time in seconds was recorded from the last blink until a rupture in the precorneal film was observed. We also estimated tear film stability by recording the self-reported tear film break-up time. The subjects were asked to keep their eyes open, and the time was recorded until they felt an urge to blink, assuming that this feeling was the appearance of a dry spot on the cornea (Wieslander et al. 1999). Measurements of break-up time were performed on three occasions in each eye: before entering the chamber, at the end of exposure, and 4 hr after exposure.
Vital staining of the eye.
We assessed epithelial damage to the cornea and conjunctiva using a semiquantitative method. We instilled 4 μL of a dye, lissamine green (1% in physiologic saline solution), into the lower conjunctival sac. After 1 min, the cornea and conjunctiva were examined by a binocular microscope with a slit lamp (Topcon SL1E), and each eye was given a score of 0–9 (Norn 1991). Vital staining was performed once, 4 hr after exposure.
Nasal lavage.
We measured inflammatory markers in nasal lavage samples before, directly after, and at 2 hr postexposure. Lavage of the nasal mucosa was collected with a 20-mL plastic syringe attached to a nose olive (Wålinder 1999). The analyses included myeloperoxidase (MPO), eosinophil cationic protein (ECP), lysozyme, and albumin and were carried out at the Department of Clinical Chemistry, University Hospital, Uppsala, Sweden. The chemical analysis of lavage biomarkers has been described in detail elsewhere (Wålinder 1999).
Transfer test.
We determined the diffusion capacity of carbon monoxide (DLCO) using a single-breath technique (transfer test; PK Morgan Ltd., Chatham, Kent, UK) (Cotes et al. 1997; Forster et al. 1954). DLCO was measured for each subject before entering the exposure chamber and 20 min after leaving the exposure chamber.
Dynamic spirometry.
Dynamic spirometric measurements were performed for each subject before entering the exposure chamber, immediately after leaving it, and 2 hr after leaving the chamber. Spirometric tests included vital capacity (VC), forced vital capacity (FVC), forced expiratory volume in 1 sec (FEV1), peak expiratory flow (PEF), and forced expiratory flow (FEF) in the middle half of FVC [FEF 25, 50, 75, the expiratory flows after one-fourth, one-half, and three-fourths, respectively, of the vital capacity has been expired (after a full inspiration)]. The tests were performed by spirometry (Vitalograph 2120 and Spirotrac 3 software for PC, version 2.0; Vitalograph, Buckingham, UK) according to the guidelines prescribed by the American Thoracic Society (1995).
Acoustic rhinometry.
We assessed nasal patency using acoustic rhinometry. The nasal volume (from the nostril and 7 cm into the nasal cavity) and the minimal cross-section area were determined as an average of three measurements in each nostril. We performed the rhinometric measurements for each subject at three occasions during the exposure day: before entering the chamber, immediately after leaving it, and 2 hr after leaving it. Data on the nasal volumes and areas are presented as the sum of the right and the left side. The rhinometer, using a single-click signal of audible frequencies with the Nasal Area-Distance Acquisition Program, version 1.0 (University of Aarhus, Aarhus, Denmark), has previously been described by Hilberg et al. (1989).
Statistical methods.
We compared the differences before and after exposure to 3-MF and air control using t-tests for paired samples for rhinometric and spirometric changes and Wilcoxon matched pairs tests for the non-normally distributed lavage data. We used repeated-measures analysis of variance (ANOVA) for subjective ratings and the blinking frequency series (Statistica for Windows, version 7.0; StatSoft Inc., Tulsa, OK, USA). Two-tailed tests and a 5% level of significance were applied when applicable.
Results
Suspected adverse reaction.
We removed one subject from the exposure series because of a two-phased pulmonary reaction to 3-MF. During the last 30 min of exposure, the subject suffered from moderate airway distress combined with acute airway obstruction. The PEF fell from 320 L/min before exposure to 170 L/min directly after. The acute dyspnea cleared up quickly, and by 3 hr after exposure, the PEF was 350 L/min. Three days after exposure, the subject had an onset of severe chest tightness, together with chills, fatigue, cough, and fever around 39°C. One week after exposure, the subject suffered from leukopenia, and obstructive symptoms remained for about a month. The subject’s chest X ray was normal, and no elevated titers for influenza virus A and B were found. Titers for total IgE and a mold-antigen panel were high. We reported the suspected adverse effect to the Regional Ethical Committee.
Symptom ratings.
The symptom ratings were not different during exposure to 3-MF compared with clean air (Figure 1). An immediate weak odor detection of 3-MF could be seen among some of the subjects, but not all (Figure E). This suggests that the exposure level was near the odor threshold and that adaptation occurred.
Eye measurements.
The blinking frequency during 3-MF exposure was significantly higher than during clean air exposure (Figure 2, Table 1). The vital staining scores of epithelial eye damage detected with lissamine green were slightly higher after 3-MF exposure, but this effect was not statistically significant. The tear-film break-up time was significantly higher at the end of the 2-hr exposure period compared with the air control (Table 1). The observed changes were similar in subjects with or without atopy.
Nasal measurements.
We observed a washout effect with decreased biomarker concentrations after repeated lavages following exposure to air. In contrast, compared with air controls, we observed an increase that was significant for MPO directly after and for lysozome 2 hr after exposure (Table 2). Nasal cavity dimensions, measured by acoustic rhinometry, were not different from air control (Table 3). Stratification by atopy did not show different reactivity for biomarkers or rhinometry, although baseline levels of ECP and albumin were doubled for subjects with atopy.
Airway measurements.
On average FVC decreased 0.1 L directly after exposure to 3-MF, which was a significant decrease compared with air control. The other lung function parameters (transfer test, VC, FEV1, PEF) were not affected by exposure to 3-MF compared with clean air (Table 4). Stratification by atopy showed that the observed effect on FVC mainly appeared among nonatopics.
Discussion
Although the exposure level of 3-MF was near the smell threshold and did not cause subjective symptoms of mucosal irritation or airway distress, the objective measurements did show effects on the eyes and airways. Considering an increase of the blinking frequency as an indicator of eye irritation together with the nasal biomarker response, it is possible that 3-MF might have mucosal effects in both the eyes and the airways. We also found an increased tear film break-up time after exposure to 3-MF. The tear film stability is dependent on the quality and amount of the fatty layer on its surface that is produced from the meibomian glands. The secretion from these glands is stimulated by the blinking movements, and a congruent increase of both blinking frequency and break-up time can be expected.
MPO is a marker of the neutrophil activity in the nasal mucosa, and lysozyme is a marker of both neutrophil activity and secretory neurogenic stimuli. Because nasal lavage was performed three times, a washout effect with decreased concentrations could be expected. We observed this decrease for all lavage bio-markers after exposure to air in contrast to an increase after exposure to 3-MF. Also, the decreased FVC after exposure to 3-MF indicates an airway effect. This pulmonary function variable is slightly more sensitive to airway irritation and hyperreactivity than is the VC measurement with slow expiration.
3-MF is metabolically activated via microsomal oxidation, cleaving the furan ring to a highly reactive unsaturated dialdehyde, methylbutenedial, that binds covalently to tissue macromolecules (Ravindranath et al. 1984). Animal inhalatory studies have revealed organ damage at high exposures. Haschek et al. (1984) reported that rats inhaling 1,000 mg/m3 3-MF for 1 hr had damaged airway epithelium with pneumonitis and necrotizing suppurative rhinitis. They also observed necrosis, fibrosis, and epithelial metaplasia in the airways at autopsy 14 days later. Previous epidemiologic results also show airway reactions related to 3-MF in indoor air (Smedje et al. 1996).
This suspected adverse reaction was previously reported (Wålinder et al. 1998) in a subject with atopy who previously had been working in a mushroom farm and with micro-fungi. This subject suffered an acute obstructive reaction and a delayed pulmonary reaction with flulike symptoms. A nonspecific airway reaction could explain the immediate effects and an infection the late reaction, but no infection was verified by laboratory tests. Instead, analyses afterward showed mold allergy and high titers of IgE (Wålinder et al. 1998). Previous exposure to fungi at work could have resulted in a sensitization causing the present reaction to 3-MF. Hypersensitivity pneumonitis is an occupational disease from exposure to organic dust, fungi, or mold. One of the manifestations is called mushroom picker’s disease. The symptoms are similar to those of the present reaction but are mostly seen after exposure to high-molecular-weight organic chemicals. There are, however, low-molecular-weight chemicals that can cause immunologic responses, for example, isocyanates and acid anhydrides. It is possible that 3-MF after bioactivation is covalently binding to proteins of the mucosa, causing both chemical injury and a protein-hapten reaction resulting in airway inflammation and a hypersensitivity pneumonitis.
Short-term experimental studies differ in many aspects in relation to real indoor exposures. Indoor exposures involve a high number of substances, typically at concentrations 10–1,000 times lower than those used in experimental studies but with possible chemical interactions. Furthermore, domestic exposures are much longer. Therefore, it has been suggested that toxic effect estimates of indoor volatile compounds should be adjusted for long-term exposures compared with shorter exposures, at least for nonirritative effects (Damgård-Nielsen et al. 1997). Using this argument, it might be justifiable to apply higher concentrations of indoor agents in experimental chamber studies. Another important issue that must be considered is a difference in individual susceptibility. A “healthy volunteer bias” could underestimate the effects compared with persons who, because of long-term daily exposures, have acquired a form of sensitivity to “sick buildings.” Because persons with atopy are considered more sensitive to dampness, mold, or other disturbances of the indoor environment, subjects with IgE-mediated allergy to common allergens were recruited for the present study. However, results do not support the statement that persons with atopy report more symptoms or have a higher reactivity to this fungal metabolite. Actually, the only difference observed in reactivity was that nonatopics had a decrease in FVC after exposure to 3-MF, whereas no such effect was seen among the subjects with atopy.
In conclusion, we have recorded acute effects from the eyes, nose, and airways indicating mucosal reactive properties of 3-MF, which is commonly found in buildings affected by microbial growth. The mucosal effects could be induced by a possible chemical injury from the bioactivation of 3-MF. More unusual but severe effects, such as hypersensitivity reactions after exposure to fungi and molds, could also be explained by a protein-hapten reaction. Therefore, the results of the present study may have relevance for the judgment of health problems due to microbial emissions.
Correction
The 3-min value for 3-MF in Figure 4E was incorrect in the original manuscript published online. The figure has been corrected here.
This study was supported by grants from the Swedish Council for Worklife Research and the Swedish Foundation for Health Care Sciences and Allergy Research.
Figure 1 Subjective ratings (median with interquartile range, total range 0–100 mm) of 10 symptoms at six times: just before entering the chamber; at 3, 60, and 118 min of exposure; and at 15 and 200 min after exposure. Discomfort in the eyes (A), nose (B), and throat or airways (C); breathing difficulty (D); odor (E); headache (F); fatigue (G); nausea (H); dizziness (I); and feeling of intoxication (J).
Figure 2 Blinking frequency during 2-hr exposures to 3-MF and air measured as mean frequencies every 2 min for 29 subjects.
Table 1 Eye measurements (mean ± SD) in 29 subjects exposed to 1 mg/m3 3-MF or clean air for 2 hr.
Measured break-up time (sec)
Self-reported break-up time (sec)
Exposure Before Aftera 4 hr afterb Before Aftera 4 hr afterb Blinking frequency during exposurec Lissamine staining after exposured
Air 36 ± 19 −3 ± 17 −3 ± 17 32 ± 19 3 ± 17 3 ± 14 5.8 ± 0.7 0.2 ± 0.3
3-MF 33 ± 21 6 ± 8* −1 ± 16 35 ± 21 2 ± 20 −4 ± 19 7.6 ± 0.8** 0.3 ± 0.5
a End of exposure compared with before exposure; negative value indicates decrease.
b Four hours after exposure compared with before exposure; negative value indicates decrease.
c Blinking frequency (blinks per minute) during exposure.
d Epithelial damage score (0–9), measured by lissamine staining, 4 hr after exposure.
* p = 0.014 by Wilcoxon rank sum test.
** p < 0.001 by repeated-measures ANOVA.
Table 2 Nasal biomarkers (mean ± SD) in 29 subjects exposed to 1 mg/m3 3-MF or clean air for 2 hr.
Lysozyme (mg/L)
ECP (μg/L)
MPO (μg/L)
Albumin (mg/L)
Exposure Before Aftera 2 hr afterb Before Aftera 2 hr afterb Before Aftera 2 hr afterb Before Aftera 2 hr afterb
Air 4.5 ± 2.3 −0.6 ± 1.9 0.2 ± 2.4 3.3 ± 4.7 −0.3 ± 3.4 −0.9 ± 3.0 42.2 ± 53.0 −10.2 ± 25.8 −14.2 ± 27.7 17.6 ± 22.4 −6.3 ± 15.2 −4.4 ± 18.0
3-MF 3.8 ± 1.9 0.3 ± 2.0 1.7 ± 3.0* 2.6 ± 4.5 0.4 ± 5.0 −0.3 ± 3.3 34.8 ± 44.2 4.8 ± 44.6* −4.9 ± 27.7 14.1 ± 21.9 −0.4 ± 14.5 1.8 ± 12.1
a End of exposure compared with before exposure; negative value indicates decrease.
b Two hours after exposure compared with before exposure; negative value indicates decrease.
* p < 0.05 by Wilcoxon rank sum test.
Table 3 Nasal measurements (mean ± SD) in 29 subjects exposed to 1 mg/m3 3-MF or clean air for 2 hr.
Volume (cm3)
MCA (cm2)
Exposure Before Aftera 2 hr afterb Before Aftera 2 hr afterb
Air 9.7 ± 1.7 −1.0 ± 0.8 −0.9 ± 1.2 0.9 ± 0.2 0.0 ± 0.1 0.0 ± 0.2
3-MF 10.0 ± 2.1 −0.8 ± 1.7 −0.8 ± 1.7 0.9 ± 0.2 −0.1 ± 0.1 0.0 ± 0.2
MCA, minimal cross-section area.
a End of exposure compared with before exposure; negative value indicates decrease.
b Two hours after exposure compared with before exposure; negative value indicates decrease.
Table 4 Pulmonary function (mean ± SD) in 29 subjects exposed to 1 mg/m3 3-MF or clean air for 2 hr.
FVC (L)
FEV1 (L)
PEF (L/min)
DLCO (μmol/sec/kPa)
Exposure Before Aftera 2 hr afterb Before Aftera 2 hr afterb Before Aftera 2 hr afterb Before Afterc
Air 4.8 ± 1.0 0.0 ± 0.2 −0.1 ± 0.2 4.0 ± 0.8 0.0 ± 0.2 −0.1 ± 0.2 510 ± 120 0 ± 30 −20 ± 40 220 ± 60 0 ± 30
3-MF 4.9 ± 1.0 −0.1 ± 0.2* −0.2 ± 0.2 4.0 ± 0.8 −0.1 ± 0.2 −0.2 ± 0.2 500 ± 120 −10 ± 40 −20 ± 40 220 ± 60 10 ± 30
a End of exposure compared with before exposure; negative value indicates decrease.
b Two hours after exposure compared with before exposure; negative value indicates decrease.
c Twenty minutes after exposure compared with before exposure.
* p < 0.05 by t-test.
==== Refs
References
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Wålinder R 1999. Nasal Reactions and the School Environment. Nasal Patency and Lavage Biomarkers in Relation to Cleaning and Some Indoor Air Pollutants [PhD Thesis]. Uppsala, Sweden:Uppsala University.
Wålinder R Norbäck D Johanson G 1998 Pulmonary reactions after exposure to 3-methylfuran vapour, a fungal metabolite Int J Tuberc Lung Dis 2 12 1037 1039 9869122
Wessen B Schoeps KO 1996 Microbial volatile organic compounds—what substances can be found in sick buildings? Analyst 121 9 1203 1205 8831278
Wieslander G Norbäck D Nordström K Wålinder R Venge P 1999 Nasal and ocular symptoms, tear film stability and bio-markers in nasal lavage, in relation to building-dampness and building design in hospitals Int Arch Occup Environ Health 72 7 451 461 10541910
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8384ehp0113-00177916330363ResearchEnvironmental MedicineCase–Control Study of an Acute Aflatoxicosis Outbreak, Kenya, 2004 Azziz-Baumgartner Eduardo 1Lindblade Kimberly 2Gieseker Karen 3Rogers Helen Schurz 1Kieszak Stephanie 1Njapau Henry 4Schleicher Rosemary 1McCoy Leslie F. 1Misore Ambrose 5DeCock Kevin 6Rubin Carol 1Slutsker Laurence 7the Aflatoxin Investigative Group* 1 National Center for Environmental Health,2 National Center for Infectious Diseases, and3 Epidemiology Program Office, Centers for Disease Control and Prevention, Atlanta, Georgia, USA4 Food and Drug Administration, Washington, DC, USA5 Preventive and Promotive Health Services, Kenya Ministry of Health, Nairobi, Kenya6 Centers for Disease Control and Prevention, Kenya Field Office, Nairobi, Kenya7 Centers for Disease Control and Prevention, Kenya Field Office, Kisumu, KenyaAddress correspondence to E. Azziz-Baumgartner, Centers for Disease Control and Prevention, Mailstop F46, 4770 Buford Hwy NE, Atlanta, GA 30341-3717 USA. Telephone: (770) 488-3412. Fax: (770) 488-3450. E-mail:
[email protected]*Members of the Aflatoxin Investigative Group include J. Nyamongo, C. Njuguna, E. Muchiri, J. Njau, S. Maingi, J. Njoroge, J. Mutiso, J. Onteri, A. Langat, I.K. Kilei, G. Ogana, B. Muture, J. Nyikal (Kenya Ministry of Health); P. Tukei, C. Onyango, W. Ochieng (Kenya Medical Research Institute); I. Mugoya, P. Nguku, T. Galgalo, S. Kibet, A. Manya, A. Dahiye, J. Mwihia, S. Likimani, C. Tetteh (Kenya Field Epidemiology and Laboratory Training Program/Kenya Ministry of Health); J. Onsongo, A. Ngindu (World Health Organization, Kenya Country Office); P. Amornkul, D. Rosen, D. Feiken, T. Thomas (CDC Kenya); P. Mensah, N. Eseko, A. Nejjar (World Health Organization, Regional Office for Africa); M. Onsongo, F. Kessel (Foreign Agricultural Service, U.S. Department of Agriculture); D.L. Park (Center for Food Safety and Applied Nutrition, Food and Drug Administration); C. Nzioka (Office of Global Health, CDC); L. Lewis, G. Luber, L. Backer, C.D. Powers, C. Pfeiffer (National Center for Environmental Health, CDC); W. Chege, A. Bowen (Epidemiology Program Office, CDC).
The authors declare they have no competing financial interests.
12 2005 9 8 2005 113 12 1779 1783 6 6 2005 9 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Objectives: During January–June 2004, an aflatoxicosis outbreak in eastern Kenya resulted in 317 cases and 125 deaths. We conducted a case–control study to identify risk factors for contamination of implicated maize and, for the first time, quantitated biomarkers associated with acute aflatoxicosis.
Design: We administered questionnaires regarding maize storage and consumption and obtained maize and blood samples from participants.
Participants: We recruited 40 case-patients with aflatoxicosis and 80 randomly selected controls to participate in this study.
Evaluations/Measurements: We analyzed maize for total aflatoxins and serum for aflatoxin B1–lysine albumin adducts and hepatitis B surface antigen. We used regression and survival analyses to explore the relationship between aflatoxins, maize consumption, hepatitis B surface antigen, and case status.
Results: Homegrown (not commercial) maize kernels from case households had higher concentrations of aflatoxins than did kernels from control households [geometric mean (GM) = 354.53 ppb vs. 44.14 ppb; p = 0.04]. Serum adduct concentrations were associated with time from jaundice to death [adjusted hazard ratio = 1.3; 95% confidence interval (CI), 1.04–1.6]. Case patients had positive hepatitis B titers [odds ratio (OR) = 9.8; 95% CI, 1.5–63.1] more often than controls. Case patients stored wet maize (OR = 3.5; 95% CI, 1.2–10.3) inside their homes (OR = 12.0; 95% CI, 1.5–95.7) rather than in granaries more often than did controls.
Conclusion: Aflatoxin concentrations in maize, serum aflatoxin B1–lysine adduct concentrations, and positive hepatitis B surface antigen titers were all associated with case status.
Relevance: The novel methods and risk factors described may help health officials prevent future outbreaks of aflatoxicosis.
albumin adductsaflatoxicosisaflatoxinKenyalysinemaize
==== Body
During January–June 2004, the Kenya Ministry of Health (MOH) and partners identified 317 cases of acute hepatic failure in eastern Kenya; 125 cases occurred in persons who subsequently died during the illness. Seven patients had serum samples analyzed at the Kenya Medical Research Institute (KEMRI), and all were negative for viruses known to cause hepatic disease in Kenya (e.g., yellow fever; Rift Valley fever; dengue; acute hepatitis A, B, and C; West Nile virus; and Chikungunya and Bunyamwera) (American Public Health Association 2000). Because aflatoxicosis outbreaks had occurred previously in that geographical area, the MOH suspected that the unusually high number of patients with acute hepatic failure might have acquired aflatoxicosis from eating contaminated maize (Ngindu et al. 1982). Public health officials sampled maize from the affected area and found concentrations of aflatoxin B1 as high as 4,400 ppb, which is 220 times greater than the 20 ppb limit for food suggested by Kenyan authorities (Onsongo 2004). Although aflatoxicosis outbreaks have occurred periodically in Africa and Asia, this outbreak resulted in the largest number of fatalities ever documented (Krishnamachari et al. 1975a, 1975b; Lye et al. 1995).
Aflatoxins are produced by Aspergillus spp. fungi that grow on a wide variety of grains and nuts (Patten 1981). The human gastrointestinal tract rapidly absorbs aflatoxins after consumption of contaminated food, and the circulatory system transports the aflatoxins to the liver (Fung and Clark 2004). From 1 to 3% of ingested aflatoxins irreversibly bind to proteins and DNA bases to form adducts such as aflatoxin B1–lysine in albumin (Skipper and Tannenbaum 1990). Disruption of proteins and DNA bases in hepatocytes causes liver toxicity (Tandon et al. 1978).
Early symptoms of hepatotoxicity from aflatoxicosis can manifest as anorexia, malaise, and low-grade fever. Aflatoxicosis can progress to potentially lethal acute hepatitis with vomiting, abdominal pain, hepatitis, and death (Etzel 2002). Because aflatoxin B1–lysine adducts are not repaired, their half-life in human serum is approximately 20–60 days (i.e., similar to that of unbound albumin) (McCoy L, personal communication; Sabbioni et al. 1987).
Information about risk factors associated with outbreaks of aflatoxicosis is limited. In addition, only a few animal studies have measured aflatoxin concentrations because unbound aflatoxins remain in the blood for a very short period of time after exposure (i.e., 13–120 min) (Unger et al. 1977; Wong and Hsiech 1978). The primary objective of our case–control study was to identify risk factors for acute aflatoxicosis. The secondary objective was to determine the concentrations of aflatoxin in maize, bound aflatoxin in serum, and hepatitis B surface antigen associated with acute aflatoxicosis.
Materials and Methods
Selection of case patients.
To focus the investigation on typical cases of presumed aflatoxicosis, our case definition was restricted to acute jaundice of unknown origin (i.e., no history of cirrhosis or obstructive liver disease) leading to hospitalization, during the peak of the epidemic, in the areas most affected by the outbreak. This case definition was based on information gathered by a descriptive epidemiology investigation conducted by the MOH and partners in May 2004. The descriptive epidemiology investigation found that a large number of patients with presumed aflatoxicosis had sought treatment at Makindu Sub-District Hospital (Makueni District) during 18 May–7 June and at Mutomo Mission Hospital (Kitui Districts) during 28 May–9 June. We did not restrict cases to live case patients or to case patients from which KEMRI had obtained blood samples because we did not want to introduce bias in our assessment of risk factors associated with disease.
To select 40 patients that met our case definition, we reviewed hospital records for the relevant time period and identified 19 case patients admitted to Makindu Sub-District Hospital and 21 case patients admitted to Mutomo Mission Hospital. All of the 29 case patients were alive at the time of the investigation, and all of the families of 11 deceased case patients verbally consented to participate in the study.
Selection of controls.
We randomly selected two controls from each case patient’s village because the descriptive epidemiology investigation suggested that these individuals would share similar soil, microclimate, and farming practices. Because the descriptive epidemiology investigation did not find a significant association among sex, case status, and case fatality, we did not match cases and controls by sex. To choose each control, we spun a bottle in front of the village elder’s home and walked to the fifth house in the direction indicated by the bottle (or to the third house in sparsely populated areas). At the selected household, we identified all residents who had slept in the house the night before, and we used a random number list to select one of these household residents. We excluded infants who were solely breast-feeding because they would not have been directly exposed to aflatoxin B1 found in maize. If selected individuals were not at their homes, we attempted to reach them wherever they were. All controls verbally consented to participate in the study.
Survey instrument.
A literature review and the descriptive epidemiology investigation allowed development of hypotheses about the relationship between aflatoxicosis and methods of handling maize. We developed a questionnaire to elicit information about maize and protein consumption, the quality of home-grown and purchased maize products, maize storage and cooking practices, and associated illness and death of family members and pet dogs. All questions related to the relevant exposure period, which was designated as 1 month before the onset of case patients’ illness or 1 month before controls heard about the outbreak.
Teams piloted the questionnaire on hospitalized patients who had presumed aflatoxicosis in Thika District. Local public health officials translated the questionnaire, which was written in English, into Kikamba and Kiswahili as needed. Teams carried measuring cups to obtain standardized information on maize food portions consumed by participants.
Food sample collection.
We obtained samples of maize products from participants to quantify personal exposure to aflatoxins. We collected samples from case households if they had maize in storage from the month before individuals developed aflatoxicosis (median date of symptom onset, 20 May 2004). We collected samples from control households if they had maize in storage from the month before hearing about the outbreak (median date of first hearing about the outbreak, 19 May 2004). We used metal cups to obtain multiple samples from different areas of the maize containers. These samples were combined in a paper bag to obtain 1 kg of maize for analysis. Collected maize products were replaced with commercial maize meal.
Blood sample collection.
We obtained blood samples from participants to quantify their exposure to aflatoxins in the preceding month. With the exception of six case-patients from whom KEMRI had banked blood in May, we collected approximately 5–10 mL of venous blood in a Vacutainer tube with gel separators from all participants. All blood samples were transported on ice to KEMRI for serum separation.
Laboratory analysis.
We analyzed maize samples using the VICAM AflaTest (VICAM, Watertown, MA, USA) immunoaffinity fluorometric method that quantitated total aflatoxin concentrations. Ground maize (50 g) that passed through a no. 20 sieve was mixed with 100 mL of a methanol:water mixture (80:20) with 5 g sodium chloride. The twice-filtered mixture (2 mL) was then passed through the immunoaffinity column at a rate of 1–2 drops/sec. The columns were washed with water, and the aflatoxins were recovered using 1 mL methanol. The methanol extract was read using a calibrated Vicam Series-4 Fluorometer set at 360 nm excitation and 450 nm emission. This method had an afla-toxin recovery of ≥85% and a detection limit of 1 ppb (VICAM 2001).
The Centers for Disease Control and Prevention (CDC) analyzed the serum specimens for aflatoxin B1–lysine albumin adducts using high-performance liquid chromatography (HPLC) and isotope dilution tandem mass spectrometry (McCoy et al. 2005). After enzymatic hydrolysis of serum albumin, aflatoxin B1–lysine adducts were extracted using solid-phase cartridges and separated using isocratic reversed-phase chromatography. We used positive ion electrospray with selected reaction monitoring mass spectrometry to measure aflatoxin B1–lysine adducts and its corresponding D4-labeled internal standard. We measured total serum albumin using a bromocresol purple binding assay and a microplate reader. The limit of detection of aflatoxin B1–lysine albumin adducts was 0.0003 ng/mg. The CDC also analyzed all remaining sera for hepatitis B surface antigen using ETI-MAK-2 PLUS enzyme immunoassay kits from DiaSorin (DiaSorin, Stillwater, MN).
Data management and analysis.
Data were analyzed using SAS, version 8.02 (SAS Institute, Cary, NC). We used conditional logistic regression to calculate odds ratios (ORs) between case status and participants’ methods of harvesting, storing, and preparing maize. We also used conditional logistic regression models to explore the relationship between case status, maize and protein consumption, aflatoxin concentrations in maize, aflatoxin B1–lysine adduct concentrations, and hepatitis B surface antigen titers in serum. We restricted mixed linear regression models to controls because we wanted to investigate the relationship between serum aflatoxin concentrations and methods of harvesting, storing, and preparing maize, daily maize and protein consumption, and total aflatoxin concentrations in maize using a sample that more closely resembled the general population. We also used Cox proportional hazards models to explore the relationship between the number of days case patients survived after the onset of jaundice and aflatoxin concentrations in maize, aflatoxin B1–lysine adducts concentrations in serum, hepatitis B surface antigen titers, and reported maize and protein consumption. Calculations were adjusted for age, sex, and participant’s district.
Results
Demographic information.
With few exceptions, case patients (n = 40) and controls (n = 80) had similar demographic characteristics (Table 1). Half of the participants lived in the Makueni District and the other half lived in the Kitui District. The mean age of case patients was similar to that of controls [22.5 years (range, 1.3–80.0 years) vs. 26 years (range, 0.5–75.0 years), respectively]. When compared with controls, more of the case patients were male (62.5% vs. 33.8%, respectively; p = 0.003). Case patients were also more likely than controls to report having family members with acute jaundice during the 2 months before the study (37.5% vs. 3.8%; p < 0.001). As of 9 August, 18 of the 40 case patients (7 additional case patients since completion of our study) had died of acute liver failure.
Food consumption and maize aflatoxin analysis.
Eating contaminated homegrown maize kernels was the primary risk factor for developing aflatoxicosis. On average, maize samples were collected 33 days (range, 8–112 days) after case-patients’ onset of symptoms. Homegrown maize kernels from case households had significantly higher aflatoxin concentrations than kernels sampled from control households [geometric mean (GM) = 354.5 ppb vs. 44.1 ppb, respectively; p = 0.04; Figure 1]. Eating homegrown maize kernels was significantly associated with case status (adjusted OR = 3.0; 95% confidence interval (CI), 1.01–8.8). Owning “bad” homegrown maize kernels (maize with colored flecks, discoloration, unusual odor, or signs of mold) was found to be a risk factor for aflatoxicosis (adjusted OR = 5.9; 95% CI, 1.9–18.2). Case patients who fed their dogs household food reported dog deaths more often (43%) than controls (15%; adjusted OR = 15.2; 95% CI, 1.8–127.4). We did not find an association between case status and the number of portions of maize, beans, or meat participants consumed on a weekly basis.
Serum aflatoxin B1–lysine adduct analysis.
On average, serum samples were collected 33 days after case-patients’ onset of symptoms. Using conditional logistic regression, we found that having aflatoxin B1–lysine adduct concentrations at or above the median (0.25 ng/mg) was a risk factor for developing aflatoxicosis (adjusted OR = 14.8; 95% CI, 3.0–72.2). Case patients who provided serum samples (n = 29) had higher aflatoxin B1–lysine adduct concentrations in their serum than did controls (n = 62; GM = 1.2 ng/mg of albumin vs. 0.15 ng/mg of albumin; p < 0.001; Figure 2). We found a positive association between concentrations of aflatoxins in homegrown maize and aflatoxin B1–lysine adduct concentrations in serum mixed linear regression adjusted for age, sex, village, and district (p < 0.05). For each milligram increase in the maize aflatoxin concentration, there was a 0.5 pg/mg increase in the logarithm of the serum aflatoxin B1–lysine adduct concentration.
Serum hepatitis B surface antigen analysis.
There was sufficient serum to analyze 72 (60%) samples for hepatitis B surface antigen. The mean age of participants with positive titers was 33 years, and most of them were female (58%). Eight (44%) of 18 cases had positive titers, while only 4 (7%) of 54 controls had positive titers (Table 2). Using conditional logistic regression, we found that having positive hepatitis B surface antigen titers was a risk factor for acute hepatic failure (adjusted OR = 9.8; 95% CI, 1.5–63.1). When we restricted the conditional logistic regression to participants with negative hepatitis B titers, we found that having aflatoxin B1–lysine adduct concentrations at or above the median for this subgroup (0.2 ng/mg) was a risk factor for developing aflatoxicosis (95% CI, 2.1–∞, p = 0.004).
Risk associated with toxin.
Case patients with known dates of death who had provided blood samples (n = 8) had higher aflatoxin B1–lysine adduct concentrations in their serum than did case patients who survived (n = 17; 3.2 ng/mg vs. 0.5 ng/mg; p = 0.07) after adjusting for age, sex, and district. In our survival analysis, we found a significant association between time from jaundice to death and serum aflatoxin B1–lysine adduct concentration (adjusted hazard ratio = 1.3; 95% CI, 1.04–1.6; p = 0.02).
Risk associated with food preparation and storage.
Storing maize that was not completely dry and storing maize inside the home rather than in a granary were both independently associated with development of aflatoxicosis (OR = 3.5; 95% CI, 1.2–10.3; OR = 12.0; 95% CI, 1.5–95.7, respectively; Table 3). Participants who reported storing their maize mixed with ash had lower concentrations of aflatoxins in their maize than those who did not (GM = 17.4 ppb vs. 142.2 ppb; p = 0.05). We did not find an association between case status, the type of container used to store maize (plastic burlap, plastic bucket, woven basket, clay pot, gourd, or sisal), the use of soda and pesticides in the storage area, or the culling of maize kernels that appeared moldy.
Discussion
Food consumption and aflatoxin analyses.
This is the first investigation to quantify the association among environmental contamination, a history of exposure, biomarker concentrations, and acute aflatoxicosis. The results of our case–control study suggest that consumption of contaminated maize kernels placed people in this region of Kenya at risk for life-threatening aflatoxicosis (case-fatality rate of 39%). Through systematic sampling of maize and serum from participants, we found a strong association between aflatoxin concentrations in homegrown maize, serum B1–albumin adducts, hepatitis B surface antigen titers, and case status.
The aflatoxin concentrations measured from the maize of case patients was comparable with those measured in other acute aflatoxicosis outbreaks. The aflatoxin B1–lysine adduct concentrations measured from the serum of case patients are the highest ever reported. This is the first study to quantify aflatoxin B1–lysine adduct concentrations in the serum of case patients during an outbreak of acute aflatoxicosis; a critical step in the elucidation of the clinically relevant action levels for aflatoxin exposure. We associated these serum aflatoxin B1–lysine adduct concentrations with the risk for life-threatening acute aflatoxicosis.
We found an association between aflatoxin concentrations in maize and aflatoxin B1–lysine adduct concentrations in serum from controls. The GM aflatoxin B1–lysine adducts concentration in serum from controls is higher than the majority of concentrations documented in population-based studies from countries with a high incidence of liver cancer (Wild et al. 1990). It is unclear why some controls with high aflatoxin B1–lysine adduct concentrations did not manifest symptoms of acute hepatitis during the time of the investigation. The concentrations found in controls were not associated with acute symptoms and may have represented chronic exposure to aflatoxins. Chronic exposure to aflatoxins is associated with impaired immunity, malnutrition, and liver cancer (the third most common cause of death from cancer in Africa) (Parkin et al. 2003; Williams et al. 2004). People chronically exposed to elevated concentrations of aflatoxins are three times more likely to develop hepatocellular carcinoma.
We also found an independent association between hepatitis B surface antigen titers and case status. Although people with hepatitis B (which is endemic in Kenya) who are chronically exposed to aflatoxins may be more likely to develop hepatocellular carcinoma, this is the first study to quantify the association between hepatitis B, aflatoxin adducts, and acute hepatitis (Keenlyside et al. 1977; Qian et al. 1994). Further research is needed to determine if the high incidence of liver cancer in eastern Kenya is attributable to chronic asymptomatic exposure to aflatoxins. In addition, clinicians working in areas where aflatoxicosis is endemic should consider obtaining a dietary history for aflatoxin exposure from cases patients with symptoms of acute hepatitis and positive hepatitis B titers.
Risk factors.
Our case–control study quantified ORs for suspected risk factors described in previous aflatoxicosis outbreaks. As in a 1974 outbreak in India (Krishnamachari et al. 1975b), we found that males were more likely to die from aflatoxicosis, in spite of eating similar quantities of maize as females. We found that acute aflatoxicosis manifests in family clusters, as reported in a 1988 outbreak in Malaysia (Lye et al. 1995). Sharing contaminated food and genetic polymorphisms of cytochrome P450 enzymes may place families at risk for aflatoxicosis (Chen et al. 2000). As reported by Ngindu (1982) in a 1981 outbreak in Kenya, we found that, more often than controls, case patients reported dog deaths before developing aflatoxicosis. In the future, reports of deaths in dogs may warn public health officials of a potential aflatoxin contamination of the food supply.
Food preparation and storage analysis.
Although maize is traditionally stored in granaries, storage inside homes occurs during periods of food shortage; this may have facilitated the contamination of maize with aflatoxins. The rainy season (from March through May) accounts for 80% of annual food production [Food and Agriculture Organization (FAO) 2000]. In 2004, an early and insufficient rainy season caused a food shortage of 156,000 metric tons of maize (Associated Press 2004). Some participants reported storing maize inside their homes to ensure it would not be stolen during the food shortage. Drought conditions stress maize plants and render them susceptible to contamination by Aspergillus spp. (Wilson and Payne 1994). The warm environment inside these windowless homes and storage of maize on the dirt floor may have promoted fungal growth in wet maize kernels.
Our case–control study suggests that traditional methods of drying and storing maize in elevated granaries were protective against aflatoxicosis. Traditional granaries are raised structures that are well ventilated, and they promote the drying of grain (FAO 1998). The granaries’ elevated platforms isolate the maize from spores and insects on the ground. We also found that storing maize mixed with ash was associated with lower concentrations of aflatoxin than storing maize without ash. Ash acts as a physical barrier against insects and helps keep maize dry.
Limitations.
Our case–control study was limited by its retrospective design. It is possible that case patients (or the family members of deceased case patients) may have recalled the amount, source, and quality of maize that was consumed differently than did controls. The aflatoxin concentrations measured in sampled maize may have differed from those consumed by case patients before they became ill with aflatoxicosis. We may not have found an association between the number of portions of maize consumed and case status due to the limited accuracy of the food questionnaires. In addition, it is possible that some case patients developed jaundice as a result of undiagnosed medical conditions unrelated to aflatoxicosis. This potential misclassification would have weakened any demonstrable associations.
Conclusion
Aflatoxins and other mycotoxins contaminate 25% of agricultural crops worldwide and are a source of morbidity and mortality throughout Africa, Asia, and Latin America (Smith et al. 1994). To prevent future aflatoxicosis outbreaks, it is necessary to explore public health interventions that promote effective production, storage, and processing of homegrown and commercial maize. In addition, surveillance that monitors aflatoxin concentrations in food and incidence of acute jaundice in humans may prevent widespread outbreaks of acute aflatoxicosis (Trucksess and Wood 1994). In the future, serum aflatoxin B1 albumin adducts may be used to diagnose acute aflatoxicosis and monitor interventions aimed at reducing aflatoxin exposure (Kensler et al. 1999). Although short-term interventions such as food replacement mitigate the loss of life during outbreaks, it is necessary to develop long-term, culturally appropriate strategies to prevent aflatoxicosis.
The use of trade names is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention, the Agency for Toxic Substances and Disease Registry, the Public Health Service, or the U.S. Department of Health and Human Services.
Figure 1 Frequency of total maize aflatoxin concentrations for participants.
Figure 2 Frequency of serum aflatoxin B1–lysine albumin adduct concentrations for participants.
Table 1 Demographic characteristics [n (%)] of jaundiced case patients (n = 40) and village controls (n = 80), Eastern Province, Kenya, 2004.
Characteristic Case patients Controls p-Valuea
District
Kitui 21 (52.5) 42 (52.5) 1.00
Makueni 19 (47.5) 38 (47.5)
Mean age (years) 22.5 26.0 0.37b
Age < 15 years 22 (55.0) 31 (38.8) 0.09
Male 25 (62.5) 27 (33.8) 0.003
Family with jaundice 15 (37.5) 3 (3.8) < 0.001
Heard of outbreak 34 (85.0) 72 (90.0) 0.33
a Values calculated using chi-square test unless indicated otherwise.
b Student’s t-test used for comparison of means.
Table 2 Serum aflatoxin B1–lysine albumin adduct concentration and hepatitis B surface antigen titers (μg/mg of albumin) in cases and controls [GM (n)].
Adduct concentration
Cases Controls
Hepatitis B positive 0.17 (8) 0.08 (4)
Hepatitis B negative 3.55 (10) 0.16 (50)
Table 3 Risk factors [n (%)] for jaundice among case patients (n = 28) and controls (n = 43) who ate maize kernels grown on their own farms, Kenya, 2004.
Characteristic Case patients Controls OR (95% CI)
Initial dryness of stored maize
Wet 15 (53.6) 11 (25.6) 3.5 (1.2–10.3)
Dry 13 (46.4) 32 (74.4) 1.0
Storage location
House 22 (81.5) 23 (53.5) 12.0 (1.5–95.7)
Granary 5 (18.5) 20 (46.5) 1.0
Preservatives added to storage
Ash 6 (15.4) 13 (17.6) 1.6 (0.4–5.6)
Insecticide 9 (23.1) 21 (28.1) 0.6 (0.2–1.8)
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References
American Public Health Association 2000. Arthropod-borne viral diseases (arboviral diseases). In: Control of Communicable Diseases Manual (Chin J, ed). 17th ed. Washington, DC:American Public Health Association, 30–31.
Chen SY Chen CJ Tsai WY Ahsan H Liu TY Lin JT 2000 Associations of plasma aflatoxin B1 -albumin adduct concentration with plasma selenium concentration and genetic polymorphisms of glutathione S -transferase M1 and T1 Nutr Cancer 38 179 185 11525595
Etzel R 2002 Mycotoxins JAMA 287 425 427 11798344
FAO 1998. African Experience in the Improvement of Post-harvest Techniques. Rome:Food and Agriculture Organization of the United Nations. Available: http://www.fao.org/documents/show_cdr.asp?url_file=/docrep/W1544E/W1544E00.htm [accessed 22 February 2005].
FAO (Food and Agriculture Organization of the United Nations) 2000. Drought in Kenya: Food Situation Is Deteriorating Rapidly - First Starvation-Related Deaths. Press Release 00/40. Available: http://www.fao.org/WAICENT/OIS/PRESS_NE/PRESSENG/2000/pren0040.htm [accessed 22 February 2005].
Fung F Clark R 2004 Health effects of mycotoxins: a toxicological overview J Toxicol 42 217 234
International Federation of Red Cross and Red Crescent Societies 2004. Kenya: Drought. Appeal no. 18/04. Available: http://www.ifrc.org/docs/appeals/04/1804.pdf [accessed 24 October 2005].
Keenlyside RA Smith DH Hirst D Zuckerman AJ Preece J 1977 The distribution and significance of hepatitis B surface antigen in a rural population in Kenya Ann Trop Med Parasitol 71 167 177 869607
Kensler TW Groopman JD Sutter TR Curphey TJ Roebuck BD 1999 Development of cancer chemopreventive agents: oltipraz as a paradigm Chem Res Toxicol 12 113 126 10027787
Krishnamachari KA Bhat RV Nagarajan V Tilak TB 1975a Hepatitis due to aflatoxicosis: an outbreak in western India Lancet 1 1061 1063 48730
Krishnamachari KA Bhat RV Nagarajan V Tilak TB 1975b Investigations into an outbreak of hepatitis in parts of western India Indian J Med 63 1036 1049
Lye MS Ghazali AA Mohan J Alwin N Nair RC 1995 An outbreak of acute hepatic encephalopathy due to severe aflatoxicosis in Malaysia Am J Trop Med Hyg 53 68 72 7625536
McCoy LF Scholl PF Schleicher RL Groopman JD Powers CD Pfeiffer CM 2005 Analysis of aflatoxin B1 lysine adduct in serum using isotope-dilution liquid chromatography/tandem mass spectrometry Rapid Commun Mass Spectrom 19 2203 2210 16015671
Ngindu A Kenya PR Ocheng DM Omondi TM Ngare W Gatei D 1982 Outbreak of acute hepatitis caused by aflatoxin poisoning in Kenya Lancet 1 1346 1348 6123648
Onsongo J 2004 Outbreak of aflatoxin poisoning in Kenya EPI/IDS Bull 5 3 4 Available: http://www.afro.who.int/csr/ids/bulletins/eastern/jun2004.pdf [accessed 20 October 2005].
Parkin DM Ferlay J Hamdi-Chérif M 2003 Liver Cancer IARC Sci Publ 153 299 314
Patten RC 1981 Aflatoxins and disease Am J Trop Med Hyg 30 422 425 7015891
Qian GS Ross RK Yu MC Yuan JM Gao YT Henderson BE 1994 A follow-up study of urinary markers of aflatoxin exposure and liver cancer risk in Shanghai, People’s Republic of China Cancer Epidemiol Biomarkers Prev 3 3 10 8118382
Sabbioni G Skipper PL Buchi G Tannenbaum SR 1987 Isolation and characterization of the major serum albumin adduct formed by aflatoxin B1 in vivo in rats Carcinogenesis 8 819 824 3111739
Smith JE Solomons GL Lewis CW Anderson JG 1994. Mycotoxins in Human Health. Brussels:European Commission.
Skipper PL Tannenbaum SR 1990 Protein adducts in the molecular dosimetry of chemical carcinogens Carcinogenesis 11 507 518 2182215
Tandon H Tandon B Ramalingaswami V 1978 Epidemic of toxic hepatitis in India of possible mycotoxic origin Arch Pathol Lab Med 102 372 376 580871
Trucksess MW Wood GE 1994. Recent methods of analysis for aflatoxins in foods and feeds. In: The Toxicology of Aflatoxins, Human Health, Veterinary, and Agricultural Significance (Eaton DL, Groopman JD, eds). San Diego, CA:Academic Press, 409–431.
Unger PD Mehendale HM Hayes AW 1977 Hepatic uptake and disposition of aflatoxin B1 in isolated perfused rat liver Toxicol Applied Pharmacol 41 523 534
VICAM 2001. About Aflatest®. Available: http://www.vicam.com/products/aflatest.html [accessed 18 February 2005].
Wild CP Jiang YZ Allen SJ Jansen LA Hall AJ Montesano R 1990 Aflatoxin-albumin adducts in human sera from different regions of the world Carcinogenesis 11 2271 2274 2265478
Williams J Phillips TD Jolly PE Stiles JK Jolly CM Aggarwal D 2004 Human aflatoxicosis in developing countries: a review of toxicology, exposure, potential health consequences, and interventions Am J Clin Nutr 80 1106 1122 15531656
Wilson DM Payne GA 1994. Factors affecting Aspergillus flanus group infection and aflatoxin contamination of crops. In: The Toxicology of Aflatoxins: Human Health, Veterinary, and Agricultural Significance (Eaton DL, Groopman JD, eds). San Diego, CA:Academic Press, 309–315.
Wong ZA Hsiech DPH 1978 Aflatoxicol: major aflatoxin B1 metabolite in rat plasma Science 200 325 327 635590
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8287ehp0113-00178416330364ResearchEnvironmental MedicineCase Report: The Clinical Toxicity of Dimethylamine Borane Tsan Yu-Tse Peng Kai-Yu Hung Dong-Zong Hu Wei-Hsiung Yang Dar-Yu Department of Emergency, Taichung Veterans General Hospital, Taichung, Taiwan, Republic of ChinaAddress correspondence to Dong-Zong Hung, No. 160, Section 3, Chung-Kang Rd., Taichung, Taiwan, Republic of China 00407. Telephone: 886-4-2359 2525. Fax: 886-4-2359 4065. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 12 8 2005 113 12 1784 1786 5 5 2005 11 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Context: Dimethylamine borane (DMAB) is a reducing agent used in nonelectric plating of semiconductors. Exposures are usually through occupational contact. We report here four cases of people who suffered from work-related exposure to DMAB.
Case presentation: Three patients exposed to DMAB decontaminated immediately by drinking a lot of water; they reported dizziness, nausea, diarrhea 6–8 hr later. The other patient did not decontaminate at once, and he suffered from more severe symptoms, including dizziness, nausea, limb numbness, slurred speech, irritable mood, and ataxia 13 hr later. Magnetic resonance imaging showed symmetric lesions with hyperintensity on T2WI and FLAIR in bilateral cerebellar dantate nuclei. This patient was readmitted to the hospital due to difficulty in walking and climbing 18 days after exposure. Lower leg weakness and drop foot were found bilaterally. A nerve conduction study revealed polyneuropathy with motor-predominant axonal degeneration. This patient receives regular outpatient followups and still walks with a clumsy gait and has difficulty with hand-grasping activity.
Discussion: This case study demonstrates that DMAB is highly toxic to humans through any route of exposure, and dermal absorption is the major route of neurotoxicity. DMAB induces acute cortical and cerebellar injuries and delayed peripheral neuropathy.
Relevance: Further investigation of the toxic mechanism of DMAB is warranted. Early decontamination with copious water is the best current treatment for exposure to DMAB.
chemical decontaminationdimethylamine boraneneurotoxicitypolyneuropathysemiconductor plating
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Dimethylamine borane [DMAB, dimethylamine-borane complex; (CH3)2NHBH3, CAS no. 74-94-2] is a strong reducing agent and is an important chemical in the semiconductor industry (Jagannathan and Krishnan 1993). DMAB is a white, crystalline solid with a molecular weight of 58.92 g/mol and melting point of 33–36°C. The chemical structure of DMAB is shown in Figure 1 (BASF 2004).
DMAB is toxic and hazardous to the environment (BASF 2004). It is an irritant and is corrosive to the skin and mucosa (BASF 2004). However, to our knowledge, there have been no published reports, to date, of human exposure. Here we report a case of occupational DMAB exposure that caused significant neurotoxicity. We also found three other cases of occupational DMAB exposure during our field investigation.
Case Presentation
A 36-year-old, healthy male was accidentally sprayed over the face and trunk with the liquid form of DMAB (Figure 2). He kept on working and did not take a shower until > 1 hr later. He developed dizziness, nausea, vomiting, sore throat, limb numbness, slurred speech, slow motion, lack of concentration, and ataxia by the next morning, 13 hr after exposure. He was admitted to a local hospital, where a normal brain computerized tomogram was noted. Because of worsening clinical conditions, including “masked” face, irritability, awkwardness, and rocking from side to side while sitting on the bed, he was transferred to our hospital 3 days later. Physical examination revealed some abnormal neurologic findings. The patient was oriented as to time and place but was easily distracted. His speech was slurred. Normal muscle power was noted for all four limbs. He could stand on a wide base with assistance but deviated to both sides when attempting a tandem gait. Impairment on finger-to-nose and heel-to-knee tests was also noted. He denied any medical problems such as hypertension, diabetes, and neurologic diseases. He smoked one pack of cigarettes per day and drank alcohol occasionally.
A routine laboratory work-up including complete blood cell count, electrolytes, blood sugar, and hepatic and renal function tests was performed. Mild hyperventilation, with arterial blood gas of pH 7.510, partial pressure of carbon dioxide 30.6 mm Hg, partial pressure of oxygen 100 mm Hg, and bicarbonate 24.7 mmol/L, was found. No drug history, including use of herbal medicine, was noted for the last 3 months. Urinalysis did not detect any illegal drugs, central nervous system-acting drugs, or other medications. Normal blood and urine lead, mercury, and aluminum levels were also noted.
Eight days after chemical exposure, the patient’s electroencephalogram (EEG) revealed diffuse background slowing, indicative of a mild diffuse cerebral dysfunction. Tests of nerve conduction velocity (NCV) for the left-side limbs were normal. Brain magnetic resonance imaging (MRI) on the eighth day showed a symmetric increase in signal intensity on FLAIR (fluid-attenuated inversion recovery), T2WI (T2-weighted intensity), and DWI (diffusion-weighted images), but low signal intensity on T1WI without postcontrast enhancement at bilateral cerebellar periventricular areas (Figure 3A). A steroid was prescribed for treatment of the possible acute inflammatory effects on neurons. The patient was discharged with stable neurologic function after 6 days of observation.
The pateint was readmitted to our neurology outpatient clinic 18 days after chemical exposure due to difficulty in walking and climbing. Physical examination revealed that the deep tendon reflexes of both knees were areflexic. Muscle power was mildly decreased in the distal and proximal parts of the upper right leg. Lower leg weakness and drop foot were also found bilaterally with muscle power of grade 2/5 in the right foot and 3/5 in the left foot. A nerve conduction study on the 29th day after poisoning showed decreased NCV and complex muscular action potential (CMAP) amplitudes for the left median, left ulnar, left peroneal, and left tibial nerves. H-reflex was absent bilaterally. Sensory conduction and sensory evoked potential tests of the nerves of the upper left and lower left limbs were normal. A brain MRI on the 37th day after poisoning showed that the previous lesions in the cerebellar dentate nuclei region had subsided (Figure 3B).
With active physical therapy, the patient could walk straight on a wide base 2 months after poisoning. No dysmetria was noted on the finger-to-nose test, but heel or toe gait was impaired. The muscle power was grade 3/5 in the flexor and extensor of the right foot; 4/5 in the flexor and extensor of the left foot, and others were all 5/5. Weakness in the flexor and extensor of both feet still remained. A repeat EEG was normal. A repeat NCV study revealed no change in polyneuropathy with motor predominant axonal degeneration. The patient receives regular outpatient followups. He still walks with a clumsy gait and has difficulty with hand-grasping activity.
Field Investigation
We performed a field investigation to study the character and mechanism of chemical exposure. According to the statement of the facility manager, the factory produces only DMAB. The liquid sprayed on the patient was 97% DMAB. The other 3% was decomposed materials including boric acid, borates, hydrogen, and dimethylamine (DMA). DMAB was the only toxic substance at the workplace.
There were three other workers with a history of DMAB contamination. Their data are summarized in Table 1 (cases 2–4). They all suffered from minor intoxication without any residual neurologic sequelae.
Discussion
To our knowledge, the human toxicity of DMAB has never been reported in the literature. In the BASF material safety data sheet, DMAB is noted to be toxic and hazardous to the environment (BASF 2004). It is harmful if swallowed or absorbed through the skin. Both vapor and solid can cause eye, skin, and respiratory tract irritation. Studies of animals exposed to high doses of DMAB have demonstrated injury to the kidneys, liver, adrenals, lungs, and central nervous system (BASF 2004). Our patients reveal that DMAB is highly toxic to humans through any route of exposure. The major route of toxicity is dermal absorption. Gastrointestinal symptoms occur the first 6–12 hr after exposure, but the toxicity of DMAB seems to be limited if prompt decontamination is performed immediately after exposure. Delayed decontamination after DMAB exposure in our patient did lead to severe toxicity, including acute cerebral and cerebellar dysfunction and delayed polyneuropathy. The cerebral and cerebellar toxicity of DMAB was temporary, as evidenced in the patient’s serial MRI and EEG examinations and clinical manifestations. The mechanism of central nervous system lesions is unknown, but from the study of serial MRI, transient demyelination, axonal degeneration, or neuron damage might be suggested (Bradley 1986). According to the clinical neurologic manifestations and EEG upon admission, we also suggest that some cortical dysfunction may have been induced by DMAB, though it was a negative finding on the image study 8 days after exposure.
Delayed peripheral neuropathy was the second important presentation in this case of DMAB poisoning. The decreased muscle power of the four limbs developed progressively during the 3 weeks after DMAB exposure. We verified the polyneuropathy with axonal degeneration by serial EEG/NCV studies.
DMAB easily decomposes to boric acid, borates, hydrogen, and DMA (BASF 2004). DMA is also toxic by inhalation, ingestion, and intravenous routes. Gases or vapors from aqueous solutions may cause irritation, conjunctivitis, and corneal damage. Inhalation may cause coughing, nausea, and pulmonary edema [American Conference of Government Industrial Hygienists (ACGIH) 1991], but no systemic effects of DMA intoxication from industrial exposure have been reported (Ballantyne et al. 1985). Boric acid is well absorbed through the gastrointestinal tract, open wounds, and serous cavities. It causes gastrointestinal symptoms (nausea, vomiting, and diarrhea) and dermal effects (erythema, desquamation). The central nervous system effects are less common in intoxication by boric acid in adults. Boric acid causes headache, lethargy, restlessness, weakness, and seizure, but cerebellar lesions have not been reported (Kiesche-Nesselrodt and Hooser 1990; Locatelli et al. 1987; Mack 1984; Siegel and Wason 1986; Von Burg 1992). Hydrogen is usually nontoxic when inhaled, but it can displace oxygen, leading to oxygen deficiency in a confined space. In a rat study, repeated administration of DMAB produced rather severe central nervous system lesions (BASF 2004). The liquid or vapor form of DMAB, in concentrations of ≥97%, might be a reason for central and peripheral neurotoxicity.
Conclusion
DMAB intoxication can lead to acute cortical and cerebellar lesions and delayed polyneuropathy. Early and prompt decontamination is indicated in an occupational setting. Further research is needed regarding the mechanism of DMAB poisoning.
Figure 1 Chemical structure of DMAB [(CH3)2NHBH3] (BASF 2004).
Figure 2 Diagram showing how the patient was exposed to DMAB during the production process. A, Tank where DMAB is produced. B, container holding DMAB product; one of the screws on the lid of the container came loose, and liquid DMAB sprayed out over the face, head, and trunk of the worker.
Figure 3 MRIs of the patient. (A) Symmetric increase in signal intensity at bilateral cerebellar periventricular area on T2WI (9 February 2004). (B) Previous cerebellar dantate nuclei region hyperintensity on T2WI has subsided (17 March 2004).
Table 1 Data of four male workers exposed to DMAB.
Case no. Age (years) Route of entry Decontamination Symptom onset time Symptoms Subside/sequelae
1 36 Sprayed on the face and head Not immediately (1 hr later) 12 hr Altered consciousness, irritable, had difficulty walking and climbing, dizziness, slurred speech, limb numbness, nausea, vomiting, gastrointestinal upset Symptoms persisted
2 32 Sprayed over the whole body Took a shower immediately 6 hr Dizziness, nausea, vomiting, and had diarrhea 3 times Symptoms subsided the next morning
3 28 Ate a particle of DMAB with rice meal Drank a lot of water immediately 6 hr Dizziness, nausea, vomiting, and had diarrhea 5 times Recovered 1 day later
4 40 Sprayed on face and mouth Took a shower immediately 8 hr Dizziness, nausea, vomiting, and had diarrhea once Recovered 1 day later
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References
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Ballantyne B Dodd DE Nachreiner DJ Myers RC 1985 The acute toxicity and primary irritancy of N -benzyl-N ,N -dimethylamine Drug Chem Toxicol 8 1–2 43 56 4017898
BASF 2004. Material Safety Data Sheet of Dimethylamine Borane. Available: http://www.basf.com/inorganics/pdf/MSDS/Boranes/DMAB.pdf [accessed 1 August 2004].
Bradley WG Jr 1986 Magnetic resonance imaging in the central nervous system: comparison with computed tomography Magnet Reson Annu 81 122
Jagannathan R Krishnan M 1993 Electroless plating of copper at a low pH level IBM J Res Dev 37 2 117 123 Available: http://www.research.ibm.com/journal/rd/372/ibmrd3702F.pdf [accessed 1 August 2004].
Kiesche-Nesselrodt A Hooser SB 1990 Toxicology of selected pesticides, drugs, and chemicals. Boric acid Vet Clin North Am Small Anim Pract 20 2 369 373 2180182
Locatelli C Minoia C Tonini M Manzo L 1987 Human toxicology of boron with special reference to boric acid poisoning G Ital Med Lav 9 3–4 141 146 3334378
Mack RB 1984 From grandma to Galen: boric acid poisoning NC Med J 45 6 401 402
Siegel E Wason S 1986 Boric acid toxicity Pediatr Clin North Am 33 2 363 367 2870462
Von Burg R 1992 Boron, boric acid, borates and boron oxide J Appl Toxicol 12 2 149 152 1556383
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8127ehp0113-00178716330365ResearchChildren's HealthPrenatal Dichlorodiphenyldichloroethylene (DDE) and Asthma in Children Sunyer Jordi 12Torrent Maties 3Muñoz-Ortiz Laura 1Ribas-Fitó Núria 1Carrizo Daniel 4Grimalt Joan 4Antó Josep M. 12Cullinan Paul 51 Unitat Recerca Respiratòria i Ambiental, Institut Municipal d’Investigació Mèdica, Barcelona, Spain2 Universitat Pompeu Fabra, Barcelona, Spain3 Area de Salud de Menorca, IB-SALUT, Menorca, Spain4 Environmental Chemistry, Consejo Superior de Investigaciones Científicas, Barcelona, Spain5 Department of Occupational and Environmental Medicine, Imperial College, London, United KingdomAddress correspondence to J. Sunyer, IMIM-Environmental Respiratory Research Unit, Institut Municipal d’Investigació Mèdica, C. Doctor Aiguader 80, 08003 Barcelona, Catalonia, Spain. Telephone: 34-93-221-1009. Fax: 34-93-221-6448. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 18 7 2005 113 12 1787 1790 18 3 2005 18 7 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Prevalence of asthma increases with increasing dichlorodiphenyldichloroethylene (DDE) levels. However, the effect of early-life exposure, the fundamental window of exposure, is unknown. We assessed the association between prenatal DDE and other organochlorine compounds, and atopy and asthma during infancy. All women presenting for antenatal care in Menorca (Spain) over 12 months starting in mid-1997 were invited to take part in a longitudinal study; 482 children were subsequently enrolled, and 468 (97.1%) provided complete outcome data up to the fourth year of study. Prenatal exposure of organochlorine compounds was measured in cord serum in 405 (83%) children. Asthma was defined on the basis of wheezing at 4 years of age, persistent wheezing, or doctor-diagnosed asthma. We measured specific immunoglobulin-E (IgE) against house dust mite, cat, and grass in sera extracted at 4 years of age. DDE (median = 1.03 ng/mL) was detected in all children, as well as hexachlorobenzene (0.68 ng/mL) and polychlorobiphenyls (0.69 ng/mL). Wheezing at 4 years of age increased with DDE concentration, particularly at the highest quartile [9% in the lowest quartile (< 0.57 ng/mL) vs. 19% in the highest quartile (1.90 ng/mL); relative risk = 2.63 (95% confidence interval 1.19–4.69), adjusting for maternal asthma, breast-feeding, education, social class, or other organochlorines]. The association was not modified by IgE sensitization and occurred with the same strength among nonatopic subjects and among those with persistent wheezing or diagnosed asthma. DDE was not associated with atopy alone. Prenatal exposure to DDE residues may contribute to development of asthma.
asthmaatopychildrenDDE dichlorodiphenyldichloroethyleneorganochlorines
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Dichlorodiphenyltrichloroethane (DDT) was extensively used around the world as an insecticide from the 1940s until the end of the 1980s. Today, it is still widely sprayed in developing countries for disease-vector control (Wendo 2004). DDT is rapidly metabolized to 1,1-dichloro-2,2-bis(p-chlorophenyl) ethylene [p,p′-DDE (dichlorodiphenyl-dichloroethylene), hereafter DDE], which is a persistent, highly lipophilic chemical that can be detected throughout the world in sediments and in the food chain. Humans are exposed mainly through foods, and infants through the placenta and breast-feeding.
Decreased lymphocyte responses were associated with DDE in wildlife species (Lahvis et al. 1985) and in experiments with rats and mice (Banarjee 1987a, 1987b; Rehana and Rao 1992). In humans, DDE was associated with changes in cellular and humoral immunity (Cooper et al. 2004; Vine et al. 2001) and particularly with changes in T-cell–mediated immune cytokines related with allergy, such as interleukin-4 (Bilrha et al. 2003; Daniel et al. 2002). Similar effects have been found with other organochlorine compounds, such as hexachlorobenzene (HCB) (Michielsen et al. 1999) and polychlorinated biphenyls (PCBs) (Van Den Heuvel et al. 2002). Japanese children with Yusho disease, from exposure to high levels of PCBs, showed a high frequency of respiratory symptoms (Nakanishi et al. 1985). In a cross-sectional study among school children in Germany, DDE was strongly related with increases in total immunoglobulin E (IgE) and asthma (Karmaus et al. 2001, 2003). An increase of asthma mortality and asthma prevalence in adults was found among an older cohort of DDT sprayers (Beard et al. 2003), and the prevalence of wheeze increased with a variety of pesticides among current applicators (Hoppin et al. 2002). These studies, however, were unable to measure the prenatal exposure that is probably the fundamental window of exposure related to subsequent health events (Gluckman and Hanson 2004).
Menorca is one of the Balearic Islands in the northwest Mediterranean Sea, which has no local pollution sources. Here a general population birth cohort was set up in 1997 within the Asthma Multicenter Infants Cohort study (Polk et al. 2004). Our aim in this study was to assess the association of cord serum levels of DDE and other organochlorine compounds with atopy and asthma during early childhood.
Materials and Methods
All women presenting for antenatal care in Menorca over 12 months (starting in mid-1997) were recruited; 482 children were subsequently enrolled, and 468 (97.1%) provided complete outcome data up to the fourth-year visit; of these children, 405 (84%) had organochlorine compounds in cord serum measured. Blood was drawn at 4 years of age in 360 children, 306 of whom had IgEs and peripheral white blood cells measured. Asthma was defined based on wheezing at 4 years of age, persistent wheezing, or doctor-diagnosed asthma. The outcome of interest was the presence of wheeze at 4 years of age or absence each year to this age. Wheezing was described on each interviewer-led annual questionnaire as “whistling or wheezing from the chest, but not noisy breathing from the nose.” One or more episodes of wheezing over 12 months constituted wheezing during any given year. Forty-seven children had wheeze at 4 years of age, 42 of whom (89.4%) did so also in a preceding year [persistent wheeze (Martinez et al. 1995)]. Parental report of doctor-diagnosed asthma at 4 years of age was alternatively used as outcome. Specific IgE against house dust mite (Der p1), cat (Fel d1), and grass was measured using the CAP method, with levels > 0.34 kU/L being considered positive. We defined atopy as a positive value to any of the allergens. The study was approved by the corresponding ethical committees, and written informed consent was obtained from the parents of all children.
Prenatal DDE and other organochlorines were measured in cord serum by gas chromatography (GC) with electron capture detection and GC coupled to chemical ionization negative-ion mass spectrometry (Sala et al. 2001).
Parents were invited to undergo skin prick testing to determine their atopic status. A wheal of ≥ 3 mm (mean of perpendicular measures) to any allergen in the presence of a positive histamine control and a negative un-coated control constituted a positive skin test. A positive skin test to at least one allergen was considered indicative of atopy (Der p1, Fel d1, or grass pollen).
The following variables came from a questionnaire administered to the pregnant mothers: number of asthmatic parents, maternal smoking, parity, education, and social class. The U.K. Registrar General’s 1990 classification was used to classify social class according to mother’s employment (Liberatos 1988). Antibiotic use, lower respiratory tract infection (LRTI), and breast-feeding data came from the first-year questionnaire. LRTI was defined as a positive response to the question “Has a doctor ever said that your [child] has had a chest infection?” Mothers reported type and duration of breast-feeding. Fish consumption was excerpted from the food frequency questionnaire filled in during pregnancy. The children’s birth weight and sex were obtained from information collected at birth.
We measured the association between DDE and wheezing by relative risk (RR) estimated using binomial regression. The RR was adjusted for known risk factors of childhood asthma (Polk et al. 2004) in a multivariate model. DDE was log-transformed to normalize its distribution; it was also categorized by quartiles of its distribution. Linear dose–response relationships were assessed using general additive modeling and tested with DDE as a continuous variable (vs. discrete variable) in the regression model. We performed stratification by atopy to specify the type of asthma. Analyses were carried out with Stata version 8 (StataCorp, College Station, TX, USA).
Results
Wheezing at 4 years of age was reported for 11.6% of all children, and absence of wheezing at any age in 41.8% of all children. Specific IgE to common allergens was positive in 12.6% of children who gave blood at 4 years of age (11.7% to house dust mite, 1.0% to cat, and 2.0% to grass pollen). The average white blood cell count was 8,453 cells/mL (range, 3,900–16,900 cells/mL), and the geometric mean of eosinophil percent was 3.04% (0.2–21.2%). The geometric mean of eosinophil counts was 244 cells/mL (25–2,099 cells/mL). Levels of organochlorines at birth are shown in Table 1. All children were born with quantifiable levels of DDE and PCB compounds.
DDE concentration increased significantly with maternal age (Table 2). Parity, high education, high social class, and low birth weight increased moderately with increasing levels of DDE, although in a non-statistically significant way. Fish consumption during gestation was poorly related with DDE. There were geographic differences in DDE within the island not explained by residence in a rural area or on a farm or by social class, education, or fish consumption. Maternal asthma, smoking, male sex, low gestational age, and no breast-feeding showed an association with wheezing at 4 years of age but not with DDE levels.
Wheezing at 4 years of age increased with DDE concentration, particularly at the highest quartile, which was also found for persistent wheezing (Table 3). Specific IgE was not associated with DDE, which also occurred after selecting a more stringent cutoff of atopy (CAP > 0.70) (p = 0.11). Eosinophil counts were higher in the last quartile of DDE, but the association was not significant. Stratification of the association between wheezing at 4 years of age and DDE by atopy did not suggest an effect modification.
DDE maintains an association with wheezing after adjusting for potential confounders [both as continuous variable (p = 0.002) and in the upper quartile (p = 0.01) (Table 4)]. Inclusion of location, residence on a farm, and maternal age did not change the association with DDE, nor did exclusion of any of the other variables in the model or the alternative use of maternal social class instead of education. This association was invariant among children sensitized to IgE or nonsensitized (p-value for interaction = 0.50). Table 4 does not show the adjusted association of wheezing with DDE among the IgE-sensitized children, given their small number (n = 37). Nevertheless, the fact that the unadjusted association between wheezing and DDE among the sensitized children [RR per each doubling of the concentration = 1.30; 95% confidence interval (CI), 0.91–1.86] was of a magnitude similar to that of the association for all the subjects reinforces a lack of interaction between DDE and atopy. All these findings were also observed for persistent wheeze. The use of doctor-diagnosed asthma (occurring in 1.9% of children) instead of wheezing as the outcome variable also resulted in a positive association (RR = 1.46; 95% CI, 0.92–2.32 ), although it was nonsignificant (p = 0.10). The association between wheezing and DDE was not modified by fish consumption, maternal asthma, or length of breast-feeding. The negative association between wheezing at 4 years of age and duration of breast-feeding was not modified by levels of DDE at birth (data not shown).
HCB (RR = 0.96; 95% CI, 0.69–1.30) per each doubling of the concentration, and PCBs (RR = 0.99; 95% CI, 0.81–1.21) did not show a significant association with wheezing (nor in quartiles), and their inclusion in the model with DDE did not change the association of DDE with wheezing.
Discussion
Wheezing at 4 years of age increased with increasing levels of DDE at birth. This association occurred independently of specific IgE. An association between DDE and asthma at school age has already been reported by Karmaus et al. (2001) in a German population with a lower DDE burden (median of 0.30 ng/mL) than in the present study (1.03 ng/mL). Karmaus et al., however, measured both DDE and asthma at the same time, procedures that preclude measurement of prenatal exposure, which is probably the fundamental window of exposure related with the further health events (Gluckman and Hanson 2004).
Two pathways—immunologic and/or hormonal—could be involved in the relationship between DDE and asthma. The immunologic effects of DDE exposure have been suggested by many studies, although its mechanisms remain unclear. Several could be implicated. In humans, DDE was associated with changes in immune cells (Vine et al. 2001), immunoglobulins (Cooper et al. 2004; Vine et al. 2001), and cytokines (Bilrha et al. 2003; Daniel et al. 2002). DDE interferes with hormonal receptors and mimics estrogen activity (Rogan and Ragan 2003), which might modulate immunologic responses (Salem et al. 2000). Nevertheless, sexual hormones have been related to asthma by routes other than immunomodulation, such as in postmenopausal asthma, by unknown mechanisms (Barr et al. 2004).
In the children of our study, we did not find any association with peripheral total cell counts or with subtypes (data not shown). Only the number of peripheral eosinophils increased among the children in the highest quartile of DDE, although the difference was not statistically significant. Eosinophils participate in the underlying inflammatory responses of asthma (Bousquet et al. 1990). Yusho children exposed to PCBs who had respiratory diseases showed an increase of Clara cells in bronchioles (Nakanishi et al. 1985), which we did not investigate.
We did not find an association between DDE and specific IgE, in contrast to a study in school children measuring total IgE (Karmaus et al. 2001, 2003). A lack of association with IgE in our study could be due to the young age of our children, because expression of IgE sensitization to common aeroallergens increases with age during childhood (Jackola et al. 2003). An alternative explanation could be that the association between DDE and asthma does not involve the immunologic cells related to specific IgE production. The unmodified association between DDE and wheezing found among nonatopic children strengthens this possibility. Two studies on other organochlorines, such as PCBs and dioxins, found a negative association with allergic reactions in children (Weisglas-Kuperus et al. 2000) and IgE sensitization in rats (Luebke et al. 2001). A final explanation could be a discordant association between total and specific IgE. In neonates, organochlorines increased cord total IgE (Reichrtova et al. 1999).
A potential decreased response to viruses and bacteria due to DDE has been assessed in epidemiologic studies in children, but with some inconsistent results. Among 199 Inuit children highly exposed to organochlorines, a moderate increase of acute infections during the first year of life was reported (Dallaire et al. 2004), but not in 343 German school children (Karmaus et al. 2003) nor in 207 Dutch infants (Weisglas-Kuperus et al. 1995). We did not find any effect of DDE on wheezing occurring only before 3 years of age (data not shown), a probable marker of LRTIs. Coincidentally, LRTI during the first year measured by questionnaire was not associated with DDE (50% of the children in the lowest quartile of DDE had LRTI vs. 48% in the upper quartile).
DDE is an agricultural and industrial residue basically incorporated through diet. Fish are an important source of persistent organic compounds, but at the same time are also the basis of fundamental oligoelements. Parents of the children in the present study had a high intake of fish (more than half of the mothers had fish more than two times per week during pregnancy). However, fish intake has not been related to DDE levels, perhaps because of a small variability in our population (only 9% of our mothers ate fish less than once per week during pregnancy). We did not find any relationship between fish intake and wheezing at any of the DDE concentrations. Virtually all categories of foods might be contaminated by DDE, particularly fish and vegetables (Shafer and Kegley 2002). However, the food products primarily related to DDE have a protective role in asthma (Farchi et al. 2003), and therefore the present findings are unlikely to be explained by a residual confounding by maternal food patterns during gestation. Margarine and butter, previously related to wheezing (Farchi et al. 2003), were not associated with cord DDE in Menorca (p > 0.6). Food patterns did not explain the geographic differences in DDE levels in children.
Breast-feeding is an important way of ingesting organochlorines during infancy. At the same time, breast-feeding is negatively associated with wheezing at 4 years of age (Oddy and Peat 2003). The stratification of breast-feeding duration by prenatal levels of DDE did not modify the association between breast-feeding and wheezing, suggesting that the postnatal effects of DDE (incorporated through breast-feeding) are probably less relevant than prenatal exposure, as some authors have suggested for neurodevelopment (Nakai and Satoh 2002).
The risk factors other than DDE associated with wheeze in the present study are those already known to play a role in asthma inception (Polk et al. 2004).
A potential limitation of the present study is nonresponse (17%). However, in most cases subjects were not included because of the small quantity of sera in the repository aliquots of cord serum. The quantity of blood was unlikely to be related to DDE levels, and participants had the same rate of wheezing as did nonparticipants (p = 0.54). Thus, non-response is unlikely to have introduced bias. The proportion of subjects lost in the analysis of atopy was larger, because around 25% of children did not provide blood at 4 years of age. However, provision of blood was unrelated to DDE concentration (p = 0.89). Selection of children could not explain the differences in DDE levels by area of residence given the lack of a geographic pattern in the nonrespondents. The geography of DDE in Menorca is unknown, but the uniformity and small dimensions of the island suggest that it is unlikely that environmental exposures play a role. Nevertheless, a further environmental study might be of interest.
Overall, the present results suggest that prenatal exposure to DDE, the organochlorine residue with the highest levels in newborns from Menorca, may contribute to the incidence of asthma. With regard to DDE, Menorca may be considered representative of areas with low background pollution because there are no local sources of DDT release. These results should be considered when evaluating the risk benefits of spraying DDT in antimalarial campaigns, because the debate about its current use in developing countries with endemic malaria remains open (Chen and Rogan 2003; Wendo 2004).
This study was funded in part by Instituto de Salud Carlos III Red de Grupos Infancia y Medio Ambiente (G03/176). This study has also been supported in part by the Fundació “La Caixa” (00/077-00) and Instituto de Salud Carlos III, Red de Centros de Investigación en Epidemiología y Salud Pública (C03/09).
Table 1 Distribution of DDE (ng/mL) and other organochlorine values in cord serum by percentiles (n = 405).
Minimum 25th 50th 75th Maximum Geometric mean
p,p′-DDE 0.04 0.57 1.03 1.94 19.54 1.06
HCBa 0.14 0.46 0.68 1.02 9.82 0.70
PCBsb 0.007 0.50 0.69 0.98 12.07 0.66
a Percent not detected = 1.2 %.
b Sum of congeners PCB-28, PCB-52, PCB-101, PCB-118, PCB-153, PCB-138, and PCB-180.
Table 2 Distribution (in percentage or mean) of women and children in the different quartiles of DDE concentration in cord serum with regard to the selected variables.
p,p′-DDE (ng/mL)
Characteristic < 0.57 (n = 102) 0.57–1.03 (n = 100) 1.03–1.90 (n = 101) > 1.90 (n = 102)
Mother (%)
Age [mean (years)]* 27 28 29 30
Maternal asthma 9 4 7 9
Maternal atopy 45 32 31 40
Smoking during pregnancy 22 26 22 22
Parity (first) 52 50 47 46
Education
Less than primary 9 6 4 8
Primary 60 47 48 54
University 8 13 17 17
Social class
Professional/managerial 9 13 16 13
Manual partly skilled 21 14 11 17
Unemployment and housewife 27 17 21 15
Fish consumption during pregnancy
< 1 per week 15 8 7 8
≥ 2 per week 52 60 55 49
Location (east)* 56 49 51 33
Rural area 15 9 13 11
Living on a farm 7 6 8 7
Child (%)
Sex (male) 59 51 38 56
Gestational age [mean (weeks)] 40 40 39 40
< 37 weeks 5 3 3 5
Birth weight [mean (g)] 3,255 3,235 3,130 3,160
< 2,500 g 5 3 7 5
Breast-feeding 76 88 86 79
> 20 weeks 42 59 50 40
Weeks of exclusive breast-feeding (mean) 12 22 20 15
* p-Value for trend < 0.05.
Table 3 Distribution of wheezing, atopy (specific IgE > 0.34 kU/L), and eosinophil counts at 4 years of age according to quartiles of DDE in cord serum.
p,p′-DDE (ng/mL)
< 0.57 0.57–1.03 1.03–1.90 > 1.90 RR (95% CI)a p-Valuea
Wheezing
Never 56.1 54.0 53.5 38.5 1
Persistentb 6.8 8.0 10.9 15.7 1.26 (1.04–1.54) 0.01
At 4 years of age 8.8 8.0 10.9 18.6 1.31 (1.09–1.58) 0.007
Atopy 16.7 13.7 9.5 10.7 0.92 (0.73–1.17) 0.51
Eosinophils (cells/mL)c 237 250 218 274 1.09 (0.96–1.25) 0.20
Wheezing at 4 years of age by atopy
Atopic 33.3 0.0 28.6 62.5 1.30 (0.91–1.86) 0.14
Nonatopic 6.8 6.3 10.4 16.4 1.37 (1.06–1.79) 0.02
Values for p,p′-DDE presented as percentage except eosinophil counts/mL.
a Unadjusted RR, 95% CI, and p-value per each doubling of DDE.
b Wheezing at 4 years of age and in a previous year.
c Geometric mean, RR on having eosinophil > 340 cells/mL, which corresponds to a percentage of total cells > 4% and which occurred in 34% of children.
Table 4 Adjusted RR (95% CI) between DDE in cord serum and wheezing at 4 years of age.
Characteristic All Nonatopic
p,p′-DDEa 1.32 (1.13–1.54) 1.30 (1.05.1.62)
Maternal asthma 2.62 (1.46–4.71) 3.45 (1.18–10.10)
Maternal smoking 1.48 (0.89–2.47) 1.03 (0.51–2.10)
Parity (≥ second child) 1.18 (0.69–2.02) 1.54 (0.74–3.24)
Maternal education
Primary 0.62 (0.26–1.46) 0.36 (0.13–1.04)
Secondary 0.80 (0.32–1.98) 0.37 (0.11–1.19)
High 0.29 (0.08–1.12) 0.17 (0.03–0.89)
Male 2.03 (1.15–3.57) 2.84 (1.21–6.68)
Gestational age (weeks) 0.87 (0.81–0.95) 0.90 (0.82–1.00)
Breast-feeding 0.57 (0.33–0.99) 0.34 (0.17–0.69)
p,p′-DDE in quartile (ng/mL)b
< 0.57 1 1
0.57–1.03 1.00 (0.41–2.43) 1.32 (0.37–4.70)
1.03–1.90 1.62 (0.70–3.74) 2.63 (0.96–7.20)
> 1.90 2.36 (1.19–4.69) 2.49 (1.00–6.19)
a Per each doubling of concentration.
b Adjusted for the variables in the table, except p,p′-DDE.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7883ehp0113-00179116330366ResearchChildren's HealthExhaled Nitric Oxide in Children with Asthma and Short-Term PM2.5 Exposure in Seattle Mar Therese F. 1Jansen Karen 1Shepherd Kristen 2Lumley Thomas 2Larson Timothy V. 3Koenig Jane Q. 11 Department of Environmental Health and Occupational Sciences,2 Department of Biostatistics, and3 Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USAAddress correspondence to J.Q. Koenig, Department of Environmental Health and Occupational Sciences, Box 357234, Room F561a, University of Washington, Seattle, WA 98195-7234 USA. Telephone: (206) 543-2026. Fax: (206) 685-3990. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 8 8 2005 113 12 1791 1794 21 12 2004 8 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The objective of this study was to evaluate associations between short-term (hourly) exposures to particulate matter with aerodynamic diameters < 2.5 μm (PM2.5) and the fractional concentration of nitric oxide in exhaled breath (FeNO) in children with asthma participating in an intensive panel study in Seattle, Washington. The exposure data were collected with tapered element oscillation microbalance (TEOM) PM2.5 monitors operated by the local air agency at three sites in the Seattle area. FeNO is a marker of airway inflammation and is elevated in individuals with asthma. Previously, we reported that offline measurements of FeNO are associated with 24-hr average PM2.5 in a panel of 19 children with asthma in Seattle. In the present study using the same children, we used a polynomial distributed lag model to assess the association between hourly lags in PM2.5 exposure and FeNO levels. Our model controlled for age, ambient NO levels, temperature, relative humidity, and modification by use of inhaled corticosteroids. We found that FeNO was associated with hourly averages of PM2.5 up to 10–12 hr after exposure. The sum of the coefficients for the lag times associated with PM2.5 in the distributed lag model was 7.0 ppm FeNO. The single-lag-model FeNO effect was 6.9 [95% confidence interval (CI), 3.4 to 10.6 ppb] for a 1-hr lag, 6.3 (95% CI, 2.6 to 9.9 ppb ) for a 4-hr lag, and 0.5 (95% CI, −1.1 to 2.1 ppb) for an 8-hr lag. These data provide new information concerning the lag structure between PM2.5 exposure and a respiratory health outcome in children with asthma.
airway inflammationasthmachildrenexhaled nitric oxideparticulate matter less than or equal to 2.5 μmshort-term exposure
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Most studies of relationships between particulate matter (PM) air pollution and health are based on 24-hr PM measurements. This approach has been driven mainly by the availability of 24-hr gravimetric monitors operated by the U.S. Environmental Protection Agency. However, there currently are several continuous PM monitors in use for documenting community exposure, and these data allow investigators to ask questions about very short-term (hourly) lags between health outcomes and PM exposure. It is important to understand the interval between exposure and health event (lag) as fully as possible because this may help our understanding of both the mechanisms underlying the event and the source of the PM.
Nitric oxide levels in airways are suggestive of the degree of airway inflammation and injury (Yates 2001; Bates and Silkoff 2003). The fractional concentration of NO in exhaled breath (FeNO) is easy to measure in exhaled breath and is a noninvasive lung measurement used to diagnose asthma (Jones et al. 2001; Kharitonov and Barnes 2000; Zeidler et al. 2004). FeNO is elevated in subjects with asthma, is elevated during an asthmatic attack (Jones et al. 2001; Silvestri et al. 2001; Yates 2001), and is reduced when subjects with asthma are treated with anti-inflammatory medications such as inhaled corticosteroids (ICS) (Beck-Ripp et al. 2002). Recently, we reported an association between 24-hr average PM with aerodynamic diameters < 2.5 μm (PM2.5) and FeNO in children with asthma participating in a panel study in Seattle, Washington (Koenig et al. 2003). We observed an approximately 4-ppb average increase in FeNO for a 10-μg/m3 increase in PM2.5. Earlier studies also found that community outdoor air was associated with changes in FeNO (Van Amsterdam et al. 1999, 2000). More recently, FeNO has been associated with PM exposure in adults with cardiovascular and respiratory disease in Steubenville, Ohio (Adamkiewicz et al. 2004) and in adults with respiratory disease in Seattle (Jansen et al. 2004). The Steubenville study evaluated short-term exposures using moving-average data to reflect cumulative exposures. They reported associations between cumulative average PM2.5 up to 12 hr before the FeNO measurement (Adamkiewicz et al. 2004).
The objective of this study was to compare short-term (hourly) exposures to PM with FeNO concentrations in children with asthma and to compare these short-term results with the earlier results. Our hypothesis was that short-term lags would show stronger associations with FeNO than would 24-hr average lags. Defining the most likely interval between exposure and FeNO response would be useful for designing future studies.
Materials and Methods
This research was part of an intensive exposure assessment and health effects panel study of susceptible subpopulations in Seattle from 1999 through 2002 (Koenig et al. 2003; Liu et al. 2003). Nineteen children, 6–13 years of age, were recruited from a local asthma and allergy clinic. All had physician-diagnosed asthma and were prescribed asthma medications daily or regularly. Each subject in the panel was asked to participate for a 10-day monitoring session in the winter of 2000–2001 and the spring of 2001. Fourteen children participated in the FeNO study during the winter heating season, and 15 children participated during spring. Ten participated in both seasons. Approximately half of the children were prescribed ICS therapy. The remainder was prescribed only inhaled albuterol as needed.
Exposure data.
Hourly PM2.5 data were collected at three fixed sites within the Seattle area by the local air agency with tapered element oscillating microbalances (TEOMs; Rupprecht and Patashnick Co./Thermo Electron, East Greenbush, NY). Descriptive statistics on covariate measurements are given in Table 1.
The average concentration of PM2.5 from the TEOM monitors for all subjects stratified by season and ICS use are shown in Figure 1. Average PM2.5 concentrations vary with exposure lag. PM2.5 concentrations are higher in the winter sessions compared with spring sessions, with winter peaks occurring in the late evening/early morning hours (FeNO measurements were taken at or about 1600 hr Pacific standard time; see Figure 2). There is little difference in PM2.5 exposure between ICS users and nonusers.
Exhaled NO.
FeNO was collected as described in a previous report (Koenig et al. 2003). Briefly, all children participated for 10 continuous days of air pollution monitoring and health measurements. Exhaled breath was collected in a Mylar balloon at approximately 1600 hr each day using an offline FeNO protocol. Exhaled breath was measured with a chemiluminescent nitrogen oxide analyzer (model 200A; API, San Diego, CA). Children were asked to refrain from eating for 1 hr before the exhaled breath collection. Pulmonary function testing was conducted after the exhaled breath because a deep inspiration may affect FeNO values (Deykin et al. 1998). Subject characteristics and FeNO measurements are presented in Table 2.
Statistical analysis.
We assessed the association between short-term effects of particulate air pollution and FeNO using a polynomial distributed lag (pdl) model for PM2.5 up to 48 hr after exposure. The pdl model allows air pollution effects at many different lags to be estimated in the same model. The model assumes that the air pollution effect varies smoothly with lag, and approximates this smooth variation by a polynomial curve. The pdl model with 3 degrees of freedom is estimated by Poisson regression using a transformed set of three exposure variables that are not highly collinear. The three estimated coefficients specify the polynomial curve, which in turn gives associations at all lags. In addition to estimating the air pollution effect over many lags, the model can be used to estimate the total air pollution effect by summing the estimates at each lag (Schwartz 2000).
Pdl models are used with time-series data where the effects of a regressor are distributed over time. This type of model constrains the coefficients to follow a polynomial that reduces the number of parameters and therefore reduces the effects of collinearity in the lag variables. Similar models have been used to look at the effect of daily lags in air pollution exposure and mortality (Goodman et al. 2004; Schwartz 2000).
Equation 1 describes the model that was used for the analysis. Each pollution variable was modeled as a difference between the daily PM2.5 level and the average exposure of the subject during his or her session because we are primarily interested in a within-subject, within-session effect. This model also included a term to account for the ambient concentrations of NO that could potentially contaminate our FeNO measurements. Koenig et al. (2003) used a similar model to look at the within-subject effects of daily increases in PM2.5 and FeNO. Model estimates were obtained using the linear mixed-effect equations and the generalized least squares (GLS) estimator in Stata (version 6.0; StataCorp, College Station, TX). As a sensitivity analysis, model estimates were also obtained using a generalized estimating equations (GEE) with an exchangeable working correlation matrix and robust standard errors.
where
W is the ambient NO concentration, ids is the PM reading for individual i on day d during session s, is is the mean PM reading for a subject during a session, i is the mean PM reading for a subject during all of their sessions, medi is an indicator variable for medication use (constant for each subject), and RH is relative humidity.
The coefficients for each lag term were obtained using
Results
The results of the polynomial distributed model for the short-term effect of PM2.5 on FeNO in subjects not taking ICS are shown in Figure 3A. Significant increases in FeNO associated with PM2.5 can be observed in the first 11 hr after exposure. There is also some suggestion of an increase in FeNO between 38 and 41 hr after exposure. The overall effect of a prolonged exposure to PM2.5 is obtained by summing up the estimated effects at each time lag. The sum of all the lag coefficients (β) over 48 hr was 7.0 ppb FeNO per 10-μg/m3 increase in PM2.5.
The short-term effects of PM2.5 on FeNO for subjects who were prescribed ICS medications are shown in Figure 3B. In general, we found no association between FeNO and PM2.5 in subjects prescribed ICS. However, a very small association was observed from the 18-hr lag to the 30-hr lag. This small increase in FeNO (ranging from 0.16 to 0.23 ppb per 10-μg/m3 increase in PM2.5) would not be of clinical significance. For ICS users, the overall effect of PM2.5 over 48 hr is a 0.3-ppb increase in FeNO per 10-μg/m3 increase in PM2.5.
The association between FeNO and PM2.5 averaged over 1 hr at various lags was also analyzed in a single-lag, linear mixed-effects regression model. These results are shown in Table 3. With the single-lag model where PM2.5 was averaged over 1 hr, we found that 7.0-ppb and 6.3-ppb increases in FeNO were associated with PM2.5 lagged 1 and 4 hr, respectively, in subjects not taking ICS. No association was found in subjects taking ICS. No associations were found with a PM2.5 exposure 8 hr previous in either group of children (Table 3).
We also tested for the lag structure in these data using a GEE model that controls for autocorrelations in the data (Figure 4). The distributed lag pattern was similar to that with the linear-effects model; however, associations between FeNO and PM2.5 dropped out for the earliest hourly lags (exposures at 1 and 2 hr before breath collection).
Discussion
The objective of this study was to evaluate the temporal relationship between prior exposure to PM2.5 and increases in FeNO in the airways of children with asthma. Using a pdl model, we found that FeNO was associated with hourly averaged PM2.5 exposure up to 10–12 hr before the health measurement in subjects not prescribed ICS. The overall effect was a 7-ppb increase in FeNO associated with a 10-μg/m3 increase in PM2.5 relative to each subject’s mean PM2.5 exposure.
The advantage of using the pdl model is the ability to reduce the collinearity in the individual lags, allowing a better understanding of the relative contribution of individual lags and, in this case, the short-term effect of PM2.5 exposure on FeNO. The similarity in results from the analyses using the linear-effects model with the GLS estimator and those using the GEE model strengthens our confidence in these results (Table 3). It is apparent from Figure 1 that associations between PM2.5 and FeNO during the 48-hr period of analysis were not predicted by the average PM2.5 concentration during that period, but rather by exposures up to 11 hr before FeNO collection. These results are dependent on the pdl model used; different models (e.g., first- and second-degree pdl) may show associations with slightly different time patterns.
Additionally, using a single lag at specific time periods (1, 4, and 8 hr before FeNO collection) for the children not prescribed ICS, we found a 7-ppb increase in FeNO for a 10-μg/m3 increase in PM2.5 exposure 1 hr earlier and a 6.3-ppb increase associated with an 10-μg/m3 increase in PM2.5 4 hr earlier. The estimate of FeNO increase is similar to that seen in the pdl model; however, the multiple-hour curve gives more complete information. The limitation of using a single-lag model is that the estimated PM2.5 effect at each of the lag hours could be confounded by the effect of other lag hours. Our single-lag model was based on 1 hr averaged PM2.5 rather than a running average of PM2.5 for a cumulative exposure effect. Although the pdl model is the preferred model, both the single-lag and the pdl models resulted in similar effect estimates. The results from our study are consistent with those reported by Adamkiewicz et al. (2004), who found increases in FeNO significantly associated with PM2.5 exposures up to 12 hr previously. That study, however, used individual hourly lag models.
The results from our analysis using the third-degree pdl model indicate that the effect of PM2.5 on FeNO is not just immediate but may have an effect up to 11 hr after exposure. Because in our study FeNO was measured at approximately 1600 hr each day, this would indicate that PM2.5 exposure from 0500 hr to 1600 hr (the time of FeNO measurement) is the relevant period of exposure. Using our time line, this would suggest that sources that predominate during daytime hours are most important.
This is one of the first studies to report short-term temporal relationships between PM2.5 and health outcomes in children with asthma. In another short-term study, hourly averages of PM were associated with respiratory symptoms in children with asthma (Delfino et al. 1998). More recently, that group, using personal monitors, reported that associations between PM and lung function derived from 1- or 8-hr PM2.5 averages did not differ from associations based on 24-hr averages (Delfino et al. 2004). These findings add more information about the relationship between PM exposure and respiratory effects and may be useful for clinicians and patients. This information also may be informative for researchers in their experiment design efforts.
The relatively wide range of exposure lags associated with increased FeNO in children with asthma that we observed suggests that more than one mechanism may be underlying changes in respiratory NO induced by air pollution. Rapid responses are associated with nervous system changes through nerve receptors or synaptic mediators, whereas delayed responses are sometimes attributed to up-regulation of gene expression and enzyme synthesis. These actions are compatible with up-regulation of NO, which has several roles in the lung (Deykin and Kharitonov 2003). Coincidentally, a recent study of allergen challenges in subjects with asthma found that FeNO was initially decreased after exposure but increased 48 hr after exposure (Ricciardolo et al. 2003). Perhaps air pollution interactions in the airways differ from those of proteins such as allergens.
In conclusion, in this study we present additional data for the use of lag structure selection in epidemiologic studies of air pollution, an area that has received considerable attention. Future studies using sequential measurements of FeNO will allow us to better identify the sources of and mechanisms underlying this health outcome.
This work was funded by the U.S. Environmental Protection Agency (EPA) (CR82717701), the Northwest Research Center for Particulate Air Pollution and Health (EPA grant CR827355), and National Institute for Environmental Health Sciences grant P30 ES07033.
This report has been subjected to agency review and approved for publication. Mention of trade names or commercial products does not constitute an endorsement or recommendation for use.
Figure 1 Comparison of mean PM2.5 for all subjects stratified by season (A) or ICS medication use (B).
Figure 2 Schematic of real-time and hourly lags (0400 hr to 1600 hr) in PM2.5 relative to FeNO collection.
Figure 3 Change in FeNO per 10-μg/m3 increase in PM2.5 (A) in subjects not prescribed ICS and (B) in subjects prescribed ICS therapy. TEOM readings were averaged from three central sites (Lynnwood, Lake Forest Park, and Kent) for hourly lags from 1 to 48. Model adjusted for temperature, relative humidity, and age. One-hour averaged PM2.5 concentrations ranged from 8.3 μg/m3 at 3-hr lag to 15.2 at 8-hr lag, suggesting that short time-lag periods rather than peak values may determine this health outcome. Error bars indicate 95% confidence intervals.
Figure 4 Change in FeNO per 10-μg/m3 increase in PM2.5 in subjects not prescribed ICS therapy. TEOM readings averaged from three sites using GEE model. Error bars indicate 95% confidence intervals.
Table 1 Summary statistics for daily averages of temperature, relative humidity, and ambient NO.
Minimum Maximum Mean ± SD
Temperature (°F) 33 68.7 44.5 ± 6.5
Relative humidity (%) 55.3 96.5 78.6 ± 10.1
Ambient NO (ppb) 0.003 0.099 0.018 ± 0.023
Table 2 Age and FeNO values stratified by age, sex, and medication use.
FeNO
No. Age (mean ± SD) Minimum Maximum Mean ± SD
Sex
Female 5 11.2 ± 1.3 5 48.1 13.3 ± 6.3
Male 14 8.2 ± 1.7 5.3 79.8 16.2 ± 10.7
Medication use
ICS 9 9.7 ± 1.4 5.3 79.8 12.7 ± 7.7
No ICS 10 8.3 ± 2.4 5 72.1 18.4 ± 11.0
Table 3 Short-term effects of air pollution on FeNO from the linear-effects model.
Metric Medication use Change in FeNO 95% Confidence interval p-Value
1-hr lag No meds 6.99 3.43 to 10.55 0
Meds −0.18 −3.33 to 2.97 0.911
4-hr lag No meds 6.30 2.64 to 9.97 0.001
Meds −0.77 −4.58 to 3.04 0.691
8-hr lag No meds 0.46 −1.18 to 2.11 0.58
Meds 0.40 −1.94 to 2.74 0.736
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References
Adamkiewicz G Ebelt S Syring M Slater J Schwartz J Suh H 2004 Association between air pollution exposure and exhaled nitric oxide in an elderly panel Thorax 58 242 245
Bates CA Silkoff PE 2003 Exhaled nitric oxide in asthma: from bench to bedside J Allergy Clin Immunol 111 256 262 12589342
Beck-Ripp J Griese M Arenz S Koering C Pasqualoni B Bufler P 2002 Changes of exhaled nitric oxide during steroid treatment of childhood asthma Eur Respir J 19 1015 1019 12108850
Delfino R Quintana P Floro J Gastanaga V Samimi B Kleinman M 2004 Associations of FEV1 in asthmatic children with personal and microenvironmental exposure to airborne particulate matter Environ Health Perspect 112 932 941 15175185
Delfino RJ Zeigr RS Seltzer JM Street DH 1998 Symptoms in pediatric asthmatics and air pollution: differences in effects by symptom severity, anti-inflammatory medication use, and particulate averaging time Environ Health Perspect 106 751 761 9799192
Deykin A Halpern O Massro AF Draxen JM Israel E 1998 Expired nitric oxide after bronchoprovocation and repeated spirometry in patients with asthma Am J Respir Crit Care Med 157 769 775 9517589
Deykin A Kharitonov SA 2003. Nitric oxide. In: Asthma and COPD (Barnes P, Drazen J, Rennard S, Thomson N, eds). New York:Academic Press, 307–314.
Goodman PG Dockery DW Clancy L 2004 Cause-specific mortality and the extended effects of particulate pollution and temperature exposure Environ Health Perspect 112 179 185 14754572
Jansen K Koenig JQ Larson TV Fields C Mar TF Stewart J 2004 Nitric oxide in subjects with respiratory disease is associated with PM2.5 and black carbon in Seattle [Abstract] Am J Respir Crit Care Med 169 A282
Jones SL Kittelson J Cowan JO Flannery EM Hancox RJ McLachlan CR 2001 The predictive values of exhaled nitric oxide measurements in assessing changes in asthma control Am J Respir Crit Care Med 164 738 743 11549525
Kharitonov SA Barnes PJ 2000 Clinical aspects of exhaled nitric oxide Eur Respir J 16 781 792 11106225
Koenig JQ Jansen K Mar TF Lumley T Kaufman J Trenga CA 2003 Measurement of offline exhaled nitric oxide in a study of community exposure to air pollution Environ Health Perspect 111 1625 1629 14527842
Liu L-JS Box M Kalman D Kaufman J Koenig JQ Larson T 2003 Exposure assessment of particulate matter for susceptible populations in Seattle, WA Environ Health Perspect 111 909 918 12782491
Ricciardolo FLM Timmers MC Sont JK Folkerts G Sterk PJ 2003 Effect of bradykinin on allergen induced increase in exhaled nitric oxide in asthma Thorax 58 840 845 14514933
Schwartz J 2000 The distributed lag between air pollution and daily death Epidemiology 11 320 326 10784251
Silvestri M Sabatini F Spallarossa D Fregonese L Battistini E Biraghi MG 2001 Exhaled nitric oxide levels in non-allergic and allergic mono- or poly-sensitised children with asthma Thorax 56 857 862 11641510
Van Amsterdam JG Nierkens S Vos SG Opperhuizen A van Lovernen H Steerenberg PA 2000 Exhaled nitric oxide: a novel biomarker of adverse respiratory health effects in epidemiological studies Arch Environ Health 55 418 423 11128880
Van Amsterdam JG Verlaan BPJ van Lovernen H Elzakker BGV Vos SG Opperhuizen A 1999 Air pollution is associated with increased level of exhaled nitric oxide in nonsmoking healthy subjects Arch Environ Health 54 331 335 10501149
Yates DH 2001 Role of exhaled nitric oxide in asthma Immunol Cell Biol 79 178 190 11264714
Zeidler MR Kleerup EC Tashkin DP 2004 Exhaled nitric oxide in the assessment of asthma Curr Opin Pulm Med 101 31 36 14749603
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7588ehp0113-00179516330367ResearchChildren's HealthAssociation of Housing Disrepair Indicators with Cockroach and Rodent Infestations in a Cohort of Pregnant Latina Women and Their Children Bradman Asa 1Chevrier Jonathan 1Tager Ira 1Lipsett Michael 2Sedgwick Jaqueline 3Macher Janet 4Vargas Ana B. 35Cabrera Elvia B. 35Camacho Jose M. 35Weldon Rosana 1Kogut Katherine 1Jewell Nicholas P. 1Eskenazi Brenda 11 Center for Children’s Environmental Health Research, School of Public Health, University of California, Berkeley, California, USA2 School of Medicine, University of California, San Francisco, California, USA3 Clínica de Salud del Valle de Salinas, Salinas, California, USA4 Division of Environmental and Occupational Disease Control, California Department of Health Services, Oakland, California, USA5 Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS), Salinas, California, USAAddress correspondence to A. Bradman, Center for Children’s Environmental Health Research, School of Public Health, UC Berkeley, 2150 Shattuck Ave., Suite 600, Berkeley, CA 94720-7380 USA. Telephone: (510) 643-3023. Fax: (510) 642-9083. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 27 7 2005 113 12 1795 1801 16 9 2004 11 7 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Health burdens associated with poor housing and indoor pest infestations are likely to affect young children in particular, who spend most of their time indoors at home. We completed environmental assessments in 644 homes of pregnant Latina women and their children living in the Salinas Valley, California. High residential densities were common, with 39% of homes housing > 1.5 persons per room. Housing disrepair was also common: 58% of homes had peeling paint, 43% had mold, 25% had water damage, and 11% had rotting wood. Evidence of cockroaches and rodents was present in 60% and 32% of homes, respectively. Compared with representative national survey data from the U.S. Department of Housing and Urban Development, homes in our sample were more likely to have rodents, peeling paint, leaks under sinks, and much higher residential densities. The odds of rodent infestations in homes increased in the presence of peeling paint [odds ratio (OR) 2.1; 95% confidence interval (CI), 1.5–3.1], water damage (OR 1.9; 95% CI, 1.2–2.7), and mold (OR 1.5; 95% CI, 1.0–2.1). The odds of cockroach infestation increased in the presence of peeling paint (OR 3.8; 95% CI, 2.7–5.6), water damage (OR 1.9; 95% CI, 1.2–2.9), or high residential density (OR 2.1; 95% CI, 1.2–3.8). Homes that were less clean than average were more prone to both types of infestations. Pesticides were stored or used in 51% of households, partly to control roach and rodent infestations. These data indicate that adverse housing conditions are common in this community and increase the likelihood of pest infestations and home pesticide use. Interventions to improve housing and promote children’s health and safety in this population are needed.
childrencockroachesenvironmentexposureHispanichome inspectionshousing qualityLatinopesticidespregnantrodentswomen
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The poor housing available to low-income families may be a chief contributor to persistent health disparities in the United States (Bashir 2002; Brugge et al. 2001; Crain et al. 2002; Kinney et al. 2002; Krieger and Higgins 2002; Krieger et al. 2002; Marsh 1982; Rauh et al. 2002; Thiele 2002). Deteriorated housing and its correlates can compromise many aspects of children’s health. For example, families in old or dilapidated homes suffer disproportionately from lead poisoning and from injuries due to household accidents (Bashir 2002; Marsh 1982; Shenassa et al. 2004). Structural deficiencies such as inadequate ventilation can contribute to dampness and mold growth, which cause or exacerbate respiratory morbidity (Bornehag et al. 2001, 2004; Brugge et al. 2001; Institute of Medicine Committee on Damp Indoor Spaces and Health 2004; Institute of Medicine Committee on the Assessment of Asthma and Indoor Air 2000; Peat et al. 1998; Ronmark et al. 1999; Spengler et al. 2004; Williamson et al. 1997). Poor housing conditions have been associated with infestations of rodents and cockroaches (Whyatt et al. 2002), both of which are allergenic, can carry infectious diseases (Baumholtz et al. 1997; Gubler et al. 2001), and can lead to increased use of home pesticides (Whyatt et al. 2002). The health burdens associated with poor housing may be particularly significant for young children, who spend the vast majority of their time inside their homes (California Air Resources Board 1991; Silvers et al. 1994).
To date, reports on housing quality have focused primarily on low-income homes in U.S. inner cities (Brugge et al. 2001; Crain et al. 2002; Kinney et al. 2002; Whyatt et al. 2002). Less attention has been paid to families in agricultural and rural communities. An unpublished report on farmworker housing prepared for the U.S. Department of Agriculture in 1980 identified severe housing shortages and substandard housing nationally (InterAmerica Research Associates, Inc. 1980). This report also documented a trend toward less employer-owned farmworker housing, leaving more farmworkers to compete for housing units on local rental markets. More recently, the Housing Assistance Council (HAC) coordinated a survey of 4,625 farm-worker homes nationwide (HAC 2001). Additionally, a community group conducted a questionnaire-based health and housing survey in the Salinas Valley and agricultural areas of Santa Cruz County, California (Applied Survey Research and the Center for Community Advocacy 2001). These studies document housing shortages, high rates of crowding, deteriorated conditions, and problems with affordability for low-income communities in agricultural areas.
In this study, we documented the housing quality in homes of Latino families with young children living in the Salinas Valley, an agricultural area in Monterey County, California. We investigated the association of housing disrepair indicators with cockroach and rodent infestations, evaluated the association of pest infestations and reported pesticide use, and examined the association of measured cockroach allergen levels and evidence of cockroach infestation to test the validity of our inspection methods.
Methods
Subjects and recruitment.
The Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) is a longitudinal birth cohort study investigating environmental exposures and children’s health in the Salinas Valley, Monterey County, California, an agricultural community (Eskenazi et al. 2003). Study participants were recruited through Clinica de Salud del Valle Salinas and the Natividad Medical Center. These clinics serve a predominantly low-income, Latina clientele. Women entering prenatal care at these facilities between October 1999 and October 2000 were screened for eligibility. Women who were a) at ≤20 weeks gestation, b) qualified to receive poverty-based government health insurance, c) ≥18 years of age, and d) planning to deliver at the Natividad Medical Center, the local county hospital, were invited to participate in the study. Of 1,130 eligible women, 601 (53%) enrolled in the CHAMACOS study. Relative to women who declined enrollment, participants in the CHAMACOS study were more likely to be Mexican-born and Spanish-speaking and to have a household member working in agriculture.
Of the 601 study participants, 511 agreed to a home visit at enrollment (mean 13.4 ± 5.3 weeks gestation). Of these, 371 consented to a second home visit when their children reached approximately 6 months of age (mean 7.3 ± 1.3 months). Thirteen families did not complete an enrollment visit but were visited at 6 months postpartum. Seventy-seven participants did not complete a home visit at either enrollment or 6 months postpartum. Women who did not complete a home visit did not differ demographically from home visit participants. They were also no more or less likely than participants whose homes we visited to report pest infestation during the office-based baseline questionnaire.
For the 371 participants with two home visits, we selected homes for our sample according to the following criteria: If a participant moved between her baseline and 6-month home visit, each of her homes is present in our housing sample as a distinct home. If a participant did not move between visits, we used housing data from her prenatal visit. Our total sample consists of 644 homes inhabited by 524 distinct families. Written informed consent was obtained from all participants in accordance with procedures approved by the Committee for the Protection of Human Subjects at the University of California, Berkeley.
Data collection.
Women were interviewed at enrollment in the study and again when their children were 6 months of age. All assessments were conducted in Spanish or English. Information collected included family demographics, household composition, and frequency of housecleaning. Shortly after each interview, study staff conducted a home inspection. The number of rooms in the household (excluding bathrooms, hallways, closets, or garages), the housing structure (i.e., detached home, duplex, multiunit apartment building), and the level of cleanliness were determined by direct observation. Cleanliness was scored on a three-point scale according to the amount of grease around stoves, the presence of dirty dishes and overflowing trash, and the presence of dust, dirt, and food particles on floors and behind cabinets, appliances, or furniture.
Indicators of home disrepair were determined by direct observation. The presence of mold was determined separately for the kitchen, living room, mother’s sleeping area, and child’s sleeping area. Mold was scored as “minimal” when growth was limited to crevices or small locations, “moderate” when growth covered < 1 m2 of wall space, and “extensive” when growth covered ≥1 m2 of wall space or was very thick in several areas. The most extensive mold growth in any room was used to represent the level of mold in the home. Water damage, leaks under sinks, peeling paint, rotting wood, and improperly vented appliances were coded as present or absent.
We measured wall moisture content in the child’s sleeping area and a central living area in 375 of the homes inspected 6 months after delivery, 130 of which are included in this analysis. Measurements were conducted with a pinless meter calibrated for sheetrock (Model CT100; Professional Equipment, Hauppauge, NY) at a point 45 cm above the floor at the horizontal midpoint of each wall. Additional measurements were conducted in areas that suggested water problems, such as heavy condensation on windows. Homes were coded as “damp” if the moisture level for any wall was ≥17%, the level at which the Monterey County Department of Health (Salinas, CA) recommends replacement of gypsum board.
Inspections for rodent infestations determined the presence or absence of mouse or rat feces, poison, or traps. Inspections for cockroaches determined the presence or absence of live or dead roaches or feces in typical habitats, including under sinks, along cabinet edges, and behind refrigerators. An infestation was considered active if we observed evidence of rodents or cockroaches or the participant reported their presence. We validated our roach infestation classification criteria by comparing cockroach allergen concentrations in a subset of homes with and without roaches.
We also completed a pesticide storage and use inventory for each home. Active ingredients were later confirmed with the California Product/Label Database, an Internet-based database operated by the California Department of Pesticide Registration (California Product/Label Database 2002).
Dust sample collection and analysis.
Cockroach allergen concentrations [Blatella germanica (Bla g1)] were measured in house dust samples collected from a subset of 99 homes during the 6-month home visit. Two dust samples were collected from each home, one from the living area floor and one from the child’s bed or crib mattress. Samples were collected using a vacuum cleaner with a Medivac dust-sampling head (Medivac Healthcare Ltd., London, U.K.). The dust was collected on a 5-to 10-μg nylon screen after passing through a 0.3-mm prefilter. In the field, all samples were kept on ice with a desiccant. For allergen extraction, 50- to 100-mg aliquots of dust were centrifuged with a borate buffer solution (sodium chloride, boric acid, and sodium hydroxide dissolved in filtered deionized water). The samples were vortexed, mixed on an orbital rotator for ≥2 hr, and centrifuged for 25 min at 2,500 rpm and a temperature of 4°C. The supernatant was transferred to a vial and stored at –80°C until analysis. Bla g1 allergens were measured by the IBT Reference Laboratory (Lenexa, KS) using monoclonal-based enzyme immunoassays with a detection limit of 0.60 U/g (Chapman et al. 1987, 1998; Pollart et al. 1991). We used the highest allergen concentration of the two samples to represent the levels in the home.
The 99 homes selected for allergen measurements comprise a nested case–control sample intended to compare the allergen levels in homes of children with and without lower respiratory symptoms. Because the homes in this subsample were not randomly selected, their allergen concentrations may not represent levels in the full CHAMACOS sample. For this article, we compared Bla g1 allergen concentrations in homes with and without cockroach infestations to validate the inspection method.
Data analysis.
We first computed the prevalence of adverse housing conditions. To investigate the association of housing disrepair indicators and pest infestations, we used contingency tables and odds ratios (ORs) to evaluate all two-by-two combinations of pest infestation and housing disrepair variables. Cockroach and rodent infestations were analyzed separately. For these analyses, the presence or absence of a pest infestation was used as the dependent variable. Indicators of housing disrepair were used as independent variables and included moderate or extensive mold or mildew, water damage, peeling paint, rotting wood, leaks under sinks, and, for the subset of homes with wall moisture measurements, moisture levels > 17%. Except for mold and mildew (coded as moderate or severe versus none or minimal), all housing disrepair indicators were coded as either present or absent.
We then developed multivariate logistic regression models using cockroach or rodent infestation as the dependent variables and housing disrepair variables and potential covariates (including building type, household characteristics, and demographic characteristics of the participant) as independent variables. Building type variables compared duplexes, multiunit apartments, and other residences (e.g., garages, trailers) to detached homes. Trailers were grouped separately from detached homes because trailer parks in the Salinas Valley are extremely dense and not comparable with detached homes with yards. Household characteristics included resident density (one or more persons per room versus less than one person per room), urbanicity (Salinas address versus living outside Salinas), household income relative to the year 2000 federal poverty level (scored as follows: poverty level or below = 1, less than 200% of poverty level = 2, 200% of poverty level or greater = 3), and level of cleanliness (more clean vs. less clean). Participants’ demographic characteristics included education level (never attended school = 1, grades 1–6 = 2, grades 7–9 = 3, grades 10–12 = 4, high school diploma or equivalent = 5, technical school = 6, some college = 7, college graduate or more = 8) and number of years in the United States (< 5 years vs. ≥5 years). We used backward-selection logistic regression to systematically evaluate and remove variables that did not significantly contribute to the overall model (p ≥0.10).
We also constructed a housing quality index according to methods used by Whyatt et al. (Whyatt et al. 2002). Each home was assigned an index value based on the total number of housing disrepair indicators present. Our index differed slightly from that of Whyatt and colleagues in that we did not include holes in ceilings or walls or recent loss of utility services, but did include rotting wood. Index values ranged from 0 to 5, with a score of 5 for homes with all five disrepair indicators present (peeling paint, water damage, moderate or extensive mold or mildew, rotting wood, and leaking sinks). We again developed multivariate logistic regression models using pest infestations as the dependent variable and the housing quality index score as the independent variable. To test the hypothesis that the log odds of pest infestation increased linearly with the number of housing disrepair indicators in a home, we used likelihood ratio tests to compare the models using continuous index scores with those containing individual indicator variables for each level (0–5) of disrepair. If the continuous model was not significantly different from the model with indicator variables (p ≥0.05), the linearity hypothesis was accepted.
To investigate whether pest infestations predicted home pesticide use, we constructed logistic regression models with infestation as the independent variable and home pesticide use as the dependent variable. We adjusted for the same covariates considered above.
Finally, to confirm the validity of our methods to identify cockroach infestations, we plotted the cumulative distributions of Bla g1 allergen levels in homes with and without cockroaches and tested the equality of these distributions with the Kolmogorov-Smirnov test. We repeated these tests separately for the homes of children with and without asthma symptoms to ensure that the relationship between cockroach presence and allergen levels was independent of inhabitants’ respiratory health.
Our demographic description of the study population is based on data collected at the first interview. However, all analyses linking demographic characteristics to housing conditions or pest infestations use questionnaire data collected concurrently with the home visit in question. All analyses were conducted using Stata software, version 8.2 for Windows (StataCorp, College Station, TX).
Results
Demographic characteristics.
Table 1 summarizes demographic and household characteristics of the study population. Participants in this study were predominantly Mexican-born (85%), Spanish-speaking (93%) women living in poverty. The mean (± SD) age of participants was 26 ± 5 years of age at enrollment, and approximately half had resided in the United States for < 5 years at the time of enrollment. Most homes (88%) we visited were either detached homes or multiunit apartment buildings, and 69% of participants lived in a home with at least one agricultural worker. Although home ownership status was not assessed at the prenatal or 6-month visits, a subsequent survey revealed that nearly all CHAMACOS participants were renters. The demographic characteristics of the participants have been described in detail in previous papers (Eskenazi et al. 2003, 2004).
Housing quality.
Figure 1 and Table 2 summarize the housing quality characteristics of the 644 homes in this sample. Pest infestations were common, with 60% and 32% of homes containing cockroaches and rodents, respectively. Housing disrepair was also common; 58% of homes had peeling paint, 43% had mold, 25% had water damage, 16% had leaks under sinks, and 11% had rotting wood. Moderate or extensive mold was present in 28% of the sleeping areas used by participating children. High resident density was also very common, with 76% of participants living in homes with > 1 person per room and 39% with ≥1.5 persons/room. As shown in Figure 1, multiple adverse housing conditions were present in the majority of homes in this population, with < 3% of homes having no adverse conditions present.
Housing characteristics and pest infestations.
Unadjusted ORs for each two-by-two combination of housing disrepair indicators, rodent infestation, and cockroach infestation are presented in Table 3. This univariate analysis is analogous to a correlation matrix, providing a measure of the association between the binary housing disrepair and pest infestations variables. Rodent infestation was strongly associated with cockroach infestation, peeling paint, water damage, rotting wood, and mold or mildew. Cockroach infestation was associated with every indicator of disrepair. Adverse housing conditions were strongly associated with each other.
Table 4 presents the final multivariate logistic regression models evaluating associations of housing disrepair with rodent and cockroach infestations. The presence of peeling paint [OR 2.1; 95% confidence interval (CI), 1.5–3.1], water damage (OR 1.9; 95% CI, 1.2–2.7), and moderate or extensive mold (OR 1.4; 95% CI, 1.0–2.1) were associated with increased odds of rodent infestations. Homes that were less clean than average were also associated with an increased odds of rodent infestations (OR 2.2; 95% CI, 1.0–4.7). Households in multiunit apartment buildings were less prone to rodent infestation than were detached homes (OR 0.6; 95% CI, 0.4–0.9).
The presence of peeling paint (OR 3.8; 95% CI, 2.7–5.6) and water damage (OR 1.9; 95% CI, 1.2–2.9) were also associated with increased odds of cockroach infestation. Other indicators of housing disrepair were not associated with cockroach infestation. Homes that were less clean than average had higher odds of cockroach infestation than cleaner homes (OR 3.7; 95% CI, 1.2–11.2). In contrast to the rodent infestation model, higher resident density was also associated with an increased odds of cockroach infestation (OR 2.1; 95% CI, 1.2–3.8). Homes in multiunit apartment buildings were more likely than detached homes to experience cockroach infestations (OR 3.0; 95% CI, 2.1–4.5). Households of recent immigrant women had higher odds of cockroach infestation than did households of women who had spent ≥5 years in the United States (OR 1.6; 95% CI, 1.1–2.4).
Each unit increase in the number of adverse housing conditions in a home was associated with an increased odds of both rodent and cockroach infestations (OR 1.5; 95% CI, 1.3–1.7 for rodents; OR 1.7; 95% CI, 1.5–2.0 for roaches). Based on evaluation of the maximum likelihood ratio, the log odds of rodent infestation increased with the total number of housing problems in a linear fashion (χ;2 = 3.9, df = 4, p = 0.4), whereas the log odds of cockroach infestation did not increase linearly (χ;2 = 16.0, df = 4, p = 0.003) (not shown).
Home pesticides inventory.
Pesticides were stored in 313 (49%) of the 644 homes. Respondents in an additional 14 homes reported having used pesticides that were no longer present in the home; conversely, respondents in 18 homes with stored pesticides reported not having used them. Overall, 309 (48%) households reported home pesticide use. Of the 644 homes in our study, 31% stored pyrethroids, 9% stored piperonyl butoxide, 6% stored carbamates, 5% stored organophosphates, 4% stored hydramethylnon, and 4% stored boric acid. Spray-application pesticides were present in 30% of homes, pellets or powders in 10% of homes, and roach bait stations in 6% of homes. Pesticide gels, bombs, or rodent food imitators were present in < 5% of homes. In the 6 months preceding the visits, professional pesticide applications to control insects had been conducted in 5% of the homes; only two homes were reported to have been professionally treated for rodents in the same timeframe. As expected, insecticide use was more common in homes with cockroach infestations (OR = 2.4; 95% CI, 1.7–3.4) than in homes without such infestations (not shown).
Cockroach allergen concentrations.
Figure 2 compares cockroach (Bla g1) allergen concentrations in a subset of 99 homes with and without identified cockroach infestations. Cockroach allergen concentrations were significantly higher in homes with evidence of infestations than in homes without infestation [median (interquartile range) = 3.0 (< 0.6–16.1) U/g for homes with cockroaches and 1.8 (< 0.6–3.4) U/g for homes without cockroaches; Kolmogorov-Smirnov statistic D = 0.28, p = 0.04], providing an external validation of our observations. The relationship between infestation and elevated cockroach allergen concentration was the same in the homes of children with and without asthma symptoms (data not shown).
Discussion
This investigation is the first population-based cohort study documenting the housing conditions of low-income, Latino families in a U.S. agricultural community. Adverse housing conditions were common in this population. Pest infestations, mold and mildew, water damage, peeling paint, leaks, rotting wood, and high residential density were widespread, with multiple problems occurring in the vast majority of homes. Many of the conditions are markers of building dampness (e.g., water damage), sources of clinically important allergens (e.g., cockroach infestations), or respiratory irritants (e.g., volatile organic compounds generated by mold metabolism). As reviewed in the introduction, building dampness, allergens, and respiratory irritants have been associated with cough, wheeze, and increased asthma symptoms and may be etiologically related to the development of asthma in children. Rodents and cockroaches are also potential carriers of infectious diseases. The level of overcrowding in these homes may pose an additional threat to children’s health, as infectious diseases can spread rapidly among individuals who share close living quarters.
Our findings on housing quality characteristics are consistent with available data for low-income agricultural populations and some urban populations (Crain et al. 2002; Whyatt et al. 2002) (Table 2). Our ability to observe infestations during home inspections may explain the higher prevalence of cockroach and rodent infestations we reported compared with the questionnaire-based survey conducted by the Center for Community Advocacy (CCA) (Table 2) (Applied Survey Research and the Center for Community Advocacy 2001). It is also possible that our methods overestimated the prevalence of live cockroach infestations because the presence of dead roaches or feces, which we defined as evidence of a current infestation, may reflect past infestations that were no longer active. However, our finding that cockroach allergen levels were higher in homes with evidence of cockroaches adds validity to our findings. The higher frequency of leaks reported in the CCA survey is likely due to their inclusion of questions about leaking faucets, which we did not record. The high prevalence of mold infestations in this study may be related to the damp, cool winters in this region, poor building quality, and household crowding, which increases ambient moisture from respiration, cooking, and bathing. Compared with national data for Hispanic households, peeling paint, rodent infestations, and leaks under sinks were more common in our sample (Table 2). Especially striking in the CHAMA-COS population was the much higher resident density; 39% of homes had ≥1.5 people per room. By comparison, only 3% of Hispanic households and 0.5% of all U.S. households experience this level of crowding (U.S. Census Bureau 2002).
Pest infestations in the homes we inspected were consistently associated with housing disrepair indicators. Our findings are very similar to the 30–60% increase in the odds of pest infestation reported to be associated with each additional adverse housing condition in New York (Whyatt et al. 2002). However, the use of a simple, linear housing disrepair index may not be appropriate for statistical analyses relating housing conditions to pest infestations. In our data, the odds of cockroach infestation did not increase linearly with the number of adverse housing conditions. This nonlinearity underscores the need to assess the shape of the relationship between environmental index scores and epidemiologic outcomes, particularly when the scale is previously untested.
Peeling paint and water damage were each independently associated with pest infestations. Whereas water damage may indicate a source of water for pests, it is unlikely that peeling paint “causes” infestation. Rather, these conditions, both of which were associated with other housing disrepair indicators, may simply be indicators of building conditions that create favorable habitats for pests. The finding that cockroaches are more common in multiunit apartment buildings is consistent with other studies (Chew et al. 1998; Kitch et al. 2000; Leaderer et al. 2002) and is not surprising, given that each infested apartment in the building is a potential source of infestation for adjacent households. The finding that rodent infestations are less common in multiunit apartments than in detached homes may be due to the number of stories between the housing unit and ground level. Although most detached homes in our study are single story, potentially offering multiple routes of ingress to ground-dwelling rodents, the apartment buildings we visited are generally one to three stories. It is possible that the distance from ground level offers protection to residents of second- and third-story units. Our finding that less-clean households are more prone to pest infestation reflects the fact that cleaner homes offer pests fewer sources of food and water (e.g., crumbs and spills on the floor). Although the vast majority of study participants frequently clean their homes, their ability to maintain better housekeeping was compromised by poor building conditions and crowded households.
About half of the families we visited used pesticides to control pests in their homes. Insecticides were used in much greater quantities than rodenticides. The high proportion of pyrethroid insecticides likely reflects industry efforts to substitute pyrethroids for organophosphate pesticides, which were recently banned for home use by the U.S. Environmental Protection Agency (EPA) (U.S. EPA 2000a, 2001). The CHAMACOS families most commonly used insecticide sprays and powders, which have a higher exposure risk compared with bait stations and gels. Hydramethylnon roach gels, which can be strategically placed out of children’s reach, were used in only a small minority of households.
There are several limitations to the analyses presented here. Study participants differed somewhat from families that declined enrollment. Thus, our findings may not be generalizable to all low-income families residing in the Salinas Valley. However, the consistency of our findings with a previous questionnaire-based survey (Applied Survey Research and the Center for Community Advocacy 2001) suggests that the housing problems we have identified represent typical conditions for low-income families in this community. Another limitation is that the associations we found between housing disrepair and pest infestations do not necessarily reflect causal relationships. As noted above, housing disrepair indicators may be proxies for the overall condition of the building and not specific building characteristics that cause pest infestations. Additionally, the population was uniformly low income. Pest infestations may be related to multiple social and physical factors that could confound the association between housing characteristics and pest infestations. For example, crowded, low-income neighborhoods in our study area may receive fewer public services such as neighborhood pest control and housing code enforcement.
Access to adequate housing is considered a basic human right (United Nations 2005). Our findings indicate that housing is inadequate in this population. Interventions to improve housing quality should focus both on individual-level behaviors and policies to improve access to quality housing. Successful interventions to reduce cockroach infestations have used integrated pest management techniques (Brenner et al. 2003). Additional research is needed to identify the best combination of physical interventions, least-toxic pest control measures, and educational strategies that are sustainable in this population. These interventions will need to be low- or no-cost and accessible to a Spanish-speaking population. Given the high use of pesticide sprays and powders in our population, a first step could be the promotion of baited roach gels, which effectively control roach populations but are less likely to expose children (Brenner et al. 2003; Schechner 2004). Other successful strategies include programs to strengthen renters’ ability to negotiate housing improvements with landlords (Krieger and Higgins 2002).
We recognize that many factors, including overcrowding and deteriorated building conditions, are beyond the control of individual, low-income families. At the county and state levels, land use and housing policies should support construction of high-quality, affordable housing. Additionally, programs to improve housing conditions should be strengthened, including increased inspections.
Our findings have several implications for national housing policy. Although young children spend most of their time inside their homes, housing quality is not currently included in the children’s environmental quality indicators tracked by the U.S. EPA (U.S. EPA 2000b). National housing quality data are currently compiled by the U.S. Department of Housing and Urban Development (HUD) (U.S. Census Bureau 2003) and could be incorporated into the U.S. EPA tracking program. Recently, Healthy People 2010 has established specific goals related to housing quality, including reducing indoor allergen levels and decreasing the proportion of families that live in substandard housing (U.S. Department of Health and Human Services 2000). The HUD Healthy Homes Initiative, created in 1997, is developing programs to support these goals. We suggest that progress on the Healthy People 2010 housing quality objectives should be monitored by distinct regions and populations to ensure that the housing quality of vulnerable groups, such as those living in low-income agricultural regions, are not averaged into larger populations with fewer problems. Given that an overarching goal of U.S. federal health and environmental agencies is to reduce health disparities (U.S. Department of Health and Human Services 2000), efforts to improve housing should be prioritized as a children’s environmental health concern with substantial opportunities for success.
We thank S. Rimando of the Monterey County Health Department for home inspection training and the anonymous reviewers for constructive comments on this manuscript. We especially thank the families that participated in this study.
This research was jointly funded by U.S. Environmental Protection Agency grant R82679-01-0 and National Institute of Environmental Health Sciences grant PO1ES09605-02.
This research has not been subjected to agency review and does not necessarily reflect the views of the funding agencies. No official endorsement should be inferred.
Figure 1 Percentage of homes with multiple adverse housing conditions within the CHAMACOS cohort.
Figure 2 Cumulative distribution of cockroach allergen levels for CHAMACOS homes with and without identified cockroach infestation (excludes nine values > 50 U/g in cockroach-infested homes).
Table 1 Demographic characteristics of CHAMACOS families who participated in home visits at enrollment or 6 months postpartum (n = 524).a
Characteristic No. (%)
Mother’s country of birth
Mexico 445 (84.9)
United States 65 (12.4)
Other 11 (2.1)
Not reported 3 (0.6)
Years mother has resided in United Statesb
< 5 250 (47.7)
≥5 274 (52.3)
Mother’s highest level of education
Some elementary school (grades 1–6) or less 226 (43.1)
Some secondary school (grades 7–12) 191 (36.5)
High school graduate or equivalent 59 (11.3)
Some education beyond high school 48 (9.2)
Language spoken at home
Spanish 462 (88.2)
Spanish and English 24 (4.6)
English 29 (5.5)
Not reported 9 (1.7)
Family income relative to federal poverty levelc
≤Poverty level 302 (57.6)
> Poverty level but < 200% poverty level 170 (32.4)
≥200% poverty level 21 (4.0)
Not reported 31 (5.9)
Housing typed
Detached home 275 (42.7)
Duplex (two apartments) 33 (5.1)
Multiunit apartment building (three or more apartments) 290 (45.0)
Other (e.g., garage, trailer) 45 (7.0)
Not reported 1 (0.2)
No. of household members
1–3 73 (13.9)
4–6 220 (42.0)
≥7 225 (42.9)
Not reported 6 (1.2)
Agricultural workers in home
0 140 (26.7)
1–3 273 (52.1)
≥4 88 (16.8)
Not reported 23 (4.4)
Frequency of housecleaninge
Daily 443 (84.5)
Several times per week 73 (13.9)
Once per week to once every 2 weeks 6 (1.2)
Not reported 2 (0.4)
Level of cleanliness in home
More clean 492 (93.9)
Less clean 30 (5.8)
Not rated 2 (0.4)
a Demographic characteristics reported for individual families that permitted home visits either at enrollment or 6 months postpartum (n = 524). These distributions are nearly identical to household characteristics for the total sample of 644 distinct homes that includes 131 movers (see text).
b Mother’s years in United States at time of entry into CHAMACOS project.
c Families’ poverty levels were calculated using the U.S. Department of Health and Human Services thresholds for the year 2000. A family of four with an annual income of ≤$17,050 was considered to be at or below the poverty level; the same family earning between $17,051 and $34,100 is within 200% of the poverty level.
d Building type for 644 distinct homes that were inspected.
e Defined as frequency with which the floor most often cleaned is mopped or vacuumed.
Table 2 Adverse housing conditions (%) in the CHAMACOS cohort and other populations.
Home characteristic CHAMACOS (n = 644) Local Farmworker Surveya (n = 780) NYC Cohortb (n = 316) HAC Surveyc (n = 4,625) Hispanic U.S.d (n = 9,814) All U.S.d (n = 106,261)
Rodents 32 18 53 19c 11 8
Cockroaches 60 48 66 19c — —
Pesticides stored in home 49 — 85 — — —
Peeling paint 58 33 42 29 4e 3e
Leak under sink 16 34a 22 — 5f 4f
Gas stove without functional ventg 35 — — — — —
Water damage 25 — 21 29 — —
Rotting wood 11 — — — — —
Moderate or extensive mold anywhere in home 43 — 17 — — —
Moderate or extensive mold in child’s sleeping areah 28 — — — — —
Wall moisture > 17%i 26 — — — — —
Density (persons/room)
≤0.5 2 — — — 42 70
0.51–1.00 22 — — — 45 28
1.01–1.50 37 — — 74.2 (> 1.0)j 10 2
≥1.51 39 — — —j 3 0.5
—, data not available.
a Data from Applied Survey Research (2001): questionnaire-based; data for leaks include faucets.
b Data from Whyatt et al. (2002): questionnaire-based; pregnant African-American and Dominican women.
c Data from HAC (2001): 19% is the proportion of homes with unsanitary conditions, including rodent and insects.
d Department of Housing and Urban Development (HUD 2001) survey of occupied U.S. homes: questionnaire-based.
e HUD data are for peeling paint and broken plaster.
f HUD data are for plumbing leaks anywhere in house.
g Includes gas stoves without vents and or with nonfunctioning vents.
h Only applicable at 6-month visit (n = 133);
i Measured in 130 homes at 6-month visit; the Monterey County Health Department suggests sheetrock replacement if moisture > 17%.
j Proportion of units with children where density exceeded 1 person per room.
Table 3 OR matrix showing the interrelationships of housing disrepair indicators and pest infestationsa (n = 619–644b).
Rodents Cockroaches Peeling paint Water damage Rotting wood Mold Leak under sink
Cockroaches 3.4**
Peeling paint 2.4** 4.2**
Water damage 2.5** 2.2** 2.1**
Rotting wood 2.2** 2.2** 6.0** 8.4**
Mold 2.0** 1.7** 1.9** 6.4** 4.3**
Leak under sink 1.4 2.1** 2.2** 4.0** 7.5** 2.2**
High density 1.1 2.7** 2.1** 2.5* 1.2 1.9* 1.1
a All variables are binary, with high density defined as > 1 person per room. ORs provide a measure of the association between the variables. We used this measure in lieu of Pearson or Spearman correlation coefficients, which are not applicable to binary variables.
b Number ranges from 619 to 644 depending on the number of missing values.
* p < 0.05.
** p < 0.01.
Table 4 Association of housing disrepair indicators with rodent and cockroach infestations: results of logistic regression models [OR (95% CI)].a,b
Home characteristic Rodent infestation (n = 640) Roach infestation (n = 629)
Peeling paint
No 1.0 1.0
Yes 2.1 (1.5–3.1) 3.8 (2.7–5.6)
Water damage
No 1.0 1.0
Yes 1.9 (1.2–2.7) 1.9 (1.2–2.9)
Mold
None or minimal 1.0 —c
Moderate or extensive 1.5 (1.0–2.1)
Resident density
< 1 person/room —c 1.0
≥ 1 person/room 2.1 (1.2–3.8)
Housing type
Detached home 1.0 1.0
Duplex 0.9 (0.4–2.0) 0.9 (0.4–2.0)
Multiunit buildingd 0.6 (0.4–0.9) 3.0 (2.1–4.5)
Othere 0.9 (0.4–1.8) 0.9 (0.4–1.8)
Level of cleanliness in home
More clean 1.0 1.0
Less clean 2.2 (1.0–4.7) 3.7 (1.2–11.2)
Years in United States
≥5 —c 1.0
< 5 1.6 (1.1–2.4)
a See “Methods” for definition of rodent or cockroach infestation.
b Covariates considered as confounders and found insignificant for rodent and cockroach infestations included maternal education level, household income, and urbanicity.
c Resident density and years in United States were not associated with rodent infestation, and mold was not associated with cockroach infestations; these variables were not included in final models for these infestations.
d Apartment building with ≥3 units.
e Includes mobile homes, converted garages, a camp in the fields, and a home inside a business. Does not include detached homes, which serve as the reference group. See “Methods” for justification.
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Whyatt RM Camann DE Kinney PL Reyes A Ramirez J Dietrich J 2002 Residential pesticide use during pregnancy among a cohort of urban minority women Environ Health Perspect 110 507 514 12003754
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7894ehp0113-00180216330368ResearchChildren's HealthOrganophosphate Urinary Metabolite Levels during Pregnancy and after Delivery in Women Living in an Agricultural Community Bradman Asa 1Eskenazi Brenda 1Barr Dana B. 2Bravo Roberto 2Castorina Rosemary 1Chevrier Jonathan 1Kogut Katherine 1Harnly Martha E. 3McKone Thomas E. 141 Center for Children’s Environmental Health Research, School of Public Health, University of California, Berkeley, California, USA2 National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA3 Environmental Health Investigations Branch, California Department of Health Services, Oakland, California, USA4 Lawrence Berkeley National Laboratory and University of California, Berkeley, California, USAAddress correspondence to A. Bradman, Center for Children’s Environmental Health Research/CHAMACOS, School of Public Health, University of California, Berkeley, 2150 Shattuck Ave., Suite 600, Berkeley, CA 94720-7380 USA. Telephone: (510) 643-3023. Fax: (510) 642-9083. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 18 7 2005 113 12 1802 1807 23 12 2004 18 7 2000 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Little information has been published about pesticide exposures experienced by pregnant women. We measured six dialkyl phosphate (DAP) urinary metabolites of organophosphate (OP) pesticides in 600 pregnant, low-income women living in the Salinas Valley, California, an agricultural area. A total of 28% were employed as farm fieldworkers during pregnancy, and 81% had at least one household member who worked in agriculture. Samples were collected twice during pregnancy (mean = 13 and 26 weeks’ gestation, respectively) and just after delivery (mean = 9 days). As in other studies, dimethyldithiophosphate levels were higher than those of other urinary OP metabolites. Total DAP metabolite levels in samples collected after delivery were higher than in samples collected during pregnancy. Median metabolite levels at the first and second prenatal sampling points and at the postpartum collection were 102.8, 106.8, and 227.2 nmol/L, respectively. Both prenatal and postpartum metabolite levels were higher in these Salinas Valley women than in a sample of women of childbearing age in the general U.S. population (National Health and Nutrition Examination Survey), although the deviation from U.S. reference levels was most pronounced after delivery. Higher DAP metabolite levels in the immediate postpartum period may have implications for estimating dose during pregnancy and for exposure during lactation.
exposureorganophosphatepesticidespregnancyprenatalurinary metaboliteswomen
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Approximately 340 million kilograms of agricultural pesticide active ingredient is used annually in the United States (Donaldson et al. 2002), and 85% of U.S. households store at least one pesticide for home use (Adgate et al. 2000; Whitmore et al. 1992). In 1993, the National Resource Council raised concerns that high levels of environmental pesticide exposure could compromise the health of U.S. children (National Research Council 1993). The Food Quality Protection Act of 1996, to address these concerns, mandates that the U.S. Environmental Protection Agency limit the amount and type of pesticides on food to levels deemed safe for children. In response to this legislation, several studies have measured the extent of pesticide exposure among the general public. Recent biologic monitoring studies indicate that pesticide exposures are widespread in the U.S. population, including children [Adgate et al. 2001; Barr et al. 2004; Centers for Disease Control and Prevention (CDC) 2001; Curl et al. 2003; Fenske et al. 2000; Koch et al. 2002; Loewenherz et al. 1997; Lu et al. 2000; O’Rourke et al. 2000; Shalat et al. 2003].
Few studies to date have focused specifically on exposure of children in utero. Those that have, however, indicate that pregnant women in the United States experience frequent exposures to pesticides (Berkowitz et al. 2003; CDC 2004; Eskenazi et al. 2004; Perera et al. 2003; Whyatt and Barr 2001; Whyatt et al. 2003). In a sample of 386 pregnant New York City women, Berkowitz et al. (2003) reported detectable urinary metabolites for pyrethroids, pentachlorophenol, and chlorpyrifos in 95, 94, and 80% of study participants, respectively. Whyatt et al. (2003) and Perera et al. (2003) have detected diazinon and chlorpyrifos in the air and dust of New York City homes and in the blood samples of pregnant women residing within them. Finally, studies have found metabolites for organophosphates (OPs), pentachlorophenol, naphthalene, ortho-phenylphenol, and several other pesticides in amniotic fluid (Bradman et al. 2003) and infant meconium (Whyatt and Barr 2001). Overall, these studies indicate that detectable pesticide exposures are occurring among pregnant women and their fetuses.
In the present study, we report OP metabolite levels in urine samples collected during and just after pregnancy from a low-income, primarily Latina cohort of women residing in an agricultural region of California.
Materials and Methods
Study location characteristics.
The Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) is a community/university partnership investigating environmental allergen, pesticide, and other toxicant exposures experienced by women and children in Salinas Valley, California, an agricultural area. In 2001, approximately 240,000 kg of OP pesticide active ingredient were applied in this area, a level typical of recent years (California Department of Pesticide Regulation 2001). Of these pesticides, 42% were dimethyl OP pesticides, 38% were diethyl OP pesticides, and 20% did not devolve into a dialkyl phosphate (DAP) metabolite. In addition, approximately 5% of study participants reported home use of OP pesticides, although > 40% used other classes of pesticides in the home (Bradman A, unpublished data).
Study population.
Between September 1999 and November 2000, 601 pregnant women were enrolled in the CHAMACOS birth cohort study. Women were contacted at six local prenatal clinics. Women were eligible to participate in this study if they were ≤20 weeks’ gestation at the time of enrollment, were ≥18 years of age, were qualified to receive poverty-based government health insurance, and planned to continue receiving prenatal care at a participating health center. Written informed consent was obtained from all participants in accordance with procedures approved by the University of California Berkeley Committee for the Protection of Human Subjects. Detailed descriptions of the Salinas Valley study area and the CHAMACOS study population have been published previously (Eskenazi et al. 2003).
Interviews.
Participants’ demographic, health, and household information was collected through personal interviews. Interviews were conducted in English or Spanish by bilingual, bicultural study staff. A baseline interview occurred shortly after enrollment in the study, generally at 13 weeks’ gestation [mean (± SD) = 13.4 ± 5.2 weeks’ gestation]. Follow-up interviews occurred at approximately 26 weeks’ gestation (mean ± SD = 25.9 ± 2.6 weeks’ gestation) and after delivery (mean ± SD = 8.8 ± 17.9 days). Urine samples were collected at each interview.
Gestational age at urine collection was calculated for most women using the clinical estimate of gestational age at birth noted in the medical record. For women who miscarried or dropped from the study before delivery, gestational age was calculated using the reported date of last menstrual period and, when possible, verified by ultrasound (Eskenazi et al. 2004).
Urine collection and analysis.
Spot urine samples were collected according to the procedures outlined by the CDC for use in the National Health and Nutrition Examination Survey (NHANES) 1999–2000 (CDC 2003). Women voided into a sterile urine cup in bathroom facilities at our field office or in the CHAMACOS mobile clinic. Specimens were aliquoted into precleaned glass containers with Teflon-lined caps, bar coded, and stored at –80°C until shipment. Samples were shipped on dry ice to the CDC and stored at –70°C until analysis.
Pesticide and creatinine measurement.
We measured six nonspecific urinary OP metabolites, including three dimethyl phosphates, dimethylphosphate (DMP), dimethyldithiophosphate (DMDTP), and dimethylthiophosphate (DMTP); and three diethyl phosphates, diethylphosphate (DEP), diethyldithiophosphate (DEDTP), and diethylthiophosphate (DETP). Urine specimens were codistilled with acetonitrile. The DAP metabolites were derivatized to their chloropropyl esters. The concentrated extracts were then analyzed by isotope dilution gas chromatography–tandem mass spectrometry (Bravo et al. 2002), which is widely regarded as the definitive technique for trace analysis with DAP metabolite detection limits of ≤1 ppb (Barr et al. 1999; Shealy et al. 1996). Creatinine concentrations in urine were determined using a commercially available diagnostic enzyme method (Vitros CREA slides, Ortho Clinical Diagnostics, Raritan, NJ).
Laboratory quality control (QC) included repeat analysis of two in-house urine pools enriched with known amounts of pesticide residues whose target values and confidence limits were previously determined (Westgard 2003). Detection limits ranged from 0.05 μg/L for DEDTP to 1.2 μg/L for DMP. A total of 135 laboratory and 121 (blind) field QC samples were analyzed, representing 16% of total samples. Mean relative recoveries for six metabolites in laboratory QC samples ranged from 98 to 105% [coefficients of variation (CVs) ranged from 11 to 15%]. Average recovery of total DAP metabolites in field spikes ranged from 92 to 103% (CVs ranged from 4 to 9%). The mean level of DAP metabolites in 32 blank field samples was < 1 μg/L.
We assigned an imputed value of the limit of detection (LOD)/√2 to levels below the detection limit (Barr et al. 1999; Hornung and Reed 1990). Because many OP pesticides devolve to more than one metabolite in their class (diethyl or dimethyl phosphates), quantities were converted to molar concentrations (nanomoles per liter) and summed to obtain the total concentrations of the diethyl and dimethyl phosphates.
The creatinine concentration in each urine sample was reported in milligrams of creatinine per deciliter of urine. One sample with missing creatinine concentration data and three urinary creatinine levels that implied unreasonably high fluid consumption rates (< 10 mg/dL) were excluded from statistical analyses.
Of the 601 women who enrolled in the study, adequate urine samples with credible creatinine levels were collected from 590 (98%) women at the first prenatal sampling point, 498 (83%) women at the second prenatal sampling point, and 489 (81%) women after delivery.
Data analysis.
The primary objective of this analysis was to present descriptive information about urinary OP metabolite levels during and soon after pregnancy. Geometric means and percentiles for individual and total dimethyl phosphate metabolites, individual and total diethyl phosphate metabolites, and total DAP metabolites were calculated for each sampling time point.
Total DAP metabolite concentrations were log-normally distributed, whereas the dimethyl and diethyl phosphate molar concentrations were not. Thus, we used the Spearman’s rank test to assess the correlation of metabolites (untransformed) between the different sampling points. We used general estimating equations (STATA GEE population-averaged model; StataCorp, College Station, TX) to determine the within- and between-individual variability for the 485 women with two measurements during pregnancy. We used paired t-tests and binary tests of proportions to compare each prenatal urinary metabolite measurement with the postpartum measurement. In addition, we calculated each participant’s ratio of DEP to DETP + DEDTP and DMP to DMTP + DMDTP metabolites at the three urine collection time points and then calculated the mean and median of these ratios. Finally, we used the Kolgomorov-Smirnov test of equality of distributions to compare the pregnancy and postpartum metabolite levels. We also used the Kolgomorov-Smirnov test and quantile regression to compare levels for the CHAMACOS cohort with levels measured at the same CDC laboratory for women participating in NHANES, a cross-sectional study of the U.S. population (CDC 2004). The 1999–2000 NHANES sample included 96 pregnant women and 271 nonpregnant women between 18 and 40 years of age (Barr et al. 2004; CDC 2004). We applied no sample weights to the NHANES data.
For statistical analyses, we present results that are not adjusted for creatinine levels. All analyses were repeated with creatinine-adjusted values to confirm our results. Analyses were conducted using STATA software (version 8.2; StataCorp).
Results
Demographic characteristics.
Eighty-five percent of CHAMACOS study participants were born in Mexico, with 48% having spent < 5 years in the United States. The mean age of participating women was 26 years, and nearly all lived within 200% of the federal poverty level. Twenty-eight percent of women were employed as farm workers at some point during their pregnancy, and 81% percent shared a home with at least one agricultural worker during their pregnancy. Additional demographic information on this population is presented in Eskenazi et al. (2004).
Creatinine.
Creatinine levels consistently decreased from the first prenatal sample through postpartum, with median levels of 98.3 mg/dL [interquartile range (IQR) = 51.6–139.3], 90.6 mg/dL (IQR = 60.8–130.7), and 85.2 mg/dL (IQR = 51.6–122.4) at the first prenatal, second prenatal, and postpartum sampling times, respectively. This trend is consistent with medical reference data that indicate lower creatinine excretion in later trimesters and early postpartum (Becker et al. 1992; Davison et al. 1980; Davison and Noble 1981).
Urinary metabolite concentration data.
Tables 1 and 2 present the unadjusted and the creatinine-adjusted geometric means, percentiles, and ranges for the six DAP metabolites and total diethyl and dimethyl phosphate molar concentrations at each of the three sampling points. Postpartum urinary metabolite levels were consistently higher than the prenatal samples, with median unadjusted total DAP metabolite levels of 102.8 nmol/L (IQR = 37.7–277.5), 106.8 nmol/L (IQR = 58.1–223.9), and 227.2 nmol/L (IQR = 96.0–554.6) and median creatinine-adjusted total DAP metabolite levels of 112.7 nmol/L (IQR = 56.3–316.0), 126.4 nmol/L (IQR = 68.8–237.8), and 283.5 nmol/L (IQR = 109.8–730.3) at the first prenatal, second prenatal, and postpartum sampling times, respectively. Detection frequencies for dimethyl, diethyl, and total DAP metabolites were higher at the second prenatal and postpartum sampling points than at the first prenatal sampling point. Dimethyl phosphate metabolite levels were higher than diethyl metabolites, a finding consistent with previous study results in other populations (Barr et al. 2004; CDC 2001
CDC 2003; Fenske et al. 2000).
As presented in Tables 1 and 2, postpartum diethyl, dimethyl, and DAP metabolite levels were higher across the entire distribution compared with the prenatal sampling points. Figure 1 presents a scatter plot of the pregnancy and postpartum total urinary DAP metabolite levels by days before and after delivery. Metabolite levels vary widely both before and after birth, although there is greater variability immediately after birth. Paired t-tests for total DAP levels found significant mean differences between prenatal and postpartum measures. The mean difference in metabolite levels between the first and second prenatal sample was 109.0 nmol/L (p = 0.98), but the mean difference between the first prenatal and postpartum samples was 423.4 nmol/L (p < 0.001), and between the second prenatal and postpartum sampling points was 566.2 nmol/L (p < 0.001). The within-individual variability across sampling points was about twice the between-individual variability (SD = 1.09 and 0.40, respectively).
The proportions of women with urinary metabolite levels increasing between the first and second prenatal samples, first prenatal sample and postpartum, and second prenatal sample and postpartum were 53, 65, and 66%, respectively. Thus, there was an approximately even chance of either decreasing or increasing metabolite levels during pregnancy, whereas a significantly higher proportion of women had higher levels after delivery compared with prenatal levels (binary test of proportions p < 0.001). Identical trends were found within each season, indicating that the pattern is pregnancy related and not a temporal trend (data not shown). Creatinine adjustment enhanced the difference between prenatal and postpartum levels [mean difference = 791.3 and 970.4 nmol/g (p < 0.001) for the first prenatal vs. postpartum samples and second prenatal vs. postpartum samples, respectively].
We found a clear upward shift in the ratio of the diethyl phosphate metabolite DEP compared with the diethyl thiophosphate metabolites (DETP, DEDTP) between the prenatal and postpartum samples. The average ratios were 2.5 (SD = 6.0), 1.2 (SD = 3.5), and 8.8 (SD = 20.7) for the prenatal 1, prenatal 2, and postpartum samples, respectively. Similarly, the median ratios were 0.9, 0.2, and 3.2 for the prenatal 1, prenatal 2, and postpartum samples, respectively. The ratios of DMP to DMTP + DMDTP, however, remained relatively constant across the sampling time points. The median DMP:(DMTP + DMDTP) ratios were 0.3, 0.4, and 0.3 for the prenatal 1, prenatal 2, and postpartum samples, respectively.
Spearman correlations between the three sampling time points for the diethyl phosphate metabolites ranged from 0.03 to 0.07 (p = 0.4), for dimethyl phosphate metabolites ranged from 0.05 to 0.10 (p < 0.05–0.3), and for the total DAP metabolites ranged from 0.04 to 0.13 (p < 0.01–0.3). Overall, the correlation analyses indicated little or no correlation between the different sampling times. Within each sampling time cross-section (prenatal sample 1, prenatal sample 2, and postpartum), the correlations of dimethyl and diethyl phosphate metabolites were 0.43 (p < 0.01), 0.30 (p < 0.01), and 0.29 (p < 0.01), respectively. This finding suggests that some participants were exposed simultaneously to dimethyl and diethyl OP pesticides.
Comparison with NHANES data.
Tables 3 and 4 present unadjusted and creatinine-adjusted geometric means, percentiles, and ranges for the total diethyl and dimethyl phosphate molar concentrations among pregnant and nonpregnant women from the NHANES population. Within the NHANES sample, DAP metabolite levels were not significantly different between pregnant and nonpregnant women or between Mexican-American and non-Hispanic women (data not shown) (Barr et al. 2004). Thus, Figure 2 shows data for CHAMACOS women and all women between 18 and 40 years of age in the NHANES sample.
The distribution of total DAP metabolite levels for CHAMACOS women’s first and second prenatal visits was significantly higher than NHANES levels (Kolgomorov-Smirnov D = 0.16, p < 0.001, and D = 0.18, p < 0.001, respectively). When we further examined the distribution using quantile regression, we found that, for the first prenatal samples, CHAMACOS total DAP metabolite levels were higher than NHANES levels at the 75th and 90th percentiles (p < 0.04 and p = 0.001, respectively); for the second prenatal samples, however, CHAMACOS total DAP metabolite levels were higher than NHANES levels at the 25th and 50th percentiles (p < 0.001 and p < 0.05, respectively) (Figure 2). The distributions of dimethyl and diethyl metabolite levels for CHAMACOS women were significantly higher than the NHANES levels in the first (dimethyl: D = 0.24, p < 0.001; diethyl: D = 0.28, p < 0.001) and second (dimethyl: D = 0.19, p < 0.001; diethyl: D = 0.24, p < 0.001) prenatal visits. Again using quantile regression, we found that dimethyl metabolite levels were consistently higher across all quartiles at the first prenatal visit (p ≤ 0.05 at 25th, 50th, and 75th percentiles; p < 0.01 at 90th percentile), but only significantly higher for the 25th and 50th percentiles at the second prenatal visit (p < 0.001 and p < 0.01, respectively). Conversely, diethyl metabolite levels were significantly higher than NHANES values at only the 25th percentile for the first prenatal visit (p < 0.001), but were significantly higher at all quartiles for the second prenatal visit (p ≤ 0.001 at 25th, 50th, 75th, and 90th percentiles).
In the postpartum period, total DAP, dimethyl phosphate, and diethyl phosphate metabolite levels from the CHAMACOS women were higher than levels for NHANES women 18–40 years of age across the distribution (D = 0.31, p < 0.001; D = 0.30, p < 0.001; and D = 0.26, p < 0.01 for total DAP, dimethyl, and diethyl metabolites, respectively). Using quantile regression, we found that total DAP, dimethyl phosphate, and diethyl phosphate metabolite levels were significantly higher than NHANES levels at every quartile (p < 0.001 at 25th, 50th, 75th, and 90th percentiles for DAPs, dimethyl and diethyl phosphate metabolites, respectively).
Creatinine levels in CHAMACOS pregnancy samples were no different than in NHANES pregnancy samples. However, non-pregnant NHANES women had significantly higher creatinine levels than did pregnant NHANES women, which is consistent with known biologic changes that occur during pregnancy (Becker et al. 1992; Davison and Noble 1981; Davison et al. 1980). CHAMA-COS participants’ creatinine-adjusted total dimethyl and diethyl phosphate and DAP metabolite levels were significantly higher than creatinine-adjusted levels for NHANES women 18–40 years of age (data not shown). In fact, the difference between the creatinine-adjusted DAP metabolite levels for the two populations was larger than the difference we found for unadjusted levels (data not shown).
Discussion
In this initial study of serial DAP metabolite levels in pregnant and early postpartum women, we detected measurable levels of DAP metabolites in nearly all urine samples collected from low-income women in the agricultural region of the Salinas Valley, California. Levels in this population were substantially higher than for the U.S. women of comparable age who participated in the NHANES 1999–2000 study. We have noted in our serial sampling that, although median metabolite levels in urine collected at approximately 13 and 26 weeks’ gestation were similar, postpartum metabolite levels were about double the pregnancy levels. In addition, we found a clear upward shift in the ratio of the diethyl phosphate metabolite DEP compared with the thiophosphate metabolites (DETP + DEDTP) between the women’s prenatal and postpartum samples. Because DEP is a known breakdown product of the bioactivated oxon form of diethyl OP pesticides (e.g., chlorpyrifos-oxon, diazinon-oxon, etc.), this shift in metabolite ratios may indicate pregnancy-related changes in hepatic cytochrome P450 metabolism (Needham 2005). However, the ratios of the dimethyl OP pesticides remained relatively constant across the sampling time points. Thus, we have no clear explanation for this finding. Creatinine adjustment accentuated the difference between prenatal and postpartum metabolite levels in the postpartum period. Women in this largely agricultural cohort had median postpartum urinary DAP metabolite levels that were 2.5 times higher than those for NHANES women.
We cannot readily explain the apparent increase in OP metabolite levels and the upward shift in the ratio of DEP to DETP + DEDTP in the postpartum period. One possible explanation is that the physiologic changes that occur during pregnancy increase the body’s capacity to store OP pesticides and/or their metabolites, but that these excess stores are excreted soon after delivery. During pregnancy, women retain approximately 4–6 L fluid, gain approximately 3.4 kg fat, and increase their blood volume by 40–45% (Cunningham et al. 1997); these changes may represent new compartments where OP pesticides or metabolites could be stored until parturition. Conversely, urinary frequency and glomerular filtration increase during pregnancy (Becker et al. 1992; Cunningham et al. 1997; Davison et al. 1980; Davison and Noble 1981), suggesting that metabolite excretion may occur more efficiently in the prenatal period than postpartum. In addition, pregnancy-related changes in participants’ diet probably occurred over the course of this study. However, because it is unlikely that the women began eating more fruits and vegetables contaminated with pesticide residues in the peripartum period, it is not known how such dietary factors could explain the observed changes in DAP metabolite levels postpartum. Finally, we found that, in the CHAMACOS population, pregnant and postpartum women’s urinary creatinine levels were lower than those of the nonpregnant women in NHANES. This is consistent with known biologic changes that occur during pregnancy (Becker et al. 1992; Davison and Noble 1981; Davison et al. 1980) and again demonstrates the many metabolic differences between pregnant and nonpregnant women. Further research is needed to determine which physiologic and dietary changes, if any, affect the excretion of OP metabolites. In the absence of this information, it is unclear whether prenatal or postpartum metabolite levels more accurately reflect exposures to OP pesticides during pregnancy.
As has been reported in previous studies (Barr et al. 2004; CDC 2001
CDC 2003; Fenske et al. 2002), CHAMACOS participants’ dimethyl metabolite levels consistently exceeded diethyl levels, with DMTP predominating. The molar ratio of dimethyl to diethyl metabolites in participants’ urine is 9:1, which is higher than would be expected given the 3:2 ratio of dimethyl to diethyl OP pesticides that the California Department of Pesticide Regulation reports are used in the Salinas Valley (California Department of Pesticide Regulation 2001). This discrepancy may be explained by the longer environmental half-lives of dimethyl than diethyl OP pesticides or by alternate exposure pathways, such as diet and home pesticide use, which we have not explored here. Regardless of the exposure pathway, the significant correlations we observed between dimethyl and diethyl phosphate metabolites within each sampling time point (Spearman r = 0.29–0.43) suggest that some participants may experience concurrent exposures to dimethyl and diethyl OP pesticides.
This study has several limitations. We have treated urinary DAP metabolite levels as an indicator of exposure to OP pesticides. Recent research suggests, however, that urinary metabolites may reflect not only an individual’s contact with pesticide parent compounds, but also contact with metabolites present in the environment (Lu et al. 2005). Lu et al. (2005) have recently reported that DAP metabolites are present in fresh fruit juices as a result of OP pesticide degradation. It is not currently known whether exposure to DAP metabolites would result in the intact excretion of these compounds. Findings from one animal study suggest that exposure to diethyl phosphate metabolites results predominately in the excretion of inorganic phosphate (Imaizumi et al. 1993). Nonetheless, DAP metabolite levels may overestimate a woman’s exposure to OP pesticides and, in fact, reflect her exposure, in part, to metabolites already present in her environment.
Another limitation of this study is that we have relied on metabolite levels from single spot urine samples collected at different times of the day to characterize participants’ exposure. Kissel et al. (2005) have reported that same-day spot samples collected from children vary in metabolite concentration, but that the first morning void tends to reflect the day’s total metabolite excretion better than do other spot samples (Kissel et al. 2005). The lack of correlation between CHAMACOS participants’ metabolite levels at different time points may be due, at least in part, to this sampling scheme. The high intraindividual variability we observed suggests that additional spot samples, a same-time sampling scheme (e.g., first morning voids for each woman at each time point), or perhaps even 24-hr samples might better characterize women’s cumulative exposure to OP pesticides during pregnancy.
Further, we assigned an imputed value of the LOD/√2 to levels below the detection limit. This method is identical to procedures adopted by the CDC and frequently used in exposure assessment (Barr et al. 1999, 2004; CDC 2003; Hornung and Reed 1990). However, results may differ depending on how LODs are considered across studies and if LODs differ in comparison populations. Further exploration is needed to determine the appropriate method of comparing large data sets with different LODs.
In summary, we found that pregnant women living in an agricultural area had higher urinary metabolite levels of OP pesticides compared with the general U.S. population. Our finding of higher levels in the immediate postpartum period may have implications for estimating dose during pregnancy and for infant exposure from breast-feeding. Our future analyses will explore possible determinants of exposure—such as fieldwork and proximity to agricultural fields—that may explain the high urinary OP metabolite levels among women in this agricultural community relative to other U.S. women. In addition, we will attempt to clarify whether documented physiologic changes among these women (e.g., prenatal weight gain) influenced the degree to which their prenatal and postpartum metabolite levels differed.
We thank the CHAMACOS field and laboratory staff and the women that participated in this study. In addition, we thank L. Caltabiano, A. Bishop, P. Restrepo, G. Weerasekera, P. Morales, M. Odetokuns, D. Walden, and J. Perez for their analytical support.
This research was jointly funded by U.S. Environmental Protection Agency (EPA) grant RD 83171001 and National Institute of Environmental Health Sciences grant PO1 ES009605. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the funding agencies. T.E.M. was supported in part by the U.S. EPA National Exposure Research Laboratory through Interagency Agreement DW-988-38190-01-0 with Lawrence Berkeley National Laboratory through the U.S. Department of Energy under contract grant DE-AC03-76SF00098.
Figure 1 Total DAP urinary metabolite levels by days before and after delivery (n = 535 CHAMACOS women). The y-axis is truncated at 10,000 nmol/L, excluding five postpartum samples with higher DAP measurements. Prenatal data are shown for pregnancies with a known delivery date; 67 samples from 61 women who miscarried or dropped from the study before delivery are excluded from this graph. Delivery = day 0.
Figure 2 Total DAP urinary metabolite levels in the CHAMACOS cohort and NHANES 1999–2000. Error bars indicate 95% confidence intervals. Statistically significant differences between CHAMACOS sample and NHANES sample: *p < 0.05; **p < 0.001.
Table 1 OP urinary metabolite levels at three time points, during pregnancy, and postpartum (nmol/L).a
Percentile
Sample Detection frequency (%) Geometric mean Range 50th 75th 90th
Prenatal sample 1 (n = 590)
DMP 50.3 14.6 3.3–1190.5 6.7 37.3 104.8
DMTP 65.6 30.0 0.9–7042.3 28.9 119.7 331.0
DMDTP 48.6 9.6 0.4–26582.3 4.5 25.9 136.7
Total DM 80.2 82.9 4.8–34362.6 74.2 232.7 648.0
DEP 60.4 7.7 0.9–1039.0 5.3 16.9 46.2
DETP 49.1 4.4 0.4–417.6 2.5 7.6 24.1
DEDTP 45.6 1.4 0.4–236.6 1.1 2.0 4.8
Total DE 74.3 16.7 1.7–1319.3 14.1 32.2 70.9
Total DAP (n = 581) 88.5 112.6 8.2–34438.1 102.8 277.5 731.6
Prenatal sample 2 (n = 498)
DMP 71.5 13.6 3.4–498.7 12.0 35.0 75.3
DMTP 97.6 37.3 1.0–2417.7 42.4 93.3 230.0
DMDTP 57.6 4.1 0.4–1456.0 6.6 20.3 72.6
Total DM 99.6 76.4 4.8–4057.6 76.3 156.3 338.6
DEP 39.8 3.3 0.9–585.3 0.9 17.2 43.9
DETP 98.6 11.5 0.4–408.4 12.5 29.1 50.4
DEDTP 12.3 0.5 0.4–93.6 0.4 0.4 2.3
Total DE 98.8 20.7 1.7–735.1 22.6 44.1 91.6
Total DAP (n = 495) 100.0 113.3 7.1–4098.3 106.8 223.9 421.7
Postpartum (n = 489)
DMP 67.1 27.7 3.4–21855.5 29.3 86.2 293.2
DMTP 85.9 60.0 1.0–20576.7 70.6 225.3 721.5
DMDTP 56.9 3.9 0.4–1329.1 4.0 15.8 61.6
Total DM 93.7 169.9 4.8–21857.3 162.4 444.9 1321.4
DEP 81.4 14.4 0.9–658.1 17.5 41.3 91.4
DETP 65.9 3.6 0.4–595.2 3.5 10.6 23.2
DEDTP 29.2 0.7 0.4–18.2 0.4 1.1 2.8
Total DE 92.2 25.0 1.7–665.6 25.7 58.4 128.1
Total DAP (n = 488) 97.1 229.5 6.5–21866.6 227.2 554.6 1348.6
Abbreviations: DE, diethyl metabolites; DM, dimethyl metabolites.
a Detection limits from multiple batches of urinary metabolite data: DMP = 0.6–1.2 μg/L; DMTP = 0.2–1.1 μg/L; DMDTP = 0.08–1.0 μg/L; DEP = 0.2–0.8 μg/L; DETP = 0.09–0.6 μg/L; DEDTP = 0.05–0.3 μg/L. Values below detection limit = LOD/√2, consistent with NHANES data published by CDC (2003).
Table 2 Creatinine-adjusted OP urinary metabolite levels at three time points during pregnancy and postpartum (nmol/g).a
Percentile
Sample Detection frequency (%) Geometric mean Range 50th 75th 90th
Prenatal sample 1 (n = 589)
DMP 50.2 17.3 1.2–3958.3 13.4 41.5 156.7
DMTP 65.6 35.5 0.7–7027.0 33.7 123.0 398.3
DMDTP 48.6 11.3 0.1–24298.2 9.5 28.3 137.1
Total DM 80.2 98.2 3.3–31410.1 85.1 258.7 728.4
DEP 60.4 9.2 0.4–1749.1 8.2 22.1 47.8
DETP 49.1 5.3 0.2–1131.8 4.7 9.5 25.5
DEDTP 45.6 1.6 0.1–242.8 1.4 3.1 6.3
Total DE 74.3 19.8 0.8–2221.0 18.1 36.4 83.5
Total DAP (n = 580) 88.5 133.4 7.0–31479.1 112.7 316.0 792.3
Prenatal sample 2 (n = 498)
DMP 71.5 15.8 1.4–710.9 15.2 40.4 89.8
DMTP 97.6 43.2 0.3–2609.1 45.5 109.9 237.8
DMDTP 57.6 4.7 0.1–862.1 6.3 23.3 69.8
Total DM 99.6 88.3 2.8–3485.8 82.0 182.1 424.7
DEP 39.8 3.8 0.3–488.5 1.9 18.8 45.3
DETP 98.6 13.3 0.4–472.4 15.0 32.4 65.8
DEDTP 12.3 0.6 0.1–82.3 0.5 0.8 2.1
Total DE 98.8 23.9 1.0–775.3 25.8 51.6 108.5
Total DAP (n = 495) 100.0 130.9 8.8–3724.8 126.4 237.8 478.6
Postpartum (n = 489)
DMP 67.1 35.2 1.1–86109.2 30.5 130.5 429.2
DMTP 85.9 76.3 0.3–34011.1 93.3 322.0 1099.6
DMDTP 56.9 4.9 0.1–1653.1 5.5 20.0 78.0
Total DM 93.7 216.0 2.5–93692.5 213.0 654.6 1796.7
DEP 81.3 18.3 0.4–795.3 22.4 52.9 115.2
DETP 65.9 4.6 0.2–1608.6 5.2 14.3 32.0
DEDTP 29.2 0.9 0.1–95.2 0.8 1.7 4.0
Total DE 92.2 31.8 1.0–1612.1 34.1 77.2 144.2
Total DAP (n = 488) 97.1 292.2 5.2–93798.6 283.5 730.3 1936.3
Abbreviations: DE, diethyl metabolites; DM, dimethyl metabolites.
a Detection limits from multiple batches of urinary metabolite data are given in Table 1. Values below detection limit = LOD/√2, consistent with NHANES data published by CDC (2003).
Table 3 Urinary DAP concentrations (nmol/L) among pregnant women (n = 96) and nonpregnant women of childbearing age (n = 271) in NHANES 1999–2000.
Percentile
Detection frequency (%) Geometric mean Range 50th 75th 90th
Pregnant womena
Total DM 82.5 48.1 4.5–2606.6 50.0 195.7 421.1
Total DE 75.3 8.2 1.5–296.1 8.9 20.3 42.8
Total DAP 92.8 70.5 6.0–2610.5 72.0 246.2 437.7
Nonpregnant women of childbearing ageb
Total DM 82.6 52.0 4.5–19721.9 54.8 159.4 378.5
Total DE 76.8 11.6 1.5–1157.1 13.7 34.0 56.7
Total DAP 90.9 82.3 2.3–19724.1 90.0 201.0 417.6
Abbreviations: DE, diethyl metabolites; DM, dimethyl metabolites.
a The distributions of OP metabolite levels in pregnant and nonpregnant women of childbearing age in the NHANES study were not statistically different (see “Data analysis”). Pregnant women ranged in age from 15 to 50 years.
b Total DAP n = 270 because of missing DMTP data.
Table 4 Creatinine-adjusted urinary DAP concentrations (nmol/g creatinine) among pregnant women (n = 96) and nonpregnant women of childbearing age (n = 271) in NHANES 1999–2000.
Percentile
Detection frequency (%) Geometric mean Range 50th 75th 90th
Pregnant womena
Total DM 82.5 49.8 1.5–3727.3 50.7 168.2 408.4
Total DE 75.3 8.5 0.7–308.5 8.4 28.4 55.8
Total DAP 92.8 73.0 3.1–3783.0 75.2 213.8 435.8
Nonpregnant women of childbearing ageb
Total DM 82.6 41.3 1.4–16713.5 44.8 125.2 313.3
Total DE 76.8 9.3 0.4–2492.8 9.2 23.0 55.8
Total DAP 90.9 65.5 2.3–16715.3 67.1 155.9 370.3
Abbreviations: DE, diethyl metabolites; DM, dimethyl metabolites.
a The distributions of OP metabolite levels in pregnant and nonpregnant women of childbearing age in the 1999–2000 NHANES study were not statistically different (see “Data analysis”). Pregnant women ranged in age from 15 to 50 years.
b Total DAP n = 270 because of missing DMTP data.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8282ehp0113-00180816330369ResearchChildren's HealthLate Pregnancy Exposures to Disinfection By-products and Growth-Related Birth Outcomes Hinckley Alison F. Bachand Annette M. Reif John S. Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USAAddress correspondence to A. Hinckley, Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523-1681 USA. Telephone: (970) 266-3558. Fax: (970) 266-3568. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 17 8 2005 113 12 1808 1813 4 5 2005 17 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Toxicologic studies have demonstrated associations between growth-related birth outcomes and exposure to high concentrations of disinfection by-products (DBPs), including specific tri-halomethane (THM) and haloacetic acid (HAA) chemical subspecies. Few prior investigations of DBPs have evaluated exposure during the third trimester of pregnancy, the time period of gestation when fetal growth may be most sensitive to environmental influences. We conducted a retrospective cohort study to examine the effects of exposure to THMs and HAAs during the third trimester and during individual weeks and months of late gestation on the risks for term low birth weight, intrauterine growth retardation, and very preterm and preterm births. The study population (n = 48,119) included all live births and fetal deaths occurring from January 1998 through March 2003 to women whose residence was served by one of three community water treatment facilities. We found evidence of associations between exposure to specific HAAs and term low birth weight as well as intrauterine growth retardation and for exposure to the five regulated HAAs (HAA5) and term low birth weight. Our findings suggest a critical window of exposure with respect to fetal development during weeks 33–40 for the effects of dibromoacetic acid and during weeks 37–40 for the effects of dichloroacetic acid. Adjustment for potential confounders did not affect the conclusions.
birth weightdisinfection by-productsepidemiologyhaloacetic acidspregnancypreterm birthtrihalomethanes
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The chemical mixture of disinfection byproducts (DBPs) has not been fully characterized but is known to contain trihalomethanes (THMs), haloacetic acids (HAAs), haloacetonitriles, and other classes of chemicals, some of which are mutagenic or carcinogenic in laboratory animals (Nieuwenhuijsen et al. 2000). Total THMs (TTHMs) are the sum of the concentrations of the THM species chloroform, bromodichloromethane (BDCM), dibromochloromethane (DBCM), and bromoform. The five regulated HAAs (HAA5) include monochloroacetic acid (MCAA), dichloroacetic acid (DCAA), trichloroacetic acid (TCAA), monobromoacetic acid (MBAA), and dibromoacetic acid (DBAA). Concerns have been raised regarding the potential effects of by-products on reproductive outcomes, supported in part by the findings that some by-products cause reproductive and developmental toxicity in laboratory animals, albeit at doses much higher than those encountered by humans. In addition, exposure to DBPs has been associated with an increased risk of impaired fetal growth in several epidemiologic studies (Bove et al. 1995; Dodds et al. 1999; Gallagher et al. 1998; Kramer et al. 1992; Savitz et al. 1995; Wright et al. 2003).
The third trimester of pregnancy is considered the period of human fetal development during which fetal growth and birth weight are maximally sensitive to environmental influences (Kline et al. 1989). The third trimester lasts from approximately the 26th week of gestation to parturition, with the actual length of time dependent on the individual pregnancy. However, only a few prior investigations of DBPs have evaluated exposure during this period (Dodds et al. 1999; Gallagher et al. 1998; Savitz et al. 1995; Wright et al. 2003). Risks for adverse birth outcomes depend on the magnitude of exposure over critical time windows. Therefore, analyses over exposure windows that are too wide may bias risk estimates. Because the critical time period for the potential effects of DBP exposure on fetal growth is uncertain, the use of multiple, shorter exposure windows may provide less biased risk estimates (Hertz-Picciotto et al. 1996). The purpose of this study was to examine the effects of exposure to THMs and HAAs during the third trimester and during individual weeks and months of late gestation on the risks for term low birth weight, intrauterine growth retardation, and very preterm and preterm births.
Materials and Methods
We conducted a retrospective cohort study in a large Arizona community served by three water treatment facilities. This community of more than half a million residents living in 24 ZIP codes is located adjacent to a major metropolitan area. Most water used by this community originates from surface water sources by means of the Salt River and Central Arizona projects.
The community was selected from the U.S. Environmental Protection Agency (EPA) Information Collection Rule database (U.S. EPA 1999) because the distribution systems displayed large temporal fluctuations (range, 7–81 μg/L) and low spatial variability in TTHM levels that permitted a natural experiment through intracommunity comparisons of exposures and outcomes. We determined spatial variability using the methods described by Hinckley et al. (2005). Briefly, we classified a facility as having low spatial variability if TTHM values measured at four points in the facility’s distribution system consistently fell, each season, within established boundaries for low, medium, and high exposure as based on concentration cut-points for TTHMs derived from prior epidemiologic studies of birth outcomes. The study population included all live births and fetal deaths for women whose residence was provided water by one of three facilities serving the community from January 1998 through December 2002. Table 1 summarizes the annual frequency of births in this population, as well as the distribution of TTHM and HAA5 concentrations by year over the period of the study. This study was approved by the human subjects institutional review boards of Colorado State University and the Arizona Department of Health Services.
Subjects were identified from Arizona birth records (n = 48,119) and were matched to a facility service area by residential ZIP code. In cases where two facilities shared the same distribution system, treatment facility employees identified service boundaries. Subjects who lived in ZIP codes that received water from more than one facility were excluded from the analysis. Maternal residence at birth was assumed to be the same as residence during the third trimester.
We estimated exposure from data obtained from each facility (facilities A–C) for the years 1998–2002. Total and individual THMs were measured quarterly during each of the 5 years, for each of the facilities. Facility A provided quarterly THM and HAA data for the entire study period, with monthly data available for 2001 and 2002. At facilities B and C, HAA5 data were available only for 2000 and 2002. Supplemental monthly and biweekly TTHM and HAA5 data were provided by facility B for 2000 and 2002, respectively. DBP concentrations were monitored at two to four locations within the distribution system of each facility. The quarterly and monthly data indicated the presence of very low levels of bromoform, MBAA, and MCAA; therefore, these chemicals were not included in the analyses. To estimate DBP values for specific study periods corresponding to months when no data were available, we performed a spline regression (Greenland 1998) for each water facility to impute the missing values using procedures similar to those used by investigators in Nova Scotia (Dodds et al. 1999; Dodds and King 2001; King et al. 2000). This nonlinear smoothing technique was applied to impute missing exposure data from existing data by generating a joined series of parabolic curve segments. HAA exposure data were not estimated before the year 2000 for facilities B and C.
Infant outcomes were identified through vital records. The date of last menstrual period was used to define the duration of gestation. We identified infants born at ≥37 completed weeks of gestation and weighing < 2,500 g as being term low birth weight. We evaluated this outcome only among term births to separate children with true growth retardation from babies that are small because of birth at a young gestational age. We identified case infants with intrauterine growth retardation as term or preterm babies that fell below the published value for the lowest 10th percentile of birth weights by race, ethnicity, and gestation age (Alexander et al. 1999). In this investigation, term low birth weight and intrauterine growth retardation were not mutually exclusive, and cases born at term may have been included in both outcome groups. Because published values for the lowest 10th percentile of birth weights were not available for extreme gestational ages, births before 23 weeks’ gestation were excluded from intrauterine growth retardation analyses for Caucasians, African Americans, and Hispanics, and births before 29 weeks’ gestation were excluded for Native Americans (Alexander et al. 1999). For intra-uterine growth retardation and term low birth weight, estimated monthly DBP exposures were averaged over the third trimester. Additionally, for DBPs associated with intrauterine growth retardation or term low birth weight, we averaged and evaluated exposure during specific time windows, corresponding to gestation weeks 25–28, 29–32, 33–36, 37–40, and 41–44, using monthly DBP concentrations.
Preterm births were defined as infants born at < 37 completed weeks of gestation. Very preterm births were defined as a birth occurring before 32 completed weeks of gestation (Martin et al. 2002). Because preterm birth outcomes are defined by time length of gestation, it was inappropriate to evaluate exposure averaged over the third trimester. Preterm births have shorter gestation lengths than the typical comparison group (term births), increasing the potential for bias (Hertz-Picciotto et al. 1996; Hinckley 2003; Hinckley et al. 2002). Therefore, for preterm and very preterm births, we evaluated exposure to DBPs only for the specific gestation week intervals mentioned above.
We abstracted information on potential confounders from birth records. These variables included maternal age, race, ethnicity, education, parity, smoking, and the Kessner index (a measure of prenatal care adequacy) (Kotelchuck 1994). By comparing the outcomes over different exposure time windows within a single community, we attempted to control for potential residual confounders that could not be evaluated individually.
We calculated tertiles of DBP concentrations using the species-specific data from all three water treatment regions in analyses for potential associations with each birth outcome. We used stratified chi-square and logistic regression analyses to evaluate the associations among demographic variables, exposure variables, and adverse birth outcomes. In addition, all covariates significantly associated with growth outcomes at the < 0.20 level in univariate analyses were retained for inclusion in multivariable analyses. After adjustment for potential confounders, we calculated odds ratios (ORs) and 95% confidence intervals (CIs) for the relationships between all individual THM species and growth and preterm birth outcomes. For gestation week and third-trimester analyses, a multivariate logistic regression model containing all individual HAAs as continuous variables was used to evaluate the possible relationship between individual HAAs in increasing the risk of growth-related outcomes. A similar model was not created for individual THMs because there was no evidence of any associations with growth-related outcomes.
Results
Table 2 summarizes characteristics of subjects and frequency of intrauterine growth retardation, term low birth weight, and preterm and very preterm births. Most mothers were white, non-Hispanic, nulliparous women with some college education. Most mothers received adequate prenatal care, and < 10% smoked during pregnancy. Subjects were excluded if there was no date for last menstrual period or no estimated date of conception. The estimated date of conception was used to estimate the last menstrual period when data on last menstrual period were missing or considered extreme (> 44 weeks before the birth date). The results were not different when using last menstrual period, estimated date of conception, or a combination of both methods; therefore, we used the combined method to minimize the number of subjects lost for this reason (n = 42).
The ORs and 95% CIs for intrauterine growth retardation and term low birth weight and exposure to DBPs during the third trimester are shown in Table 3. We found no evidence of an association with either outcome for exposure to TTHMs or specific brominated and chlorinated THMs. We also found no association between exposure to HAA5 and intrauterine growth retardation. The second and third tertiles of exposure to HAA5 showed evidence of a weak association with term low birth weight [OR = 1.26 (95% CI, 0.96–1.65), and OR = 1.25 (95% CI, 0.96–1.64), respectively] compared with referent exposure levels.
Exposures to the highest tertiles of DCAA and TCAA were associated with an increased risk of intrauterine growth retardation [OR = 1.28 (95% CI, 1.08–1.51), and OR = 1.19 (95% CI, 1.01–1.41), respectively]. DCAA and TCAA were also associated with intrauterine growth retardation when analyzed as continuous variables. Weak associations were found for exposure to the highest tertile of DBAA and DBAA analyzed as a continuous variable, although the 95% CIs for those results all included 1.0. Analyses of intrauterine growth retardation were adjusted for parity, smoking, maternal education, and Kessner index.
The risk of term low birth weight was increased (OR = 1.49; 95% CI, 1.09–2.04) among women exposed to average DBAA concentrations of ≥5 μg/L during the third trimester compared with those who were exposed to the referent category of < 4 μg/L. Continuous (unit) increases in average exposure to DBAA also indicated a weak association with term low birth weight (OR = 1.17; 95% CI 1.03–1.32). Analyses of term low birth weight were adjusted for maternal age, parity, education, race, ethnicity, smoking, and Kessner index.
Table 4 presents ORs and 95% CIs for exposure to HAA5 and individual HAAs over specific gestation time windows for intrauterine growth retardation and term low birth weight. Because the potential for bias due to averaging was reduced when examining shorter time intervals, exposure values were generally slightly higher or slightly lower over the specific gestation week intervals than over the third trimester. In analyses for intrauterine growth retardation, small increases in risk were observed for DBAA concentrations ≥5 μg/L (OR = 1.15; 95% CI, 0.98–1.35) and for DBAA analyzed as a continuous variable (OR = 1.06; 95% CI, 1.01–1.12) over gestation weeks 25–28. The largest risk was observed with exposure to DCAA ≥8 μg/L (OR = 1.27; 95% CI, 1.02–1.59) during gestation weeks 37–40. In addition, an increased risk was observed for exposure to moderate concentrations of TCAA (OR = 1.58; 95% CI, 1.02–2.46) and DCAA (OR = 1.51; 95% CI, 0.98–2.32) during gestation weeks 41–44, but the risk estimates were lower at higher levels of estimated exposure.
Exposure to DBAA was associated with an increase in risk for term low birth weight in analyses by gestation week. Between gestation weeks 33 and 36, the second and third tertiles of exposure to DBAA showed evidence of a dose-dependent trend [OR = 1.29 (95% CI, 0.94–1.79), and OR = 1.49 (95% CI, 1.10–2.02), respectively] compared with referent exposure levels. Similarly, moderate exposure to DBAA between gestation weeks 37 and 40 was associated with an increased risk for term low birth weight (OR = 1.38; 95% CI, 1.02–1.86).
No associations were observed between preterm or very preterm birth and exposure to TTHMs, HAA5, or specific DBPs during any gestation week interval. ORs for the associations between individual HAAs and term low birth weight and intrauterine growth retardation were not affected by inclusion of other HAAs in the logistic regression model.
Discussion
Reduced fetal weight is one of the most consistent developmental effects observed with exposure to high concentrations of DBPs in laboratory animals (Nieuwenhuijsen et al. 2000). The biologic mechanisms for DBP-induced growth retardation are not well understood. In animal studies, reductions in birth weight have been commonly described after exposure to THMs, especially chloroform (Murray et al. 1979; Ruddick et al. 1983; Schwetz et al. 1974; Thompson et al. 1974). Two studies by Smith et al. (1989, 1992) found reductions in rat pup body weight after exposure to DCAA and TCAA. Recently, Christian et al. (2001) found that DBAA administration (of 250, 500, and 1,000 mg/L) was associated with exposure-related decreases in rat pup body weight. This effect, however, was thought to be due to reduced parental water consumption.
The epidemiologic evidence for an association between exposure to THMs and indicators of fetal growth is relatively sparse and inconsistent, and few studies have investigated this relationship with respect to HAAs. Four prior epidemiologic studies have evaluated exposure to total and individual THMs in relation to intrauterine growth retardation. In a study by Kramer et al. (1992), a dose-related trend was observed for intrauterine growth retardation at the 5th percentile for exposure to chloroform ≥10 μg/L and BDCM ≥4 μg/L, with ORs of 1.8 (95% CI, 1.1–2.9) and 1.7 (95% CI, 0.9–2.9), respectively. Bove et al. (1995) also found an increased risk of intrauterine growth retardation (adjusted OR = 1.50; 90% CI, 1.19–1.86) with exposure to TTHMs > 100 μg/L during pregnancy. In a Massachusetts cohort, Wright et al. (2003) found increased risk of intra-uterine growth retardation (10th percentile) for mean exposures to TTHMs > 80 μg/L throughout pregnancy (adjusted OR = 1.14; 95% CI, 1.02–1.26) and during the second trimester (adjusted OR = 1.13; 95% CI, 1.03–1.24). However, Dodds et al. (1999) found no association between intrauterine growth retardation (10th percentile) and TTHM exposure ≥100 μg/L in a large cohort of Nova Scotia women.
Three studies have evaluated term low birth weight and exposure to TTHMs. Gallagher et al. (1998) found an adjusted OR of 5.9 (95% CI, 2.0–17.0) for term births, although only six cases were analyzed. Bove et al. (1995) also observed a positive, but smaller, association between TTHM exposures averaged over the entire pregnancy and term low birth weight with an OR of 1.42 (50% CI, 1.22–1.65). In a study by Wright et al. (2003), no associations were reported between term low birth weight and trimester-specific exposures or entire pregnancy exposures to TTHMs. Six studies have evaluated preterm birth or very preterm birth; none found a significant relationship with DBPs (Bove et al. 1995; Dodds et al. 1999; Gallagher et al. 1998; Kramer et al. 1992; Savitz et al. 1995; Wright et al. 2003).
As a group, these studies differed in their selection of a referent group for exposure, in their ability to control for potential confounding, and in their assessment of exposure during the third trimester or late stages of pregnancy. To evaluate the relationship between TTHMs and growth-related birth outcomes, Bove et al. (1995) averaged quarterly TTHM concentrations over each subject’s entire pregnancy. In a study of miscarriage, low birth weight and preterm delivery in North Carolina, Savitz et al. (1995) assigned exposure by using the quarterly value nearest the 28th week of pregnancy. Gallagher et al. (1998) used the median of all quarterly measurements taken during the third trimester. For children born in the second or third month of the quarter, Wright et al. (2003) used the average quarterly values for the third trimester; children born in the first month of the quarter were assigned the preceding quarterly averages. In Nova Scotia, Dodds et al. (1999) used linear regression of quarterly data to estimate average exposures during the last 3 months of pregnancy. Our method of assigning exposure included estimating some periodic study time exposures using a spline regression based on quarterly sampling values. Further, all data were interpolated from month midpoint and converted to ordinal study time, to better align with gestation time (Yang et al. 2005). This regression method, which is similar to that used in the Nova Scotia studies, permitted estimation of exposure for time periods when data were missing or when sampling was not performed. We performed a sensitivity analysis by systematically repeating the spline regression with varying subsets of exposure data. By this method, we found that the model consistently predicted existing data points to within ± 5%. However, the spline regression technique requires additional validation in other distribution systems.
Our study is the first to examine associations between exposures to specific HAAs and impaired fetal growth. We found evidence of associations between exposure to specific HAAs and term low birth weight and intra-uterine growth retardation. The second and third tertiles of exposure to HAA5 were also associated with a small increase in risk for term low birth weight when evaluated over the third trimester (Table 3). The increased risk in the second tertile did not seem to be due to a higher risk from DBAA, DCAA, or TCAA. HAA5 concentration is currently regulated in the United States, but concentrations of specific HAAs are not. Our findings suggest a critical window of exposure during weeks 33–40 for the effects of DBAA on fetal development. To our knowledge, this is the first time that DBAA has been investigated in an epidemiologic study of developmental outcomes. Studies of exposure to HAAs are relatively new, and none have been performed in communities where DBAA concentrations in drinking water were above detection (King et al. 2005; Wright et al. 2004). In this investigation, the levels of DBAA were well above the 90th percentile concentrations reported by the U.S. EPA (1998).
We also observed evidence of an association between intrauterine growth retardation and exposure to chlorinated HAAs during specific critical time windows of gestation, with modest increases in risk for third-trimester exposure to DCAA and slightly lower estimates for TCAA. When analyzed as continuous variables, exposure to DCAA and TCAA also showed slight increases in risk of intrauterine growth retardation between weeks 29 and 40 of gestation. The risk estimates remained consistent during the gestation week windows comprising this time period.
Our study is the first to examine exposure to DBPs during specific gestation week intervals of exposure. In previous studies, exposure for fetal development was usually averaged over the longer third-trimester window. Averaging a variable exposure over longer time periods such as the third trimester is likely to introduce misclassification over the critical time periods and lead to biased risk estimates (Hertz-Picciotto et al. 1996). However, for the highest level of exposure to DBAA, we observed the same OR for exposure averaged over the third trimester as for exposure averaged over gestation weeks 33–36. The CIs were narrower for DBAA exposure during weeks 33–36 than for the entire third trimester, reflecting increased precision due to the slightly larger sample population retained for analysis of gestation week intervals. Because the third trimester is a longer time period, it is more likely to fall outside of the study initiation and termination (or beginning and end) date than are single week-long periods (Hinckley 2003; Hinckley et al. 2002).
Windows of exposure have been historically important in epidemiologic investigations of thalidomide, retinoic acid (vitamin A), maternal rubella, and radiation (O’Rahilly and Muller 2001). For exposures during the first 2 weeks of gestation, few congenital abnormalities are observed because the teratogen either damages most cells, resulting in cell and embryonic death, or affects only a few cells that can be repaired without resultant birth defects (Moore and Persaud 1998). After the first 2 weeks, the tissue or organ that is most susceptible to malformation is the part undergoing critical development when the teratogen is active. Exposures that occur later in gestation have a less drastic effect and are thought to primarily affect fetal growth.
The strengths of this study include the large number of birth records, high quantity of exposure data (including some biweekly data), and the ability to evaluate multiple time periods of exposure to specific THMs and HAAs. By comparing subjects within the same community with respect to exposure levels, we may have reduced potential residual confounding. We also selected this community to minimize misclassification due to spatial variability within the distribution systems (Hinckley et al. 2005).
Our study was limited by the use of birth records to ascertain individual exposure information. Maternal residence was identified from birth records to assign the appropriate water service, but residential mobility during pregnancy may have introduced exposure misclassification. Potential exposure misclassification could also have resulted from lack of information regarding exposures from inhalation or dermal exposure from showering, bathing, and washing. Exposure estimates were based on distribution system DBP concentrations and did not account for variability in personal habits affecting ingestion, such as the use of bottled water (Zender et al. 2001). Finally, exposure misclassification could have resulted from exposures outside the service area (e.g., at work) of the designated water treatment system.
In summary, despite toxicologic evidence of growth retardation after exposure to DBPs, few human studies have been conducted on this relationship. The pervasive nature of the exposure suggests that even small effects may be important. This work explored this relationship using seasonal variability and intracommunity comparisons to define a natural experiment. We improved on previous exposure assessments by considering total and individual THMs and HAAs, and multiple time periods of exposure in late gestation. Further studies are needed to confirm our observations for DBAA, TCAA, and DCAA as well as other relationships between DBPs and growth outcomes.
We thank water facility departments in Tempe and Mesa, Arizona, for assistance in data collection and exposure assignment. We also thank J. Nuckols and the Environmental Health Advanced Systems Laboratory for support of this work.
Funding was provided by the College Research Council, College of Veterinary Medicine and Biomedical Sciences, Colorado State University.
Table 1 Distribution of births/fetal deaths and TTHM and HAA5 concentrations [mean (range)] by year for the Arizona study community.
Year No. of births/fetal deaths TTHM concentrations (μg/L) HAA5 concentrations (μg/L)
1998 9,117 56.9 (30.1–80.8) 31.8 (9.8–48.9)a
1999 9,571 45.8 (15.7–71.3) 20.6 (13.9–25.2)a
2000 9,976 48.9 (27.8–65.9) 17.0 (7.5–25.7)
2001 10,149 46.1 (16.6–68.0) 14.4 (4.2–22.3)a
2002 9,306 43.4 (7.8–70.3) 13.7 (3.1–23.0)
a Based on data from one facility.
Table 2 Characteristics of subjects and frequency of intrauterine growth retardation, term low birth weight, and preterm births [n (%)].
Characteristic Study population (% of total) Intrauterine growth retardationa Term low birth weight Very preterm birth Preterm birth
Total births (% of total) 48,119 (100) 4,346 (9.5) 1,010 (2.1) 564 (1.2) 4,008 (8.3)
Maternal age (years)
< 20 5,609 (11.7) 671 (15.4) 150 (14.9) 100 (17.7) 527 (13.1)
20–29 28,366 (59.0) 2,536 (58.4) 573 (56.7) 299 (53.0) 2,190 (54.6)
≥30 14,076 (29.3) 1,135 (26.1) 285 (28.2) 164 (29.1) 1,281 (32.0)
Unknown 68 (0.1) 4 (0.1) 2 (0.2) 1 (0.2) 10 (0.3)
Maternal race
Caucasian 43,026 (89.4) 4,085 (94.0) 844 (83.6) 493 (87.4) 3,490 (87.1)
African American 1,347 (2.8) 114 (2.6) 52 (5.1) 41 (7.3) 173 (4.3)
Native American 1,715 (3.6) 147 (3.4) 33 (3.3) 7 (1.2) 146 (3.6)
Other 1,701 (3.5) — 64 (6.3) 14 (2.5) 151 (3.8)
Unknown 330 (0.7) — 17 (1.7) 9 (1.6) 48 (1.2)
Maternal ethnicity
Non-Hispanic 30,803 (64.0) 2,741 (63.1) 623 (61.7) 349 (61.9) 2,575 (64.2)
Hispanic 15,140 (31.5) 1,453 (33.4) 340 (33.7) 191 (33.9) 1,228 (30.6)
Unknown 2,176 (4.5) 152 (3.5) 47 (4.7) 24 (4.2) 205 (5.1)
Maternal education
≥1 year of college 22,435 (46.6) 1,769 (40.7) 391 (38.7) 217 (38.5) 1,779 (44.4)
High school graduate 13,427 (27.9) 1,266 (29.1) 281 (27.8) 171 (30.3) 1,139 (28.4)
< 12th grade 11,172 (23.2) 1,220 (28.1) 293 (29.0) 153 (27.1) 970 (24.2)
Unknown 1,085 (2.3) 91 (2.1) 45 (4.5) 23 (4.1) 120 (2.99)
Parity
0 18,886 (39.3) 2,023 (46.5) 440 (43.6) 249 (44.1) 1,560 (39.9)
1 14,341 (29.8) 1,180 (27.2) 291 (28.8) 165 (29.3) 1,121 (28.0)
2 8,348 (17.4) 653 (15.0) 158 (15.6) 70 (12.4) 682 (17.0)
3 3,728 (7.8) 282 (6.5) 67 (6.6) 40 (7.1) 342 (8.5)
≥4 2,724 (5.7) 198 (4.6) 53 (5.2) 37 (6.5) 285 (7.1)
Unknown 92 (0.2) 10 (0.2) 1 (0.0) 3 (0.5) 18 (0.5)
Prenatal care (Kessner index)
Adequate 36,271 (75.4) 3,059 (70.4) 663 (65.6) 350 (62.1) 2,604 (65.0)
Intermediate 9,117 (19.0) 941 (21.7) 243 (24.1) 124 (22.0) 948 (23.7)
Inadequate 2,731 (5.7) 346 (8.0) 104 (10.3) 90 (16.0) 456 (11.4)
Maternal smoking
No 44,139 (91.7) 3,741 (86.1) 856 (84.7) 484 (85.8) 3,527 (88.0)
Yes 3,409 (7.1) 540 (12.4) 136 (13.4) 71 (12.6) 417 (10.4)
Unknown 571 (1.2) 65 (1.5) 18 (1.8) 9 (1.6) 64 (1.6)
Maternal alcohol
No 47,176 (98.0) 4,233 (97.4) 976 (96.6) 547 (97.0) 3,906 (97.5)
Yes 291 (0.6) 39 (0.9) 11 (1.1) 5 (0.9) 29 (0.7)
Unknown 652 (1.4) 74 (1.7) 23 (2.3) 12 (2.1) 73 (1.8)
a Does not include births at < 23 weeks’ gestation for Caucasians, African Americans, and Hispanics or births at less than 29 weeks’ gestation for Native Americans. Does not include Asian or “other” births. For intrauterine growth retardation analyses, total number of births in data set = 41,682.
Table 3 ORs and 95% CIs for the association between exposure to DBPs averaged over the entire third trimester and intrauterine growth retardation and term low birth weight.
Intrauterine growth retardationa Term low birth weightb
DBP (μg/L) Cases (n) OR (95% CI) Cases (n) OR (95% CI)
TTHMs (n)c 39,954 38,096
< 40 1,208 — 269 —
40–53 1,198 0.98 (0.90–1.07) 284 1.06 (0.89–1.25)
≥53 1,354 1.09 (1.00–1.18) 306 1.11 (0.94–1.31)
Continuous 3,760 1.00 (1.00–1.01) 859 1.00 (1.00–1.01)
Chloroform
< 10 1,216 — 265 —
10–16 1,258 1.02 (0.94–1.11) 312 1.18 (1.00–1.39)
≥16 1,286 1.01 (0.93–1.10) 282 1.04 (0.88–1.23)
Continuous 3,760 1.00 (1.00–1.01) 859 1.00 (1.00–1.01)
BDCM
< 13 1,251 — 274 —
13–18 1,173 0.93 (0.85–1.01) 290 1.05 (0.89–1.24)
≥18 1,336 1.03 (0.95–1.12) 295 1.04 (0.88–1.23)
Continuous 3,760 1.00 (1.00–1.01) 859 1.00 (0.99–1.02)
DBCM
< 12 1,288 — 286 —
12–16 1,164 0.96 (0.89–1.05) 269 1.00 (0.84–1.18)
≥16 1,308 1.01 (0.94–1.10) 304 1.05 (0.89–1.24)
Continuous 3,760 1.01 (1.00–1.01) 859 1.01 (0.99–1.02)
HAA5 (n) 14,350 13,981
< 15 462 — 97 —
15–19 466 1.00 (0.87–1.15) 124 1.26 (0.96–1.65)
≥19 513 1.08 (0.94–1.23) 126 1.25 (0.96–1.64)
Continuous 1,441 1.0 (1.00–1.01) 347 1.01 (1.00–1.02)
DBAA (n)d 9,576 9,312
< 4 322 — 70 —
4–5 301 1.04 (0.88–1.23) 71 1.01 (0.72–1.41)
≥5 347 1.12 (0.95–1.32) 103 1.49 (1.09–2.04)
Continuous 970 1.05 (0.98–1.12) 244 1.17 (1.03–1.32)
DCAA
< 6 268 — 76 —
6–8 332 1.15 (0.97–1.36) 81 1.04 (0.75–1.43)
≥8 370 1.28 (1.08–1.51) 87 1.10 (0.80–1.50)
Continuous 970 1.05 (1.02–1.09) 244 1.02 (0.96–1.08)
TCAA
< 4 277 — 73 —
4–6 309 1.00 (0.84–1.18) 81 0.94 (0.68–1.30)
≥6 384 1.19 (1.01–1.41) 90 1.00 (0.73–1.37)
Continuous 970 1.04 (1.02–1.07) 244 1.01 (0.96–1.05)
a Adjusted for parity, education, smoking, and Kessner index.
b Adjusted for maternal age, parity, education, race, ethnicity, smoking, and Kessner index.
c Sample sizes used in analysis of chloroform, BDCM, and DBCM were equal for third-trimester analyses.
d Sample sizes used in analysis of DBAA, DCAA, and TCAA were equal for third-trimester analyses.
Table 4 ORs (95% CIs) for term low birth weight and intrauterine growth retardation by gestation week (GW) according to level of DBP exposure.
DBP (μg/L) GW 25–28 GW 29–32 GW 33–36 GW 37–40 GW 41–44
Intrauterine growth retardationa
HAA5 (n) 14,350 14,953 15,414 14,929 2,634
< 14 —
14–19 1.02 (0.89–1.17) 1.00 (0.86–1.13) 0.93 (0.81–1.06) 0.99 (0.86–1.13) 1.22 (0.86–1.72)
≥19 1.12 (0.98–1.29) 1.11 (0.98–1.27) 1.00 (0.88–1.14) 0.98 (0.85–1.13) 0.91 (0.63–1.33)
Continuous 1.01 (1.00–1.01) 1.01 (1.00–1.01) 1.00 (0.99–1.01) 1.00 (0.99–1.01) 1.00 (0.98–1.01)
DBAA (n)b 9,576 10,302 10,945 10,875 1,913
< 3 —
3.5–5 1.00 (0.84–1.18) 0.97 (0.82–1.14) 1.14 (0.97–1.34) 0.95 (0.81–1.12) 1.23 (0.82–1.85)
≥5 1.15 (0.98–1.35) 1.06 (0.91–1.24) 1.08 (0.93–1.27) 0.90 (0.77–1.06) 0.91 (0.60–1.40)
Continuous 1.06 (1.01–1.12) 1.03 (0.99–1.08) 1.01 (0.96–1.06) 0.99 (0.94–1.04) 0.97 (0.85–1.10)
DCAA
< 6 —
6–8 1.00 (0.85–1.18) 1.06 (0.90–1.25) 0.92 (0.78–1.08) 1.00 (0.85–1.18) 1.51 (0.98–2.32)
≥8 1.04 (0.85–1.29) 1.11 (0.90–1.35) 1.03 (0.84–1.26) 1.27 (1.02–1.59) 1.41 (0.85–2.33)
Continuous 1.02 (0.99–1.04) 1.03 (1.01–1.05) 1.03 (1.01–1.05) 1.03 (1.01–1.05) 1.03 (0.98–1.08)
TCAA
< 4 —
4–6 0.96 (0.81–1.14) 1.05 (0.89–1.24) 0.91 (0.78–1.07) 1.12 (0.95–1.32) 1.58 (1.02–2.46)
≥6 1.01 (0.86–1.19) 1.15 (0.98–1.34) 1.07 (0.92–1.24) 1.15 (0.98–1.35) 1.48 (0.96–2.31)
Continuous 1.01 (1.00–1.03) 1.02 (1.01–1.04) 1.03 (1.01–1.05) 1.02 (1.01–1.04) 1.02 (0.98–1.06)
Term low birth weightc
HAA5 (n) 13,981 14,593 15,195 15,780 2,755
< 14 —
14–19 0.91 (0.69–1.18) 1.04 (0.79–1.36) 1.04 (0.80–1.36) 1.12 (0.87–1.44) 0.74 (0.28–1.98)
≥19 1.05 (0.81–1.36) 1.30 (1.00–1.69) 1.20 (0.93–1.55) 1.08 (0.83–1.40) 1.03 (0.39–2.72)
Continuous 1.01 (0.99–1.02) 1.01 (1.00–1.02) 1.00 (1.01–1.02) 1.00 (0.99–1.02) 0.99 (0.95–1.04)
DBAA (n)b 9,312 10,043 10,778 11,484 2,003
< 3 —
3.5–5 0.97 (0.69–1.35) 1.05 (0.77–1.45) 1.29 (0.94–1.79) 1.38 (1.02–1.86) 0.99 (0.34–2.85)
≥5 1.16 (0.86–1.58) 1.18 (0.87–1.61) 1.49 (1.10–2.02) 1.22 (0.90–1.66) 0.38 (0.10–1.43)
Continuous 1.03 (0.94–1.12) 1.07 (0.98–1.17) 1.11 (1.01–1.21) 1.10 (1.01–1.20) 0.88 (0.62–1.23)
DCAA
< 6 — —
6–8 0.80 (0.58–1.09) 0.84 (0.61–1.15) 0.94 (0.69–1.27) 0.98 (0.74–1.32) 0.45 (0.13–1.60)
≥8 0.87 (0.64–1.18) 0.99 (0.74–1.32) 0.98 (0.73–1.30) 0.91 (0.68–1.21) 0.93 (0.33–2.57)
Continuous 1.01 (0.97–1.05) 1.02 (0.98–1.06) 1.00 (0.96–1.04) 1.01 (0.97–1.05) 0.94 (0.81–1.09)
TCAA
< 4 —
4–6 0.79 (0.57–1.09) 0.91 (0.67–1.24) 1.05 (0.77–1.42) 1.15 (0.86–1.53) 0.40 (0.11–1.30)
≥6 0.89 (0.66–1.20) 0.98 (0.73–1.33) 1.05 (0.78–1.41) 0.93 (0.69–1.25) 0.64 (0.23–1.79)
Continuous 1.01 (0.98–1.05) 1.01 (0.98–1.04) 1.00 (0.96–1.03) 1.00 (0.96–1.03) 0.93 (0.80–1.09)
a Adjusted for parity, education, smoking, and Kessner index.
b Sample sizes used in analysis of DBAA, DCAA, and TCAA were equal for each gestation age interval.
c Adjusted for maternal age, parity, education, race, ethnicity, smoking, and Kessner index.
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References
Alexander GR Kogan MD Himes JH 1999 1994–1996 U.S. singleton birth weight percentiles for gestational age by race, Hispanic origin, and gender Matern Child Health J 3 225 231 10791363
Bove FJ Fulcomer MC Klotz JB Esmart J Dufficy EM Savrin JE 1995 Public drinking water contamination and birth outcomes Am J Epidemiol 141 850 862 7717362
Christian MS York RG Hoberman AM Diener RM Fisher LC Gates GA 2001 Biodisposition of dibromoacetic acid (DBA) and bromodichloromethane (BDCM) administered to rats and rabbits in drinking water during range-finding reproduction and developmental toxicity studies Int J Toxicol 20 239 253 11563419
Dodds L King W Woolcott C Pole J 1999 Trihalomethanes in public water supplies and adverse birth outcomes Epidemiology 10 233 237 10230830
Dodds L King WD 2001 Relation between trihalomethane compounds and birth defects Occup Environ Med 58 443 446 11404448
Gallagher MD Nuckols JR Stallones L Savitz DA 1998 Exposure to trihalomethanes and adverse pregnancy outcomes Epidemiology 9 484 489 9730025
Greenland S 1998. Introduction to regression models. In: Modern Epidemiology (Rothman KJ, Greenland S, eds). 2nd ed. New York:Lippincott Willimans and Wilkins, 392–394.
Hertz-Picciotto I Pastore LM Beaumont JJ 1996 Timing and patterns of exposures during pregnancy and their implications for study methods Am J Epidemiol 143 597 607 8610677
Hinckley AF 2003. Disinfection By-products and Prenatal Development [PhD Thesis]. Fort Collins, CO:Colorado State University.
Hinckley AF Bachand AM Nuckols JR Reif JS 2002 Adverse reproductive outcomes and third trimester exposure to disinfection by-products [Abstract] Epidemiology 15 S156
Hinckley AF Bachand AM Nuckols JR Reif JS 2005 Identifying public water facilities with low spatial variability of disinfection by-products for epidemiologic investigations Occup Environ Med 62 494 499 15961627
King WD Dodds L Allen AC 2000 Relation between stillbirth and specific chlorination by-products in public water supplies Environ Health Perspect 108 883 886 11017894
King WD Dodds L Allen AC Armson BA Fell D Nimrod C 2005 Haloacetic acids in drinking water and risk for stillbirth Occup Environ Med 62 124 127 15657195
Kline J Stein Z Susser M 1989. Conception to Birth: The Epidemiology of Prenatal Development. New York:Oxford University Press.
Kotelchuck M 1994 An evaluation of the Kessner adequacy of prenatal care index and a proposed adequacy of prenatal care utilization index Am J Public Health 84 1414 1420 8092364
Kramer MD Lynch CF Isacson P Hanson JW 1992 The association of waterborne chloroform with intrauterine growth retardation Epidemiology 3 407 413 1391132
Martin JA Hamilton BE Ventura SJ Menacker F Park MM 2002 Births: final data for 2000 Natl Vital Stat Rep 50 1 101
Moore KL Persaud TVN 1998. Essentials of Embryology and Birth Defects. 5th ed. Philadelphia:W.B. Saunders Company.
Murray FJ Schwetz BA McBride JG Staples RE 1979 Toxicity of inhaled chloroform in pregnant mice and their offspring Toxicol Appl Pharmacol 50 515 522 516065
Nieuwenhuijsen MJ Toledano MB Eaton NE Fawell J Elliott P 2000 Chlorination disinfection byproducts in water and their association with adverse reproductive outcomes: a review Occup Environ Med 57 73 85 10711274
O’Rahilly R Muller F 2001. Human Embryology and Teratology. 3rd ed. New York:John Wiley & Sons.
Ruddick JA Villeneuve DC Chu I Valli VE 1983 A teratological assessment of four trihalomethanes in the rat J Environ Sci Health B 18 333 349 6875216
Savitz DA Andrews KW Pastore LM 1995 Drinking water and pregnancy outcome in central North Carolina: source, amount, and trihalomethane levels Environ Health Perspect 103 592 596 7556013
Schwetz BA Leong BK Gehring PJ 1974 Embryo- and fetotoxicity of inhaled chloroform in rats Toxicol Appl Pharmacol 28 442 451 4851839
Smith MK Randall JL Read EJ Stober JA 1989 Teratogenic activity of trichloroacetic acid in the rat Teratology 40 445 451 2623633
Smith MK Randall JL Read EJ Stober JA 1992 Developmental toxicity of dichloroacetate in the rat Teratology 46 217 223 1523579
Thompson DJ Warner SD Robinson VB 1974 Teratology studies on orally administered chloroform in the rat and rabbit Toxicol Appl Pharmacol 29 348 357 4283698
U.S. EPA 1998. Occurrence Assessment for Disinfectants and Disinfection Byproducts in Public Drinking Water Supplies. EPA 815B980004; NTIS PB 99-111320. Washington, DC:U.S. Environmental Protection Agency.
U.S. EPA 1999. ICR Auxiliary 1 Database, Version 5.0; Query Tool, Version 2.0 (CD-ROM). Washington, DC:U.S. Environmental Protection Agency.
Wright JM Schwartz J Dockery DW 2003 Effect of trihalomethane exposure on fetal development Occup Environ Med 60 173 180 12598663
Wright JM Schwartz J Dockery DW 2004 The effect of disinfection by-products and mutagenic activity on birth weight and gestational duration Environ Health Perspect 112 920 925 15175183
Yang J Hartmann KE Herring AH Savitz DA 2005 Reducing misclassification in assignment of timing of events during pregnancy Epidemiology 16 121 123 15613955
Zender R Bachand A Reif JS 2001 Exposure to tap water during pregnancy J Expo Anal Environ Epidemiol 11 224 230 11477520
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7911ehp0113-00181416330370ResearchMini-MonographEvolving Partnerships in Community Srinivasan Shobha Collman Gwen W. Division of Extramural Research and Training, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USAAddress correspondence to G.W. Collman, Division of Extramural Research and Training, NIEHS, NIH, DHHS, P.O. Box 12233, MD EC–21, 111 T.W. Alexander Dr., Research Triangle Park, NC 27709 USA. Telephone: (919) 541-4980. Fax: (919) 316-4606. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 18 7 2005 113 12 1814 1816 28 12 2004 18 7 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In recent years there have been a significant number of publications on the benefits and challenges of community-based participatory research (CBPR). In this introduction we give an overview of three projects presented in this mini-monograph and highlight their commonalities and differences in developing community–university partnerships. While the studies presented here were not required to use CBPR strategies in their work, they did engage community members in a participatory manner. In this mini-monograph we examine how these multifaceted research questions are addressed while simultaneously negotiating complex relationships among researchers and communities as they strive for a more equitable partnership—not only in the distribution of resources but also in power/authority, the process of research, and its outcome. The three papers in this mini-monograph offer insights into various ways of forming, working, and sustaining community–university partnerships in conducting CBPR. They illustrate both the potential benefits and some of the challenges involved with establishing partnerships between community groups and researchers committed to the mutual goal of promoting environmental health. They suggest the importance of nonprescriptive frameworks for conducting community-based participatory research that focuses on more equitable power relationships to address health disparities to help alleviate environmental health problems.
community-based participatory researchcommunity–university partnershipenvironmental exposureshealth disparitiesmethodology
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In recent years there have been a significant number of publications on benefits and challenges of community-based participatory research (CBPR) (Shepard et al. 2002). The National Institute of Environmental Health Sciences (NIEHS), through several extramural activities, promotes CBPR that encourages partnerships between community members and academic researchers in public health. Information can be found on the NIEHS web-site (http://www.niehs.nih.gov/translat/home.htm). One of these initiatives is the Health Disparities: Linking Biological and Behavioral Mechanisms with Social and Physical Environments Program (henceforth referred to as the Health Disparities Program, HDP) that was initiated in 2000. The purpose of this program is to foster multidisciplinary research to elucidate underlying mechanisms by which interactions of physical exposure with the social environment lead to health disparities. The research projects in the HDP were based in the community, and requests for application to obtain grants sponsored by the program required partnerships between social/behavioral scientists and biomedical scientists. While the proposed research projects were required to have a community outreach and education program (COEP), they were not required to use CBPR strategies in their work. However, several of the projects funded, including the three highlighted in this mini-monograph series, did engage community members in a participatory manner.
The three articles in this mini-monograph (based in and referred to as the Detroit, Akwesasne, and North Carolina projects) are funded through the HDP. The authors of these articles discuss the partnerships established between communities, community-based organizations, and researchers; the evolution and development of those partnerships in relation to the research effort; the partnerships’ contributions to the evolution and development of the research; and lessons learned in that process. The projects funded within the HDP address complex scientific research questions that try to determine the mechanisms through which the social and physical environments influence biological processes and behaviors that ultimately contribute to health disparities.
The starting point for these research projects is the recognition in the public health community that most human diseases are related in some manner to social factors and forces (Kaplan 1999; Link and Phelan 1995; Schulz et al. 2000; Williams and House 1991). However, the terminology used to describe this relationship is as numerous as the research specialties that study this area (behavioral, social, epidemiologic, etc.).
Diseases may be said to be socially mediated, or to be distributed, patterned, or determined by social factors, as implied in the paragraph immediately above. The presence or distribution of virtually all disease can be related to social factors either in regard to their origin (human impacts on ecology), transmission, or distribution within, between, or among societies. This awareness of the social determinants or dimension of diseases may be the result of a new and better understanding of the etiology of diseases. Understanding disease in contemporary human populations requires in-depth analysis and understanding of the role of social factors. The most complete picture may be obtained by understanding the relevant social forces as much as possible, and linking this knowledge to the biology of disease processes. Because social factors or forces can vary tremendously among and within cultures and societies, obtaining detailed and authentic information of particular social forces may be facilitated through partnerships with community members. Such research requires the involvement of communities at multiple levels—not only to obtain better understanding of the concerns and issues of the communities but also to promote change by involving members of communities who live in these areas and are affected by these processes.
For this type of research, the “community” may or may not be defined geographically, for example, as a neighborhood or town. Because health disparities are prevalent among people of the same socioeconomic class and spread across different municipalities or geographic areas, the community boundary may not coincide with the usual denominators in epidemiologic work. In some cases the community may define itself by using standards of social identification foreign to the researchers or by identifying social boundaries that have not been recognized by the scientific partners but that may be instrumental in disease causation.
The three projects featured in this mini-monograph actively engage members of communities that experience disproportionate burdens of disease or ill health in the following manner.
The Detroit project focuses on race-based residential segregation and its potential link to cardiovascular disease in inner-city Detroit, Michigan. The project also examines the influence of past economic divestment in shaping both physical and social environments that have left the African American and Hispanic communities in substandard housing and in high-crime communities that lack infrastructure.
The Akwesasne project is based in upstate New York, although it includes a significant population that lives in Canada. This project focuses on the effects of polychlorinated biphenyls (PCB) in the St. Lawrence River and how the Mohawk tribe’s culture of interaction with the physical environment exposes the community to pollutants that may affect physical, cognitive, and social well-being. The Akwesasne Mohawk people are faced with a dominant and dominating American culture within which they seek to retain and strengthen their own cultural and religious practices.
The North Carolina project focuses on occupational roots of health disparity among women employed in poultry processing in the rural northeastern region of the state, an area that suffers from a declining local economy. The project examines the interaction of physical exposures at work with the social environment in the workplace. Occupational health and safety issues of African American women, in general, have been poorly researched. More specifically, women employed in the poultry processing industry in the rural South have not been systematically studied.
While all three projects conceptually address physical or chemical exposures, they each emphasize social processes that may influence these exposures—to a greater extent than issues of personal or individual choice. This interaction between physical–chemical exposures and social forces can be much more complicated than mere socioeconomic status. Understanding and appreciation of context are important; for example, the rural women employed in the North Carolina poultry processing industry were not involved in municipal decisions that placed a low-wage company as a dominant employer in their community. Similarly, the Akwesasne people were not in control of the decisions that located toxic waste sites near their tribal lands.
In this mini-monograph we examine how these multifaceted research questions are addressed while simultaneously negotiating complex relationships among researchers and communities as they strive for a more equitable partnership—not only in the distribution of resources but also in power and authority, the process of research, and its outcome. The three projects differ in their study approach; some provide more detail regarding the role of community partners in the development of study design, while others focus on the collaborative process itself. These studies reflect the entire HDP, where some projects had existing structured partnerships with community-based groups while others had identified communities where their research would be based. Some of these projects had more extensive community involvement from conceptualization of the proposal through the outreach component at the end of the project, while others had limited community involvement.
History of Partnerships
Both the Detroit and Akwesasne projects have longer histories of community–university partnerships with formalized steering committees, established processes for reviewing potential research in the community, publication, and dissemination. The Detroit project can trace its beginnings to the establishment of the Detroit Urban Research Center (URC). In 1995 the Centers for Disease Control and Prevention funded the URC to improve health in selected areas of Detroit through CBPR. The URC board, comprised of representatives from community-based organizations, health service providers, and academic researchers, identified environmental health and social determinants of health as priorities. The Healthy Environments Partnership (HEP) was developed in response to those priorities. Representatives from community-based organizations and local health professionals provided input into the research questions and study design, and several of these people continue as members of the HEP Steering Committee along with some new members. The current Akwesasne project grew from relationships started with the Akwesasne community in the 1980s, whereas the project in the HDP started in 1995. The Akwesasne Mohawk Nation, as a Native American community, has been the subject of numerous studies, and the protracted nature of the relationship with the preexisting partnership was helpful to the current research project. Furthermore, because Akwesasne is a sovereign territory, collaboration developed very deliberately and through a dual process that was familiar on one level to academics and on another level to the Native American community. In the North Carolina project the most recent of the collaborations, the academic researchers were approached by women in the community to address specific issues of concern. Initially, the academic team viewed its role more as one of providing technical assistance to the community. However, as the work and the academic-community partnership evolved, many aspects of CBPR became evident.
Shared Premises and Evolving Partnerships
Despite marked diversity across various issues, including research topics and methodologies, communities of study and history of collaborations, and diversity of the academic teams, these projects demonstrate a number of shared premises, commitments, and processes. In all three projects there is a shared commitment to scientific rigor to provide credible information, with the recognition that credibility is essential to the ability to negotiate change.
All these research projects required community involvement at various levels and in multiple dimensions. All had community involvement in defining research questions, development of tools, recruitment of participants, and collection of data. In each case the partnership with community members is viewed as essential in obtaining detailed and authentic information about the complex constructs of interest based on the specific aims in each project.
Although the partnerships in all three projects have quite different histories and longevities, these collaborations between the community and academic partners are viewed as a process evolving and, it is hoped, improving over time. This process is analogous to an evolving scientific undertaking in which investigators learn from the work of others and strive to build on their own previous work as well. Because the partnerships between communities and universities are of varying lengths, the partners did not come to the table to discuss the research project with the same levels of experience. Significant time was spent on building relationships based on mutual respect, on establishing the process of communication, and on developing skill (on both the academic and community sides). For example, because in all these projects the primary funding was given to the university partner, significant time was invested in establishing processes by which community members can voice their opinions to influence the research process. Developing a working relationship is a shared and an evolving process because all the project partners need to come to the table to determine how the research will be conducted, develop the protocol, devise data collection methods and procedures, and establish strategies for dissemination and publication. Thus, every issue in the research process is thoroughly discussed together by the community and the academic partners.
When deciding whether there is an equitable distribution of resources, many times community members may not be comfortable requesting salaries higher than those of most of the other community members with whom they work and live. Therefore, it is important that community members decide the appropriate salary or level of reimbursement. In all three projects, community staff and participants are paid for their time, in salary or incentives, as an indication of respect for their valuable time and effort. A higher salary than the earning capacity of other community members may create income disparity and could subsequently lead to differential power among community members. Besides salary it is also important to acknowledge community partnership contributions to public meetings, conferences, presentations, and publications, and to provide opportunities for co-authorship of papers and presentations as well. In recognition of the crucial contribution of communities to the research, the three projects provide funds for travel of community members to various national meetings.
Conclusion
Some research scientists who use principles of CBPR may be wary of acknowledging that they are indeed involved in CBPR because, in some cases, there was no structured community–university partnership until they received funding for the project. The three projects highlighted in this mini-monograph offer invaluable insights into the ways of forming partnerships and working with communities. Given very different research questions, unique communities, and varying lengths of association with the community, they illustrate a wide range of strategies and processes for working toward establishing mutually respectful scientific collaboration that benefits the communities and researchers alike.
Partnership structure.
A question that arises in such partnerships is how relationships between partners should be structured. Typical arrangements include subcontracting with community organizations, hiring community members as university employees, and contracting with consultants; each of these possibilities has its challenges and benefits. The three highlighted projects hired community partners as staff members on the project for different reasons. Sometimes community members hired as staff may not feel they have independent voices in the process; however, the experiences in the projects described here show that this method can be successful. The project manager for the Detroit project is a long-time community resident who is also a health professional. In the Akwesasne project the community partner hired works closely with the research team and along with the project’s Steering Committee, which is made up of community members, health professionals, academic researchers, and representatives from the Mohawk community. The addition of the Steering Committee complements the relationships between members of the research team and the community. The North Carolina project in its first year faced difficult decisions when the community organization with which it had partnered was unable to meet its obligations to the community members and the research project. Ultimately, community members who were part of the originally subcontracted community organization left the organization and were hired as staff members on the project. These staff members have had considerable involvement in all aspects of the work, and they were the reason for the current level of success in recruiting research participants, maintaining followup with women in their communities, and developing a wider community outreach.
Scientific rigor.
Although the ultimate outcome of the research project may be different for the researcher and the community partner, the success of these projects can be linked directly to the commitment of both to scientific rigor and promoting health. Although the community may be more interested in solving and alleviating the present health problems than in the production of scientific knowledge per se, their commitment to the research is the result of a focus on addressing immediate health problems. This synergy has resulted in the Akwasasne people developing several research studies with other academic partners. This community is also concerned that the research on the possible human health effects of toxicant exposure be credible within the scientific community because negotiations with industry regarding remediation are still under way. While the ultimate motive for the research may not be identical for communities and researchers alike, scientific rigor is valued by both partners to the extent that it moves forward changes to improve community health and that credibility is essential to negotiate change.
Power differences.
It is important to note that researchers and communities bring with them, among other issues, unequal power relations and cultural, racial/ethnic, linguistic, and socioeconomic differences, all of which can affect who has influence within the community, as well as who has influence in relation to researchers. While overcoming these differences can be difficult, the challenges can be surmounted through the process of establishing a partnership based on mutual respect. All three projects surmounted these barriers and challenges because it was the community that chose the topic and approached the researchers. In the North Carolina project, the process of allowing members of the existing community organization to be hired as employees on the project group was empowering to those community members because they felt that they consequently had a greater representation and voice in the process of research.
Sustainability.
One of the biggest concerns for both communities and researchers is sustaining the partnerships beyond the funding period of the grant–especially if there is no financial support for the work. Sustainability of partnerships is extremely challenging and depends ultimately on the abilities of the researchers and the communities to sustain the research or any future endeavors with the relationships they have developed in working together on the basis of shared ideologies.
The three projects described in this mini-monograph offer invaluable insight into forming partnerships and working with community partners, given that they pose very different research questions in different types of communities, and the length of association with the community partners varies widely. These projects illustrate both the potential benefits and some of the challenges involved with establishing partnerships between community groups and researchers committed to the mutual goal of promoting environmental health. Finally, the projects suggest the importance of nonprescriptive frameworks for conducting CBPR that focuses on more equitable power relationships to address health disparities to help alleviate environmental health problems.
This article is part of the mini-monograph “Community-Based Participatory Research.”
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References
Kaplan GA 1999 What is the role of the social environment in understanding inequalities in health? Ann NY Acad Sci 896 116 119 10681892
Link BG Phelan J 1995 Social conditions as fundamental causes of disease J Health Soc Behav 27 80 94 7560851
Schulz AJ Krieger J Galea S 2002 Addressing social determinants of health: community-based participatory approaches to research and practice Health Educ Behav 3 287 295 12038739
Shepard PM Northridge ME Prakash S Stove G 2002 Preface: Advancing environmental justice through community–based participatory research Environ Health Perspect 110 suppl 2 139 140 11836141
Williams DR House JS 1991 Stress, social support, control and coping: a social epidemiological view WHO Reg Publ Eur Ser 37 147 172 1817534
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7913ehp0113-00181716330371ResearchMini-MonographSocial and Physical Environments and Disparities in Risk for Cardiovascular Disease: The Healthy Environments Partnership Conceptual Model Schulz Amy J. 1Kannan Srimathi 2Dvonch J. Timothy 2Israel Barbara A. 1Allen Alex III3James Sherman A. 4House James S. 5Lepkowski James 61 Health Behavior and Health Education, and2 Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA3 ISLES, Inc., Trenton, New Jersey USA4 Duke University, Durham, North Carolina, USA5 Survey Research Center and Department of Sociology, and6 Institute for Social Research and Department of Biostatistics, University of Michigan, Michigan, Ann Arbor, USAAddress correspondence to A.J. Schulz, Health Behavior and Health Education, School of Public Health, University of Michigan, 5134 SPH II, 1420 Washington Heights, Ann Arbor, MI 48109 USA. Telephone: (734) 647-0221. Fax: (734) 763-7379. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 18 7 2005 113 12 1817 1825 28 12 2004 29 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The Healthy Environments Partnership (HEP) is a community-based participatory research effort investigating variations in cardiovascular disease risk, and the contributions of social and physical environments to those variations, among non-Hispanic black, non-Hispanic white, and Hispanic residents in three areas of Detroit, Michigan. Initiated in October 2000 as a part of the National Institute of Environmental Health Sciences’ Health Disparities Initiative, HEP is affiliated with the Detroit Community–Academic Urban Research Center. The study is guided by a conceptual model that considers race-based residential segregation and associated concentrations of poverty and wealth to be fundamental factors influencing multiple, more proximate predictors of cardiovascular risk. Within this model, physical and social environments are identified as intermediate factors that mediate relationships between fundamental factors and more proximate factors such as physical activity and dietary practices that ultimately influence anthropomorphic and physiologic indicators of cardiovascular risk. The study design and data collection methods were jointly developed and implemented by a research team based in community-based organizations, health service organizations, and academic institutions. These efforts include collecting and analyzing airborne particulate matter over a 3-year period; census and administrative data; neighborhood observation checklist data to assess aspects of the physical and social environment; household survey data including information on perceived stressors, access to social support, and health-related behaviors; and anthropometric, biomarker, and self-report data as indicators of cardiovascular health. Through these collaborative efforts, HEP seeks to contribute to an understanding of factors that contribute to racial and socioeconomic health inequities, and develop a foundation for efforts to eliminate these disparities in Detroit.
community-based participatory research partnershipsracial segregation and cardiovascular diseasesocial and physical environments and cardiovascular disease
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Cardiovascular disease (CVD) is the largest contributor to all-cause mortality in the United States and accounts for one-third of the excess mortality experienced by non-Hispanic black compared with non-Hispanic white Americans (Wong et al. 2002). Although CVD risk has declined substantially over the past 30 years, this decline has been uneven across both socioeconomic position (SEP) and racial or ethnic groups, resulting in increasing disparities (Cooper et al. 2000; Williams 1999). Although socioeconomic disparities and racial disparities in health risks and health outcomes between non-Hispanic black and non-Hispanic white Americans have been well established (Cooper et al. 2000; Cubbin et al. 2001; Diez-Roux et al. 1999; Kaplan and Keil 1993; Lynch et al. 1996, 1997; Wong et al. 2002), mixed results are reported in the literature on CVD risk and mortality among Mexican Americans (Hunt et al. 2003; Luepker 2001; Pandey et al. 2001; Sorlie et al. 1993; Sundquist and Winkleby 2000; Winkleby et al. 1999).
Understanding the patterns and processes associated with racial disparities in CVD is an important priority for health professionals, and perhaps more so for the communities that disproportionately experience CVD morbidity and mortality. The Healthy Environments Partnership (HEP) is a community-based participatory research (CBPR) partnership that brings together representatives from community-based organizations, public health organizations, and academic institutions to examine the contributions of social and physical environmental contexts to the risk of CVD. In this article we describe the conceptual model that guides HEP’s work, the study design, and the processes used to facilitate engagement among these diverse partners in the development and implementation of this study.
Background
Cardiovascular disease in Detroit.
Residents of Detroit experience age-adjusted risks of death due to heart disease that are considerably higher than either the Michigan or the national rates (Table 1). CVD mortality rates for non-Hispanic black Detroiters were not substantially higher than for non-Hispanic blacks in Michigan or nationally (relative risk, 1.1 and 1.2, respectively), but mortality rates among non-Hispanic white Detroit residents were substantially higher than either the Michigan or the national rates (relative risk, 1.5–1.6, respectively). Although data were not available for Detroit’s predominantly Mexican American Hispanic population, the literature for Mexican Americans elsewhere in the United States is mixed: some report lower risk of CVD (Mitchell et al. 1990) or mortality (Sorlie et al. 1993), whereas others report similar or higher rates of CVD mortality among Mexican Americans compared with non-Hispanic whites (Hunt et al. 2003; Luepker 2001; Pandey et al. 2001). Sundquist and Winkleby (2000), reporting on a national sample of Mexican American women and men from the Third National Health and Nutrition Examination Survey (NHANES III), note the heterogeneity of the Mexican American population and suggest the importance of examining variations in both individual characteristics and contextual factors in understanding variations in cardiovascular risk.
CVD mortality rates also vary within Detroit. The 3-year age-adjusted average CVD mortality rate (2000–2002) on Detroit’s east side was 523.9; in northwest Detroit, 395.3; and in southwest Detroit, 426.9 (Michigan Department of Community Health 2003). Understanding the factors that account for these variations requires understanding contemporary and historical relationships between the city and the surrounding region, and within the city itself.
A thriving and prosperous community with a strong blue-collar middle class for much of the twentieth century, like many similar urban areas, Detroit experienced population out-migration and economic disinvestment beginning in the 1950s and escalating in the 1970s and 1980s. As Detroit’s population declined, surrounding suburban areas experienced unprecedented economic and population growth. These economic and population shifts were fueled by white fears of racial integration and the departure of most of the city’s white residents to suburban neighborhoods as African American residents moved into previously all-white Detroit neighborhoods (Sugrue 1996). The racial composition of Detroit shifted from 16% African American in 1950 to 83% in 2000 (Schulz et al. 2002). For the past two decades, Detroit has been among the most racially segregated metropolitan areas in the United States (Glaeser and Vigdor 2001; Sugrue 1996). Concurrently, employment opportunities relocated to outlying areas, contributing to an exponential growth in areas of concentrated poverty within the city.
House and Williams (2000) have noted that SEP shapes “people’s experience of and exposure to virtually all psychosocial and environmental risk factors for health. . . . [T]hese in turn operate through a very broad range of physiological mechanisms to influence the incidence and course of virtually all causes of disease and death” (p 83). SEP, whether measured by education, income, occupation, or a composite measure aggregating two or more of these indicators, is predictive of mortality across a wide range of health outcomes, including, but not limited to, CVD (House 2002). The pervasiveness of these influences has led some to suggest that SEP is a “fundamental factor” influencing health by shaping access to multiple resources needed to maintain health and avoid disease (Link and Phelan 1995). More recently, Williams and Collins (2001) have extended this argument, suggesting that race-based residential segregation is a fundamental factor influencing health disparities shaping differential access to multiple resources—including but not limited to, education, income, and wealth—necessary to maintain health. The HEP project focuses on explicating the effects of race-based residential segregation in concentrating access to political, economic, and social resources and the resulting implications for health.
The Detroit HEP.
HEP was initiated in October 2000 as a part of the National Institute of Environmental Health Sciences Health Disparities Initiative and is affiliated with the Detroit Community–Academic Urban Research Center (URC) (Israel et al. 2001). The URC board, composed of representatives from community-based organizations, health service and public health institutions, and academic institutions, identified the contribution of environmental factors to health disparities as a priority. HEP contributes to this goal by examining aspects of the social and physical environments and their association with health status across areas within Detroit and by disseminating results from these analyses within the study communities as well as peer-reviewed venues.
HEP investigates the prevalence of biologic indicators of CVD and the extent to which these inequalities are mediated through social and physical environmental exposures, with implications for proximate factors such as health-related behaviors, psychosocial stressors and responses, and social integration. In addition, HEP aims to disseminate and translate findings to inform new and established intervention and policy efforts through HEP’s community outreach and education program (COEP).
HEP engages researchers based in academic institutions and representatives from health service organizations and community-based organizations in a collaborative effort to address these questions (see acknowledgments footnote on page 1 of this article for a list of HEP partner organizations). Representatives from the partner organizations comprise the HEP steering committee (SC), which is involved, in varying degrees, in all aspects of the research process. In 2001 the SC adopted a set of CBPR principles that emphasizes involving community, practitioner, and academic partners in all major phases of the research process; strengthening collaboration among all partners; conducting research that is beneficial to the communities involved; enhancing the capacity of all partners; and disseminating findings to community members in ways that are understandable and useful (Israel et al. 1998, 2005).
The Healthy Environments Partnership Conceptual Model
The conceptual model that guides HEP’s work builds on previous CBPR efforts undertaken by the URC (Israel et al. 2001, 2002; Parker et al. 2001; Schulz et al. 2001); the literature describing relationships between SEP, racial segregation, and access to resources necessary to maintain health (House and Williams 2000; Link and Phelan 1995; Schulz et al. 2002; Schulz and Northridge 2004; Williams and Collins 2001); and the extensive literature on CVD. The HEP conceptual model shown in Figure 1 posits that the social and physical environments serve to mediate relationships between racial and socioeconomic inequalities (expressed in patterns of race-based residential segregation and concentrated poverty) and more proximate social, psychological, behavioral, and biologic indicators of CVD risk.
Fundamental Factors: Race-Based Residential Segregation and Concentrated Poverty
Race-based residential segregation and economic inequality appear on the left of Figure 1 as fundamental factors influencing intermediate and proximate risks for CVD. Racial or ethnic status remains a major determinant of SEP in the United States as a result of interpersonal and institutional discrimination that constrains housing, educational, and employment opportunities (Conley 2000; House and Williams 2000). Similarly, there are steep gradients in risk for CVD mortality by SEP, whether measured as income, education, or occupation at the individual level (Cooper 2001; Kaplan and Keil 1993; National Heart, Lung, and Blood Institute 1995) or by indicators of income inequality (Cooper R, Casper M, Barnett E, unpublished data). The evidence linking race-based residential segregation to income inequality, as well as to constrained educational and economic opportunities within many predominantly black residentially segregated urban communities (Massey and Denton 1993; Orfield 1993), suggests mechanisms through which race-based residential segregation may contribute to CVD risk. At least one study (Cooper 2001) found an effect of race-based residential segregation on cardiovascular mortality above and beyond the effect of income inequality. HEP’s conceptual model posits that race-based residential segregation and associated economic inequalities influence the social and physical environments in which people live (Figure 1, arrows 1 and 2).
Intermediate Factors: Social and Physical Environments
Our model conceptualizes social environments as social, economic, and political relationships at the local level, for example, workplace conditions, citizen engagement and influence, indicators of community investment, and municipal supports such as street maintenance and the capacity and cultural competence of the police force. The physical environment includes the built environment, such as age and quality of housing stock, transportation systems, and age and location of industrial activities, which in turn influence residents’ exposures to, for example, airborne pollutants.
To illustrate the concepts represented by arrows 1 and 2 in Figure 1, processes that concentrate poverty in racially segregated communities affect both household income and area tax bases (Farley et al. 2000; Wacquant and Wilson 1989; Wilson 1996). The availability of personal and municipal economic resources in turn influences the infrastructure that supports community life, such as the adequacy and competence of the police force, fire-fighting services, and other municipal supports (Sugrue 1996; Wacquant and Wilson 1989). Race-based residential segregation influences the distribution of educational and employment opportunities (Massey and Denton 1993; Orfield 1993, 2001); services and retail outlets (Sugrue 1996); health care providers and pharmacies (McLafferty 1982; Whiteis 1992); and parks and recreational facilities, grocery stores, and fast food and liquor establishments (LaVeist and Wallace 2000; Zenk et al. 2005a).
Differential access to economic resources also has implications for residents’ ability to influence local political decisions. Areas with high concentrations of poverty contain fewer individuals with the economic resources and political influence to shape decisions regarding, for example, land use or the enforcement of existing environmental regulations (arrow 3). Concentrating residents with few political and economic resources into specific areas of the city weakens political influence (Cohen and Dawson 1993) and contributes to increased risk of exposure to hazards in the physical environment (Maantay 2001). Among these is exposure to airborne particulate matter (PM), which is linked to increased risk of CVD (Pope et al. 2004; Samet et al. 2000; Verrier et al. 2002).
Effects of airborne PM on CVD have been demonstrated at levels below the U.S. National Ambient Air Quality Standards (Peters et al. 2001). Detroit residents experience considerable fluctuations in air quality, and all of metropolitan Detroit has been designated as a nonattainment area for PM ≤2.5 μm in aero-dynamic diameter (PM2.5) as of 2004. Recent measurements also suggest that residents of some areas within Detroit may be disproportionately exposed to elevated levels of respirable particles (Keeler et al. 2002). This may affect cardiovascular risk factors (arrow 6). In addition, aspects of the built environment and airborne PM may also influence cardiovascular risk indirectly, through more proximate factors such as physical activity, social integration and social supports, and exposure to chronic stressors (arrow 5).
Proximate Factors and Cardiovascular Risk
Environmental conditions may influence a variety of more proximate risk factors, including perceived stressors, health-related behaviors, social integration and support, and psychosocial responses to stressors (arrows 4 and 5). Established variations in these risk factors by racial status and SEP may arise, at least in part, through the effects of the social and physical environments, exposure to stressful life conditions, health-related behaviors, social integration, and social support. Although a comprehensive review of this literature is beyond the scope of this article, we highlight established relationships between several proximate factors and CVD.
Stressful life conditions.
Exposure to stressful life events varies by SEP and race or ethnicity (Bosma et al. 1997; Marmot et al. 1997; Schulz et al. 2001; Williams et al. 1997), and the HEP conceptual model suggests that these variations are, at least in part, shaped by aspects of the social and physical environment. For example, residents of areas with few employment opportunities may experience higher levels of stressors related to job insecurity or inflexibility, or financial insecurity (Heslop et al. 2002; Pickering 1999; Wilson 1996). Similarly, in communities in which the tax base is inadequate to support police, firefighting, and other city services, residents may experience heightened concerns about crime, police effectiveness, and safety (Morenoff and Sampson 1997; Schulz and Lempert 2004).
Laboratory research on allostatic load, the body’s response to chronically stressful life conditions, has established that these physiologic responses experienced over time can lead to altered functioning of the hypothalamic–pituitary–adrenal axis and to increased risk of CVD (Björntorp 2001; Esch et al. 2002; McEwen 2000; Vitaliano et al. 2002). Excess cortisol produced under chronically stressful circumstances contributes to central adiposity (deposits of fat in the midsection of the body), an established risk factor for CVD (Björntorp 2001). Chronic exposure to stressful life conditions is linked to primary hypertension (Björntorp 2001) and may contribute to chronic inflammatory processes culminating in atherosclerosis (Black and Garbutt 2002).
Health-related behaviors.
Differences in health-related behaviors by race, ethnicity, and SEP may be influenced by differences in local social and physical environments (Lantz et al. 1998; Lynch et al. 1997; Zhang and Wang 2004). For example, both household income and residence in areas of concentrated poverty are associated with reduced intake of micronutrients that are protective against CVD (Kaufman et al. 1997). Residents of areas with high concentrations of poverty often experience reduced access to essential nutritional resources (Laraia et al. 2004; Nestle and Jacobson 2000; Swinburn et al. 1999; Zenk et al. 2005a). Intake of some micronutrients, including vitamins B6 and B12, which are cofactors in the metabolism of homocysteine, may interact with exposure to airborne PM to influence oxidative stress, a risk factor for CVD (Ford et al. 2002).
Inverse relationships have also been established between social class and smoking and may reflect in part a response to stressful life conditions associated with economic hardship (James 1999). Physical activity, another protective factor against CVD, may be influenced by conditions in the physical and social environment (Brownson et al. 2001; Lantz et al. 1998; Swinburn et al. 1999). Crespo et al. (1996), using NHANES data, found that 40% of African American women, who are disproportionately likely to live in communities with poorly maintained sidewalks and to have reduced access to recreational facilities, reported no leisure-time physical activity. Furthermore, a study of Latina women in an urban area found that concerns about safety were an impediment to outdoor physical activity (Kieffer et al. 2002).
Social integration and social support.
Social network ties, support, and integration vary in relation to SEP and are strongly associated with premature death and disease (Cacioppo et al. 2002; Heaney and Israel 1997), including CVD (Berkman et al. 1992; Case et al. 1992; Kawachi et al. 1996). The availability of social support when faced with stressful life conditions is also associated with depression and psychological distress (Israel et al. 2002; Lepore 1997; McEwen and Seeman 1999). There is some evidence that chronically stressful life conditions can contribute to erosion of these protective social relationships (Barrera 2000; Green and Rodgers 2001).
Psychosocial indicators.
Finally, a number of psychosocial characteristics have also been associated with increased risk of CVD, including anger or hostility (Carroll et al. 1997), and John Henryism, a high-effort coping response to stressful life conditions, with patterns that appear to be sensitive to social context (Dressler et al. 1998; James and Thomas 2000). Important health outcomes in their own right, symptoms of depression and psychological distress have also been found to be associated with cardiovascular mortality (Sheps and Sheffield 2001; Stansfield et al. 2002).
Cardiovascular Risk and Protective Markers
The proximate risk factors described in the preceding discussion have been linked to physiologic indicators for CVD (arrow 7). These include blood pressure, body mass, hip:waist ratio, and hemostatic (e.g., cholesterol) indicators of cardiovascular risk. There is substantial evidence that these cardiovascular risks are differentially distributed by race, ethnicity, and SEP. Rates of hypertension and cardiovascular mortality (Mensah et al. 2005), abdominal obesity (Sundquist and Winkleby 2000), and diabetes (Harris et al. 1998) vary by race, ethnicity, and socioeconomic indicators.
In sum, Figure 1 describes pathways through which established racial and socioeconomic differences in CVD risk may be shaped by race-based residential segregation and income inequalities, mediated through social and physical environments. This conceptual model has guided the HEP’s efforts to examine independent and cumulative contributions of aspects of the environment to patterns of CVD in Detroit. In the remainder of this article we describe the HEP study design.
Study Design
Data Collection
The HEP study design was initially developed through discussion among members of the URC board before submission of the grant proposal. The URC had previously worked in two of the areas of the city included in this study; the board recommended adding the third (northwest Detroit) after discussing the research questions, to increase variation across study communities in air quality. The URC board helped to develop the HEP study design, and once funding was received, board members identified several new organizations from areas of the city involved in the study to join the HEP SC. In keeping with the principles of CBPR, members of the HEP SC worked together to design specific components of the study. As we describe each of the areas of the study below, we also describe briefly how members of the partnership worked together to design, implement, and interpret results from the study. [For additional details on the participatory processes involved, see Schulz et al. (2005a) and Zenk et al. (2005a).]
HEP used a wide range of data collection methods to address the study questions. These included data from decennial censuses (1970–2000; U.S. Census Bureau 2005); administrative sources (e.g., land use documents); neighborhood observation checklist (NOC); airborne PM ≤10 μm in aerodynamic diameter (PM10) and PM2.5 monitored in each of three study communities over a 3-year period (January 2000 through December 2002); a stratified random-sample community survey administered to residents of the three study communities; and biomarker data collected from a subset of survey participants. Approval was granted for the HEP study in January 2001 by the University of Michigan Institutional Review Board for Projection of Human Subjects.
Census and administrative data.
Data from the 1990 decennial census (U.S. Census Bureau 2005) were used to identify the three HEP study areas, based on evidence of variations in racial/ethnic and socioeconomic composition, as well as preliminary air quality data indicating variations in airborne PM. During the study period, a doctoral research assistant worked with the HEP SC to identify additional census data of interest and to compile data relevant to the study questions (e.g., percentage below/above poverty; median home value), for decennial censuses conducted between 1970 and 2000 (U.S. Census Bureau 2005). A postdoctoral scholar worked with the SC to identify sources of relevant administrative data (e.g., crime reports, location of parks and recreational facilities, toxic waste sites).
Neighborhood observation checklist.
A subcommittee of the HEP SC developed a systematic NOC to document characteristics of selected blocks within the areas from which survey respondents were sampled (see survey sampling description in “Community survey”). This subcommittee worked with a doctoral research assistant to adapt items from several existing instruments (Caughy et al. 2001; Farquhar 2000; Morenoff JD, House JS, Raudenush SW, unpublished data; Perkins et al. 1992) and to develop new items for this checklist through an extensive process (for a more complete description of this process, see Zenk et al. 2005b). The final 140-item checklist assessed aspects of the social and built environments for each study block (e.g., condition of homes and businesses, vacant lots, streets and sidewalks, traffic patterns, and parks and recreational facilities). Neighborhood raters completed a 36-hr initial training period followed by group and individual practice sessions, and feedback of interrater reliability (IRR) statistics based on practice blocks. Eleven observers were certified and collected data using the HEP NOC on 551 blocks across the three study neighborhoods during a 15-week period in the summer and early fall of 2003 (Zenk SN, Schulz AJ, Mentz G, House JS, Miranda P, Gravlee CC, et al., unpublished data; Zenk et al. 2005b). The sample for the NOC consisted of 147 blocks in which one or more HEP survey respondents resided, and 404 blocks that shared a common border with those blocks (so-called rook neighbors) (Lee and Wong 2001).
Physical environment: airborne particulate matter.
PM10 and PM2.5 were measured seasonally over a 3-year period (January 2000 through December 2002) as indicators of the physical environment in the three study communities. Data collected included a historical assessment of exposure to ambient PM10, as well as a multiyear assessment of exposure to fine aerosols, PM2.5, and the chemical components of PM2.5. This multiyear approach allowed proper characterization of community level exposure to PM10 and PM2.5 and attention to the contribution of point or localized sources of ambient air pollution (e.g., motor vehicle traffic, industrial facilities).
PM2.5 and PM10 samples were collected daily onto 47-mm Teflon membrane filters (Pall Life Sciences, Ann Arbor, MI) during seasonal measurement intensives [four times per year, 2 weeks duration each; see Keeler et al. (2002) for additional detail] using the dichotomous sequential air sampler Partisol-Plus (model 2025; Rupprecht and Patashnick Co., Inc., East Greenbush, NY), for subsequent chemical and elemental characterization of fine and coarse particles as previously described (Keeler et al. 2002). The dichotomous configuration of the sampler permits the differentiated mass determination and chemical composition of the fine (≤2.5 μm aerodynamic diameter) and coarse (2.5–10 μm) particles contained in PM10, which can aid in further source identification. Consistent with other aspects of the project, HEP SC members were involved as members of analysis and writing teams examining and disseminating the PM results.
Community survey.
The HEP community survey was developed by a survey subcommittee of the SC that worked together for over a year to develop and pretest the survey instrument. In doing so, this subcommittee drew on results from community focus groups, the literature on cardiovascular risk and protective factors, and extensive discussions between April 2001 and April 2002 (Schulz et al. 2005a). Survey data collection began March 2002 and ended March 2003.
The HEP survey sample is a stratified, two-stage equal probability sample of occupied housing units (or households) in the three areas of Detroit in which air quality was monitored (see “Physical environment: airborne particulate matter”). In each of the three areas, all respondents lived in a compact area with at most a 1.3-mile radius. The sample was designed to obtain 1,000 completed interviews with persons 25 or more years of age in the three study areas.
In each area, households were to be selected to attain approximately equal representation across racial and ethnic groups and by SEP. This design was intended to allow for comparisons across racial and ethnic and socioeconomic status while holding air quality constant (i.e., within geographic areas). It also allows comparisons of residents with similar social and economic characteristics across air quality exposures (i.e., across geographic areas of the city). The racial and ethnic distributions of the Detroit population did not allow study goals to be met completely. White residents were oversampled in northwest and southwest Detroit, and census tracts in southwest Detroit where most of the Hispanic population resides were oversampled. No effort was made to select Hispanic respondents from the two study areas in which Hispanic residents made up < 1% of the population, or white respondents in the east side of the city where there were fewer than 3% white residents.
In the first stage of selection, blocks were selected with probabilities proportionate to Census 2000 (U.S. Census Bureau 2000) counts of households. Households within sample blocks were listed by study staff, and a sample of approximately equal numbers of housing units per block were selected with probabilities inversely proportionate to size. The products of the probabilities of selection were equal for housing units in each study area.
Interviewers visited each sampled housing unit to complete the last stage of selection. They attempted to obtain a list of all residents 25 or more years of age. Respondents were randomly selected from the list of eligible household members using an objective respondent selection procedure (Kish 1965). Probabilities of selection were varied to achieve target numbers of non-Hispanic black, non-Hispanic white, and Hispanic participants of low and moderate socioeconomic status.
Study enrollment projections and results of field sampling are shown in Table 2. In east-side Detroit, which was 97% non-Hispanic black according to Census 2000 (U.S. Census Bureau 2000), 97% of HEP survey respondents reported their race as African American. In northwest Detroit, white respondents were oversampled, and interviews were completed with 162 non-Hispanic black and 93 non-Hispanic white respondents as well as 13 respondents with other or unspecified racial or ethnic identity. In southwest Detroit, both non-Hispanic white and Hispanic respondents were oversampled, and interviews completed with 93 non-Hispanic black, 99 non-Hispanic white, and 177 Hispanic respondents, most of whom identified as of Mexican origin. The number of survey participants with household incomes above and below the poverty line by race and area of the city are also shown in Table 2 compared with enrollment targets. Interviews were conducted in Spanish or in English according to the preference of the respondents: 106 interviews were completed in Spanish.
Of the 2,517 housing units in the initial sample, 1,297 were invalid (e.g., vacant, under construction), were unable to be screened after repeated attempts (no one contacted after 12+ attempts, refused screener), or contained no eligible respondent (e.g., no one 25 or more years of age). Of the 1,220 households in which an eligible respondent was identified, interviewers were unable to contact the identified respondent after repeated attempts in 193 (16%). Of the 1,027 eligible respondents contacted, 105 (10%) refused to be interviewed, and interviews were completed with 922 respondents (90%), three of whom were subsequently determined to be ineligible. Assuming an 80% eligibility rate for noncontacted households, we estimate that there were 1,663 housing units within the sample frame with an eligible respondent. The overall response rate (number of completed interviews from the number of households in sample estimated to have an eligible respondent) was 55% (919 of 1,663); interviews were completed with 75% of households in which an eligible respondent was identified, and in 90% of the total households in which an eligible respondent was contacted. Sample weights were constructed to adjust for differential selection and response rates, allowing us to estimate population effects from the HEP sample.
For each community member who agreed to participate in the study, data gathered included demographic information (age, income, education); self-reported stressors (life events, police stress, discrimination, safety stress, financial stress); assessments of health-related behaviors; self-reported exposure to airborne PM in home and workplace settings; indicators of social support, integration, and community connectedness; responses to stressful life conditions; self-reported medical history and conditions; anthropomorphic and hemodynamic measures; and nutrition data collected using a food frequency questionnaire. A detailed list of scales used in the survey and supporting documentation are available in Schulz et al. (2005b).
HEP contracted with a survey research organization to manage the day-to-day aspects of the survey and worked closely with this organization to develop and conduct interviewer training and to assist in survey administration. On the basis of recommendations from the HEP SC, survey interviewers were Detroit residents. Members of the HEP SC and other members of their organizations assisted with the 32-hr training in survey interviewing techniques and procedures and instruction in the collection of anthropomorphic and hemodynamic measures. At the completion of training, interviewers received certification and were required to be recertified in collection of survey, anthropomorphic, and hemodynamic measures on a monthly basis. Quality controls included review of completed survey by field supervisors, and additional review of completed surveys by research staff for quality assurance and completeness. The administrator of the subcontracting organization attended monthly meetings of the full HEP SC to provide reports on survey progress and to discuss the quality and progress of the survey.
Biomarker data collection.
At the completion of the survey interview, each respondent was invited to participate in the clinical portion of the study, which involved collection of blood and saliva samples. This component of the study allowed for analysis of associations between exposure to social stressors, PM10, PM2.5, and biomarkers for CVD and, within each focal area, analysis of the potential mediating effects of micronutrients on biomarkers. Of the 919 survey participants, 367 participated in the clinical component of the study, a substantially larger number than the 200 initially anticipated. Each participant was provided with a saliva sample collection kit (Sarstedt Corp., Montreal, Canada) with stepwise instruction for collecting saliva samples adapted from sample collection procedures described in the literature (King et al. 2000). Participants were instructed to collect saliva samples over 2 consecutive days and were asked to store the samples in their home freezer or refrigerator. They were instructed to bring the stored saliva samples to the community site on the day of their scheduled blood draw. Participants were scheduled for their biomarker assessment at a community site (e.g., a community-based partner organization) set up for the purpose of the HEP project in three areas of the city—eastside, northwest, and southwest Detroit. Participants received a reminder phone call from the HEP staff 3 days before their scheduled appointment.
Participants were instructed to fast for 10–12 hr before their appointment and to bring their saliva samples to the site. At the site, their resting blood pressure was measured three times by a team of trained and certified phlebotomists. Participants then completed a brief questionnaire that characterized their use of vitamin, mineral, and herbal supplements, use of prescription and nonprescription medications, and ongoing infection symptomatology. Venous blood was drawn from the participants and aliquoted for processing. Biomarker site staff were trained and required to demonstrate competency and certified in collecting, handling, transporting, and processing of the biomarker samples (Kannan S, Arya I, Schulz A, Wyman L, Roy R, Benjamin A, et al., unpublished data). Training was provided in biohazard safety procedures modified from the Occupational Safety and Environmental Health (OSEH) Laboratory Biosafety Manual (OSEH 2005) procedures. Biomarker data collection began in May 2002 and ended in April 2003.
Follow-up with Results
At the time of data collection, each survey respondent received a card indicating the mean of the second and third measures of blood pressure (systolic, diastolic) taken by the survey interviewer and recommendations for follow-up according to American Heart Association (AHA) guidelines. Reports with results from the food frequency questionnaires and, where relevant, biomarker results were designed by a working group of HEP SC members and computerized by a team of graduate students (Kannan S, Arya I, Benjamin A, Wyman L, Roy R, Schulz A, et al., unpublished data). The dietary reports were produced in Spanish or English, depending on the language in which the survey was conducted, and provided summarized feedback on participants’ dietary intakes based on their responses to the food consumption questionnaire, as well as data on height, weight, and systolic and diastolic blood pressure readings derived from the survey. In addition to feedback about their dietary intakes, blood pressure, height, and weight, suggestions consistent with the AHA nutrition and weight for height recommendations were incorporated within the report. The 367 participants of the biomarker component of the study were provided a second report of their blood pressure (systolic, diastolic) as measured at the biomarker site and blood lipid levels derived from their biomarker site sample. With written permission of participants, in the event that biomarker results indicated elevated risk of CVD based on AHA guidelines, biomarker feedback reports were also mailed to the respondents’ designated health care provider.
Study respondents who indicated an interest received annual mailings with summary results from the study and community outreach and educational activities. Results from HEP data analysis are also disseminated widely through community forums, newsletters, and translation to local decision makers, as well as through peer-reviewed publications. HEP SC members are actively involved in these efforts.
Data Management and Analysis
Neighborhood observation checklist.
NOC data were collected by trained community raters on 551 blocks using a PDA, and data were downloaded electronically to a SAS database (version 8.0, SAS Institute Inc., Cary, NC). IRR across 220 NOC variables was evaluated in two ways. First, we evaluated IRR across the 12 observers, including a gold standard rating on four blocks, using a κ-statistic designed for multiple observers by Gwet (2002) (κ = 0.77). Second, IRR was assessed based on 221 street segments that were rated by two different observers using Cohen’s κ-statistic (κ = 0.77). In addition, test–retest reliability on 54 street segments that the same observer rated when observing adjacent blocks at different time points was high (κ = 0.86). Ecometrics (reliability and validity) for scales created using NOC items were evaluated using processes developed by Raudenbush and Sampson (Raudenbush 2003; Raudenbush and Sampson 1999; Zenk SN, Schulz AJ, Mentz G, House JS, Miranda P, Gravlee CC, et al., unpublished data).
Airborne particulate matter.
All filters collected as part of HEP for PM characterization were prepared and analyzed at the University of Michigan Air Quality Laboratory (Keeler et al. 2002; Yip et al. 2004). The detection limit for mass determination, calculated as 3 times the standard deviation of seven replicate filter measures, is 5.1 μg. Upon completion of gravimetric analysis, PM samples collected on Teflon filters were analyzed for trace element composition. Teflon sample filters were wetted with 150 μL of ethanol before extraction in 20 mL of 10% HNO3 and sonication for 48 hr in an ultrasonic bath. Samples were then diluted with Milli-Q water to 4% vol/vol solutions before passive acid digestion for 1 month. The extracts were then analyzed for a suite of elements by high-resolution inductively coupled plasma-mass spectrometry (ELEMENT2; Finnigan MAT, Austin, TX) similar to methods previously described (Moore et al. 1996).
Community survey.
Survey data were entered into a database by data entry personnel at Automated Resources Management Inc. (Ann Arbor, MI), an independent data management corporation. The food frequency questionnaire was entered into a separate database using a modified version of the Block data analysis software (Block et al. 1986, 1994). Each respondent was identified by a code number, with a key listing that matched code numbers to each survey respondent allowing data collected through various mechanisms (survey, biomarker, NOC, air quality) to be linked for analyses. All data gathered in the face-to-face interviews were entered into a database and linked with data from the NOC, census data, air quality data, and biomarker data to create a comprehensive database. Standardized scales assessing stressors, health-related behaviors, social support, and psychosocial responses to stress were constructed by aggregating individual items into the psychosocial constructs described in the preceding sections. Psychometric properties (Cronbach’s α) were calculated for each scale.
Intakes of micro- and macronutrients were calculated by multiplying the frequency of consumption of each unit of food by the nutrition content of the specified portions on the food frequency questionnaire. Food frequency questionnaires were analyzed for macro- and micronutrients using a modified version of the Block diet analysis program (Block et al. 1986, 1994; Kannan S, Arya I, Benjamin A, Wyman L, Roy R, Schulz A, Dvonch JT, et al., unpublished data). Micronutrient intakes were characterized to determine food group contributions to intakes.
The HEP sample deliberately selected specified race or ethnic groups at higher rates in two of the three neighborhoods in order to obtain large enough sample sizes for race by class comparisons across areas of the city. Furthermore, within each selected household, one person was selected at random from all eligible persons who usually resided in the household. This led to an overrepresentation of respondents from households with fewer eligible persons. Finally, response rates varied across the three neighborhoods, and across different sets of sample blocks within neighborhoods.
Weights were constructed to adjust for these design features. The weights consist of two components: an unequal probability of selection adjustment and a poststratification adjustment. The latter adjustment was designed to make the weighted distribution for each neighborhood resemble the distribution of adults 25 or more years of age obtained in Census 2000 (U.S. Census Bureau 2000). The unequal probability of selection adjustment was computed as the inverse of the probability of selection of each household and person with in the household (probabilities of selection were computed for all units at the time of sample selection and retained for just this purpose). The unequal probability adjusted weights were then further adjusted by a poststratification factor to make the weighted sample look like the Census 2000 population in each neighborhood. This poststratification adjustment provides compensation for differential non-response and noncoverage that arose in the survey. The application of these weights to analyses conducted using the HEP sample allows us to estimate population effects from the HEP sample.
Biomarker data.
The validity of all bio-marker measurements was checked through examination of biomarker outliers and external quality control programs, such as routine measurement of biomarkers from phantom samples and lab performance in independent quality maintenance programs such as the Micronutrient Measurement Quality Assurance Programs offered by the National Institutes for Standards and Technology (Gaithersburg, MD) and the Centers for Disease Control and Prevention Lipids Standardization Program (Myers et al. 1994). Blood samples were centrifuged to separate the plasma and serum, which were then stored in a –70°C freezer until further analysis of the samples. Measurements will be made for several biomarker domains of lipids, lipoproteins, lipid peroxidation, and homosyteine metabolite concentrations and for oxidative damage and stress.
Integrated data analysis.
As described above, unique identification numbers were used to link data gathered through various components of the study. Census data, administrative data, and data from the NOC were located in separate databases and linked to survey respondents using census block, block group, and tract numbers. Air quality data for each of the three areas of the study were linked for analysis using aerial indicators (northwest, eastside, southwest). Linking of data from various sources allows for analysis across the various sources and levels of data collected for HEP (e.g., contextual and behavioral).
Data analysis for the HEP study will test a series of hypotheses regarding relationships among the components of the conceptual model described in this article (Figure 1). Specifically, the analyses will examine bivariate relationships between the intermediate, proximate, and health outcome variables to establish relationships among these various levels of the model. In addition we will conduct multiple regression analyses to examine independent and cumulative effects of exposures in, for example, the social and the physical environments and to test for interactions among predictor variables. Hierarchical linear modeling techniques will then be used to estimate relationships between indicators of neighborhood built environment (e.g., condition of housing, path characteristics), social environment (e.g., territoriality), psychosocial and behavioral risk factors (e.g., perceived stressors, symptoms of depression, physical activity), and cardiovascular risk factors (e.g., systolic blood pressure), controlling for individual characteristics (e.g., age) derived from survey data. Members of the HEP SC are actively engaged in the data analysis process, in interpretation of findings, as co-authors of peer-reviewed journal articles, as presenters at scientific meetings, and in community forums. In keeping with the community outreach and education plan component of this effort, findings will be disseminated through both peer-reviewed publications and presentations at professional meetings and also through a wide range of local, state, and regional audiences, including community residents and city, state, and regional decision makers. The HEP SC prioritized study findings for dissemination, identified media through which to reach these audiences (e.g., local newspapers, community forums, newsletters), and will participate actively in dissemination of results through these venues.
Discussion
CVD is a major contributor to morbidity and mortality and varies substantially across racial and ethnic groups as well as by SEP. As a CBPR effort, the HEP brings together representatives from community-based organizations, health service organizations, and academic institutions to collectively investigate the contributions of social and physical environments to racial and socioeconomic inequalities in the risk of CVD. Our goal is to contribute to an understanding of, and to inform efforts to eradicate, these disparities.
HEP emerged from priorities identified by the Detroit URC to examine the contributions of environmental factors to health disparities, and the conceptual model that guides the HEP study builds on previous work conducted by partners involved with the URC. This model integrates prior empirical research, the experience and insights of members of the partnership, conceptual models of race-based residential segregation and health, and a vast literature on CVD. This model guides HEP’s analysis of social and physical environments as intermediate factors contributing to CVD risk, mediating relationships between fundamental factors such as race-based residential segregation and concentrated poverty, and more proximate factors (e.g., physical activity, dietary practices) associated with CVD. Representatives from community-based organizations, health service organizations, and academic institutions have been, and will continue to be, involved in all aspects of HEP, from establishing the priorities for research (the contribution of environmental factors to CVD disparities) to informing the conceptual model, determining the study communities, and development and implementation of the data collection processes.
As the largest contributor to all-cause mortality in the United States as well as to racial disparities in mortality, it is essential to understand the factors that contribute to excess cardiovascular mortality among racial, ethnic, and socioeconomic subgroups of the U.S. population. Members of adversely affected communities join health practitioners and academic researchers in their profound interest in understanding and addressing the pathways and processes through which these disparities are produced and sustained (O’Fallon and Dearry 2002). The wide range of measures of both the physical and social environments and the ethnic diversity of the sample are unique features and major strengths of this study, as is the community-based participatory nature of the process with which it was carried out. The wide range of measures permits comparisons that may provide important insights about relationships among racial or ethnic group status, SEP, social environments, physical environments, and more proximate risk factors for CVD. The community-based participatory process allows community residents and representatives from community-based organizations, health service providers, and academic researchers to pool their skills, resources, and knowledge to extend our understanding of the complex pathways through which local environments influence risk of CVD. Perhaps more important, because these diverse groups engage in the process of developing knowledge about CVD, the capacity to disseminate results widely to local decision makers, health care providers, and community residents, as well as through the scientific literature, is enhanced, along with the potential to facilitate effective interventions and policy changes to reduce racial and socioeconomic disparities in CVD.
This article is part of the mini-monograph “Community-Based Participatory Research.”
The Healthy Environments Partnership (HEP) is a project of the Detroit Community–Academic Urban Research Center. We thank the members of the HEP Steering Committee for their contributions to the work presented here, including representatives from Boulevard Harambee, Brightmoor Community Center, Detroit Department of Health and Wellness Promotion, Detroit Hispanic Development Corporation, Friends of Parkside, Henry Ford Health System, Southwest Detroit Environmental Vision, Southwest Solutions, University of Detroit Mercy, and the University of Michigan Schools of Public Health, Nursing, and Social Work and Survey Research Center. Finally, we thank S. Andersen for assistance with preparation of the manuscript.
HEP is funded by National Institute of Environmental Health Sciences (NIEHS) RO1 ES10936-0. Work by the Michigan Department of Environmental Quality and funding from the Michigan Center for the Environment and Children’s Health (U.S. Environmental Protection Agency R826710-01, NIEHS P01-ES09589-01 and R01-ES10688-03) helped to support air quality data analyzed as part of HEP. For additional information, see http://www.hepdetroit.org
Figure 1 Conceptual model and data sources for HEP: social and physical environmental factors and disparities in cardiovascular risk. Arrows 1–7 indicate relationships between components of the conceptual model. Solid arrows indicate the main hypothesized effect. Dashed arrows indicate that some reciprocal effect may be present. Letters in the box “Data sources” refer to footnotes in other boxes in the figure.
Table 1 Age-adjusted heart disease mortality rates for non-Hispanic black and non-Hispanic white residents of the United States (1999), Michigan (2000), and Detroit (2002).a
All Non-Hispanic black Non-Hispanic white
United Statesb 260.4 336.5c 263.5c
Michigand 285.3 366.5 275.7
Detroitc 401.1 409.1 408.8
a All rates are per 100,000 population. Data from
b U.S. Department of Health and Human Services (2001) and Minimo and Smith (2001);
c MDCH (2004);
d Michigan Department of Community Health (MDCH) (2003).
Table 2 Racial and ethnic distribution goals and results (number of respondents) for the Healthy Environments Survey for eastside, northwest, and southwest Detroit.
Eastside Detroit
Northwest Detroit
Southwest Detroit
Total
Goal
Actual
Goal
Actual
Goal
Actual
Goal
Actual
APa BP AP BP AP BP AP BP AP BP AP BP AP BP AP BP
Non-Hispanic black 134 133 132 126 67 67 102 60 66 67 49 42 267 267 283 228
Hispanic 0 0 0 2 0 0 1 0 100 100 90 87 100 100 91 89
Non-Hispanic white 0 0 2 0 66 67 63 30 67 66 50 49 133 133 115 79
Other 0 0 1 1 0 0 3 5 0 6 8 0 0 10 14
Subtotal 134 133 135 129 133 134 169 95 233 233 195 186 500 500 499 410
Missinga — 3 — 4 — 3 — 8
Total 267 267 267 268 466 384 1,000 917
Abbreviations: AP, above poverty; BP, below poverty.
a Respondents missing data on race and income and therefore uncategorizable.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7914ehp0113-00182616330372ResearchMini-MonographHealth Disparities and Toxicant Exposure of Akwesasne Mohawk Young Adults: A Partnership Approach to Research Schell Lawrence M. 12Ravenscroft Julia 2Cole Maxine 3Jacobs Agnes 3Newman Joan 4Akwesasne Task Force on the Environment 51 Department of Epidemiology, University at Albany, State University of New York, USA2 Department of Anthropology, University at Albany, State University of New York, USA3 First Environment Research Projects, Akwesasne Mohawk Nation, Akwesasne, New York, USA4 Department of Educational Psychology and Statistics, University at Albany, State University of New York, USA5 Akwesasne Mohawk Nation, Akwesasne, New York, USAAddress correspondence to L.M. Schell, 1400 Washington Ave., AS 237, University at Albany, Albany, NY 12222 USA. Telephone (518) 442-4714. Fax: (518) 442-4563. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 18 7 2005 113 12 1826 1832 28 12 2004 11 7 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In this article we describe a research partnership between the Akwesasne Mohawk Nation and scientists at the University at Albany, State University of New York, initiated to address community and scientific concerns regarding environmental contamination and its health consequences (thyroid hormone function, social adjustment, and school functioning). The investigation focuses on cultural inputs into health disparities. It employs a risk-focusing model of biocultural interaction: behaviors expressing cultural identity and values allocate or focus risk, in this instance the risk of toxicant exposure, which alters health status through the effects of toxicants. As culturally based behaviors and activities fulfill a key role in the model, accurate assessment of subtle cultural and behavioral variables is required and best accomplished through integration of local expert knowledge from the community. As a partnership project, the investigation recognizes the cultural and socioeconomic impacts of research in small communities beyond the production of scientific knowledge. The components of sustainable partnerships are discussed, including strategies that helped promote equity between the partners such as hiring community members as key personnel, integrating local expertise into research design, and developing a local Community Outreach and Education Program. Although challenges arose during the design and implementation of the research project, a collaborative approach has benefited the community and facilitated research.
adolescentsAkwesasne Mohawk Nationcommunity-based participatory researchhealth disparitiesNative Americanpartnership researchpolychlorinated biphenyls
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There is considerable concern about the possible effects of endocrine-disrupting compounds such as polychlorinated biphenyls (PCBs) on the development of thyroid function (Brouwer et al. 1998; Osius et al. 1999; Persky et al. 2001; Ribas-Fito et al. 2003) and neurobehavioral maturation (Guo et al. 1994; Jacobson et al. 1990; Schantz et al. 2003). Risk of exposure to environmental contaminants such as PCBs is not an individual choice but is related to larger political and economic factors. Minority communities are often at special risk of exposure, as they are more often affected by toxic landfills, incinerators, dumping, mining, and other environmentally damaging activities (Akwesasne Notes 1993; Bryant et al. 1992; Chavis et al. 1983; Commission for Racial Justice, United Church of Christ 1987; Mohai and Bryant 1992; Schell and Czerwinski 1998). Native Americans especially suffer from a combination of these risk factors as they strive to maintain cultural identity, are often economically disadvantaged, and are perceived by mainstream society as ethnically distinct.
The Mohawk Nation at Akwesasne, New York, “the land where the partridge drums” (LaDuke 1999, p 11), has shared a disproportionate amount of environmental injustice since the construction of the St. Lawrence Seaway and the St. Lawrence–FDR Power Project in the 1950s. Cheap hydroelectric power led to the development of several major industries directly upstream, upwind, and upgradient from the community. The industrial sites have contaminated the St. Lawrence with PCBs (Ecology and Environment, Inc. 1992; RMT, Inc. 1986; Woodward-Clyde Associates 1991), and Akwesasne now sits directly adjacent to a National Priority Superfund Site while two New York State Superfund sites are nearby and immediately upriver [U.S. Environmental Protection Agency (EPA) 1984]. Some local species of fish, birds, amphibians, and mammals have PCB levels that exceed the U.S. Food and Drug Administration’s tolerance limits for human consumption (Forti et al. 1995;Lacetti 1993; Sloan and Jock 1990). Akwesasne is a potential candidate for designation as an Environmental Justice Community (EJC) by the U.S. EPA because of elevated PCB levels in adjacent lands and traversing waterways, and the potential for inhalation exposure from volatized PCB particulates.
In this community, diet, particularly consumption of fish and other aquatic animals, is an important route of exposure. Another route for newborns and infants is via breast milk (Fitzgerald et al. 2001, 1998, 1992). PCBs are lipophilic, and, consequently, a mother’s PCB burden is passed on through breastfeeding. The passage of PCB body burdens across generations highlights the importance of exploring the household and familial contexts that pattern exposure risk. From a public health perspective, a solution is to implement educational intervention programs that discourage community residents from engaging in activities that might increase their exposure to local contaminants. However, Native Americans “are unique cultural and political groups who have very distinct environmental problems” (U.S. EPA 1992), as risk of exposure to environmental contaminants is embedded within active participation in the culture of this community and, indeed, within cultural survival itself.
Thus, the Mohawk community at Akwesasne has found itself with two alternatives, neither of which is fully acceptable to the community. The first is to continue dietary and cultural practices that increase exposure to environmental contaminants; this is, of course, not an option for many community members because of the health risks to adults, children, and generations to come. The second is to ask community members to avoid dietary and cultural practices related to exposure. The Mohawk community has followed the recommendations of tribal and state fish advisories implemented in the 1980s and early 1990s, and the levels of PCBs in breast milk and serum have fallen (Fitzgerald et al. 1995, 1998, 2004). At first glance this history may appear to be a public health policy success story, but this interpretation does not consider the specific cultural context and implications. For many Native communities, subsistence-based activities are part of the surviving traditional culture and identity. A fundamental component of Mohawk identity is that the ties linking individuals, families, and groups to specific locations and land have symbolic and sacred meaning. For the Mohawk community, being asked to avoid activities that reaffirm Mohawk identity is not a solution to this problem but a bigger problem in and of itself (Arquette et al. 2002). Because the cultural integrity and continuation of the Mohawk people is at stake, before any intervention process begins, those activities that truly place individuals at risk should be identified [Akwesasne Task Force on the Environment (ATFE) 1997] and placed in the context of Native rather than Western, economic, environmental, or social priorities (Arquette et al. 2002).
The goal of the present research is to identify adverse health effects among older adolescents and young adults stemming from exposure to local pollutants through behaviors that express cultural values and affirm cultural identity. If we can determine the behavioral pathways to health effects of interest, it may be possible for the community to continue activities that contribute to national and cultural sovereignty while not harming well-being. The health outcomes of interest are effects on thyroid function and measures of school performance and community adjustment. These outcomes were chosen because they represent the intersection of prior scientific investigations on PCBs and child development (Brouwer et al. 1998; Guo et al. 1994; Jacobson et al. 1990; Osius et al. 1999; Persky et al. 2001; Ribas-Fito et al. 2003; Schantz et al. 2003; Schell et al. 2002, 2004; Winneke G, Bucholski A, Heinzow B, Kramer U, Plabmann S, Schmidt E, et al., unpublished data) and community concerns for the social, spiritual, and physical well-being of their youth. In this article we describe the community–academic partnership that developed and implemented a program of research to investigate the impact of environmental contaminants on the health of young adults.
Conceptual Framework—Scientific Model and the Need for Indigenous Knowledge
This research is based on the risk-focusing model (Schell 1997). The model was developed during a study of multigenerational effects of lead exposure. The model described how disability from lead exposure in early life leads to many outcomes, including reduced cognitive performance, reduced educational opportunity, reduced opportunities for employment, and greater chances of residing in an area of higher lead exposure in which another generation is exposed (Schell 1992). The model acknowledges that risk in stratified societies is apportioned according to the social and biological characteristics of individuals. Risk is said to be “focused” because several different types of risk commonly occur simultaneously in individuals with shared characteristics, and the risks are compounded over generations. None of the events in such a sequence are the result of individual choices, but each results from larger economic and social forces.
The risk-focusing model is a complement to models of resource allocation common in health disparities research in which resources are allocated on the basis of socioeconomic characteristics (Schell 1997). Risk focusing also recognizes that risk may be allocated on the basis of the socioeconomic characteristics that are themselves the consequences of previous exposures in one or several generations. A general model first relates social position to risk of exposures (environmental, occupational, etc.), then exposures to disabilities and suboptimal health, and, finally, disabilities to social position and further stratification (Figure 1). Any social group, whether defined in terms of biological characteristics or ethnicity or occupation, can experience suboptimal health and disabilities through exposure to environmental pollutants and then reduced opportunities for socioeconomic rewards because of poorer health.
Studies of environmental crises and disasters, such as the Exxon Valdez oil spill (Palinkas et al. 1992) and other similar incidents (Curtis 1992; Grinde and Johansen 1995; Harris and Harper 1997; Hild 1998), suggest that Native groups might be disproportionately affected by environmental pollution because of subsistence systems and a cultural ethos that involve greater contact with the physical environment. The meaning of “land,” and the environmental contamination of that land, has a spiritual significance that not only contributes to individual health but also affects identity and well-being at a group level (Arquette et al. 2002; Ransom and Ettenger 2001). Environmental contamination not only disrupts sacred ties and connections to place but also disrupts the practice of many activities such as consuming locally caught fish, trapping, hunting, gardening, and gathering materials for basket-making that express and reaffirm Mohawk identity and culture. These activities have important cultural and spiritual meaning but place individuals in direct contact with local contaminants that may increase exposure. Thus, contamination of the local environment is experienced by the community as a threat to cultural identity because avoidance of PCBs involves the inability to practice activities that are important to the Mohawk way of life and connection to the land.
In applying a risk-focusing model to Akwesasne (Figure 2), socioeconomic position now refers to behaviors that affirm Mohawk identity as well as usual components of socioeconomic status. The model allows us to capture the transgenerational pathway of exposure to PCBs via prenatal and lactational pathways that stem from maternal exposure. The model also includes susceptibility factors that may be allocators of risk, such as age, sex, differences in metabolism and storage of PCBs, and concurrent exposure to other toxicants. Data on toxicant levels and growth from a previous study (Gallo et al. 2005; Schell et al. 2003) of the same cohort when members were 10–17 years of age [the Mohawk Adolescent Well-Being Study (MAWBS), 1995–2000] are integrated to provide additional context, time depth, and control variables.
In applying the model to the current study, household socioeconomic position is related to possible “exposure behaviors” identified by Mohawk community members, such as fishing, hunting, picking berries and herbs, and cultivating gardens, that may allocate exposure. Because diet is another possible exposure pathway, extensive data are collected on adolescent consumption of locally caught fish and game over the last five years. A history of maternal local food consumption before and during her pregnancy with the participating adolescent is also collected to assess cross-generational dietary patterns. The ability of these behaviors to increase exposure of mothers and children to contaminants is assessed at two time points by determining the levels of PCBs and other contaminants in serum of the adolescents between 10 and 17 years of age while participating in the MAWBS and during the current project, the Young Adult Well-Being Study (YAWBS).
The primary health outcomes of interest are thyroid hormone function and measures of neurobehavioral maturation that pertain to school performance and community adjustment. The model considers pathways by which toxicants may affect such important domains directly and indirectly through hyperactivity or/and alterations in thyroid function. These relationships are assessed in individuals at 17 years of age.
Measures of thyroid function (levels of thyrotropin, total and free triiodithyronine, and thyroxine) are conducted following standard laboratory procedures. School functioning and adjustment is assessed in terms of grades, standardized test scores from state or provincial testing, and indicators of disciplinary action and school absences. Teachers complete two rating scales to describe the adolescent’s school behavior. The first of these is the Conners’ Rating Scales–Revised: Teacher Form (Conners 1997). Because of evidence from previous studies of problems of attention and activity level associated with PCB exposure, the teachers are also asked to complete a second rating scale, the Attention Deficit Disorders Evaluation Scale (McCarney 1995). Those 17-year-old individuals not attending school are interviewed to determine their age at dropping out of school, current employment status, membership in community organizations, and delinquency (any arrests or probation). Attendance records for the last year of school are also sought. Information about community membership, involvement, and delinquency is obtained from those adolescents who are still attending school.
The model tests pathways between cultural values, actualizing behavior, exposure, and health effects. It involves variables that differ considerably in their degree of standardization. Outcomes are measured with standard techniques (i.e., measures of toxicant exposure, thyroid function, standardized tests of hyper-activity), while measures of proximate and distal causes of these outcomes (values and culturally expressive behaviors) are tailored to the specific cultural context to obtain a detailed sociocultural analysis. Clearly, the accurate and reliable measurement of causal variables in the model depends on knowledge of the community—knowledge that may be provided best by the community itself.
Rationale for a Partnership Study
To address community concerns, a partnership developed between academic researchers at the University at Albany and the Akwesasne Mohawk Nation. Optimal research partnerships with Native communities should reflect that each Native community is a unique entity with specific historical, social, political, economic, and cultural contexts (Holkup et al. 2004; Ransom and Ettenger 2001) and challenges. There is no one research “template” or strategy to apply across all Native peoples. Because the Akwesasne community is burdened by environmental contamination and exposure to toxicants, any partnership with researchers must be aimed at resolving this burden.
Past research at Akwesasne, as in many Native communities, has often proceeded in a manner that benefited those performing the research, in the form of academic advancement and grant support, rather than benefiting the community itself (Arquette et al. 2002; Ransom and Ettenger 2001; Schell and Tarbell 1998). Often, research at Akwesasne progressed with research agendas dictated by researchers with little or no opportunities for community input, and there was no clear presentation of any results or findings by scientists to the community when the research was finished (Schell and Tarbell 1998).
In the mid-1980s, as the community faced an ongoing environmental crisis caused by industrial pollution, the need to become active in the research occurring within Akwesasne territory became evident to many in the community. In response, the Akwesasne Task Force on the Environment (ATFE), a community-based organization, was founded to “conserve, preserve, and protect the natural and cultural resources within the territory of Akwesasne” (ATFE 1996; ATFE and Research Advisory Committee 1997). In 1995 the ATFE established a subcommittee, the research advisory committee (RAC), to review and comment on proposals for research to be conducted at Akwesasne. The RAC developed and published a research protocol (ATFE 1996; ATFE and Research Advisory Committee 1997) that included a set of research requirements to help outside academic researchers become collaborative partners to benefit both academia and the community.
Conceptual Framework of the Partnership: The Akwesasne Research Protocol
The RAC developed three guiding principles for research based on the Haudenosaunee (“People of the Longhouse,” of which the Mohawk Nation is one of six Iroquois Nations) principles of peace, good mind, and strength. It is the emerging behaviors that flow from these guiding principles that serve to inform the research process and “channel the inherent good will of humans to work toward peace, justice, and unity to prevent the abuse of human beings and mother earth” (ATFE and Research Advisory Committee 1997, p 95). The protocol also provides specific guidelines to researchers regarding community expectations relating to full disclosure to the community of the proposed study’s methods and goals, funding sources, ongoing review of the research process and opportunities for community feedback, benefits to the community, and capacity-building through local training and hiring. By following such guidelines, a collaboration between researchers and the community is built that is based upon respect, equity, and empowerment, and which produces what the community calls “a good research agreement.”
Equity for the community can include the provision of monies to hire community researchers and/or an administration fee to support the infrastructure of a community organization. Empowerment includes not only training community people to conduct research but also that the university partners provide expertise regarding environmental contaminants and possible adverse health effects. Health effects research is valuable for the knowledge it produces, but to attain the full value of this knowledge, it must be applied in the development of health prevention strategies, new policies, and amendments to existing policy and legislation. Within the university, areas of expertise exist that can provide the community with a broad base of information and support to pursue policy change.
In addition to equity and empowerment, to develop a good research agreement the researchers and the community must generate respect for each other. Respect is generated by understanding each others’ social, political, and cultural structures (Harrison 2001; Holkup et al. 2004; O’Fallon and Dearry 2002). Examples of respect are good communication strategies that work for both partners, cultural sensitivity training for the researchers, and community awareness presentations that are clarified and questioned by each partner. Ultimately, if the need arises, consensus and mediation processes can be used to develop procedures that can be honored by both the researchers and the community.
Roles and Responsibilities: Components of a Sustainable Partnership
The Akwesasne model recognizes that research has profound effects on any community and seeks to channel these influences to produce benefits for the community while also respecting the researchers’ needs. The economic benefits for communities are valued in the partnership, but researchers may not perceive the benefits of research projects beyond the new knowledge produced. Research for career advancement without concern for community impacts is termed “stepping-stone research” by Akwesasne partners, because it uses the community only as a stepping-stone for career advancement. It is the antithesis of partnership research, as it does not empower, respect, or promote equity in the community. Therefore, the first step in our partnership was recognizing the mutual and individual benefits of research and consciously apportioning group effort toward achieving each partner’s goals. The honesty and candor required for this activity was a trust-building exercise.
Taking time to learn about one another.
The current team became acquainted with the Akwesasne community when planning MAWBS, which was part of a Superfund Basic Research Program grant (1995–2000). MAWBS was conducted after a process of personnel and project vetting by community members and organizations in which possible projects were described and scientists were introduced to community members interested in environmental and health problems. The conversations between researchers and community members, leaders, and organizations gave researchers the opportunity to hear the specific concerns of the community. At this point, three different driving forces were recognized: a) the research questions and activities the sponsor would support, b) what the community was interested in learning and the activities the community would support or allow, and c) the areas of academic researchers’ expertise. Community members and academic researchers then determined the fit among these three forces, and they agreed to submit a grant proposal.
It is also beneficial for academic researchers to attempt to understand the community’s history, politics, and culture (Harrison 2001; Holkup et al. 2004; Minkler 2004). Community members at Akwesasne mentioned frequently that when outside scientists made no attempt to at least learn a little about local culture, it appeared that the scientists were either disinterested or lacked respect for the community. At the beginning of the project, cultural sensitivity training sessions were scheduled to allow researchers to gain greater understanding of Mohawk culture and thereby produce the relationship most beneficial to both partners. In an effort to build awareness about the community, academic researchers also attended community events and assisted the community in various activities, with the intent of having a presence within the community. The ultimate goal was to build a trusting relationship for mutual benefit.
Partnering with a community before the research project is launched is best to devise a mutually beneficial research investigation (Israel et al. 1998, 2001; O’Fallon and Dearry 2002), but paying for community members’ input maybe difficult. While compensation for community input is ethical, grantors typically will not allow payments for work conducted before the funded project period begins. If other sources of compensation are not available, the community partners should be informed of the entrepreneurial and risky nature of research applications in order to budget their time and involvement.
Understanding styles of decision making.
Researchers and the Akwesasne community differ in their styles of decision making. Among scientists the general model of decision-making is a balance of majority rule and deferring to an expert opinion, then moving on as quickly as possible to the next decision to be made. The Mohawk style of decision making is based on consensus building and everyone having the opportunity to speak. In Native communities already dealing with many factors that promote divisiveness, group solidarity is an important principle; therefore, a research project that promotes dissension (even inadvertently) will be harmful. Native governments and organizations often need time to consider the proposed project and make a decision about participation. Although it is not the role of the researcher to solve longstanding problems in Native communities, there is a need to appreciate the resources, time, and commitment that are necessary to promote community consensus.
The communication needed for reaching a consensus usually entails a longer process of decision-making. For scientists, consensus building can appear to be an exhaustive and time-consuming process. In a society where time means money and the production of new knowledge is routinely weighed against its cost, extending this process may be viewed negatively. However, obtaining community input and consensus was crucial to the success of research at Akwesasne. It enabled us to identify problems appropriately, formulate research questions, select appropriate methodologies, identify evaluation strategies, and select effective means of dissemination and education. Thus, we as researchers learned to adjust our work schedule and to build in time for this process.
The role of community partners in the development of research design and protocols.
Native communities/governments have a primary responsibility to ensure that their citizens who participate in research do not have their human rights exploited and that protections are in place to guard participants’ health and safety. The ATFE has managed the safeguarding of human rights through the development of culturally based research guidelines. The guidelines require submitting research proposals to the RAC for review to ensure that external researchers and organizations adhere strictly to the community’s established research guidelines, particularly to how the community as a whole should be approached for review of proposed studies. When individual community members are approached regarding proposed studies, individuals cannot ensure that their or the community’s best interests will be served. The RAC was created to ensure that the rights of the community are addressed, and, consequently, that the rights of individuals are preserved, because individual rights are nested within protections afforded the community.
Promoting equitable benefits.
One key principle of our partnership is that there should be benefits to both the community and the scientists and that these benefits serve one another. The prime community benefit is greater knowledge that enables choosing the most effective steps to alleviate the community’s toxicant burden. However, other benefits can include increased capacity in community leadership and in research performance, and these benefits can be long lasting. To build leadership capacity, community members are included as key project personnel. This practice also promotes equitable participation and influence by community partners in the project and creates structured lines of communication. Additional benefits accrue when community members are trained to do the research so that the training stays in the community (Israel et al. 2001).
These actions have direct benefits to the project as well. In our project, individually and through the RAC, community partners have played an integral role in research design and development of research protocols, including questionnaires and instruments. The RAC reviewed the grant application with the community project staff members and provided feedback regarding the cultural appropriateness and acceptability of interview questions and study procedures, other more effective ways to ask certain questions, suggestions on how to streamline data collection to minimize the burden on the participant, and additional questions that should be asked that were not readily apparent to researchers. For example, we wanted to ask about the young adults’ consumption of local foods. Through working closely with the community we were able to identify the full range of locally trapped, hunted, fished, and grown food sources; issues of seasonality that would affect data collection; and the most appropriate time units in which to collect the data.
Community members hired as part of the project were trained in the skills necessary to carry out the project, such as anthropometric measurement, food frequency assessment, in-person interviews, and data coordination. Local community members served as the experts on how such methods might best be implemented in their particular community, and their expertise and knowledge was integrated into data collection strategies. The community-based personnel collecting data provided continuous feedback to researchers in Albany regarding the utility of the instruments. When encountering problems with specific questions or an instrument, data collectors were able to make insightful suggestions on how to restructure the question or instrument to collect the data of interest in a way that was understandable and acceptable to the participant.
The community-based staff member who recruited participants received training in human “subjects” protocols, and she also capitalized on her intimate knowledge of the community, specifically of the children, to facilitate recruitment. This researcher had to consider that, in this Mohawk community, there was a specific protocol to follow regarding involvement of children in any activity. In a matrilineal society such as the Akwesasne, the mothers’ responsibilities are to nurture and care for their children; therefore, the researcher routinely approached the mother first, as the primary caregiver, then the father second. In addition community members on the research team knew appropriate avenues to publicize the study and were available to discuss the project one-on-one at informal community events.
Other complexities of this community may not be familiar to outside researchers. The Akwesasne community has a traditional government and two imposed elected governments because it straddles the U.S./Canadian border. Many parents are employed by these governments, and their children attend schools following varying schedules. Data collection appointments had to be scheduled without interfering with tribal ceremonies or Canadian and U.S. school and work holidays. The community researcher also accommodated the adolescents’ school and extracurricular activities within their own schedules. For example, because fasting blood specimens were needed, the time the families felt was most convenient for venipunctures was at home before their children left for school. The researcher then took the individual to school if necessary. To make participants comfortable and improve retention, interviews and measures of height and weight also were conducted in the home. However, other accommodations depended on knowing details of local norms. For example, a standard research practice is to maintain friendly eye contact with the participant during the interview, but at the Akwesasne community, researchers noted that minimal eye contact made participants in this young age group more comfortable. In short, the knowledge held by local project personnel served as the basis for the successful recruitment and retention of participants. In the on-going study our recruitment rate is currently 65%, a highly successful rate for a follow-up study of a hard-to-reach age-group.
In the data analysis and report writing stage, community partners contribute to interpreting variables in analyses (especially variables representing social constructs), because the community has interpretive insights that may not be apparent to the academic partners. Thus, valuing and integrating local expert knowledge enables the community to become active participants in the research process and improves design, recruitment, data collection, and interpretation of results (Holkup et al. 2004; Israel et al. 2001; Stevens and Hall 1998).
Local expert knowledge is also essential for the creation of an effective Community Outreach and Education Program (COEP). The COEP was developed by the community, with researchers’ influence limited to the interpretation of the sponsor’s requirements. A community member from Akwesasne who had experience working on the previous project (MAWBS) invested personal time to write the COEP component for the current research project (YAWBS). That individual was aware of the RAC/ATFE research guidelines and used these as a foundation to prepare the proposal. Accordingly, the individual consulted with other community members and gained feedback at the monthly ATFE meeting, a process that identified which outreach programs would be of most use to the community. After funding was awarded, this individual was hired as the director for the COEP. Community leadership of the COEP acknowledges the community’s agenda and enables community members at Akwesasne to prioritize activities for support with grant funds. One of the welcomed COEP activities is sponsorship of a bilingual local radio show during which environmental messages are conveyed in the Mohawk language Kahniakeha.
Another strategy that helps to maintain equity between the partners is clearly outlining mutually agreed-upon protocols. This practice provides a road map of each partner’s expectations. For example, the process of disseminating results to the community and reporting results for publication and to sponsors was perceived as a considerable challenge (Israel et al. 1998; O’Fallon and Dearry 2002), and this was discussed at length. Community members unfamiliar with epidemiologic research expected results to appear during the process of data collection. Previous studies at Akwesasne have been very slow to report results, and this memory can affect recruitment for the current project. In contrast, researchers perceive that final analyses cannot be completed until the entire sample is collected, often a long process in a small community, and results should not be disseminated until “vetted” by the process of peer review.
We developed a protocol in which results are categorized as those concerning the individual and those concerning the community, which involves a system of checks and balances whereby research results are reviewed by members of the Akwesasne community before they become final. Results pertaining to individual participants such as tests of toxicant levels, results from cognitive and behavioral tests, and physical growth assessments (height and weight percentiles) are returned to individuals and their physicians, as appropriate, as soon as they are available and well before data collection ceases. This process produces immediate benefits to the participants because they can then act on the information provided to improve their health (McCauley et al. 2001; O’Fallon and Dearry 2002). The protocol also improves the community’s trust and belief that final results will be returned to the community while reducing pressure on the research partners to deliver final results.
A second aspect of the protocol guides communication of results relating to the community, for example, results pertaining to the relationships among variables of interest on the population level. The main problem historically has been the public release of results about the community to research sponsors, and to scientific journals and eventually the press before the participants and the community at Akwesasne are informed. The result was that the community learned about themselves from others, including groups who were potential adversaries in legal action (e.g., polluting industries and government agencies that may be sued for hindering remediation). This route of communication is contrary to community culture and is an essentially disempowering process. A related problem was that researchers, when asking for community comment, often did so when the final report was complete and did not ask for input during the development of the analysis or the writing of the report.
We developed the Albany–Akwesasne Protocol to guide the distribution of results and to incorporate community input during the process of report writing (Table 1). Initially, science and community partners prioritize report writing in the context of the project’s specific aims. Community partners are invited to share writing duties, and a preliminary draft is developed by the writing team. The draft is presented to the partnering community group or groups, and later a meeting is held to discuss comments on all aspects of the work but especially regarding the accurate depiction of the community. We try to allow at least 2 weeks between transmittal of the draft and the discussion session to allow community partners to read and discuss the manuscript. Revision of the manuscript occurs with community group partners, and when all authors agree, it is submitted to a peer-reviewed journal. Because changes are often required before publication, the editor’s comments are conveyed to the community partners involved in modifying the text and responding to the editor. When the manuscript is accepted but before publication, the results are presented to the community and to study participants at a community meeting. This process is designed to ensure that a) the community learns about itself directly, b) the community has input before the manuscript is completed, and c) the research itself has received the stamp of peer-reviewed approval so that the results disseminated to the community-at-large are accurate and not insulting. The process also enables those community members who have made significant contributions to the report to be co-authors.
Besides the obvious benefits of a more informed analysis of the data by virtue of community input, the process teaches researchers important details of community culture that are likely to be helpful in understanding the social production of health disparities. It also familiarizes community members with the culture of science, including its epistemology and economics, leading to greater understanding and sustainable partnerships with scientists (Israel et al. 1998; O’Fallon and Dearry 2002; Wallerstein 1999). In addition, it builds capacity in the community to write scientific reports and grants of their own (Israel et al. 2001; Stevens and Hall 1998).
The Albany–Akwesasne protocol facilitates equity by allowing both partners to receive credit for their work, input, and assistance. The policy of including community members or organizations as authors of scientific papers acknowledges the value of local expertise and recognizes the merit of contributions that have been made throughout the research process, from identifying research questions of interest to providing feedback and interpretation on papers for publication (Israel et al. 2001; Wallerstein 1999).
Two-way communication.
Much of the partnership relationship is based on frequent and open two-way communication that is equally privileged (Holkup et al. 2004). As the project has progressed, communication content has varied with the phase of the research. Strong and frequent internal and external communication is required to maintain the working relationship between the academic researchers, the local project personnel, and the community. Very frequent contact is needed to ensure that community project personnel stay in the loop regarding what is occurring with the academic research team and vice versa. The examples described previously illustrate how such communication is essential for development of locally appropriate research design, data collection instruments, recruitment, report writing, and dissemination (Israel et al. 1998; O’Fallon and Dearry 2002).
The Potential of Partnership Research to Understand Health Disparities
Understanding the relationship between health disparities and social and physical environments involves a detailed, highly contextualized, and carefully nuanced analysis of the myriad factors that are included in the simple words “social and physical environments.” Creating a model that depicts these relationships and then operationalizing the model for hypothesis testing is a formidable challenge. We believe the task is impossible without integrating the detailed knowledge of community members with scientific research methodologies. The local knowledge and input of community members has facilitated the development and successful implementation of our research design, which includes a rigorous data collection protocol in a population that is already burdened with grave social, political, and economic challenges related to 500 years of genocidal policies and neglect.
Few studies have been conducted on the health effects of toxicants on adolescents or young adults and even fewer on Native American youth. The intimate knowledge of the community by the local project personnel is the basis for the successful recruitment of this age group. Community knowledge has been invaluable in the development of the questionnaires and instruments during the project. Through community collaboration, the project has been able to develop culturally sensitive, as well as culturally relevant, instruments that capture complex pathways of exposure. Because several pathways of exposure in this community are potentially linked to culturally based activities that are closely connected to Mohawk identity, building a trusting partnership in research is critical. The Mohawk community at Akwesasne has many good reasons to be distrustful of outsiders, in general, and academic researchers specifically. It is only through a collaborative framework based on relationships of respect that a detailed investigation of the role of the social and physical environments on toxicant exposure can occur.
Mechanisms to reduce health disparities.
One of the most important contributions of partnership research is its potential to build capacity in the communities where the research takes place. The long history of colonialism, forced containment to reservations, and ongoing federal policies directed at Native Americans has had profound effects on Native communities. Such deeply entrenched social disparities at Akwesasne have contributed to the placement of polluting industries next door to the community, and, consequently, to pattern exposure to environmental toxicants. Access to employment and education is limited within the community. Community members are closely tied to the land where they live, which is part of their ancestral Iroquois territories, yet they have three industrial waste sites as their current neighbors. These factors are not easily changed and are connected to larger political and economic forces that can be linked to changes at the global level.
On the local level the work of the community will continue after the research is finished. Actions as well as policy decisions are needed to resolve the environmental and health issues. As the ATFE has reviewed the risk assessments that have been completed, it has become obvious that while frameworks for risk assessments have evolved over time, there remains a void in the assessment of health. The void is grounded in a definition of health within Mohawk society that differs remarkably from that of mainstream society. Not only must the physical health of an individual be considered but also what has become known as the emotional, mental, and spiritual being of the person. Considering only the physical part of the individual does not address the health and well-being of the individual; therefore, overall health is at greater risk. Without this consideration, any risk assessment is lacking and cannot address the very issue it is supposed to address. The definitions of health used by Tribal/First Nations are strikingly different from those of Western health-based professionals and scientists. Moreover, there is a critical need to expand the current definition of health and incorporate traditional knowledge into all facets of decision making regarding health issues.
The results of this project will provide part of the picture regarding risk of exposure and possible health effects in the community. Work is ongoing at Akwesasne to develop a more holistic model of risk-based decision making (Arquette et al. 2002). Results from this project will be integrated with information from many other community sources so that a full picture of the impact of toxicants in the community can be created. For the Mohawk community it is critical to identify correctly those cultural and subsistence-based pathways placing them at risk and then for them, as a community, to decide what is acceptable risk.
Research relationships with communities do not end when the funding does, as academic partners may be called on to assist with intervention and policy issues for years and perhaps decades into the future. Sustainability and reciprocity of the partnership relationship are the truest forms of benefit for the community (Holkup et al. 2004), for these will aid the community to reduce or effectively eliminate persistent racial and socioeconomic disparities in health in the future.
This article is part of the mini-monograph “Community-Based Participatory Research.”
We thank the adolescents and their families at Akwesasne for their time and participation in the project. We also gratefully acknowledge the community members of Akwesasne for their invaluable contributions during the partnership process.
The project is funded by National Institute of Environmental Health Sciences grant ES10904-05.
Figure 1 Risk-focusing model.
Figure 2 Research design. Model of primary relationships presents a diagram of relationships among primary variables in YAWBS but also includes some variables from MAWBS to indicate some longitudinal components. SES, socioeconomic status; YAWBS, Young Adult Well-Being Study. Susceptibility factors are indicated by letters: D, diet; H, mercury; L, lead; M, metabolism. For clarity, some covariates [e.g., non-focal toxicants such as hexachlorobenzene, mirex, and dichlorophenyldichloroethylene (DDE)] are not depicted but will be examined.
Table 1 Albany–Akwesasne Protocol for dissemination of results.
Researchers and community partners discuss and choose hypotheses to be tested. Partners are invited to collaborate on writing.
Development of preliminary draft.
Draft manuscript is presented to the partnering community group or groups.
Comments are received by authors concerning all aspects of the work, especially regarding the accurate depiction of the community.
Writing partners revise the manuscript to joint satisfaction.
Manuscript is submitted to a peer-reviewed journal.
Comments from the editor are shared with partners; modifications to the manuscript and responses to the editor are constructed by partners.
Upon acceptance and before publication, the results are presented to the community at large and study participants at a community meeting.
Publication
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References
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7912ehp0113-00183316330373ResearchMini-MonographExploration of Work and Health Disparities among Black Women Employed in Poultry Processing in the Rural South Lipscomb Hester J. 1Argue Robin 1McDonald Mary Anne 1Dement John M. 1Epling Carol A. 1James Tamara 12Wing Steve 3Loomis Dana 341 Division of Occupational and Environmental Medicine, Department of Community and Family Medicine, Duke University Medical Center, Durham, North Carolina, USA2 Occupational and Environmental Safety, Duke University, Durham, North Carolina, USA3 Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA4 Department of Environmental Sciences, School of Public Health, University of North Carolina, Chapel Hill, North CarolinaAddress correspondence to H.J. Lipscomb, 2200 West Main St., Suite 700, Durham, NC 27705 USA. Telephone: (919) 286-1722, ext 256. Fax: (919) 286-1620. E-mail:
[email protected] authors declare they have no competing financial interests.
12 2005 18 7 2005 113 12 1833 1840 28 12 2004 21 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. We describe an ongoing collaboration that developed as academic investigators responded to a specific request from community members to document health effects on black women of employment in poultry-processing plants in rural North Carolina. Primary outcomes of interest are upper extremity musculoskeletal disorders and function as well as quality of life. Because of concerns of community women and the history of poor labor relations, we decided to conduct this longitudinal study in a manner that did not require involvement of the employer. To provide more detailed insights into the effects of this type of employment, the epidemiologic analyses are supplemented by ethnographic interviews. The resulting approach requires community collaboration. Community-based staff, as paid members of the research team, manage the local project office, recruit and retain participants, conduct interviews, coordinate physical assessments, and participate in outreach. Other community members assisted in the design of the data collection tools and the recruitment of longitudinal study participants and took part in the ethnographic component of the study. This presentation provides an example of one model through which academic researchers and community members can work together productively under challenging circumstances. Notable accomplishments include the recruitment and retention of a cohort of low-income rural black women, often considered hard to reach in research studies. This community-based project includes a number of elements associated with community-based participatory research.
African American womenblack womencommunity-based participatory researchhealth disparitiesmusculoskeletal disorder
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Classic epidemiologic studies and historical research document that in the past, blacks in particular were openly selected for unpleasant, “dirty” jobs regarded as unsuitable for other workers (Baron 1983; Cherniak 1986; Lloyd 1971; Taylor and Murray 2000). Historically, employers have also sought to reduce labor costs by hiring workers from less-advantaged groups—notably racial minorities, immigrants, women, and children—who are perceived to be willing to accept lower pay and poorer working conditions and to be less likely to organize (Green 1983; Levine 1989).
The U.S. labor force continues to be segregated by race and gender (Darrity 2003; Thompson et al. 2005; Tomaskovic-Devey 1993). Blacks are employed in hazardous occupations more frequently than whites, and black men experience higher occupational fatality rates than white men employed in the same jobs (Loomis and Richardson 1998). Compared with other women, African American women have higher rates of nonfatal occupational injuries treated in emergency departments in the United States (Chen and Hendricks 2001), with differences in employment by racial group suggesting explanations for this pattern. Recent evidence indicates that working conditions may be particularly dangerous for nonwhite workers in the Southern United States (Richardson et al. 2004).
Inequities in rights to medical care and wage replacement under workers’ compensation are also present. Claim rejection for carpal tunnel syndrome has been reported to be strongly related to ethnicity and socioeconomic status, with claims by nonwhites, low-wage earners, and union members more likely to be challenged. Although more than 96% of the adjudicated cases in this report were eventually approved, the mean time involved in the process was over 1 year (Herbert et al. 1999). In Canada a recent report indicates that women are less likely to have contested claims for musculoskeletal disorders (MSDs) accepted by an appeals tribunal than their male counterparts (Lippel 2003). The mislabeling and early mismanagement of these disorders may affect their clinical course (Andersson et al. 1995; Buckle 1997; Oschner et al. 1998) and may have adverse consequences for women who balance multiple roles at home and work.
The Safety and Health of Working Women (SHOWW) project is a collaboration of academic researchers and community representatives in rural northeastern North Carolina designed to explore occupational roots of health disparities. Both the tradition of hiring the less advantaged in less desirable and often more dangerous jobs and the failure to acknowledge their occupational health concerns are at the core of this research. We present the conceptual framework for the project and describe the development of the collaboration as an example of one model through which academic investigators and community members can work effectively under challenging circumstances.
Project History and Setting
In 1999 women working for a small nonprofit advocacy organization sought an academic partner to document health effects of employment on black women working in poultry-processing plants. Several of these women had experience with community and academic collaboration surrounding environmental health issues (Wing et al. 1996). Upper extremity MSDs and acute injuries were viewed as the primary, but not the only, outcomes contributing to poor health among these working women. Women maintained that management and health care providers often attributed their musculoskeletal complaints to obesity, child-care responsibilities, or conditions existing prior to their employment.
The study area, which includes five counties located in northeastern North Carolina, is poor and sparsely populated. Fifty-five percent of the five-county population is black, compared with 22% in North Carolina and 12% throughout the United States (U.S. Census Bureau 2004a). This black majority is a legacy of the large slave population of the antebellum plantation system (Powell 1988). The area is characterized by poor health indices, including high age-, race-, sex-adjusted total mortality, low birth weight, and high infant and fetal mortality. These five counties rank in the top 15% in the state for years of potential life lost, with two of the counties being the highest in the state (Center for Health Services Research and Development 2003). In North Carolina 78% graduate from high school (80% in the United States), whereas in the study area, only 66% graduate. Nearly one-third of the population lives below the poverty level (U.S. Census Bureau 2004b, 2004c, 2004d, 2004e, 2004f, 2004g). Although North Carolina has enjoyed rapid economic expansion and high population growth during the last 20 years, the study area has remained economically underdeveloped. Poultry processing is the largest employer in the area, providing yearly salaries in the range of $17,000.
The development of the modern poultry industry and the economy of the rural South are closely connected. The production, processing, and marketing of poultry products have undergone a massive transformation. In 1960, 300 commercial suppliers and a larger number of family farms supplied chickens for the retail market. By 1990, fewer than 50 firms remained, and the top five among them supplied over a third of the market. Both production and processing of poultry are concentrated in the rural South (Fink 2003; Griffith 1993; Hall 1989).
Modern poultry-processing plants are highly organized industrial structures for slaughter, disassembly, and packaging of birds (Campbell 1999). Rapid line speed and extreme division of labor characterize the assembly-line work. The concentration of this low-wage industry in depressed rural areas and the employment of large numbers of black and Hispanic women help producers keep costs low (Fink 2003; Griffith 1993; Hall 1989) contributing to an environment that fosters disparities in working conditions and health.
The poultry plants in our study area have a history of occupational health and safety problems. In 1989, N.C. Occupational Safety and Health Administration (OSHA) inspectors cited the two plants in the area for serious repetitive motion problems with some jobs documented to require over 10,000 repetitions per shift (North Carolina Department of Labor 1989). After the citations the National Institute for Occupational Safety and Health (NIOSH) conducted Health Hazard Evaluations (HHE) in these plants that processed over 400,000 birds daily (Kiken et al. 1990). At the two plants, 36% and 20%, respectively, of employees who participated in the NIOSH evaluation had work-related cumulative trauma disorders in the last year, as determined by questionnaire alone; 20% and 8%, respectively, had current work-related disorders based on both questionnaire responses and physical exam. At both plants the high-exposure group included larger percentages of women, blacks, and younger people. At one plant, all high-exposure participants were black. The HHE clearly demonstrated risk. However, annual turnover rates in both plants were high (50–70%), and because workers with painful disorders tend to leave employment, this cross-sectional study of disease prevalence may underestimate the magnitude of morbidity because of “survivor bias.”
Conceptual Framework for the Project
Musculoskeletal disorders are a frequent reason for seeking medical care and common causes of chronic health problems and long-term disability (Badley et al. 1994). Despite a body of literature linking occupational exposures such as repetitiveness, force requirements, posture, vibration, and lifting with MSDs, especially at high levels of exposure (NIOSH 1997), the majority of studies add little to understanding natural history (including latency and cumulative exposure) or resulting impairment and disability because of their cross-sectional nature.
From this background, through discussions with the women who requested the research partnership, interviews with workers, and review of historical information on the poultry industry and the geographic area, we developed the conceptual framework illustrated in Figure 1. The framework draws heavily on a conceptual model of work-related neck and upper-limb MSDs described by Armstrong et al. (1993) that incorporates relationships among exposure, dose, response, and worker capacity.
Exposure refers to the external factors, or work requirements such as repetition, force, and postures, that produce the internal dose (tissue loads, metabolic demands) on the worker. Dose refers to factors that disturb the individual mechanically, physiologically, or psychologically. Response includes the changes that occur in the individual in response to dose. Capacity, physical or psychological, refers to the ability of the individual to resist destabilization and is influenced by prior dose as well as other factors including health conditions.
The model posits that response at one level can affect dose at another level, and that the relationship between dose and response can be altered by previous dose. For example, repeated and prolonged exertions can result in desirable adaptation, as in a training effect on muscle or in undesirable reduced capacity, when a muscle is fatigued repeatedly without sufficient time to recover. Besides the direct effect of dose on tissue, the response of one tissue can affect another tissue. Connective tissue can thicken as it adapts to mechanical stress, and the thickening can lead to pressure on neural structures. Changes in upper-extremity function may represent pain and/or early tissue changes that, in turn, affect the individual’s capacity to withstand additional dose.
In addition to the physical job requirements, worker capacity, skill, and the social and physical organization of the work environment may influence the development and/or expression of MSDs. Yet these relationships are highly complex and contextual (Hagberg 1992). For example, work longevity, job stress, and organizational factors such as levels of psychological demand and control (Karasek et al. 1998) are potentially important modifiers of work exposures. In the case of MSDs, the control a person has over how she works might influence her work speed, breaks, and voluntary task rotation. Individuals with more experience may learn to use tools more efficiently with less force (Dempsey and McGorry 2004).
We also view perceptions and coping mechanisms as potentially significant modifiers of exposure and individual capacity. A worker with impaired upper extremity function who alters the manner in which she does the work changes her subsequent exposures. In a poultry plant, a woman’s assertive behaviors might alter physical exposures by securing job rotation or sharper tools; conversely, situations could arise where complicity might gain favor from superiors. James et al. (1987) have described “John Henryism,” a strong personality predisposition to cope actively with psychosocial environmental stressors. The scale developed to measure this attribute includes Likert-scaled items such as “When things don’t go the way I want them to, that just makes me work even harder.” The potential for high active coping among disadvantaged women may be maladaptive, leading them to experience greater damage through harder work and greater internalized stress. Poultry-processing workers represent the lower end of the socioeconomic distribution among workers, and we are interested in how their level of socioeconomic disadvantage influences decisions about work. These complex relationships of workplace exposures with adaptive and pathological responses may be most appropriately considered as interdependent rather than independent effects, as indicated by the double-headed arrows in Figure 1.
Obviously, both job availability and assignment determine individual exposures. However, to emphasize the importance of context, the sphere in which the model sits represents the underlying industrial structure. Unstated policies, such as institutional racism, sexism, and classism, as well as stated ones, such as economic development plans, cannot be measured through a focus on individuals as independent actors. In seeking to understand contributions of work to health disparities, it is important to understand what happens in workplaces and how workers respond, but also how workplaces come to be in certain communities and what employment alternatives workers may or may not have.
Research Design and Methods
To address the concerns of women in the community and to improve cross-sectional investigations, we are conducting a longitudinal study of a volunteer cohort of women employed in poultry processing. Based on community concerns and the history of the industry’s poor labor relationships (Fink 2003; Griffith 1993; Human Rights Watch 2000), we decided to conduct the study in a manner that did not require the cooperation of the employer. This critical decision influenced the research methods and, particularly in a nonunion environment such as this, necessitated community involvement.
Volunteers participate in serial interviews and physical exams conducted at 3- to 6-month intervals over a maximum of 3 years. Community staff recruited participants over a 23-month period providing, by design, variable lengths of follow-up time and, consequently, variable cumulative occupational exposures. Although we initially limited recruitment to new hires to the industry, we later included longer-term employees to increase the overall efficiency of the study.
Table 1 outlines key variables. The primary outcomes of interest ( “responses” in our conceptual framework) are upper extremity musculoskeletal symptoms and disorders. Disorders are defined by a constellation of reported symptoms and signs identified by standardized physical exams performed by study nurses. The longitudinal design of our study allows us to explore relationships among health outcomes, tenure in the plant, exposure differences, and coping strategies. We also will be able to investigate upper extremity function, health-related quality of life, and depressive symptoms as outcomes, as well as subsequent modifiers of the relationships among our primary outcomes and exposures.
We developed a multidimensional strategy of exposure assessment that will result in two streams of data for analyses: one based on group-level exposure assignment by department and job, and the second derived from individual-level self-report of exposure. The process began with in-depth, semistructured interviews with 37 workers from different departments and jobs. We are using the information from these workers, combined with general knowledge of the poultry-processing industry (OSHA 2004), to construct an industry-specific job exposure matrix (JEM) (Bouyer and Hemon 1993; Kauppinen et al. 1998; Le et al. 1998). The matrix will be used to assign levels of exposure for key variables such as repetition, force, and joint posture by department and job. JEMs have been used effectively to combine observational or direct exposure measurements with past work histories to derive a measure of overall exposure for both surveillance and etiologic research. They provide a global evaluation of a job category that can be used to estimate exposures by job, or task, with cumulative exposures based on length of employment. Relevant to this project, JEMs facilitate exposure assessment when the workplace is not accessible to the researcher (Siemiatycki et al. 1981).
In addition, information from these key informant interviews guided the development of a self-report exposure assessment tool. The aggregated self-report data from women in the same jobs will be used in the JEM to help assign categories of exposure. The potential importance of individual behaviors, even in this assembly-line work, emerged in the analyses of the interview data. Long-term workers described behaviors that could potentially alter physical exposures, such as demanding sharper tools, taking unauthorized breaks, or refusing part of a job rotation or task. These are included in the assertiveness measure referenced in Table 1, and they may help us understand differences in the group-level exposures based on the JEM and individual reports of exposure.
The final component of our research design involves the documentation of workers’ life histories through in-depth, ethnographic interviews that explore how their work affects their lives and how they make decisions about their employment. These interviews, separate from those conducted early in the project to inform the exposure assessment, are designed to provide a broader understanding of quality of life and a view of more dimensions of the poultry workers’ lives. We are interviewing women who have worked in the industry for variable periods of time, as well as some who are no longer employed in poultry processing. These interviews are typically conducted in several sessions (up to three) and over 1–3 years. We intentionally tried to find women who represented ranges of age, length of employment in the plant, satisfaction with work, and injury and disability, and who lived throughout the five-county study area. We relied on SHOWW community–based staff to recommend women to be interviewed. They suggested women they knew through social networks or kinship, as well as women who were enrolled in the longitudinal study, who seemed open and forthcoming during the staff-administered study questionnaire sessions. This was not intended to be a random sample; when using ethnographic interviews, the goal is to find people who are knowledgeable, representative of the population of interest, and willing to talk (Fetterman 1998; Patton 2002; Ulin et al. 2005). Twenty-two different women were interviewed in 35 sessions. The ethnographic interviews covered life history, employment choices, family responsibility, hopes and fears, role of the church in their lives, social relationships within the plant, and many other topics.
Collaboration of Community and Academic Research Team
Our research design not only affords opportunities for, but requires, collaboration. Early in the project the community nonprofit agency that was our original partner was unable to meet the obligations of the research project. Since then, the project has not had an independent community partner, but rather community members who serve as paid staff at a project office located in the study area. With this arrangement the staff serving as community representatives are not agents of a community organization but work directly with the research team as university employees. Key elements of this collaboration and support provided to community staff are presented in Table 2.
The five members of the community-based team are all black women raised in northeastern North Carolina and range from 19 to 60 years of age. Although demographic similarities exist among the staff, their life and work experiences vary considerably; they bring diversity of expertise and skill to the study. All five women obtained a high school diploma; two continued on for an associate degree. Their collective work backgrounds include positions in poultry processing (nearly 30 years) and sewing factories (5 years); grocery stores (manager for 12 years and clerk for 1 year); nursing homes (2 years); beauty salons (3 years); and community-based service organizations (20 years). These women are our main connection to the community and actively participate in shaping the research.
The community-based staff had no experience with a participatory or collaborative work-place. To augment their initial enthusiasm, to ensure that they would see themselves as integral to the project, and to foster equitable participation, we designed the project training using the elements of participatory learning (Arnold et al. 1991; Wallerstein and Rubenstein 1993). Key tenets of this method are equality among leaders and learners, and reciprocal learning. Participatory training methods enabled us to build on their existing knowledge of the poultry industry and of their communities. By creating a training where participants’ knowledge was valued, we strived to set the tone for collaboration based on equality and mutual respect.
These women work independently in an office 2.5 hr away from the university team, which gives them autonomy and responsibility. Community staff recruit participants and schedule their initial and follow-up interviews and examination appointments. They provide transportation, offer childcare and activities (crayons, books, or toys) for those who need to bring their children, and offer to conduct interviews in workers’ homes to facilitate participation. Data are collected through in-person interviews conducted by the staff using flip charts. Workers are able to view options to closed-ended questions as well as see visual cues, allowing participation by individuals who might otherwise have difficulty completing a lengthy questionnaire.
In addition to identifying avenues for outreach in their community and attending various community events or meetings to discuss the project, the community staff provide valuable outreach to the academic community through participation in university classes in occupational epidemiology, community-driven research, and political science, providing important community perspectives to students and faculty.
The academic project manager meets weekly with staff in the community. The meetings serve several purposes including information sharing, timely quality control of data, and problem solving. This regular forum promotes communication and allows dedicated time when input from staff is actively solicited and decisions can be made collectively.
Although the academic team made the decision to conduct a longitudinal study, that decision was influenced by expressed concerns over the inability to document work-related disorders among community women, and the women agreed with the need for and logic of a longitudinal design. The community staff have remained routinely involved in design decisions. They influenced the decision to incorporate ethnographic methods by requesting ways in which interested workers with longer tenure in the plant or former workers could be involved. The community staff also felt that longer-term workers should have the opportunity to participate in the longitudinal study and that their involvement would facilitate recruitment of new hires to the industry.
Input from current and former workers in the plants has also been critical in developing a self-report tool to capture personal exposure information. They provided information on potential sources of exposure variability that could be overlooked even in ergonomic assessments where investigators had access to the workplace. In addition, community members recruited participants and were compensated for this assistance at the suggestion of the staff.
Challenges of Collaboration
The community-based staff came to this work with no research experience and limited general office skills. To address these challenges, their training included questionnaire administration, recruitment strategies, and skill-building training to allow them to manage the community office (effective communication; scheduling and record keeping; management; computer skills). The staff continue to develop these skills. The academic researchers came to the project with different limitations. Many of us had little prior experience with this community and were naïve about the lives of women in this rural area 130 miles from our universities. Like our community partners, we continue to learn.
Differences in communication, planning, and time management emphasized the cultural divide. Community-based staff correctly described the inability of participants to plan weeks or even days in advance for study appointments. This necessitated creating flexible office hours for exam times with study nurses. Even without the cultural differences between white academic researchers and black rural community members, the distance between the study site and institution limits daily interactions and makes the weekly meetings with the project manager an essential link.
The protection of the privacy of research participants takes on added dimensions within the context of both occupational health and community-based research (McPhaul and Lipscomb 2005). The planning, early participatory training, and ongoing conduct of the work have all revolved around maintaining privacy of participants, and the community staff are sensitive to these issues. Interviews conducted for exposure assessment and the ethnographic work took place in workers’ homes, private rooms in restaurants, friends’ houses, motel rooms, and our community office, always at the discretion of the woman being interviewed; they were recorded using aliases to protect privacy. Ongoing team discussions reinforce the importance of not revealing that any individual is a study participant in community encounters or in attempts to locate women for follow-up visits, as well as maintaining the confidence of all information shared in the data collection process.
We are asking workers to participate in a demanding protocol over several years. Both academic and community partners agreed that workers should be compensated for their time. Participants receive $40 for each data collection point involving an hour and a half interview and examination. Concern about effects of employment on health varies among participants, so compensation demonstrates respect for their time and their contribution to an effort for which they may not receive other direct benefits.
Both the academic and community staff are affected by the difficult lives of the poultry workers. We are saddened when participants must immediately use their incentive to purchase food for their children or kerosene to heat their trailer. We are frustrated by the complexities of occupational health concerns, especially when participants choose not to seek medical care for fear of losing their jobs or being identified as patients. We do not know if these fears are justified, but workers perceive these as real possibilities and consequently they influence behaviors.
The research process, including developing working relationships, building skills, recruiting workers, and maintaining their participation in this longitudinal study, has been time consuming. Recruitment, although designed to occur over time, has taken longer than initially planned. In economically depressed areas the immediate need for jobs can outweigh concerns about long-term health effects in the eyes of the community at large and in the eyes of workers. Low-wage industries such as poultry processing depend on a supply of unskilled labor; consequently, the industries have a vested interest in keeping economic growth low (Griffith 1993). Anxiety about job availability in the study area has been compounded by a poultry plant closure in August 2003. We believe the tension between health effects of employment and job concerns made the recruitment process more challenging, and it will likely affect perceptions about community outreach and education efforts.
Severance of the original community subcontract occurred at a pivotal point shortly before we were to begin enrollment of women in the longitudinal component of the project. The community staff members, who had participated in the early exposure interviews with workers and months of training for their project roles, maintained their commitment to the project. They immediately focused on the logistics of the work, conducting regular staff meetings and the early baseline interviews with participants in their own homes and by finding space for the project office.
Rewards of Collaboration
There are significant rewards from our community-academic collaboration despite the challenges. The most notable achievement to date is the recruitment and retention of a cohort of 291 women, which would not have been possible without the community-based staff. Over 85% (87–97%) of those who were working at each follow-up period have remained in the study; 162 women (55.7%) remain in the cohort being followed. Data collection continues, with some participants having completed their seventh follow-up visit. Black women have not been adequately represented in epidemiologic studies, particularly studies related to occupational health, despite their high participation in the labor force and their higher-than-average levels of morbidity and mortality (Dennis and Neese 2000). Particularly in the face of a demanding protocol, a paternalistic employer, lack of alternative jobs, and the rural environment, this level of participation is significant.
Low-income black women are often labeled a “hard-to-reach” population by researchers (Wyatt et al. 2003). Barriers to their recruitment and retention in research studies are significant and include lack of transportation, costs, burdensome procedures, competing family responsibilities, lack of awareness, and distrust of investigators (Brown et al. 2000; Sengupta et al. 2000). Community-based staff intuitively deal with such issues. They have a thorough knowledge of the community, they understand the demands on working women, and they actively sought methods to accommodate the participants. Consistent with other community-based work (Corbie-Smith et al. 2003), there are indicators of trust associated with the project, not only as a safe place to participate in the research project, but also as a resource for other concerns. Women have come to the office to request a blood pressure check or have sought out the study nurses for information about pregnancy and delivery, for example.
The quality of data collection from our lengthy and complex questionnaire would not have been possible without in-person interviews. Our early analyses provide indicators of good face validity of the data. The musculoskeletal symptom reports are consistent with the hand-intensive nature of poultry-processing work and differ from those of other predominantly female occupational groups with different types of work exposures (Daraiseh et al. 2003). The distribution of upper extremity function scale measures among those with hand pain is similar to reports of Pransky et al. (1997) among employed clinic patients with upper-extremity disorders. These findings are consistent with successful reports of the use of workers to collect health outcome and exposure information from their peers (Dement et al. 2003; Lipscomb et al. 2003a, 2003b) and add to the evidence that community members can be successful in circumstances where academic researchers might not be (Avery et al. 2004).
Using descriptions from the interviews with the early key informants, a series of Likert-scaled questionnaire items were developed for behaviors that we believe represent plant-specific assertiveness. The interview data allowed us to frame the items using the women’s own words such as, “How likely are you to tell your line leader to ‘back off’?” Using factor analytic techniques (DeVellis 1991), we have identified a group of 11 items with good scale properties (Cronbach’s alpha 80.0). This will allow us to explore whether these behaviors are associated with longer tenure in the plant, their relationship to symptom development, and whether women with higher scores on the scale are more likely to alter the physical exposures of interest and thus introduce exposure variability that might otherwise be unrecognized. The approach we took was out of necessity, yet we would have been unlikely to identify this potentially important construct using standard methods of ergonomic assessment such as direct observation or videotape and review of several work cycles for each job.
The involvement of other community members in multiple facets of the study is conducive to outreach and community education. Formative work provided initial contacts with members of the community. While these women provided us with information about their work, we were able to provide them with information about the research effort. The same is true of women we interviewed for the life history portion of the study.
Discussion
Community-based participatory research (CBPR) encompasses a wide range of research methods, with varying levels of involvement from communities and researchers. Guidelines for this approach have been described for work with communities in general (Green 2004; Metzler et al. 2003; Minkler 2004) and more specifically related to occupational and environmental health (Arcury et al. 2000; Mergler 1987; O’Fallon and Deary 2002). In actual practice, CBPR has depth and variation, and the SHOWW project has variable shades or dimensions of this approach (Israel et al. 1994, 1998; Sullivan 2001).
In contrast to situations in which academic investigators seek to study a population defined by their own research interests, this collaboration developed as academic researchers responded to a request of women in rural North Carolina to document health effects of work in poultry processing, the largest employer in their area. As such, the project provides an example of truly community-driven research.
The academic team viewed the request to some extent as a request for technical assistance. Early decisions about research design were made by the academic team based on input from women in the community, including the feasibility of the longitudinal design. Regardless of access, the fear and distrust of the industry among the worker community made the industry an unsuitable partner, and given this constraint, the research required community collaboration from the outset.
Equitable participation and influence between researchers and community is another tenet of CBPR. We believe this is a lofty goal to strive for but also one difficult to measure and seldom realized (Buchanan 1996; Wallerstein 1992). Our community partners are our local staff; they do not control funding or share equal power with the academic principal investigator. Nonetheless, within the confines of this structure, the staff have a strong voice in the research.
Responsibility was central to the decision to break ties with the original community partner. However, the change altered power structures and the decision was not made lightly. We had a responsibility to the poultry workers to find a model in which we could conduct the work, and we also felt responsible to the community staff who had become our active partners. In the model under which we continue our project, the staff do not work under the umbrella of a community organization or labor union but rather as employees of the university. As such, they do not have a separate organization from which to draw power, which at least theoretically, would give them strength and independence within the project. However, the community-based staff were actively involved in this decision. They openly reported and quickly demonstrated empowerment from the break, and they believe the independent project office has provided added legitimacy and recognition.
The restricted ability of many small community-based organizations to provide benefits of employment is often ignored in discussions of power and equity in community-based research. Health and life insurance, retirement contributions, and even paid vacation and sick leave are expected standards for university staff. The implications increase as the time committed by community staff to the project increases. In this project the community-based staff manage the project office, and their time commitments are greater than those of any other members of the academic team (30–35 hr per week). As university employees, they are eligible for corresponding benefits. These benefits, possible through our large university risk pool and the support afforded by substantial benefit rates on government grants, are often prohibitively expensive for small nonprofit and community organizations.
One could argue that failure to acknowledge these issues and realistically evaluate what is gained by not working under the perceived power structure of a community agency contributes to the very disparities in health we seek to address in our collaborative research. Is it equitable for university researchers not only to enjoy but to expect these benefits, yet deny them to our community colleagues because it is not the community standard? In attempts to guarantee substantial community involvement and leveling of the playing field between community and academic teams, proscribing the proportions of funds that must be designated to community organizations in CBPR projects may not have the desired result as the research is operationalized. The issues are not easy ones to address, but they cut to very nature of this collaboration focused on the health of working women.
We recognize that our project has both limitations and strengths. Occupational health research conducted without industry cooperation has merits and challenges. The volunteer cohort constitutes neither a full enumeration of potential workers nor a random sample of the population at risk. This will be a significant limitation of cross-sectional analyses of baseline findings in which subjects ideally would be selected based on exposure or disease. However, the longitudinal analyses focused on internal comparisons reduce the potential for bias. This does not negate the possibility of a healthy worker survivor effect (Checkoway et al. 2004), although it is diminished by successful follow-up of participants and the inclusion of a substantial number of women who were new to the industry.
The design we chose, which recruits and follows employed women, does not have an unexposed group. Unemployed women of working age may have characteristics that could confound results. They may belong to a different socioeconomic group, may not need to work, or may already have a work disability. Collecting detailed exposure assessment was not feasible for many work sites, so we focused our work only on women employed in poultry processing and made a conscious decision to trade external validity for improved internal validity.
Lack of access to the workplace constrains our ability to observe and directly measure exposures. Instead, we are using a multidimensional, indirect approach to exposure assessment. Previous comparative studies have shown that this approach, using qualitative or semiquantitative exposure assessments by workers and health and safety professionals, can provide a valid ranking of exposure levels relative to quantitative measurements (Flynn et al. 1991). In our case the development of the JEM also provides a method for assigning exposures that occurred before our observation period. This is particularly important in understanding cumulative exposures for the longer-term workers in our study. This process also engaged community women early in the project and improved the cultural relevance of questionnaire items.
Through the ethnographic life history interviews, women gave us insight into complex and subtle processes that may contribute to disparities in health. The women’s life stories revealed the effects that poorly funded schools, de facto segregation, teen childbearing, inadequate health care, cultural norms for work expectations, and a declining industrial base in the region may have on creating and maintaining disparities. Jointly, as academic researchers and representatives of the community, we intend to reach out to the community of poultry workers, the wider black and white communities, and academic, medical, and health policy circles. We have already found in sharing information from this project that the mix of quantitative epidemiologic data and qualitative ethnographic reports appeals to a wide variety of audiences.
We present our project as an example of how one collaboration developed with the calculated tradeoffs in design and logistics as challenges were faced. The model for community collaboration is not the one originally planned, but we do believe it allowed a modicum of success to date that would not have been realized otherwise. Research projects evolve as they move from plans on paper to actual work. Community collaborations add another dimension that we believe requires substantial flexibility to allow each to be effective under unique and unpredictable circumstances. We are not proposing that our model is ideal but rather one through which we have worked effectively while more equitably sharing benefits and resources.
In conclusion, in this research we focus on the physical environment of the workplace and the social environment, which includes the organization of work. However, it is important to see the workplace and the workers in the poultry plants as part of the larger economic and social environment. Working conditions—and occupational hazards—also vary by race and gender in ways that could affect worker health. Our research must be viewed within the broad context of economic realities of North Carolina, the United States, and the world. The migration of low-wage industry to economically disadvantaged areas of the rural South has led to the placement of poultry-processing plants in areas with a disproportionate share of women of color, many of whom are single heads of household with few economic alternatives.
Because “the discovery of hazards and proposed remedies have the potential to adversely affect the profit margin of business (Murray 2003, p 223),” there has been longstanding neglect for occupational safety and health that is not limited to this community or industry. Neither the academic research team nor the community collaborators think there are easy solutions, and we also recognize that the solutions do not lie solely in this community. The health of workers is influenced not just by work exposures but also by a complicated web in which government policy, racial history, geographic variation, economic opportunity, and longstanding patterns of exploitation may contribute to existing conditions and disparities in health among poor working women. The complexities of conducting occupational health research in this context, which places low-wage and nonwhite workers at risk of dangerous exposures and work conditions while at the same time diminishing their power and the ability of researchers to access the points of exposure effectively, requires creative designs and methods. Despite the challenges, we see the potential to document health conditions, gain a better understanding of the complex processes that influence them, and begin steps toward change through collaboration of community and academic researchers.
This article is part of the mini-monograph “Community-Based Participatory Research.”
The study population is referred to as black, as opposed to African American, throughout the manuscript, based on the preference of the community-based staff.
The questionnaire used to collect musculoskeletal symptom data was adapted from the questionnaire developed by the National Institute for Occupational Safety and Health Research Program for the Prevention of Work-Related Musculoskeletal Disorders. The physical exam protocol is based in large part on the protocol developed by E. Viikari-Juntura for the Washington State Department of Labor and Industries Safety and Health Assessment and Research Program (SHARP) upper extremity musculoskeletal study.
We acknowledge K. Wicker for her assistance in preparing the manuscript and for her many efforts coordinating the project. We thank C. Slatin from the University of Massachusetts at Lowell for his thoughtful comments on an earlier version of the manuscript. We also acknowledge the substantial and essential contributions of the community-based staff, including E. Pender, R. Perry, C. Rankins, and C. Rodgers. L. Williams is acknowledged posthumously. She was the inspiration behind this project, seeking academic partners to address issues of health disparities in her community. Her death in September 2003 has been a challenge for those of us who valued her as a colleague and friend.
This project is funded by the National Institute of Environmental Health Sciences and the National Institute of Arthritis Musculoskeletal and Skin Diseases grant R01 ES10939-01.
Figure 1 Conceptual framework for the study of MSDs among rural women employed in poultry processing. Figure modified from Armstrong et al. (1993).
Table 1 Key variables based on conceptual framework—SHOWW project.
Variables Measures used/source
Outcomes or responses Musculoskeletal symptoms by body region Modified from NIOSH symptom report items (NIOSH 2000)
Hand diagram (Katz et al. 1990)
Signs from physical exam Modified from SHARP physical exam protocol (Viikari-Juntura 2000)
MSDs Combinations of signs and symptoms used to define working case definitions (Sluiter et al. 2001; Palmer et al. 2000 and Walker-Bone et al. 2002; Gerr et al. 2002)
Acute work-related injury Self-report
Health-related quality of lifea SF-12 (Ware et al. 1996)
Upper extremity functiona Upper extremity function scale (Pransky et al. 1997)
Depressive symptomsa CES-D (Radloff 1977)
Exposures Work requirements Repetition, posture, force, temperature, tool use Key informant interviews, project-specific self-report exposure tool
Modifiers Work organization (decision latitude, control, demand, social support, job satisfaction) Job Content Questionnaire (Karasek et al. 1998)
Discrimination and response Self-reported by race or gender and usual response (Krieger and Sidney 1996)
Assertiveness at work Scale measure from self-reported items; developed from key. informant interviews
Coping style John Henryism Active Coping Scale (James et al. 1987)
Socioeconomic strain Self-report of “Weeks you could be out of work without pay before loss of income would be a major problem.”
Other health conditions Medical history (select items based on possible relationship to MSDs—pregnancy, hormonal therapies, diabetes, etc.)
Abbreviations: CES-D, Center for Epidemiologic Studies Depression Scale; SF-12, SF-12 health survey; SHARP, Safety and Health Assessment and Research Program.
a Outcome of interest and potential modifier.
Table 2 Community contributions to SHOWW study and support provided to community-based staff.
Community members
Initiated request for academic assistance
Influenced decision to circumvent industry involvement
Influenced decision to conduct longitudinal study
Informed exposure assessment and other data collection tools
Participate in longitudinal research
Participate in ethnographic interviews
Recruit eligible participants
Community members as paid staff/collaborators
Provide valuable insight and helpful strategies for working with community
Participate in key decision making
Influenced decision to include ethnographic work
Arranged key informant interviews early in study development process
Selected location and site for community office
Recruit and retain participants using social networks and “snowball”method of recruitment
Manage daily office activities including recruitment incentives and advertising
Coordinate office schedules with study nurses
Transport participants; provide childcare
Participate in local community outreach and education
Provide outreach and education to academic community
Support provided to community-based staff
Participatory training—study design, recruitment/retention methods, interviewing skills, protection of participants’ confidentiality
Weekly staff meetings with project manager
Topic specific training
Office management (record keeping, database management)
MSDs
Principles of ergonomics
Daily availability by phone/e-mail of university team member as resource; study physician always available by pager
University benefits of employment—health and life insurance, paid vacation and sick leave, etc.
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U.S. Census Bureau 2004g. Highlights from the Census 2000 Demographics Profiles for Northampton County, North Carolina. Available: http://factfinder.census.gov/servlet/SAFFFacts?_event=Search&geo_id=05000US37117&_geoContext=01000US%7C04000US37%7C05000US37117&_street=&_county=northampton&_cityTown=northampton&_state=04000US37&_zip=&_lang=en&_sse=on&ActiveGeoDiv=geoSelect&_useEV=&pctxt=fph&pgsl=050 [accessed 19 February 2004].
Viikari-Juntura E 2000. Exam Protocol Developed for the Safety and Health Assessment and Research Program (SHARP) Upper Extremity Musculoskeletal Study. Olympia, WA: Washington State Department of Labor and Industries.
Walker-Bone K Byng P Linaker C Reading I Coggon D Palmer KT 2002 Reliability of the Southampton examination schedule for the diagnosis of upper limb disorders in the general population Ann Rheum Dis 63 1103 1106 12429544
Wallerstein N 1992 Powerlessness, empowerment, and health: implications for health promotion programs Am J Health Promot 6 3 197 205 10146784
Wallerstein N Rubenstein HL 1993. Teaching about Job Hazards. A Guide for Workers and Their Health Care Providers. Washington, DC:American Public Health Association.
Ware J Kosinski M Keller SD 1996 A 12-item short-form health survey: construction and scales and preliminary tests of reliability and validity Med Care 34 3 220 233 8628042
Wing S Grant G Green M Stewart C 1996 Community based collaboration for environmental justice: south-east Halifax environmental reawakening Environ Urban 8 129 140
Wyatt SB Diekelmann N Henderson F Andrew ME Billingsley G Felder SH 2003 A community-driven model of research participation: the Jackson Heart Study participant recruitment and retention study Ethn Dis 13 4 438 455 14632263
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==== Front
Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0079416330328PerspectivesEditorialInflammatory Genomics Gant Timothy W. Medical Research Council Toxicology Unit, University of Leicester, Leicester, United Kingdom, E-mail:
[email protected] author declares he has no competing financial interests.
Timothy W. Gant is head of the Systems Toxicology group at the Medical Research Council Toxicology Unit, United Kingdom. The primary interests of the group are the application of genomics in molecular toxicology with emphasis on the genetic basis of susceptibility and resistance to toxicity.
12 2005 113 12 A794 A795 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
As a University of London undergraduate beginning a module on pathology, I remember Professor Frank Fairweather opening his lecture by pointing to a large boil on his forehead as an example of acute inflammation. He then proceeded to describe the gross pathological characteristics of acute inflammation: weal, brief blood vessel constriction, followed by blood vessel dilation and associated redness. Such was my introduction to the most common consequence of tissue damage—and contributor to disease pathogenesis—inflammation.
Inflammation is mediated by chemical activators, collectively known as chemokines, secreted in the area of the tissue damage. Chemotactant proteins are expressed on the endothelial cell of the dilated blood vessels that serve as recruitment factors for lymphocytes. Blood vessel dilation causes a decrease in local blood flow, and activated neutrophils, attracted by the chemokines, attach to the chemotactant proteins, squeeze themselves through the endothelial cell walls of the locally dilated blood vessels, and follow the scent of the chemokines to the site of damage (for additional information, see Schmidt 2005).
Toxicogenomics has led to an additional description of inflammation based on the differential expression of genes associated with the inflammatory process. One of the first toxicogenomics reports published was that of the differential expression of genes in response to lipopolysaccharide-induced inflammation (Heller et al. 1997). Several reports now link the expression of certain genes, in particular the attachment genes Vcam1 and Icam1, to, for example, inflammation in the liver (Davies et al. 2005; Gant et al. 2003; Huang et al. 2004; Jiang et al. 2004). To date, a quantitative fingerprint of gene expression associated with inflammation has not been defined. In the GeneOntology (GO) database (GO 2005), genes associated with, but not necessarily quantitative for, inflammation are identified in biological processes as “inflammatory response.” Under inflammatory response in the GO, there are 371 genes listed for Homo sapiens. Tumor necrosis factor-α(TNF-α) is included among these 362 genes, but interleukin 6 (IL-6) is not, although IL-6 is used as a plasma biomarker of inflammation (Curran et al. 2005). Similarly, a recent study in the liver has associated three genes PGS6 (pregnancy-specific β-1-glycoprotein), GSTM4/M2 (glutathione S-transferase mu 4 and mu 2), and OAT (ornithine ketoacid aminotransferase) with inflammation in human liver (Younossi et al. 2005); these genes, like IL-6, are not categorized as inflammation genes in the GO. Thus, not all genes associated with inflammation are defined as such in GO, and none are quantitatively associated. Therefore, to provide a repository of data for making future associations, we need a maintained sub-database of differential gene expressions that are quantitatively associated with measured pathological responses. Such quantitative association of gene expression with altered pathology, known as “phenotypic anchoring” (Moggs 2005; Moggs et al. 2004; Paules 2003; Waters and Fostel 2004), includes measurement of both gene expression and degree of pathological change. Few data sets in Gene Expression Omnibus (GEO 2005) or ArrayExpress (European Bioinformatics Institute 2005) contain a histopathological quantitation of inflammation of sufficient quality to allow retrospective phenotypic anchoring of differential gene expression at the present time. More data sets need to include an actual measure of pathological change. In particular, toxicogenomic data should be collected before and during the onset of measured pathological change.
However, before embarking on the development of a phenotypically anchored database of signature gene expression, we must ask the following question: Does toxicogenomics have any advantage over histopathology in the assessment and characterization of pathological change? For inflammation, as for other pathologies, the answer to this question depends on whether toxicogenomics can a) detect inflammation before it becomes histopathologically observable, b) provide a more quantitative assessment of its severity, and c) distinguish between the acute and chronic forms and other pathologies. If we are referring to the most informative genes, the answer to these questions is probably “yes,” but more data is necessary to derive conclusive answers. Thus, the generation of more gene expression data is necessary in targeted pathologies such as inflammation, and a phenotypically anchored database should be targeted to specific common pathologies in the first instance so critical data masses of gene expression data can be collected.
In the early development of microarrays and their application in toxicology, some predictions were made that histopathologists would become an endangered species, made redundant by the new technology. This has not happened, and even the reverse could be argued to have occurred; toxicogenomics has proven so challenging for interpretation that there has been a retreat into the “gold standard” methods of analysis (Albertini 2005). Toxicogenomics has the potential to inform and append histopathological assessment, injecting a degree of instrumental precision into the analysis and assisting in the differentiation of difficult-to-discern lesions (Gant 2002, 2003; Lakhani and Ashworth 2001). Although there is still much work to be done, toxicogenomics will gradually gain a central role in the toxicologists’ armory—as long as expectations are reasonable, quality is good, interpretation is expert, and conclusions are balanced. Genomics has much to offer in pathological assessment, but its application should be collaborative, not inflammatory.
==== Refs
References
Albertini S 2005 Toxicogenomics in the pharmaceutical industry: hollow promises or real benefit? Mut Res 575 102 115 15924886
Curran JE Jowett JB Elliott KS Gao Y Gluschenko K Wang J 2005 Genetic variation in selenoprotein S influences inflammatory response Nat Genet 37 1234 1241 16227999
Davies R Schuurman A Barker CR Clothier B Chernova T Higginson FM 2005 Hepatic gene expression in protoporphyic Fech mice is associated with cholestatic injury but not a marked depletion of the heme regulatory pool Am J Pathol 166 1041 1053 15793285
European Bioinformatics Institute 2005. ArrayExpress. Available: http://www.ebi.ac.uk/arrayexpress/ [accessed 7 November 2005].
Gant TW 2003 Application of toxicogenomics in drug development Drug News Perspect 16 217 221 12942151
Gant TW Baus PR Clothier B Riley J Davies R Judah DJ 2003 Gene expression profiles associated with inflammation, fibrosis, and cholestasis in mouse liver after Griseofulvin Environ Health Perspect 111 847 853
GEO 2005. Gene Expression Omnibus. Available: http://www.ncbi.nlm.nih.gov/geo/ [accessed 7 November 2005].
GO 2005. GeneOntology, Biological Process. Available: http://www.geneontology.org/ontology/process.ontology [accessed 7 November 2005].
Heller R Schena M Chai A Shalon D Bedilion T Glilmore J 1997 Discovery and analysis of inflammatory disease-related genes using cDNA microarrays Proc Natl Acad Sci USA 94 2150 2155 9122163
Huang Q Jin X Gaillard ET Knight BL Pack FD Stoltz JH 2004 Gene expression profiling reveals multiple toxicity endpoints induced by hepatotoxicants Mutat Res 549 147 168 15120968
Jiang Y Kang YJ Liu J Waalkes M 2004 Changes in the gene expression associated with carbon tetrachloride-induced liver fibrosis persist after cessation of dosing in mice Toxicol Sci 79 404 410 15056808
Lakhani S Ashworth A 2001 Microarray and histopathological analysis of tumours: the future and the past? Nat Rev 1 151 157
Moggs J 2005 Molecular responses to xenoestorgens: mechanistic insights from toxicogenomics Toxicology 213 177 193 15996808
Moggs J Tinwell H Spurway T Chang HS Pate I Lim F 2004 Phenotypic anchoring of gene expression changes during estrogen-induced uterine growth Environ Health Perspect 112 1589 1606 15598610
Paules R Phenotypic anchoring:linking cause and effect 2003 Environ Health Perspect 111 A338 A339 12760838
Schmidt CW Critical care: applying genomics to inflammation outcomes Environ Health Perspect 113 A816 A821 16330338
Waters MD Fostel JM 2004 Toxicogenomics and systems toxicology: aims and prospects Nat Rev Genet 5 936 948 15573125
Younossi Z Baranova A Ziegler K Del Giacco L Schlauch K Born T 2005 A genomic and proteomic study of the spectrum nonalcoholic fatty liver disease Hepatology 42 665 674 16116632
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Environ Health Perspect. 2005 Dec; 113(12):A794-A795
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==== Front
Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0079516330329PerspectivesEditorialNote from the Editor: Good Bye and Thank You Goehl Thomas J. Editor-in-Chief, EHP, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services12 2005 113 12 A795 A795 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
Environmental Health Perspectives (EHP) is like one of the lighthouses that dot the North Carolina coast; the journal acts as a beacon warning people of potential environmental dangers and, at the same time, welcomes people whose goal is to improve global health. Although we report areas of concern about how our environment can negatively affect us, we also provide information that can give a sense of hope for the improvement of human health. Regardless of the information to be shared, EHP tries to provide the balance between voices that sometimes have competing interests.
I will be retiring at the end of December and have been blessed to have a fulfilling career dating back to 1969 that allowed me to experience industrial, academic, and governmental service. Along the way I have had the honor to work with many wonderful people. However, none of my experiences has been more fulfilling than my time at NIEHS during which I served with the National Toxicology Program and now with EHP. The talent, energy, tenacity, and altruism of these wonderful people are beyond compare.
NIEHS has been the benevolent sponsor of EHP since its inception over 30 years ago. NIEHS is an exceptional institution with exceptional people, who are at the forefront of research in environmental health issues. During this time EHP has given a voice to the institute and to the field of environmental health.
Although the future sponsorship of EHP is uncertain, the journal will continue in its mission of serving “as a forum for the discussion of the interrelationships between the environment and human health by publishing in a balanced and objective manner the best peer-reviewed research and most current and credible news of the field.”
My immediate plans are to go on extended camping trips across the United States with my wife, Marilyn (who is not just another pretty face!). I hope to use this time to contemplate new ways to contribute to scientific capacity building and information dissemination in the developing world, which has been my passion at EHP. I am very grateful for, and will always remember, the support and dedication of the EHP staff and the NIEHS administration during my tenure.
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==== Front
Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0079616332545PerspectivesDirector's PerspectivePhysician-Scientists in Environmental Health Schwartz David A. MDDirector, NIEHS and NTP, E-mail:
[email protected] 2005 113 12 A796 A796 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
I believe that the physician-scientist represents an endangered species in biomedical research, and is absolutely essential to achieving our vision for the NIEHS. As a physician-scientist, my thoughts on this topic are somewhat biased and reflect my parochial views, as well as the more reasoned concerns raised by others (Goldstein and Brown 1997; Ley and Rosenberg 2005; Rosenberg 2000; Wyngaarden 1979; Zerhouni 2005), on the fragile future of our species. Despite this admonition, I believe that our field will benefit enormously by expanding our cadre of physicians committed to the science of environmental health. While many people with whom I’ve talked emphasize the “E” in NIEHS, I think we need to pay more attention to the “H” in both NIEHS and NIH.
In general, the motivation that drives the quest for new knowledge in biomedical research is decidedly different for the physician-scientist. Although I spend most of my time in the research arena, the reason that I’m involved in research and the type of research that I do is largely inspired by my patients with unexplainable illnesses. The very question that patients and families frequently ask me—why one person (when challenged with an environmental agent) develops an illness while another person remains healthy—represents the focus of my research and has led me to the NIEHS. In fact, the longer I practice medicine (25 years and counting), the more I realize how much we don’t know about the science of medicine. We have so much more to understand about disease development, pathogenesis, treatment, and prevention. For the general public, this medical science gap usually doesn’t hit home until someone in the family develops a disease, or the message gets across from one of the research advocacy agencies, like Research America. The physician-scientist can help to identify the major opportunities in biomedical research that are likely to have the biggest impact on human health.
Our vision at the NIEHS, to use environmental sciences to understand human disease and to improve human health, requires that physician-scientists actively engage in the process. As we shift the focus of research supported by the NIEHS to emphasize human disease, the need for physician-scientists becomes compelling. While there are numerous examples of PhD-trained scientists who have had major effects on human health, MD-trained scientists are simply more familiar with the varied manifestations of human disease. Moreover, physicians bring the bedside to the science through their experiences, such as the clear memory of the patient who responded in an unusual way, which highlights some of the research opportunities in medicine. In this context, physicians have the unique ability to focus their research on scientific questions that are clinically relevant. While physician-scientists may lead scientific projects or teams studying a particular disease, it is just as likely that they will serve as the “glue,” helping a group of basic or public health investigators to focus their interests on clinically relevant areas of human pathophysiology.
As we conceive the future for the NIEHS, we need to consider the role of the physician-scientist in our institute. When we benchmark our institute to others at the NIH and consider independently funded investigators, it is clear that we have a much lower percentage of physicians as principal investigators than most of the institutes. I would suggest that we need to increase the percentage of physician-scientists at the NIEHS to at least 30% if we’re serious about shifting our focus to human health and disease.
To accomplish this, we are in the process of developing a number of extramural and intramural programs that focus on training, career development, independent research support, and specialized centers in integrative (translational) research. For instance, we recently established the Outstanding New Environmental Scientist (ONES) Award, an RFA that will fund first-time R01 recipients who are using environmental science to understand a human disease. In addition, we have decided to establish a Clinical Research Unit (CRU) within the Division of Intramural Research at the NIEHS. The CRU is being developed by Perry Blackshear, director of the Office of Clinical Research, and will be located on our campus at the NIEHS to afford the physician-scientist every opportunity to collaborate with basic and public health scientists. This will also enable our intramural scientists to take an interdisciplinary approach to broad themes in environmental health that cross methodological disciplines, such as reproductive health and epigenetics, neurosciences, immune-mediated diseases, and metabolism. The goal of this effort is to integrate basic, clinical, and public health science to have the biggest impact on human health.
If we keep our eye firmly fixed on the goal of understanding human disease and improving human health, we will surely have an impact. I believe that the physician-scientist will prove to be critical to the success of these efforts.
Principal Investigators with MDs at Benchmark NIH I/Cs
==== Refs
References
Goldstein JL Brown MS 1997 The clinical investigator: bewitched, bothered, and bewildered—but still beloved J Clin Invest 99 12 2803 2812 9185499
Ley TJ Rosenberg LE 2005 The physician-scientist career pipeline in 2005: build it, and they will come JAMA 294 11 1343 1351 16174692
Rosenberg LE 2000 Young physician-scientists: internal medicine’s challenge Ann Intern Med 133 10 831 832 11085847
Wyngaarden JB 1979 The clinical investigator as an endangered species N Engl J Med 301 23 1254 1259 503128
Zerhouni EA 2005 Translational and clinical science—time for a new vision N Engl J Med 353 15 1621 1623 16221788
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==== Front
Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0080116330331PerspectivesCorrespondenceTungsten Alloy and Cancer in Rats: Link to Childhood Leukemia? Schell John D. Blasland, Bouck & Lee, Inc., Houston, Texas, E-mail:
[email protected] author provides consulting sevices for Kennametal, Inc., a company with a facility in Fallon, Nevada.
12 2005 113 12 A801 A802 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
We read with interest the article by Kalinich et al. (2005) on the generation of rhabdomyosarcomas from “embedded weapons-grade tungsten alloy.” Although the study design and the reported findings are of great interest, we are concerned about certain statements made in both the “Introduction” and the “Discussion” of the article. In these sections the authors make reference to the allegation that “several cancer clusters in the United States are associated with elevated levels of tungsten in the environment” (Kalinich et al. 2005) Although they accurately point out that “no definitive link … has been established,” they suggest that the cancer clusters are part of “a growing list of health concerns related to tungsten exposure.” However, the conditions at Fallon, Nevada, and the investigations into a purported link between naturally occurring tungsten and childhood leukemia are very different from the experimental conditions that exist in the implantation study by Kalinich et al. (2005).
The Centers for Disease Control and Prevention (CDC) conducted a thorough investigation into the Fallon cancer cluster; in fact, it was the largest cancer cluster investigation ever undertaken in the United States. The scientists from the CDC and state health departments concluded that exposure to tungsten was not associated with the incidence of childhood leukemia in Fallon (CDC 2003). The genesis of the leukemia cases is still an area of interest and speculation as shown by a recent letter in EHP (Daughton 2005). Because Kalinich et al. (2005) inferred that tungsten somehow played a role in the Fallon leukemias while presenting data suggesting that implanted tungsten alloy caused metastatic tumor formation, readers may confuse the issues and assume that somehow the two effects (rhabdomyosarcoma and childhood leukemia) are related.
We are not questioning the quality of the work presented by Kalinich et al. (2005) or their finding that implanted pellets of a specific combination of tungsten/nickel/cobalt alloy caused an apparent increase in rhabdomyosarcoma with subsequent metastasis to the lung. Rather, we recommend that the authors remain focused on this finding. Suggesting that these results can be linked to, or somehow shed light on, childhood leukemia and exposure to environmental tungsten is both inappropriate and misleading.
==== Refs
REFERENCES
CDC 2003. Cross-Sectional Exposure Assessment of Environmental Contaminants in Churchill County, Nevada. Final Report. Atlanta, GA:Centers for Disease Control and Prevention. Available: http://www.cdc.gov/nceh/clusters/Fallon/study.htm [accessed 2 November 2005].
Daughton CG 2005 Overlooked in Fallon? [Letter] Environ Health Perspect 113 A224 A225 15811811
Kalinich JF Edmond CA Dalton TK Mog SR Coleman GD Kordell JE 2005 Embedded weapons-grade tungsten alloy shrapnel rapidly induces metastatic high-grade rhabdomyosarcomas in F344 rats Environ Health Perspect 113 729 734 15929896
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