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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0078815471707PerspectivesEditorialEditorial: Embracing Scrutiny Goehl Thomas J. Editor-in-Chief, EHP, Research Triangle Park, North Carolina, E-mail:
[email protected] 2004 112 14 A788 A788 Publication 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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Scientists are accustomed to scrutiny such as experimental protocol reviews, oversight on research conduct, and critiques provided during the peer-review publication process.
We especially anticipate the post-publication period during which others attempt to reproduce, validate, and then build on our work. All this scrutiny is basic to establishing the credibility of our findings. We therefore should not be put off by the greater attention now being focused on full disclosure of competing financial interests. What is essential to us as scientists is credibility. If our work is to contribute to the scientific and medical knowledge base, full disclosure is just one more process that must be embraced to establish and maintain the credibility of scientific and medical research.
“The potential for conflict of interest can exist whether or not an individual believes that the relationship affects his or her scientific judgment” (Davidoff et al. 2001); this quote should be the rule upon which authors lean when deciding on the necessity of providing a financial interest disclosure. Authors should also realize that disclosing financial support does not automatically diminish the credibility of the research. However, failure to disclose a competing financial interest that is subsequently discovered immediately opens the authors to questions about objectivity.
The need for full disclosure has become even more compelling as commercial organizations provide an increasing percentage of research support. Recognizing the growing importance of full disclosure, EHP clarified its policies in 2003. We feel that our disclosure requirements, which focus only on competing financial interests, are clear. However, a recent survey of the disclosure statements of some of our authors raised doubts about compliance.
The Center for Science in the Public Interest (CSPI 2004) surveyed four top medical and scientific journals: the New England Journal of Medicine, the Journal of the American Medical Association (JAMA), EHP, and Toxicology and Applied Pharmacology. The author of the report, Merrill Goozner, was quoted in USA Today (Davis 2004) as saying that “these journals were picked because they have the best policies.”
The CSPI investigative study covered December 2003 through February 2004, during which time EHP published 37 scientific studies. In a letter to EHP, Goozner (2004) stated that
Only 2 of the studies indicated they were funded by industry, and 2 studies included conflict of interest disclosure statements for at least some of the authors. … The CSPI investigated the first and last authors involved in the 35 studies who did not disclose conflicts of interest. Our investigation revealed at least 3 articles (8.6%) where either the first or last authors should have disclosed conflicts in accordance with the disclosure policy.
A fourth article was noted, but not included, because the issue identified would require a very strict interpretation of conflict of interest policies.
In an e-mail to Goozner, I expressed EHP ’s gratitude for the work done by the CSPI to help EHP achieve its goal of full disclosure of competing financial interests. I mentioned that we have a standing policy that encourages our readership to scrutinize disclosure issues. I further noted the difficulty that journal editorial offices would have if we undertook the task of checking on the financial interests of each of our authors. I promised Goozner that we would discuss this issue with our editorial board members and publish his letter along with responses that we would solicit from the named authors. These letters appear in this issue of EHP beginning on page A794.
The authors named by the CSPI (2004) have provided explanations for why they did not provide disclosure to EHP. After careful review of all the responses and discussions with our editorial board members, I am confident that there were no willful attempts to hide any competing financial interests. I judge that the authors named in the CSPI report (CSPI 2004) have made good faith efforts to comply with EHP disclosure policy.
However, lessons learned from examination of the four cases identified in the CSPI report (CSPI 2004) do provide guidance for future authors. In reporting affiliations, authors must ensure that they see the final formatted manuscript before submission. In deciding how in-depth their funding sources should be investigated, authors are expected to make a diligent effort to identify sources of funding and report that information. However, when funds come from a funding group that combines contributions from multiple sources, a failure to note a minor contributor is understandable. The probability is low that this minor percentage could impact the research findings or the personal finances of an author. Another clear requirement is that any relationship that could be perceived to have the potential for improperly influencing an author’s research should always be reported. In regard to patent disclosures, only existing, relevant patents issued before submission of a paper need to be disclosed. The issue of disclosure is quite complex. I counsel authors to always err on the side of caution. When in doubt, report!
Considering the issues raised by the CSPI report (CSPI 2004), we feel that it is appropriate for EHP to continue to update our disclosure policy. We now clearly instruct our authors to err on the sign of caution, and we have added the admonition that authors are to disclose all competing financial interests that might in any way be perceived as representing a competing financial interest. As has been our practice, EHP will continue to publish all disclosures made by our authors.
Our previous policy did not outline specific punitive measures that would be taken when our policies are violated. Because we feel that full disclosure is an absolute requirement, we are now adding clear consequences for any ethical violations. From now on, we will impose a 3-year ban on publication on authors who willfully fail to disclose a competing financial interest. Implementation of the ban will be made in consultation with our editorial board. If complete disclosure of possible conflicts would have caused the journal to have rejected the manuscript, the paper will be retracted. If the paper is not retracted but an ethical omission has occurred, an Expression of Concern will be written, published in the journal, and added to the online version of the article.
Once again, I encourage the scientific community to embrace scrutiny of our competing financial interests as we embrace the scrutiny of our research. Full disclosure is in the best interest of the individual scientists, the journals, and society, which must have complete faith that our research is not only of the highest quality but also is open, honest, and unbiased.
==== Refs
References
CSPI 2004. Unrevealed: Non-Disclosure of Conflicts of Interest in Four Leading Medical and Scientific Journals. Washington, DC:Center for Science in the Public Interest. Available: http://cspinet.org/new/pdf/unrevealed_final.pdf [accessed 2 September 2004].
EHP 2003 Instructions to authors Environ Health Perspect 111 A828 A833
Davidoff 2001 Sponsorship, authorship, and accountability Lancet 358 854 856 11567695
Davis R 2004. Journals, authors cited for conflicts of interest. USA Today (13 July 2004). Available: http://www.keepmedia.com/pubs/USATODAY/2004/07/13/505420 [accessed 3 September 2004].
Goozner M 2004 Study on failures to disclose conflicts of interest in Environmental Health Perspectives Environ Health Perspect 112 A794 A795 15471711
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0078915471708PerspectivesEditorialThe National Children’s Study: A Critical National Investment Trasande Leonardo Center for Children’s Health and the Environment, Department of Community and Preventive Medicine, New York, NY, E-mail:
[email protected] Philip J. Center for Children’s Health and the Environment, Department of Community and Preventive Medicine, New York, NY, E-mail:
[email protected] Trasande is assistant director of the Center for Children's Health and the Environment, and an instructor in Community and Preventive Medicine and in Pediatrics at the Mount Sinai School of Medicine. He also holds a lectureship in Pediatrics at Harvard Medical School.
Philip J. Landrigan is the Ethel H. Wise Professor and chair of the Department of Community and Preventive Medicine, and director of the Center for Children's Health and the Environment at the Mount Sinai School of Medicine.
10 2004 112 14 A789 A790 Publication 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
Patterns of disease in American children have changed dramatically in the past 200 years. Acquired immunodeficiency syndrome (AIDS), severe acute respiratory syndrome (SARS), and tuberculosis notwithstanding, vaccines, antibiotics, and improved hygiene have controlled the classic infectious diseases. Infant mortality has decreased by 90%. Life expectancy has nearly doubled. Yet amid this success a new challenge has arisen. Chronic diseases have increased sharply in incidence and have become the leading causes of childhood illness:
Asthma incidence and mortality have more than doubled [Centers for Disease Control and Prevention (CDC) 1995a, 1995b].
Despite declining mortality, incidence of acute lymphocytic leukemia increased by 61.7% from 1973 to 1999 (Robison et al. 1995).
Incidence of primary brain cancer increased by 39.6% from 1973 to 1994 (Schechter 1999).
Birth defects of the male reproductive system, such as hypospadias, doubled in frequency from 1970 to 1993 (Paulozzi et al. 1997).
Neurodevelopmental disorders—including learning disabilities, dyslexia, mental retardation, attention deficit disorder, and autism—are highly prevalent and affect 5–10% of the 4 million babies born in the United States each year (Bertrand et al. 2001; CDC 2004a, 2004b; LeFever et al. 1999; Safer et al. 1996; Zito et al. 2000).
Prevalence of childhood obesity has trebled (Galvez et al. 2003).
Incidence rates of chronic neurodegenerative diseases of late life such as Parkinson disease and dementia and of certain cancers have increased markedly, raising the possibility of early-life antecedents (Cory-Slechta et al., unpublished data).
Although much remains to be learned about the causes of these trends, evidence is accumulating that environmental factors make important contributions.
Airborne fine particulates have been shown to cause asthma and to trigger asthmatic atttacks (Salam et al. 2004).
Ozone, oxides of nitrogen, environmental tobacco smoke, and indoor air pollutants all are now recognized triggers for asthma (Suh et al. 2000; Wallace et al. 2003).
Childhood cancer has long been linked to ionizing radiation. More recently, benzene, 1,3-butadiene, and pesticides have been etiologically associated (Daniels et al. 2001; Lee et al. 2004).
Neurobehavioral impairment has been observed following exposure of the fetal brain to even low levels of lead (Baghurst et al. 1987; Canfield et al. 2003; Dietrich et al. 1987; Lanphear et al. 2000; Opler et al. 2004; Wasserman et al. 2000), methyl mercury (Grandjean et al. 1997, 2004; Kjellstrom et al. 1986, 1989; Murata et al. 2004; National Research Council 2000), pesticides (Berkowitz et al. 2004; Perera et al. 2003), polychlorinated biphenyls (Jacobson and Jacobson 1996), and ethanol (Lupton et al. 2004). A recent National Academy of Sciences study (2000) suggests that at least 28% of developmental disabilities in children are caused by environmental factors acting alone or in concert with genetic susceptibility.
Until now, progress in elucidating the role of the environment in chronic childhood disease has been slow and incremental. Nearly all studies have examined relatively small populations of children; have considered only one chemical toxicant at a time; have had little statistical power to examine interactions among chemical, social, and behavioral factors in the environment; have had limited ability to examine gene–environment interactions (Olden 2004); and have suffered from brief duration of follow-up. Also, many previous studies have been retrospective in design and thus have been forced to estimate past exposures from limited and sometimes biased historical data.
To overcome these difficulties, the President’s Task Force on Environmental Health and Safety Risks to Children recommended in 1998 (U.S. Department of Health and Human Services 2004) that a large prospective epidemiologic study of American children be undertaken. In response, the U.S. Congress through the Children’s Health Act of 2000 authorized the National Institute of Child Health and Human Development (NICHD) “to conduct a national longitudinal study of environmental influences (including physical, chemical, biological and psychosocial) on children’s health and development” (Children’s Health Act 2000). The National Institute of Environmental Health Sciences (NIEHS), the CDC, and the U.S. Environmental Protection Agency (EPA) join the NICHD in planning and conducting this study.
Key features of this far-reaching study—now termed the National Children’s Study (NCS)—are that it will follow a representative sample of 100,000 American children from early pregnancy through age 21; a subset maybe recruited before conception. Exposure histories and biologic samples will be obtained during pregnancy and from children as they grow, obviating the need for retrospective assessments of exposures. The large sample size will facilitate simultaneous examination of the effects of multiple chemical exposures, of interactions among them, and of interactions among biologic, chemical, behavioral, and social factors. Each child will be screened genetically, thus permitting study of gene–environment interactions. Follow-up will extend over decades. For the past four years, working groups convened by NICHD have been developing the NCS: formulating core hypotheses, delineating research protocols, and planning logistics. The study is now ready for the field.
Previous major prospective epidemiologic studies have yielded invaluable gains in knowledge of disease causation and have provided critical tools for prevention and treatment. The Framingham Heart Study (Framingham, MA), for example, was established in 1948 at a time when heart disease and stroke were epidemic in the United States. The goal was to identify preventable risk factors. Within a few years, data from Framingham identified cigarette smoking (Dawber 1960) and elevated cholesterol and hypertension as preventable causes of cardiovascular disease (CVD) (Kannel et al. 1961, 1978); later analyses elucidated the role of elevated triglycerides, sedentary lifestyle, and diabetes. This information provided the blueprint for the major reductions in incidence of CVD that we have achieved in the United States over the past four decades (CDC 1999).
We anticipate that the NCS will yield equally enormous societal benefits. Six of the chronic diseases that the study plans to examine —obesity, injury, asthma, diabetes, schizophrenia, and autism—cost America $642 billion per year (Bromet and Fennig 1999; CDC 2004a, 2004b, 2004c, 2004d, 2004e, 2004f, 2004g; National Alliance for Autism Research 2002; U.S. Department of Health and Human Services 2001; Weiss 2001; Yeargin-Allsop et al. 2002). If the NCS were to produce a reduction of only 1% in incidence of these diseases, the annual savings would amount to $6.4 billion, far more than the $2.7 billion price tag of the study over 25 years.
Despite the enormous potential of the NCS, its funding is in critical jeopardy. In each of the past 4 years, the annual budget has been $12 million, a sum provided by contributions from the NICHD, the NIEHS, the CDC, and the U.S. EPA. But now to move the study forward, there is need in 2005 to establish a data-coordinating center, a repository for secure storage of biologic samples, and a series of regionally distributed vanguard recruitment sites. For these tasks, NICHD needs $15 million in new dollars above their regular budget. Without at least $27 million in federal funding in 2005, NICHD will likely be forced to cancel or at least postpone the study.
The NCS has benefited from strong and bipartisan leadership in Congress and from the support of a broad-based coalition that includes the American Academy of Pediatrics, the U.S. Conference of Catholic Bishops, the American Chemistry Council, the Learning Disabilities Association, and the March of Dimes. But still it is in dire fiscal peril.
The NCS represents an extraordinary opportunity. If the study receives the funding that it needs in 2005, it will begin as early as 2009 to produce data that will save children’s lives and improve their health. The NCS is a national investment in the future that for the sake of our children we must make today.
==== Refs
References
Baghurst PA Robertson EF McMichael AJ Vimpani GV Wigg NR Roberts RR 1987 The Port Pirie Cohort Study: lead effects on pregnancy outcome and early childhood development Neurotoxicology 8 3 395 401 2443882
Berkowitz GS Wetmur JG Birman-Deych E Obel J Lapinski RH Godbold JH 2004 In utero pesticide exposure, maternal paraoxonase activity, and head circumference Environ Health Perspect 112 388 391 14998758
Bertrand J Mars A Boyle C Bove F Yeargin-Allsopp M Decoufle P 2001 Prevalence of autism in a United States population: the Brick Township, New Jersey, investigation Pediatrics 108 5 1155 1161 11694696
Bromet EJ Fennig S 1999 Epidemiology and natural history of schizophrenia Biol Psychiatry 46 7 871 881 10509170
Canfield RL Henderson CR Jr Cory-Slechta DA Cox C Jusko TA Lanphear BP 2003 Intellectual impairment in children with blood lead concentrations below 10 microg per deciliter N Engl J Med 348 16 1517 1526 12700371
CDC 1995a Children at risk from ozone air pollution, 1991–1993 MMWR Morb Mortal Wkly Rep 44 309 312 7715588
CDC 1995b Asthma—United States, 1982–1992 MMWR Morb Mortal Wkly Rep 43 952 955 7799908
CDC 1999 Achievements in public health, 1900–1999: decline in deaths from heart disease and stroke—United States, 1900–1999 MMWR Morb Mortal Wkly Rep 48 30 649 656 10488780
CDC 2004a. Developmental Disabilities. Available: http://www.cdc.gov/ncbddd/dd/default.htm [Accessed 21 June 2004].
CDC 2004b Economic costs associated with mental retardation, cerebral palsy, hearing loss, and vision impairment—United States 2003 MMWR Morb Mortal Wkly Rep 53 03 57 59 14749614
CDC 2004c. National Asthma Control Program. Available: http://www.cdc.gov/nceh/airpollu-tion/asthma/asthmaAAG.htm [Accessed 30 August 2004].
CDC 2004d. National Diabetes Fact Sheet: General Information and National Estimates on Diabetes in the United States. Available: http://www.cdc.gov/diabetes/pubs/factsheet.htm [Accessed 30 August 2004].
CDC 2004e. National Center for Injury Prevention and Control. Injuries among Children and Adolescents. Available: http://www.cdc.gov/ncipc/factsheets/children.htm [Accessed 30 August 2004].
CDC 2004f. Cost of Injury in the United States: A Report to Congress. Available: http://www.cdc.gov/ncipc/pub-res/cost_of_injury/ch2-3.pdf [Accessed 13 September 2004].
CDC 2004g. National Center for Health Statistics. Asthma Prevalence, Health Care Use and Mortality, 2000–2001. Available: http://www.cdc.gov/nchs/products/pubs/pubd/hestats/asthma/asthma.htm [Accessed 30 August 2004].
Children’s Health Act of 2000 2000. Public Law 106–310.
Daniels JL Olshan AF Teschke K Hertz-Picciotto I Savitz DA Blatt J 2001 Residential pesticide exposure and neuroblastoma Epidemiology 12 1 20 27 11138814
Dawber TR 1960 Summary of recent literature regarding cigarette smoking and coronary heart disease Circulation 22 164 66 13814553
Dietrich KN Krafft KM Bornschein RL Hammond PB Berger O Succop PA 1987 Low-level fetal lead exposure effect on neurobehavioral development in early infancy Pediatrics 80 5 721 730 2444921
Grandjean P Murata K Budtz-Jorgensen E Weihe P 2004 Cardiac autonomic activity in methylmercury neurotoxicity: 14-year follow-up of a Faroese birth cohort J Pediatr 144 2 169 176 14760255
Grandjean P Weihe P White RF Debes F Araki S Yokoyama K 1997 Cognitive deficit in 7-year-old children with prenatal exposure to methylmercury Neurotoxicol Teratol 19 6 417 428 9392777
Galvez MP Frieden TR Landrigan PJ 2003 Obesity in the 21st century Environ Health Perspect 111 A684 A685 14527853
Jacobson JL Jacobson SW 1996 Intellectual impairment in children exposed to polychlorinated biphenyls in utero N Engl J Med 335 783 789 8703183
Kannel WB Dawber TR Kagan A Revotskie N Stokes JI 1961 Factors of risk in the development of coronary heart disease—six year follow-up experience; the Framingham Study Ann Intern Med 55 33 50 13751193
Kannel WB Wolf PA Dawber TR 1978 Hypertension and cardiac impairments increase stroke risk Geriatrics 33 71 83 680566
Kjellstrom T Kennedy P Wallis S Mantell C 1986. Physical and Mental Development of Children With Prenatal Exposure to Mercury From Fish. Stage I: Preliminary Tests at Age 4. Report 3080. Solna, Sweden:National Swedish Environmental Protection Board.
Kjellstrom T Kennedy P Wallis S Stewart A Friberg L Lind B 1989. Physical and Mental Development of Children With Prenatal Exposure to Mercury From Fish. Stage II: Interviews and Psychological Tests at Age 6. Report 3642. Solna, Sweden:National Swedish Environmental Protection Board.
Lanphear BP Dietrich K Auinger P Cox C 2000 Cognitive deficits associated with blood lead concentrations < 10 microg/dL in US children and adolescents Public Health Rep 115 6 521 529 11354334
Lee WJ Cantor KP Berzofsky JA Zahm SH Blair A 2004 Non-Hodgkin’s lymphoma among asthmatics exposed to pesticides Int J Cancer 111 2 298 302 15197786
LeFever GB Dawson KV Morrow AL 1999 The extent of drug therapy for attention deficit-hyperactivity disorder among children in public schools Am J Public Health 89 1359 1364 10474553
Lupton C Burd L Harwood R 2004 Cost of fetal alcohol spectrum disorders Am J Med Genet 127C 1 42 50 15095471
Murata K Weihe P Budtz-Jorgensen E Jorgensen PJ Grandjean P 2004 Delayed brainstem auditory evoked potential latencies in 14-year-old children exposed to methylmercury J Pediatr 144 2 177 183 14760257
National Academy of Sciences, Committee on Developmental Toxicology 2000. Scientific Frontiers in Developmental Toxicology and Risk Assessment. Washington, DC:National Academies Press.
National Alliance for Autism Research 2002. What is Autism? Available: http://www.naar.org/aboutaut/whatis.htm [Accessed 30 August 2004].
National Research Council 2000. Toxicological Effects of Methylmercury. Washington, DC: National Academies Press.
Olden K 2004 Genomics in environmental health research—opportunities and challenges Toxicology 198 1–3 19 24 15138025
Opler MG Brown AS Graziano J Desai M Zheng W Schaefer C 2004 Prenatal lead exposure, δ-aminolevulinic acid, and schizophrenia Environ Health Perspect 112 548 552 15064159
Paulozzi LJ Erickson JD Jackson RJ 1997 Hypospadias trends in two US surveillance systems Pediatrics 100 5 831 834 9346983
Perera FP Rauh V Tsai WY Kinney P Camann D Barr D 2003 Effects of transplacental exposure to environmental pollutants on birth outcomes in a multiethnic population Environ Health Perspect 111 201 206 12573906
Robison LL Buckley JD Bunin G 1995 Assessment of environmental and genetic factors in the etiology of childhood cancers: the Childrens Cancer Group epidemiology program Environ Health Perspect 103 suppl 6 111 116 8549456
Safer DJ Zito JM Fine EM 1996 Increased methylphenidate usage for attention deficit disorder in the 1990s Pediatrics 98 1084 1088 8951257
Salam MT Li YF Langholz B Gilliland FD Children’s Health Study 2004 Early-life environmental risk factors for asthma: findings from the Children’s Health Study Lancet 363 9403 119 125 14726165
Schechter CB 1999 Re: Brain and other central nervous system cancers: recent trends in incidence and mortality J Natl Cancer Inst 91 2050 2051 10580036
Suh HH Bahadori T Vallarino J Spengler JD 2000 Criteria air pollutants and toxic air pollutants Environ Health Perspect 108 suppl 4 625 633 10940240
U.S. Department of Health and Human Services The President's Task Force on Environmental Health Risks and Safety Risks to Children. Available: http://nationalchildrensstudy.gov/about/task_force.cfm [Accessed 13 September 2004].
U.S. Department of Health and Human Services Public Health Service, Office of the Surgeon General. 2001. The Surgeon General’s Call to Action to Prevent and Decrease Overweight and Obesity. Available: http://www.surgeongeneral.gov/topics/obesity/calltoaction/CalltoAction.pdf [Accessed 30 August 2004].
Wallace LA Mitchell H O’Connor GT Neas L Lippmann M Kattan M 2003 Particle concentrations in inner-city homes of children with asthma: the effect of smoking, cooking, and outdoor pollution Environ Health Perspect 111 1265 1272 12842784
Wasserman GA Liu X Popovac D Factor-Litvak P Kline J Waternaux C 2000 The Yugoslavia Prospective Lead Study: contributions of prenatal and postnatal lead exposure to early intelligence Neurotoxicol Teratol 22 6 811 818 11120386
Weiss KB Sullivan SD 2001 The health economics of asthma and rhinitis. I. Assessing the economic impact J Allergy Clin Immunol 107 3 8 11149982
Yeargin-Allsopp M Rice C Karapurkar T Doernberg N Boyle C Schendel D 2002 Prevalance of autism in a US metropolitan city JAMA 289 1 49 55 12503976
Zito JM Safer DJ dosReis S Gardner JF Boles M Lynch F 2000 Trends in the prescribing of psychotropic medications to preschoolers JAMA 283 1025 1030 10697062
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00795PerspectivesCorrespondenceConflicts of Interest: Owens’ Response Owens J. William The Procter and Gamble Company, Central Product Safety, Cincinnati, Ohio, E-mail:
[email protected] author is employed by The Procter and Gamble Company.
10 2004 112 14 A795 A796 Publication 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
To clarify comments from Goozner and the Center for Science in the Public Interest (CSPI), I would like to provide the pertinent facts on my compliance with EHP’s policy on competing financial interest disclosure.
First, I was on loan or “seconded” from Procter & Gamble (P&G) to the Organisation for Economic Co-operation and Development (OECD) in Paris. The secondment was arranged at the request of the OECD, and I was officially under contract and employed by them from October 2002 through October 2003. I offer the former manager of the department, Herman Köeter; the current manager, Andrew Wagner; and their manager, Robert Visser, as references for these points.
Second, in this approximate period, I was a corresponding or contributing author on 11 relevant publications on assays that were either undergoing formal validation at the OECD or were being developed along similar lines for endocrine mechanisms (Ashby et al. 2002a, 2002b, 2003, 2004; Fang et al. 2003; Kanno et al. 2001, 2003a, 2003b; Owens and Ashby 2002; Owens and Köeter 2003; Owens et al. 2003; Yamasaki et al. 2003). In all cases but one, my P&G address was used.
Third, the one exception involved the article questioned by Goozner (Yamasaki et al. 2003). Kanji Yamasaki of the Chemical Evaluation and Research Institute in Japan was the corresponding author in this case. Because work on this manuscript (Yamasaki et al. 2003) occurred while I was at the OECD, Yamasaki used the OECD affiliation when submitting the manuscript to EHP. I simply did not notice the affiliation. If I had, I would have changed it to use my P&G affiliation to be consistent with the other 10 publications during this period.
Last, neither I nor P&G have any financial interest in the publications or their subject matter. Rather, this work was done to progress both development and validation work on safety tests that have potential for use to serve a diverse range of parties, including regulators, industry, and the public. Therefore, I do not see how allegations of a competing financial interest are valid in this case, even considering the oversight on the affiliation.
I hope this fully clarifies the record on this matter and lays the CSPI allegations to rest.
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References
Ashby J Lefevre PA Tinwell H Odum J Owens W 2004 Testosterone-stimulated weanling rats as a replacement for castrated rats in the Hershberger anti-androgen assay Regul Toxicol Pharmacol 39 229 238 15041151
Ashby J Owens W Deghenghi R Odum J 2002a Concept evaluation: an assay for receptor-mediated and biochemical antiestrogens using pubertal rats Regul Toxicol Pharmacol 35 393 397 12202054
Ashby J Owens W Lefevre PA 2002b Concept evaluation: androgen-stimulated immature intact male rats as an assay for antiandrogens Regul Toxicol Pharmacol 35 280 285 12052012
Ashby J Owens W Odum J Tinwell H 2003 The intact immature rodent uterotrophic bioassay: possible effects on assay sensitivity of vomeronasal signals from male rodents and strain differences Environ Health Perspect 111 1568 1570 12948899
Fang H Tong W Branham W Hong H Xie Q Moland CL 2003 Structure-activity relationships of 202 natural, synthetic and environmental chemicals for binding to the androgen receptor Chem Res Toxicol 16 1338 1358 14565775
Kanno J Onyon L Haseman J Fenner-Crisp P Ashby J Owens W 2001 The OECD program to validate the rat uterotrophic bioassay to screen compounds for in vivo estrogenic responses: Phase 1 Environ Health Perspect 109 785 794 11564613
Kanno J Onyon L Peddada S Ashby J Jacob E Owens W 2003 The OECD program to validate the rat uterotrophic bioassay. Phase 2: coded single dose studies Environ Health Perspect 111 1550 1558 12948897
Kanno J Onyon L Peddada S Ashby J Jacob E Owens W 2003 The OECD program to validate the rat uterotrophic bioassay. Phase 2: dose–response studies Environ Health Perspect 111 1530 1549 12948896
Owens JW Ashby J 2002 Critical review and evaluation of the uterotrophic bioassay for the identification of possible estrogen agonists and antagonists: in support of the validation of the OECD uterotrophic protocols for the laboratory rodent CRC Crit Rev Toxicol 32 445 520
Owens W Koëter HBMW 2003 The OECD special program to validate the rat uterotrophic bioassay: an overview Environ Health Perspect 111 1527 1529 12948895
Owens W Ashby J Odum J Onyon L 2003 The OECD program to validate the rat uterotrophic bioassay. Phase 2: dietary phytoestrogen analyses Environ Health Perspect 111 1559 1567 12948898
Yamasaki K Sawaki M Ohta R Okuda H Katayama S Yamada T 2003 OECD validation of the Hershberger assay in Japan: phase 2 dose response of methyltestosterone, vinclozolin, and p,p ’-DDE Environ Health Perspect 111 1912 1919 14644666
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0079715471717PerspectivesCorrespondenceChildhood Leukemia, Military Aviation Facilities, and Population Mixing Kinlen Leo J. University of Oxford, Radcliffe Infirmary, Oxford, United Kingdom, E-mail:
[email protected] author declares he has no competing financial interests.
Editor’s note: In accordance with journal policy, Steinmaus et al. were asked whether they wanted to respond to this letter, but they chose not to do so.
10 2004 112 14 A797 A798 Publication 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 a recent article on the striking cluster of childhood leukemia in 2000–2001 near the Fallon Naval Air Station in Nevada, Steinmaus et al. (2004) referred to the potential relevance of rural–urban population mixing. The population-mixing hypothesis was generated by the observation of excesses of childhood leukemia in two remote and isolated areas in Great Britain that had experienced influxes of significant numbers of workers as a result of the construction and operation of two large nuclear facilities (Kinlen 1988). Such mixing will increase the level of contacts between susceptible (more prevalent in rural areas) and infected individuals, promoting localized (frequently subclinical) epidemics of infections. If childhood leukemia is a rare response to a common—but unidentified—infection, then these localized epidemics will produce excess cases of the unusual complication, childhood leukemia.
Studies of all known examples of extreme rural–urban population mixing in Britain in the past 60 years have, in each instance, revealed significant temporary excesses of childhood leukemia (Kinlen 1995, 2000). These findings have been supported by studies conducted in other countries, most recently by an excess of childhood leukemia in isolated rural counties of the United States where substantial population increases have occurred (Wartenberg et al. 2004). None of these “mixing” situations, however, can compare in intensity with the indirect exposure of the small town of Fallon, Nevada (population 7,536), in only a few years, to over 100,000 military personnel from outside the area receiving training at the naval air station, reaching the extraordinary level of 55,000 in 2000 (GlobalSecurity.org 2003; . U.S. Navy 2002). That the world’s most sharply defined cluster of childhood leukemia (Alexander 1993; Steinmaus et al. 2004) should occur in association with the most extreme example of rural–urban population mixing could not be more arresting (Kinlen and Doll 2004).
Every opportunity should be taken to investigate the role that infection may have played in this extraordinary cluster of childhood leukemia. Unlike most studies of marked population mixing, where the relevant circumstances occurred some time ago, this recent cluster provides researchers with the chance to thoroughly study the cases (and other members of the population) for evidence of exposure to the relevant infectious agent. It is an opportunity that should not be missed.
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References
Alexander FE 1993 Viruses, clusters and clustering of childhood leukaemia: a new perspective? Eur J Cancer 29A 1423 1424
GlobalSecurity.org 2003. Naval Air Station Fallon. Available: http://www.globalsecurity.org/military/facility/fallon.htm [accessed 5 April 2004].
Kinlen L 1988 Evidence for an infective cause of childhood leukaemia: comparison of a Scottish new town with nuclear reprocessing sites in Britain Lancet 2 1323 1327 2904050
Kinlen LJ 1995 Epidemiological evidence for an infective basis in childhood leukaemia Br J Cancer 71 1 5 7819022
Kinlen LJ 2000 Infection, childhood leukaemia and the Seascale cluster Radiol Prot Bull 226 9 18
Kinlen L Doll R 2004 Population mixing and childhood leukaemia: Fallon and other US clusters [Editorial] Br J Cancer 91 1 3 15226760
Steinmaus C Lu M Todd RL Smith AH 2004 Probability estimates for the unique childhood leukemia cluster in Fallon, Nevada, and risks near other U.S. military aviation facilities Environ Health Perspect 112 766 771 15121523
U.S. Navy 2002. Naval Air Station, Fallon, Nevada. History. Available: http://www.fallon.navy.mil/history.htm [accessed 12 May 2004].
Wartenberg D Schneider D Brown S 2004 Childhood leukemia incidence and the population mixing hypothesis in US SEER data Br J Cancer 90 1771 1776 15150603
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0080215471718EnvironewsForumChildren’s Health: The Opposite of Obesity: Undernutrition Overwhelms the World’s Children Potera Carol 10 2004 112 14 A802 A802 Publication 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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An alarming number of studies report that overnutrition and the resulting obesity are a growing health problem for children in industrialized nations and even some developing ones. The explosion of such studies might seem to suggest that starvation is a thing of the past, yet children in many developing countries still go hungry. Furthermore, a lack of calories and nutrients—or undernutrition—can worsen the effects of infectious disease, and thereby causes half of all child deaths worldwide, report public health experts at The Johns Hopkins University and the World Health Organization in the 1 July 2004 issue of the American Journal of Clinical Nutrition.
This new finding supports a 1995 study coordinated by David Pelletier, an associate professor of nutrition sciences at Cornell University, which provided the first evidence of how often child deaths are attributable to under-nutrition. The latest study goes a step further: Johns Hopkins nutritionist Laura Caulfield and her colleagues answer the important question of whether undernutrition exacerbates the effects of infectious diseases.
Caulfield headed a team that analyzed data from 10 large studies of child deaths in sub-Saharan Africa and Southeast Asia. These studies included data about the average weight-for-age status of children relative to healthy U.S. reference children. Unlike Pelletier’s work, the studies reviewed by Caulfield’s team contained information about the cause of death, allowing the team to tease out the role of undernutrition in deaths caused by diarrhea, malaria, measles, and pneumonia.
Weight-for-age is the most widely used indicator of child nutritional status in developing countries. Caulfield’s team compared the weight-for-age of children relative to the “international growth reference” established by the National Center for Health Statistics. Children who fall below –2 standard deviations are classified as moderately to severely undernourished (in developing countries, 30–50% of children fall into this category). The team then used a statistical model to relate weight-for-age scores to the death rate.
Overall, the team found having a low weight-for-age score is a leading risk factor for child deaths, accounting for 52.5% worldwide. Among individual diseases studied, undernutrition is responsible for 60.7% of deaths from diarrhea, 57.3% of deaths from malaria, 52.3% of deaths from pneumonia, and 44.8% of deaths from measles.
Moreover, children do not need to be severely undernourished to be at heightened risk of dying if an infectious disease strikes. “Our analysis shows that even children who are small [for their age], but who would not be classified as malnourished based on their weight, are twice as likely to die as children of normal weight,” says Caulfield. “Undernutrition increases the susceptibility to illness and increases the likelihood that an illness will be severe.”
Before Caulfield’s study and the earlier one by Pelletier, experts estimated that undernutrition accounted for no more than 5% of child deaths; cause of death was attributed only to obvious disease symptoms, such as diarrhea or fever. These earlier estimates “did not capture the underlying effect of malnutrition in making a disease more severe,” says Pelletier, who calls undernutrition “the silent killer.”
Public health experts and policy makers historically look to immunizations, drug treatments, and sanitation as ways to prevent child deaths. Programs such as the Millenium Development Goals of the United Nations (which promises to cut the mortality rate of children under age 5 by two-thirds by the year 2015) and vaccination accessibility and research projects funded by the Bill & Melinda Gates Foundation suggest that the international community is committed to improving child health through such means.
But disease treatment and prevention are not enough, says Pelletier; money must also go toward educational and agricultural programs to abate undernutrition. “The impact of undernutrition is not as well appreciated,” agrees Caulfield. Her findings emphasize the need to invest in nutrition programs globally to reduce child deaths.
The new findings are a wakeup call to policy makers about the implications of undernutrition. “The data are there,” says Caulfield, “but we need to translate them for policy makers so that they can understand what it means for a child to weigh less than normal.” In addition to preventing child deaths, correcting undernutrition contributes to quality of life. Even if antibiotics and immunizations keep children alive, “their quality of life is miserable if they’re malnourished,” says Pelletier.
Hunger persists. A severely malnourished 4-year-old in Ethiopia is typical of thousands of children around the world whose health and lives are devastated by lack of adequate food.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00805EnvironewsForumEHPnet: NIEHS Environmental Health Science Education Dooley Erin E. 10 2004 112 14 A805 A805 Publication 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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Although the NIEHS is known primarily as a research institution, the institute is also charged with making its findings openly accessible to the public. As one of its efforts in this area, the NIEHS has developed its Environmental Health Science Education website to provide teaching materials for students and educators on topics including asthma, carcinogens, herbal medicines, nanotechnology, obesity, and risk assessment. To aid in the ease of finding materials, the site’s homepage, located at http://www.niehs.nih.gov/science-education/home.htm, has three portals, one for each of the three audiences—students, teachers, and scientists—that may be accessing the site.
The Students portion contains a library of homework resources, sorted by type of publication: factsheet, pamphlet, news article, or video. The page shows the ages for which each resource is intended. Within the Online Activities subsection are links to games, puzzles, tips for healthy living, environment-related coloring books, and storybooks. Most of these activities are intended for elementary or middle school students, although there is one tool, Project Greenskate, that has been developed for high school students. The Students portion also contains information on summer employment and training opportunities for high school and college students.
In the Teachers portion of the site, educators can find curricular materials, sorted by keywords, that include a real-time air quality activity, guides to performing scientific techniques such as the Ames assay, classroom role-playing scenarios such as the Hydroville Challenge project, and staff development units. Also within the curricular materials section are links to PDF presentations, web-based activities, and videos. Teachers can access lesson resources such as pamphlets and other print materials. Under the Professional Development subsection of the Teachers portion are links to the NIEHS Summers of Discovery program and other teacher training programs around the country.
The Scientists portion of the site, meanwhile, offers resources that environmental health professionals can use in making classroom presentations on subjects related to their work. The resources are annotated with the subject, format, and intended audience. Also within this section is a Professional Opportunities page, which links to EHP’s online Career Opportunities and Fellowships listings, as well as training, fellowship, and career development opportunities through the NIEHS Division of Extramural Research and Training.
All of the materials on the website are fully searchable from the homepage. There is also a menu bar across the top of the homepage to quickly access the most often requested pages. Visitors can find information on how to schedule tours to the NIEHS campus in Research Triangle Park, North Carolina, and how to request classroom visits by NIEHS scientists. A number of the resources available on the site are also available in Spanish. These can be accessed through a link on the top menu bar.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0080615471719EnvironewsNIEHS NewsMission: Educational Manuel John 10 2004 112 14 A806 A809 Publication 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 mission of the NIEHS is to reduce the burden of human illness and dysfunction from environmental causes by understanding the interrelationship between environmental factors, individual susceptibility, and age. The institute pursues this mission primarily through biomedical research, but in order for these findings to impact human health, they must be relayed to the public—and that includes students, from kindergarteners who are just beginning to learn about air, water, and plants, to college students preparing for careers as scientists.
“Most American schoolkids understand the need to ‘save the environment,’ [but] most do not understand the interaction between environment and human health,” says Marian Johnson-Thompson, director of education and biomedical research development at the NIEHS. “Given how much that interaction can affect them personally and the importance of an informed citizenry in supporting wise government policies, we need to be involved in environmental health science education.”
Education is a component of many activities taking place throughout the NIEHS. Indeed, says Liam O’Fallon, program analyst for the NIEHS science education and outreach grant programs, “Science education is really a part of all our jobs here at the institute.”
Curriculum for Change
O’Fallon chairs the newly formed NIEHS Science Education Committee, which brings together the diverse educational activities throughout the institute and focuses on how these can better address student and teacher needs at the local and national level. One of the outcomes of this collaborative effort has been the development of a comprehensive NIEHS environmental health science education website (http://www.niehs.nih.gov/science-education/). The website provides access to an enormous amount of information on environmental health science, including homework resources and online activities for students, lesson resources and classroom activities for teachers, and presentation materials for scientists. The site also lists opportunities for professional development, summer research and job opportunities, and tours and visits of the campus. [For more information on the site, see “NIEHS Environmental Health Science Education, p. A805 this issue.]
The bulk of the institute’s education efforts are aimed at boosting environmental health science education in kindergarten through twelfth grade (K–12). Various studies, including the High School and Beyond survey of the National Center for Education Statistics, have cited a steady decline in both the scientific literacy of American students and the number of students interested in pursuing careers in natural science or engineering. Through its K–12 educational programs, the NIEHS seeks to help reverse both these trends.
Starting in 1994, the institute provided grants to universities to develop K–12 level instructional materials on such topics as cell biology, toxicology, risk assessment, scientific process and methodology, and indoor and outdoor air pollution. These instructional materials were meant to be incorporated into existing curricula.
The institute followed these instructional materials development grants with grants aimed at teacher enhancement and development. Grantees provided teachers with materials and curricula pertaining to environmental health science, funding to attend workshops, and opportunities to interact with environmental health scientists in the field. Under this initiative, grantees trained more than 7,500 classroom teachers to incorporate environmental health science into their classrooms. Some curricula, such as the My Health My World series produced by the Baylor College of Medicine for grades 2–4, have been so successful that they are being promoted nationally.
The latest initiative, Environmental Health Sciences as an Integrative Context for Learning (EHSIC), is intended to improve overall academic performance as well as enhance students’ comprehension of and interest in environmental sciences. These grants, which offer up to $250,000 per year for seven years, support projects designed to integrate environmental health science into a variety of school curricula. The nine recipients, several of whom received earlier grants for instructional materials and teacher development, are now entering the fifth year of their projects and are showing impressive results.
Reaching the Grassroots
The University of Rochester Medical Center has used its EHSIC grant to develop multidisciplinary curriculum units for grades 5–12. All units have a problem-based learning component, include hands-on activities, and integrate science with other subjects such as health, English, and social studies. Kim LaCelle, formerly a science teacher at Marion High School in western New York and now a science educator at the University of Rochester Life Science Learning Center, describes activities that the students in the rural community of Marion found particularly relevant.
“We addressed local environmental health issues, such as how farmers handle agricultural waste,” LaCelle says. “With the NIEHS grant, we bought equipment to test wells for fecal coliforms. Another group mapped out the waterways that collected runoff from the fields and tested those for pesticides. The kids really enjoyed designing their own experiments. They developed a lot of confidence in their ability to do science.”
Cathy Hoppe, a special education teacher working with schools in west Rochester, found the activities well suited to her learning-disabled students. “The problem-based learning unit engaged my kids right from the start,” Hoppe says. “We presented them with a story about a child who discovers a polluted creek. They had to find out what kind of pollution it was. They used the Internet and went on field trips. It’s wonderful for them to be able to get out of the classroom and do field studies.”
In addition to its grant programs promoting environmental health science education in the schools, the NIEHS reaches the general population through its 25 NIEHS centers. The NIEHS requires each center to develop and maintain a Community Outreach and Education Program (COEP). Each center defines the community and/or region it serves and develops outreach efforts that are specific to the environmental health issues of greatest concern to that community.
For example, the COEP at the University of Texas Medical Branch (UTMB) offers a program called the Youth Environmental Studies Lab School, or YES! The program was designed to provide an intense, passionately taught, language-rich, small class environment to at-risk middle school children from the Galveston school system. At Central Middle School, all lessons in environmental science, math, reading, writing, and social studies coordinate around the environmental theme of the week. Students study the environment in a pattern of concentric circles: their own neighborhood, Galveston Island, the county, southeast Texas, and eventually, by extrapolation, the natural world. In another UTMB effort, the Bench Tutorials program pairs high school students with a university graduate student, postdoctoral fellow, or faculty mentor for supervised instruction and research in field study on the molecular biology of asthma.
“I feel our educational efforts through the schools have been very successful,” says Sharon Petronella, an assistant professor of pediatrics at UTMB.
“We don’t yet have a means of determining the impact of our educational programs on morbidity, but that may one day be possible. Last year, we conducted a survey of twenty thousand school kids in the Galveston area. The responses, including such information as number of days missed and reason for absence, may actually become a part of the students’ school health record. We will be able to see where the health problems are.”
Materials and resources developed by all 25 COEPs can be found at the COEP Resource Center (http://www.apps.niehs.nih.gov/coeprc/), a central repository of educational outreach materials produced by NIEHS grantees.
Opening Doors to the Future
The NIEHS conducts a variety of science education programs in and around its Research Triangle Park, North Carolina, campus. Prominent among these are annual teacher workshops cosponsored with groups such as the North Carolina Association of Biomedical Research. During the one-day workshops, teachers hear from NIEHS researchers about the latest developments in toxicology research and visit the institute’s extensive lab facilities. They are provided with a curriculum titled Chemicals, the Environment, and You for use in the classroom. On average, the NIEHS sponsors two workshops per year attracting 40–50 teachers from the local area.
NIEHS also serves as a resource for programs at nearby universities and organizations that expose local high school and college students to possibilities for research and science careers. Students with, for example, the Research Apprenticeship Program developed by the University of North Carolina at Chapel Hill or Summer Ventures, a statewide program in which nearby North Carolina Central University participates, can visit the NIEHS campus, where they hear scientific presentations from institute staff, engage in informal discussions about career options and summer internship opportunities, and visit the laboratories.
Through its Summers of Discovery program, the NIEHS provides high school, college, and graduate-level students, science teachers, and college faculty with two-to three-month research internships in an NIEHS lab. Participants receive one-on-one mentoring with an institute scientist and attend weekly seminars where they discuss current research being conducted at the institute with the scientists in charge. At the end of the summer, students participate in a poster session at the NIEHS, where they make a brief oral presentation on their research and respond to questions as they would at a scientific society meeting. As a result of their internships, some students end up getting their names on peer-reviewed papers and/or being hired at the NIEHS.
Lessons for Learning
According to Johnson-Thompson, statistics show that by the third grade, girls and minorities tend to lose interest in science because of cultural expectations that they pursue other careers, and minorities in particular don’t see any role models in science. One effort to break this trend is the Bridging Education, Science, and Training (BEST) Program, in which the NIEHS and the NIH partner with public schools in nearby Durham to nurture interest in environmental health science among economically disadvantaged students.
Through BEST, the institute provides schools with surplus supplies and equipment. Staff members give presentations at schools, and act as mentors and science fair judges. And the NIEHS supports science-based programs in the public schools and hosts Durham students in mini summer intern-ships, student research presentations, and awards programs.
Two of the schools in the BEST Program are C.C. Spaulding Elementary School and Shepard Magnet Middle School. C.C. Spaulding is designated as a Biosphere Magnet with a curriculum that has a strong focus on the environment. The school features a Life Lab Biostation containing several live ecosystems, which promotes scientific thinking and learning. Shepard, meanwhile, served as a pilot site for teaching the national Biological Sciences Curriculum Study science curriculum, which teaches science in the context of themes and issues relevant to the students themselves. Shepard currently is participating in Technology Enhanced Learning in Science, a National Science Foundation program that uses innovative, technology-enhanced curricula to teach scientific concepts and methods.
Another BEST experience points out what else is needed to successfully implement such programs. In 1996, the NIEHS worked with Durham’s Hillside High School to construct a Molecular Biology Laboratory and Training Center. The institute loaned the school $60,000 worth of state-of-the-art lab equipment, trained teachers in its use, mentored students, and provided judges for science contests. The center scored some notable successes early on, with several students winning area science competitions and performing summer internships at universities and corporations in Research Triangle Park.
But despite intensive financial and staff support from the NIEHS, the Hillside center has not proven to be a sustainable resource. According to Kenneth Cutler, former Hillside science teacher and now project director of the Berkeley, California–based Project SEED (Summer Educational Experience for the Disadvantaged), too few students had the skills and experience necessary to take advantage of the lab. Cutler offers some lessons about introducing science education programs to high school students.
“In order for students to take advantage of a sophisticated science laboratory, they need to be prepared in the fundamentals—mathematics, reading, writing,” Cutler says. “This needs to happen early, well before they reach high school. Students especially need to know how to write in order to communicate their findings and to make presentations. Students should be encouraged to take higher-level courses to prepare them for scientific thinking and methodology. And they should be provided with paid summer research internships to keep them involved and motivated. Finally, you’ve got to have support for the program at every educational level.”
Free for Teachers
Besides the institute’s numerous funding opportunities, the NIEHS Office of Communications and Public Liaison produces educational booklets for use by school audiences and the general public. Students can use booklets such as Environmental Diseases from A to Z and It’s Your Scene, Teen for a variety of in-class activities. The office publishes eight brochures aimed at K–12 audiences, covering such topics as common environmental hazards, genetic predisposition, environment-related diseases, and air pollution. Teachers can request up to 60 copies of each publication for free by calling 919-541-3345 or e-mailing the NIEHS at [email protected].
Along with the formal educational programs sponsored by the institute, individual staff members devote countless hours to education-related activities. By all accounts, NIEHS scientists enjoy the opportunity to get out of the lab and interact with the public. Perhaps more importantly, they also consider it their responsibility to play a role in guiding the next generation of environmental health scientists and ensuring that students evolve into scientifically literate citizens.
“It’s one of the more pleasurable things we do,” says NIEHS senior investigator Jerry Yakel. “Students get really jazzed up by the science, and some of them do, in fact, end up pursuing careers in the field.”
Over the last decade, science education activities at the NIEHS have positively impacted many lives across the nation, O’Fallon says. Through these activities, he says, students have won awards for academic performance in science, competed successfully for internships, and engaged in community-based activities aimed at improving local environmental conditions. Teachers have implemented engaging environmental health curricula in their classrooms. Communities have made policy changes aimed at improving the local environment. The result is a citizenry that better understands the connections between environment and health.
Class-y materials. The NIEHS produces a number of free environmental health educational materials for teachers to use in the classroom.
Education abroad. The NIEHS provides grants for environmental health education programs around the country. At Marion High School in New York (above), students test well water for fecal coliforms. The Bench Tutorials program in Galveston, Texas, (left) pairs high school students with graduate students, postdoctorate fellows, and faculty mentors to learn to conduct field studies of environmental toxicants such as air pollutants.
Education at home. The BEST Program provides equipment and supplies to local public schools (above left). Through the Summers of Discovery program, students and teachers are invited annually to train in NIEHS labs. At the end of the summer, participants present their own research projects (right).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00809EnvironewsNIEHS NewsHeadliners: Chemical Exposures and Childhood Leukemia: Parental Chemical Exposures and ras Mutations in Children Phelps Jerry 10 2004 112 14 A809 A809 Publication 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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A variety of carcinogens have been shown to induce ras mutations in animal and human tumor models, and ras proto-oncogene mutations have been implicated in the development of many malignancies including pancreatic and breast cancers. However, few data exist associating parental exposures and ras mutations in their children. Now a team including NIEHS grantee Leslie L. Robison of the University of Minnesota report that parents’ chemical exposures may be associated with distinct ras mutations in their children with acute lymphoblastic leukemia (ALL).
This study used data from a large case–control study of childhood ALL conducted by the Children’s Oncology Group in Southern California. DNA samples from the study children were examined for ras mutations. A total of 127 out of 837 ALL cases exhibited ras mutations in the K- or N-ras genes. Earlier studies have reported a 5–20% frequency of ras mutations among patients with ALL.
A number of parental chemical exposures were associated with significantly increased risks for ras mutation in the children. Use of drugs such as marijuana, LSD, and cocaine was associated with increased risk of N-ras mutation (three-fold higher risk for maternal use and two-fold higher risk for paternal use). Paternal use of amphetamines or diet pills was associated with a four-fold increase in N-ras mutation. Maternal exposure to solvents and plastics during pregnancy raised the risk of K-ras mutation about three-fold and seven-fold, respectively, and maternal exposure to plastics after pregnancy was associated with an eight-fold higher risk. Maternal and paternal exposure to oil and coal products and other hydrocarbons before and during pregnancy was associated with about a two-fold greater risk of N-ras mutation.
In previous studies, parental occupational exposure to hydrocarbons (such as chlorinated solvents, benzene, and paints) has been linked to elevated childhood leukemia risk. The present study has extended these findings to include drugs of abuse and additional chemical exposures, and to link them to ras mutations. The authors conclude that parental exposures to hydrocarbons and mind-altering drugs, chemicals that have been previously suggested to increase the risk of childhood leukemia, are related to specific ras mutations in childhood ALL.
Shu XO, Perentesis JP, Wen W, Buckley JD, Boyle E, Ross JA, Robison LL; Children’s Oncology Group. 2004. Parental exposure to medications and hydrocarbons and ras mutations in children with acute lymphoblastic leukemia: a report from the Children’s Oncology Group. Cancer Epidemiol Biomarkers Prev 13(7):1230–1235.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0081015515226EnvironewsNIEHS NewsNational Meeting Breaks the Mold Wakefield Julie 10 2004 112 14 A810 A811 Publication 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 the 2004 hurricane season nears its end after an unprecedented run of flooding and other water damage, attention is turning once again to the health effects of toxic mold infestation. Exposure to mold in residential, public, and commercial buildings is thought to have caused health problems ranging from bleeding lungs to hair loss—even to death. But debate continues over many key questions, such how to best treat exposed individuals. In an effort to push through questions that still constrain the field, participants at the June 2004 National Meeting on Mold-Related Health Effects: Clinical, Remediation Worker Protection, and Biomedical Research Issues established a consensus on mold-related health effects and discussed clinical recommendations and a future research agenda for the evaluation, diagnosis, treatment, and management of these health problems.
The meeting was aimed at an interdisciplinary cross-section of policy makers, researchers, engineers, advocacy group members, and clinicians. Sponsors included the NIEHS, the Society for Occupational and Environmental Health, the Association of Occupational and Environmental Clinics, the Johns Hopkins Bloomberg School of Public Health, the Urban Public Health Program of Hunter College, the University of Medicine and Dentistry of New Jersey School of Public Health, and the NIH Office of Rare Diseases.
A Gamut of Questions
Outstanding research questions on the health effects of mold exposure run a broad gamut. Do airborne fungi produce known or unknown compounds that modulate immunity? Does co-exposure to multiple molds and other allergens occur, and how, and with what effect? Does mold exposure produce neurophysiologic and neurobehavioral abnormalities in children? And how can we best develop registries to chronicle exposures to mold and fungi?
One leading question is whether exposure to high levels of allergens in buildings triggers new-onset allergies. Some clinicians at the meeting had examined individual cases in which mold-contaminated environments appeared to have caused new-onset adult asthma, but population-based research is needed to confirm these findings. Exposure in children seems to cause other respiratory tract disorders besides allergies. These include rhinosinusitis, cognitive and developmental effects, psychological effects, and other nonimmunologic health effects.
To study mold-related health effects, standard assessment tools such as clinical questionnaires for tracking symptoms and effects are needed, as are exposure assessment indicators. To date, questionnaires have proven valuable in assessing population response to abatement. But there are no good, clinically useful biological markers of exposure for nonallergic health outcomes, contended Clifford Mitchell, director of the occupational medicine residency program at the Bloomberg School of Public Health. Participants recommended that diagnostic testing be symptom-based and that exploratory tests for neurobehavioral, neurologic, immunologic, and allergic effects be developed.
Direct and indirect measures should be further developed and validated, said J. David Miller, an industrial researcher in fungal allergens and toxins at Carleton University. Markers of early biological effects might be related to cumulative exposures in moist or contaminated environments. Key questions presented by Michigan State University food scientist James Pestka included whether toxicokinetics and tissue concentrations in animals correlate with in vitro effects, and whether airborne exposure data or human tissue levels correlate with thresholds for immune effects in animals. Participants produced a detailed list of research questions, which participants prioritized through a survey after the meeting. The list will be available in a meeting report due out this winter.
The Public Health Perspective
Without a consensus on specific aspects of mold-related health effects, the primary concern from a public health perspective is that affected people need to be treated and returned to a safe environment. In addition, the mold and the conditions that led to it need to be corrected.
It is difficult to measure people’s exposures to molds, fungi, and their constituents and metabolic products from different sources. For example, many molds and fungi produce mycotoxins that further complicate health effects by acting in a synergistic fashion. Current techniques are limited in their sensitivity and what they can measure, especially given the wide distribution of fungi and complex aspects of growth and metabolism. Factoring in cumulative exposures and all clinically relevant exposures is beyond current capabilities. In general, large integrated samples are needed for accurate exposure assessment.
“The bottom line,” explained Miller, “is that indoor exposure [involves] much more than just fungal material—it’s a lot of stuff.” And from a public health point of view, he said, what’s most important is mitigating and treating the exposure. He acknowledged that the details—for example, knowing the biologically active agent or the specific spore present—may make a difference for policy makers, lawyers, and others.
Once a mold problem is identified, exposed individuals should first be removed from the exposure. Then they should receive treatment depending on symptoms and diagnosis using the tools of evidence-based medicine. Participants noted that treatment for cumulative and toxic exposures should be further researched; doctors do not currently advise prophylactic treatment based on known exposure alone, although symptoms, of course, are treated. The effectiveness of health and remediation interventions also needs probing. It is also important to clearly communicate with exposed populations after interventions to let them know what the exposure means to their health and how to best manage it, Mitchell said.
Yet even after abatement, Mitchell added, some individuals may be symptomatic. “It’s important for everybody to realize there is not a one hundred percent fix for [mold contamination and exposure], and this is a message that needs to go to the clinical world as well as the policy world.”
Cleanup and Prevention
Many issues remain to be resolved around sampling. Generally, participants agreed that for home abatements, sampling is likely not worth the expense, and it makes more sense financially to just solve the problem. In large buildings (particularly office environments), on the other hand, sampling may be useful to pinpoint the source of exposure, both for legal reasons and for cleanup purposes.
But many remain skeptical of sampling’s ultimate utility. “Sampling does little to add to the diagnosis, management, or correction of the problem,” said Gregg Recer, a research scientist with the New York State Department of Health. And in practice, determining when a building is safe for individuals who experienced mold-related health problems remains a thorny issue. Most experts agree that visual and olfactory inspection by a competent authority with appropriate personal protective equipment before and after abatement is the best strategy.
Work is also needed in developing better guidance for maintenance and remediation workers. There are no standards or requirements for training, said Susan Klitzman, an urban public health professor at Hunter College. Some outfits offer certification, she said, but no hands-on experience—a component that experts at the conference felt was vital.
For now, there is a general consensus that, at a minimum, workers need some type of respiratory protection and gloves. “We can come up with general guidelines, but there’s no one-size-fits-all approach,” Klitzman said. “Professional experience and professional judgment are really paramount here.”
Most of the existing guidance doesn’t cover in sufficient detail other categories of workers who may work in an exposed area on a regular basis, such as maintenance workers, construction workers, teachers, and office workers. Participants will compile new guidance for all groups of workers as a product of the meeting. As Ted Outwater, a public health educator in the NIEHS Division of Extramural Research and Training, concluded, “We’re into this because we view workers as our first line of environmental defense.”
As with many environmental threats, preventing exposure is key for mold; in this case, prevention largely involves correcting moisture problems and housekeeping deficiencies. Participants agreed that remediation goals should include addressing underlying moisture problems, removing or cleaning moldy and damaged materials, protecting workers and occupants, and using containment procedures appropriate for the conditions. Remediation techniques depend on moisture source, condition of the structure and furnishings, building materials, location of mold contamination, presence of additional contaminants, and effects on operations (for example, whether a business will have to be closed down for weeks).
“We have to think very carefully about [performing] outcome studies,” said Mitchell. “At this point we certainly know enough that we have to correct the problem. And figuring out which part of the problem is most important to correct and what that question means for population health is an important research question.” At the same time, he said, we need to understand how those corrective interventions pay off in terms of public health.
Public health menace.
Stachybotrys chartarum hyphae is just one of many toxic molds whose spores can cause serious adverse health effects when inhaled.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00811EnvironewsNIEHS NewsBeyond the Bench: Hunting Down Fugitive Literature Colopy Karalyn R. 10 2004 112 14 A811 A811 Publication 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 first step to becoming a successful fugitive is to abandon all forms of conventional identification—driver’s license, passport, checking account. People who stay out of the commercial realm are extremely hard to find. The same is true of literature. Libraries, with their online catalogs and helpful reference librarians, make it easy to find just about any piece of commercially published material. But lurking beyond the reach of the card catalog are thousands of materials such as reports, fact-sheets, newsletters, meeting transcripts, lesson plans, presentations, manuals, and interactive websites—so-called “fugitive literature”—that have never darkened the library’s door. Today, some of those fugitives have been found: the COEP Resource Center website (http://www-apps.niehs.nih.gov/coeprc/) offers visitors a bibliographic database for searching and reading about more than 600 environmental health materials developed by the Community Outreach and Education Programs, or COEPs, associated with each of the 25 NIEHS centers.
Since 1996, when the NIEHS established COEPs as an essential component of its Core Center Program, NIEHS grantees have been generating large volumes of fugitive literature. Charged with increasing public understanding of environmental health science research, the 25 COEPs carry out diverse projects. They host public forums and town meetings, offer professional development opportunities to teachers and health care providers, bring students to their laboratories for tours and summer science camps, and arrange for scientists to give presentations at local schools. They also develop curricula on environmental health for students in kindergarten through twelfth grade. These curricula are based on the latest research and are designed to meet state and national education standards.
The documents created during the course of the COEPs’ activities represent a wealth of environmental health information, innovative ideas, creative teaching approaches, lesson plans, videos, posters, brochures, training manuals, and successful outreach strategies. These materials are usually free and ready to use in a variety of education and outreach settings. However, until recently the people who could most use them—teachers, parents, nurses, community groups—were unlikely to find them.
That changed in 2000, when the NIEHS developed the COEP Resource Center to collect and catalog the products of the COEPs’ projects. Today, most printed resources are available for download in PDF format, and the database provides an abstract and ordering information for nonprint materials such as videos and CD-ROMs. The site also posts information about upcoming events, news, related links, and contact information for each COEP grantee.
The COEP Resource Center is now expanding its scope by incorporating materials produced by grantees in several other NIEHS programs besides the Core Center Program. Additions are planned over the next few months.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0081215515227EnvironewsNIEHS NewsSister Study Launched Nationwide Medlin Jennifer 10 2004 112 14 A812 A812 Publication 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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Breast cancer is the second most commonly diagnosed form of cancer among U.S. women, according to the National Cancer Institute (NCI), and the second leading cause of cancer deaths in this group. For the year 2003 alone, the NCI estimated more than 212,000 new diagnoses and more than 40,000 deaths from breast cancer. A woman’s risk increases with her age: breast cancer is the most common cause of cancer death after age 65, and nearly half of all breast cancers are found in this age group—a figure that is likely to increase significantly as baby boomers age into the next decade. Plus, black women have the highest death rates from breast cancer. To address these concerns, the NIEHS-sponsored Sister Study plans to explore on a nationwide basis, beginning in October 2004, how genetic and environmental influences may work together to cause breast cancer.
The study has been in development since 2001 and under way in pilot form for the past year. A total of 50,000 female volunteers aged 35–74 whose sisters have been diagnosed with breast cancer will be recruited and receive health evaluations over a period of 10 or more years. First-degree relatives, including sisters, have about twice the risk as the average woman of developing breast cancer.
Past studies have focused on pesticides, solvents, and electromagnetic fields as possible contributors to breast cancer, but have failed to find consistent links to the disease. Study authors Dale Sandler, chief of the NIEHS Epidemiology Branch, and Clarice Weinberg, chief of the Biostatistics Branch, believe this study—unlike past studies—will be able to effectively characterize levels of a participant’s environmental exposure prior to onset of cancer, a feat that can’t be accurately accomplished retrospectively.
Recruiting began in fall 2002 for a pilot phase in four cities—Phoenix, St. Louis, Tampa, and Providence—involving a total of 2,000 participants. “We wanted to go slowly at first,” explains Sandler. The pilot phase gave the researchers time to fine-tune recruiting strategies and data collection methods, arrange for field staff training, and streamline the lengthy questionnaire. The original four cities were chosen for their economic, ethnic, and geographic diversity, Sandler says, and by early 2004 the study spread beyond city limits to encompass the entire states of Arizona, Missouri, Florida, and Rhode Island. In August the study also began recruiting in Illinois, Ohio, Virginia, and North Carolina.
Getting adequate participant diversity is important, Sandler says. She believes the study results will be useful to all U.S. women only if a diverse cross-section of women participate. “With a diverse population, we will have a wider range of [health and environmental] exposures, increasing our ability to detect associations,” she says.
The original eight states were chosen in part because they had community- and church-based breast cancer awareness programs, heavy minority interest, and what Sandler pragmatically terms “good connections” in both public and private sectors. “Our contacts in the breast cancer advocacy community help with grassroots recruitment,” she says.
Participants receive a welcome kit by mail with instructions on how to prepare for the study. A staff member calls first to walk the participant through the kit and later to conduct the survey, which takes about two hours. Next, independent phlebotomists working under contract to the NIEHS make home visits to draw blood samples, collect household dust samples and toenail clippings, and take blood pressure, weight, height, and body measurements.
“Even though it’s an enormous national study, we’re doing everything we can to make it as personal as possible,” Sandler says. “We want to make sure that the women get something back, thus we have a duty to let them know what we learn from the study. We plan to contact them regularly over the years with news from the study.”
Sandler and Weinberg will closely evaluate the expected 1,500 women who will develop breast cancer within five years of the study’s start, analyzing environmental, genetic, and health data captured from the very beginning. “We’ve learned what works [in terms of study design and implementation], and what doesn’t,” Sandler says. “We’re ready.”
Sisters are doing it for themselves. The Sister Study, now recruiting nationwide, will yield new information on how genes and environment may interact to cause breast cancer.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0081415471720EnvironewsFocusSetting a New Syllabus: Environmental Health Science in the Classroom Brown Valerie J. 10 2004 112 14 A814 A819 Publication 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 the last few decades, the importance of the relationship between humans and the environment has become prominent in the social consciousness. Recognition of this importance has come from new understanding of environmental health issues, including evidence that many environment-related health problems, such as asthma and neurological damage from lead exposure, affect children disproportionately. A number of efforts are under way around the nation to educate children about the concepts and principles of environmental health with the goal of expanding science education, empowering children to avoid some adverse environmental exposures, and helping them to grow into informed citizens who can assess and affect important public health issues.
Development of curricula and teacher competency in environmental health for kindergarten through twelfth grade (K–12) began in the early 1990s. Since then, environmental health education has been implemented in many individual schools, but still has far to go to be adopted into the standard curricula followed by most school districts and states. For the most part, it is the initiative of individual teachers that brings environmental health into classrooms.
But while environmental health may not figure prominently on the radar screen of the educational establishment, education definitely figures on that of environmental health professionals. Institutions including the NIEHS, the NIH, the American Association for the Advancement of Science, and some museums have committed significant resources to creating environmental health curricula that may eventually become part of a standard education in the United States.
Environmental Health: Health or Environment?
Although many, if not most, K–12 science curricula include environmental science components such as ecology and pollution remediation, few focus on environmental health. Environmental health is a multidisciplinary concept involving principles and methods from toxicology, epidemiology, endocrinology, public health, and other specialties. Whereas environmental science tends to address how human beings affect the rest of the biosphere, environmental health focuses on how the environment affects human health. Many environmental influences on human health are man-made—for example, pesticides, industrial chemicals, and air pollution—but environmental health also encompasses broad public health issues including tobacco use, infectious disease, indoor air quality and allergies, and sanitation.
Science and health tend to be separate tracks in typical curriculum frameworks. Marian Johnson-Thompson, director of education and biomedical research development at the NIEHS, says, “The likelihood of an environmental health course being taught is slim unless you’re at a specialized high school or a private school.” Moreover, says Lloyd Sherman, director of the Center for Excellence in Youth Education at Mount Sinai School of Medicine, if environmental health is taught, it’s usually an elective.
Although there are many professional and citizen groups in the United States devoted either to the environment or to education, not many are working on environmental health education per se. The National Environmental Education & Training Foundation, a nonprofit environmental literacy group in Washington, D.C., has issued a position statement advocating environmental health education, but it is aimed at raising awareness of environmental factors in health among medical clinicians, not teachers. A 2003 meeting report by the Washington, D.C.–based nonprofit National Council for Science and the Environment titled Recommendations for Education for a Sustainable and Secure Future advocated that “education for sustainability” be incorporated into six broad aspects of elementary and secondary education: teacher education; standards and assessment; community education; school partnerships and real-world knowledge; curriculum development and distribution; research; and funding. None of these six “essential learnings,” as the council calls them, specifically mentions environmental health, although environmental health could be incorporated into any or all of them. The State Education and Environment Roundtable (SEER), a consortium of 16 state departments of education, advocates using local natural and community surroundings as a context for learning in its “Environment as an Integrating Context” model of educational practices. But according to SEER director Gerald Lieberman, the consortium has not worked directly on environmental health education.
Curriculum Conundrums
There are three realities of modern education that shape all public school classroom content: the National Science Education Standards (NSES), state curriculum standards, and the Elementary and Secondary Education Act, also known as the No Child Left Behind Act of 2001. Between them, these elements exert considerable influence over teachers’ options in teaching environmental health.
The NSES strongly influences science curricula and incorporates a variety of environmental principles. For example, one age-specific NSES benchmark is the requirement that K–2 students understand that “plants and animals need certain resources for energy and growth (e.g., food, water, light, air).” Students in grades 3–5 should know that “all organisms (including humans) cause changes in their environments, and these changes can be beneficial or detrimental.” A similar benchmark for grades 9–12 requires knowing “ways in which humans can alter the equilibrium of ecosystems, causing potentially irreversible effects,” for example through population growth, harvesting, pollution, and atmospheric changes.
The NSES thus sets broad goals that environmental health curricula can meet, but does not specifically mandate the teaching of environmental health topics. State curriculum standards are based—sometimes loosely, sometimes rigorously—on the NSES benchmarks. Like those benchmarks, state curriculum standards tend to require broad general skills rather than detailed subject skills.
No Child Left Behind is a different matter. It requires that students pass competency tests in basic skills, starting with reading and mathematics. Laura Hemminger, codirector of tthe Resource Center of the Environmental and Occupational Health Sciences Institute at the University of Medicine and Dentistry of New Jersey, says, “With No Child Left Behind, there’s this feeling that, for whatever is used in the classroom, there has to be a direct link to improving test scores.”
Dina Markowitz, director of the Center for Science Education and Outreach at the University of Rochester, agrees. “Mandated testing drives the lesson plans,” she says. Markowitz administers an NIEHS Environmental Health Sciences as an Integrative Context for Learning (EHSIC) grant as well as grants from several other sources to develop multidisciplinary material for K–12 teachers [for more information on EHSIC grants, see “Mission: Educational,” p. A806 this issue]. She believes the burden of required standardized testing is a barrier to developing usable environmental health curricula. She has had what she describes as “heated” discussions with federal education officials about it. She has told them that she is very grateful for the federal funding that allows her program to create unique curriculum units, but that testing requirements discourage teachers from using them.
Part of teachers’ nervousness about straying from the test subjects stems from No Child Left Behind’s provision that schools whose students do not show adequate progress are placed on a special list with annually progressive consequences for continued poor showings. No Child Left Behind can also introduce a skew by grade level into school curricula. Nancy Moreno, an associate professor of family and community medicine at Baylor College of Medicine Center for Community Outreach in Houston, says that No Child Left Behind requires only certain subject tests in certain years, and teachers increasingly tend to use class time to prepare students for specific tests.
“The emphasis on standardized testing is leading to concentrations of subjects in grade levels where there’s a mandated assessment,” Moreno says. “When the emphasis is on reading, language arts, and mathematics—which of course are important—the time allocated to science can end up being reduced.” On the other hand, Moreno says, in places where absolutely no science was being taught at all, children are enriched by the presence of required science testing in certain grades.
A further issue, primarily in elementary grades, is that many teachers are reluctant to teach science because they have little or no science background themselves. At the high school level, most science teachers do have at least some science background and are more autonomous in their teaching decisions, Moreno adds.
For those K–12 teachers who do have the interest and time to develop their own environmental health curricula, there are many materials available from government programs and other sources, especially on the Internet. For example, the National Library of Medicine Tox Town website (http://toxtown.nlm.nih.gov/) introduces visitors to basic toxicology concepts, shows where toxic chemicals might be located in a town or city, provides information about specific chemicals, and links to a database where visitors can learn whether their own communities have such chemicals in them. Tox Town is aimed at high school and college students, according to Cynthia Love, a technical specialist with the National Library of Medicine in Bethesda, Maryland.
The U.S. Environmental Protection Agency sponsors the SunWise Program (http://www.epa.gov/sunwise/), which suggests ways for teachers to convey health risks associated with sun exposure to children in grades K–8. SunWise offers a “toolkit” of lesson ideas, a website including a UV Index with information about the dangers of various degrees of UV exposure, a video, and other materials.
Another excellent resource is Science NetLinks, a website created by the American Association for the Advancement of Science and the MarcoPolo Education Foundation, a project of the telecommunications company MCI conducted as part of the still larger Internet Content for the Classroom project by a consortium of international education organizations and the MCI Foundation. The MarcoPolo site offers teachers free grade-specific web-based lessons and newsy reports on such topics as the Asian “brown cloud” of particulate pollution over the Indian Ocean. The lessons have been structured for easy adaptability to individual state standards. The site also links to other online resources screened for reliable and appropriate content.
But few teachers have the time to sort through the plethora of environmental health information to find not only educational materials but also those at an appropriate level of difficulty and content. Even if environmental health education were mandated at the school district or state level, most teachers would be hard-pressed to take on the extra work. Markowitz says Rochester public school teachers are already overwhelmed by the standard curriculum in a district that is the third largest in the state and one of the poorest. “At Rochester we can’t possibly mandate another thing to our teachers” without making sure the new material addresses existing curriculum requirements, she says.
“The trick is making [an environmental health] curriculum fit the requirements and making it easy on the teachers so that it’s something they can implement in the classroom very quickly,” says Sarah Weppner, environmental health education and assessment program director at the Idaho Department of Health and Welfare. Weppner heads a small educational outreach program funded by the Agency for Toxic Substances and Disease Registry (ATSDR) aimed at increasing community awareness of health risks associated with Superfund and other hazardous waste sites in Idaho. Her program has produced lesson plans and sponsors an annual essay and poster competition for middle school students.
Fortunately for those teachers who do have relevant methods and materials and are comfortable using them, state standards are often broad enough that environmental health can be used to fulfill some science and health curriculum requirements. In New York, says Marissa Maggio, who teaches environmental health in that state, it’s relatively easy to meet the state standards, at least at the secondary level. “For anything you do, you can find a standard that fits what you’re teaching, and you can teach beyond what standards require,” Maggio says.
K–12 Initiatives
Because of the constraints on teaching science, an effective strategy for designing a K–12 environmental health curriculum is to make it kill several birds with one stone—that is, to structure it so that it challenges students not only in environmental science and health, but also in reading, mathematics, social studies, and other standard subjects. Moreno has found that when she and her team present this integrated multidisciplinary approach to teachers at professional development workshops, “they’re kind of surprised. Once they start thinking about it, they find it fits in with what’s happening in the classroom.”
The NIEHS has been a pioneer in supporting K–12 environmental health education, including development of interdisciplinary curricula through its EHSIC grants. Johnson-Thompson says when she joined the NIEHS in 1992, director Kenneth Olden asked her to immediately address how the institute could develop a K–12 science educational activity. Initially, she says, the program focused on getting more students interested in science with the goal of fostering the next generation of scientists. The program later took on the additional focus of ensuring that all children become scientifically literate citizens.
In 1993 the NIEHS issued its first request for applications to develop environmental health curricula. Moreno and Hemminger were among the early recipients of NIEHS funding. Hemminger’s program developed a curriculum for grades K–9 called ToxRAP to teach environmental health using risk assessment concepts, while Moreno’s group developed a package of environmental health educational materials for grades 2–4 called My Health My World. This package integrates environmental health topics such as infectious disease and chemical pollution with language arts, math, and other standard curriculum subjects, meeting science education standards along the way. Today the NIEHS continues to fund a variety of environmental health education projects [for more information on these programs, see “Mission: Educational,” p. A806 this issue].
Science-based activities for elementary school children are notoriously scarce; where possible, their teachers often turn to museums for learning opportunities. Moreno’s program at Baylor teamed up with the Children’s Museum of Houston to create a traveling exhibit called My Home Planet Earth, which is based on the My Health My World curriculum. Funded by the NIEHS and the NIH Science Education Partnership Award program of the National Center for Research Resources, the exhibit is bilingual in Spanish and English. It will be at the Children’s Museum of Seattle until January 2005.
Chris Cooper, director of external affairs at the Seattle museum, says the exhibit “is a terrific match for our audience.” The exhibit features the characters Riff and Rosie, who are squirrels, and Mr. Castor Slaptail, a professorial beaver. Riff and Rosie introduce children to issues such as allergy triggers and indoor air quality, eutrophication and toxic waste in water supplies, and microbial activity in leftover foods in the refrigerator. Along the way the children learn scientific concepts: a jar full of 999,999 yellow cupcake sprinkles and a single black one illustrates parts per million; an “achoo” pinball game shows how particulate matter enters lungs.
The most popular activity is a Rube Goldberg–like contraption full of blue balls representing clean water. To learn how pollutants behave, children can put slightly smaller brown balls in the machine, which other children try to remove before they reach the main water supply. “Kids just love it,” Cooper says. “A good exhibit should hold kids’ attention for twenty minutes. We have kids playing [at the ball machine] for an hour and a half.” Cooper adds that during the summer the exhibit contributed to a 150% increase in group visits to the museum over the year before, and he expects school groups during the academic year to pick up considerably as well.
The Secondary Level
At the secondary level there are also very few established environmental health programs, but one shining example stands out at the High School for Environmental Studies (HSES) in New York City, a specialty secondary school focusing on the environment. The program has existed since 1994 as a collaboration with the Mount Sinai School of Medicine environmental health outreach program, which is directed by Sherman. It is an outgrowth of Mount Sinai’s Superfund grant to study organochlorine contaminants in the Hudson River. The high school’s collaboration with Superfund investigators has resulted in the creation of the environmental health curriculum, which has been adopted by the high school as an elective.
Maggio teaches the environmental health course, and helps implement a zebrafish research project and a host of weekly activities that bring students and Superfund investigators together in this year-round program to increase environmental health literacy among inner-city students. Because classes are small and the health aspect is already embedded in a general environmental science context, Maggio has the luxury of focusing on advanced knowledge, such as epidemiology and toxicology, to a far greater extent than most high school teachers can manage.
In its entrance requirements, HSES relies as much on student interest as it does on specific knowledge or skill levels, according to Sherman. HSES students don’t take tests. Instead, Maggio requires them to do research projects in which they search the scientific literature, write reports including proper citation of their sources, and present results to the class. “I have seventeen-year-old students reading peer-reviewed journals,” Maggio says proudly.
The program is structured so that the students learn to do investigative work themselves. Jennifer Vasquez, a 2003 graduate of HSES and now a sophomore at Connecticut College, focused on endocrine disruption in the health class. She says Sherman and Maggio inspired and challenged her. “They forced you to learn [by] yourself. It’s actually better that way, and it sticks with you,” she says. Vasquez is considering an environmental studies major.
Vasquez, senior Jamie Ahn, and several other students have spent the last two years developing a zebrafish breeding system. This summer they conducted their first experiment with the fish by exposing them to DDT. They conducted a literature review, kept a lab log, and presented their results to a group of Mount Sinai doctors, reporting that DDT exposure affected both zebrafish reproduction and survival, and that it may affect the fishes’ swim bladders as well.
The HSES environmental health program has been successful enough that Maggio is now getting calls from teachers at other New York City schools who are interested in teaching the subject, and a second environmental high school recently opened in the city. At the start of the 2004 HSES school year, 26 students had enrolled in the environmental health course, up from 18 last year.
Environmental justice is another facet of environmental health that is taught at HSES; it is part of the school’s environmental ethics class, which is required for juniors. The HSES student body is highly multicultural, with many disadvantaged and minority students for whom health disparities are a personal matter. The students become aware of environmental justice issues directly relevant to themselves, their families, and their friends, and Maggio says the subject is one of their “favorite issues to talk about in ethics class.” For example, in studying the Super-fund problems in the Hudson River, they learn that the river’s contamination affects poor and minority residents disproportionately because those populations eat the fish they catch. During literature searches, Maggio adds, the students often discover articles relating to their own neighborhoods.
Because of this immediacy, Sherman says, “kids get up at six in the morning and don’t leave until six at night—because everything they study has meaning. When education connects with that, there’s no stopping it. The appetite is sensational.”
Ahn, who hopes to pursue a career in environmental toxicology or environmental policy, appreciates the way environmental health brings its related issues and concepts very close to home. “I have applied my knowledge to my and my family’s life,” she says. “I have learned about the dangers [from polychlorinated biphenyls] of eating fish from the Hudson River, and the effects of industrial [toxicants] on human health.”
Vasquez was surprised to learn of the health threat to Hudson anglers, who tend to be minorities, poor, or immigrants. But she says the ethics class and her study of endocrine disruption have empowered her to believe she can make a difference in the world and that “it’s cool to know you can help.”
Across the state, in Rochester—where childhood lead poisoning is a huge problem and many neighborhoods are poor—Markowitz’s program incorporates social studies as focused through the environmental justice lens to get students asking questions about health disparities. One alternative high school she works with is built on a former auto repair shop across the street from a landfill. A science teacher there introduced the issue of indoor air quality in the school itself as an environmental health education topic. Soon the students—whom the teacher had told Markowitz were “reluctant readers”—were enthusiastically using Internet search engines to gather data about indoor air quality, studying websites, and presenting information to each other in class. According to Markowitz, the teacher was able to take the time for this project in part because the school is an alternative one whose students are exempt from the testing mandated by No Child Left Behind. Another Rochester teacher has used environmental health as subject matter in an English as a Second Language class.
Graduating to the Next Level
Hard numbers about the extent to which environmental health is being taught in grades K–12 nationally are not available. Hemminger estimates that about 4,500 teachers in 23 states are using the ToxRAP materials. Moreno says she and her colleagues have trained more than 5,000 teachers, representing almost every U.S. state, in the My Health My World curriculum. Weppner believes about 500 students in two schools participated in the 2004 Idaho State/ATSDR essay and poster contest. Sherman estimates that between 120 and 200 students in all have passed through the HSES environmental health program.
While the NIEHS has perhaps the most broad-based approach, there are also many more narrowly focused resources available to educators. Taken together, these initiatives are making it easier for teachers who want to teach environmental health to find curriculum ideas, lesson plans, professional development, and ways to use environmental health to teach mandated subject areas.
There are no nationwide curriculum requirements in place for environmental health, but there are pockets where it is being taught, usually in environmental science classes; a very few specialized secondary schools place more emphasis on it. But environmental health education experts believe that if environmental health topics can be integrated with standard curriculum requirements, especially through multidisciplinary projects, they are much more likely to be used by teachers already laden with state curriculum standards and the strictures of the No Child Left Behind Act. And while formal incorporation into standard school curricula is still some distance off, Sherman says, there is no way that environmental health can not be part of the continuing educational narrative, because people everywhere are making the connection between human diseases and environmental factors. “Environmental health education,” he says, “is a genie you cannot put back in the bottle.”
A world of opportunities. Curricula that use environmental health as a teaching model can give children a global perspective on science.
Bringing the message home. The National Library of Medicine’s Tox Town website encourages students to investigate chemical exposures in their own communities.
Bright idea. The Environmental Protection Agency’s SunWise program educates students on the dangers of sun exposure and gives them concrete steps they can take to protect their own health.
Hands-on learning. The traveling museum exhibit My Home Planet Earth teaches children about environmental health topics such as (clockwise from left) remediation (“Clean It Up!”), research (“Video Microscope”), indoor air exposures (“Allergen House”), and pollution (”Mucky Water”).
Scientists in the making. Students at the High School for Environmental Studies learn by conducting actual environmental health experiments. Clockwise from left: Gabrielle Torres and Gabrielle Niccolls harvest brine shrimp, which were hatched to feed baby zebrafish; students set up the zebrafish lab at school; Jamie Ahn dissects a striped bass, a fish native to the Hudson River.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0082015471721EnvironewsSpheres of InfluenceThe Fat of the Land: Do Agricultural Subsidies Foster Poor Health? Fields Scott 10 2004 112 14 A820 A823 Publication 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. Ever since the Great Depression, American farmers have been the beneficiaries of a medley of subsidies and support programs meant to stabilize crop prices, keep farmers farming, and provide U.S. families with an affordable, reliable supply of food. But these programs may have had an unintended side effect. Rather than keep Americans healthy, critics say, these policies have contributed to today’s obesity pandemic and other nutrition problems as well.
Writing in the 2004 Annual Review of Nutrition, James Tillotson, a professor of food policy and international business at Tufts University, argues that U.S public policy encourages obesity at the expense of sound nutritional practices. “You have a whole régime here that’s worked to increase agricultural efficiency,” Tillotson says. And what U.S. farmers are most efficient at producing, he says, are just a few highly subsidized crops—wheat, soybeans, and especially corn.
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Support for these few crops, critics say, has compelled farmers to ignore other crops such as fruits, vegetables, and other grains. The market is flooded with products made from the highly subsidized crops, including sweeteners in the form of high-fructose corn syrup (HFCS), fats in the form of hydrogenated fats made from soybeans, and feed for cattle and pigs. This flood, in turn, drives down the prices of fattening fare such as prepackaged snacks, ready-to-eat meals, fast food, corn-fed beef and pork, and soft drinks. Worse yet, some scientists say, paltry support for foods other than these staples increases the contrast between prices of fat-laden, oversweetened foods and those of healthier alternatives, offering poor folks little choice but to stock their pantries with less nutritious foods.
Hogwash, say other researchers and agricultural industry professionals, who cite a number of other changes that are making Americans fat. Less physical activity is one major lifestyle change that has led to more obesity. Longer work weeks and more two-worker households both mean less time for nutritious home-cooked meals. They also mean more “latchkey children” left home alone from the time they leave school until their parents get home from work—children who tend to be less active and eat more fattening snacks. Technological innovations have contributed as well—for example, advances in cutting and peeling technology, freezing technology, coating technology (McDonald’s fries have a coating of sugar and beef flavoring), transportation technology, and cooking technology have put fattening french fries on nearly every restaurant menu in America. Persuasive television commercials and just plain personal taste are also making Americans fat, they contend.
Even if price supports were eliminated entirely, says Larry Mitchell, CEO of the American Corn Growers Association in Washington, D.C., prices for subsidized commodities wouldn’t increase significantly, and they might even drop. Furthermore, says Sam Willett, senior director of public policy for the National Corn Growers Association, also in Washington, demand for products, not agricultural subsidies, determines what farmers choose to grow. “Connecting farm programs to obesity is quite a leap,” he says. “When you examine the data, it doesn’t support the theory. The fact is, farmers are capturing less and less of the total food dollar.”
Any way you look at it, the number of factors involved makes it hard to encapsulate the relationship between farm support and obesity in a neat cause-and-effect equation. Even the U.S. Department of Agriculture (USDA)—which administers both the agricultural subsidy programs and a host of nutrition research and education programs—has put scant, if any, thought into a possible relationship between the two, says Elizabeth Frazao, a nutrition scientist for the USDA Economic Research Service. Department spokeswoman Jean Daniel says USDA research indicates simply that overweight children and adults are eating too much and not getting enough physical activity.
Support for Farmers
American farm subsidies have evolved over the last 80-plus years from an emergency stopgap into an apparently inviolable institution that, despite efforts to scale back, is bigger than ever. The first American agricultural assistance programs started in the 1920s to address ramped-up growing patterns that farmers had developed in support of the World War I effort. When the war ended, farmers continued to grow crops at a record pace. The result was a glut of produce followed soon by plummeting prices, which the Agricultural Credits Act of 1923 was unable to stop.
Since then, the U.S. government has employed a chain of programs that at times have attempted to manage what and how much American farmers produce. As early as 1929 the government bought cotton and grains on the open market when production outstripped demand in an attempt to stabilize prices. That just encouraged farmers to grow more. Later techniques included fixing quotas for certain farm products, removing surplus products from the marketplace, and paying farmers not to plant crops that were flooding the market.
According to Richard Wiles, a senior vice president for the nonprofit Environmental Working Group, these programs have become entrenched in America’s heartland. Although farm subsidies began to taper off in the early 1960s, during the first term of the Nixon administration an unfortunate convergence of a poor growing year and an agreement to sell millions of bushels of grain to the Soviet Union caused shortages and a spike in prices. In response, the government developed a suite of programs meant to increase production. The result, Wiles says, was a surplus of basic commodities—primarily wheat, corn, soybeans, and cotton—and falling prices for these products on the open market. In 1996 an attempt was made to eliminate subsidies altogether. This so-called Freedom to Farm Act eliminated crop subsidies, but instead gave farmers fixed amounts of money based on what they had grown in earlier years.
According to Wiles, however, the act was fatally flawed. “It grandfathered everybody who received subsidies at that time so that they could get subsidies forever, whether or not they grow anything. It turned the commodity payments into commodities themselves that could be passed around, sold, and traded.”
By 2000 these fixed payments had reached $22 billion, about three times the pre-reform level of 1996, according to the 2002 report Landowners’ Riches: The Distribution of Agricultural Subsidies by Ohio State University agricultural economist Barry K. Goodwin and colleagues. The 2002 Farm Bill abandoned this attempt to eliminate subsidies and reduce farm payments. Instead, says Landowners’ Riches, it is scheduled to distribute about $190 billion by 2012, an increase of about $72 billion when compared to the programs it replaced. Supporters call this provision a vital safety net for America’s most vulnerable workers—small family farmers with few resources. Some critics, on the other hand, call it welfare that benefits huge agricultural corporations—giant farms, grain brokers, food processors, fast-food chains, and prepackaged food companies—more than family farms.
From Farm Fields to Grocery Bills
This support may indeed drive down the price of commodities such as corn, wheat, and soybeans. To Marion Nestle, a professor of nutrition, food studies, and public health at New York University, that’s one of the reasons the relationship between agricultural subsidies and obesity is clear. Because prices of these staples are low, so are those of HFCS, hydrogenated fats, and corn-fed meats. And the cheapest way to make foods taste good, she says, is to add sugars and fat.
Compounding the problem, says Barry Popkin, a professor of nutrition at the Carolina Population Center of the University of North Carolina at Chapel Hill, is that fattening foods are supported whereas healthy fare isn’t. “We put maybe one-tenth of one percent of our dollar that we put into subsidizing and promoting foods through the Department of Agriculture into fruits and vegetables,” he says. As a result, the price gap between high-sugar, high-fat foods and more nutritionally valuable fruits and vegetables is artificially large. That means in supermarkets and restaurants, red meats, sugar-and fat-loaded products, and fast foods not only appear to be the best buys but in proportion to even moderate salaries are downright cheap. The proportion of income required to buy food in the United States is among the lowest in the world and has declined steadily since the 1950s, according to the USDA. If anything, the 2002 Farm Bill will result in record crops planted on even fewer acres than under previous support programs, Willett says.
A shopping cart filled with inexpensive food rolls right to an overweight population, says Darius Lakdawalla, an economist at the RAND Corporation and the National Bureau of Economic Research who investigates trends in U.S. obesity. “One of the things we’ve looked at was simply the falling price of food. The price of food has fallen a lot over the past couple of decades. According to our estimates, declining food prices can account for as much as half of the increase in obesity that we’ve seen,” he explains. “In a sense it’s a very simple explanation. People face cheaper food. They eat more. And they weigh more.”
The very poorest American people, says Lakdawalla, are undernourished and thinner than the general population. But if you exclude the poorest of the poor, obesity is associated with poverty. One reason is that the fattening foods found at convenience stores and fast-food restaurants are the cheapest and sometimes the only available foods in poor neighborhoods, according to Thomas Robinson, an associate professor of pediatrics and medicine at the Stanford School of Medicine Prevention Research Center. A poor, overweight person therefore isn’t necessarily a completely nourished person, says Lakdawalla. Furthermore, poorer people can’t afford health clubs and may live in neighborhoods in which it is too dangerous to exercise outside. And because poverty is inversely related to education, poor people may be unaware of sound nutritional practices.
The Effect on Children
As the American obesity pandemic has gathered momentum, the hazards of obesity—heart problems, diabetes mellitus, some cancers, skeletal and musculature stress, shortened life expectancy—have been much discussed in the popular press. Less well understood is the relationship between consuming too many calories and an absence of some essential minerals and vitamins, especially in children.
“Children who are obese or overweight are actually also often lacking the appropriate nutrients,” Lakdawalla says. “It’s called ‘mis-nourishment’ rather than ‘malnourishment.’” These improperly nourished children—who as of 1992 numbered about 12 million in the United States alone, according to a February 1996 Scientific American article—can encounter serious physical and mental development problems, such as stunted growth and cognitive impairment, says J. Larry Brown, executive director of the Brandeis University Center on Hunger and Poverty and coauthor of the Scientific American article.
We put maybe one-tenth of one percent of our dollar that we put into subsidizing and promoting foods through the Department of Agriculture into fruits and vegetables.
Barry Popkin, Carolina Population Center, UNC-CH
Shanthy Bowman, a nutrition scientist for the USDA Agricultural Research Service, says department research shows that when children eat foods that contribute to obesity, they miss out on the nutrients found in healthier foods. Bowman and colleagues at Harvard University reported n the January 2004 issue of Pediatrics how eating fast food affects the quality of children’s diets. On any given day, about 30% of the study’s 6,200 children aged 2–19 consumed some fast food. On those days they took in about 187 extra calories, more energy per gram of food (which generally translates to less dietary fiber), more fat, more carbohydrates, more added sugars, less milk, and fewer fruits and vegetables. Fast foods give children practically nothing in the way of fruits, vegetables (not counting potatoes), or milk, Bowman says.
Many of the empty calories children are taking in come from sweetened beverages, largely soft drinks, which in American homes are increasingly displacing milk and contributing to calcium deficiencies, Bowman says. Between 1965 and 1996, adolescents’ milk consumption decreased by 36% as soft drink consumption increased by 287% in boys and 224% in girls, according to research by Popkin and colleagues published in the July 2000 issue of Archives of Disease in Childhood. People who consume more than 18% of their calories in added sugars (and U.S. consumption of added sugars increased 28% between 1982 and 1997) have lower-than-normal levels of essential micronutrients, especially vitamin A, vitamin B12, folate, magnesium, and iron, Bowman says.
HFCS: A Double-Edged Sword
In America, soft drinks are sweetened with HFCS. (In Europe beet sugar is used in soft drinks; HFCS is not allowed in order to protect European beet farmers.) Until a few decades ago, most American foods were sweetened with cane sugar from warm climates or, less often, beet sugar grown domestically. In the late 1960s, however, Japanese scientists developed a way to use enzymes to convert cornstarch into HFCS, which is sweet enough to replace other types of sucrose-based sugars. Since then, HFCS has been a success story for corn growers, but—says George Bray, a professor of nutrition at Louisiana State University—a tragedy for American health.
Between 1970—just after HFCS was developed—and 1990, consumption of HFCS in the United States increased 1,000%, according to a commentary published in the April 2004 issue of the American Journal of Clinical Nutrition by Bray, Popkin, and colleague Samara Joy Nielsen. It now represents 40% of the non-calorie-free sweeteners added to U.S. foods and is virtually the only source of sweeteners for soft drinks. It has also worked its way into baby food, fruit drinks, ketchup, yogurt, candies, cakes, muffins, and too many other products to count. On average, Bray says, Americans over age 2 consume at least 132 calories of HFCS per day—and that’s a conservative estimate. Americans who are in the top 20% of sweetened product consumers take in about 216 calories a day from HFCS.
It is no coincidence, Bray says, that as HFCS’s sales figures have increased, American waistlines have kept pace. HFCS is cheap, which has allowed for 25¢ snack cakes, 60¢ candy bars, and—especially—bargain-priced, giant-sized soft drinks in convenience stores, at restaurants, and on grocery store shelves.
Bray says the human body processes fructose differently than it does glucose. Glucose triggers the pancreas to release insulin, suppressing appetite. Fructose, however, is processed only in the liver, so no insulin is released. As a result, he says, people are more likely to habitually overindulge in HFCS-sweetened products. (Nestle says, however, that the percentage of fructose is the same in HCFS and cane or beet sugar—about 50%. Although there are small variations in the fructose content between the types of sugar, she says, they are not enough to affect how the body reacts to them.)
Most significantly, according to Bray, HFCS products just taste sweeter than foods made with cane or beet sugar. That trains people to expect ever-increasing levels of sweetness. Children, especially, learn quickly to crave HFCS. “We may be damaging the neuronal circuitry in the brain during this highly plastic period of development,” he says. He adds that soft drinks are especially troublesome because experimental research in the June 2000 issue of the International Journal of Obesity and Related Metabolic Disorders demonstrated that people will consume more calories when sweetened products are offered as liquids than when offered as solids. About two-thirds of the HFCS consumed in the United States is in beverages.
Agricultural interests stand by their product, however. The nonprofit Corn Refiners Association issued a 25 March 2004 press release in response to Bray and Popkin’s 2004 commentary, which was titled “HFCS Is Not a Unique Contributor to Obesity.” The release stated, “The facts are simple . . . HFCS and table sugar are indistinguishable to the human body; . . . HFCS is safe to consume and can be part of a healthy, balanced diet.” The association declined to comment further for this article.
The Bottom Line
HFCS’s market success may be at least partly a result of two complementary government policies. Farm subsidies may reduce its cost, and tariffs plus quota restrictions on imports of foreign sugar make it a better buy than alternatives. But even eliminating farm subsidies entirely wouldn’t affect how much soda pop people drink, how many cupcakes they snack on, or even how much meat they eat, says Bruce Babcock, an economics professor at Iowa State University.
“We did an analysis that showed that if corn and soybeans were not subsidized, the price would rise at most by between five and seven percent,” Babcock explains. According to the unpublished analysis, which Babcock performed in June 2004 for the National Corn Growers Association, that much of an increase in the price of corn wouldn’t affect the price of HFCS because most of its cost is in manufacturing rather than raw materials, he says; it would affect other products, although again not by much. “A five- to seven-percent increase in the price of corn would lead to, at most, a one-percent increase in the price of meat,” says Babcock. “But meat consumption doesn’t respond dramatically to price. So what that would do is reduce consumption by point-three percent.”
If you raise the price of those inputs like corn and soybean oil, you have a very, very small impact on the prices consumers see when they make their food choices.
Bruce Babcock, Iowa State University
The problem with linking farm subsidies to the cost of fattening foods, Babcock says, is that farmers just don’t see much of the consumer’s food dollar. “The final prices of products—meat, bread, milk—don’t have a whole lot to do with the price of farm products,” he says. “So if you raise the price of those inputs like corn and soybean oil, you have a very, very small impact on the prices consumers see when they make their food choices.”
But if America is going to subsidize agriculture, the least it could do is subsidize healthy foods, says Richard Atkinson, a professor of medicine and nutritional sciences at the University of Wisconsin–Madison and president of the nonprofit American Obesity Association. “There are a lot of subsidies for the two things we should be limiting in our diet, which are sugar and fat, and there are not a lot of subsidies for broccoli and Brussels sprouts,” he says. “What would happen if we took away the subsidies on the sugar and fat? Probably not much. They might go up a little bit, but the cost of the food is not the actual cost of the final products. But if we’re trying to look for something political that might make a difference, try subsidizing fruit and vegetable growers so the cost is comparatively lower for better foods.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00827AnnouncementsNIEHS Extramural UpdateMerit Award Winners 10 2004 112 14 A827 A827 Publication 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 NIEHS is pleased to announce that Raymond F. Burk, a professor of pathology and director of the Clinical Nutrition Research Unit at Vanderbilt University Medical Center, and Gerd P. Pfeifer, a professor and chair of the Division of Biology at the City of Hope Beckman Research Institute, have each received an award under the Method to Extend Research in Time (MERIT) Award Program. MERIT awards are offered to investigators who have demonstrated superior skill and outstanding productivity during the course of their previous research endeavors. MERIT awards relieve investigators from writing frequent renewal applications by providing the opportunity to gain up to 10 years of support in two segments.
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Burk’s research has centered on the characterization of the biological function of selenium, an essential human nutrient found in virtually all tissues, and the determination of selenoproteins responsible for that function. Early work on this grant identified and characterized selenoprotein-P (Se-P), a plasma protein made primarily in the liver that carries selenium to the brain and testis. Mice lacking the protein have male infertility and brain injury; conversely, a high-selenium diet prevents brain injury.
Toxicity studies by this lab demonstrated that selenium deficiency upregulates enzymes important to the detoxification of some compounds, while the same deficiency increases the toxicity of other compounds. Burk has shown that cirrhosis is associated with low plasma Se-P and has collaborated in studies showing that selenium deficiency is not necessary for the development of colorectal adenomas. Current studies are evaluating the possibility that the Se-P delivery system to the brain can be impaired and lead to human neurodegeneration. Burk is also investigating the mechanism of transport of selenium via Se-P from mother to fetus.
As a clinical physician and former director of the Clinical Nutrition Research Unit, who has received awards for his work from the American Institute of Nutrition, Burk is in a unique position to translate research conducted in animals to human clinical research.
Pfeifer’s research has contributed greatly to our understanding of the molecular mechanisms associated with skin carcinogenesis in response to ultraviolet (UV) light injury and to the study of site specificity of UV photoproducts. He demonstrated that a different pattern of damage is observed when cells (as opposed to naked DNA) are irradiated with UV light, and he characterized the methylation and nucleosome locations of the p53 genes.
With colleague Gerald P. Holmquist, Pfeifer applied ligation-mediated polymerase chain reaction to the study of DNA damage at the nucleotide level. This allowed the critical observation that differences in mutation frequency depend upon the efficiency of DNA repair rather than lesion induction alone. The Pfeifer lab demonstrated an important role of the DNA base 5-methylcytosine in UV damage formation and mutagenesis.
Pfeifer proposes to characterize DNA-damaging and mutagenic properties of UVA irradiation, a component of the solar spectrum that has been linked to melanoma. He then will attempt to demonstrate a molecular link between sunlight exposure and melanoma. Studies will determine the in vivo roles of DNA polymerases and other proteins likely to be involved in UV mutagenesis.
Pfeifer’s basic science findings are leading to a better understanding of the mechanisms of UV light and also lay the foundation for prevention and treatment of skin cancers.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00828AnnouncementsFellowships, Grants, & AwardsFellowships, Grants, & Awards 10 2004 112 14 A828 A829 Publication 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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Novel Approaches to Enhance Animal Stem Cell Research
The purpose of this program announcement (PA) is to encourage research to enhance animal stem cells as model biological systems. Innovative approaches to isolate, characterize, and identify totipotent and multipotent stem cells from nonhuman biomedical research animal models, as well as to generate reagents and techniques to characterize and separate those stem cells from other cell types, is encouraged. Embryonic and other stem cells are valuable biomedical research models for the study of biological and disease processes and the creation of disease models. In addition, these cells hold promise as model systems for development of therapeutics and of replacement tissues.
Thus far, embryonic stem cells have been isolated from some biomedically important nonhuman research models. In addition, stem cells with a more restricted potential have been characterized from post-embryonic tissue types. However, research is needed to provide for a full array of totipotent and multipotent stem cells from nonhuman biomedical research animal models, as well as to provide the research tools to identify, characterize, and purify those cells.
This PA supports the isolation and characterization of embryonic and other multipotent stem cells in a variety of nonhuman animal species. Examples of research areas appropriate to this PA include, but are not limited to, projects to 1) expand the number of nonhuman animal model systems in which embryonic stem cells are available; 2) identify, isolate, culture, and characterize multipotent stem cell populations derived from nonhuman embryonic stem cells; 3) identify, isolate, culture, and characterize multipotent stem cells from postfetal tissue types; 4) generate and use panels of markers for stem cell attributes common across species for use in characterization and isolation of stem cells in a range of animal species or tissues; and 5) create universal methods of culture to maintain the undifferentiated state of embryonic or other characterized multipotential stem cells across nonhuman animal species.
Projects supported by the National Center for Research Resources under this PA are intended to generate research tools, reagents, or stem cells of utility to research on a broad range of tissue or cell types and of interest to more than one categorical or disease-oriented NIH institute or center. Projects that will focus on research on tissues or disease processes specific to the mission of an institute or center should be directed to the respective facility.
This PA will use the NIH R01 and R21 award mechanisms. As an applicant, you will be solely responsible for planning, directing, and executing the proposed project. R21 applications should meet the requirements for this mechanism as recently redefined in PA-03-107, available at http://grants.nih.gov/grants/guide/pa-files/PA-03-107.html. In brief, by using the R21 mechanism, the NIH seeks to foster the introduction of novel scientific ideas, model systems, tools, agents, targets, and technologies that have the potential to substantially advance biomedical research. These studies may involve considerable risk but may lead to breakthroughs, developments, or applications that could have a major impact on a field of biomedical, behavioral, or clinical research.
Applications for R21 awards should describe projects distinct from those supported through the traditional R01 mechanism. For example, long-term projects or projects designed to increase knowledge in a well-established area will not be considered for R21 awards. Applications submitted under this mechanism should be exploratory and novel. These studies should break new ground or extend previous discoveries toward new directions or applications.
Applications for R21 awards may request a project period of up to two years with a combined budget for direct costs of up to $275,000 for the two-year period. The request should be tailored to the needs of the project. Normally, no more than $200,000 may be requested in any single year.
This PA uses just-in-time concepts. It also uses the modular budgeting as well as the nonmodular budgeting 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 budgeting format. Otherwise, follow the instructions for nonmodular budgeting research grant applications. This program does not require cost sharing as defined in the current NIH Grants Policy Statement at http://grants.nih.gov/grants/policy/nihgps_2003/NIHGPS_Part2.htm.
Applications must be prepared using the PHS 398 research grant application instructions and forms (rev. 5/2001). Applications must have a Dun and Bradstreet Data Universal Numbering System (DUNS) number as the Universal Identifier when applying for federal grants or cooperative agreements. The DUNS number can be obtained by calling 1-866-705-5711 or through the website at http://www.dunandbradstreet.com/. The DUNS number should be entered on line 11 of the face page of the PHS 398 form. The PHS 398 document is available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance, contact GrantsInfo by calling 301-435-0714 or e-mailing [email protected].
Applications submitted in response to this PA will be accepted at the standard application deadlines, which are available at http://grants.nih.gov/grants/dates.htm. Application deadlines are also indicated in the PHS 398 application kit.
Contact: For the complete listing of contacts, please consult the full PA, available online at http://grants1.nih.gov/grants/guide/pa-files/PA-04-125.html. Reference: PA No. PA-04-126
Supplements to Promote Reentry into Biomedical and Behavioral Research Careers
The participating institutes and centers of the NIH, along with the Office of Research on Women’s Health, announce a continuing program for administrative supplements to research grants to support individuals with high potential to reenter an active research career after taking time off to care for children or attend to other family responsibilities. The aim of these supplements is to encourage such individuals to reenter research careers within the missions of all the program areas of the NIH. This program will provide administrative supplements to existing NIH research grants for the purpose of supporting full-time or part-time research by these individuals in a program geared to bring their existing research skills and knowledge up to date. It is anticipated that at the completion of the supplement, the reentry scientist will be in a position to apply for a career development (K) award, a research award, or some other form of independent research support.
The NIH recognizes the need to increase the number of underrepresented racial and ethnic groups, women, individuals with disabilities, and people from disadvantaged backgrounds in biomedical, behavioral, clinical, and social science research careers. Among the reasons for the low representation of women may be the fact that women bear a majority of the responsibilities surrounding child and family care. To address this issue, this program is designed to offer opportunities to women and men who have interrupted their research careers to care for children or parents or to attend to other family responsibilities. A second objective of the program is to mentor and guide those who receive support to reestablish careers in biomedical, behavioral, clinical, or social science research. Participating NIH institutes and centers are listed at the end of the online version of this announcement, located on the Internet at http://grants1.nih.gov/grants/guide/pa-files/PA-04-126.html.
Only the following active NIH award mechanisms at domestic institutions are eligible for Supplements to Promote Reentry into Biomedical and Behavioral Research Careers: R01, R10, R18, R24, R35, R37, P01, P40, P41, P50, P51, U54, P60, U01, and U10. Principal investigators on such awards are invited to submit a request for an administrative supplement to the awarding component of the parent grant to support an eligible candidate interested in reestablishing a research career. The parent grant should have at least two years of support remaining at the time of the proposed beginning date of the supplemental funding. The rationale for this policy is to ensure ample opportunity for the candidate to further develop her or his research skills. A maximum of three years of supplemental support can be awarded under this program. Usually, a research grant or a subproject of a multiproject grant would support only one administrative supplement, including Research Supplements to Promote Diversity in Biomedical, Clinical, and Behavioral Research Careers. Grants most likely to support more than a single administrative supplement are multiproject awards.
Candidates must have a doctoral degree, and must have had sufficient prior research experience to qualify for a doctoral-level research staff or faculty position at the time they left active research. Candidates who have begun the reentry process through a fellowship, traineeship, or similar mechanism are not eligible for this program. Awards will be limited to citizens or noncitizen nationals of the United States or to individuals who have been lawfully admitted for permanent residence (i.e., who possess an Alien Registration Receipt Card) at the time of application.
The following guidelines will generally be applied at the discretion of the individual institutes and centers. In general, the duration of the career interruption should be for at least one year and no more than eight years. Examples of qualifying interruptions would include child-rearing; an incapacitating illness or injury of the candidate, spouse, partner, or a member of the immediate family; relocation to accommodate a spouse, partner, or other close family member; pursuit of nonresearch endeavors that would permit earlier retirement of debt incurred in obtaining a doctoral degree; and military service. The program is not intended to support additional graduate training and is not intended to support career changes from nonresearch to research careers for individuals without prior research training. Generally, at the time of application, a candidate should not be engaged in full-time paid research activities. Because NIH institutes and centers may have varying degrees of flexibility in interpreting and implementing the reentry program, potential applicants should consult with the contact at the NIH awarding component at the earliest possible stage to discuss his or her unique situation.
In all cases, the proposed research must be directly related to the funded, approved, ongoing research of the parent grant or cooperative agreement. The individual supported under this supplemental award must be afforded the opportunity to act as a full participant in the research project and must be given an opportunity to update and enhance her or his research capabilities. This will allow the candidate to begin the process of establishing or reestablishing a career as a productive, competitive research investigator. Supplemental awards will be consistent with the goals of strengthening the existing research program and with the overall programmatic balance and priorities of the funding program of the NIH. Administrative supplements provided under this program may be for either part-time or full-time support for the candidate, and all supported time is to be spent updating and enhancing research skills. Proposed part-time appointments may not be less than 50% effort.
The requested salary and fringe benefits for a reentry candidate must be in accordance with the salary structure of the grantee institution, consistent with the level of effort. An additional amount up to $10,000 may be requested for supplies, domestic travel, and publication costs relevant to the proposed research. Equipment may not be purchased as a part of this supplement without justification and specific prior approval of the NIH.
The decision to fund a supplement will take approximately 10 weeks from the time all of the necessary information is received by the awarding institute or center in an acceptable format. During the first budget period, funds will be provided as an administrative supplement to the parent grant. In subsequent years, continued funding for the supplement is contingent on funding of the parent grant and the reentry candidate’s progress, and cannot extend beyond the current competitive segment of the parent grant.
For general information about the reentry supplements, candidates and principal investigators should contact the program official of the parent grant at the appropriate awarding institute or center. Candidates who have not yet made contact with a principal investigator are encouraged to contact the program official whose institute or center is specific to the research interest.
Contact: For the complete listing of contacts, please consult the full announcement, available online at http://grants1.nih.gov/grants/guide/pa-files/PA-04-126.html. Reference: PA No. PA-04-126.
Understanding and Promoting Health Literacy
The participating institutes, centers, and offices of the NIH and the Agency for Healthcare Research and Quality (AHRQ) invite investigators to submit research grant applications on health literacy. The goal of this program announcement (PA) is to increase scientific understanding of the nature of health literacy and its relationship to healthy behaviors, illness prevention and treatment, chronic disease management, health disparities, risk assessment of environmental factors, and health outcomes including mental and oral health. There is a need for increased scientific knowledge of interventions that can strengthen health literacy and improve the positive health impacts of communications between health care/public health professionals (including dentists, health care delivery organizations, and public health entities) and consumer or patient audiences that vary in health literacy. Applicants may propose secondary goals of modeling the potential impact of new interventions on future national trends and/or determining the impact of targeted cancer control interventions on population outcome (i.e., evaluating optimal cancer control strategies).
Health literacy is the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions. Many factors affect individuals’ ability to comprehend, and in turn use or act on, health information and communication. Proficiency in reading, writing, listening, interpreting, oral communication, and visual analysis is necessary as the modern health system typically relies on a variety of interpersonal, textual, and electronic media to present health information. Individuals and families both must be able to 1) communicate with health professionals; 2) understand the health information in mass communication; 3) understand how to use health-related print, audiovisual, graphic, and electronic materials; 4) understand basic health concepts (e.g., that many health problems can be prevented or minimized) and vocabulary (e.g., about the body, diseases, medical treatments, etc.); and 5) connect this health-related knowledge to health decision making and action taking.
Too often, people with the greatest health burdens have limited access to relevant health information. In addition, health care providers may not communicate effectively with individuals with limited levels of literacy. Low health literacy is a widespread problem, affecting more than 90 million adults in the United States. Low health literacy results in patients’ inadequate engagement in and benefit from health care advances, as well as medical errors. Low health literacy is likely to be a major contributor to adverse health outcomes. Research has linked low or limited health literacy with such adverse outcomes as poorer self-management of chronic diseases, less healthful behaviors, higher rates of hospitalizations, and overall poorer health.
This PA invites applications to develop research on health literacy in general areas that include, but are not limited to, the following: 1) modeling and measuring the nature and scope of health literacy; 2) variation in health literacy over the life course or among native and nonnative speakers of English; 3) mediators and moderators of low health literacy; 4) the impact of low health literacy on health outcomes, diseases, behaviors, and treatments, including the contribution of health literacy to informed decision making, adherence to preventative or therapeutic regimens, utilization of health care services, risk avoidance strategies, and other consumer health care–related actions; 5) the identification of effective preventive and other interventions to improve health literacy among populations and to enable the health care and public health systems to communicate effectively across different health literacy levels; and 6) the development of effective methods and new technologies in health literacy research.
Applications should be relevant both to the objectives of the PA and to at least one of the participating institutes’ general research interests. Prior to preparing an application, researchers are strongly encouraged both to review the general research interests of the participating institutes and to contact program staff of the relevant institutes to discuss the proposed research.
A wide variety of research approaches are encouraged under this PA: basic research that investigates or describes the nature of health literacy and the magnitude of health literacy problems, and applied research addressing issues pertinent to health literacy practices (e.g., systems-level interventions) and research in practice (e.g., active potential end users participate as supportive research partners). Applications also may develop theoretical models, refine research constructs, improve methods and measurements, and establish causal relationships (e.g., between low health literacy and lack of effective health promotion). Researchers also may address the effectiveness of interventions, or adapt and test existing programs (including those that are not research-based) to reduce low health literacy and its adverse consequences (e.g., interventions implemented by health care systems and systems outside of health care such as systems of public education).
The research must involve either 1) health literacy, or one of its many components, as a key outcome; 2) health literacy as a key explanatory variable for some other outcome; 3) methodological or technological improvement to strengthen research on health literacy; or 4) health literacy–focused preventions and interventions. Studies to develop or evaluate the readability or utility of specific materials that are intended for single uses or single audiences are not responsive to this PA unless these investigations are integral to testing a significant research hypothesis related to health literacy.
Projects may employ any one or combination of study designs, research approaches, and data collection techniques. Secondary analyses of existing data sets as well as meta-analytic studies are also suitable for this PA. Multilevel, multidisciplinary, and interdisciplinary research is also encouraged, especially studies that incorporate individual, family, community, and societal mediators of health literacy in childhood and adulthood, or state-of-the-art health communication theory and knowledge.
Researchers are encouraged to address ongoing investigations of prevention, healthy living, chronic disease management, patient-based health care, cultural competence, and health disparities to inform the research on health literacy. Research questions can focus on consumers, patients, clients, or other population groups; the strategies and tactics used by providers of medical and health information and communication to enable them to effectively reach literacy-challenged populations; or the influences of health literacy upon interactions between consumers, patients, clients, providers, and organizations or systems.
The Institute of Medicine’s 2004 report Health Literacy: A Prescription to End Confusion reviews the current body of knowledge about health literacy, and identifies actions for the promotion of health literacy in society. Applicants are encouraged to consult this report as a general reference.
This PA will use the NIH R01 award mechanism. As an applicant, you will be solely responsible for planning, directing, and executing the proposed project. This PA uses just-in-time concepts. It also uses the modular budgeting format (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 budgeting format. This program does not require cost sharing as defined in the current NIH Grants Policy Statement at http://grants.nih.gov/grants/policy/nihgps_2003/NIHGPS_Part2.htm.
Applications must be prepared using the PHS 398 research grant application instructions and forms (rev. 5/2001). Applications must have a Dun and Bradstreet Data Universal Numbering System (DUNS) number as the Universal Identifier when applying for federal grants or cooperative agreements. The DUNS number can be obtained by calling 1-866-705-5711 or through the website at http://www.dunandbradstreet.com/. The DUNS number should be entered on line 11 of the face page of the PHS 398 form. The PHS 398 document is available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance, contact GrantsInfo by calling 301-435-0714 or e-mailing [email protected].
Applications submitted in response to this PA will be accepted at the following receipt dates: 13 October 2004, 13 October 2005, and 13 October 2006.
Contact: For the complete listing of contacts, please consult the full PA, available online at http://grants1.nih.gov/grants/guide/pa-files/PAR-04-116.html. Reference: PA No. PAR-04-116
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0792a15471710PerspectivesCorrespondenceAssociation between Air Pollution and Adverse Pregnancy Outcomes in Vancouver Bukowski John A. ExxonMobil Biomedical Sciences, Inc., Annandale, New Jersey, E-mail:
[email protected] author is employed by ExxonMobil Biomedical Sciences, Inc.
10 2004 112 14 A792 A792 Publication 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 the November 2003 issue of EHP, Liu et al. (2003) concluded that “relatively low concentrations of gaseous air pollutants are associated with adverse effects on birth outcomes.” Although this may be true from a purely statistical sense, there appear to be limitations of this research that suggest cautious interpretation of the findings.
Liu et al. (2003) evaluated individual-level birth certificate data, which is an improvement over the ecologic designs of past time-series studies on pollution. However, birth records do not contain most of the variables that are important predictors of low weight and preterm births. These include smoking, alcohol and/or drug abuse, low socioeconomic status (SES), small maternal weight or height, complications of the current or previous pregnancy [e.g., pregnancy-induced hypertension, previous low birth weight (LBW), spontaneous abortion], insufficient weight gain during pregnancy, maternal illness (e.g., fever), and job-related exertion (Berkowitz and Papiernik 1993; Holmes and Soothill 1996; Kramer 1987, 2003; Lang et al. 1996; Moore 2003). Many of these are major factors that substantially affect risk. For example, maternal smoking during pregnancy, which has a prevalence of 10–20% in the United States (Ebrahim et al. 2000; O’Campo et al. 1995), is associated with a 2- to 4-fold increase in risk of LBW or growth restriction (Kramer 1987; Lang et al. 1996; Nordentoft et al. 1996). Therefore, there is considerable room for uncontrolled confounding that might account for the small odds ratio of 1.05–1.10 observed by Liu et al. (2003).
Liu et al. (2003) argued that uncontrolled or residual confounding is an unlikely explanation for their results because a) there is no evidence that these factors are associated with air pollution; b) ecologic measures of SES did not modify the associations; and c) “there were only slight differences between crude and adjusted estimates,” and “individual characteristics ... did not attenuate the risk estimates.” However, these arguments have limitations.
First, there may not be evidence that important risk factors co-vary with pollution, but it seems reasonable that many might correlate with residential location. Liu et al. (2003) linked pollution measurements in 13 census subdivisions to births within those subdivisions. If gaseous pollutant measurements and other factors (e.g., SES, smoking prevalence) co-vary by census subdivision, then confounding could occur. Second, ecologic measures are poor surrogates for individual-level ones, which can result in confounder misspecification and residual confounding (Greenland 1980; Liu 1988; Marshall and Hastrup 1996; Morgenstern 1998). Third, the individual-level covariates included in some of the models did appear to have substantive impacts. For example, the odds ratio for the association between LBW and first-month sulfur dioxide exposure changed from a crude value of 0.95 to a significant 1.11 after adjustment for confounding. This is a 17% absolute increase in risk and a change in coefficient from –0.05 to +0.10 per 5 ppb. In other instances the adjustment caused a significant elevation to become a deficit (e.g., association between preterm birth and first-month exposure to ozone) or a null value to become a significant protective effect (preterm birth and last-month ozone exposure). This apparent impact of confounding was caused by variables (e.g., maternal age and season of birth) that are weaker risk factors than many missing variables, such as smoking, SES, and weight gain (Berkowitz and Papiernik 1993; Kramer 1987; Lang et al. 1996). This suggests considerable potential for residual confounding.
The findings of Liu et al. (2003) also lack biological coherence with the literature. The authors invoked a biological mechanism for air pollution similar to cigarette smoking. For smoking, the risk is predominantly during the third trimester, primarily from decreased fetal growth, which has been attributed to decreased maternal and fetal nutrition among smokers and hypoxia from inhaled carbon monoxide (Holmes and Soothill 1996; Kramer 1987; Petridou et al. 1990). However, most of the significant increases reported by Liu et al. (2003) were associated with exposures during the first month or trimester, with no effects seen during the third trimester. It is unclear how these early, low-level pollution exposures, which lack the substantive impact of smoking, would alter fetal growth.
Liu et al. (2003) also do not discuss the potential for spurious results due to multiple comparisons. The authors reported 36 associations within the tables, and many more were likely performed, including multipollutant models. Therefore, at least some of the significant results may be due to chance.
In conclusion, the above limitations could easily account for the findings reported by Liu et al. (2003), without invoking novel effects from air pollution.
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References
Berkowitz GS Papiernik E 1993 Epidemiology of preterm birth Epidemiol Rev 15 414 443 8174665
Ebrahim SH Floyd RL Merritt RK Decoufle P Holtzman D 2000 Trends in pregnancy-related smoking rates in the United States, 1987-1996 JAMA 283 361 366 10647799
Greenland S 1980 The effect of misclassification in the presence of covariates Am J Epidemiol 112 564 569 7424903
Holmes RP Soothill PW 1996 Intrauterine growth retardation Curr Opin Obstet Gynecol 8 148 154 8734133
Kramer MS 1987 Determinants of low birth weight: methodological assessment and meta-analysis Bull WHO 65 663 737 3322602
Kramer MS 2003 The epidemiology of adverse pregnancy outcomes: an overview J Nutr 133 1592S 1596S 12730473
Lang JM Lieberman E Cohen A 1996 A comparison of risk factors for preterm labor and term small-for-gestational-age birth Epidemiology 7 369 376 8793362
Liu K 1988 Measurement error and its impact on partial correlation and multiple linear regression analysis Am J Epidemiol 127 864 874 3354551
Liu S Krewski D Shi Y Chen Y Burnett RT 2003 Association between gaseous ambient air pollutants and adverse pregnancy outcomes in Vancouver, Canada Environ Health Perspect 111 1773 1778 14594630
Marshall JR Hastrup JL 1996 Mismeasurement and the resonance of strong confounders: uncorrelated errors Am J Epidemiol 143 1069 1078 8629614
Moore ML 2003 Preterm labor and birth: what have we learned in the past two decades? J Obstet Gynecol Neonatal Nurs 32 638 649
Morgenstern H 1998. Ecological studies. In: Modern Epidemiology (Rothman KJ, Greenland S, eds). Philadelphia:Lippincott-Raven, 459–480.
Nordentoft M Lou HC Hansen D Nim J Pryds O Rubin P 1996 Intrauterine growth retardation and premature delivery: the influence of maternal smoking and psychosocial factors Am J Public Health 86 347 354 8604759
O’Campo P Davis MV Gielen AC 1995 Smoking cessation interventions for pregnant women: review and future directions Semin Perinatol 19 279 285 8560293
Petridou E Panagiotopoulou K Katsouyanni K Spanos E Trichopoulos D 1990 Tobacco smoking, pregnancy estrogens, and birth weight Epidemiology 1 247 250 2081260
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0792b15471710PerspectivesCorrespondenceAir Pollution and Adverse Pregnancy Outcomes: Response Liu Shiliang Krewski Daniel Shi Yuanli Chen Yue Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, E-mail:
[email protected] Richard T. Healthy Environments and Consumer, Safety Branch, Health Canada, Ottawa, Ontario, CanadaThe authors declare they have no competing financial interests.
10 2004 112 14 A792 A794 Publication 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 thank Bukowski for his critical comments on our article (Liu et al. 2003), in which we reported associations between ambient air pollution and adverse pregnancy outcomes in Vancouver, Canada. In recent years, air pollution has come to be recognized as an important risk factor for a number of adverse health outcomes, particularly cardiorespiratory morbidity (Burnett et al. 1997, 2001; Lin et al. 2002, 2003; Yang et al. 2003) and mortality (Burnett et al. 1997; Dockery et al. 1993; Pope et al. 1995; Villeneuve et al. 2003).
The adverse effects of air pollution on pregnancy outcomes, such as low birth weight (LBW), preterm birth, intrauterine growth retardation (IUGR), and developmental anomalies are of increasing concern. Before our study, there were reports of associations between particulate (total suspended particulate) and gaseous (carbon monoxide, sulfur dioxide, and nitrogen dioxide) air pollutants and adverse pregnancy outcomes from southern California (Ritz et al. 2000, , 2002), China (Wang et al. 1997; Xu et al. 1995), and the Czech Republic (Bobak 2000; Dejmek et al. 1999). Replication of these findings in different populations under different conditions of exposure is an important aspect of epidemiologic research, with consistency of results strengthening the weight of evidence for a true association between exposure and outcome.
Data on important predictors of adverse pregnancy outcomes were not available to us for use in our study (Liu et al. 2003). Although numerous risk factors have been identified (including maternal age, parity, infant sex, and season of birth, as well as gestational age and birth weight, in the case of LBW and preterm birth, respectively, which we were able to take into account), our understanding of the etiology of adverse pregnancy outcomes remains far from sufficient (Kramer 2003). The omission of known or unknown risk factors for birth anomalies may lead to uncontrolled or residual confounding of the association between air pollution and adverse pregnancy outcomes, as Bukowski suggests. However, the extent to which residual confounding might occur in our data is unclear. Schwartz and Morris (1995) have argued that the estimated effects of air pollution are unlikely to be confounded by these factors because they are unlikely to be correlated with daily air pollution levels.
Exposure assessment is always a critical factor in environmental epidemiology (Rothman 1993). Like most other studies of air pollution and population health, our study (Liu et al. 2003) relied on ecologic rather than personal indicators of exposure, with average ambient air pollution concentrations determined using one or more fixed site monitors within census areas in Vancouver. Janssen et al. (1998, 1999) have suggested that air pollution levels from outdoor monitoring stations can provide useful surrogates for personal exposure. Exposure misclassification due to the use of fixed site ambient monitors rather than personal dosimeters is likely to underestimate rather than overestimate the effect of air pollution on birth outcomes (Mallick et al. 2002; Zeger et al. 2000).
The weight of evidence that air pollution is causally related to adverse pregnancy outcomes would be considerably increased through understanding of biological mechanisms by which such effects could occur.
Burkowski notes that we (Liu et al. 2003) included a number of statistical tests of the strength of association between air pollution and adverse pregnancy outcomes, and observes that multiple testing raises the risk of false positives. Our a priori strategy for hypothesis testing focused on predetermined stages of pregnancy (month or trimester), which are thought to represent periods of differential susceptibility to exogenous exposures. Findings from both epidemiologic and toxicologic studies suggest that the fetus is most susceptible to the effects of air pollution during the first trimester (Generoso et al. 1987; Rutledge 2000). Human studies also have suggested that initial changes leading to IUGR might be triggered in early pregnancy, around the time of implantation (Duvekot et al. 1995; Khong et al. 1986). Air pollutants may be absorbed into the maternal bloodstream, cross the placental barrier, and have direct toxic effects on the fetus.
Our a priori strategy for the development of appropriate risk models focused on single-pollutant models, with adjustment for relevant covariates available to us, as we reported in Tables 4–7 (Liu et al. 2003). Our strategy also called for an assessment of the robustness of the associations between pregnancy outcomes and specific pollutants against adjustment for copollutants. Although this strategy does involve a moderately large number of statistical tests of the significance of logistic regression coefficients associated with specific pollutants, our evaluation of the data is based more on the evidence provided by this set of hypothesis tests as a whole, rather than on the results of individual tests alone.
Overall, our data suggest that adverse pregnancy outcomes are associated with exposures to air pollutants during pregnancy, particularly in early gestation. Because of limitations of our study, we (Liu et al. 2003) concluded that
“these effects require further examination in other populations, and further research also needs to be conducted with more detailed information on personal exposures, effect modifiers, and other adverse pregnancy outcomes such as birth defects and spontaneous abortion.”
Our data need to be interpreted in the context of the emerging body of scientific evidence on air pollution and adverse pregnancy outcomes, to which we have made a contribution.
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References
Bobak M 2000 Outdoor air pollution, low birth weight, and prematurity Environ Health Perspect 108 173 176 10656859
Burnett RT Dales RE Brook JR Raizenne ME Krewski D 1997 Association between ambient carbon monoxide levels and hospitalizations for congestive heart failure in the elderly in 10 Canadian cities Epidemiology 8 162 167 9229208
Burnett RT Smith-Doiron M Stieb D Raizenne ME Brook JE Dales RE 2001 Association between ozone and hospitalization for acute respiratory diseases in children less than 2 years of age Am J Epidemiol 153 444 452 11226976
Dejmek J Selevan SG Benes I Solansky I Srám RJ 1999 Fetal growth and maternal exposure to particulate matter during pregnancy Environ Health Perspect 107 475 480 10339448
Dockery DW Pope CA III Xu X Spengler JD Ware JH Fay ME 1993 An association between air pollution and mortality in six U.S. cities N Engl J Med 329 1753 1759 8179653
Duvekot JJ Cheriex EC Pieters FA Peeters LL 1995 Severely impaired growth is preceded by maternal hemodynamic maladaptation in very early pregnancy Acta Obstet Gynecol Scand 74 693 697 7572102
Generoso WM Rutledge JC Cain KT Hughes LA Braden PW 1987 Exposure of female mice to ethylene oxide within hours after mating leads to fetal malformation and death Mutat Res 176 269 274 3807937
Janssen NA Hoek G Brunekreef B Harssema H Mensink I Zuidhof A 1998 Personal sampling of particles in adults: relation among personal, indoor, and outdoor air concentrations Am J Epidemiol 147 537 547 9521180
Janssen NA Hock G Harssema H Brunekreef B 1999 Personal exposure to fine particles in children correlates closely with ambient fine particles Arch Environ Health 54 95 100 10094286
Khong TY De Wolf F Robertson WB Brosens I 1986 Inadequate maternal vascular response to placentation in pregnancies complicated by pre-eclampsia and by small-for-gestational age infants Br J Obstet Gynecol 93 1049 1059
Kramer MS 2003 The epidemiology of adverse pregnancy outcomes: an overview J Nutr 133 1592S 1596S 12730473
Lin M Chen Y Burnett RT Villeneuve PJ Krewski D 2002 The influence of ambient coarse particulate matter on asthma hospitalization in children: case-crossover and time-series analyses Environ Health Perspect 110 575 581 12055048
Lin M Chen Y Burnett RT Villeneuve PJ Krewski D 2003 Effect of short-term exposure to gaseous pollution on asthma hospitalization in children: a bi-directional case-crossover analysis J Epidemiol Community Health 57 50 55 12490649
Liu S Krewski D Shi Y Chen Y Burnett RT 2003 Association between gaseous ambient air pollutants and adverse pregnancy outcomes in Vancouver, Canada Environ Health Perspect 111 1773 1778 14594630
Mallick R Fung F Krewski D 2002 Adjusting for measurement error in the Cox proportional hazards regression model J Cancer Epidemiol Prev 7 155 164 12846486
Pope CA III Thun MJ Namboodiri MM Dockery DW Evans JS Speizer FE 1995 Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults Am J Respir Crit Care Med 151 669 674 7881654
Ritz B Yu F Chapa G Fruin S 2000 Effect of air pollution on preterm birth among children born in Southern California between 1989 and 1993 Epidemiology 11 502 511 10955401
Ritz B Yu F Fruin S Chapa G Shaw GM Harris JA 2002 Ambient air pollution and risk of birth defects in southern California Am J Epidemiol 155 17 25 11772780
Rothman KJ 1993 Methodologic frontiers in environmental epidemiology Environ Health Perspect 101 suppl 4 19 21 8206029
Rutledge J 2000 Perimplantation teratology and the placenta Teratology 61 246 247 10716742
Schwartz J Morris R 1995 Air pollution and hospital admissions for cardiovascular disease in Detroit, Michigan Am J Epidemiol 142 23 35 7785670
Villeneuve PJ Burnett RT Shi Y Krewski D Goldberg MS Hertzman C 2003 A time series study of air pollution, socioeconomic status, and mortality in Vancouver, Canada J Expo Anal Environ Epidemiol 13 427 435 14603343
Wang X Ding H Ryan L Xu X 1997 Association between air pollution and low birth weight: a community-based study Environ Health Perspect 105 514 520 9222137
Xu X Ding H Wang X 1995 Acute effects of total suspended particulate and sulfur dioxides on preterm delivery: a community-based cohort study Arch Environ Health 50 407 415 8572718
Yang Q Chen Y Shi Y Burnett RT McGrail K Krewski D 2003 Association between ozone and respiratory admissions among children and the elderly in Vancouver, Canada Inhal Toxicol 15 1297 1308 14569494
Zeger SL Thomas D Dominici F Samet JM Schwartz J Dockery D 2000 Exposure measurement error in time-series studies of air pollution: concepts and consequences Environ Health Perspect 108 419 426 10811568
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0794a15471712PerspectivesCorrespondenceBhopal: No Silver Linings Gupta J.P. Department of Chemical Engineering, Indian Institute of Technology, Kanpur, India, E-mail:
[email protected] Kimberly Thigpen News Editor, EHP, E-mail:
[email protected] author declares he has no competing financial interests.
Editor’s note: The caption referred to by Gupta was written by me, not the author of the article, and I take full responsibility for it. In no way did I intend to trivialize the tragedy at Bhopal. I wanted to make the point that sometimes beneficial lessons may be learned from tragic situations, but my attempt to be “clever” was unfortunate. I regret my choice of wording and that it caused offense.
10 2004 112 14 A794 A794 Publication 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 read with interest the article “Lessons Learned? Chemical Plant Safety since Bhopal,” by Ernie Hood (2004). I would recommend it to all interested in safety in chemical plants or safety in other fields.
As mentioned in the article (Hood 2004), this year is the 20th anniversary of the Bhopal tragedy. An international conference on the 20th anniversary of the tragedy, Bhopal and Its Effects on Process Safety, will be held 1–3 December 2004 at the Indian Institute of Technology in Kanpur, India, with a visit to the Bhopal plant planned on 4 December for those who are interested; details are available online (http://www.iitk.ac.in/infocell/announce/bhopal). Although the deadline for abstracts has passed, we will still consider outstanding papers.
I would also like to comment on the legend for the figure on page A354 of Hood’s article (Hood 2004): “A toxic cloud’s silver lining?” The question mark does indicate that there are some doubts whether the death of many thousands and the continued suffering of a still larger number should be considered to have a silver lining. In predictable accidents, the large number of deaths produce only untold suffering and not proportionate advantages to the society. The earlier leakages at the Union Carbide Bhopal plant were well known and documented in the newspapers, but neither the company nor the government took enough actions to save the city from the expected accident. According to Charles Perrow of Yale University (Perrow 1999), this is one accident that could not have been worse, contrasting the common cliche “we were lucky it wasn’t worse,” which is used to describe many other accidents and deliberate actions, such as the 9/11 attacks on the World Trade Center (WTC) in New York City. If the explosions in the WTC had taken place later in the day, many more people would have been inside the two towers and many more would have died. No one should say the deaths at the WTC and the Pentagon provide a silver lining to the war against terrorism. Terrorist acts were already being conducted in several places in Asia, Spain, Northern Ireland, Latin America, and other locations, except the world as a whole decided to look the other way and let individual countries respond. Similarly, because the problems caused by fascism were known or could be foreseen, World War II did not have to happen and cause many millions of deaths. The Allies recently observed the 60th anniversary of the D-Day invasions of several beaches in France; so many deaths and much misery was not necessary for us to understand what fascism could do.
Therefore, I hope that people would reconsider their comments of silver linings on others’ sufferings. The use of the question mark indicates that Hood (2004) was not sure of this, and I commend that hesitant punctuation mark.
==== Refs
References
Hood E 2004 Lessons learned? Chemical plant safety since Bhopal Environ Health Perspect 112 A352 A359 15121534
Perrow C 1999. Normal Accidents. Princeton, NJ:Princeton University Press.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0794b15471711PerspectivesCorrespondenceStudy on Failures to Disclose Conflicts of Interest in Environmental Health Perspectives Goozner Merrill Integrity in Science Project, Center for Science in the Public Interest, Washington, DC, E-mail:
[email protected] author is employed as the director of the Integrity in Science Project at the Center for Science in the Public Interest.
10 2004 112 14 A794 A795 Publication 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 Center for Science in the Public Interest (CSPI) recently investigated conflict of interest disclosures in a cross-section of leading scientific and medical journals, including EHP, to determine adherence to their own policies.
EHP’s conflict of interest disclosure policy (EHP 2003) outlines a comprehensive list of “competing financial interests” that an author must disclose along with a published article. They include “grant support, employment (recent, present, or anticipated), … travel, consultancies, advisory board positions, patent and royalty arrangements, stock shares, … and the like.” It limits disclosure to situations where an author “may gain or lose financially through publication.” The editors also eschew any effort at enforcement, relying instead on the veracity of authors. EHP encourages its readers to scrutinize disclosure statements and offers to publish letters that address alleged inaccuracies.
During the study period of December 2003 through February 2004, EHP published 37 scientific studies. Only 2 of the studies indicated they were funded by industry, and only these 2 studies included conflict of interest disclosure statements for at least some of the authors.
The CSPI investigated the first and last authors involved in the 35 studies who did not disclose conflicts of interest. Our investigation revealed at least 3 articles (8.6%) where either the first or last authors should have disclosed conflicts in accordance with the disclosure policy.
First, a Procter and Gamble (P&G) scientist, William Owens, was identified only as a representative of the Organisation for Economic Co-operation and Development. The article (Yamasaki et al. 2003) validated an assay that may be used on P&G products. Owens did not disclose his corporate affiliation in this article, despite having disclosed his P&G employment in a previous EHP article (Owens and Köeter 2003).
Second, a Quebec, Canada, group led by Pierre Ayotte of CHUQ-Laval University Medical Center studied the effects of organochlorines and methyl mercury on a remote coastal population (Bilrha et al. 2003). Although there was no disclosure of a conflict of interest, the study was funded in part by the Canadian Network of Toxicology Centers, which is funded in part by the Canadian Chemical Producer Association, an industry trade group. Several of Ayotte’s previous studies were funded in part by the Canadian Chemical Producer Association and the Canadian Chlorine Coordinating Committee, although Ayotte was not directly compensated for this work.
The third group of authors who did not disclose conflicts of interest are scientists at Macquarie University who investigated the sources of lead in children near a zinc–lead smelter (Gulson et al. 2004). Brian Gulson, a professor in Macquarie’s graduate school of the environment, did not disclose that he is listed as an adviser on the website of a consulting group that advised Pasminco Ltd. (Laboratory Quality Management Services Pty Ltd. 2003), the company that ran the smelter. In a subsequent e-mail communication, Gulson informed the CSPI that one of his coauthors, Karen Mizon, was the wife of the owner of the consulting group.
In each of those cases, at least one of the authors of a study may gain financially from publication of the article, thus meeting the test of the EHP policy. Owens was directly employed by a company that might be affected by his findings. Gulson’s colleagues stood to gain financially from a company directly affected by the subject matter of their articles.
Ayotte’s study, although a borderline case, should have contained a disclosure statement because he had collaborated on industry-funded studies in the past and the study in question was funded by an industry-supported group. When a field of research is so closely tied to an industry, future funding for research may involve the goodwill of that industry. The spirit of conflict of interest disclosure is best served by full disclosure in such cases.
A fourth case of nondisclosure was not included in our results. In February 2004, EHP published a study funded by the National Institutes of Health that claimed polychlorinated biphenyls (PCBs) inhibit a receptor involved in clearing foreign substances from humans (Tabb et al. 2004). One of the authors, Bruce Blumberg, of the Department of Developmental and Cell Biology at the University of California, Irvine, is listed as a co-inventor on a patent granted in 2002 on the gene for an unrelated receptor in frogs. The use claimed for the frog gene is that it may be useful in identifying compounds that affect the receptor.
Should Blumberg disclose that patent as a potential conflict of interest? In an e-mail communication, he defended his failure to disclose by asking, “Do you seriously believe that a patent for a Xenopus (frog) nuclear receptor has any bearing on a paper about a mammalian receptor with an entirely different function?” Strict conflict of interest policies would argue “yes,” because it is impossible to predict how the information in the new study will affect the use of previous knowledge or what inventions considered irrelevant today may be extremely useful (and lucrative) in the future. Even though EHP’s policy (EHP 2003) says “may gain,” we did not count his nondisclosure in our statistics.
Judging from the findings of our limited survey, a significant percentage of articles published in EHP (8.6%) fail to disclose relevant conflicts of interest of authors. We cannot determine whether authors are not disclosing the relevant information to EHP, or if they are providing the information but EHP is not publishing the disclosures. Considering the importance of providing readers with such information, it would seem that EHP needs to develop mechanisms to minimize failures to provide “full disclosure of competing financial interests” (EHP 2003). One possible mechanism for improving compliance would be for EHP not to accept for 3 years any papers submitted by authors who failed to disclose information about conflicts of interest.
I look forward to your response.
==== Refs
References
Bilrha H Roy R Moreau B Belles-Isles M Dewailly É Ayotte P 2003 In vitro activation of cord blood mononuclear cells and cytokine production in a remote coastal population exposed to organochlorines and methyl mercury Environ Health Perspect 111 1952 1957 14644672
EHP 2003. Instructions to authors. Available: http://ehp.niehs.nih.gov/docs/admin/edpolicy.html [accessed 11 June 2004].
Gulson BL Mizon KJ Davis JD Palmer JM Vimpani G 2004 Identification of sources of lead in children in a primary zinc–lead smelter environment Environ Health Perspect 112 52 60 14698931
Laboratory Quality Management Services Pty Ltd 2003. Brian L. Gulson - Consultant. Available: http://www.lqms.com.au/information/expertise/brian.asp [accessed 11 June 2004].
Owens W Koëter HBMW 2003 The OECD special program to validate the rat uterotrophic bioassay: an overview Environ Health Perspect 111 1527 1529 12948895
Tabb MM Kholodovych V Grun F Zhou C Welsh WJ Blumberg B 2004 Highly chlorinated PCBs inhibit the human xenobiotic response mediated by the steroid and xenobiotic receptor (SXR) Environ Health Perspect 112 163 169 14754570
Yamasaki K Sawaki M Ohta R Okuda H Katayama S Yamada T 2003 OECD validation of the Hershberger assay in Japan: phase 2 dose response of methyltestos-terone, vinclozolin, and p,p ’-DDE Environ Health Perspect 111 1912 1919 14644666
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0796aPerspectivesCorrespondenceConflicts of Interest: Ayotte’s Response Ayotte Pierre Public Health Research Unit, CHUQ-Laval University Medical Center, Quebec, Canada, E-mail:
[email protected] author declares he has no competing financial interests.
10 2004 112 14 A796 A796 Publication 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
Goozner contacted me by e-mail on 24 June 2004, and I promptly, honestly, and to the best of my knowledge answered questions regarding his claim that I did not disclose a conflict of interest while publishing our article (Bilrha et al. 2003) in EHP. Although I thought I made it clear that he was wrong in his allegations, he nevertheless chose to go ahead and include my name in a report published on the Center for Science in the Public Interest’s (CSPI) website (CSPI 2004) and in his letter to EHP. Here are the facts.
In our 2003 manuscript (Bilrha et al. 2003), we acknowledged funding from the Canadian Network of Toxicology Centers (CNTC). As the corresponding author, I read the conflict of interest statement and indicated that I had nothing to declare, neither for me nor for the coauthors. I did not know at that time that the Canadian Chemical Producer Association was partly funding the CNTC. On their website (CNTC 2004), the CNTC indicates being funded mostly by public sources (90%) and does not mention the identity of private sources. In any case, had I known this at the time of publication, it would not have changed anything, because I never personally received any funds (or compensation of any sort, or stood to gain financially) from the Canadian Chemical Producer Association or the Canadian Chlorine Coordinating Committee. Goozner is wrong when he mentions that several of my previous studies were funded by these interest groups. I was a coauthor on two articles published previously in EHP, in which funding from these interest groups was acknowledged, along with other sources of funding (Sandau et al. 2000, 2002). I collaborated in the work, but I was not the recipient of funds obtained from the Canadian Chemical Producer Association or the Canadian Chlorine Coordinating Committee. Funds from these organizations went to the principal investigator at Carleton University, and not a penny was transferred to me.
I truly believe that the authors of scientific manuscripts should disclose relevant conflicts of interest, and I support enforcing disclosure by appropriate means. However, because Goozner elected to choose the easy way by conducting an Internet-based research, without actually talking to me, he wrongly associated my name with scientific misconduct. I am presently seeking legal advice on this matter.
==== Refs
References
Bilrha H Roy R Moreau B Belles-Isles M Dewailly É Ayotte P 2003 In vitro activation of cord blood mononuclear cells and cytokine production in a remote coastal population exposed to organochlorines and methyl mercury Environ Health Perspect 111 1952 1957 14644672
CNTC 2004. Canadian Network of Toxicology Centres. Available: http://www.uoguelph.ca/cntc/ [accessed21 July 2004].
CSPI 2004. Unrevealed: Non-Disclosure of Conflicts of Interest in Four Leading Medical and Scientific Journals. Washington, DC:Center for Science in the Public Interest. Available: http://cspinet.org/new/pdf/unrevealed_final.pdf [accessed 15 July 2004].
Sandau CD Ayotte P Dewailly É Duffe J Norstrom RJ 2000 Analysis of hydroxylated metabolites of PCBs (OH-PCBs) and other chlorinated phenolic compounds in whole blood from Canadian Inuit Environ Health Perspect 108 611 616 10903613
Sandau CD Ayotte P Dewailly É Duffe J Norstrom RJ 2002 Pentachlorophenol and hydroxylated polychlorinated biphenyl metabolites in umbilical cord plasma of neonates from coastal populations in Québec Environ Health Perspect 110 411 417 11940460
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0796bPerspectivesCorrespondenceConflicts of Interest: Gulson’s Response Gulson Brian Graduate School of the Environment Macquarie University Sydney, New South Wales, Australia E-mail:
[email protected] author declares he has no competing financial interests.
10 2004 112 14 A796 A796 Publication 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 commend the Center for Science in the Public Interest (CSPI) for their investigation into conflicts of interest statements in leading scientific and medical journals, but we take exception to our inclusion (Gulson et al. 2004) as an example of providing misinformation to EHP.
Goozner’s sweeping statement that my colleague had previously received research funding, compensation, or stood to gain financially from Pasminco Ltd. is highly inaccurate. We received no research funding, compensation, or financial gain from the company to undertake this study. In fact, if Goozner had read even the abstract of our article, he would have noted that the findings were detrimental to the company, as the dominant source of lead in the environment and children probably derived from smelter emissions. Furthermore, the smelter closed in September 2003 and the company no longer exists. With respect to the association of my colleague, Karen Mizon, to her husband’s company and the (consulting) work undertaken for Pasminco Ltd., the work [an International Organization for Standardization (ISO) Guide 25 accreditation assessment of the company’s on-site laboratory (ISO/International Electrotechnical Commission 1990)] was undertaken by the owner for the accreditation body while he was employed by a federal government research organization, and he was not paid for this audit.
==== Refs
Reference
Gulson BL Mizon KJ Davis JD Palmer JM Vimpani G 2004 Identification of sources of lead in children in a primary zinc–lead smelter environment Environ Health Perspect 112 52 60 14698931
ISO/International Electrotechnical Commission 1990. ISO Guide 25: General Requirements for the Competence of Calibration and Testing Laboratories. 3rd ed. Geneva: International Organization for Standardization.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0796cPerspectivesCorrespondenceConflicts of Interest: Blumberg’s Response Blumberg Bruce Department of Developmental and Cell Biology University of California, Irvine Irvine, California, E-mail:
[email protected] author is the co-inventor of U.S. Patent 6,756,491, “Steroid-activated nuclear receptors and uses therefore,” issued on 29 June 2004. This patent is owned and controlled by the Salk Institute for Biological Studies, La Jolla, California, but is likely to generate income to the inventors as a result of licensing.
10 2004 112 14 A796 A797 Publication 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
In his letter, Goozner suggested that I may have failed to disclose a competing financial interest in regard to an article published in EHP. In this article, “Highly Chlorinated PCBs Inhibit the Human Xenobiotic Response Mediated by the Steroid and Xenobiotic Receptor (SXR)” (Tabb et al. 2004), we described differences in how humans and rodents respond to highly chlorinated polychlorinated biphenyls (PCBs). We concluded that rodents may not be appropriate models for exposure to the class of PCBs discussed in the article and suggested that previous research using rodent models to predict the effects of these PCBs on humans may need to be re-evaluated in light of our findings.
In his letter, Goozner noted that I am the co-inventor of U.S. Patent 6,391,847 (“Method, polypeptides, nucleotide sequence of XOR-6, a vitamin D-like receptor from Xenopus”). This patent describes a frog nuclear receptor, now referred to as the benzoate “X” receptor (BXR). As I pointed out on 24 June 2004 in an e-mail message to Goozner, I am not the owner of this patent; the patent is owned and controlled by the Salk Institute for Biological Studies, where I was employed from 1992 to 1998.
It is difficult to understand how Goozner reasons that the frog BXR patent is related in any way to our article on rodent and human SXR (Tabb et al. 2004). My laboratory (Grün et al. 2002) and another laboratory (Moore et al. 2002) have shown that BXR and SXR (also known as PXR) are functionally distinct and that BXRs do not function as xenobiotic receptors. Therefore, there is no functional link between BXR and SXR/PXR, as I also pointed out to Goozner in my e-mail.
The EHP Instructions to Authors (EHP 2003) defines a competing financial interest thusly: “Competing financial interests may include, but are not limited to, grant support, employment (recent, present, or anticipated), and personal financial interests by the authors, immediate family members, or institutional affiliations that may gain or lose financially through publication.” Therefore, for a competing financial interest to exist, there must be at least some realistic probability at the time of submission that publication of the article in EHP would lead to financial gain or loss to the authors, their immediate family members, or institutional affiliations. Considering that there is no functional similarity between frog BXR and rodent and human SXR, it is not reasonable to infer that publication of the article regarding the function of SXR in rodents and humans (Tabb et al. 2004) would have any influence on financial interests related to U.S. Patent 6,391,847. Therefore, no potential competing financial interest existed at the time of submission or publication of this manuscript, and as a result, none was disclosed.
In view of these ongoing discussions of interpreting and perhaps heightening the standards regarding disclosure, I wish to inform you of a patent that I just learned was recently issued: U.S. Patent 6,756,491, “Steroid-activated nuclear receptors and uses therefore” was issued on 29 June 2004, over 4 months after the publication of our article (Tabb et al. 2004) in EHP. I am the co-inventor of this patent, which teaches the sequence of SXR and its nucleotide response elements. Because this patent is owned and controlled by the Salk Institute, I was unaware of its status. Had this patent been issued at the time of submission or publication of the article (Tabb et al. 2004) (or had I known that it would issue shortly), I would have disclosed it as a potential competing financial interest. In contrast to the BXR patent, this patent meets the tests described above. It is functionally connected to the subject matter of the article, it clearly has commercial value, and it is foreseeable that I will receive some fraction of whatever income the Salk Institute receives in the course of licensing it to interested parties. Whether or not publication of the article in EHP will lead to a financial gain or loss as required by EHP policy remains to be seen.
I fully support EHP’s competing financial interest policy. Goozner argues in his letter to EHP, and in e-mails to me, for a relatively extreme interpretation of what constitutes a competing financial interest, which, as far as I understand it, is beyond the scope of the current EHP policy. Whether such an interpretation will become the norm for the scientific community is a matter for future discussion. Although scientists make a good faith effort to comply with disclosure clauses, most are not well trained in understanding the legal nuances involved. It would be very helpful if policies ultimately adopted by journal editorial boards were clearly stated and included appropriate examples so that authors can readily understand the requirements and more effectively comply with the policy.
==== Refs
References
EHP 2003. Instructions to authors. Available: http://ehp.niehs.nih.gov/docs/admin/edpolicy.html [accessed 12 December 2003].
Grün F Venkatesan RN Tabb MM Zhou C Cao J Hemmati D 2002 Benzoate X receptors α and β are pharmacologically distinct and do not function as xenobiotic receptors J Biol Chem 277 43691 43697 12198127
Moore LB Maglich JM McKee DD Wisely B Willson TM Kliewer SA 2002 Pregnane X receptor (PXR), constitutive androstane receptor (CAR), and benzoate X receptor (BXR) define three pharmacologically distinct classes of nuclear receptors Mol Endocrinol 16 977 986 11981033
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0803a15515224EnvironewsForumLead: Sweet Candy, Bitter Poison Medlin Jennifer 10 2004 112 14 A803 A803 Publication 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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For years, scientists and health experts have traced child lead exposures to paint, leaded gasoline, soils, dust, lead-soldered cans and water pipes, and lead-glazed pottery. More recently, tests have shown that something as presumably innocuous as candies—specifically certain ones made in Mexico—may also be a source of toxic lead. Efforts are under way to combat this source of exposure, but success to date has varied.
Lead poisoning causes a host of problems, many worse in children, including decreased intelligence, impaired neurobehavioral development, stunted physical growth, hearing impairment, and kidney problems. Blood lead levels as low as 10 micrograms per deciliter (μg/dL) are known to cause adverse health effects. The U.S. Food and Drug Administration (FDA) recommends that children under age 6 consume less than 6.0 μg lead daily from all food sources.
Since the early 1990s, the FDA, the California Department of Health Services, and independent laboratories have shown that certain Mexican candies contain sometimes hazardous levels of lead. Historically, some Mexican candy manufacturers have had two versions of their product lines: a cleaner version that meets FDA standards and is designed for export to the United States, and a dirtier—and cheaper—version for the Mexican market. The latter is imported via the “grey market”—pickup trucks that drive shipments over the border to small, family-owned stores, particularly in California, Texas, New Mexico, and Arizona. These stores serve local Latinos, who enjoy the comfort and familiarity of candy from home.
The huge number of these small, informal shipments makes surveillance very difficult. Yet according to the San Diego–based nonprofit Environmental Health Coalition, California has traced 15% of its child lead poisoning cases to lead-contaminated candy—roughly the same percentage attributed to lead-based paint.
Lead-glazed clay pots and candy wrappers printed with lead-based ink were the focus of the FDA’s earliest tests. Tamarind candy packaged in lead-glazed pots elevated blood lead levels in some children to 40–50 μg/dL. And in May 2001, the FDA cautioned consumers to stay away from tamarind Bolirindo lollipops. Tests had found lead concentrations of 21,000 parts per million (ppm) in the lollipops’ wrappers.
California and FDA officials have also found lead in a common ingredient in many Mexican candies—chili powder. Several potential contamination sources have been suggested: soil residue from fields, air-drying or storage where the chilies can accumulate dust from exhaust emissions, metal particles accumulated during the grinding process, and drying over open petrochemical fires. One chili-coated candy tested by the FDA, Chaca Chaca, contained as much as 0.3–0.4 μg lead per gram of product, with one piece of candy alone weighing 35 grams.
Public health and advocacy groups have had some success in warning consumers about lead-contaminated candy, and other efforts to solve the problem are under way. In July 2004, California attorney general Bill Lockyer sued Mexican candy manufacturers and distributors, including two subsidiaries of the Virginia-based Mars candy company, for neglecting to advise consumers that many of their products contain lead. A statement issued by the defendants’ counsel downplayed the extent and gravity of the problem, insisting the companies complied fully with U.S. and international food safety laws. “Traces of minerals such as lead are . . . found in virtually all foods, including fish, meats, grains, fruits and vegetables, and candy,” the statement read. “People have been eating those foods safely for generations.”
Earlier in the summer, California assemblyman Marco Firebaugh proposed a bill that would have banned candies containing more than 0.2 ppm. But the bill died on the floor in August, in large part because other law makers felt the 0.2-ppm safety margin could undermine the Lockyer lawsuit.
The FDA continues to work with the Mexican government to identify the agricultural and manufacturing practices that cause the contamination, although Mexico largely does not have the analytical support, laboratories, and instrumentation to carry out the necessary product testing. Because this contamination appears to be avoidable, the FDA is proposing to lower the amount of lead allowed in candy products and ingredients in the near future.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0803b15515224EnvironewsForumThe Beat Dooley Erin E. 10 2004 112 14 A803 A805 Publication 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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e-Hospitals for Better Care
In July 2004 DHHS secretary Tommy Thompson announced a 10-year plan to improve the delivery of health care across the nation using electronic health records. The plan—initiated by President Bush in April—calls for continuously updated and accessible electronic health records, which can give physicians life-saving information in real time. A recent Institute of Medicine report found that implementation of electronic medical record systems could reduce the tens of thousands of deaths and injuries caused by medical mistakes each year. Bush has called for electronic medical records for most Americans within 10 years. A panel appointed by Thompson should provide a full cost–benefit analysis of health information technology this fall.
WHO Launches Children’s Health Resources
The World Health Organization has unveiled several new resources on children’s environmental health. An “e-library” CD-ROM contains more than 100 documents concerning children’s environmental health. Children’s Health and the Environment: A Global Perspective is a reference manual for health care providers and policy makers that includes case studies of environmental illnesses, tips on taking pediatric environmental histories, and ways people can take action to improve children’s health. And the Atlas of Children’s Environmental Health and the Environment is a compendium of facts on environmental hazards to children. These resources are intended to underscore for stakeholders how greatly children are impacted by the environment. It is estimated that over 40% of the environment-related disease burden falls on children under the age of 5, with over 3 million children dying from environmental causes each year.
Tragic Trinkets
The Consumer Product Safety Commission issued a record-breaking recall in July 2004 when it announced that 150 million toy bracelets, rings, and necklaces should be removed from the market or discarded by parents because they contain high levels of lead. The vending machine trinkets had already been recalled twice before during the preceding year, but the problem persisted. Tests have found that some of the items, which are made in India, contain as much as 69% lead by weight. The four importers of the jewelry have agreed to the recall, while the commission has posted photos of the items on the website http://www.toyjewelryrecall.com.
Preservation Masterpiece
Ozone-depleting methyl bromide was once the fumigant of choice for protecting museum artifacts against insects and mold. But with the chemical’s scheduled phase-out in January 2005, an alternative is gaining ground in museums around the world. The new method uses commercially available oxygen absorbers and airtight bags to kill pests by depriving them of oxygen. The technology was first developed by NASA to prevent rusting, and was first used for artifact preservation in the early 1990s in two California museums. Today it is being used in North America, Australia, Europe, Latin America, and Japan. Recently the director of Japan’s Maritime Self-Defense Force Sasebo Historical Museum reported that the museum had preserved more than 3,000 artifacts with the method and that all remain in good condition.
European Child Health Plan Adopted
In June 2004, the Fourth Ministerial Conference on Environment and Health adopted the Children’s Environment and Health Action Plan for Europe. The plan includes regional priority goals for ensuring children’s access to safe drinking water and adequate sanitation, reducing accidental deaths and injuries, promoting child-friendly urban planning, reducing respiratory disease, and reducing exposure to hazardous chemicals. The plan calls on the represented nations to implement national children’s environmental health plans by 2007. The meeting also sponsored a young journalists workshop and a youth parliament as part of the European Commission’s efforts to enlist young people as environmental stakeholders.
A Vaccine for Medical Waste Burning
The NGO Health Care Without Harm has teamed with the Philippines Department of Health to prove large-scale immunizations can be conducted without the burning of medical waste. Incineration of syringes, needles, and other medical waste releases toxicants such as dioxin, mercury, and lead into the air. During the program, conducted during February 2004, about 18 million Filipino children were vaccinated for measles using an estimated 19.5 million syringes. The used syringes were collected in safety boxes and treated in either autoclave or microwave facilities, then buried in waste pits or put in concrete vaults. In 1999 the Philippines was the first nation to ban the burning of all waste, including medical waste.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0804a15515225EnvironewsForumRecycling: Funds for Phones Dahl Richard 10 2004 112 14 A804 A804 Publication 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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Americans will retire about 100 million cell phones this year when they switch to new models or new carriers, according to INFORM, a nonprofit environmental research group. Many go into the trash, ending up in landfills, and still more are tossed into closets and drawers, where they await the same ultimate destination. As University of Florida researchers concluded in the July 2004 report RCRA Toxicity Characterization of Computer CPUs and Other Discarded Electronic Devices, cell phones often release enough lead under test conditions to be classified as hazardous waste under federal law [see “Electronics, Lead, and Landfills,” EHP 112:A734 (2004)]. But while this growing mountain of old phones is drawing the attention of people who are worried about its potential impact on the environment, it has also been discovered by people who see it as a commodity that still retains market value.
In recent years, companies have emerged that buy old cell phones from individuals or groups that collect them on fundraising drives. These companies then sell the phones to foreign wireless carriers, who refurbish and resell them, or recyclers, who extract metals such as gold, silver, and copper. One company, RMS Communications Group, is currently taking in about 80,000 phones per month, according to its marketing and communications manager, Lynda Gorsuch. Like many such companies, RMS emphasizes charity tieins, offering people the opportunity to make a “virtual contribution” of the dollar value of the phone they sent in to a variety of charities listed on the RMS web-sites http://www.cellforcash.com/ and http://www.wirelessfundraiser.com/. Another such firm is CollectiveGood (http://www.collectivegood.com/), which offers a similar smorgasbord of charities to which people can contribute the value of their old phones.
Meanwhile, youth organizations have found that cell phone collection drives offer unparalleled fundraising opportunities. On Earth Day 2004, a group of Boy Scouts in West Jordan, Utah, collected cell phones that they sold to RMS. RMS paid the Scouts from a set price list ranging from $3 to $100 per phone. The public response was so overwhelming that the Scouts are continuing the program and expect to make $6,000 from it this year, says David Bresnahan, an adult advisor who set up the project.
“We’re trying to promote environmental protection, which is a great lesson for the kids,” Bresnahan says, “and we’re putting the money into a fund that’s used to help kids who can’t afford to go to camp or can’t afford a backpack.”
A 2003 INFORM report, Calling All Cell Phones, analyzed various U.S. cell phone collection and recycling programs and concluded that, while they are providing a “critically important” service, they are not nearly enough. INFORM senior researcher Bette Fishbein says industry programs will absorb about 1% of this year’s discarded phones, and independent programs such as RMS and CollectiveGood will absorb a bit more. But the total amount of phones being taken in is still well under 5% of the 100 million that will be discarded, she says.
“It’s a step in the right direction,” Fishbein concludes. “But if you’re going to address the issue of toxics entering our environment through disposal facilities, you’ve got to take back a lot more.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0804b15515225EnvironewsForumAgriculture: Farm Chore Checkup Barrett Julia R. 10 2004 112 14 A804 A804 Publication 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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Nearly 23,000 children are hurt doing farmwork in the United States each year, and approximately 100 children are killed, according to the National Agricultural Statistics Service. To combat this problem, the National Children’s Center for Rural and Agricultural Health and Safety in Marshfield, Wisconsin, developed the North American Guidelines for Children’s Agricultural Tasks (NAGCAT) in the late 1990s to help farm parents assess whether their children aged 7–16 are developmentally ready to safely complete various farm tasks. A recent study now offers evidence that NAGCAT has been effective at preventing injuries to children working on farms.
The study was led by pediatric scientist Anne Gadomski of the Bassett Research Institute in Cooperstown, New York, and published in the October 2004 American Journal of Public Health. It is the first randomized, controlled trial to test the efficacy of these guidelines in preventing farm injuries.
William Pickett, a researcher on agricultural injury at Queens University in Kingston, Ontario, says many interventions are directed at children, which may not be an effective strategy. “You can have the most highly educated, most informed child around,” he says, “but they are not necessarily the ones making the decisions about what they do on the farm and where they are allowed to go.”
NAGCAT, on the other hand, provides guidance for those who do make such decisions. The guidelines are conveyed through a professional resource manual and parent booklets. Each booklet covers a set of related farm tasks, such as animal care or tractor work, using a poster format to describe the task, adult responsibilities, potential hazards, and necessary safety precautions. The posters also list developmental abilities a child must possess to perform the task.
The study involved 2,454 children on 845 farms in central New York. Some of the farms received NAGCAT information, while others did not. Over 21 months, the researchers collected data on children’s injuries, what they were doing when injured, their general responsibilities, and the number of hours worked.
Although the two groups did not differ significantly in overall injury incidence, farms that received NAGCAT information reported fewer injuries related to tasks described in the guidelines. Gadomski says, “We suspect that the average age of the child is going up in terms of being assigned certain tasks now that the parent has a guideline to help them make that assessment. The other issue is [parents now have] some idea of how much supervision certain aged children require in order to do the job safely.” For the most part, children under 7 are not ready to engage in productive agricultural work, says Nancy Esser, an agricultural youth safety specialist at the center. The center therefore recommends that young children not be involved in such work.
The study is a welcome addition to the literature in an area where there are few published trials, says Pickett. But NAGCAT addresses only one portion of the pediatric farm injury problem, he says. Similar efforts are needed to address injuries that occur among young children—not necessarily workers—who accompany their worker parents into the farm environment.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0824aEnvironewsScience SelectionsCellular Energy Crisis: Particulate Hitchhikers Damage Mitochondria Weinhold Bob 10 2004 112 14 A824 A824 Publication 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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One of the body’s most important processes—energy production by mitochondria in the cell—can be significantly disrupted by exposure to ultrafine particulates, according to a team of researchers from the University of California, Los Angeles, and the University of Southern California [EHP 112:1347–1358]. Furthermore, the researchers say, the primary culprits are substances that are attached to particles. These findings provide the first insights into the specific mechanism by which ultrafine particles, increasingly recognized as environmental villains, damage mitochondria, says principal investigator Andre Nel.
The researchers conducted a series of experiments that evaluated the effects on mouse liver mitochondria of either diesel exhaust particles (DEPs), ambient ultrafine particles collected in the Los Angeles area, or engineered nanoparticles with no attached chemicals. Using the DEPs and ultrafines collected in Los Angeles, the team isolated organic “hitchhiker” substances such as polycyclic aromatic hydrocarbons (PAHs) and quinones that had attached to the particle cores. These chemicals were tested for a variety of effects on cells and mitochondria.
Among the adverse effects observed were mitochondrial structural decomposition, mitochondrial swelling due to increased membrane porosity and rupturing, increased production of free radicals, and induction of cellular death. Although some of these effects were dependent on the presence of calcium, others were caused by direct damage to the mitochondrial membrane.
The mitochondrial effects varied with the specific hitchhiker substances tested. For instance, polar fractions high in quinones were much more potent in inducing cell death, whereas aromatic compounds high in PAHs had a more moderate effect, and aliphatic compounds had no apparent effect.
Even within a class of compounds, not all substances proved to be equally destructive. For instance, among quinones, phenanthraquinone and 1,2-naphthoquinone caused mitochondrial swelling, while anthraquinone did not. This difference may depend on the ability of particular quinones to participate in reactions that generate reactive oxygen species (ROSs), unstable compounds that can quickly react with and damage other substances.
The team speculates on the biological mechanism behind the observed effects, laying the groundwork for future research. In the case of quinones, they suggest that the substances may redirect electron transfers in the inner mitochondrial membrane to molecular oxygen, thereby generating ROSs that can damage the mitochondria as well as exert proinflammatory effects. These effects could be important in the exacerbation of asthma.
Regardless of the specific mechanism, the consistent culprits in damaging mitochondria were the organic substances attached to particle cores. In contrast, engineered polystyrene nanoparticles with no organics attached had no apparent effects, leading the team to speculate that the small size of engineered nanoparticles may not be solely responsible for inducing mitochondrial and cellular damage.
This is of considerable interest to the burgeoning field of nanotechnology, where there is concern that nanoparticles may be toxic based on small size alone. The researchers acknowledge that smaller particles tend to penetrate better than larger particles and possibly are more bioavailable, due to their higher surface-to-volume ratio. These characteristics may allow organic chemicals that are attached to particle surfaces to better penetrate tissues than if they are not carried along by tiny transporters.–Bob Weinhold
Energy drains. New research conducted on mouse liver cells shows that toxicants that “hitchhike” on particulates severely interfere with the ability of mitochondria (framed, above) to produce energy.
Source: Li N, Sioutas C, Cho A, Schmitz D, Misra C, Sempf J, et al. Ultrafine particulate pollutants induce oxidative stress and mitochondrial damage. Environ Health Perspect 111:455–460 (2003).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0824bEnvironewsScience SelectionsMeasuring by Hand: Arsenic Picked Up from the Playground Fields Scott 10 2004 112 14 A824 A825 Publication 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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Several nations today ban or severely restrict the use of wood preserved with chromated copper arsenate (CCA), but many existing structures still remain—for example, about 70% of existing U.S. single-family homes and 14% of public playgrounds incorporate CCA-treated wood. In recent years, scientists have studied how arsenic leaches from CCA-treated wood, but they have only inferred exposure levels from measurements of arsenic concentrations in soil and sand near treated wood structures. In this issue, Elena Kwon of the University of Alberta and colleagues report on direct measurements they made of arsenic on the hands of children playing in playgrounds, some with CCA-treated wood structures and others without [EHP 112:1375–1380]. The team reports that although playing on treated structures increases the amount of arsenic on children’s hands, washing the children’s hands after playing may be enough to avoid the health risks associated with CCA.
For several decades, CCA-treated wood was widely used in the United States, Canada, and other countries for playground equipment, fences, and backyard decks. Bans and restrictions on the use of CCA-treated wood have been driven largely by concerns that treated wood could release chromium and arsenic, posing risks to human health. Especially vexing was the possibility that children who contacted CCA-treated wood structures were, because of their propensity for hand-to-mouth contact, especially at risk for ingesting arsenic. Although touching treated wood will not liberate the 70- to 170-milligram dose of arsenic that is fatal to humans, ingesting lower doses of the substance has been linked to several cancers and other ailments.
The scientists measured arsenic on the hands of 130 children who visited 16 public playgrounds in Edmonton, Canada, over the period 5–21 August 2003. They tested all children who visited the playgrounds during randomized observation times and whose parents allowed them to participate in the study. The children averaged 4.75 years of age and spent an average of 1.25 hours on the playground.
When each child was finished playing, his or her hands were rinsed for 1 minute in a Ziploc bag of deionized water. The water and any soil/sand rinsed from the child’s hands were analyzed separately in the laboratory for arsenic content. The team also collected soil/sand samples from each playground; samples from near the structures provided a measure of the arsenic that had leached from the wood, while those taken far from the structures indicated how much arsenic was present naturally.
In comparing playgrounds with and without CCA-treated wood structures, the team found no statistically significant difference in the amount of arsenic in the soil/sand samples or in the soil/sand washed from the children’s hands. However, children who had played in the treated-wood environment had an average of 0.50 micrograms of soluble arsenic rinsed from their hands—more than five times as much as the children who did not play on treated structures.
EPA research indicates that ingestion, rather than inhalation or dermal absorption, is the primary route of exposure related to arsenic-related ailments. Children aged 2–6 typically ingest about half of whatever they collect on their hands. But even assuming that the children in the study managed to ingest all of the arsenic on their hands, their average dosage was less than the average Canadian child’s daily dose of arsenic through food and water (about 0.6 micrograms per kilogram body weight).
The scientists also found that the first rinsing removed most of the arsenic from the children’s hands. That could be the prescription for parents whose children frequent playgrounds with treated-wood structures—and who want to play it safe.
Picked up on the playground. Arsenic from treated wood play structures is transferred to children’s hands, but washing can remove most of it.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0844aAnnouncementsBook ReviewChildren’s Health, The Nation’s Wealth: Assessing and Improving Child Health Ettinger Adrienne S. Adrienne S. Ettinger is currently assistant professor of health policy and management and epidemiology at Johns Hopkins Bloomberg School of Public Health. Her research involves pediatric and perinatal epidemiology, specifically applied to children’s environmental health issues, and the translation and application of epidemiologic evidence to public health policy.10 2004 112 14 A844 A844 Publication 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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National Research Council and Institute of Medicine
Washington, DC:National Academies Press, 2004. 210 pp. ISBN: 0-309-09249-3, $44.95 cloth.
Children’s Health, The Nation’s Wealth: Assessing and Improving Child Health examines the information needed to help policymakers and program service providers improve and monitor children’s health. This report from the Committee on Evaluation of Children’s Health (National Research Council and Institute of Medicine) focuses on population health issues related to children and provides a framework for measurement of children’s health. It does not recommend specific measures to monitor children’s health. The report summarizes what is known about the health of children and why children’s health is important. The term “children” is used to refer to the ages between birth and 18 years of age. Children’s health is defined as “the extent to which individual children or groups of children are able or enabled to (a) develop and realize their potential, (b) satisfy their needs, and (c) develop the capacities that allow them to interact successfully with their biological, physical, and social environments.” The committee highlights a new conceptual model of children’s health and its influences, which are multiple, interactive, and changing over the course of childhood. This model depicts the relative importance and interaction of social environment, biology, physical environment, and behavior for children’s health over the course of development, as well as the service and policy contexts in which children live.
Critical differences between children and adults are emphasized, as are the special developmental issues of different age groups of children. The authors note improvements in U.S. children’s health over the past century, as measured by reduced infant mortality, reduced morbidity and mortality from infectious diseases and accidental causes, increased access to health care, and reduced environmental contaminants, such as lead. Despite progress in these areas, national indicators suggest that significant disparities in children’s health still exist. A thorough summary of how national health surveys address children’s health is provided; however, these data do not provide enough detail at the state and local levels for examining the origins of these differences. Major questions remain about how to assess the status of children’s health, what factors should be monitored, and the appropriate measurement tools that should be used. Insufficient data exist at the federal, state, and local levels to design and evaluate public health prevention and intervention programs and to monitor their effectiveness. Existing data systems are underused for these purposes and present several challenges for use in examining trends in children’s health indicators.
The title of the report is somewhat misleading because there is little emphasis on the connection between children’s health and the national economy (“wealth”) either in terms of burden of disease or cost-effectiveness analyses of programs. However, socioeconomic disparities in health are discussed throughout in the context of missing or insufficient data. This new report describes the information needed to improve decision making and devotes significant attention to the need for coordination and cooperation among agencies regarding data integration, standardization, and data sharing agreements. The new children’s health model recommends that policymakers should adopt a broader view of children’s health and implement innovative methodologies to assess both current conditions and emerging threats to children’s health.
Clearly, the title asserts that children are vital assets to our society. Overall, the report is an important contribution to a strategic plan for ensuring the health of future generations.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0844bAnnouncementsNew BooksNew Books 10 2004 112 14 A844 A844 Publication 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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America’s Environmental Report Card: Are We Making the Grade?
Harvey Blatt
Cambridge, MA:MIT Press, 2004. 272 pp. ISBN 0-262-02572-8, $27.95
Climate Change—Environment and Civilization in the Middle East
Arie S. Issar, Mattanyah Zohar
London:Springer-Verlag, 2004. 252 pp. ISBN: 3-540-21086-5, $137.99
Defying Ocean’s End: An Agenda for Action
Linda K. Glover, Sylvia A. Earle, eds.
Washington, DC:Island Press, 2004. 250 pp. ISBN: 1-55963-753-6, $55
Democracy’s Dilemma: Environment, Social Equity, and the Global Economy
Robert C. Paehlke
Cambridge, MA:MIT Press, 2004. 316 pp. ISBN 0-262-66188-8, $16.95
Diamond: A Struggle for Environmental Justice in Louisiana’s Chemical Corridor
Steve Lerner
Cambridge, MA:MIT Press, 2004. 344 pp. ISBN 0-262-12273-1, $27.95
Elements and their Compounds in the Environment: Occurrence, Analysis and Biological Relevance. 3 Volumes
Ernest Merian, Manfred Anke, Milan Ihnat, Markus Stoeppler, eds.
Hoboken, NJ:John Wiley & Sons, 2004. 1,600 pp. ISBN: 3-527-30459-2, $680
Encyclopedic Reference of Immunotoxicology
Hans-Werner Vohr, ed.
London:Springer-Verlag, 2004. 1,000 pp. ISBN: 3-540-44172-7, $329.72
Environmental Governance Reconsidered: Challenges, Choices, and Opportunities
Robert F. Durant, Daniel J. Fiorino, Rosemary O’Leary, eds.
Cambridge, MA:MIT Press, 2004. 536 pp. ISBN: 0-262-54174-2, $35
Environmental Impact Assessment of Recycled Wastes on Surface and Ground Waters: Chemodynamics, Toxicology, and Modeling Series
Tarek A Kassim, Kenneth J. Williamson, eds.
London, England:Springer-Verlag, 2004. 450 pp. ISBN: 3-540-00268-5, $268.82
Hope’s Horizon: Three Visions for Healing the American Land
Chip Ward
Washington, DC:Island Press, 2004. 400 pp. ISBN: 1-55963-977-6, $27
Implementing Climate and Global Change Research: A Review of the Final U.S. Climate Change Science Program Strategic Plan
Committee to Review the U.S. Climate Change Science Program Strategic Plan, National Research Council
Washington, DC:National Academies Press, 2004. 108 pp. ISBN: 0-309-08865-8, $30
Occupational Toxicology, 2nd Edition
Neill H. Stacey, Chris Winder
Boca Raton, FL:CRC Press, 2004. 624 pp. ISBN: 0748409181, $89.95
Pesticide Toxicology and International Regulation
Timothy C. Marrs, Bryan Ballantyne, eds.
Hoboken, NJ:John Wiley & Sons, 2004. 592 pp. ISBN: 0-471-49644-8, $140
Pharmaceuticals in the Environment: Sources, Fate, Effects and Risks, 2nd ed.
Klaus Kümmerer, ed.
London:Springer-Verlag, 2004. 528 pp. ISBN: 3-540-21342-2, $110.20
Protein Synthesis and Ribosome Structure: Translating the Genome
Knud H. Nierhaus, Daniel N. Wilson, eds.
Hoboken, NJ:John Wiley & Sons, 2004. 592 pp. ISBN: 3-527-30638-2, $180
Tutorials in Biostatistics, Volume 1—Statistical Methods in Clinical Studies
Ralph B. D’Agostino
Hoboken, NJ:John Wiley & Sons, 2004. 350 pp. ISBN 0-470-02365-1, $150
Tutorials in Biostatistics, Volume 2—Statistical Modeling of Complex Medical Data
Ralph B. D’Agostino
Hoboken, NJ:John Wiley & Sons, 2004. 430 pp. ISBN 0-470-02370-8, $150
Water Follies: Groundwater Pumping and the Fate of America’s Fresh Waters
Robert Jerome Glennon
Washington, DC:Island Press, 2004. 304 pp. ISBN: 1-55963-400-6, $20
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7047ehp0112-00144715531427ResearchReviewListing Occupational Carcinogens Siemiatycki Jack 12Richardson Lesley 3Straif Kurt 3Latreille Benoit 4Lakhani Ramzan 4Campbell Sally 4Rousseau Marie-Claude 1Boffetta Paolo 351Département de Médecine sociale et préventive, Université de Montréal, Montréal, Québec, Canada2Department of Epidemiology and Biostatistics, McGill University, Montréal, Québec, Canada3International Agency for Research on Cancer, Lyon, France4INRS-Institut Armand-Frappier, Laval, Québec, Canada5Division of Clinical Epidemiology, German Cancer Research Center, Heidelberg, GermanyAddress correspondence to J. Siemiatycki, Département de Médecine sociale et préventive, Université de Montréal, P.O. Box 6128, stn Centre-Ville, Montréal, Québec, Canada, H3C 3J7. Telephone: (450) 686-5676. Fax: (450) 686-5599. E-mail:
[email protected] work was in part supported by funds from the Centre de recherche du CHUM and from the Canada Research Chair Program.
The authors declare they have no competing financial interests.
11 2004 15 7 2004 112 15 1447 1459 19 2 2004 14 7 2004 Publication 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 occupational environment has been a most fruitful one for investigating the etiology of human cancer. Many recognized human carcinogens are occupational carcinogens. There is a large volume of epidemiologic and experimental data concerning cancer risks in different work environments. It is important to synthesize this information for both scientific and public health purposes. Various organizations and individuals have published lists of occupational carcinogens. However, such lists have been limited by unclear criteria for which recognized carcinogens should be considered occupational carcinogens, and by inconsistent and incomplete information on the occupations and industries in which the carcinogenic substances may be found and on their target sites of cancer. Based largely on the evaluations published by the International Agency for Research on Cancer, and augmented with additional information, the present article represents an attempt to summarize, in tabular form, current knowledge on occupational carcinogens, the occupations and industries in which they are found, and their target organs. We have considered 28 agents as definite occupational carcinogens, 27 agents as probable occupational carcinogens, and 113 agents as possible occupational carcinogens. These tables should be useful for regulatory or preventive purposes and for scientific purposes in research priority setting and in understanding carcinogenesis.
cancerenvironmentepidemiologyoccupationreview
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Occupational carcinogens occupy a special place among the different classes of human carcinogens. The occupational environment has been a most fruitful one for investigating the etiology and pathogenesis of human cancer. Up to the 1970s, most recognized human carcinogens were substances or circumstances found primarily in the occupational environment, and although this may no longer be true with the growing list of recognized non-occupational carcinogens, they still represent a large fraction of the total. Although it is important to discover occupational carcinogens for the sake of preventing occupational cancer, the potential benefit of such discoveries goes beyond the factory walls because most occupational exposures find their way into the general environment, sometimes at higher concentrations than in the workplace.
There is a large volume of epidemiologic and experimental data concerning cancer risks in different work environments. It is important to synthesize this information for both scientific and public health purposes. Various national and international bodies have published lists of carcinogens, but available lists of occupational carcinogens have been limited in various ways. Among the issues that are often missing, or treated rather casually, are a coherent assessment of which substances should be considered occupational carcinogens; information on the occupations and industries in which the carcinogenic substances may be found; and the target sites of cancer. The present article represents an attempt to summarize, in tabular form, current knowledge on occupational carcinogens, the occupations and industries in which they are found, and their target organs.
Methods and Results
Difficulties in listing occupational carcinogens.
Although it seems like a simple enough task, it is very difficult to draw up an unambiguous list of occupational carcinogens. The first source of ambiguity concerns the definition of an “occupational” carcinogen. Most occupational exposures are also found in the general environment, and/or in consumer products; most general environmental exposures and consumer products, including medications, foods, and others, are found in some occupational environments. The distinctions can be quite arbitrary. For instance, although tobacco smoke, sunlight, and immunosuppressive medications are not primarily considered to be occupational exposures, there certainly are workers whose occupations bring them into contact with these agents. Also, although asbestos, benzene, and radon gas are considered to be occupational carcinogens, they are also found widely among the general population, and indeed, it is likely that many more people are exposed to these substances outside than inside the occupational environment. There is no simple rule to earmark occupational carcinogens as opposed to nonoccupational ones. Further, some carcinogens are chemicals that are used for research purposes and to which few people would ever be exposed, whether occupationally or nonoccupationally. Our operational criterion for designating occupational carcinogens is outlined below.
A second source of ambiguity derives from the rather idiosyncratic nature of the evidence. In some instances, we know that an occupational or industrial group is at excess risk of cancer, and we have a good idea of the causative agent; for example, scrotal cancer among chimney sweeps and polyaromatic hydrocarbons (PAHs) in soot (Waldron 1983), and lung cancer among asbestos miners and asbestos fibers [International Agency for Research on Cancer (IARC) 1977]. In some instances, we know that a group experienced excess risk but the causative agent is unknown or at least unproven [e.g., lung cancer among painters (IARC 1989c), bladder cancer among workers in the aluminum industry (IARC 1987)]. The strength of the evidence for an association can vary. For some associations, the evidence of excess risk seems incontrovertible [e.g., liver angiosarcoma and vinyl chloride monomer (IARC 1979b), bladder cancer and benzidine (IARC 1982b)]. For some associations, the evidence is suggestive [e.g., lung cancer and diesel engine exhaust (IARC 1989a), bladder cancer and employment as a painter (IARC 1989c)]. Among the many substances in the industrial environment for which there are no human data concerning carcinogenicity, there are hundreds that have been shown to be carcinogenic in some animal species and thousands that have been shown to have some effect in assays of mutagenicity or genotoxicity. These considerations complicate the attempt to devise a list of occupational carcinogens.
IARC Monographs.
For this task we drew on the authoritative IARC Monograph Program and its evaluation of carcinogenic risks to humans (IARC 1987). The objective of the IARC Monograph Program, which has been operating since 1971, is to publish critical reviews of epidemiologic and experimental data on carcinogenicity for chemicals, groups of chemicals, industrial processes, other complex mixtures, physical agents, and biologic agents to which humans are known to be exposed, to evaluate the data in terms of human risk, and to indicate where additional research efforts are needed.
Substances are selected by IARC for evaluation on the basis of two main criteria: a) humans are exposed, and b) there is reason to suspect that the substance may be carcinogenic. Direct evidence concerning carcinogenicity of a substance can come from epidemiologic studies among humans or from experimental studies of animals (usually rodents). Additional evidence comes from the results of studies of chemical structure–activity analysis, absorption and metabolism, physiology, mutagenicity, cytotoxicology, and other aspects of toxicity. In the IARC Monographs, all types of data contribute to the evaluation.
In this article, we outline the IARC process because it is important to understand how decisions are made in order to properly interpret these decisions. IARC evaluations are carried out during specially convened meetings that typically last a week. The meetings may evaluate only one agent, such as silica, or they may address a set of related agents or even exposure circumstances such as an occupation or an industry. For each such meeting, and there have typically been three per year, IARC convenes an international working group, usually involving from 15 to 30 experts on the topic(s) being evaluated, from four perspectives, a) exposure and occurrence of the substances being evaluated, b) human evidence of cancer risk (i.e., epidemiology), c) animal carcinogenesis, and d) other data relevant to the evaluation of carcinogenicity and its mechanisms. The working group is asked to review all of the literature relevant to an assessment of carcinogenicity. In the first part of the meeting, four subgroups (based on the four perspectives mentioned above) review and revise drafts prepared by members of the subgroup, and each subgroup develops a joint review and evaluation of the evidence on which they have focused. Subsequently, the entire working group convenes in plenary and proceeds to derive a joint text. They determine whether the epidemiologic evidence supports the hypothesis that the substance causes cancer, and, separately, whether the animal evidence supports the hypothesis that the substance causes cancer. The judgments are not simply dichotomous (yes/no), but rather they allow the working group to express a range of opinions on each of the dimensions evaluated. Table 1 shows the categories into which the working groups are asked to classify each substance, when examining only the epidemiologic evidence and when examining only the animal experimental evidence. The operational criteria for making these decisions leave room for interpretation, and the scientific evidence itself is open to interpretation. It is not surprising, then, that the evaluations are sometimes difficult and contentious.
For our purpose, there are several limitations to bear in mind. First, IARC does not provide any explicit indication as to whether the substance evaluated should be considered an occupational exposure. Second, although the working groups certainly study the evidence in relation to cancer sites, until recently the formal evaluations did not identify which sites of cancer may be at risk. Site-specific information needs to be gleaned from the working group’s report and other literature. Third, the evaluations are anchored in the time that the working group met and reviewed the evidence; it is possible that evidence appearing after the IARC review could change the evaluation.
Current knowledge on occupational carcinogens.
From 1972 through 2003, the IARC Monograph Program published 83 volumes, representing evaluations of more than 880 substances, complex mixtures, and industrial processes. Of these, 89 have been classed as definite human carcinogens, 64 as probable, and 264 as possible human carcinogens (IARC 2003). We reviewed each one and earmarked those that we consider to be “occupational exposures.”
In developing a decision rule, we considered the following dimensions: whether the evidence of an effect drew on studies in exposed workers, whether the agent was found more often in the occupational or nonoccupational environments, and the numbers of workers exposed. In the end, the first two dimensions became redundant when we applied the third. Thus, a substance was considered an occupational exposure if there are, or have been, significant numbers of workers exposed to the substance at significant levels. The fact that some workers were exposed to a substance was not enough to label it as an occupational carcinogen. There are many carcinogens to which few workers are exposed, and we did not want to dilute the lists with such obscure agents.
Unfortunately, the knowledge base for determining how many workers are or have been exposed, and at what levels, is very fragmentary. We relied on available documentation such as the IARC Monographs, surveys by the National Institute for Occupational Safety and Health (NIOSH 1990), the National Toxicology Program (NTP) Report on Carcinogens, Tenth Edition (NTP 2002), and informed guesses on the part of expert industrial hygienists. Where we could come up with approximate numbers of workers exposed, we had to have some type of operational threshold for what should be considered a significant number. As a rule of thumb, we used > 10,000 workers exposed worldwide or > 1,000 in any country, presently or at any time in the past. These were the guidelines against which we measured our imprecise and semisubjective estimates. We also had to operationalize the notion of a level of exposure that was significant. This was even less explicit than the criteria used for numbers of workers exposed; it depended, inter alia, on the known range of exposure levels to the agent.
Despite the fact that they may be found in occupational environments, some classes of agents were summarily excluded from consideration on the grounds that the exposures are rare or very infrequent or at very low doses. These included hormones, pharmaceuticals, microbiologic agents, and dietary constituents. Pharmaceuticals represent a special case. Many have been evaluated, and many are considered to be carcinogenic. Although the main population exposed consists of patients undergoing therapy, there can also be exposure of workers who produce the drugs and of health care workers who administer them. But because the exposure doses are orders of magnitude higher among patients than among workers, we have not listed these as occupational carcinogens. Analogously, we have not listed carcinogenic viruses, notably, human immunodeficiency virus (HIV) and hepatitis B and C viruses, although health care workers may be at risk.
With these criteria, we derived the following lists of occupational carcinogens:
28 definite human occupational carcinogens (IARC group 1; Table 3)
27 probable human occupational carcinogens (IARC group 2A; Table 4)
113 possible human occupational carcinogens (IARC group 2B; Table 5)
18 occupations and industries that possibly, probably, or definitely entail excess risk of cancer (IARC groups 1, 2A, and 2B; Table 6).
Tables 3–6 only include agents and circumstances that were reviewed and published by the IARC Monograph Program as of 2003. As discussed above, the evaluations are rooted in the information base that was available at the time of the IARC evaluation. As evidence accumulates, the evaluation of an agent can change, as has already occurred in some cases (e.g., cadmium, acrylonitrile). This is why we have included in the tables a reference to the IARC volume in which the substance was evaluated and its date. Evaluations with early dates are more vulnerable to being out of date.
In a special review published in 1987 (Supplement 7), all substances and occupations covered in the first 15 years of the program were reevaluated (IARC 1987). Thus, every substance for which the Supplement 7 reference is cited had an earlier monograph. For many of the substances, there was little, if any, new information, and consequently, we have quoted the original monograph for those without any new data in 1987. For those substances referenced as Supplement 7, new data were available for the reevaluation.
For the agents in Tables 3–5, we devised a set of subheadings to help the reader digest the long lists of often obscure chemical names: physical agents, respirable dusts and fibers, metals and metal compounds, PAHs, wood and fossil fuels and their by-products, monomers, intermediates in plastics and rubber manufacturing, chlorinated hydrocarbons, aromatic amine dyes, azo dyes, intermediates in the production of dyes, pesticides, nitro compounds, and others. Tables 3–5 indicate some of the main occupations or industries in which each listed substance is found, and the strength of evidence from human and animal studies. In Tables 3 and 4, we show the type(s) of cancer affected, with an indication of the strength of evidence for each type listed. Information on target organ is not shown in Table 5 because, for agents listed as possible carcinogens, evidence concerning humans is either conflicting or not available at all.
For many of the agents listed, but not all, there has been some epidemiologic evidence of carcinogenicity among exposed workers. For most of the agents listed, but not all, the occupational environment represents the most common locale of exposure. The most prominent exceptions to this rule are aflatoxins, sunlight, involuntary tobacco smoking, and radon. Whether these cause more cases of cancer as a result of occupational or nonoccupational exposure depends on numbers exposed and exposure levels in the two types of milieu. It is plausible that there may be more cases resulting from nonoccupational exposure.
The IARC Monograph Program has occasionally addressed cancer risk in various occupations and industries, as well as agents. However, although the monograph program aims at a systematic evaluation of agents and complex mixtures, it is not intended to provide a systematic review of cancer risk by industries and occupations. That is, those reviews were conducted where there were particular concerns or anticipated insights regarding specific potential carcinogens. Sometimes this was done when there appeared to be strong evidence of risk in an occupation but little indication of what the responsible agent might be (e.g., rubber industry, painters). Sometimes the impetus for an occupation or industry review came from the attempt to evaluate some agent, but it was realized that the evidence regarding that agent was rooted in epidemiologic evidence regarding some occupation or industry (e.g., glass industry, hairdresser). Table 6 shows those occupations and industries that IARC has evaluated as definitely, probably, or possibly entailing a carcinogenic risk. Because there has been no pretense of exhaustiveness in evaluating occupations and industries, the absence of an occupation or industry in Table 6 does not carry the same significance as the absence of an agent in Tables 3–5. That is, it does not signify that there is no known risk for that occupation or industry.
Because our inclusion criteria admitted substances to which workers were exposed in the past, we included some substances that have been banned or virtually eliminated in some countries, such as mustard gas, bis(chloromethyl) ether, tris(2,3-dibromopropyl) phosphate, and 4,4′-methylene bis(2-chloroaniline) (MOCA), as well as some industries that no longer exist (viz., production of auramine and magenta). These are mentioned partly for historic interest and partly because it is possible that these might yet be used in some places at some time.
It is important to note that the substances, occupations, and industries listed in Tables 3–6 are not mutually exclusive. Certainly, some of the occupations and industries listed in Table 6 may be there because of some of the substances that are listed in Tables 3–5. But further, the substances relate to each other in complicated ways. Some families of substances include some specific substances that are also listed (e.g., nonarsenical insecticides, which includes DDT; benzidine-based dyes, which includes benzidine). Also, there are some complex mixtures (e.g., diesel exhaust) that contain a substance on the list (e.g., nitro-PAHs) that may be responsible for the carcinogenicity of the mixture.
The listing of affected cancer sites in Tables 3 and 4 does not come explicitly from the IARC Monographs. Sometimes the affected target organ(s) was rather evident, but sometimes it required that we evaluate the evidence, including evidence published more recently than the IARC evaluation in question. Table 7 shows the same agents listed in Tables 3 and 4 but organized by site of cancer. Again, we indicate clearly which associations are strong and which are only suggestive. The lung is the target organ that has most often been linked to occupational carcinogens.
The evolution of knowledge.
In order to appreciate how knowledge has evolved, we searched for information on the current occupational carcinogens at two earlier time periods. As mentioned above, IARC carried out a comprehensive cumulative synthesis in 1987 (IARC 1987). In that report, the results were presented with the same rating system (group 1, 2A, 2B, 3) as is used today, rendering the lists comparable. In 1964, even before the establishment of IARC, the World Health Organization (WHO) commissioned an expert panel to survey available knowledge on human carcinogens (WHO 1964). In the WHO report, there was no explicit rating system. It was a discursive presentation of knowledge and opinions that we attempted, with some license, to translate into a simple system corresponding to definite, probable/possible, or not mentioned. From these two reports, we searched for references to the 168 substances presented in Tables 3–5 and that are currently considered to be definite, probable, or possible occupational carcinogens.
Table 8 shows how the current occupational carcinogens were considered in two earlier times. Half of today’s recognized definite occupational carcinogens were already recognized as such by 1964, in the early period of cancer epidemiology. Nearly 90% were considered to be definite or probable as of 15 years ago. In contrast, > 95% of today’s probable and possible occupational carcinogens had not even been mentioned as of 1964, and about one-third were not mentioned as of 1987. Although it is possible for the classification of agents to change over time in either direction, in practice there have been rather few instances of agents being “downgraded” between successive periods. Notable counterexamples include the following:
3,3-Dichlorobenzene, which was considered a definite carcinogen in 1964 but was only considered as a possible carcinogen as of 1987 and 2002
Acrylonitrile and propylene oxide, which were considered probable carcinogens in 1987, but only as possible carcinogens in 2002
Glass wool was considered a possible carcinogen in 1988 but was downgraded to unclassifiable in 2002
Ionizing radiation, a special case, was considered a definite carcinogen in 1964 and is so considered today, but it had not been reviewed by IARC before the 1990s; therefore, we had to classify it as “unrated” in 1987.
Discussion
Many of the recognized definite occupational carcinogens were first suspected before the era of modern epidemiology (i.e., before 1950). The significance of this observation is unclear. It may be that there were only a limited number of strong occupation–cancer associations, and these were sufficiently obvious that they could produce observable clusters of cases for astute clinicians to notice. It may be that levels of exposure to occupational chemicals were so high before the 1950s as to produce high cancer risks and cancer clusters, but that improvements in industrial hygiene in industrialized countries have indeed decreased risks to levels that are difficult to detect. The number of occupational agents rated by IARC as group 1 carcinogens has tapered off since 1987, whereas the proportion of group 2B evaluations has increased. This reflects the fact that, when the monograph program began, there was a “backlog” of agents for which strong evidence of carcinogenicity had accumulated, and, naturally, these were the agents that IARC initially selected for review. Once the agents with strong evidence had been dealt with, IARC started dealing with others. It would be wrong to infer that the historic trend in IARC designations signals that we are approaching the end of the period of potential to discover occupational carcinogens. There are many thousands of chemicals in workplaces, and new ones are continuously being introduced. Most recognized occupational carcinogens were first suspected on the basis of case reports by clinicians or pathologists (Doll 1975). These discoveries were usually coincidental (Siemiatycki et al. 1981). It is thus reasonable to suspect that there may be some, perhaps many, as yet undiscovered occupational carcinogens. Only a small fraction of occupational agents have been adequately investigated with epidemiologic data. There are many reasons for this including, inter alia, the magnitude of the numbers of agents to be investigated, a shift away from occupational cancer research in the epidemiologic community and into new areas of epidemiologic interest, the difficulty and challenge of exposure assessment, and increasing barriers to accessing human subjects for occupational studies. These are problems that deserve attention, or we will fail in our responsibilities.
Many countries have agencies that list carcinogens. In the United States the two primary sources of information on occupational carcinogens, at least in the form of lists, are NIOSH and the NTP. NIOSH publishes a list of agents that it considers to be occupational carcinogens (NIOSH 2004). Currently there are 133 agents on this list. There is no further information in the NIOSH list regarding the degree of evidence for different agents, the occupations where these may occur or on the target organs, or the criteria and methods used to establish and update this list. The NTP has been mandated under the Public Health Service Act (1978) to maintain a list of human carcinogens and to provide data on each one concerning exposure circumstances and regulatory policies (NTP 2002). This list uses a two-category scale: “known to be a human carcinogen” and “reasonably anticipated to be a human carcinogen.” Currently, there are 52 agents listed in the first category and 176 in the second. Information concerning each agent is described in a brief report that includes some exposure data as well as health effects data and regulatory data (NTP 2002). The substances on these lists are not limited to occupational agents, and there is no tabular summary of occupational agents, the occupations in which these may occur, or the target organs. It is beyond the scope of this article to carry out a comparison of the procedures and lists of the various national bodies. Suffice it to say that most of them draw heavily on the IARC program and adapt it to their purposes.
There is sometimes a tendency to interpret tables of carcinogens in too categorical a fashion. Although it may be convenient for lobbyists and regulators to divide the world of chemicals and occupational circumstances into “good guys” and “bad guys,” such a dichotomy is simplistic. The determination that a substance or circumstance is carcinogenic depends on the strength of evidence at a given point in time. The evidence is sometimes clear-cut (which would correspond to evaluations of group 1 or group 4), but more often it is not. The balance of evidence can change in either direction as new data emerge.
The characterization of an occupation or industry group as a “high-risk group” is strongly rooted in time and place. For instance, the fact that some groups of nickel refinery workers experienced excess risks of nasal cancer does not imply that all workers in all nickel refineries will be subject to such risks. The particular circumstances of the industrial process, raw materials, impurities, and control measures may produce risk in one nickel refinery but not in another or in one historic era but not in another. The same can be said of rubber production facilities, aluminum refineries, and other industries and occupations. Labeling a chemical substance as a carcinogen in humans is a more timeless statement than labeling an occupation or industry as a high-risk group. However, even such a statement requires qualification. Different carcinogens produce different levels of risk, and for a given carcinogen there may be vast differences in the risks incurred by different people exposed under different circumstances. Indeed, there may be threshold effects or interactions with other factors, environmental or genetic, that produce no risk for some exposed workers and high risk for others.
This raises the issue of quantitative risk assessment, which is an important tool in prevention of occupational cancer. Unfortunately, our tables provide no basis for gauging the strength of the effect of each carcinogen, either in relative risk terms or in absolute risk terms, or in terms of dose–response relationships. The IARC evaluations provide no such indications, and although it would be most desirable to have such information, for most agents the information base to support such quantification is fragmentary.
In summary, the listing of occupational carcinogens is important. It provides a yardstick of our knowledge base, it provides guidance in setting research priorities, and it provides an important tool for prevention of cancer. Regulatory procedures and other aspects of cancer prevention depend on the listing of carcinogens. The IARC Monograph Program has been an indispensable component of this process. The tables presented herein, based on IARC Monographs but augmented in various ways, will be useful to researchers in setting research priorities and in furthering our understanding of carcinogenesis, and to those interested in preventing occupational cancer.
Table 1 Classifications used in the IARC Monographs to characterize evidence of carcinogenicity.
Category of evidence In humans In animals
Sufficient evidence of carcinogenicity A causal relationship has been established between exposure to the agent, mixture, or exposure circumstances and human cancer. That is, a positive relationship has been observed between the exposure and cancer in studies in which chance, bias, and confounding could be ruled out with reasonable confidence. A causal relationship has been established between the agent or mixture and an increased incidence of malignant neoplasms or of an appropriate combination of benign and malignant neoplasms in a) two or more species of animals or b) in two or more independent studies in one species carried out at different times or in different laboratories or under different protocols.
Limited evidence of carcinogenicity A positive association has been observed between exposure to the agent, mixture, or exposure circumstance and cancer for which a causal interpretation is considered to be credible, but chance, bias, or confounding could not be ruled out with reasonable confidence. The data suggest a carcinogenic effect but are limited for making a definitive evaluation because, for example, a) the evidence of carcinogenicity is restricted to a single experiment; b) there are unresolved questions regarding the adequacy of the design, conduct, or interpretation of the study; or c) the agent or mixture increases the incidence only of benign neoplasms or lesions of uncertain neoplastic potential, or of certain neoplasms that may occur spontaneously in high incidences in certain strains.
Insufficient evidence of carcinogenicity The available studies are of insufficient quality, consistency, or statistical power to permit a conclusion regarding the presence or absence of a causal association between exposure and cancer, or no data on cancer in humans are available. The studies cannot be interpreted showing either the presence or absence of a carcinogenic effect because of major qualitative or quantitative limitations, or no data on cancer in experimental animals are available.
Evidence suggesting lack of carcinogenicity There are several adequate studies covering the full range of levels of exposure that human beings are known to encounter, which are mutually consistent in not showing a positive association between exposure to the agent, mixture, or exposure circumstance and any studied cancer at any observed level of exposure. Adequate studies involving at least two species are available which show that, within the limits of the tests used, the agent or mixture is not carcinogenic.
Table 2 Guidelines used by the IARC Monographs Program in evaluating human carcinogenicity based on the synthesis of epidemiologic, animal, and other evidence.a
Combinations that fit in this group
Group Description of group Epidemiologic evidence Animal evidence Other evidence
1 The agent, mixture, or exposure circumstance is carcinogenic to humans Sufficient Any Any
Less than sufficient Sufficient Strongly positive
2A The agent, mixture, or exposure circumstance is probably carcinogenic to humans Limited Sufficient Less than strongly positive
Inadequate or not available Sufficient Strongly positive
2B The agent, mixture, or exposure circumstance is possibly carcinogenic to humans Limited Less than sufficient Any
Inadequate or not available Sufficient Less than strongly positive
Inadequate or not available Limited Strongly positive
3 The agent, mixture, or exposure circumstance is not classifiable as to its carcinogenicity to humans Inadequate or not available Limited Less than strongly positive
Not elsewhere classified
4 The agent, mixture, or exposure circumstance is probably not carcinogenic to humans Suggesting lack of carcinogenicity Suggesting lack of carcinogenicity Any
Inadequate or not available Suggesting lack of carcinogenicity Strongly negative
a This table shows our interpretation of the IARC Monographs Program guidelines to derive the overall evaluation from the combined epidemiologic, animal, and other evidence. However, the IARC working groups can, under exceptional circumstances, depart from these guidelines in deriving the overall evaluation (IARC 2003s). For example, the overall evaluation can be downgraded if there is less than sufficient evidence in humans and strong evidence that the mechanism operating in animals is not relevant to humans.
Table 3 Substances and mixtures that have been evaluated by IARC as definite (group 1) human carcinogens and that are occupational exposures.
Substance or mixture Occupation or industry in which the substance is founda IARC Monograph volume (year)b Human evidencec Animal evidencec Site(s)
Physical agents
Ionizing radiation and sources thereof, including, notably, X rays, γ rays, neutrons, and radon gas Radiologists; technologists; nuclear workers; radium-dial painters; underground miners; plutonium workers; cleanup workers following nuclear accidents; aircraft crew Vol. 75 (2000a) Sufficient Sufficient Boned
Vol. 78 (2001a) Leukemiad
Lungd
Liverd
Thyroidd
Othersd
Solar radiation Outdoor workers Vol. 55 (1992b) Sufficient Sufficient Melanomad
Skind
Respirable dusts and fibers
Asbestos Mining and milling; by-product manufacture; insulating; shipyard workers; sheet-metal workers; asbestos cement industry Suppl. 7 (1987) Sufficient Sufficient Lungd
Mesotheliomad
Larynxe
GI tracte
Erionite Waste treatment; sewage; agricultural waste; air pollution control systems; cement aggregates; building materials Suppl. 7 (1987) Sufficient Sufficient Mesotheliomad
Silica, crystalline Granite and stone industries; ceramics, glass, and related industries; foundries and metallurgical industries; abrasives; construction; farming Vol. 68 (1997b) Sufficient Sufficient Lungd
Talc containing asbestiform fibers Manufacture of pottery, paper, paint, and cosmetics Suppl. 7 (1987) Sufficient Inadequate Lungd
Mesotheliomad
Wood dust Logging and sawmill workers; pulp and paper and paperboard industry; woodworking trades (e.g., furniture industries, cabinetmaking, carpentry and construction); used as filler in plastic and linoleum production Vol. 62 (1995b) Sufficient Inadequate Nasal cavities and paranasal sinusesd
Metals and metal compounds
Arsenic and arsenic compounds Nonferrous metal smelting; production, packaging, and use of arsenic-containing pesticides; sheep dip manufacture; wool fiber production; mining of ores containing arsenic Suppl. 7 (1987) Sufficient Limited Skind
Lungd
Liver (angiosarcoma)e
Beryllium Beryllium extraction and processing; aircraft and aerospace industries; electronics and nuclear industries; jewelers Vol. 58 (1993a) Sufficient Sufficient Lungd
Cadmium and cadmium compounds Cadmium-smelter workers; battery production workers; cadmium-copper alloy workers; dyes and pigments production; electroplating processes Vol. 58 (1993a) Sufficient Sufficient Lungd
Chromium compounds, hexavalent Chromate production plants; dyes and pigments; plating and engraving; chromium ferro-alloy production; stainless-steel welding; in wood preservatives; leather tanning; water treatment; inks; photography; lithography; drilling muds; synthetic perfumes; pyrotechnics; corrosion resistance Vol. 49 (1990a) Sufficient Sufficient Lungd
Nasal sinusese
Selected nickel compounds, including combinations of nickel oxides and sulfides in the nickel refining industry Nickel refining and smelting; welding Vol. 49 (1990a) Sufficient Sufficient Lungd
Nasal cavity and sinusesd
Wood and fossil fuels and their by-products
Benzene Production; solvents in the shoe production industry; chemical, pharmaceutical, and rubber industries; printing industry (rotogravure plants, bindery departments); gasoline additive Suppl. 7 (1987) Sufficient Limited Leukemiad
Coal tars and pitches Production of refined chemicals and coal tar products (patent-fuel); coke production; coal gasification; aluminum production; foundries; road paving and construction (roofers and slaters) Suppl. 7 (1987) Sufficient Sufficient Skind
Lunge
Bladdere
Mineral oils, untreated and mildly treated Production; used as lubricant by metal workers, machinists, engineers; printing industry (ink formulation); used in cosmetics, medicinal and pharmaceutical preparations Suppl. 7 (1987) Sufficient Inadequate Skind
Bladdere
Lunge
Nasal sinusese
Shale oils or shale-derived lubricants Mining and processing; used as fuels or chemical-plant feedstocks; lubricant in cotton textile industry Suppl. 7 (1987) Sufficient Sufficient Skind
Soots Chimney sweeps; heating-unit service personnel; brick masons and helpers; building demolition workers; insulators; firefighters; metallurgical workers; work involving burning of organic materials Vol. 35 (1985) Sufficient Inadequate Skind
Lungd
Esophaguse
Monomers
Vinyl chloride Production; production of polyvinyl chloride and co-polymers; refrigerant before 1974; extraction solvent; in aerosol propellants Suppl. 7 (1987) Sufficient Sufficient Liver (angiosarcoma)d
Liver (hepatocellular)e
Intermediates in plastics and rubber manufacturing
Bis(chloromethyl) ether and chloromethyl methyl ether (technical grade) Production; chemical intermediate; alkylating agent; laboratory reagent; plastic manufacturing; ion-exchange resins and polymers Suppl. 7 (1987) Sufficient Sufficient Lung (oat cell)d
Aromatic amine dyes
4-Aminobiphenyl Production; dyestuffs and pigment manufacture Suppl. 7 (1987) Sufficient Sufficient Bladderd
Benzidine Production; dyestuffs and pigment manufacture Suppl. 7 (1987) Sufficient Sufficient Bladderd
2-Naphthylamine Production; dyestuffs and pigment manufacture Suppl. 7 (1987) Sufficient Sufficient Bladderd
Pesticides
Ethylene oxide Production; chemical industry; sterilizing agent (hospitals, spice fumigation) Vol. 60 (1994) Limited Sufficient Leukemiad
2,3,7,8-Tetrachlorodibenzo-para-dioxin (TCDD) Production; use of chlorophenols and chlorophenoxy herbicides; waste incineration; PCB production; pulp and paper bleaching Vol. 69 (1997a) Limited Sufficient All sites combinedd
Lunge
Non-Hodgkin lymphomae
Sarcomae
Others
Aflatoxin Feed production industry; workers loading and unloading cargo; rice and maize processing Vol. 82 (2002b) Sufficient Sufficient Liverd
Involuntary (passive) smoking Workers in bars and restaurants; office workers Vol. 83 (2004) Sufficient Sufficient Lungd
Mustard gas Production; used in research laboratories; military personnel Suppl. 7 (1987) Sufficient Limited Larynxd
Lunge
Pharynxe
Strong inorganic-acid mists containing sulfuric acid Pickling operations; steel industry; petrochemical industry; phosphate acid fertilizer manufacturing Vol. 54 (1992a) Sufficient Not available Larynxd
Lunge
a Not necessarily an exhaustive list of occupations/industries in which this agent is found; not all workers in these occupations/industries are exposed. The term “production” is used to indicate that this substance is man-made and that workers may be exposed in the production process.
b Most recent IARC evaluation; for those referenced to Supplement 7 (IARC 1987), it is possible that the 1987 review was quite perfunctory and that the essential evidence was cumulated at an earlier date.
c As judged by the IARC working group; we added the notation “not available” to signify those substances for which there was no evidence at all.
d We judged that evidence for an association with this site was strong.
e We judged that evidence was suggestive.
Table 4 Substances and mixtures that have been evaluated by IARC as probable (group 2A) human carcinogens and that are occupational exposures.
Substance or mixture Occupation or industry in which the substance is founda IARC Monograph volume (year)b Human evidencec Animal evidencec Site(s)
Physical agents
Ultraviolet radiation (A, B, and C) from artificial sources Arc welding; industrial photoprocesses; sterilization and disinfection; phototherapy; operating theaters; research laboratories; ultraviolet fluorescence in food industry; insect traps Vol. 55 (1992b) Inadequate Sufficient Melanomad
Polyaromatic hydrocarbons
Benz[a]anthracene Work involving combustion of organic matter; foundries; steel mills; firefighters; vehicle mechanics Vol. 32 (1983b) Not available Sufficient Lungd
Bladderd
Skind
Benzo[a]pyrene Work involving combustion of organic matter; foundries; steel mills; firefighters; vehicle mechanics Vol. 32 (1983b) Not available Sufficient Lungd
Bladderd
Skind
Dibenz[a,h]anthracene Work involving combustion of organic matter; foundries; steel mills; firefighters; vehicle mechanics Vol. 32 (1983b) Not available Sufficient Lungd
Bladderd
Skind
Wood and fossil fuels and their by-products
Creosotes Brickmaking; wood preserving Vol. 35 (1985) Limited Sufficient Skind
Diesel engine exhaust Railroad workers; professional drivers; dock workers; mechanics Vol. 46 (1989a) Limited Sufficient Lungd
Bladderd
Intermediates in plastics and rubber manufacturing
4,4′-Methylene bis(2-chloroaniline) Production; curing agent for roofing and wood sealing Vol. 57 (1993b) Inadequate Sufficient Bladderd
Styrene-7,8-oxide Production; styrene glycol production; perfume preparation; reactive diluent in epoxy resin formulations; as chemical intermediate for cosmetics, surface coating, and agricultural and biological chemicals; used for treatment of fibers and textiles; in fabricated rubber products Vol. 60 (1994) Inadequate Sufficient
Chlorinated hydrocarbons
α-Chlorinated toluenes Production; dye and pesticide manufacture Vol. 71 (1999a) Limited Sufficient Lungd
Polychlorinated biphenyls Production; electrical capacitor manufacturing Suppl. 7 (1987) Limited Sufficient Liver and biliary
tractd
Tetrachloroethylene Production; dry cleaning; metal degreasing Vol. 63 (1995a) Limited Sufficient Cervixd
Esophagusd
Non-Hodgkin lymphomad
Trichloroethylene Production; dry cleaning; metal degreasing Vol. 63 (1995a) Limited Sufficient Liver and biliary tractd
Non-Hodgkin lymphomad
Renal celld
Monomers
Acrylamide Chemical industry; water and wastewater treatment; textile, steel, and lumber industries; petroleum refining; mineral processing; sugar production; hospitals Vol. 60 (1994) Inadequate Sufficient Pancreasd
1,3-Butadiene Chemical and rubber industries Vol. 71 (1999a) Limited Sufficient Lymphohematopoieticd
Epichlorohydrin Production and use of resins, glycerine, and propylene-based rubbers; used as a solvent Vol. 71 (1999a) Inadequate Sufficient Lungd
CNSd
Vinyl bromide Production; production of vinyl bromide polymers and monoacrylic fibers for carpet backing material; rubber and plastic production Vol. 71 (1999a) Not available Sufficient
Vinyl fluoride Production; polyvinyl fluoride and fluoropolymer production Vol. 63 (1995a) Not available Sufficient
Aromatic amine dyes
Benzidine-based dyes Production; used in textile, paper, leather, rubber, plastics, printing, paint, and lacquer industries Suppl. 7 (1987) Inadequate Sufficient Bladderd
4-Chloro-ortho-toluidine Dye and pigment manufacture; textile industry Vol. 77 (2000b) Limited Sufficient Bladderd
ortho-Toluidine Production; manufacture of dyestuffs, pigments, optical brightener, pharmaceuticals, and pesticides; rubber vulcanizing; clinical laboratory reagent; cleaners and janitors Vol. 77 (2000b) Limited Sufficient Bladderd
Intermediates in the production of dyes
Dimethylcarbamoyl chloride Production; manufacture of pharmaceuticals, pesticides, and dyes Vol. 71 (1999a) Inadequate Sufficient
Pesticides
Captafol Production; fungicide Vol. 53 (1991b) Not available Sufficient
Ethylene dibromide Production; pest control; petroleum refining and waterproofing; leaded gasoline additive; chemical intermediate and solvent in gums, waxes, resins, dyes, and pharmaceutical preparations Vol. 71 (1999a) Inadequate Sufficient
Nonarsenical insecticides Production; pest control and agricultural workers; flour and grain mill workers Vol. 53 (1991b) Limited Not available Braind
Leukemiad
Lungd
Multiple myelomad
Non-Hodgkin lymphomad
Others
Diethyl sulfate Ethanol production Vol. 71 (1999a) Not available Sufficient
Formaldehyde Production; pathologists; medical laboratory technicians; plastics; textile industry Vol. 62 (1995b) Limited Sufficient Leukemiad
Nasal sinusesd
Nasopharynxd
Tris(2,3-dibromopropyl) Production; used in the textile phosphate industry; in phenolic resins (for electronics industry), paints, paper coatings, and rubber Vol. 71 (1999a) Inadequate Sufficient
CNS, central nervous system.
a Not necessarily an exhaustive list of occupations/industries in which this agent is found; not all workers in these occupations/industries are exposed. The term “production” is used to indicate that this substance is man-made and that workers may be exposed in the production process.
b Most recent IARC evaluation; for those referenced as Supplement 7 (IARC 1987), it is possible that the 1987 review was quite perfunctory and that the essential evidence was cumulated at an earlier date.
c As judged by the IARC working group; we added the notation “not available” to signify those substances for which there was no epidemiologic evidence at all.
d We judged that the evidence was suggestive.
Table 5 Substances and mixtures that have been evaluated by IARC as possible (group 2B) human carcinogens and that are occupational exposures.
Substance or mixture Occupation or industry in which the substance is founda IARC Monograph volume (year)b Human evidencec Animal evidencec
Respirable dusts and fibers
Glass wool Production; construction and insulation Vol. 81 (2002a) Inadequate Sufficient
Palygorskite (long fibers > 5 μm) Miners and millers; production of waste absorbents, fertilizers, and pesticides Vol. 68 (1997b) Inadequate Sufficient
Refractory ceramic fibers Production; furnace insulators; ship builders; heat-resistant fabric manufacture Vol. 81 (2002a) Inadequate Sufficient
Rock wool Production; thermal or acoustical insulation Vol. 81 (2002a) Inadequate Limited
Slag wool fireproofing Production; thermal or acoustical insulation Vol. 81 (2002a) Inadequate Limited
Special-purpose glass fibers such as E-glass and “475” glass fibers Reinforced plastic industry Vol. 81 (2002a) Not available Sufficient
Metals and metal compounds
Antimony trioxide Ore processing; glass and ceramic production Vol. 47 (1989c) Inadequate Sufficient
Cobalt and cobalt compounds Miners; processing of copper and nickel ore; glass and ceramic production Vol. 52 (1991a) Inadequate Sufficient
Lead and inorganic lead compounds Lead smelters; plumbers; solderers; occupations in battery recycling smelters Suppl. 7 (1987) Inadequate Sufficient
Methyl mercury compounds Pesticide and fungicide production; paint industry Vol. 58 (1993a) Inadequate Sufficient
Nickel: metallic and alloys Nickel miners; metal fabrication, grinding, electroplating, and welding Vol. 49 (1990a) Inadequate Sufficient
Wood and fossil fuels and their by-products
Benzofuran Production; intermediate in coumarone-indene resin polymerization; coke production; coal gasification and combustion Vol. 63 (1995a) Not available Sufficient
Bitumens, extracts of steam-refined and air-refined Production/refining; road construction; roofing and flooring Suppl. 7 (1987) Inadequate Sufficient
Carbon black Production; paint, ink, plastic and rubber industries Vol. 65 (1996) Inadequate Sufficient
Diesel fuel, marine Petroleum refineries; marine fuel; distribution Vol. 45 (1989b) Inadequate Limited
Fuel oils, residual (heavy) Petroleum refineries; distribution; marine fleets; most large diesel engines operated on land; industrial heating systems Vol. 45 (1989b) Inadequate Sufficient
Gasoline Petroleum refineries; transportation; mechanics and service station attendants Vol. 45 (1989b) Inadequate Limited
Gasoline engine exhaust Transportation and vehicle maintenance workers; drivers; toll attendants; traffic controllers Vol. 46 (1989a) Inadequate Limited
Naphthalene Production; insecticide, resin, and pharmaceutical production Vol. 82 (2002b) Inadequate Sufficient
Polyaromatic hydrocarbons
Benzo[b]fluoranthene Work involving combustion of organic matter Vol. 32 (1983b) Not available Sufficient
Benzo[j]fluoranthene Work involving combustion of organic matter Vol. 32 (1983b) Not available Sufficient
Benzo[k]fluoranthene Work involving combustion of organic matter Vol. 32 (1983b) Not available Sufficient
Dibenz[a,h]acridine Production; used in dye synthesis; biochemical laboratory workers; work involving combustion of organic matter Vol. 32 (1983b) Not available Sufficient
Dibenz[a,j]acridine Production; dye synthesis; work involving combustion of organic matter Vol. 32 (1983b) Not available Sufficient
Dibenzo[a,e]pyrene Production; biochemical laboratory workers; work involving combustion of organic matter Vol. 32 (1983b) Not available Sufficient
Dibenzo[a,h]pyrene Production; biochemical laboratory workers; work involving combustion of organic matter Vol. 32 (1983b) Not available Sufficient
Dibenzo[a,i]pyrene Work involving combustion of organic matter Vol. 32 (1983b) Not available Sufficient
Dibenzo[a,l]pyrene Production; biochemical laboratory workers; work involving combustion of organic matter Vol. 32 (1983b) Not available Sufficient
Monomers
Acrylonitrile Production; acrylic textile fiber and plastic production Vol. 71 (1999a) Inadequate Sufficient
Chloroprene Production; manufacture of polychloroprene (synthetic rubber) Vol. 71 (1999a) Inadequate Sufficient
Ethyl acrylate Production; plastic molding occupations using acrylate resins Vol. 39 (1986a) Not available Sufficient
Isoprene Production; synthetic rubber and plastics industries Vol. 71 (1999a) Not available Sufficient
Styrene Polyester resin manufacture; production of packaging materials and fiberglass-reinforced polyester Vol. 82 (2002b) Limited Limited
Toluene diisocyanates Production; production of polyurethane foams and wire coating; insulation workers; ship builders Vol. 71 (1999a) Inadequate Sufficient
Urethane Production; amino-resin production Vol. 7 (1974a) Not available Sufficient
Vinyl acetate Production; plastics, paint, and adhesive industries Vol. 63 (1995a) Not available Limited
Intermediates in plastics and rubber manufacturing
Acetaldehyde Acetic acid production workers; dyestuff, plastic and synthetic rubber industries Vol. 71 (1999a) Inadequate Sufficient
Acetamide Production; plastics and chemical industries Vol. 71 (1999a) Not available Sufficient
2,4-Diaminotoluene Production; chemical intermediate in TDI production; dyes for textiles; leather; furs; wood; biologic stain; photo developer Vol. 16 (1978) Not available Sufficient
1,2-Epoxybutane Production; metal degreasing; plastics industry Vol. 71 (1999a) Not available Limited
Ethylbenzene Production; ink, paint, and plastic production Vol. 77 (2000b) Inadequate Sufficient
Ethylene thiourea Production; vulcanization in the rubber industry; manufacture of ethylenebisdithiocarbamate pesticides; electroplating baths; dyes; pharmaceuticals; synthetic resins Vol. 79 (2001b) Inadequate Sufficient
Phenyl glycidyl ether Production; epoxy resins; casting and molding Vol. 71 (1999a) Not available Sufficient
Propylene oxide Production; polyurethane foam and glycol production, fumigant Vol. 60 (1994) Inadequate Sufficient
Chlorinated hydrocarbons
Carbon tetrachloride Production; industrial degreasing occupations; dry cleaners; refrigerant production Vol. 71 (1999a) Inadequate Sufficient
Chlorinated paraffin of average carbon-chain length C12 Production; polyvinyl chloride processing industry Vol. 48 (1990b) Not available Sufficient
Chloroform Refrigerant production; dyes, solvents, and pesticides Vol. 73 (1999b) Inadequate Sufficient
1,2-Dichloroethane Vinyl chloride production workers Vol. 71 (1999a) Inadequate Sufficient
Dichloromethane Production; painters and furniture restorers; pharmaceutical and electronic production Vol. 71 (1999a) Inadequate Sufficient
Hexachloroethane Production; aluminum refinery; industrial firefighters Vol. 73 (1999b) Inadequate Sufficient
Aromatic amine dyes
Auramine (technical grade) Production; textiles, plastic, and printing Suppl. 7 (1987) Inadequate Sufficient
Benzyl violet 4B Production; food; drugs; cosmetics; textiles Vol. 16 (1978) Not available Sufficient
CI Basic Red 9 Production; textiles; printing; biologic stains (basic fuchsin dye in laboratories) Vol. 57 (1993b) Inadequate Sufficient
2,4-Diaminoanisole Dyestuff industry; barbers and cosmetologists; furriers Vol. 79 (2001b) Not available Sufficient
3,3′-Dimethylbenzidine (o-tolidine) Production; dye or intermediate in dye and pigment production; polyurethane elastomers; coating; plastics; clinical laboratories Vol. 1 (1972) Not available Sufficient
2,6-Dimethylaniline (2,6-xylidine) Production; dyestuffs and pharmaceutical manufacturing Vol. 57 (1993b) Not available Sufficient
3,3′-Dichlorobenzidine Production; dyestuff manufacturing Vol. 29 (1982b) Inadequate Sufficient
4,4′-Diaminodiphenyl ether Production; polyamide-type resin manufacturing Vol. 29 (1982b) Not available Sufficient
Disperse Blue 1 Production; hair coloring; textiles and plastics Vol. 48 (1990b) Not available Sufficient
HC Blue No. 1 Production; hair dye Vol. 57 (1993b) Not available Sufficient
4,4′-Methylenedianiline Production; production of diisocyanates, polyisocyanates, and epoxy resins Vol. 39 (1986a) Not available Sufficient
Magenta containing CI Basic Red 9 Production; textiles and printing; biologic stains in laboratories; photography Vol. 57 (1993b) Not available Sufficient
Azo dyes
ortho-Aminoazotoluene Production; textiles and leather Vol. 8 (1975) Not available Sufficient
para-Aminoazobenzene Production; textiles and leather Suppl. 7 (1987) Not available Sufficient
CI Acid Red 114 Production; textiles and leather Vol. 57 (1993b) Not available Sufficient
CI Direct Blue 15 Production; textiles and paper Vol. 57 (1993b) Not available Sufficient
Citrus Red No. 2 Production; used for food coloring Vol. 8 (1975) Not available Sufficient
para-Dimethylaminoazobenzene Production; textiles; laboratories Vol. 8 (1975) Not available Sufficient
Oil orange SS Production; dyes/pigments for varnishes, oils, fats, and waxes Vol. 8 (1975) Not available Sufficient
Ponceau 3R Production; textiles Vol. 8 (1975) Not available Sufficient
Ponceau MX Production; textiles; leather; inks; paper; wood stains; food; biology laboratories Vol. 8 (1975) Not available Sufficient
Trypan blue Production; textiles and printing; biologic stains in life science laboratories; used by ophthalmologists Vol. 8 (1975) Not available Sufficient
Intermediates for the manufacture of dyes
para-Cresidine Production; manufacture of dyes, pigments, and perfumes Vol. 27 (1982a) Not available Sufficient
3,3′-Dimethoxybenzidine (ortho-dianisidine) Production; manufacture of dyes and pigments; dye for leather, paper, plastics, rubber, textiles, and laboratories Suppl. 7 (1987) Inadequate Sufficient
2-Methyl-1-nitro anthraquinone (of uncertain purity/impurity) Production; synthesis of anthraquinone dyes Vol. 27 (1982a) Not available Sufficient
4,4′-Methylene bis (2-methylaniline) Production; manufacture of dyes and pigments Suppl. 7 (1987) Inadequate Sufficient
2-Nitroanisole Production; manufacture of the dye intermediates ortho-anisidine and ortho-dianisidine Vol. 65 (1996) Not available Sufficient
4,4′-Thiodianiline Production; manufacture of dyes Vol. 27 (1982a) Not available Sufficient
Nitro compounds
2,4-Dinitrotoluene Production; manufacture of diisocyanates and munitions Vol. 65 (1996) Inadequate Sufficient
2,6-Dinitrotoluene Production; manufacture of diisocyanates and munitions Vol. 65 (1996) Inadequate Sufficient
Nitrobenzene Production; manufacture of dyestuffs, detergents, and cosmetics Vol. 65 (1996) Not available Sufficient
2-Nitrofluorene Underground miners using diesel-powered machinery Vol. 46 (1989a) Not available Sufficient
2-Nitropropane Production; ink, paint, explosives industries Vol. 71 (1999a) Not available Sufficient
1-Nitropyrene Production; manufacture of azidopyrene; particulate emissions Vol. 46 (1989a) Not available Sufficient
4-Nitropyrene Production; used only as a laboratory chemical; probably present before 1980 in carbon black used in photocopy machines Vol. 46 (1989a) Not available Sufficient
Tetranitromethane Production; diesel fuel additive; TNT manufacturing Vol. 65 (1996) Not available Sufficient
Pesticides
Aramite Production; in miticides in greenhouses, nurseries, and orchards Vol. 5 (1974b) Not available Sufficient
Chlordane Production; termite control Vol. 79 (2001b) Inadequate Sufficient
Chlordecone Production; insecticide Vol. 20 (1979a) Not available Sufficient
Chlorophenoxy herbicides Production; defoliant Suppl. 7 (1987) Limited Inadequate
Chlorothalonil Production; fungicide, bactericide, and nematocide Vol. 73 (1999b) Not available Sufficient
DDT (p,p′-DDT) Production; nonsystemic insecticide Vol. 53 (1991b) Inadequate Sufficient
1,2-Dibromo-3-chloropropane Production; pesticide, nematocide, and soil fumigant Vol. 71 (1999a) Inadequate Sufficient
para-Dichlorobenzene Production; pesticide Vol. 73 (1999b) Inadequate Sufficient
Dichlorvos Production; insecticide and miticide Vol. 53 (1991b) Inadequate Sufficient
Heptachlor Production; termite control Vol. 79 (2001b) Inadequate Sufficient
Hexachlorobenzene Production; in chlorinated pesticides and fungicides; dye manufacture and synthesis of organic chemicals and rubber; plasticizer for polyvinyl chloride; wood preservative; by-product of the production of a number of chlorinated solvents Vol. 79 (2001b) Inadequate Sufficient
Hexachlorocyclohexanes (most common form is Lindane) Production; woodworkers; farm workers Suppl. 7 (1987) Inadequate Sufficient
Mirex Production; fire-retardant additive; insecticide; workers at hazardous waste sites Vol. 20 (1979a) Not available Sufficient
Nitrofen Production; herbicide Vol. 30 (1983a) Not available Sufficient
Sodium ortho-phenylphenate Production; fungicide; chemical intermediate Vol. 73 (1999b) Not available Sufficient
Toxaphene (polychloronated camphenes) Production; insecticide Vol. 79 (2001b) Inadequate Sufficient
Others
Butylated hydroxyanisole (BHA) Production; food and pharmaceutical industries Vol. 40 (1986b) Not available Sufficient
Catechol Production; insecticide and pharmaceutical production; tanneries Vol. 71 (1999a) Not available Sufficient
Diglycidyl resorcinol ether Production; liquid spray epoxy resin in electrical, tooling, adhesive, and laminating applications; production of epoxy resins and rubber; aerospace industry Vol. 71 (1999a) Not available Sufficient
1,4-Dioxane Production; chlorinated solvents; textile processing; mixed with pesticides Vol. 71 (1999a) Inadequate Sufficient
Hydrazine Production; manufacture of agricultural chemicals and chemical blowing agents; water treatment; spandex fibers; rocket fuel; oxygen scavenger in water boilers and heating systems; scavenger for gases; plating metals on glass and plastics; solder fluxes; photographic developers; reactant in fuel cells in the military; reducing agent in electrodeless nickel plating; chain extender in urethane; textile dyes; explosives Vol. 71 (1999a) Inadequate Sufficient
Nitrilotriacetic acid and its salts Production; textiles; electroplaters; tanners Vol. 73 (1999b) Not available Sufficient
Polychlorophenols and their sodium salts (mixed exposure) Herbicide production; wood, textile and leather manufacturing Vol. 71 (1999a) Limited Inadequate
Potassium bromate Production; bakeries Vol. 73 (1999b) Not available Sufficient
Thiourea Production; photoprocessing; dyes; rubber industry Vol. 79 (2001b) Not available Sufficient
Welding fumes Metal fabricating industry Vol. 49 (1990a) Limited Inadequate
TDI, toluene diisocyanate.
a Not necessarily an exhaustive list of occupations/industries in which this agent is found; not all workers in these occupations/industries are exposed. The term “production” is used to indicate that this substance is man-made and that workers may be exposed in the production process.
b Most recent IARC evaluation; for those referenced as Supplement 7 (IARC 1987), it is possible that the 1987 review was quite perfunctory and that the essential evidence was cumulated at an earlier date.
c As judged by the IARC working group; we added the notation “not available” to signify those substances for which there was no epidemiologic evidence at all.
Table 6 Occupations or industries that have been evaluated by IARC as definitely (group 1), probably (group 2A), or possibly (group 2B) entailing excess risk of cancer among workers.
Occupation or industry Suspected substance IARC Monograph volume (year)a Group Site(s)
Aluminum production Pitch volatiles; aromatic amines Suppl. 7 (1987) 1 Lung,b bladderb
Auramine manufacture 2-Naphthylamine; auramine; other chemicals; pigments Suppl. 7 (1987) 1 Bladderb
Boot and shoe manufacture and repair Leather dust; benzene and other solvents Suppl. 7 (1987) 1 Leukemia,b nose,b paranasal sinuses,b bladderc
Carpentry and joinery Wood dust Suppl. 7 (1987) 2B
Coal gasification Coal tar; coal-tar fumes; PAHs Vol. 34 (1984) 1 Skin (including scrotum),b bladder,b lungb
Coke production Coal-tar fumes Suppl. 7 (1987) 1 Skin (scrotum),b lung,b bladder,c kidneyc
Dry cleaning Solvents and chemicals used in “spotting” Vol. 63 (1995a) 2B
Furniture and cabinet making Wood dust Suppl. 7 (1987) 1 Nose and sinonasal cavitiesb
Hairdressers and barbers Dyes (aromatic amines, amino-phenols with hydrogen peroxide); solvents; propellants; aerosols Vol. 57 (1993b) 2A Bladder,c lung,c non-Hodgkin lymphoma,c ovaryc
Hematite mining, underground, with radon exposure Radon daughters; silica Suppl. 7 (1987) 1 Lungb
Iron and steel founding PAHs; silica; metal fumes; formaldehyde Suppl. 7 (1987) 1 Lungb
Isopropanol manufacture, strong-acid process Diisopropyl sulfate; isopropyl oils; sulfuric acid Suppl. 7 (1987) 1 Paranasal sinuses,b larynx,b lungc
Magenta manufacture Magenta; ortho-toluidine; 4,4′-methylene bis(2-methylaniline); ortho-nitrotoluene Vol. 57 (1993b) 1 Bladderb
Painters Vol. 47 (1989c) 1 Lung,b bladder,c stomachc
Petroleum refining PAHs Vol. 45 (1989b) 2A Bladder,c brain,c leukemiac
Printing processes Solvents; inks Vol. 65 (1996) 2B
Production of art glass, glass containers, and pressed ware Lead; arsenic; antimony oxides; silica; asbestos; other metal oxides; PAHs Vol. 58 (1993a) 2A Lungc
Rubber industry Aromatic amines; solvents Suppl. 7 (1987) 1 Bladder,b stomach,c larynx,c leukemia,c lungc
Textile manufacturing industry Textile dust in manufacturing process; dyes and solvents in dyeing and printing operations Vol. 48 (1990b) 2B
a Most recent IARC evaluation; for those referenced as Supplement 7 (IARC 1987), it is possible that the 1987 review was quite perfunctory and that the essential evidence was cumulated at an earlier date.
b We judged that the evidence for an association with this site was strong.
c We judged that the evidence was suggestive.
Table 7 Definite or probable occupational carcinogens and carcinogenic circumstances, by site.
Site Strength of evidencea High-risk substance or circumstance
Pharynx and nasopharynx Suggestive Mustard gas; formaldehyde
Nasal cavities and paranasal sinuses Strong Boot and shoe manufacture and repair; furniture and cabinet making; isopropanol manufacture, strong acid process; selected nickel compounds, including combinations of nickel oxides and sulfides in the nickel-refining industry; wood dust
Suggestive Chromium compounds, hexavalent; formaldehyde; mineral oils, untreated and mildly treated
Esophagus Suggestive Soots; tetrachloroethylene
Stomach Suggestive Painters; rubber industry
Gastrointestinal tract Suggestive Asbestos
Liver and biliary tract Strong Aflatoxin; ionizing radiation
Suggestive Polychlorinated biphenyls; trichloroethylene
Liver (angiosarcoma) Strong Vinyl chloride
Suggestive Arsenic and arsenic compounds
Liver (hepatocellular) Suggestive Vinyl chloride
Pancreas Suggestive Acrylamide
Larynx Strong Isopropanol manufacture, strong acid process; inorganic acid mists containing sulfuric acid; mustard gas
Suggestive Asbestos; rubber industry
Lung Strong Aluminum production; arsenic and arsenic compounds; asbestos; beryllium; cadmium and cadmium compounds; chromium compounds, hexavalent; coal gasification; coke production; hematite mining, underground, with radon exposure; involuntary (passive) smoking; ionizing radiation; iron and steel founding; selected nickel compounds, including combinations of nickel oxides and sulfides in the nickel refining industry; painters; silica, crystalline; soots; talc containing asbestiform fibers
Suggestive Benz[a]anthracene; benzo[a]pyrene; α-chlorinated toluenes; coal tars and pitches; dibenz[a,h]anthracene; diesel engine exhaust; epichlorohydrin; hairdressers and barbers; inorganic acid mists containing sulfuric acid; isopropanol manufacture (strong acid process); mineral oils (untreated and mildly treated); nonarsenical insecticides; mustard gas; production of art glass, glass containers, and pressed ware; rubber industry; TCDD
Lung (oat cell) Strong Bis(chloromethyl) ether and chloromethyl methyl ether (technical grade)
Bone Strong Ionizing radiation
Melanoma Strong Solar radiation
Suggestive Ultraviolet radiation (A, B and C) from artificial sources
Skin Strong Arsenic and arsenic compounds; Coal tars and pitches; coal gasification; coke production; dibenz[a,h]anthracene; mineral oils, untreated and mildly treated; shale oils or shale-derived lubricants; solar radiation; soots
Suggestive Benz[a]anthracene; benzo[a]pyrene; creosotes
Mesothelioma Strong Asbestos; erionite; talc containing asbestiform fibers
CNS Suggestive Epichlorohydrin
Sarcoma Suggestive TCDD
Cervix Suggestive Tetrachloroethylene
Ovary Suggestive Hairdressers and barbers
Kidney Suggestive Coke production
Kidney (renal cell) Suggestive Trichlorethylene
Bladder Strong Aluminum production; 4-aminobiphenyl; auramine manufacture; benzidine; coal gasification; magenta manufacture; 2-naphthylamine; rubber industry
Suggestive Benz[a]anthracene; benzidine-based dyes; benzo[a]pyrene; boot and shoe manufacture and repair; 4-chloro-ortho-toluidine; coal tars and pitches; coke production; dibenz[a,h]anthracene; diesel engine exhaust; hairdressers and barbers; 4,4′-methylene bis(2-chloroaniline); mineral oils, untreated and mildly treated; ortho-toluidine; painters; petroleum refining
Brain Suggestive Nonarsenical insecticides; petroleum refining
Thyroid Strong Ionizing radiation
Non-Hodgkin lymphoma Suggestive Hairdressers and barbers; nonarsenical insecticides; TCDD; tetrachloroethylene; trichloroethylene
Lympho-hematopoietic system Suggestive 1,3-Butadiene
Multiple myeloma Suggestive Nonarsenical insecticides
Leukemia Strong Benzene; boot and shoe manufacture and repair; ethylene oxide; ionizing radiation
Suggestive Formaldehyde; nonarsenical insecticides; petroleum refining; rubber industry
Other sites Suggestive Ionizing radiationb
All sites combined Strong TCDDc
CNS, central nervous system; TCDD, 2,3,7,8-tetrachlorodibenzo-para-dioxin.
a Our judgment of strength of evidence regarding each site.
b There is suggestive evidence of an effect of ionizing radiation on several sites in addition to those shown here.
c The evidence for an association with TCDD only becomes strong when data are combined for all cancer sites.
Table 8 Evolution in knowledge regarding current (2003) IARC occupational carcinogens.
Earlier evaluation
Current rating Past rating IARC 1987 WHO 1964
1 (n = 28) 1 19 13
2A 4 4
2B 1
3 0 NA
Unrated 4 11
Total 28 28
2A (n = 27) 1 0 0
2A 16 0
2B 6
3 2 NA
Unrated 3 27
Total 27 27
2B (n = 113) 1 0 1
2A 2 5
2B 63
3 9 NA
Unrated 39 107
Total 113 113
NA, not applicable.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7143ehp0112-00146015531428ResearchArticlesOrganochlorine Exposure and Colorectal Cancer Risk Howsam Mike 1Grimalt Joan O. 2Guinó Elisabet 3Navarro Matilde 3Martí-Ragué Juan 4Peinado Miguel A. 5Capellá Gabriel 3Moreno Victor 3for the Bellvitge Colorectal Cancer Group 1Laboratoire Universitaire de Médécine du Travail, Lille, France2Consejo Superior de Investigaciones Cientificas, Department of Environmental Chemistry, Institute of Chemical and Environmental Research, Barcelona, Catalonia, Spain3Catalan Institute of Oncology, Barcelona, Catalonia, Spain4Ciudad Sanitaria i Universitaria de Bellvitge, University of Barcelona, Barcelona, Catalonia, Spain5Oncology Research Institute, Barcelona, Catalonia, SpainAddress correspondence to V. Moreno, Servei d’Epidemiologia i Registre del Cancer, Institut Catala d’Oncologia, Gran Via km 2.7, L’Hospitalet, 08907 Barcelona, Catalonia, Spain. Telephone: 34-93-260-7812; Fax: 34-93-260-7787. E-mail:
[email protected]*Members of the Bellvitge Colorectal Cancer Study Group: Victor Moreno, Matilde Navarro, Joan Martí-Ragué, Javier de Oca, Alfonso Osorio, Carlos del Río, Sebastiano Biondo, Josep Mª Badosa, María Cambray, Felip Vilardell, Belén Lloveras, Valeri Novell, Elisabet Guinó, Laura Pareja, Miguel A. Peinado, and Gabriel Capellá.
We thank E. Marco, R. Mas, R. Chaler, and D. Fanjul for their assistance with organochlorine analysis.
The study was supported by grants from the Fundació La Marató de TV3 (48/95), the Spanish Fondo de Investigaciones Sanitarias (FIS 96/0797, FIS 00/0027, FIS 01/1264, FIS 03/0114, ISCIII, Red de Centros RCESP C03/09, and Red de Centros de Cancer C03/10) and Comisión Interministerial de Ciencia y Tecnología (SAF 00/81), and a European Union Marie Curie Fellowship for M.H. (HPMF-CT-2000-00888).
The authors declare they have no competing financial interests.
11 2004 15 7 2004 112 15 1460 1466 1 4 2004 14 7 2004 Publication 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. Organochlorine compounds have been linked to increased risk of several cancers. Despite reductions in their use and fugitive release, they remain one of the most important groups of persistent pollutants to which humans are exposed, primarily through dietary intake. We designed a case–control study to assess the risk of colorectal cancer with exposure to these chemicals, and their potential interactions with genetic alterations in the tumors. A subsample of cases (n = 132) and hospital controls (n = 76) was selected from a larger case–control study in Barcelona, Catalonia, Spain. We measured concentrations in serum of several organochlorines by gas chromatography. We assessed point mutations in K-ras and p53 genes in tissue samples by polymerase chain reaction/single-strand conformation polymorphism and assessed expression of p53 protein by immunohistochemical methods. An elevated risk of colorectal cancer was associated with higher serum concentrations of mono-ortho polychlorinated biphenyl (PCB) congeners 28 and 118. The odds ratio for these mono-ortho PCBs for middle and higher tertile were, respectively, 1.82 [95% confidence interval (CI), 0.90–3.70] and 2.94 (95% CI, 1.39–6.20). α-Hexachlorocyclohexane, hexachlorobenzene, and p,p′-DDE (4,4′-dichlorodiphenyltrichloroethene) showed nonsignificant increases in risk. Risk associated with mono-ortho PCBs was slightly higher for tumors with mutations in the p53 gene but was not modified by mutations in K-ras. Mono-ortho PCBs were further associated with transversion-type mutations in both genes. These results generate the hypothesis that exposure to mono-ortho PCBs contributes to human colorectal cancer development. The trend and magnitude of the association, as well as the observation of a molecular fingerprint in tumors, raise the possibility that this finding may be causal.
case–control studycolorectal cancerK-ras mutationsorganochlorinesp53 mutationsPCBs
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Colorectal cancer is the third most common human cancer and the second most important cause of cancer-related death in Western countries, affecting men and women about equally. The etiology of sporadic colorectal cancer is relatively poorly understood, although diet is thought to play an important role in modifying risk. Vegetables, fruit, and dietary fiber are protective, whereas red and processed meats, fat, total energy intake, and obesity all increase risk (Potter 1996).
Diet is also an important source of exposure to many synthetic organic chemicals used in industry, agriculture, or accidentally released to the environment. Among them, the industrial organochlorine compounds (OCs) hexachlorobenzene (HCB) and polychlorinated biphenyls (PCBs) and the pesticides DDT (dichlorodiphenyltrichloroethane) and lindane [γ-hexachlorocyclohexane (HCH), including α-HCH and β-HCH isomers] have been classified as “probably” or “possibly” carcinogenic to humans [International Agency for Research on Cancer (IARC) 1987, 1991]. Most of the data for these classifications come from animal studies, although evidence for carcinogenic effects in humans is accumulating from occupational and nonoccupational exposure studies [Agency for Toxic Substances and Disease Registry (ATSDR) 2000, 2002].
Despite reductions in their use and fugitive release, OCs remain one of the most important groups of persistent pollutants to which humans are exposed, primarily via dietary intake. More lipophilic OCs, and those that are not easily metabolized, accumulate in adipose tissue, and the half-lives of these compounds in the body can be on the order of years or decades, whereas those compounds that are more water soluble or more easily metabolized have half-lives on the order of hours or days. Eventually, OCs recirculate in blood and are excreted in feces (Moser and McLachlan 2001). Serum concentrations are strongly correlated with fecal concentrations, particularly for the more lipophilic compounds that accumulate in adipose tissue and are generally more chlorinated (Juan et al. 2002; Moser and McLachlan 2001). Exposure of tissue in the gastrointestinal tract to OCs is the result, therefore, not only of uptake from food but also of depuration from the tissue to the lumen. The long residence time (1–2 days) of feces in the colon offers potentially greater opportunity for exchange of OCs between the lumen and the epithelium than elsewhere in the gastrointestinal tract. However, the physicochemical characteristics of the compound (specifically, its solubility in water) will be more important in determining the relative importance of these exchange processes in the colon than in the small intestine, given the predominantly aqueous nature of the colonic milieu (Moser and McLachlan 2001; Schlummer et al. 1998). Therefore, colon epithelium is likely to be a major target for putative carcinogenic effects of OCs via luminal and blood-borne exposure.
OCs have been shown to mimic hormones, and this has been postulated as a mechanism for carcinogenesis in hormone-dependent cancers (Davis et al. 1993). Although colorectal cancer cannot be considered a hormone-dependent cancer, there is evidence that hormones play a role, at least in women: hormone replacement therapy and, possibly, high parity and oral contraceptive use are all protective factors (Potter 1999). Studies of cancers of the pancreas and breast have shown that OCs may interact with genetic alterations in tumors such as K-ras mutations or p53 overexpression (Hoyer et al. 2002; Porta et al. 1999; Slebos et al. 2000). Research on these interactions is relevant because they are frequent in colorectal cancer, and one potential mechanism of OC toxicity may be the induction of mutations in these genes.
Despite this, few studies of OC exposure and colorectal cancer risk have been published. Some studies in occupationally exposed cohorts have shown mixed results (Acquavella et al. 1996; Bertazzi et al. 1987; Brown 1987; Cantor and Silberman 1999; Leet et al. 1996; Wilkinson et al. 1997), whereas studies with data on individual plasma levels are rare. One study of colorectal cancer in children from rural areas exposed to pesticides showed no differences in serum OC levels compared with controls (Caldwell et al. 1981). Higher (nonsignificant) serum concentrations of DDT and β-HCH in cases than in controls were reported in a small study of farmers in Egypt (Soliman et al. 1997).
To test the hypothesis that exposure to OCs plays an etiologic role in colorectal cancer, we determined serum concentrations in a subset of samples from a hospital-based case–control study. In addition, we investigated the relationship between OC concentrations and mutations of the K-ras and p53 genes.
Materials and Methods
Study population.
We conducted a hospital-based case–control study to assess gene–environment interactions in relation to colorectal cancer risk. A detailed description of the population and study methods has already been published (Landi et al. 2003). Cases were patients with a new diagnosis of colorectal adenocarcinoma attending a university hospital in Barcelona, Catalonia, Spain, between January 1996 and December 1998. Controls were selected from admissions to the same hospital during this period using age and sex as frequency-matching criteria. To avoid selection bias of controls, the reason for admission had to be a disease not previously diagnosed for that patient. This criterion was used to avoid inclusion of patients with chronic diseases who might be repeatedly admitted to hospital and modify their habits because of their disease. This procedure paralleled the criterion for cases, who were also newly diagnosed incident cases. Main diagnosis groups of controls were acute digestive surgery (19%), urology (17%), gastroenterology (16%), and orthopedic surgery (15%). The study protocol was cleared by the ethics committee of the hospital, and all individuals gave written, informed consent to participate and for genetic analysis of their samples to be performed.
Of 523 identified cases, there were 13 (2%) refusals and 74 (14%) exclusions, with reasons including mental or other impairment and death or discharge before recruitment. We identified a total of 488 controls, with 36 (7%) refusals and 22 (5%) exclusions (reasons as described above). From these 436 cases and 430 controls, we selected a random sample of 140 cases and 80 controls for OC analyses for budgetary reasons. The sample size and imbalance in number between cases and controls was chosen to retain statistical power for our study of K-ras and p53 mutations among cases [80% power to detect odds ratios (ORs) of 2.25 for the exposures of interest]. Controls for this study were selected using strata based on age, sex, and energy intake, the latter being used as a frequency-matching criterion because diet is a main source of exposure to OCs. Twelve samples were lost or excluded because of bad quality during laboratory analyses, and statistical tests were conducted on 132 cases and 76 controls.
Interviews.
Cases and controls were interviewed by trained personnel using a structured questionnaire. A dietary history questionnaire, previously developed and validated in the framework of the EPIC study [European Prospective Investigation into Cancer and Nutrition (EPIC) Group of Spain 1997], focused on average food consumption 1 year before diagnosis. Food groups based on bromatologic properties were calculated from reports of items consumed. Other risk factors measured were body mass index (BMI) at diagnosis and 10 years before, parity in women, and life-long history of nonsteroidal anti-inflammatory drugs (NSAIDs), tobacco, and alcohol use.
Organochlorine analysis.
Selection of OCs for analysis was based on a literature search of commonly studied contaminants and included representatives of industrial and agrochemical compounds. Among the large family of PCB congeners, those often referred to as the ICES 7 (from International Council for the Exploration of the Sea), were selected: mono-ortho PCB congeners 28 and 118 and di-ortho PCBs 52, 101, 138, 153, 180. These PCB congeners are frequently found at high concentrations in humans and wildlife.
Nonfasting blood samples were obtained at diagnosis, and serum was isolated and analyzed “blind” for OCs. We calculated lipid-corrected concentrations of OCs by dividing serum OC concentrations by total plasma lipid concentrations estimated using the formula of Phillips et al. (1989), and data are expressed in nanograms of OC per gram of lipid.
OCs were analyzed by gas chromatography after serum samples were subjected to liquid–liquid extraction and cleanup using concentrated sulfuric acid and hexane (Porta et al. 1999). A mass spectrometer in negative chemical ionization mode was used to quantify α-, β-, γ-, and δ-HCH isomers; pentachlorobenzene (Pe-CB), HCB, PCB congeners, p,p′-DDT, and p,p′-DDE (4,4′-dichlorodiphenyltrichloroethene) were quantified on a gas chromatograph with electron capture detection. On both instruments, quantification was performed by external standards using PCB-142 as an injection standard to correct for volume. This method performed satisfactorily in an international intercalibration exercise within the Arctic Monitoring and Assessment Programme (AMAP 2004).
Point mutations in K-ras and p53 genes, and expression of p53 protein.
Fresh tumor tissue and normal mucosa samples were obtained from surgically extracted specimens of cases. K-ras gene mutations in codons 12 and 13 were detected and characterized by polymerase chain reaction (PCR) followed by single-strand conformation polymorphism (SSCP) analysis (Tortola et al. 1999). Aspartic acid mutations at codons 12 and 13 were confirmed by means of artificially introduced restriction fragment length polymorphism. Similarly, p53 exons 4–9 were analyzed by PCR and SSCP followed by direct sequencing whenever necessary. Expression of the p53 protein was determined by immunohistochemistry, using commercially available antibodies (ab-6 Pantropic; Oncogene Research Products, VWR International GmBH, Darmstadt, Germany). We studied microsatellite instability by analyzing five microsatellite sequences (Gonzalez-Garcia et al. 2000).
Statistical analysis.
OC concentrations were categorized using tertiles based on all subjects. Some compounds were not detected in a high proportion of individuals. For these, the reference category consisted of values below the detection limit (LOD). When the proportion of individuals with detectable values was > 50%, the values were further divided into two groups using the median value of those with values > LOD.
Logistic regression was used to test the association between OCs and colorectal cancer. When cases were subdivided into groups according to genetic alterations, we used polytomous logistic regression, comparing each group of cases with the whole set of controls. All analyses were adjusted for age, sex, total energy intake, and BMI at diagnosis because these had been used as frequency-matching criteria or, in case of BMI, because of concerns about potential confounding of OC exposure by BMI, given that cases showing lower BMI might have undergone lipid-stored OC mobilization. The impact of these adjustments on risk estimates was minimal, however. We also excluded an association between OCs and tumor stage that would be indicative of bias, because more advanced tumors are associated with greater weight loss. Potential confounding by other variables associated with disease (alcohol, NSAIDs, and food groups) was explored and rejected. These were not included in the models to avoid losing efficiency in the estimates owing to excessive stratification.
ORs and 95% confidence intervals (CIs) were calculated for each group compared with the reference category. We tested for linear trend using the categorized variable as quantitative after assigning codes 1 to 3 to each category defined by tertiles. Interactions between exposures and genetic alterations in tumors were tested with logistic regression models comparing cases with the alteration to cases without it. All p-values calculated were two sided.
Results
Characteristics of cases and controls are shown in Table 1. Sex, age, and energy intake were used as frequency-matching criteria. BMI estimated 10 years before diagnosis was not related to disease. In contrast, BMI at diagnosis was inversely related to disease, probably due to cancer-related weight loss, although no association was found between plasma lipids and disease, or between plasma lipids and OC concentrations. In this population, alcohol intake, but not smoking, was a strong risk factor for colorectal cancer, both in duration and in average daily consumption. NSAIDs other than aspirin were protective but not significantly so.
OC levels.
Lipid-corrected concentrations of OCs are shown in Table 2. The most abundant compound was p,p′-DDE, followed by HCB and β-HCH. Summed PCB concentrations were similar to those of β-HCH. The proportions of samples with values lower than LODs are also shown in Table 2. PCB-28 and PCB-52 were detected in only a small proportion of cases, although the LOD was very low. These compounds are more easily metabolized than are other PCBs because of the arrangement of their chlorine atoms and their lower degree of chlorination (Borlakoglu and Walker 1989). δ-HCH was not detected in any individual, and although Pe-CB and PCB-101 were detected in a few samples, their LODs were higher than other OCs; these three compounds were therefore excluded from statistical analyses.
Compounds were divided into two groups according to water solubility, and cases had slightly higher levels than did controls of both groups (data not shown). PCBs were grouped according to their structure, which is related to toxicity (ATSDR 2000; Safe 1990). Mono-ortho PCBs 28 and 118 have a relatively “planar” configuration compared with the remaining, di-ortho PCBs measured here. Exposure to mono-ortho PCBs 28 and 118 was almost double in cases compared with controls, whereas exposure to di-ortho PCBs was only slightly higher for cases than for controls.
OC levels and colorectal cancer risk.
Table 3 shows median lipid-corrected concentrations and 5th and 95th percentiles for each compound, separated for cases and controls. ORs for the association of OCs with colorectal cancer, adjusted for age, sex, BMI, and energy intake, are shown in Table 4. Those significantly associated with an increased risk were mono-ortho PCBs 28 and 118, each with individual ORs > 2 for the more exposed category. The OR for serum concentrations of the mono-ortho PCB group that combines PCB-28 and PCB-118 was 1.82 (95% CI, 0.90–3.70) in the middle tertile (LOD, 147 ng/g lipid) and 2.94 (95% CI, 1.39–6.20; p-value for trend = 0.004) for the highest tertile. Concentrations of di-ortho PCBs were not related to disease, nor was the classification of OCs according to their lipophilicity. High levels of α-HCH, HCB, and p,p′-DDE showed a nonsignificant increase in risk. Levels of p,p′-DDT were lower in cases than in controls in this population.
OC levels and interaction with other variables.
Dietary variables, which are both sources of exposure and risk factors for colorectal cancer, were explored as potential confounders. Specific food groups explored were vegetables, fruit, legumes, potatoes, meat, fish, eggs, dairy products, added fats, and pastries. Spearman’s correlation coefficients between food groups and serum levels of specific OCs were in general low and nonsignificant (maximum r = 0.19). Exceptions worth mentioning were dairy products, which correlated with p,p′-DDT and PCB-101; fresh meats, which correlated with PCB-101, PCB-153, and PCB-180; and fish, which correlated with PCB-153 and PCB-180. The significance of these correlations was poor, and in addition to those correlations mentioned, we observed several negative correlations. We also used a multivariate logistic regression model with all food groups to generate a “dietary propensity factor,” which could be interpreted as a score integrating the risk of colorectal cancer associated with diet. Adjusting OCs for this dietary propensity factor did not modify the risk estimates, showing again that diet was not a confounding factor. Hence, after careful consideration, adjustment of the analyses for food groups was found to be unnecessary because foods were not clearly related to serum OC levels and, more important, did not modify the risk estimates.
An exploratory analysis of interactions between mono-ortho PCBs and several variables was carried out. The magnitude of the risk of colorectal cancer observed for these OCs was not modified by sex, age, BMI, smoking, NSAIDs use, or parity in women. Analysis of interactions with other hormonal variables in women could not be performed because only six women were exposed to oral contraceptives and only two to postmenopausal hormone replacement treatments. Because alcohol was an important risk factor in this study and alcohol intake produces metabolic induction similar to that produced by some OCs (Mochizuki and Yoshida 1989), interactions between OCs and alcohol were explored in detail, but none was significant.
OC levels and mutations at the K-ras and p53 genes.
K-ras was mutated in 50 (38%) cases. Most frequent mutations were GAT (n = 16) and GTT (n = 15) in codon 12. In 6 cases, codon 13 was mutated to GAC. Other, less frequent mutations in codon 12 were TGT (n = 5), AGT (n = 4), GCT (n = 3), and CGT (n = 1). Twenty-six mutations were transitions (G:C → A:T), and 24 were transversions (G:C → T:A).
Tumor suppressor gene p53 was mutated in 59 (60%) of the tumors analyzed (data not available for 34 cases). Twenty-four mutations were located in hotspots: codon 175 (n = 4), codon 245 (n = 3), codon 248 (n = 3), codon 273 (n = 6), and codon 282 (n = 8). Four cases harbored mutations at codon 158, the remaining being detected at 24 different codons with frequencies ≤ 2. Forty mutations were transitions, and 15 were transversions; in the remaining four cases, deletions were detected. The most frequent base changes were C → T and G → A, each in 17 cases, mainly at hotspot sites. G → T changes were observed in eight cases and distributed with a nonclustered profile. The immunohistochemical analyses showed that 76% of the cases overexpressed the p53 protein.
Table 5 shows the analysis of selected OCs where the cases have been stratified by mutations in K-ras and p53. This analysis is based on polytomous logistic regression, and each group of cases is compared with controls in a unified model. Exposure to mono-ortho PCBs 28 and 118 increased risk in similar magnitude for both mutated and wild-type K-ras. PCB-118, but not PCB-28, showed higher risk for p53-mutated tumors, although the interaction was not significant. This stratification of cases by genetic alterations also showed some significant interactions with other OCs. High levels of p,p′-DDE were associated with increased risk of cancer with wild-type K-ras. Also, p,p′-DDE and α-HCH had significant effects on p53-mutated tumors. A similar pattern was evident when tumors were stratified according to p53 overexpression: p,p′-DDE and PCB-118 increased the risk only for tumors overexpressing p53 protein. Tumors with microsatellite instability were rare (n = 11, 8%), and these patients had similar OC levels to other cases.
Overall, 81% of the tumors harbored K-ras and/or p53 gene mutations, and a combined analysis was performed. Exposure to high levels of the mono-ortho PCB-118 was associated with more mutations (OR = 2.89; 95% CI, 0.66–12.7), with transversions three times more likely than transitions (Table 6). Other OCs analyzed in this way were not related to the type of mutation.
Discussion
OCs have previously been associated with increased risk of colorectal cancers in studies of occupationally exposed individuals (Acquavella et al. 1996; Soliman et al. 1997; Wilkinson et al. 1997). Other studies have failed to show this association (Hardell 1981; Hoar et al. 1985; Settimi et al. 2001) or even have shown inverse associations with colon cancer (Cantor and Silberman 1999; Wang et al. 2002). These mixed results are not surprising because most occupational studies lack individual exposure indicators and are prone to confounding. To overcome these limitations, we studied a population not occupationally exposed using serum OC levels as exposure markers. We found an elevated risk of colorectal cancer associated with high levels of mono-ortho PCBs 28 and 118. Other abundant OCs such as p,p′-DDE and α-HCH also showed an increased risk of relevant magnitude; although the associations for these OCs were not significant for the entire population of cases, they were significant for the subset of tumors harboring mutations of the p53 gene.
We have taken serum concentrations of OCs as indicative of the body burden. Serum concentrations are strongly correlated with those in adipose tissue and feces and reflect the historical legacy of uptake and depuration, and as such, they may be taken as markers for long-term, low-level exposure (Alcock et al. 2000; Juan et al. 2002; Moser and McLachlan 2001). Many studies that also used plasma OC concentrations as exposure markers have reported mixed results for breast cancer (Calle et al. 2002) and non-Hodgkin lymphoma (Cantor et al. 2003; De Roos et al. 2003; Rothman et al. 1997) but increased risk of pancreatic cancer (Hoppin et al. 2000; Porta et al. 1999; Slebos et al. 2000). Altogether, these results suggest that a role for OCs in the etiology of several tumors is likely. Nevertheless, the mechanisms underlying carcinogenicity will differ among both OCs and target organs because chemical properties and toxicity mechanisms of these compounds are diverse. In addition, target organs differing in lipid content might determine distinct degree of local exposure.
In our population, high body burdens of mono-ortho PCBs 28 and 118 were associated with an elevated risk of colorectal cancer. These OCs are among the most toxic of the PCBs, together with non-ortho PCBs. The relatively flat (or “planar”) orientation of their biphenyl rings allows them to bind to the aryl hydrocarbon (Ah) receptor in a similar way to polychlorinated dibenzodioxins and dibenzofurans and may be responsible for our finding (Safe 1990). This binding induces phase I and phase II metabolic enzymes; mono-ortho PCBs may therefore induce CYP1A and CYP2B enzymes that, in the absence of substrate, can produce reactive oxygen species. Although PCB-118 has a toxic equivalency factor (TEF) of 0.0001, based on its toxicity relative to 2,3,7,8-tetrachlorinated dibenzo-p-dioxin (Van den Berg et al. 1998), PCB-28 has not been assigned a TEF under this scheme and may not, therefore, bind strongly to the Ah receptor and/or significantly induce CYP enzymes. Nevertheless, higher serum levels of PCB-28 were associated with increased risk of colon cancer in this study. Some studies on breast cancer have also reported increased risk being limited to mono-ortho PCBs (Aronson et al. 2000; Demers et al. 2002; Lucena et al. 2001).
PCBs themselves are hydroxylated by cytochrome P450 enzymes, and their metabolites have been shown to induce DNA adducts in vitro (McLean et al. 1996). We have not observed a clear differential risk for PCB-28 or PCB-118 in relation to K ras or p53 mutations. However, when mutations in both genes were classified according to their molecular nature, an association between transversions (less likely to occur spontaneously) and exposure to mono-ortho PCBs was observed. This could be interpreted as indirect evidence for a molecular fingerprint associated with exposure to these pollutants.
The role of other OCs in colorectal cancer risk may be more complex. Compounds such as p,p′-DDE and α-HCH that had an overall moderate association with colorectal cancer showed a significant increase in risk for tumors with mutation of the p53 gene. A similar result was reported in a Danish study, where higher risk of breast cancer with mutated p53 was observed among women exposed to high levels of dieldrin and PCBs (Hoyer et al. 2002). In our population, p,p′-DDE also increased risk for tumors with wild-type K-ras but not when this oncogene was mutated. Previous studies in pancreatic cancer have provided mixed results regarding OC exposure and K-ras mutation status associated with DDE levels (Porta et al. 1999; Slebos et al. 2000).
Several factors suggest that these findings may be causal: the strength and trend of the associations observed; the restriction of the findings to mono-ortho PCBs that have a relatively planar, dioxin-like structure; the existence of similar findings in tumors from other organs; the plausibility of carcinogenic mechanisms; and the association with specific mutation types. We are aware, however, that this study also has several limitations. Owing to the limitations of our analytical method, we did not study non-ortho PCBs that have a greater affinity for the Ah receptor than do mono-ortho PCBs as a result of their “coplanar” molecular structure, which may have strengthened our hypothesis. The retrospective assessment of exposure in the case–control design is not ideal, and the use of hospital controls may introduce information and selection bias. We minimized these biases by careful design, the use of validated questionnaires administered by trained interviewers, and robust analytical methods. However, potential sources of non-differential bias would tend to decrease the magnitude of any positive relationship; in particular, if OC exposure increased the risk of hospital admission, this would bias the associations toward the null hypothesis. Furthermore, potential confounding factors were explored in detail and rejected. Special effort was devoted to explore whether the weight loss observed among cases could explain the results, because OCs accumulate in lipids, and changes in BMI due to metabolism of adipose tissue in cancer patients could result in higher serum levels of OCs. However, three observations combine to negate this possibility: a) adjusting for BMI had only a minor effect on risk estimates; b) although more advanced cases exhibited severe weight loss, there was no association between OC levels and tumor stage; and c) if lipid mobilization was the reason for observing higher OC levels among cases, this would be true for all OCs but especially the more lipophilic compounds, whereas we observed increased risk only for selected OCs, and these were less lipophilic than p,p′-DDT and PCB-138, PCB-153, and PCB-180, for which no increased risk was observed. Nevertheless, we cannot exclude chance as an explanation of some of the results because this study was conceived as exploratory or hypothesis generating and we performed many statistical comparisons.
In conclusion, these results suggest that exposure to mono-ortho PCBs is associated with an increased risk of colorectal cancer. The trend and magnitude of the association and the specific toxic action of these PCBs, linked with transversion-type mutations, suggest that this finding may be causal. This hypothesis merits confirmation in other studies that would benefit from including other mono-ortho PCBs, as well as non-ortho PCBs and perhaps dioxins/furans.
Table 1 Population characteristics.
Controls No. (%) Cases No. (%) OR (95% CI) p-Valuea
Sex
Male 43 (57) 75 (57) 1.00
Female 33 (43) 57 (43) 1.07 (0.60–1.92)
Age (years)
24–63 26 (34) 46 (35) 1.00
63–73 25 (33) 47 (36) 1.09 (0.54–2.20)
73–92 25 (33) 39 (30) 0.91 (0.45–1.83)
Energy intake (calories/day)
663–1,733 24 (32) 46 (35) 1.00
1,733–2,280 26 (34) 43 (33) 0.82 (0.39–1.73)
2,280–4,418 26 (34) 43 (33) 0.83 (0.38–1.82)
BMI
16.8–25.0 24 (32) 56 (42) 1.00 0.09
25.0–30.0 33 (43) 51 (39) 0.65 (0.34–1.25)
30.0–40.7 19 (25) 25 (19) 0.53 (0.24–1.17)
BMI 10 years before diagnosis
17.6–25.0 19 (25) 42 (32) 1.00 0.79
25.0–30.0 42 (55) 57 (43) 0.61 (0.31–1.20)
30.0–43.1 15 (20) 33 (25) 0.95 (0.41–2.18)
Plasma lipids (g/L)
2.58–4.94 26 (34) 44 (33) 1.00 0.49
4.94–5.85 21 (28) 48 (36) 1.37 (0.67–2.81)
5.85–9.55 29 (38) 40 (30) 0.79 (0.39–1.57)
Alcohol duration (years)
0 34 (45) 42 (32) 1.00 0.05
1–40 19 (25) 38 (29) 2.22 (0.93–5.30)
40–74 23 (30) 52 (39) 2.40 (1.02–5.63)
Alcohol consumption (g/day)
0 36 (47) 42 (31) 1.00 0.001
1–60 35 (46) 66 (50) 2.42 (1.13–5.17)
60–410 5 (6) 24 (18) 7.39 (2.13–25.7)
Tobacco use (menb)
Nonsmoker 9 (20) 20 (26) 1.00 0.47
Ex-smoker 23 (53) 37 (49) 0.75 (0.29–2.00)
Smoker 11 (25) 18 (24) 0.66 (0.21–2.12)
Smoking duration (years, menb)
0 9 (21) 20 (27) 1.00 0.81
1–40 20 (47) 22 (29) 0.48 (0.17–1.36)
40–79 14 (33) 33 (44) 1.05 (0.38–2.96)
Parity (women)
0–2 6 (18) 16 (28) 1.00 0.78
2–4 20 (61) 25 (44) 0.55 (0.17–1.76)
4–9 7 (21) 16 (28) 1.25 (0.31–5.11)
NSAID use
None 52 (68) 101 (76) 1.00 0.12
Aspirin 12 (15) 22 (16) 0.92 (0.42–2.04)
Other 12 (15) 9 (6) 0.43 (0.16–1.11)
a Test for linear trend adjusted for age, sex, energy intake, and BMI.
b Only 6% of women ever smoked in this population.
Table 2 Lipid-corrected serum concentrations of OCs.
OC Mediana (cutpoints)b LODc Percent of samples > LODd
p,p′-DDE 3,686 (2,574–5,565) 0.2 100
p,p′-DDT 444 (336–740) 1.5 100
HCB 1,753 (1,344–2,522) 77.9 98
α-HCH 17 (< 2.8–30) 2.8 61
β-HCH 1,057 (779–1,422) 34.4 100
γ-HCH 10 (< 1.5–46) 1.5 56
PCB-28 < 3.5 (< 3.5) 3.5 25
PCB-118 92 (< 21.0–127) 21.0 62
PCB-52 < 4.6 (< 4.6) 4.6 9
PCB-153 362 (255–487) 16.0 100
PCB-138 308 (245–404) 15.3 98
PCB-180 252 (190–333) 16.0 98
a Lipid-corrected concentrations expressed as ng/g lipid.
b Cutpoints were 33rd and 66th percentiles, except for compounds for which the 33rd percentile was < LOD, in which case median of measured values were used.
c Lipid corrected (ng/g lipid) using average lipid concentration in the population (5.44 g lipid/L serum).
d Proportion of samples with values > LOD.
Table 3 Lipid-corrected serum concentrations [median (5th–95th percentile); ng/g lipid] of OCs in cases and controls.
OC Cases Controls
p,p′-DDE 3,936 (600–11,804) 2,977 (611–13,608)
p,p′-DDT 396 (124–2,077) 609 (137–3,848)
HCB 1,753 (487–5,777) 1,763 (720–4,848)
α-HCH 21 (< 2.8–94) 14 (< 2.8–55)
β-HCH 1,042 (315–2,971) 1,119 (345–3,654)
γ-HCH 10 (< 1.5–121) 10 (< 1.5–399)
PCB-28 < 3.5 (< 3.5–228) < 3.5 (< 3.5–43)
PCB-118 103 (< 21–319) 63 (< 21–404)
PCB-52 < 4.6 (< 4.6–49) < 4.6 (< 4.6–< 4.6)
PCB-153 381 (118–1,018) 340 (118–917)
PCB-138 327 (121–992) 301 (91–830)
PCB-180 259 (87–741) 238 (72–650)
Table 4 Risk of colorectal cancer associated with organochlorine body burden.
Controls No. (%) Cases No. (%) OR (95% CI) p-Valuea
p,p′-DDE
Low 31 (41) 38 (29) 1.00 0.19
Medium 21 (28) 49 (37) 2.17 (1.03–4.54)
High 24 (32) 45 (34) 1.60 (0.79–3.25)
p,p′-DDT
Low 25 (33) 44 (33) 1.00 0.12
Medium 18 (24) 52 (39) 1.58 (0.74–3.36)
High 33 (43) 36 (27) 0.56 (0.27–1.17)
HCB
Low 29 (38) 40 (30) 1.00 0.23
Medium 22 (29) 47 (36) 1.72 (0.83–3.54)
High 25 (33) 45 (34) 1.60 (0.62–4.15)
α-HCH
Low 33 (43) 48 (36) 1.00 0.081
Medium 27 (36) 37 (28) 0.99 (0.49–1.98)
High 16 (21) 47 (36) 2.02 (0.95–4.29)
β-HCH
Low 26 (34) 44 (33) 1.00 0.81
Medium 23 (30) 45 (34) 1.14 (0.55–2.36)
High 27 (36) 43 (33) 0.88 (0.39–2.02)
γ-HCH
< LOD 30 (39) 61 (46) 1.00 0.28
Medium 22 (29) 36 (27) 0.79 (0.38–1.61)
High 24 (32) 35 (27) 0.69 (0.34–1.38)
PCB-28
< LOD 65 (86) 91 (69) 1.00 0.006
> LOD 11 (14) 41 (31) 2.75 (1.29–5.83)
PCB-118
< LOD 36 (47) 43 (33) 1.00 0.045
Medium 20 (26) 39 (30) 1.63 (0.80–3.31)
High 20 (26) 50 (38) 2.02 (1.00–4.08)
PCB-52
< LOD 69 (91) 120 (91) 1.00 0.77
> LOD 7 (9) 12 (9) 1.16 (0.42–3.19)
PCB-153
Low 30 (39) 39 (30) 1.00 0.57
Medium 22 (29) 48 (36) 1.50 (0.74–3.07)
High 24 (32) 45 (34) 1.22 (0.59–2.52)
PCB-138
Low 26 (34) 43 (33) 1.00 0.79
Medium 26 (34) 44 (33) 0.85 (0.41–1.75)
High 24 (32) 45 (34) 0.90 (0.43–1.90)
PCB-180
Low 29 (38) 41 (31) 1.00 0.63
Medium 24 (32) 44 (33) 1.20 (0.59–2.42)
High 23 (30) 47 (36) 1.19 (0.57–2.49)
a p-Value for trend adjusted for age, sex, energy intake, and BMI.
Table 5 Colorectal cancer risk for selected organochlorines in relation to K-ras and p53 mutations.
Wild-type
Mutated
No. (%) OR (95% CI) No. (%) OR (95% CI) p-Valuea
K-ras
p,p′-DDE
Low 16 (20) 1.00 22 (44) 1.00 0.012
Medium 34 (41) 3.51 (1.49–8.24) 15 (30) 1.07 (0.42–2.72)
High 32 (39) 2.78 (1.21–6.35) 13 (26) 0.72 (0.29–1.81)
α-HCH
Low 28 (34) 1.00 20 (40) 1.00 0.55
Medium 27 (33) 1.22 (0.57–2.61) 10 (20) 0.64 (0.25–1.66)
High 27 (33) 2.16 (0.94–4.97) 20 (40) 1.75 (0.70–4.37)
PCB-28
< LOD 56 (68) 1.00 35 (70) 1.00 0.88
> LOD 26 (32) 2.78 (1.24–6.25) 15 (30) 2.83 (1.13–7.06)
PCB-118
< LOD 26 (32) 1.00 17 (34) 1.00 0.42
Medium 25 (30) 1.78 (0.81–3.90) 14 (28) 1.35 (0.53–3.43)
High 31 (38) 2.27 (1.04–4.96) 19 (38) 1.64 (0.67–4.01)
p53
p,p′-DDE
Low 12 (31) 1.00 11 (19) 1.00 0.047
Medium 16 (41) 2.24 (0.81–6.16) 20 (34) 2.94 (1.11–7.76)
High 11 (28) 1.09 (0.39–3.05) 28 (47) 3.44 (1.39–8.47)
α-HCH
Low 18 (46) 1.00 16 (27) 1.00 0.045
Medium 10 (26) 0.76 (0.29–2.01) 17 (29) 1.39 (0.58–3.35)
High 11 (28) 1.22 (0.44–3.34) 26 (44) 3.44 (1.40–8.45)
PCB-28
< LOD 29 (74) 1.00 44 (75) 1.00 0.98
> LOD 10 (26) 2.16 (0.79–5.91) 15 (25) 2.06 (0.85–5.01)
PCB-118
< LOD 14 (36) 1.00 18 (31) 1.00 0.19
Medium 13 (33) 1.72 (0.65–4.53) 13 (22) 1.31 (0.53–3.26)
High 12 (31) 1.40 (0.52–3.75) 28 (47) 2.79 (1.22–6.37)
a p-Value for interaction between OC and the genetic alteration adjusted for age, sex, energy intake, and BMI; tests for differences in the OR between wild-type and mutated cases.
Table 6 Colorectal cancer risk for PCB-118 in relation to K-ras and p53 mutation type.
Transitionsa Transversionsa
No. (%) OR (95% CI) No. (%) OR (95% CI) p-Valueb
PCB-118
< LOD 20 (37) 1.00 6 (18) 1.00 0.071
Medium 17 (32) 1.09 (0.28–4.28) 12 (35) 3.29 (0.66–16.4)
High 17 (32) 1.93 (0.41–9.09) 16 (47) 6.52 (1.16–36.6)
Trend test p-valuec 0.42 0.038
a Mutations in K-ras and p53 classified as transitions (G:C→A:T) or transversions (G:C→T:A).
b p-Value for interaction between PCB-118 and the mutation type; tests for differences in the OR between transitions and transversions.
c Trend test p-value for the specific mutation type adjusted for age, sex, energy intake, and BMI.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6990ehp0112-00146715531429ResearchArticlesAndrogenic and Estrogenic Response of Green Mussel Extracts from Singapore’s Coastal Environment Using a Human Cell-Based Bioassay Bayen Stéphane 12Gong Yinhan 23Chin Hong Soon 3Lee Hian Kee 1Leong Yong Eu 3Obbard Jeffrey Philip 21Department of Chemistry,2Tropical Marine Science Institute, and3Department of Obstetrics and Gynecology, National University of Singapore, Republic of SingaporeAddress correspondence to S. Bayen, Tropical Marine Science Institute, 14 Kent Ridge Rd., Singapore 119223. Telephone: 65-6774-9920. Fax: 65-6774-9654. E-mail:
[email protected] thank the research group of K. Jones (Department of Environmental Science, Lancaster University, UK) for their valuable technical support.
This study is part of a scientific program (Marine Environment Monitoring, Impact Assessment and Enhancement in Singapore) funded by the Agency for Science, Technology and Research, Singapore.
The authors declare they have no competing financial interests.
11 2004 15 7 2004 112 15 1467 1471 29 1 2004 14 7 2004 Publication 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 the last decade, evidence of endocrine disruption in biota exposed to environmental pollutants has raised serious concern. Human cell-based bioassays have been developed to evaluate induced androgenic and estrogenic activities of chemical compounds. However, bioassays have been sparsely applied to environmental samples. In this study we present data on sex hormone activities in the green mussel, Perna viridis, in Singapore’s coastal waters. P. viridis is a common bioindicator of marine contamination, and this study is a follow-up to an earlier investigation that reported the presence of sex hormone activities in seawater samples from Singapore’s coastal environment. Specimens were collected from eight locations around the Singapore coastline and analyzed for persistent organic pollutants (POPs) and heavy metals. Tissue extracts were then screened for activities on androgen receptors (ARs) and estrogen receptors (ER-α and ER-β) using a reporter gene bio-assay based on a HeLa human cell line. Mussel extracts alone did not exhibit AR activity, but in the presence of the reference androgenic hormone dihydrotestosterone (DHT), activities were up to 340% higher than those observed for DHT alone. Peak activities were observed in locations adjacent to industrial and shipping activities. Estrogenic activities of the mussel extract both alone and in the presence of reference hormone were positive. Correlations were statistically investigated between sex hormone activities, levels of pollutants in the mussel tissues, and various biological parameters (specimen size, sex ratio, lipid and moisture content). Significant correlations exist between AR activities, in the presence of DHT, and total concentration of POPs (r = 0.725, p < 0.05).
androgenendocrine disruptionestrogengreen musselheavy metalspersistent organic pollutantsreporter gene bioassaySingapore
==== Body
Endocrine disruption is now evident at the global scale for humans (Norgil Damgaard et al. 2002), mammals (Kirk et al. 2003), and aquatic organisms (Oberdörster and Cheek 2001). Both natural and anthropogenic chemicals have been implicated as the cause for this problem. Concern is mounting about the number of potential endocrine-disrupting compounds (EDCs) now at large in the biosphere. For instance, 80,000 chemicals are estimated to be in use in U.S. commerce alone, but only a small fraction has been screened for endocrine-disrupting potential (Tully et al. 2000). Estrogens were recently quantified in coastal waters of the United States, and peak concentrations were observed near sources of sewage, highlighting the importance of anthropogenic sources of EDCs in the marine environment (Atkinson et al. 2003). In 2001, the Stockholm Convention under the auspices of the United Nations Environmental Program (UNEP) specified a suite of persistent organic pollutants (POPs) considered as potential EDCs in the environment (UNEP 2001). The “red list” defined at the Stockholm Convention includes dichlorodiphenyltrichloroethane (DDT), chlordanes, lindane, hexachlorobenzene, aldrin, endrin, dieldrin, heptachlor, toxaphene, mirex, and polychlorinated biphenyls (PCBs), dioxins, and furans.
A number of techniques exist for assessing the endocrine effect of anthropogenic chemicals on wildlife. Common assays include in vivo tests such as uterine growth bioassays or the use of in vitro biomarkers such as vitellogenin proteins, gene transcription, and cell proliferation (Jimènez 1997). So far, no assay has been proven to deliver a comprehensive evaluation of endocrine disruption effects in environmental samples. Furthermore, in most cases, results between assays are not comparable (Jimènez 1997).
Human cell-based gene receptor bioassays enable the comparison of hormonal activity in a sample relative to standard hormones. This type of assay has been principally used to test activities of single congeners such as PCBs (Schrader and Cooke 2003), as well as pesticides (Tully et al. 2000). Reporter gene assays have also been applied to environmental samples such as fresh water (Shen et al. 2001). More recently, Legler et al. (2003) reported the use of a gene receptor bioassay for determining estrogenic activity in sediments and marine organism extracts, including fish and mussels.
In a previous study (Gong et al. 2003), we developed a robust methodology to measure both androgenic and estrogenic activities in seawater samples using a HeLa human cell-based assay. Analysis of samples using this assay revealed that Singapore’s coastal waters displayed high levels of both androgenic and estrogenic activity. This finding poses questions as to the potential biological impact of EDCs in Singapore’s coastal environment. Mussels represent the most common species of shellfish cultivated in the world, with more than 1.1 million tons produced in 1998 (Gosling 1992). The green mussel, Perna viridis, is the mussel species naturally prevalent in Asia-Pacific coastal waters (Gosling 1992). As a filter-feeding organism, green mussels have been used as a bioindicator species for various POPs, including organochlorine pesticides (OCPs), PCBs, and polybrominated diphenyl ethers (PBDEs) (Bayen et al. 2003). In this study we report the use of a human cell-based bioassay for the determination of sex hormone activity in extracts of P. viridis sampled from Singapore’s coastal waters. Specifically, mussel extracts were screened for hormonal activities on androgen receptors (ARs) and estrogen receptors (ER-α and ER-β), either alone or in the presence of well-known hormones, androgenic dihydrotestosterone (DHT) or estrogenic 17β-estradiol (E2). To our knowledge, this study represents the first measurement of both androgenic and estrogenic activities of an environmental biological tissue extract using a human cell-based bioassay. Data on sex hormone activities in P. viridis samples collected from the coastal waters of Singapore were then correlated statistically to various parameters measured in the mussels, including contaminant burden, to evaluate the possibility of using this bio-assay as an indicator of the presence of EDCs in biological samples.
Materials and Methods
Chemicals.
All organic solvents used for the bioassay were of HPLC grade and were obtained from Fisher Scientific (Fairlawn, NJ, USA) and J.T. Baker (Philipsburg, NJ, USA). We obtained ultrapure water using Nanopure treatment (Barnstead, Dubuque, IA, USA). DHT and E2 were purchased from Sigma (St. Louis, MO, USA). Chemicals used for POP and heavy metal analysis have been previously described (Bayen et al. 2003, 2004).
Green mussel collection and preparation of tissue homogenates.
Perna viridis specimens were collected from eight sample stations along the coastline of Singapore’s main island between March and April 2002 (Figure 1). Specimens were taken from floating structures and shore defense walls. We collected 20 mussels from each location, but some of these were later rejected so that only the largest specimens and those most similar in size were analyzed for each location. Samples were transported in polyethylene bags in ice boxes to the laboratory for analysis.
In the laboratory, we recorded the sex and size of each specimen. Sex is easily ascertained for P. viridis because female tissues are red in color and male tissues are creamy white (Gosling 1992). The soft tissues in the mussel samples were removed from the shell and homogenized in a stainless steel blender to form a single batch sample for each sampling site. These samples were then frozen at −20°C in glass containers.
Green mussel tissue extraction and human cell-based bioassay.
Green mussel homogenate samples (5.2 ± 0.2 g) were extracted via microwave-assisted extraction using a Mars X oven (CEM, Matthews, NC, USA), with 30 mL methanol/ethanol/dichloromethane/n-hexane/ethyl acetate mixture (1:1:1:1:1 by volume). The extraction temperature was increased to 110°C within 10 min and then held for 3 min at this value, using 60% of 1,200 W power. The maximum pressure allowed was set to 200 psi. The extract was then filtered, dried under purified nitrogen, and reresolved in 6 mL methanol/DMSO (1:1 vol/vol). Then 1.2 μL extract was added in 0.6 mL culture media for screening androgenic and estrogenic activities. The cell-based gene receptor bioassay procedure has been described and validated in a previous study (Gong et al. 2003). Briefly, HeLa cells were transiently cotransfected with two plasmids using a lipofectamine technique. The first plasmid consisted of DNA encoding for AR or ER (ER-α and ER-β), and the second an appropriate luciferase reporter gene to drive the androgen response or estrogen response element. After 36 hr incubation, the cells were lysed and collected for measurement of luciferase activity. Bioactivity of the extracts was expressed as percentages of luciferase activity of positive control cells. The gene reporter tests were conducted in duplicate for each sample.
POP and heavy metal analysis.
The analytical methods for determination of POP and heavy metal concentrations have been reported in previous studies (Bayen et al. 2003, 2004). Briefly, POPs were extracted using accelerated Soxhlet extraction followed by a two-step cleanup procedure that included adsorption chromatography on acid silica gel followed by gel permeation chromatography. Quantification of POPs was performed by gas chromatography/mass spectrometry for 41 PCB congeners, 21 PBDE congeners, p,p′-DDT, p,p′-DDD, o,p′-DDE and p,p′-DDE, α- and γ-chlordane, mirex, hexachlorobenzene, penta-chloronitrobenzene, and heptachlor.
Samples were digested for heavy metal analysis using an oxidizing acid mixture exposed to microwave energy. Digested solutions were analyzed by inductively coupled plasma/mass spectrometry. Quantification was performed for arsenic, chromium, copper, nickel, lead, zinc, and cadmium.
Validation and quality assurance of the analytical procedure were conducted as described previously (Bayen et al. 2003, 2004). Analytical quality assurance for POPs included a recovery test using 13C-labeled PCBs, analysis of standard reference material (SRM2978; National Institute of Standards and Technology, Gaithersburg, MD, USA), reproducibility tests, and standard solution and procedural blank analysis. Quality assurance for heavy metals included recovery tests, reproducibility checks, and procedural blank analysis. Analytical methodology and results were acceptable for the various quality criteria set for individual contaminant determination in the green mussel tissues.
Data analysis.
All statistical data analyses were performed using XSTAT 6.19 software (Addinsoft, Brooklyn, NY, USA). We assessed differences in the activities for the various sites using the Kruskal-Wallis test, setting an adjusted p-value of < 0.05 for significance. Pearson correlation analysis was used to detect any proportional relationships between sex hormone activity and the concentrations of contaminants in the mussel sample tissue. The Pearson correlation r coefficient measures the proportional (i.e., linear) relationship between two parameters, where the r coefficient varies in the interval [−1.00, +1.00] and a value of 0.00 represents a lack of correlation. Values of −1.00 and +1.00 represent, respectively, perfect negative and positive correlations, respectively. A Pearson matrix of correlation is the summary of all the Pearson coefficients for a specified set of parameters. The significance of the correlation was evaluated for a t-test using a p-value of 0.05.
Results
Endocrine activities of the mussel samples.
Sex hormone activities of P. viridis extracts are presented in Figure 2. AR activities in the P. viridis extract alone were comparably low between sample locations (< 1% of 0.1 nM DHT). In contrast, AR activity in the P. viridis extract in the presence of 0.1 nM DHT ranged from 112 to 340% of the DHT alone, thereby indicating a strong increase in hormone activity in the presence of androgens. Differences in AR activity in the presence of 0.1 nM DHT were significant between sample locations (Kruskal-Wallis, p < 0.05). The strongest effects were found in P. viridis samples taken from stations M3, M4, and M8 (Figure 1).
ER-α activity in the P. viridis extract alone reached 49.6% of 10 nM E2 at station M1 and was generally constant in samples from all other locations (18.3% ± 2.2% of 10 nM E2). The 10 nM E2 estrogenic reference hormone displayed higher ER-α activity in the presence of the P. viridis extracts for all locations except M2 and M6, ranging from 98.1 to 216.9% of the activity observed for E2 alone. Differences in ER-α activity, in the presence of 1 nM E2, were significant between each sample site (Kruskal-Wallis, p < 0.05). The greatest increase in ER-α activity was observed for samples taken from stations M3 and M4.
ER-β activity in the P. viridis extract alone was more variable than ER-α activity, where peak values were found in samples taken from station M1 (31.3% of 10 nM E2) and M4 (16.0% of 10 nM E2). The ER-β activity of 10 nM E2 in the presence of the P. viridis extract ranged from 54.9 to 115.4% of the 10 nM E2 alone. ER-β activity in the presence of 1 nM E2 in samples M1, M2, M7, and M8 were significantly lower than the activity of E2 alone (Kruskal-Wallis, p < 0.05) and therefore inhibited the ER-β activity of E2. The highest increase in ER-β activity was observed for samples from stations M3, M4, and M6.
Biological parameters and chemical levels in green mussels.
Biological parameters, levels of specific contaminants in green mussels, and geographical distribution are presented in previous reports (Bayen et al. 2003, 2004). Peaks of POPs and heavy metals were generally found in stations M3, M4, and M8. Biological parameters, such as sex ratio and lipid and moisture content, did not show obvious trends. Ranges are presented in Table 1 for reference.
Statistical analysis.
The Pearson correlation analysis was used to detect relationships among 23 measured biological and chemical parameters of the P. viridis samples. These parameters include sex hormone activity, individual POP and heavy metal contaminant levels, and specimen biological parameters (specimen size, moisture and lipid content, and batch sample sex ratio). In addition, sum concentrations of OCPs (∑OCPs), POPs (∑POPs), and heavy metals (∑HMs) were included in the statistical analysis because these contaminants may exert a combined EDC effect. Heavy metal elements, including Pb, Cd, and Zn, were also measured but are not discussed here because P. viridis tissue concentrations were at or below analytical limits of detection. Details on the parameters correlated are given in Table 1. Levels of contaminants are presented as molar concentrations to allow comparison with endocrine activities. The matrix of correlation factors between parameters for Pearson analysis is presented in Table 2.
The Pearson r coefficient reveals that ER (both α and β) activities of the P. viridis extracts alone have a significantly similar geographical distribution profile (r = 0.955, p < 0.05). AR and ER-α activities of the green mussel extracts in the presence of the reference hormone also have a similar profile (r = 0.928, p < 0.05). Statistical analysis reveals that specific individual OCPs, that is, DDTs, chlordanes, and mirex, in P. viridis samples have a similar relative concentration profiles among the P. viridis tissues from all sample locations (0.895 < r < 0.998; p < 0.05). On the contrary, OCPs had a different profile than PCBs (r = 0.544, p > 0.05) and PBDEs (r = 0.031, p > 0.05).
As shown in Figure 2, AR activity in the presence of 0.1 nM DHT is the sex hormone activity with the greatest variability in P. viridis tissues between sample locations (i.e., 112–340% of 0.1 nM DHT). AR activity in the presence of 0.1 nM DHT has a significant (p < 0.05) and positive correlation with the sum of α- and γ-chlordane levels (r = 0.759), as well as the total concentration of POPs (r = 0.725; Figure 3). ER-α activity in the presence of 1 nM E2 shows similar trends, although the r coefficient is weaker and not significant (i.e., r = 0.582 with total concentration of POPs). In contrast, activities of the mussel samples in the presence of reference hormones do not show any strong linear correlation with any heavy metal or biological parameters of the mussels (i.e., specimen size, moisture and lipid content, and batch sample sex ratio). Activities of samples alone do not show any strong proportional correlations with either heavy metal or POP tissue concentrations. ER-α and ER-β activities of mussel extracts alone are significantly and negatively correlated with the lipid content of mussel tissues (r < −0.749) and positively correlated with moisture content (r > 0.728).
Discussion
Sex hormone activity distribution in Singapore’s green mussels.
The AR activity of mussel extract alone was very low in samples from all locations (< 1% of 0.1 nM DHT). However, the samples displayed a strong increase in activity in the presence of 0.1 nM DHT, with clear geographical variation, indicating a synergistic response of the mussel extract in the presence of the reference androgenic hormone. The highest increases in AR endocrine activities were found at sample locations close to ship maintenance yards or industrial areas (i.e., stations M4, M3, and M8). The lowest increases in AR activities were found in P. viridis samples taken from stations M2 and M6. These sites are adjacent to fish and bivalve aquaculture farms located in the middle of the West and East Straits of Johore and are not directly exposed to industrial and shipping activity.
For ER activity, the mussel extract alone exhibited activities in both ER-α and ER-β bioassays. Endocrine disruption has been previously observed for mussels exposed to environmental pollution, including sewage effluent (Gagné and Blaise 2003). However, it must be noted that E2 and other steroids are naturally present in the metabolism of a variety of invertebrates, including oysters and mussels (Matsumoto et al. 1997). Zhu et al. (2003) also detected E2 in the gonadal tissues of the blue mussel, Mytilus edulis, highlighting its role in the reproductive process of mussels. Therefore, the presence of naturally occurring estrogens in the green mussel may partially account for the variability of activities on ER-α and ER-β receptors found in our study. Finally, the negative significant correlation between ER activities and lipid content might reflect an influence of lipids on the human cell-based bioassay. Therefore, the increase of ER activities for mussel extracts in the presence of E2 cannot be clearly interpreted, but it is noteworthy that a similar profile of activity can be observed between sample stations (Figure 2B,D).
Our data suggest that exposure to anthropogenic activities in near-shore coastal waters with reduced hydrodynamic mixing results in a higher EDC load and endocrine activity in P. viridis. In a previous study in our laboratory (Gong et al. 2003), sex hormone activities were measured in marine water samples collected from Singapore’s coastal environment. Although differences between exact sample locations and collection time prevent a direct comparison between studies, androgenic and estrogenic peak activities in seawater occurred in confined marine areas and declined rapidly with distance from the coastline. Similarly, investigations of well-known EDCs, including POPs, in harbors in Japan have revealed distinct spatial relationships of contamination, with peak concentrations occurring in the innermost and most confined areas of the harbors (Hosokawa et al. 2003). It is known that coastal waters that receive inputs of pollutants through sewage discharges readily accumulate EDCs in weakly mixed water bodies (Atkinson et al. 2003).
Relationship between the endocrine profile and POP levels in P. viridis.
In the present study, peaks of AR or ER activity in the presence of the reference hormone corresponded to the sites where heavy metal and POP contamination peak. Pearson correlation analysis shows that the concentration of total POPs (∑POPs) has a positive and significant correlation (p < 0.05) with the pattern of the AR activity of the P. viridis extracts in the presence of 0.1 nM DHT (Table 2). In contrast, no significant correlation was apparent for sex hormone activities of the mussel extracts in the presence of reference hormones and heavy metal concentrations or any measured biological parameter. This information suggests a relationship between the presence of POPs in the mussel extracts and the androgenic activity of the bioassay (Figure 3A,B). Sex hormone activities in reporter gene assays using human cell lines have been previously assayed for POPs, including chlordanes (Legler et al. 1999), DDT (Legler et al. 1999; Maness et al. 1998), and PCBs (Schrader and Cooke 2003). Endocrine disruption has also been demonstrated for mirex in mice (Dai et al. 2001) and intimated for PBDEs in a study on seals (Hall et al. 2003). Despite concerns over these EDCs, there is no previous report of a bioassay for mirex and PBDEs based on a human cell line. The sum of total POP concentrations in wet mussel tissue ranged from 14 × 10−12 to 84 × 10−12 mol/g (Table 1). After extraction and dilution, these concentrations correspond to 0.023–0.140 nM used in the bioassay, which are well below threshold concentrations observed for single contaminants previously reported (Legler et al. 1999; Schrader and Cooke 2003). Still, mixtures of single EDCs are known to induce synergistic responses in endocrine bioassays when present at levels below their individual threshold concentrations (Silva et al. 2002).
However, the association between the bioassay and a specific congener should be considered carefully. First, the effects of single chemicals are very complex, and even a single PCB congener, for example, can exhibit both estrogenic and antiestrogenic effects (Gregoraszczuk et al. 2003). Additionally, our extraction technique was designed for monitoring the summation effects of all potential EDCs present in green mussel samples. Other chemicals, including dioxins, alkyl phenols, phthalate esters, toxaphene, contaminant metabolites, estrogenic drugs, and steroids, are all known EDCs (Sonnenschein and Soto 1998) and are likely to be extracted with the solvent mixture (Camel 2000; Schmidt and Steinhart 2002). In vitro activity on HeLa cell-based assays are known to be responsive to chemicals such as phthalate esters (Zacharewski et al. 1998) and hydroxylated PCBs (Moore et al. 1997), and many xenobiotic compounds are known to have synergistic endocrine effects (Kortenkamp and Altenburger 1998). The presence of other EDCs in the mussel tissues, such as dioxins, alkyl phenols, or phthalate esters, may therefore account for the remaining variability observed for endocrine activity of the mussel extracts. Therefore, the bioassay should be regarded as a tool to monitor cumulative effects of all potential EDCs in the mussel tissue extracts, which provides a more holistic measure of the impact of complex multichemical mixtures on marine biota.
To our knowledge, this is the first reported use of a human cell-based gene receptor bioassay applied to biological samples for both AR and ER activities in the same sample. Our data show that POP levels and AR activity of the marine mussel extracts, in the presence of 0.1 nM DHT, are significantly and positively related and that the enhanced activity of reference hormones in the presence of biological extracts can be usefully applied as an indicator of EDCs in marine biota.
Figure 1 Geographical location of Singapore (A) and sampling locations of P. viridis (M1–M8) in Singapore’s coastal environment (B).
Figure 2 Sex hormone activities of extracts of P. viridis (mean ± SD) as a percentage of the reference hormone: AR agonist (A) and antagonist (B); ER-αagonist (C) and antagonist (D); and ER-βagonist (E) and antagonist (F). (A), (C), and (E) represent the activities of the mussel extracts alone. (B), (D), and (F) represent the mussel extracts in the presence of the reference hormone.
Figure 3 (A) Relationship between AR activity in the presence of DHT and total levels of POPs in P. viridis tissues (r = 0.725; p < 0.05). (B) Total levels of POPs in green mussel tissues collected around Singapore.
Table 1 P. viridis parameters used in Pearson matrix of correlation and range.
Parameter Range
AR (androgenic activity alone) 0.45–0.85%
ER-α (estrogenic α activity alone) 14.7–49.6%
ER-β (estrogenic β activity alone) 3.4–31.3%
AR + hormone (androgenic activity in presence of hormone) 112–340%
ER-α + hormone (estrogenic α activity in presence of hormone) 98–217%
ER-β + hormone (estrogenic β activity in presence of hormone) 55–116%
As (molar concentration of arsenic) 24–93 × 10−9 mol/g
Cr (molar concentration of chromium) 4.2–9.0 × 10−9 mol/g
Cu (molar concentration of copper) 53–115 × 10−9 mol/g
Ni (molar concentration of nickel) 14–49 × 10−9 mol/g
Zn (molar concentration of zinc) 0.39–1.25 × 10−6 mol/g
∑HMs (sum of the heavy metal concentrations) 0.49–1.43 × 10−6 mol/g
∑CHLs (molar concentration of chlordanes) 1.0–8.1 × 10−12 mol/g
∑DDTs (molar concentration of DDTs) 2.2–41.4 × 10−12 mol/g
∑PCBs (molar concentration of PCBs) 3.8–44.4 × 10−12 mol/g
∑PBDEs (molar concentration of PBDEs) 0.6–16.0 × 10−12 mol/g
Mirex (molar concentration of mirex) 0.08–0.62 × 10−12 mol/g
∑OCPs (sum of the molar concentrations of mirex, DDTs, and chlordanes) 5.5–50.2 × 10−12 mol/g
∑POPs (sum of the molar concentrations of OCPs, PCBs, and PBDEs) 15–84 × 10−12 mol/g
Sex ratio (ratio of female to male P. viridis samples collected) 0.25–1.00
Size (size of the mussel) 8.4–10.7 cm
Moisture (moisture content of the mussel) 78–86%
Lipid (lipid content of the mussel) 0.7–2.0%
Molar concentrations are based on wet weight.
Table 2 Pearson matrix of correlation for 23 measured parameters (biological and chemical) of the P. viridis samples.
ER-α + horm ER-β+horm AR + horm ER-α ER-β AR Cr Cu Zn As Ni ∑HMs ∑DDTs ∑CHLs Mirex ∑OCPs ∑PCBs ∑PBDEs ∑POPs Sex ratio Size Moisture Lipid
Er-α + horm 1 0.530 0.928* 0.094 0.302 0.065 −0.166 0.053 0.087 0.449 −0.248 0.096 0.638 0.650 0.631 0.648 0.145 0.570 0.582 −0.048 −0.145 −0.159 0.278
Er-β + horm 1 0.254 −0.020 0.068 −0.452 −0.149 0.088 −0.206 0.198 −0.154 −0.172 0.354 0.326 0.356 0.355 −0.076 0.397 0.252 −0.651 0.224 0.002 0.091
AR + horm 1 0.030 0.272 0.165 −0.094 0.201 0.319 0.556 −0.214 0.318 0.687 0.759* 0.692 0.707 0.357 0.533 0.725* 0.182 −0.279 −0.252 0.397
ER-α 1 0.955* 0.143 −0.528 −0.668 −0.564 −0.556 −0.318 −0.585 −0.073 −0.314 −0.172 −0.110 −0.238 −0.173 −0.230 −0.058 0.057 0.817* −0.890*
ER-β 1 0.192 −0.519 −0.500 −0.433 −0.341 −0.372 −0.449 0.213 −0.030 0.113 0.179 −0.044 −0.123 0.057 −0.046 −0.126 0.728* −0.749*
AR 1 0.300 0.194 0.347 0.164 0.489 0.348 0.269 0.205 0.264 0.264 0.186 −0.450 0.165 0.427 −0.406 0.098 −0.113
Cr 1 0.624 0.503 0.684 0.779* 0.556 0.091 0.279 0.235 0.122 −0.183 0.134 0.000 0.614 −0.075 −0.719* 0.505
Cu 1 0.850* 0.802* 0.543 0.880* 0.527 0.749* 0.630 0.568 0.602 0.032 0.671 0.138 −0.255 −0.583 0.720*
Zn 1 0.640 0.606 0.997* 0.294 0.631 0.401 0.349 0.659 0.153 0.597 0.316 −0.044 −0.482 0.688
As 1 0.348 0.687 0.689 0.860* 0.797* 0.724* 0.283 0.382 0.663 0.407 −0.377 −0.797* 0.785*
Ni 1 0.630 −0.132 0.113 −0.006 −0.097 −0.037 −0.005 −0.079 0.348 0.283 −0.282 0.233
∑HMs 1 0.326 0.657 0.438 0.380 0.632 0.160 0.603 0.327 −0.065 −0.521 0.707*
∑DDTs 1 0.895* 0.985* 0.998* 0.524 −0.010 0.875* −0.007 −0.708* −0.221 0.326
∑CHLs 1 0.944* 0.923* 0.616 0.269 0.939* 0.108 −0.453 −0.449 0.615
Mirex 1 0.992* 0.504 0.082 0.880* 0.074 −0.649 −0.346 0.435
∑OCPs 1 0.544 0.031 0.896* 0.010 −0.680 −0.259 0.374
∑PCBs 1 −0.222 0.820* −0.194 −0.357 0.022 0.344
∑PBDEs 1 0.105 0.171 0.479 −0.468 0.465
∑POPs 1 −0.064 −0.498 −0.239 0.505
Sex ratio 1 −0.238 −0.476 0.225
Size 1 0.171 −0.123
Moisture 1 −0.899*
Lipid 1
horm, hormone.
*Statistically significant values (p < 0.05).
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7039ehp0112-00147215531430ResearchArticlesCumulative Dietary Energy Intake Determines the Onset of Puberty in Female Rats Odum Jenny 1Tinwell Helen 1Tobin Graham 2Ashby John 11Syngenta Central Toxicology Laboratory, Macclesfield, Cheshire, United Kingdom2Harlan Teklad UK, Bicester, Oxfordshire, United KingdomAddress correspondence to J. Ashby, Syngenta Central Toxicology Laboratory, Alderley Park, Macclesfield, Cheshire, SK10 4TJ UK. Telephone: 44-0-1625-512833. Fax: 44-0-1625-590249. E-mail:
[email protected] authors are employed by Syngenta and Harlan Teklad.
11 2004 21 7 2004 112 15 1472 1480 17 2 2004 21 7 2004 Publication 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. Laboratory animal diets for studies to determine the endocrine-disrupting potential of chemicals are under scrutiny because they can affect both assay control values and assay sensitivity. Although phytoestrogen content is important, we have previously shown that a phytoestrogen-rich diet and a phytoestrogen-free diet were equally uterotrophic to rats and advanced vaginal opening (VO) when compared with the standard diet RM1. Abolition of the effects by the gonadotrophin-releasing hormone antagonist Antarelix indicated that these effects were mediated through the hypothalamus–pituitary–reproductive organ axis. In the present study, we investigated the relationship between cumulative energy intake and sexual maturation in female rats. Infant formula (IF) at different concentrations and synthetic diets, with a wide range of metabolizable energy (ME) values, were used to modulate energy intake. Increasing energy intake was associated with an increase in uterine weight (absolute and adjusted for body weight) for both IF and the synthetic diets. In both cases, the increased uterine weight was directly proportional to energy intake. Body weight was unaffected by IF consumption but, in the case of the diets, was increased proportionally with energy consumption. Antarelix abolished the uterine weight increases with both formula and the diets, whereas body weight was unaffected. The mean day of VO was also advanced by high-ME diets and IF, whereas body weight at VO was unaffected. VO occurred at an energy intake of approximately 2,300 kJ/rat determined by measuring total food intake from weaning to VO, indicating that this cumulative energy intake was the trigger for puberty. ME is therefore a critical factor in the choice of diets for endocrine disruption studies.
energy intakemetabolizable energyphytoestrogenspubertysoyuterotrophic assay
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The choice of laboratory animal diet for rodent studies to determine the endocrine-disrupting potential of chemicals is currently under intense scrutiny (Lawton 2003; Odum et al. 2001; Owens et al. 2003; Owens and Koëter 2003; Thigpen et al. 2003). This is because the diet selected can affect both assay control values and assay sensitivity; for example, uterine weight in control animals needs to be low to maximize the dynamic range of the uterotrophic assay. One contributing factor is the phytoestrogen content of the diet. Most of the commonly used laboratory animal diets are formulated with soy extracts, which contain the isoflavones genistein (GEN) and daidzein, and/or alfalfa (lucerne), which contains coumestrol (Patisaul and Whitten 1999). These phytoestrogens are estrogenic to rodents, causing effects such as increased uterine weight and advanced vaginal opening (VO) in immature animals, similar to effects observed with xenobiotic estrogens (Bickoff et al. 1962; Boettger-Tong et al. 1998; Casanova et al. 1999; Medlock et al. 1995; Thigpen et al. 1999; Tinwell et al. 2000; Whitten et al. 1992).
An analysis conducted as part of the recent Organisation for Economic Co-operation and Development (OECD) evaluation of the immature rat uterotrophic assay indicated that isoflavone levels greater than 325–350 mg GEN equivalents/kg diet should be avoided to maintain optimal assay sensitivity and dynamic range (Owens et al. 2003). The phytoestrogen content of diets is not, however, the only factor of importance. This is shown by our earlier demonstration that the phytoestrogen-rich diet Purina 5001 (Purina Mills, Inc., Richmond, IN, USA) and the phytoestrogen-free diet AIN-76A are equally uterotrophic to rodents, compared with the standard diet RM1, and that each is able to advance the mean day of VO in rats, again compared with RM1 (Odum et al. 2001). Further, we showed that coadministration of the gonadotrophin-releasing hormone (GnRH) antagonist Antarelix (ANT; Europeptides, Argenteuil, France) abolished the uterotrophic activity of both diets, indicating that these effects were mediated at the level of the hypothalamus to influence GnRH secretion (Odum et al. 2001). ANT is a synthetic peptide that was shown to be a GnRH antagonist in several animal models, including suppression of ovulation in rats and leutinizing hormone release in rams (Deghenghi et al. 1993). In a related series of experiments, we observed a correlation between the quantity of infant formula (IF) consumed by immature rats and mice and the magnitude of the resultant uterotrophic effect (Ashby et al. 2000). The uterotrophic effects were independent of the phytoestrogen content of the IF because they were abolished by inhibition of GnRH with ANT. In contrast, the uterotrophic effect of the reference synthetic estrogen diethylstilbestrol (DES) was unaffected by ANT (Ashby et al. 2000). These findings suggest that the type of food consumed by female rodents could influence the time of their puberty but that these influences were independent of phytoestrogen intake at the levels present in the foods used in these studies.
Energy intake is known to affect the onset of puberty in mammals; for example, pigs and rats with inadequate nutrition have retarded sexual development (Frisch et al. 1975; Kirkwood et al. 1987; Trentacoste et al. 2001). Energy balance in mammals is controlled by a series of complex central mechanisms that allow adaptive responses to situations of energy abundance or insufficiency. Two of the key hormones are leptin and ghrelin, which act as signals at either end of the spectrum (Zigman and Elmquist 2003). Leptin is secreted by adipocytes in response to increased food intake and energy balance. Its action on the brain and peripheral tissues results in activation of pathways suppressing food intake and increasing energy expenditure (Friedman and Halaas 1998). Ghrelin is released from endocrine cells in the stomach in response to decreased food intake and has the opposite effect to leptin (Gualillo et al. 2003). A definitive role for leptin in the onset of puberty has not yet been demonstrated (Ahima et al. 1977; Cunningham et al. 1999), but the importance of energy balance in sexual development led us to consider whether the effects described previously (Odum et al. 2001) were associated with the metabolizable energy (ME) of the diets/formulas evaluated and hence energy intake during the prepubertal period. However, the range of the ME densities of the diets used was small, and no useful correlation was found (Odum et al. 2001). Subsequently, Thigpen et al. (2002, 2003) evaluated several proprietary rodent diets containing phytoestrogens and with a wider range of ME densities. They observed a primary correlation of the phytoestrogen level of the diet, and a secondary correlation of the ME density of the diet, with the uterotrophic/VO activity of the diet to immature mice. However, food intake by the mice was not monitored, and this precluded accurate assessments of energy intake. Further, the analysis was complicated by studying concomitant differences in both dietary phytoestrogen levels and dietary ME values.
In the present experiments, we have investigated the relationship between total (cumulative) energy intake and sexual maturation in female rats. Two types of dietary modification were used. In one, IF (at different concentrations) and sugar solutions were used to modulate metabolic energy intake. In the second, open-formula synthetic phytoestrogen-free diets, with a wide range of metabolizable energies (8–22 kJ/g), were evaluated. Some experiments were conducted in the presence and absence of ANT to evaluate of the role of the hypothalamus–pituitary–reproductive organ axis on the effects observed.
Materials and Methods
Chemicals.
DES (> 99% pure), glucose, sucrose, and arachis oil (AO) were obtained from Sigma Chemical Co. (Poole, Dorset, UK). ANT was a gift from Europeptides, a Division of Asta Medica (Argenteuil, France). GEN was obtained from ChemService (West Chester, PA, USA). Halothane anesthetic was obtained from AstraZeneca (Alderley Park, Cheshire, UK).
Animals.
Alpk:APfSD (Wistar-derived) rats, obtained from the AstraZeneca breeding unit (Alderley Park, Macclesfield, UK), were used in all studies. Studies were performed in accordance with the U.K. Animals (Scientific Procedures) Act (1986). Animal care and procedures were carried out according to in-house standards as described previously (Odum et al. 2001).
In the uterotrophic assays with IF and glucose, we used rats that were postnatal days (PND)21–22 on arrival into the laboratory (where birth is PND0). In the uterotrophic assays with the synthetic diets, we used weanling rats on PND18–19. This was because the former studies were carried out using the specifications described by Odum et al. (1997), and the latter studies followed the specifications required by the OECD evaluation of the uterotrophic assay (Kanno et al. 2003a, 2003b). Control uterine blotted weights for both series of studies were similar, generally between 20 and 30 mg. The sexual maturation study with IF was carried out in weanling rats on PND21–22 on arrival in the laboratory, whereas the study with the synthetic diets used weanling rats that were PND18–19 on arrival. To avoid confounding effects due to litter-mates or initial body weights, the weanling rats were taken from multiple litters and were randomly allocated to groups such that the initial group mean body weights were similar within experiments. In all experiments, animals were weaned on RM3 diet (Special Diet Services Ltd., Witham, Essex, UK) in the breeding unit and then fed the appropriate test diet upon arrival at the laboratory and for the duration of the assay. All solid diets, fluid diets, and drinking water solutions were available ad libitum.
IF and sugar drinks.
IF (Infasoy; Cow and Gate, Trowbridge, Wiltshire, UK) was purchased from several outlets in Cheshire and Staffordshire (UK). It was prepared according to manufacturer instructions using sterile deionized water (considered as 100% strength throughout). The basic constituents are shown in Table 1. In one study, a dilute solution (33% recommended strength) and a more concentrated solution (200%) of IF were used. A glucose (6.6% wt/vol) solution in water was similarly prepared and evaluated. All drinking water solutions were prepared and replaced on a daily basis.
Diets.
Two proprietary natural ingredient diets, Rat and Mouse No. 3 (RM3) and Rat and Mouse No. 1 (RM1) were supplied by Special Diet Services Ltd. (Witham, Essex, UK). RM1 has been consistently used as the standard diet in our postweaning studies since 1997 (Odum et al. 2001). A series of open-formula synthetic diets with a range of MEs (diets A–E) were produced by Harlan Teklad UK (Bicester, Oxfordshire, UK) and were based on AIN-76A (Knapka 1983). The constituents and proportions for all diets used, as well as the unique Harlan Teklad reference numbers for diets A–E, are listed in Table 1. AIN-76A and RM1 were included to provide links to our previous findings (Odum et al. 2001). In order to derive a wide range of ME densities, a base diet (designated diet B) was created. Diets with increasing ME densities were then achieved by substituting increasing proportions of lard for cellulose (diets C–E). A diet with an additional decrease in ME (diet A) was obtained by reducing the proportion of sucrose and maltodextrin. All diets were prepared as pellets.
We estimated ME densities of the synthetic diets using the values for protein, fat, and carbohydrate given by Blaxter (1989). The figure for casein protein takes into account the fact that it contains 10% moisture and 1% fat. The energy in the minerals and vitamins was derived from the excipients. Protein, vitamins, and fatty acids were maintained at a constant level in all the diets. The diets lowest in fat contained sufficient essential fatty acids to meet normal dietary requirements. The values for RM1 and IF were as reported by the manufacturer (Table 1). Total ME intake over the duration of the studies was calculated from solid and liquid food consumption data and the ME content of the diets and drinks.
Analysis of diets for phytoestrogens.
We analyzed IF for daidzein and GEN content using the method described previously by Odum et al. (2001) and Owens et al. (2003). The limits of detection were 0.1 μg/g diet. The phytoestrogen aglycone contents of diet B (as representative of the phytoestrogen-free synthetic diets A–E) and RM1 were determined as described in detail by Wiseman et al. (2002). Portions of the diet (200 mg) were extracted by shaking with aqueous methanol at 60°C for 1 hr. The extracts were defatted with hexane and hydrolyzed to the aglycones with dilute hydrochloric acid. The aglycones were then extracted with ether. Daidzein, GEN, glycitein, and coumestrol were detected and quantified against reference samples by liquid chromatography coupled with mass spectroscopy. Data were adjusted for extraction efficiency. Quality control was determined by the concurrent analysis of a soy flour of known daidzein and GEN content, and results were < 9% different from those expected. The limit of detection was 0.05 μg/g diet for daidzein, GEN, and glycitein and 0.1 μg/g diet for coumestrol.
Animal studies.
In all experiments, weanling rats were fed IF or synthetic diets upon arrival in the laboratory. Uterotrophic assays were based on the protocol described by Kanno et al. (2003a, 2003b) where the basic end point is uterine weight. In the sexual maturation studies, dietary modulation continued from weaning to postpuberty, and end points related to puberty (e.g., VO) were monitored. A scheme of the experiments and the hypotheses that they were designed to address are shown in Table 2.
Uterotrophic assays.
Uterotrophic assays were conducted using IF at different concentrations selected to provide a concentration-dependent response (experiments 1 and 2). In experiment 3, we administered a 6.6% glucose solution, either alone or in addition to coadministration of 5 mg/kg body weight GEN. In these experiments, the normal drinking water supply was replaced with either IF or glucose solutions. RM1 was provided as an optional solid food. Control rats were fed RM1 and water. Three uterotrophic assays were conducted using the pelleted synthetic diets (experiment 4 of 4 days’ duration and experiments 5 and 6 of 6 days’ duration). Control rats were fed RM1. Rats were housed up to five per cage. Food and fluid were available ad libitum and monitored (by cage) daily.
In experiments 1, 4, and 6, the GnRH antagonist ANT was coadministered at a dose of 300 μg/kg/day by subcutaneous (sc) injection (dosing volume, 1.5 mL/kg; Odum et al. 2001). In experiment 3, GEN was coadministered orally at 5 mg/kg/day in AO (dosing volume, 5 mL/kg). DES was used as a positive control in all studies. In experiments 1–3, DES was administered in the drinking water at either 10 or 20 μg/L, starting on the day of arrival of the rats at the laboratory and continuing throughout the experiment. In experiments 4–6, it was administered by sc injection at 5 μg/kg/day with a dosing volume of 1.5 mL/kg. Some animals received both DES and ANT or DES and AO, administered by two successive sc injections made within 5 min of each other. Rats administered DES were fed RM1. Control animals received vehicle only. Dosing of compounds (by sc or oral routes) commenced on the day after the rats had been placed on the test diets and continued daily. Animals were killed by an overdose of halothane 24 hr after the final chemical administration. Uteri were removed, blotted, and weighed as described previously (Odum et al. 1997).
Sexual maturation studies.
Weanling rats were provided with IF solutions, in place of drinking water supply, and RM1 diet in experiments 7 and 8. In experiment 9, weanling rats were fed either diet B or diet D instead of RM1. Control animals were fed RM1 in all experiments. Experiment 7 also contained a group of “heavy” control animals consisting of a group of the heaviest animals selected from the required weight range. Consequently, in the sexual maturation studies, the initial weights of the standard RM1 control group and the IF group were similar, whereas the weight of the “heavy control group” was greater. DES (30 μg/L in the drinking water) was administered in experiment 9 as a positive control with RM1 as the diet. This concentration of DES has previously been shown to decrease the mean age at VO by 7 days in the absence of changes in body weight (Odum et al. 2002). All diets and drinking water were available ad libitum. Rats in experiment 7 were housed singly, whereas rats in experiments 8 and 9 were housed in groups of five. Food and fluid consumption were monitored daily. VO was monitored daily from PND21, and individual body weights on the day of VO were recorded.
The age at first and second estrus were determined in experiment 8 by analysis of daily vaginal smears that were taken from the day of VO to PND65. First estrus was defined as the first day on which only cornified epithelial cells were observed on the vaginal smear. Second estrus was defined as the day on which a smear indicating estrus fell within a run of smears clearly showing the correct cyclic sequence of proestrus, estrus, metestrus, and diestrus.
Animals were killed on PND41, when all animals had open vaginas (experiments 7 and 9), or PND118, after second estrus (experiment 8). Liver, kidney, and uterine weights were determined at necropsy.
Statistical methods.
For uterotrophic assays, we analyzed uterine weights by covariance with the terminal body weights. Terminal body weights were analyzed by covariance with initial body weights. Differences from control values (RM1 or RM1 with AO, as appropriate) were assessed statistically using a two-sided Student’s t-test based on the error mean square from the analysis of covariance (ANCOVA). Relationships between energy intake and body or uterine weight were analyzed by linear regression.
For sexual maturation studies, analysis of variance (ANOVA) was carried out on body weights, food consumption, and organ weights. Organ weights were also analyzed by covariance with the terminal body weights (Shirley 1996). VO was analyzed by Fisher’s exact test on the proportions of animals recorded each day with VO and by ANOVA for the observed days of VO and body weights at the time of VO. Differences from control values in all cases were assessed statistically using a two-sided Student’s t-test based on the error mean square from the ANOVA or ANCOVA. Analyses were carried out with SAS software (Version 8; SAS Institute, Inc., Cary, NC, USA).
Results
Diet analyses.
The synthetic diets A–E were free from daidzein, GEN, glycitein, and coumestrol. RM1 contained low levels of the phytoestrogens daidzein, GEN, and glycitein (11, 9, and 2 μg/g diet, respectively) and non-detectable levels of coumestrol. IF contained 45.7 μg daidzein and 133.4 μg GEN per gram dry formula (glycitein and coumestrol were not analyzed). ME values for the diets and IF are shown in Table 1.
Uterotrophic assays.
In experiments 1 and 2 (Table 3), IF gave a positive uterotrophic response except when 33% IF was used (experiment 2, Table 3). All increases in uterine weight (compared with RM1 controls) occurred without significant effects on final body weights, except for the 200% IF group (Table 3). Energy intake was also increased above the RM1 controls in animals consuming IF. In experiment 2, uterine weight was increased proportionally with increasing IF concentration and energy intake (Figure 1). Coadministration of the GnRH antagonist ANT abolished the uterine weight increases induced by IF but did not affect the response given by DES (experiment 1, Table 3; Figure 2).
Administration of a solution of glucose to rats in 4-day uterotrophic assays (experiment 3, Table 3) had no effect on uterine weight. The concentrations were chosen based on the presence of 6.6% glucose in IF. Uterine weight was also unaffected by GEN at 5 mg/kg/day; this was the calculated daily intake of isoflavone in human infants consuming IF (at 100% concentration). The lack of effect is as expected from the dose response of GEN in the uterotrophic assay (Kanno et al. 2003a).
The results of the uterotrophic assays with the synthetic diets are shown in Tables 4 and 5. In all cases, uterine wet weight increased as energy intake increased in animals fed the synthetic diets. Body weights also increased, but uterine weights adjusted for covariance with terminal body weights were still increased.
In experiment 4, rats were fed diets B–D and AIN-76A for 4 days, and the energy content of the synthetic diets ranged from 12.1 to 20.3 kJ/g. Absolute and adjusted uterine weight was significantly increased from 21 mg (RM1 control) to a maximum of approximately 35 mg by all the synthetic diets. The increase was abolished by coadministration of the GnRH antagonist ANT, when all diet groups attained absolute uterine weights of 17–19 mg (Table 4, Figure 2). Coadministration of ANT to the DES group had no effect on uterine weight (Figure 2). In experiment 5, the duration of the experiment was increased to 6 days in an attempt to enhance the sensitivity of the assay. Absolute and adjusted uterine weights for rats consuming diets B–D were significantly increased to a maximum of approximately 48 mg, and energy consumption was increased concomitantly (Table 5). In experiment 6, two more diets (diets A and E) were evaluated, expanding the ME range to 8.2–22.3 kJ/g. Diets were also fed for 6 days. The body weight curves (Figure 3) display a clear relationship between increasing energy intake and body weight, with body weights of animals fed diets B, C, D, and E being significantly increased relative to the RM1 control. Total energy intake over the 6 days was proportional to the ME of the diets (Figure 4). Coadministration of ANT had no effect on body weight (Table 5). Absolute and adjusted uterine weights were again significantly increased in animals fed the synthetic diets B–E compared with those fed RM1. In animals receiving diet A, with the lowest ME, absolute uterine weight was not significantly increased, but the increase in adjusted uterine weight was significant. A plateau was reached at absolute uterine weights of approximately 50–55 mg, suggesting that this may be the limit of prepubertal stimulation of uterine growth by manipulation of energy intake (Table 5). ANT again abolished the increases in uterine growth, reducing all absolute uterine weights to 16–19 mg (Table 5). The relationship between energy intake and either final body weight, absolute uterine weight, or uterine weight adjusted for body weight, for the data from experiment 6 (Table 5), was analyzed by linear regression (Figure 5).
In the uterotrophic assays with synthetic diets, the SDs for uterine weights were generally at least double those obtained with RM1. The reason for this is not clear. When ANT was coadministered, the SDs for uterine weight were smaller and less variable. We carried out an experiment in which rats were fed diet D under conditions of both single and group housing—in case competition for food within the cage was a factor—but SDs in both cases were similarly large (data not shown). We also attempted to reduce the uterine weight of rats fed diet D to that of rats fed diet B by restricting food (and therefore caloric) intake. The restriction achieved, however, was only partial because the animals ate their allocated amount of diet D so quickly that they would have been without food for long periods of the night. This was considered to be unacceptable for our animal license, and therefore the rats were given more food. A total energy intake of 889 kJ over 6 days was achieved in restricted animals fed diet D compared with 831 and 935 kJ for animals fed diets B and D, respectively, ad libitum. Uterine weights were 47.5 ± 14.4 mg in animals fed restricted amounts of diet D compared with 44.4 ± 9.3 and 51.0 ± 6.9 mg for animals fed diets B and D, respectively, ad libitum. This followed the trend established in Figure 4, but the reduction in uterine weight for the restricted animals was not statistically significant.
Sexual maturation studies.
Rats consuming IF (at 100% concentration) achieved VO approximately 2 days earlier than those fed RM1 alone, whereas body weights at VO were lighter (experiment 7, Table 6) or unaffected (experiment 8, Table 6; Figure 6). The group of heavy control animals (experiment 7) was significantly heavier at VO than were the “standard” controls, but age at VO was not different. Age at first and second estrus was significantly reduced by approximately 2 days for rats consuming IF (experiment 8, Table 6). No differences in the length of the estrus cycle were observed between groups.
Animals fed the synthetic diets D and B had increased body weights (from PND19.5 and 28.5, respectively) when compared with RM1 controls (data not shown). Rats receiving RM1 plus DES had reduced body weights from PND33.5 forward (experiment 9, Table 7). Consumption of diet D was reduced from PND26.5 compared with that of the RM1 controls, whereas consumption of diet B was similar to that of controls (data not shown). Compared with the control group fed RM1, VO occurred 1.3 days earlier in rats fed diet B and 5.2 days earlier in rats fed diet D (both advances statistically significantly different from the RM1 values and from each other). Body weight at VO for animals fed diet B was not different from those fed RM1, but animals fed diet D were significantly lighter at VO (the difference between diets B and D was also statistically significant; Table 7). Cumulative energy intake at the age of VO gave a consumption of approximately 2,300 kJ/rat at the time of VO for each of the three diets (Table 7, Figure 6). There was no statistical difference in energy intake up to the age of VO across the three diets. The figure of approximately 2,300 kJ/rat to day of VO is similar to the values observed in the IF studies (Table 7, Figure 6).
DES treatment resulted in an 11.2 day advance in VO, and the body weight at VO and the energy intake up to the time of VO were dramatically reduced (Table 7). DES intake was calculated to be 5.3 μg/kg/day over the whole study.
Absolute and adjusted liver and kidney weights were increased in animals fed diets B and D. There were no changes in uterine weights with either synthetic diet (Table 8), nor were there any organ weight changes after DES treatment. No organ weight changes were observed in animals fed IF (data not shown). The increase in relative liver and kidney weights was observed for AIN-76A previously (Odum et al. 2001) and has no obvious explanation.
Discussion
There is a current concern that the diets used in rodent endocrine toxicity studies may influence, either qualitatively or quantitatively, the outcomes of those studies (Ashby et al. 2000; Boettger-Tong et al. 1998; Casanova et al. 1999; Lawton 2003; Odum et al. 2001; Owens et al. 2003; Thigpen 1999, 2002, 2003; Tinwell et al. 2000). Most of the studies cited above have concentrated on the possible effects of dietary phytoestrogen on rodent sexual maturation, but some studies have also attempted to evaluate the possible effects of changes in the ME of the diets (Ashby et al. 2000; Odum et al. 2001; Thigpen et al. 2002; 2003; Tinwell et al. 2000). However, these attempts have been rendered opaque by the concomitant effects induced by dietary phytoestrogens, failure to monitor food intake (reliance being placed solely on the stated ME values of the diets), and the use of proprietary diets that provide only a narrow range of MEs. The present studies have overcome these problems by using synthetic diets devoid of phytoestrogens—but with a wide range of ME values—and by monitoring food intake, leading to accurate assessments of cumulative energy intake. The soy-based IF was similarly studied, after establishing that the levels of phytoestrogen present in it are below those that affect the sexual maturation end points evaluated. Our conclusions from this study are as follows.
Increasing the ME density of the synthetic diet increases body weight (Tables 4 and 5, Figure 3) and total energy intake (Tables 4 and 5, Figure 4) proportionally. Body weight is increased for diets A–E over 6 days (Figure 3) and is maintained until PND41 for the two diets evaluated over that period (diets B and D, Table 7). RM1 diet, which is substantially different in its makeup from diets A–E, produces a growth curve consistent with its ME being between that of diets A and B (Figure 3).
Faced with the choice between RM1 diet and IF solution, rats select the latter, the strength of the preference being in proportion to the strength of the IF solution (experiment 2, Table 3). As the IF intake increases, so also does the total energy intake of the animals (Table 3). Unlike with the diets, increased energy intake from drinking IF is not closely associated with an increase in body weight. Only in the case of the 200% IF solution did body weight increase significantly over 4 days (Table 3), and exposures over longer periods led to variable effects on body weight (Table 6).
Increasing energy intake is associated with an increase in uterine weight for both IF (Table 3) and the diets A–E (Tables 4 and 5). In the case of IF, the increase in uterine weight over the concurrent controls was directly proportional to the percentage of energy intake via drink (Figure 1). We have shown previously (Ashby et al. 2000) that the uterotrophic activity of the IF brand used in this study is shared by two other proprietary brands of soy-based IF. We have also shown that rats elect to drink much less of a cow’s milk–based formula than they drink soy-based formula (Ashby et al. 2000). Consequently, energy intake through the cows’ milk–based formula is low (about the same as 33% IF; Table 3), and only marginal activity was observed for it in the uterotrophic assay (Ashby et al. 2000). In the case of the synthetic diets, the increase in uterine weight was proportional to the ME of the diets and to the total energy intake during the experiment (Tables 4 and 5, Figure 5). The nonresearch diets RM1 and AIN-76A also gave increases in uterine weight proportional to their ME values and total energy intake (Tables 4 and 5). Unlike with IF, body weights of the animals on the synthetic diets increased proportionally to the ME of the diets (Tables 4 and 5, Figure 5).
The uterotrophic activities of IF and the diets were abolished by coadministration of the GnRH antagonist ANT, but the uterotrophic activity of DES was unaffected (Tables 3–5, Figure 2). This confirms that the uterotrophic activity of DES, and by analogy the dietary phytoestrogens studied by Thigpen et al. (2003), act directly on the uterus, whereas the uterotrophic activity of the present synthetic diets, and IF, is stimulated by their effects on the hypothalamus. The independence of the uterotrophic effects from changes in body weight, discussed above for IF, is shown by the data in Figure 5 to apply equally to the diets. Figure 5 establishes that body weight increases induced by the diets, themselves in proportion to the ME of the diet, are not affected by ANT, whereas the concomitant increases in absolute and adjusted uterine weights are abolished by it. There are, therefore, two discrete influences at work in this study: increases in energy intake usually lead to increases in body weight, and increases in energy intake always lead to increases in uterine weight. Body weight is not always a good indicator of energy balance (the difference between intake and expenditure). It may be influenced by differences in gut contents, particularly when the nature of the diets is different (e.g., dry matter digestibility), and by the nature of the body constituents (body fat has eight times the energy content of bone-free lean tissue per gram) (Armitage et al. 1983). Differences in protein:energy ratio, such as seen in the diets in this study, can significantly affect the relative deposition of body fat and lean tissue (Blaxter 1975).
The prepubescent increase in uterine weight, and the advance in puberty induced by IF, was instigated by the rats through their voluntary drinking of IF in preference to eating the RM1. When presented with a 6.6% glucose solution (equivalent to the glucose content of IF), they obtained only approximately 20% of their energy intake from this solution, and this was insufficient to increase uterine weight (Table 3). Likewise, when the glucose solution was supplemented with 5 mg/kg GEN (the dose of phytoestrogens ingested by human infants drinking 100% IF), uterine weights did not increase significantly (Table 3). Further, the estimated daidzein and GEN intake during the sexual maturation study of IF (Table 6) was approximately 14 mg/kg, levels that are inactive in uterotrophic assays (Farmakalidis et al. 1985; Kanno et al. 2003a). The uterotrophic activity of the IF solution is therefore independent of its sugar content or its constituent phytoestrogens.
An advance in the day of VO mirrors the increases in uterine weight observed for animals maintained on diets B and D (Table 7) from weaning to PND41. Similar advances in the day of VO, together with advances in the day of first and second estrus, are seen for animals exposed to 100% IF from weaning to PND41 (Table 6). The day of VO for the four energy sources evaluated (RM1, diets B and D, and IF) correlates better with total energy intake up to the day of VO than it does with body weight on the day of VO (Figure 6). Supporting the secondary role of body weight on the day of VO is the fact that preselected heavy control animals maintained on RM1 have the same day of VO as do normal-weight control animals maintained on RM1, yet they have significantly heavier body weights at the day of VO (experiment 7, Table 6).
At the simplest level, these combined data indicate that events associated with the onset of puberty in female rats can be accelerated by increasing energy intake, enabled either by the use of high-ME diets or by the animals electing to drink large quantities of the relatively low-energy IF. The two peripubertal events monitored were the premature growth of the uterus and the early onset of puberty (VO and first estrus). These effects may be mechanistically distinct. The increases in uterine weight induced (from ~ 20 mg to ~ 40 mg) consistently fall short of the maximum uterine weight achieved at puberty, or after pseudoprecocious puberty induced by DES acting directly on the uterus (~ 130 mg). This suggests that the food effects are caused by the hypothalamus maximizing the prepubescent release of estrogens, as opposed to initiating full puberty. Previous studies (Ashby et al. 2000) have shown that the IF-induced uterine weight increase is inhibited by the estrogen receptor antagonist fulvestrant. This endogenous release of estrogens may be from the ovaries, initiated by GnRH acting on the pituitary-gonad axis via follicle-stimulating hormone, or from the adrenal glands via hypothalamic corticotrophin-releasing hormone acting on the pituitary–adrenal axis (Harvey and Everett 2003). The inhibition of uterine growth by ANT suggests the former, consistent with the demonstration by Branham and Sheehan (1995) that ovariectomy or adrenalectomy of PND6 rats decreased uterine growth. However, the early onsets of VO and, in the case of IF, of first and second estrus provide clear evidence of an advance in full hypothalamic puberty subsequent to achievement of a cumulative energy intake of approximately 2,300 kJ/rat post-weaning. Administration of ANT to rats during this period results in a total block on puberty (Ashby et al. 2002), so by definition, it was not possible use ANT to prove the involvement of the hypothalamus in these energy-induced pubertal effects.
Although there will be small variations in the efficiency with which the different proportions of dietary fat and carbohydrate in diets A–E would be converted to body fat, it is probable that the amount of fat deposited for any level of energy intake from these diets would be similar (not determined here). Body fat is an accurate measure of energy balance because it integrates the varying and sometimes small daily differences in intake and expenditure of energy. It is likely, therefore, that body fat content, rather than energy intake per se, is the critical variable. However, body weight does not provide an invariable indicator of body fat because of differences in the proportions of individual components of body weight, such as gut fill, body fat, and lean tissue—factors that may vary with the amount and nature of the diet, feeding pattern, and environment.
Earlier studies have failed to demonstrate clearly the central involvement of cumulative energy intake of puberty because of the concomitant presence of biologically active doses of phytoestrogens in the foods (Odum et al. 2001; Thigpen et al. 2002, 2003). It is therefore important to be aware of both the phytoestrogen content of diets and dietary energy intake during rodent studies evaluating the endocrine activities of chemicals. The latter will involve knowledge of the ME of the diet and awareness of differences in food intake between test and control groups.
The present observations relate directly to chemical safety assessments in rodents. However, the dramatic and continuing increase in human energy intake (Figure 7) indicates that the present observations may have a more general relevance to human health. For example, health breads such as Burgen (advertised as containing plant estrogens from soy and linseed; Allied Bakeries, Maidenhead, UK) show similar effects to those described here (Ashby and Tinwell 1998), and gross energy intake may also be associated with reports of a reduction in the age at which human females are entering puberty (Herman-Giddens et al. 1997). The present observations on IF are also relevant and are suggested to have the following implications for human infants drinking soy-based IF. First, the phytoestrogen content of these formulas leads to an average daily intake of approximately 4.5–10 mg/kg total phytoestrogens/day [Ministry of Agriculture, Fisheries and Food (MAFF) 1998; Setchell et al. 1997]. This level is known from other experiments to be devoid of reproductive effects in rodents (Lewis et al. 2003). Second, the observations made herein relate to the onset of puberty and are therefore of no relevance to infant humans exposed during the first few years of their life. Third, although the soy-based IF has the same ME as cows’ milk formulas, they may be more palatable to infants than cows’ milk formulas, and this may lead to excess energy intake, as happened in the present rodent studies. Anecdotal information indicates that mothers sometimes allow such excess intake to induce sleep. The only toxicologic implication for human infants is therefore one of possible excess calorific/energy intake.
Figure 1 Total energy intake for rats drinking IF (33%, 100%, or 200% solutions) shown plotted against the increase in uterine weight above control levels (RM1 diet and water; all animals had access to RM1 diet). R2 = 0.99, p < 0.01. Data are based on experiment 2 (Tables 2 and 3).
aUterine weight increase not significant. **Uterine weight increase significant at p< 0.01
Figure 2 The effect of ANT (0.3 mg/kg/day, sc) on adjusted blotted uterine weights of rats fed IF or synthetic diets or dosed with DES [10 μg/L in drinking water (experiment 1) or 5 μg/kg/day sc (experiment 4)] in 4-day immature rat uterotrophic assays (Tables 3 and 4, respectively). Values are ANCOVA-adjusted means.
**p < 0.01 compared with RM1 control. #p < 0.01 compared with RM1/ANT control.
Figure 3 Body weights of female rats fed synthetic diets with different ME content in the uterotrophic assay (experiment 6, Table 5). For clarity, groups receiving ANT and/or DES are not shown. Statistically significant reductions (p < 0.01 compared with RM1 control) occurred with diet A on days 2 and 3 and increases occurred with diet B from day 4 onward and with diets C, D, and E from day 2 onward.
Figure 4 The relationship between total energy intake (kJ/rat) and the ME content (kJ/g) of RM1 and diets A–E between PND19 and PND25 for groups not receiving ANT in experiment 6 (Table 5).
R2 = 1.00, p < 0.01.
Figure 5 Final body weight (A; –ANT R2 = 0.86, p < 0.05; + ANT: R2 = 0.85, p < 0.05) and absolute (B; –ANT: R2 = 0.82, p < 0.05) and adjusted (C; –ANT: R2 = 0.78, p < 0.05) uterine weight plotted as a function of increasing total energy intake for animals fed diets A–E over 6 days (experiment 6, Table 5).
Figure 6 Body weights at VO (A) and cumulative energy consumption from weaning to the mean day of VO (B) for rats in the female sexual maturation studies (experiments 8 and 9, Tables 6 and 7). Values shown are mean ± SD.
**p < 0.01 for body weights at VO for diet D (and RM1/DES 30 μg/L drinking water) compared with RM1. #p < 0.01 for body weights at VO for diet B compared with diet D; there were no statistically significant differences in energy intake between diets RM1 and IF (experiment 8) or RM1 and diets B or D (experiment 9).
Figure 7 Intake of calories (A), protein (B), and fat (C) per capita from 1961 to 2000. Plotted using data from the database of the Food and Agricultural Organisation of the United Nations (FAO 2003).
Table 1 Composition and ME content of the diets.
RM1a IF (Infasoy)b AIN-76A
Diets A–E (%)
Constituent g/100 g Constituent g/100 mL Constituent g/100 g Constituent A 02171c B 01364 C 01365 D 02332 E 01366
Wheat/barley/wheat 88.5 Glucose syrup NS Casein 20 Casein 20 20 20 20 20
middlings Carbohydrates 6.7 Sucrose 50 Sucrose 17.5 32.5 32.5 32.5 27.5
Soybean meal 6.0 Vegetable oils NS Corn starch 15 Maltodextrin 5 15 15 15 15
Whey powder 2.5 Fat 3.6 Cellulose 5 Cellulose 50 25 13.75 2.5 0
Soy oil 0.5 Soy protein isolate NS Corn oil 5 Lard 2.5 2.5 13.75 25 32.5
Minerals Minerals 0.4 Minerals 3.5 Minerals 3.5 3.5 3.5 3.5 3.5
Vitamins 2.5 Vitamins Vitamins 1 Vitamins 1 1 1 1 1
Amino acids dl-Methionine 0.3 dl-Methionine 0.3 0.3 0.3 0.3 0.3
Choline 0.2 Choline 0.2 0.2 0.2 0.2 0.2
Ethoxyquin 0.001 Ethoxyquin 0.001 0.001 0.001 0.001 0.001
Total protein content (% wt/wt) 14.7 Total protein content (% wt/vol) 1.8 Total protein content (% wt/wt) 20 Total protein (% wt/wt) 20 20 20 20 20
Total ME (kJ/g diet) 10.9 Total ME (kJ/g diet) 2.8 Total MEd (kJ/g diet) 15.7 Total MEd (kJ/g diet) 8.2 12.1 16.2 20.3 22.3
a All values for RM1 are as stated on the manufacturer’s data sheet.
b Major constituents as stated on the Infasoy packaging; the quantities of glucose syrup, vegetable oils, and soy protein isolate were not specified (NS), but proportions of carbohydrates, fat, and protein were given.
c Unique Harlan Teklad reference numbers of the synthetic diets.
d ME was calculated using the following values (kJ/g constituent): casein, 16 kJ/g; sucrose, 16 kJ/g; corn starch, 16 kJ/g; maltodextrin, 16 kJ/g; cellulose, 0.3 kJ/g; corn oil, 37 kJ/g; lard, 37 kJ/g; minerals, 1.9 kJ/g; vitamins, 15.7 kJ/g; dl-methionine, 17 kJ/g; choline, 0 kJ/g; ethoxyquin, 0 kJ/g. The composition of the synthetic diets A–E was based on that of AIN-76A such that the protein content was identical but the carbohydrate and fat content were adjusted to give varying total ME values.
Table 2 Experimental scheme and hypotheses.
Experiment Hypothesis Treatment Duration
Uterotrophic studies
Experiment 1 IF consumption increases uterine weight IF, ANT,a DESb 4 days (PND21–25)
ANT antagonizes IF-induced uterine weight increase
ANT does not antagonize DES-induced uterine weight increasec
Experiment 2 IF-induced uterine weight increase is dependent on IF concentration IF (33–200%), DES 4 days (PND21–25)
Experiment 3 Glucose and GEN increase uterine weight Glucose, GEN, DES 4 days (PND21–25)
Experiment 4 Consumption of synthetic diets with higher ME than RM1 increases uterine weight over 4 days Synthetic diets, ANT, DES 4 days (PND18–22)
ANT antagonizes synthetic diet-induced uterine weight increase
ANT does not antagonize DES-induced uterine weight increase
Experiment 5 Consumption of synthetic diets with higher ME than RM1 gives greater uterine weight increase over 6 days Synthetic diets, DES 6 days (PND18–24)
Experiment 6 Consumption of synthetic diets with low–high ME range shows correlation of ME with uterine weight Synthetic diets, ANT, DES 6 days (PND18–24)
ANT antagonizes synthetic diet-induced uterine weight increase
ANT does not antagonize DES-induced uterine weight increase
Sexual maturation studies
Experiment 7 IF consumption reduces age at VO IF 20 days (PND21–41)
Age-matched heavy controls have earlier VO
Experiment 8 IF consumption reduces age at VO and age at first and second estrus IF 97 days (PND21–118)
Energy intake after weaning determines age at VO
Experiment 9 Consumption of synthetic diets with higher ME than RM1 reduces age and body weight at VO Synthetic diets, ANT, DES 23 days (PND18–41)
Energy intake after weaning determines age at VO
DES treatment reduces age and body weight at VOd
Experiment 9 Consumption of synthetic diets affects organ weight Synthetic diets, DES 23 days (PND18–41)
a ANT is a GnRH antagonist used to determine whether GnRH mediates uterine weight increases.
b DES was used throughout as a positive control.
c As demonstrated previously (Ashby et al. 2000).
d As demonstrated previously (Odum et al. 2002).
Table 3 Immature rat uterotrophic assays with IF and sugar drinks (experiments 1–3).
Experiment/treatment Total energy intake (kJ)a/rat Percent energy intake as drink Absolute uterine blotted weight (mg)b Adjusted uterine blotted weight (mg)c Final body weight (g)b No.
Experiment 1
RM1 238 0 20.7 ± 2.8 20.4 52.3 ± 4.5 10
RM1, IF 100% 416 88 29.9 ± 4.9** 29.8** 53.8 ± 3.3 10
RM1, DES 10 μg/L 243 0 41.8 ± 11.9** 41.6** 53.8 ± 4.1 10
RM1, ANT 237 0 16.3 ± 1.5 16.0 52.7 ± 4.1 10
RM1, IF 100%, ANT 431 89 16.9 ± 1.1 16.7 56.1 ± 3.7 10
RM1, DES 10 μg/L, ANT 254 0 39.7 ± 13.8# 39.5# 52.8 ± 4.4 10
Experiment 2
RM1 311 0 27.3 ± 4.6 28.0 62.1 ± 5.4 9
RM1, IF 33% 341 22 31.5 ± 5.2 32.4 61.8 ± 5.8 10
RM1, IF 100% 464 62 40.7 ± 11.6** 40.5** 63.3 ± 5.2 10
RM1, IF 200% 547 75 44.7 ± 16.2** 43.3** 64.9 ± 5.3* 10
RM1, DES 10 μg/L 321 0 40.7 ± 6.0** 39.3** 65.0 ± 4.5 5
Experiment 3
RM1, AO 222 0 22.2 ± 5.8 23.3 54.5 ± 6.4 9
RM1, glucose 6.6% 333 19.6 22.0 ± 6.0 22.7 55.2 ± 7.2 9
RM1, GEN 5 mg/kg/day ND 0 21.3 ± 2.4 21.1 56.6 ± 7.4 9
RM1, glucose 6.6%, GEN 5 mg/kg/day 253 22.6 23.9 ± 5.9 24.8 54.8 ± 7.3 9
RM1, DES 20 μg/L 239 0 73.9 ± 15.3** 73.0** 57.8 ± 6.4 9
ND, not determined. DES was administered in drinking water.
a Total energy intake was calculated from the total amount of liquid and solid food consumed per rat over the duration of the study and their MEs. The ME value for RM1 was taken from the manufacturer’s data sheet; the ME value of IF was taken from information supplied by the manufacturer and adjusted for concentration where necessary; and the ME value of 16 kJ/g for glucose/sucrose was adjusted for concentration.
b Mean ± SD.
c Uterine weights adjusted for covariance with terminal body weights.
*p < 0.05 and
**p < 0.01 compared with RM1 or RM1/AO control.
# p < 0.01 compared with RM1/ANT control.
Table 4 Immature rat uterotrophic assays (4 days’ duration) using synthetic diets of different ME content (experiment 4).
Treatment Diet ME intake (kJ/g diet)a Total energy intake (kJ)b/rat Absolute uterine blotted weight (mg)c Adjusted uterine blotted weight (mg)d Final body weight (g)c No.
RM1/AO 10.9 222 21.4 ± 3.2 22.4 51.2 ± 7.2 10
Diet B/AO 12.1 243 29.2 ± 7.4** 30.7** 50.0 ± 8.0 10
AIN-76A/AO 15.7 325 35.8 ± 6.4** 34.9** 55.8 ± 8.0** 10
Diet C/AO 16.2 316 34.7 ± 9.1** 34.5** 54.0 ± 7.8** 10
Diet D/AO 20.3 218 34.4 ± 5.6** 32.5** 58.2 ± 7.6** 10
RM1/DES 5 μg/kg 10.9 434 105.1 ± 3.4** 106.1** 51.3 ± 6.6 4
RM1/ANT 10.9 222 17.1 ± 1.9 18.5 50.0 ± 6.8 10
Diet B/ANT 12.1 227 17.3 ± 2.4 18.8 49.8 ± 7.8 10
AIN-76A/ANT 15.7 336 17.8 ± 1.2 17.0 55.6 ± 6.4# 10
Diet C/ANT 16.2 314 18.1 ± 2.0 18.3 53.2 ± 8.3# 10
Diet D/ANT 20.3 422 18.8 ± 2.2 16.9 58.3 ± 6.5# 10
RM1/DES 5 μg/kg/ANT 10.9 232 119.2 ± 7.3# 120.6# 49.6 ± 7.5 4
ND, not determined. DES was administered sc.
a The ME value for RM1 was taken from the manufacturer’s data sheet.
b Total energy intake was calculated as the product of the total amount of food consumed per rat over the duration of the study and the ME of the diet.
c Mean ± SD.
d Uterine weights adjusted for covariance with terminal body weights.
**p < 0.01 compared with RM1 or RM1/AO control.
# p < 0.01 compared with RM1/ANT control.
Table 5 Immature rat uterotrophic assays (6 days’ duration) using synthetic diets of different ME content (experiments 5 and 6).
Experiment/treatment Diet ME intake (kJ/g diet)a Total energy intake (kJ)b/rat Absolute uterine blotted weight (mg)c Adjusted uterine blotted weight (mg)d Final body weight (g)c No.
Experiment 5
RM1 10.9 485 27.6 ± 3.1 31.0 57.7 ± 6.5 10
Diet B 12.1 483 36.6 ± 6.7* 40.2** 57.5 ± 5.1 10
AIN-76A 15.7 696 45.0 ± 12.4** 42.1** 66.4 ± 5.1** 10
Diet C 16.2 666 44.7 ± 7.2** 42.9** 64.9 ± 5.8** 10
Diet D 20.3 907 47.5 ± 8.3** 42.0** 70.0 ± 6.4** 10
RM1/AO 10.9 471 30.4 ± 4.0 38.1 51.8 ± 8.2** 10
RM1/DES 5 μg/kg 10.9 530 131.2 ± 18.0** 133.9** 60.3 ± 5.6** 4
Experiment 6
RM1/AO 10.9 520 26.4 ± 5.3 29.3 62.3 ± 5.4 10
Diet A/AO 8.2 426 33.2 ± 6.7 36.9* 61.0 ± 6.8 10
Diet B/AO 12.1 555 39.7 ± 7.8** 39.8** 66.5 ± 7.0* 10
Diet C/AO 16.2 897 50.8 ± 16.2** 49.0** 69.3 ± 5.0** 10
Diet D/AO 20.3 1,010 55.6 ± 18.6** 51.9** 72.2 ± 5.5** 10
Diet E/AO 22.3 481 50.9 ± 15.4** 48.0** 71.0 ± 4.1** 10
RM1/DES 5 μg/kg 10.9 434 122.4 ± 17.2** 124.6** 60.7 ± 6.3 4
RM1/ANT 10.9 493 16.1 ± 1.2 20.1 60.6 ± 4.6 10
Diet A/ANT 8.2 408 17.4 ± 1.5 22.6 58.9 ± 5.6# 10
Diet B/ANT 12.1 816 18.6 ± 2.1 21.5 62.3 ± 8.6 10
Diet C/ANT 16.2 314 19.2 ± 1.5 14.9 72.9 ± 6.3# 10
Diet D/ANT 20.3 991 18.4 ± 1.9 15.6 70.9 ± 5.1# 10
Diet E/ANT 22.3 1,131 19.1 ± 2.6 14.7 73.1 ± 6.9# 10
RM1/DES 5 μg/kg/ANT 10.9 465 153 ± 20.8# 154.5# 58.7 ± 2.7 4
DES was administered subcutaneously.
a The ME value for RM1 was taken from the manufacturer’s data sheet.
b Total energy intake was calculated as the product of the total amount of food consumed per rat over the duration of the study and the ME of the diet.
c Mean ± SD.
d Uterine weights adjusted for covariance with terminal body weights.
*p < 0.05 and
**p < 0.01 compared with RM1 or RM1/AO control.
# p < 0.01 compared with RM1/ANT control.
Table 6 Female sexual maturation of rats given IF (experiments 7 and 8).
First estrus
Second estrus
Experiment/treatment Body weight at PND21 (g) Body weight at PND41 (g) Cumulative energy intake at VOa (KJ) Age at VO (PND) Body weight at VO (g) Age (PND) Weight (g) Age (PND) Weight (g) No.
Experiment 7
RM1 48.0 ± 5.6 145.1 ± 15.5 ND 33.7 ± 1.9 111.3 ± 8.5 ND ND ND ND 10
RM1 heavy control 56.5 ± 1.7* 159.5 ± 13.2* ND 33.3 ± 1.5 124.8 ± 12.9* ND ND ND ND 10
RM1/IF 100% 48.0 ± 5.6 164.6 ± 11.8** ND 31.1 ± 1.5** 99.2 ± 12.4* ND ND ND ND 10
Experiment 8
RM1 37.1 ± 5.7 140.7 ± 11.6 2,181 ± 425 34.5 ± 2.0 102.4 ± 13.2 37.1 ± 3.9 115.2 ± 19.8 44.6 ± 5.9 149.6 ± 23.0 45
RM1/IF 100% 37.5 ± 5.7 143.2 ± 14.2 2,249 ± 368 32.4 ± 1.2** 91.5 ± 10.8 35.3 ± 2.7* 108.3 ± 18.7 42.8 ± 5.4* 148.6 ± 28.4 61
ND, not determined. Values shown are mean ± SD.
a Cumulative energy intake was calculated from the amount of IF and food (and their MEs) consumed per rat up to VO.
*p < 0.05 and
**p < 0.01 compared with RM1 control; there were no statistically significant differences in energy intake at VO between RM1 and IF (experiment 8).
Table 7 Female sexual maturation of rats fed synthetic diets (experiment 9).
Treatment Body weight at PND21 (g) Body weight at PND41 (g) Cumulative energy intake at VOa (kJ) Age at VO (PND) Body weight at VO (g) No.
RM1 40.9 ± 4.0 137.5 ± 11.9 2,404 ± 108 36.1 ± 1.7 114.2 ± 9.1 20
Diet B 38.3 ± 4.2 147.8 ± 5.4** 2,214 ± 151 34.8 ± 1.5* 117.2 ± 9.9 20
Diet D 43.5 ± 4.1** 166.1 ± 10.6** 2,281 ± 208 30.9 ± 1.0**,# 105.1 ± 8.5**, # 20
RM1/DES 30 μg/L 39.7 ± 3.0 127.1 ± 12.8** 479 ± 58** 24.9 ± 0.7** 55.7 ± 7.4** 10
Values are mean ± SD. DES was administered in the drinking water.
a Total energy intake was calculated from the amount of food (and the MEs of the diets) consumed per rat up to VO.
*p < 0.05,
**p < 0.01 compared with RM1 control.
# p < 0.01 for age and body weight at VO for diets D and B; there were no statistically significant differences in energy intake at VO between RM1 and diets B and D when either RM1 or diet B was used as the control.
Table 8 Organ weights of female rats (at PND41) fed synthetic diets (experiment 9).
Treatment Liver (g) Kidney (g) Uterus (mg) No.
RM1
Absolute 6.7 ± 0.7 1.2 ± 0.1 177 ± 43 20
Adjusted 7.2 1.3 178
Diet B
Absolute 8.2 ± 0.6** 1.8 ± 0.2** 203 ± 62 20
Adjusted 8.3** 1.8** 203
Diet D
Absolute 9.2 ± 0.9** 1.8 ± 0.2** 205 ± 43 20
Adjusted 8.5** 1.7** 203
RM1/DES (30 μg/L)
Absolute 5.8 ± 1.0 1.1 ± 0.1 188 ± 52 10
Adjusted 6.9 1.29 180
Values shown are mean ± SD. DES was administered in the drinking water.
a Organ weights adjusted for covariance with terminal body weights.
**p < 0.01 compared with RM1 control.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7022ehp0112-00148815531432ResearchArticlesEffects of 1,3-Butadiene, Isoprene, and Their Photochemical Degradation Products on Human Lung Cells Doyle Melanie 1Sexton Kenneth G. 1Jeffries Harvey 1Bridge Kevin 1Jaspers Ilona 21Environmental Sciences and Engineering, and2Center for Environmental Medicine, Asthma, and Lung Biology, University of North Carolina, Chapel Hill, North Carolina, USAAddress correspondence to I. Jaspers, Center for Environmental Medicine, Asthma, and Lung Biology, CB #7310, 104 Mason Farm Rd., Room 528 EPA Human Studies Facility, Chapel Hill, NC 27599-7310 USA. Telephone: (919) 966-8657. Fax: (919) 966-9863. E-mail:
[email protected] work was funded by U.S. Environmental Protection Agency (EPA) grants R829762 and CR829522 and American Chemistry Council grant 2324.
This publication has not been formally reviewed by the American Chemistry Council. The views expressed in this document are solely those of the authors. Although the research described in this article has been funded wholly or in part by the U.S. EPA, it has not been subjected to the agency’s required peer and policy review and therefore does not necessarily reflect the views of the agency, and no official endorsement should be inferred. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
The authors declare they have no competing financial interests.
11 2004 16 8 2004 112 15 1488 1495 11 2 2004 16 8 2004 Publication 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. Because of potential exposure both in the workplace and from ambient air, the known carcinogen 1,3-butadiene (BD) is considered a priority hazardous air pollutant. BD and its 2-methyl analog, isoprene (ISO), are chemically similar but have very different toxicities, with ISO showing no significant carcinogenesis. Once released into the atmosphere, reactions with species induced by sunlight and nitrogen oxides convert BD and ISO into several photochemical reaction products. In this study, we determined the relative toxicity and inflammatory gene expression induced by exposure of A549 cells to BD, ISO, and their photochemical degradation products in the presence of nitric oxide. Gas chromatography and mass spectrometry analyses indicate the initial and major photochemical products produced during these experiments for BD are acrolein, acetaldehyde, and formaldehyde, and products for ISO are methacrolein, methyl vinyl ketone, and formaldehyde; both formed < 200 ppb of ozone. After exposure the cells were examined for cytotoxicity and interleukin-8 (IL-8) gene expression, as a marker for inflammation. These results indicate that although BD and ISO alone caused similar cytotoxicity and IL-8 responses compared with the air control, their photochemical products significantly enhanced cytotoxicity and IL-8 gene expression. This suggests that once ISO and BD are released into the environment, reactions occurring in the atmosphere transform these hydrocarbons into products that induce potentially greater adverse health effects than the emitted hydrocarbons by themselves. In addition, the data suggest that based on the carbon concentration or per carbon basis, biogenic ISO transforms into products with proinflammatory potential similar to that of BD products.
1,3-butadieneair pollutionatmospheric chemistryhydrocarbonsin vitrointerleukin-8irradiative chambersisoprenelung epithelial cellsphotochemical products
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The interest in 1,3-butadiene (BD) is not a new topic of concern within the environmental community. The Clean Air Act Amendments of 1990 (1990) added BD to the U.S. Environmental Protection Agency (EPA) hazardous air pollutants list. It ranks 36th among the top 50 most produced chemicals within the United States [Occupational Safety and Health Administration (OSHA) 2002] and is one of the top 33 in the Toxic Release Inventory (U.S. EPA 2001). Although the only natural form of BD known is from biomass combustion during natural forest fires, it has been used in production on a large scale since the 1930s [International Agency for Research on Cancer (IARC) 1992]. Although BD is used mainly in polymer manufacturing, it can also be found in large quantities in vehicle exhaust emissions, cooking oils, fungicides (captan and captafol), and cigarette smoke (Hughes et al. 2001; IARC 1992; Sorsa et al. 1996; Thorton-Manning et al. 1997). Although reported emissions fluctuate and can reach up to 3,000 tons/year within the United States (IARC 1992), outdoor concentrations typically range as low as 1–10 ppb in rural areas, with higher peaks closer to emitting industrial facilities (Hughes et al. 2001; IARC 1992).
Isoprene (ISO), like BD, is also emitted both naturally and anthropogenically into the environment. Biologic production of ISO is driven by photosynthesis and depends on both temperature and solar radiation (Guenther et al. 1993). ISO is the most predominant hydrocarbon emitted by a number of deciduous forest species (IARC 1994). It is also released into the air from rubber, thermoplastic and polymer industries, and cigarette smoke and is produced endogenously within the human body, being exhaled during respiration [National Toxicology Program (NTP) 2002]. Worldwide, ISO global emissions from vegetation range from 175 to 503 tons/year (Guenther et al. 1993), compared with the much larger estimated BD emissions in the United States alone. Similar to BD, outdoor concentrations of ISO range from 1 to 21 ppb but are generally < 10 ppb (IARC 1994; NTP 2002; Reimann et al. 2000). Although outdoor concentrations do not seem large enough to initiate immediate concern, indoor concentrations can range 5–10 times that of outdoor concentrations because of smoke from cigarettes, which emit both ISO and BD at about 3,100 and 400 μg/cigarette, respectively, into the air (NTP 2002).
Both ISO and BD react in the atmosphere through partially known chemical mechanisms, such as reactions with hydroxyl and other radicals as well as ozone, which are all created during photochemical processes. These reactions result in the formation of chemically specified and unspecified products, including those products that are not yet fully chemically identified or quantified (Atkinson and Arey 2003; Claeys et al. 2004; Feltham et al. 2000; Geiger et al. 2003; Jenkin et al. 1998; Larsen et al. 2001). Some of the unspecified products include multi-functional carbonyls such as hydroxyl carbonyls, dicarbonyls, and hydroxyl dicarbonyls (Chien et al. 1997; Liu et al. 1999; Tuazon and Atkinson 1990; Yu et al. 1995). Known ISO photochemical degradation products include methacrolein, methyl vinyl ketone, formaldehyde, 3-methylfuran, acetaldehyde, carbon monoxide, O3, organic nitrates, isoprene monoxide, peroxyacetyl nitrate (PAN), glycolaldehyde, hydroxyacetone, glyoxal and methylglyoxal, biacetyl, and Criegee biradicals (Atkinson and Arey 2003; Carter 1996; Palen et al. 1992; Sauer et al. 1999; Tuazon and Atkinson 1990; Yu et al. 1995). Known BD photochemical degradation products include acrolein, formaldehyde, organic nitrates, butadiene monoxide, CO, carbon dioxide, O3, PAN, 1,2-epoxy-3-butene, glycoladehyde, glycidaldehyde, 3-hydroxypropionaldehyde, and malonaldehyde (Atkinson 1990; Atkinson et al. 1994; Liu et al. 1999; Wayne et al. 1991; Yu and Jeffries 1997; Yu et al. 1995). Many of these products, for example, O3 and formaldehyde, are not merely formed through reactions with ISO and BD, and are generated during other photochemical transformation processes.
In this study, A549 cells were exposed to ISO, BD, or their photochemical degradation products. A549 cells are a model of respiratory epithelial cells with some type II-like cell characteristics that have been extensively used to assess the toxicity of air pollutants. Both ISO and BD alone are known sensory irritants with observable effects to the eyes, nasal passages, throat, and lungs (Alarie et al. 1998; U.S. EPA 2003; Wilkins et al. 2001). Many of the photochemical products for both ISO and BD have known health effects that have been studied in animals, humans, or both. For ISO, according to Rohr et al. (2002), the inflammatory effects from the products generated by reacting ISO and O3 were greater than the effects of either O3 or ISO alone. Wilkins et al. (2001) observed ISO products formaldehyde, formic acid, acetic acid, methacrolein, and methyl vinyl ketone causing sensory irritations for mice. Methacrolein alone causes irritation of the upper respiratory tract, painful sensation in human nasal cavities, and sensitization of the trigeminal nerve endings (Larsen and Nielsen 2000). Methyl vinyl ketone is a direct-acting irritant that targets the upper respiratory tract causing nasal lesions (Cunningham et al. 2001). Acrolein is considered highly acutely toxic, is a known sensory and upper respiratory irritant, and causes changes in respiratory function such as decrease in respiratory rate, rhythm, and amplitude [Gomes and Meek 2002; World Health Organization (WHO) 1992]. Studies show that formaldehyde is a moderate sensory irritant with carcinogenic evidence from occupational exposures (Liteplo et al. 2002; WHO 1989). Exposures to acetaldehyde alone also caused irritation of the eyes and mucous membranes, reddening of the skin, pulmonary edema, headache, and sore throat in humans and degeneration changes in the respiratory epithelium, trachea, and larynx in rats and hamsters (WHO 1995). With respect to all of the products created during photochemical reactions, O3 has been studied the longest and most in-depth. O3 toxicity includes inflammatory responses in sensory nerves, morphologic injury with inactivation of alveolar macrophage secretory enzymes, epithelial cytotoxicity, changes in airway resistance and respiratory rate, epithelial permeability, bronchoactive challenges, and changes in other pulmonary functions (Krishna et al. 1997; Lippmann 1989; Mehlman and Borek 1987; NTP 1994; WHO 1978). O3 is also a known animal carcinogen causing lesions in both F344/N rats and B6C3F1 mice (NTP 1994). Although respiratory health effects induced by some of the photochemical degradation products of ISO and BD are known, the toxicity of the entire photochemical reaction product mixture as it would occur in the atmosphere is unknown. In this study, we interfaced human lung epithelial cells with a smog chamber, allowing us to assess the toxicity of ISO, BD, and their photochemical degradation products in parallel.
Materials and Methods
Smog chambers.
Environmental irradiation chambers (also called smog chambers) that use sunlight can be used to study systems of natural transformation chemistry of pollutants (Jeffries et al. 1976; Jeffries and Fowler 2000; Sexton et al. 2004). Dual 150,000-L University of North Carolina (UNC) outdoor smog chambers made of FEP Teflon film (Livingstone Coating Corporation, Charlotte, NC) were used as photochemical reactors during the experiments. The chambers are located in Chatham County, North Carolina. Descriptive information about the chambers has been previously published (Jeffries et al. 1976; Jeffries and Fowler 2000). These chambers are ideal for studying chemical systems that are part of the real photochemical phenomena that occur within the atmosphere because the FEP Teflon film allows ultraviolet and visible regions of sunlight to react with the chemical components in the chamber.
Each chamber experiment was given a unique identification name symbolizing the date the experiment was performed. For example, AU2703 is an experiment performed on 27 August 2003. In addition, both chambers of the dual-chamber system were used during each experiment to expose two sets of cells simultaneously to different chemical mixtures, either reacted or unreacted with natural sunlight, using the same environmental conditions.
For the experiments presented here, we used two injection protocols. For the first protocol, hydrocarbon (ISO or BD) and nitric oxide mixtures were either injected into a chamber and allowed to react with the sunlight or injected into a chamber after sundown. This protocol permitted the cells to be exposed either to photochemically generated reaction products or to the unreacted hydrocarbon and NO mixture. For these exposures, one chamber included a photochemically active system with 50 ppb NO and 200 ppb by volume (ppbV) of ISO or BD, whereas the other chamber contained the initial amount of hydrocarbon and NO but was kept in the dark (without sunlight) and therefore unreacted. These experiments were performed on different days with slightly different environmental conditions, including temperature, amount of sunlight, and humidity. In the second experimental protocol, one chamber was operated with 200 ppbV ISO and 50 ppb NO, whereas the other chamber had 200 ppbV BD and 50 ppb NO. This protocol directly compared the effects of photochemical products generated with ISO or BD under the exact same environmental conditions (sunlight, humidity, and photochemical reaction time). To prevent condensation inside the chamber after sundown, we used a chamber dehumidification system before the photochemical experiment to lower the dew point below the expected low temperature. Condensation would cause product loss because of adsorption into the moisture on the walls. In each experiment, a very small amount of carbon tetrachloride (CCl4), used as a tracer, was injected and then monitored to calculate the dilution within each chamber. During the first experimental protocol comparing ISO or BD and their photochemical products, the initial injections of 200 ppbV ISO (99%; Sigma-Aldrich, St. Louis, MO) or 200 ppbV BD (National Specialty Gases, Durham, NC) and 50 ppb NO were injected into one chamber at 4 hr before sunset (1500–1600 hr eastern daylight time) and allowed to react in the remaining sunlight. At sundown, these same initial amounts were injected into the opposite chamber side. During the second experimental protocol comparing the photochemical reaction products of ISO and BD, both 200 ppbV of ISO and BD and 50 ppb NO were added simultaneously into opposite sides of the chambers 4 hr before sunset and allowed to react with the remaining sunlight. The exposure to the lung cells began after sundown when photochemical reactions were terminated from the absence of sunlight. This short photochemical experimental design, conducted at the end of the day, stops the photo-oxidation sequence where the first-generation primary products are at their maximum concentration. Cells were exposed to the gaseous mixtures for 5 hr (Figure 1). After the exposures, all sets of cells were kept in the exposure chamber with control air plus 5% CO2 until transported to the laboratory.
Cell culture and in vitro exposure.
A549 cells, a human lung epithelial cell line that has retained several alveolar type II-like cell characteristics, were used throughout this study. A549 cells were grown on membranous support (Costar-Clear Transwell inserts; Costar, Cambridge, MA) as described by Jaspers et al. (1997) in complete medium (F12K, 10% fetal bovine serum, antibiotics; all from Invitrogen, Carlsbad, CA). Upon confluency, the medium was exchanged for serum-free medium [F12K, 1.5 μg/mL bovine serum albumin (BSA), antibiotics; all from Invitrogen] several hours before exposure. Just before transport to the smog chamber site, media located in the apical chamber were aspirated, whereas media in the basolateral compartment remained. This facilitates direct exposure of lung epithelial cells to gaseous pollutants without significant interface of media while the cells are maintained with nutrients from the basolateral side. Triplicate sets of A549 cells were separately exposed to ISO plus nitrogen oxides (NOx), BD plus NOx, photochemical degradation products of ISO plus NOx, or photochemical degradation products of BD plus NOx for 5 hr. Nine hours after exposure, cells were examined for cytotoxicity and interleukin-8 (IL-8) gene expression, as an indicator of inflammatory responses.
Primary human bronchial cells were obtained from healthy nonsmoking adult volunteers by cytologic brushing at bronchoscopy after they provided informed consent. The protocols for the acquisition of the primary human bronchial epithelial cells were reviewed and approved by the University of North Carolina Institutional Review Board. Primary human bronchial epithelial cells were expanded to passage 2 in bronchial epithelial growth medium (Cambrex Bioscience Walkersville, Inc., Walkersville, MD) and then plated on collagen-coated filter supports with a 0.4 μM pore size (Trans-CLR; Costar) and cultured in a 1:1 mixture of bronchial epithelial cell basic medium and Dulbecco modified Eagle medium:high glucose with l-glutamine with SingleQuot supplements (Cambrex Bioscience Walkersville), bovine pituitary extracts (13 mg/mL), BSA (1.5 μg/mL), and nystatin (20 U). Upon confluency, all-trans-retinoic acid was added to the medium, and air–liquid interface (ALI) culture conditions (removal of the apical medium) were created to promote differentiation. Mucociliary differentiation was achieved 18–21 days post-ALI.
Smog chamber–lung cell exposure system.
The schematic shown in Figure 1 illustrates how the smog chambers were coupled to the in vitro exposure system. Inside tissue culture incubators we placed 8-L modular, cell-exposure chambers (MIC-10, Billups-Rothenberg, Del Mar, CA) that hold the tissue culture plates. Humidification of the exposure chamber was achieved by placing a dish of sterile water inside the chamber. The 8-L cell-exposure chambers have an inlet and an outlet connection for flowing gas through the exposure chamber. Sample lines directly coupled to the smog chambers through two externally circulated sample manifolds were used to provide chamber gases to the cells during exposure (Sexton et al. 2004). Three cell-exposure chambers were used throughout these studies, two of which were supplied with the gas mixtures from the smog chambers and one of which was supplied with medical-grade clean air. For each experiment, we exposed one set of A549 cells to clean air to control for potential variations induced by tissue culture or transport of the cells. In addition, the clean air control cell-exposure chamber was used to hold the cells during preexposure and postexposure periods. The cell-exposure chambers were ventilated with either humidified medical-grade air from a cylinder or with chamber air, both of which were mixed with 5% CO2. The addition of CO2 to chamber air was achieved using small pumps on the exhaust side and mass flow controllers (AALBORG, Orangeburg, NY). Humidification of the control cell exposure chamber was achieved by passing medical-grade air from the gas cylinder through two midget impingers in series (Ace Glass, Vineland, NJ), containing 15 mL HPLC-grade water (Fisher Scientific, Fairlawn, NJ). The impingers were heated at 28°C, which resulted in 50% relative humidity to match that in the smog chambers.
Chemical analysis.
During each experiment, we used five chromatographic methods to monitor volatile organic compounds within the chambers. We used a Carle gas chromatograph (GC; Chandler Engineering, Tulsa, OK) to measure total hydrocarbon, which we used for assuring low background concentrations and measuring the initial injections. Samples were taken continually throughout the experiment, once each hour from each chamber, and analyzed with two Carle GCs using packed isothermal columns coupled to flame ionization detectors (FIDs). We used a Varian 3700 GC (Varian Inc. Scientific Instruments, Cary, NC) with electron capture detector, to measure CCl4 (our dilution tracer), PAN, and other N- or O-containing compounds; this GC was also used continually every 30 min throughout the duration of the experiment. We used a Varian 3400 capillary GC-FID with a Varian Saturn 2000 ion trap mass spectrometer (MS) to analyze air samples taken before, at the beginning, and during each exposure to help analyze for both known and unknown products created during the photochemical reactions. Formaldehyde was measured continuously, using the automated Dasgupta-diffusion-tube sampler to obtain aqueous formaldehyde, which is then mixed with buffered 2,4-pentanedione and measured with fluorescence (Dasgupta et al. 1988). O3 was measured using a U.S. EPA standard reference method based on photometry with a Thermo Environmental Instruments monitor (model 49; Thermo Environmental Instruments Inc., Franklin, MA). We measured NOx using a U.S. EPA standard reference method based on chemiluminescence with a Monitor Labs monitor (model 98-41; Teledyne Monitor Labs Inc., Englewood, CO).
Analysis of cytotoxicity and IL-8 expression.
Approximately 9 hr after exposure, basolateral supernatants from the exposed cells were collected and stored at –80°C until analysis for cytotoxicity and IL-8 expression. To determine cytotoxicity, the basolateral supernatants were analyzed for the release of cell lactate dehydrogenase (LDH) using a coupled enzymatic assay (Promega, Madison, WI), following the manufacturer’s instructions. Cytotoxicity was expressed as fold increase in LDH levels over the individual clean air control sample.
Total RNA was isolated using Trizol (Invitrogen) following the manufacturer’s instructions and analyzed for IL-8 mRNA levels by real-time reverse transcription polymerase chain reaction (RT-PCR) as described previously (Jaspers et al. 2001).
Basolateral supernatants were analyzed for IL-8 protein levels by ELISA (R&D Systems, Minneapolis, MN), following the manufacturer’s instructions. IL-8 protein levels were adjusted to account for the differences in viable cells that could produce and release IL-8 into the supernatant and expressed as fold increase over the individual clean air control sample.
Statistical analysis.
The Student’s t-test was performed to compare the means of each set of results. To use this test, we made three assumptions: a) both sets of values have approximately normal distributions; b) they have roughly the similar variances; and c) each of the samples produced independent results. Data are presented as mean ± SEM, and a p-value < 0.05 was considered to be significant.
Results
The products generated during photochemical transformations of ISO or BD were identified by GC and confirmed using GC/MS. Table 1 summarizes the average levels and maximum concentrations of the known photochemical products derived from ISO or BD during the cellular exposure period on the indicated days. During the ISO experiments, the cells were exposed to practically the same concentrations of the first-generation products, primarily methacrolein, methyl vinyl ketone, and formaldehyde (within 0.030 ppm). The BD photochemical product concentrations, primarily acrolein, within the chamber during the exposure were also very similar (within 0.020 ppm). Therefore, each set of toxicologic results from the experiments performed on different days, but with the same hydrocarbon mixture, is comparable because of similar product concentrations generated and available during the exposure period. Figures 2 and 3 illustrate the photochemical smog chemistry within the two sides of the chamber during the experiment directly comparing photochemical reaction products formed with ISO or BD. These time-series concentration plots show the concentrations of the organic and inorganic species found within each chamber throughout the experiment. The formation and degradation of the inorganic species are shown in Figures 2A and 3A. After the initial NO injection, nitrogen dioxide, PAN, and O3 are generated from the reactions between the hydroxyl radicals and the initial injections of the ISO or BD and NO. Figures 2B and 3B show the photochemical degradation of the initial ISO or BD with the formation of the first-generation photochemical products from the time of injection through the end of the exposure period. Figures 2 and 3 show that the cell exposure to the chamber mixtures occurred directly after peak concentrations of the first-generation products were generated. The products generated during photochemical transformations of ISO or BD were identified by GC and confirmed using GC/MS. Data shown in Figures 2 and 3 and Table 1 indicate that, although photochemical reactions using ISO or BD as hydrocarbon precursors generate some of the same products (e.g., O3 or formaldehyde), several products are specific for either ISO or BD. In addition, the levels of formaldehyde and O3 produced by photochemical reactions with ISO or BD are different even though the initial carbon concentration reacted within the chamber was the same. For example, the O3 levels generated by photochemical reactions with ISO and exposed to the cells ranged from 0.118 to 0.130 ppm, whereas the O3 levels generated by photochemical reactions with BD ranged from 0.146 to 0.178 ppm.
To determine whether the products generated by photochemical reactions with ISO or BD affect cell viability, we analyzed relative cytotoxicity induced by exposure to ISO, BD, or their photochemical product mixtures approximately 9 hr postexposure. Results from experiments performed on different days were combined, and cytotoxicity induced by ISO, BD, or their photochemical reaction products were expressed as fold increase over the respective control exposure to clean air. Figure 4A shows that ISO photochemical products induce a significant increase in cytotoxicity compared with ISO plus NO alone. Similarly, BD photochemical products are significantly more cytotoxic than BD plus NO alone, as shown in Figure 4B. Furthermore, directly comparing the cytotoxicity induced by photochemical products generated with ISO or BD, as shown in Figure 4C, suggests that ISO photochemical products have effects on cell viability similar to those of BD photo-chemical products. The results from the repeated ISO versus BD photochemical experiment using differentiated human bronchial cells derived from multiple individuals (Figure 4D) show that both ISO and BD photochemical products induced no significant change in cytotoxicity compared with the clean air exposure.
To examine whether photochemical reactions alter proinflammatory potential of ISO or BD, we compared the effects of ISO, BD, or their photochemical reaction products on IL-8 expression in both A549 and differentiated human bronchial cells. Figure 5A shows that ISO photochemical products induced a greater change in IL-8 expression (IL-8 protein induced) compared with clean air control. In contrast, ISO plus NO alone had no significant effect on IL-8 expression. Figure 5B demonstrates that BD photochemical products induced a significantly greater IL-8 expression compared with BD plus NO alone, which also enhanced IL-8 expression compared with the clean air control. Directly comparing the effects of photochemical products using A549 cells from either ISO or BD on IL-8 expression suggests that the products generated from ISO have a greater effect on IL-8 expression than do BD photochemical products (Figure 5C), although these data were not statistically significant (p = 0.06). The same experimental protocol was used to expose differentiated human bronchial cells to both ISO and BD photochemical degradation products. No significant changes in IL-8 release were seen (Figure 5D) in cells exposed to the photochemical mixtures compared with the air-exposed control cells.
We also measured IL-8 mRNA levels in experiments performed with A549 cells, as shown in Figure 6A and B. Similar to IL-8 protein levels released into the basolateral supernatants, IL-8 mRNA levels were enhanced by both ISO and BD photochemical products, although the levels did not reach statistical significance.
Discussion and Conclusions
Previous research has shown that many photochemical products of ISO and BD are known sensory irritants to either animals or humans. However, the toxicities of these photochemical product mixtures produced in irradiative smog chambers using realistic atmospheric chemistry have not been previously studied. The advantage of using this approach, the smog chamber–cell exposure interface, to produce the photochemical mixtures is that all of the photochemical products are generated in the proper relative ratio, including the unspecified products. In this study, we examined cytotoxicity and IL-8 gene expression induced by these photochemical gaseous mixtures using A549 cells, a human alveolar type II-like cell line, and differentiated human bronchial cells.
The data presented here demonstrate that ISO and BD photochemical products increased LDH release as well as IL-8 expression compared with their clean air controls. The IL-8 mRNA and protein data indicate that both biogenic ISO and anthropogenic BD, as they react within the atmosphere, generate products that are more potent inducers of IL-8 gene expression than the unreacted volatile organic compounds. Although exposure to ISO and BD photochemical products generated significant levels of cytotoxicity and IL-8 expression in A549 cells, no significant effects were observed in differentiated human bronchial epithelial cells. These data indicate that differentiated human bronchial epithelial cells are less sensitive to the products generated after photochemical transformation of ISO and BD than A549 cells. Differentiated human bronchial epithelial cells release and are covered by a thin layer of mucus (Gray et al. 1996), which serves as a protectant against xenobiotics and inhaled gases (Schlosser 1999). Interactions and partitioning of inhaled agents within the mucus layer covering the epithelium of the tracheal-bronchial region can prevent these agents from reaching the underlying cell layer to induce cellular responses (Medinsky and Bond 2001). Alveolar epithelial cells lack such a protective mucus layer and could therefore be inherently more sensitive to inhaled gaseous species. Hence, the differences observed here may represent regional differences in sensitivity within the respiratory epithelium. Another reason for the observed differences in responses to ISO and BD photochemical products in A549 cells and differentiated human bronchial epithelial cells is that A549 cells are an immortalized cell line, whereas differentiated human bronchial epithelial cells are primary cells with a finite life span. Previous studies have shown that exposure of primary human bronchial epithelial cells and a bronchial epithelial cell line to O3 induced greater effects in the epithelial cell line than the in primary bronchial epithelial cells (Samet et al. 1992).
One concern when analyzing the results of the photochemical degradation products for both ISO and BD was the production of O3 and its known toxic health effects. These experiments were designed to produce the smallest concentration of O3 while continuing to keep the desired primary products within realistic ambient exposure concentration ranges. Previous studies have shown that A549 epithelial cells exposed to O3 concentrations less than those produced in these studies did increase IL-8 production (Jaspers et al. 1997), which would suggest that O3 is the major photochemical product causing the increase in IL-8 production. However, during the side-by-side experiment of photochemical products of ISO and BD, the average concentration of O3 inside the chambers was 120 ppb for ISO and 140 ppb for BD. Hence, if IL-8 production was solely based on O3 concentrations, the photochemical products of BD would produce a greater IL-8 response than would those of ISO. Because this is not the case, these data indicate that O3 is not the sole inducer of IL-8 protein secretion, but that other photochemical products generated during the reactions of ISO or BD with sunlight are causing the increase in IL-8 production. In addition, this suggests that although O3 concentrations are a good indicator of the adverse health potential of photochemical smog, one has to examine the entire photochemical mixture to estimate its toxic effect on the exposed population.
Analyses by GC-FID and GC/MS of resulting photochemical ISO and BD atmospheric reaction products showed the formation of many known products. The main ISO first-generation photochemical products formed were methacrolein, methyl vinyl ketone, formaldehyde, and O3, whereas BD formed acrolein, formaldehyde, and O3. Both experiments produced trace amounts of acetaldehyde, CO, PAN, and their respective monoxides. The sample results from GC and GC/MS analysis have not been explored for unknown products. Thus, several other photochemical products, besides O3, could have significant effects on IL-8 production in human lung cells. Data derived from these experiments could suggest potential agents in both the ISO and BD experiments that enhanced IL-8 production. In addition, in this article we describe the analysis of only known photochemical products formed during the ISO and BD reactions. Extensive chemical mechanism studies have been reported in the literature for both pollutants (Atkinson 1990; Atkinson and Arey 2003; Atkinson et al. 1994; Carter 1996; Liu et al. 1999; Palen et al. 1992; Sauer et al. 1999; Tuazon and Atkinson 1990; Wayne et al. 1991; Yu and Jeffries 1997; Yu et al. 1995), but the complete carbon balances have yet to be identified. Some of the unspecified products include multifunctional carbonyls such as hydroxyl carbonyls, dicarbonyls, and hydroxyl dicarbonyls (Chien et al. 1997; Liu et al. 1999; Tuazon and Atkinson 1990; Yu et al. 1995). Besides the mechanistic history of both chemicals, other experimental limitations of both sample collection and exposure design did not allow for the lung cells to be exposed to more polar compounds, nor could these compounds be quantified. Ongoing changes in our experimental setup will allow for these more polar products to be studied in the future.
In this study, we evaluated the quantification of inflammatory potential for primary photochemical products. Within the atmosphere these primary products typically continue to react, forming secondary and tertiary products. For outdoor exposures, then, the results shown here emphasize, by design, the primary pollutants. These first-generation products are continuously being produced as the precursors are continuously being emitted. But these primary pollutants continue to react as they are formed, not only limiting their maximum concentration but also producing secondary and tertiary products, which themselves have toxicity characteristics. The overall toxicity of pollutants must allow inclusion of residual primary, secondary, and tertiary products formed. Forthcoming experiments will include the comparison of primary, secondary, and tertiary products for different toxicity end points.
In this study we have demonstrated the importance of considering photochemistry when making decisions for outdoor air quality guidelines. Currently, air quality models include a predictive but not complete ISO mechanism; that is, they do not include detailed chemistry of the products formed. The data presented here demonstrate that the first-generation ISO photochemical products are more toxic than ISO itself, which indicates the need to better understand and to include the chemical mechanisms of the photochemical products in air quality models. Some studies suggest that ISO oxidation can contribute significantly to the total O3 production rate during O3 episodes (Biesenthal et al. 1997). O3 and its known adverse effects are of great health concern when creating guidelines related to exposure and release of pollutants that have the potential to produce a large amount of O3 once released into the atmosphere. This indicates the importance of increasing the inclusion of detailed chemistry of chemicals known to be biogenic such as ISO in photochemical models used to investigate O3 formation and health concerns to rural and urban outdoor exposures. This would enable people within the risk assessment community to make more realistic exposure guidelines using all sources of relevant toxicity, including nonanthropogenic sources that were previously overlooked.
Figure 1 Schematic for the smog chamber–lung cell exposure setup.
Figure 2 Representative time-series concentration plots for ISO photochemical reaction products for the experiment performed 13 October 2003. Abbreviations: LDT, local daylight time; ppmC, parts per million carbon; VOC, volatile organic compound. (A) Time course of NO2 and O3 formation after photochemical reaction of ISO and NOx. (B) Decay of ISO and production of methacrolein, methyl vinyl ketone, formaldehyde, and PAN (the concentration for ISO is given on the left y-axis, and the concentration for the other compounds is given on the right y-axis). The 200 ppbV ISO and 50 ppb NO underwent photochemical reactions from 4 hr before sunset until sundown, producing primarily first-generation photochemical degradation products. The dashed lines represent the 5-hr period the cells were exposed to the chamber contents.
Figure 3 Representative time-series concentration plots for BD photochemical reaction products for the experiment performed 13 October 2003. Abbreviations: LDT, local daylight time; ppmC, parts per million carbon; VOC, volatile organic compound. (A) Time course of NO2 and O3 formation after photochemical reaction of ISO and NOx. (B) Decay of BD, and production of acrolein, formaldehyde and PAN (the concentration for BD is given on the left y-axis, and the concentration for the other compounds is given on the right y-axis). The 200 ppbV BD and 50 ppb NO underwent photochemical reactions from 4 hr before sunset until sundown, producing primarily first-generation photochemical degradation products. The dashed lines represent the 5-hr period the cells were exposed to the chamber contents.
Figure 4 Analysis of cytotoxicity induced by exposure to NO (50 ppb) and ISO (200 ppbV), BD (200 ppbV), or their photochemical degradation products and expressed as fold increase of LDH over the control (mean ± SEM). (A) ISO without and with photochemical reaction. (B) BD without and with photochemical reaction. (C) ISO in one chamber and BD in the other chamber with photochemical reaction. (D) ISO in one chamber and BD in the other chamber with photochemical reaction. A549 cells were used in (A, B, and C) and differentiated human bronchial cells were used in (D); in all experiments, cells were exposed to mixtures for 5 hr. After exposure, supernatants were collected and evaluated for cytotoxicity (LDH release). See “Materials and Methods” for details of experiments. The dashed line indicates the normalized control value. *Significantly different from the control (p < 0.05). **Significantly different from the other side of the chamber (p < 0.05).
Figure 5 Analysis of IL-8 protein induced by exposure to NO (50 ppb) and ISO (200 ppbV), BD (200 ppbV), or their photochemical degradation products and expressed as fold increase over the control (mean ± SEM). (A) ISO without and with photochemical reaction. (B) BD without and with photochemical reaction. (C) ISO in one chamber and BD in the other chamber with photochemical reaction. (D) ISO in one chamber and BD in the other chamber with photochemical reaction. A549 cells were used in (A, B, and C) and differentiated human bronchial cells were used in (D); cells in all experiments were exposed to these mixtures for 5 hr. After exposure, supernatants were collected from the basolateral chambers and analyzed for IL-8 protein levels. See “Materials and Methods” for details of experiments. The dashed line indicates the normalized control value. *Significantly different from the control (p < 0.05). **Significantly different from the other side of the chamber (p < 0.05).
Figure 6 Analysis of IL-8 mRNA levels released by exposure to NO (50 ppb) and ISO (200 ppbV), BD (200 ppbV), or their photochemical degradation products in A549 cells and expressed as mean fold increase over the control (mean ± SEM). GADPH, glyceraldehyde-3-phosphate dehydrogenase. (A) ISO without and with photochemical reaction. (B) BD without and with photochemical reaction. A549 cells were exposed to these mixtures for 5 hr; after exposure, total RNA was isolated and analyzed for IL-8 mRNA levels by real-time RT-PCR. See “Materials and Methods” for details of experiments. The dashed line indicates the normalized control value.
Table 1 Chemical analysis of chamber constituents.
ISO
MACR
MVK
ISO MON
BD
Acrolein
Acetaldehyde
Form
PAN
O3 NO
NO2
Chamber side Ave Max Ave Max Ave Max Ave Max Ave Max Ave Max Ave Max Ave Max Ave Max Ave Max Ave Max Ave Max
AU2703 ISO+NO+light vs. ISO+NO
Light 0.076 1.138 0.201 0.209 0.176 0.228 0.002 0.024 0.0 0.0 0.0 0.0 0.030 0.037 0.161 0.177 0.011 0.011 0.130 0.155 0.0 0.0 0.026 0.030
Dark 0.870 1.054 0.032 0.052 0.005 0.032 0.001 0.005 0.0 0.0 0.0 0.0 0.023 0.034 0.018 NA 0.001 0.001 0.008 0.012 0.0 0.0 0.026 0.077
ST1003 BD+NO+light vs. BD+NO
Light 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.042 0.777 0.197 0.237 0.013 0.047 0.096 0.109 0.001 0.002 0.178 0.210 0.005 0.018 0.012 0.025
Dark 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.593 0.738 0.029 0.171 0.010 0.054 0.004 0.004 0.001 0.001 0.011 0.295 0.010 0.031 0.022 0.045
ST2403 BD+NO+light vs. BD+NO
Light 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.125 0.713 0.184 0.255 0.023 0.044 0.083 0.093 0.001 0.002 0.154 0.203 0.001 0.022 0.001 0.013
Dark 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.686 0.753 0.017 0.033 0.005 0.017 0.004 0.004 0.001 0.001 0.011 0.060 0.001 0.019 0.022 0.037
OC1303: ISO+NO+light vs. BD+NO+light
ISO 0.116 1.040 0.176 0.202 0.153 0.205 NA NA 0.0 0.0 0.0 0.0 NA NA 0.102 0.103 0.010 0.011 0.118 0.156 0.001 0.005 0.013 0.022
BD 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.170 0.625 0.197 0.206 NA NA 0.087 0.092 0.001 0.002 0.146 0.177 0.001 0.007 0.012 0.024
Abbreviations: AU, August; Ave, average concentration of exposure over the 5-hr period; Form, formaldehyde; ISO MON, ISO monoxide; MACR, methacrolein; Max, maximum concentration produced during the experiment; MVK, methyl vinyl ketone; NA, not available; OC, October; ppmC, parts per million carbon; ST, September. Light indicates the side that was photochemically reacted during each experiment, and dark indicates the side that remained unreacted.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7083ehp0112-00149615531433ResearchArticlesLead Sources in Human Diet in Greenland Bjerregaard Peter 1Johansen Poul 2Mulvad Gert 3Pedersen Henning Sloth 3Hansen Jens C. 41National Institute of Public Health, Copenhagen, Denmark2National Environmental Research Institute, Roskilde, Denmark3Primary Health Care Center, Nuuk, Greenland4Centre for Arctic Environmental Medicine, University of Aarhus, Aarhus, DenmarkAddress correspondence to P. Bjerregaard, Section for Research in Greenland, National Institute of Public Health, Svanemøllevej 25, DK-2100 Copenhagen Ø, Denmark. Telephone: 45-3920-7777. Fax: 45-3927-3095. E-mail:
[email protected] study was supported by Karen Elise Jensen’s Foundation.
The authors declare they have no competing financial interests.
11 2004 21 7 2004 112 15 1496 1498 9 3 2004 21 7 2004 Publication 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 blood lead levels have declined in Greenland, they are still elevated despite the fact that lead levels in the Greenland environment are very low. Fragments of lead shot in game birds have been suggested as an important source of dietary exposure, and meals of sea birds, particularly eider, contain high concentrations of lead. In a cross-sectional population survey in Greenland in 1993–1994, blood lead adjusted for age and sex was found to be associated with the reported consumption of sea birds. Participants reporting less than weekly intake of sea birds had blood lead concentrations of approximately 75 μg/L, whereas those who reported eating sea birds several times a week had concentrations of approximately 110 μg/L, and those who reported daily intake had concentrations of 170 μg/L (p = 0.01). Blood lead was not associated with dietary exposure to other local or imported food items.
dietGreenlandInuitleadlead shotsea birds
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Lead has been recognized as a poison for millennia and has recently been the focus of public health regulations in most of the developed world. Consequently, fatalities and symptomatic lead poisoning have declined dramatically during the latest decades and are continuing to decline (Kaufmann et al. 2003). In recognition of the particular sensitivity of the developing brain to lead effects, much of this legislation has addressed the prevention of childhood lead poisoning. Of particular importance is the accumulation of data suggesting that there are toxicologic effects in children at low levels of exposure (Winneke et al. 1996). Lanphear et al. (2000) found that deficits in cognitive and academic skills in children 6–16 years of age were associated with lead exposure at blood lead concentrations < 50 μg/L. In addition, evidence shows that certain genetic and environmental factors can increase the detrimental effects of lead on neural development (Lidsky and Schneider 2003; Long et al. 2002). Long-term deficits in cognitive function are the principal effects of lead exposure in children and can be modeled in experimental animals (Guilarte et al. 2003). During the past two decades, the proportion of U.S. children who have blood lead concentrations ≥100 μg/L declined by > 80% after the elimination of leaded gasoline and lead solder from canned foods and a ban on leaded paint used in housing (Lanphear et al. 2003). Furthermore, lead is associated with elevated blood pressure and cardiovascular mortality. The association is weak, but there is a small dose response through the range of blood concentrations ≥350 μg/L (Hertz-Picciotto and Croft 1993).
Blood lead levels in samples collected before 1980 from the indigenous (Inuit) adult population of Greenland were found to be similar to those of populations in western European cities, where leaded gasoline was the main source (Hansen 1981; Hansen et al. 1983). Because local sources of lead in Greenland were considered unlikely to be significant, the cause of the unexpected high levels was proposed to be long-distance atmospheric transport of lead particles in combination with an increased intestinal absorption due to a diet low in calcium and rich in iron and protein (Hansen 1988; Milman et al. 1994). This assumption was supported by the finding that lead was being transported and deposited in the Greenland ice cap from remote sources (Boutran et al. 1995). Further support for the assumption is that in samples collected after 1980, blood lead concentrations have gradually declined in parallel with the reduced use of leaded gasoline on a global scale (Hansen et al. 1991). However, blood lead levels in Greenland appear still to be elevated compared with those in other Arctic regions and Scandinavia. Levels among Greenland Inuit mothers (geometric means, 31–50 μg/L) were found to be similar to the moderately increased levels among some of the Canadian Inuit [Arctic Monitoring and Assessment Programme (AMAP) 2003; Bjerregaard and Hansen 2000].
In Canada, elevated levels of blood lead in children were proposed to be caused by the consumption of birds containing lead shot (Smith and Rea 1995), and high levels of lead were subsequently detected in bird meat (Scheuhammer et al. 1998). Later research on lead isotope signatures in the Canadian Arctic has indicated that elevated blood lead levels were likely caused by the use of lead shot and thus its presence in the wild game consumed (Dewailly et al 2000). High levels of lead in tissue were also found in Greenland by Johansen et al. (2001), who concluded that birds killed by lead shot probably is the most important source of lead in the diet of many people in Greenland. In 2004, lead shot is still commonly used in Greenland for hunting, and a recent study has shown that game birds contain even higher concentrations of lead than found earlier (Johansen et al. 2004). The lead shots are fragmented on impact, and the meat of the birds is consequently contaminated by microscopic particles of lead. These researchers concluded that in some cases safe limits of lead intake by humans would be largely exceeded.
The purpose of the study reported here was to analyze the association of blood lead with the consumption of traditional and store-bought food items in a cross-sectional population survey in Greenland conducted in 1993–1994. Since then, there have been no changes in the use of lead shot for hunting. Also, traditional food (fish, birds, and mammals) still constitutes an important part of the diet in Greenland, although with large variation among regions and individuals.
Materials and Methods
The total population of Greenland was 55,000 in 1993, of whom 86% were born in Greenland (a proxy measure of Inuit ethnicity). Genetically, the Greenlanders are Inuit with a substantial admixture of European genes. They are historically, culturally, and genetically closely related to the Inuit of Canada and the Inupiat of Alaska and speak mutually intelligible dialects of the same language. The population is scattered along the coastline in 17 towns and 60 villages, most of which are situated on the west coast between the 60th and the 75th parallels. Only 19% of the 18- to 59-year-old male Greenlanders rely on hunting or fishing for a living, but subsistence hunting as a supplement to a paid job is common (Bjerregaard et al. 1995).
During 1993–1994, a sample of the inhabitants in Greenland, selected at random from the central population register, was asked to participate in a health interview survey. From the 1,728 participants, a subsample of Greenlanders (Inuit) from three towns and four villages on the west coast of Greenland were selected at random for the present study (n = 228). Among these, the interview was supplemented with a clinical examination and blood sampling. The subsample consisted of fewer men than the total Inuit population of Greenland (42 and 51%, respectively), and the 18- to 34-year-old age group was especially underrepresented.
The dietary questionnaire was developed specifically for this study because there are no standardized questionnaires on traditional Greenlandic food available. The questionnaire was of the food frequency type and included questions on traditional Greenlandic food and certain categories of imported food. Among 17 food categories, four included traditional Greenlandic food items: seal, whale, wild fowl (the vast majority of which are sea birds), and fish. The frequency categories were daily, 4–6 times a week, 1–3 times a week, 2–3 times a month, once a month or less, and rarely.
Blood samples were obtained after overnight fasting. The blood was separated, frozen at −20°C, and shipped to Denmark, where the samples were analyzed for lead by atomic absorption spectrometry at the National Environmental Research Institute of Denmark (Department of Arctic Environment, Roskilde, Denmark) (Asmund and Cleemann 2000). The detection limit of the method was calculated to be 6 μg/L.
Data analysis was performed with SPSS/ Windows (version 11.5; SPSS Inc., Chicago, IL, USA). p-Values were calculated by analysis of variance from log-transformed blood lead values. The association between several dietary variables and blood lead was explored in a general linear model with control for age and sex (Figure 1). Nonsignificant variables were removed by backward elimination.
Ethical approval was obtained from the Commission for Scientific Research in Greenland. Informed consent was obtained verbally.
Results
Dietary information was obtained from 222 participants, and blood lead analysis was carried out on 162 of these (73%). The sub-sample consisted of 67 men (median age, 43 years; range, 23–77 years) and 94 women (median age, 39 years; range, 20–78 years). After the removal of one outlier with a lead concentration of 1.0 μg/L, the mean ± SD concentration of blood lead in this population was 94.4 ± 69.6 μg/L (range, 7–351 μg/L). Lead concentrations increased significantly with age (p < 0.001) and were higher among men than among women (103 and 88 μg/L; p = 0.05) but were not significantly associated with body mass index, smoking, or consumption of alcohol.
Blood lead concentrations were not associated with the reported consumption of any of the imported food items but were associated with several local marine food items, particularly sea birds (Table 1). The dietary variables were all closely associated with each other and with both age and sex. Bivariate correlations between the dietary variables ranged from 0.29 between sea birds and whale meat to 0.56 between sea birds and fish (p < 0.01 for all correlations). In a multivariate analysis age, sex, and consumption of sea birds were retained in the model, whereas the other dietary variables were not. Adjusted for these covariates, consumption of sea birds was still significantly associated with blood lead (p = 0.01; Figure 1).
A linear regression analysis of blood lead as a predictor for blood pressure with age and body mass index as covariates did not show any association between lead and blood pressure.
Discussion
Our results indicate that Greenlanders who report consuming sea birds several times a week have a blood lead level > 50% higher than those who report eating sea birds only a few times per month or less often. In combination with a study conducted in 2003 (Johansen et al. 2004) of lead concentrations in breast meat of eider and murre—the two most consumed species of birds in Greenland—this strongly indicates a causal relationship between the consumption of sea birds and blood lead concentrations.
The association between blood lead and the consumption of fish and whale is not considered causal, because lead concentrations in fish and whales (and other local food) are very low (Johansen et al. 2000). The cause is rather that people who eat many birds also eat much fish and whale.
We made a rough estimate of the lead intake from birds in Greenland. The dominating species in Greenland bird hunting are the thick-billed murre and the common eider. From 1994 to 1999, the annual reported hunt ranged from 187,685 to 254,728 murres and from 72,109 to 83,810 eiders (Anonymous 2001). Johansen et al. (2004) calculated the mean lead intake to be 146 μg lead from one murre meal and 1,220 μg lead from one eider meal. If we assume that murres are eaten three times as often as eiders (as could be indicated by the hunting figures), the lead intake from “an average bird meal” will be 0.25 × 1,220 μg lead + 0.75 × 146 μg lead = 425 μg lead.
The Food and Agriculture Organization (FAO)/World Health Organization (WHO) (1993) has established a provisional tolerable weekly intake (PTWI) equivalent to 1,500 μg lead/week for a person weighing 60 kg (25 μg/ kg/day). This recommendation was maintained at the meeting of the Joint WHO/FAO Expert Committee of Food Additives in 1999 (WHO 2000). The calculation shows that the PTWI of 1,500 μg for a 60-kg person will be exceeded when eating four bird meals or more per week. Birds are, however, eaten seasonally, and the reported average consumption of seabirds cannot be meaningfully compared with blood lead levels at one point in time. Further studies where bird consumers are followed before, during, and after the bird hunting season are needed to establish the association between consumption of birds and blood lead concentrations as well as peak concentrations.
The lead intake from other dietary sources is estimated to be significantly lower than that from tissue contaminated with lead shot. The lead intake from the traditional diet in Greenland has been estimated to be only 15 μg per person per week (Johansen et al. 2000), and the lead intake from imported food is also considered to be at a low level because food (with low lead levels) imported from Denmark dominates the market in Greenland. In Denmark the mean lead intake from food is estimated to be 126 μg/week (Larsen et al. 2002).
Earlier theories that elevated blood levels in Greenland were caused by long-range transport of lead, mainly from leaded gasoline (Hansen 1988; Milman et al. 1994), must be rejected based on this study and others pointing to lead shot as the main source in Greenland, Canada, and Russia (Hanning et al. 2003; Johansen et al. 2004; Odland et al. 1999; Scheuhammer et al. 1998; Smith and Rea 1995). This is supported by the finding that the atmospheric lead concentration at Station Nord in remote northeast Greenland showed no change from 1990 to 2001 (Heidam et al. 2004), whereas it has declined by about a factor of 10 in Copenhagen (Kemp and Palmgren 2002). Also, the lead concentration in the air in Greenland is approximately 10 times lower than in Denmark, whereas the blood lead concentration is lower in Denmark than in Greenland (Nielsen et al. 1998). Considering that the lead concentration would be diluted during long-range transport, it seems unlikely that such lead could be significant as a direct source when breathing the air in Greenland.
However, blood lead levels in Greenland have declined over the past 20 years (AMAP 2003; Hansen 1981; Hansen et al. 1983). It is possible that the decline has been caused by a lower consumption of birds. Another possible explanation is that leaded gasoline was phased out during the 1990s and is no longer used in Greenland; this may have been a significant local source earlier, both from combustion and from handling of gasoline.
Figure 1 Blood lead concentration according to reported consumption of sea birds adjusted for age and sex (arithmetic means with 95% confidence intervals): Greenland 1993–1994 (n = 161).
Table 1 Blood lead concentrations (μg/L) according to diet (arithmetic means): Greenland 1993–1994 (n = 161).
Reported frequency of consumption Seal Whale Sea birds Fish
Rarely (n = 12) 81 98 74 —
Once a month (n = 39) 86 71 71 92
2–3 times per month (n = 36) 74 97 70 60
1–3 times per week (n = 53) 96 112 114 96
4–6 times per week (n = 15) 93 102 127 109
Daily (n = 6) 131 169 181 139
p-Values p = 0.22 p = 0.04 p < 0.001 p = 0.001
p-Values were calculated from log-transformed concentrations.
==== Refs
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Guilarte TR Toscano CD McGlothan JL Weaver SA 2003 Environmental enrichment reverses cognitive and molecular deficits induced by developmental lead exposure Ann Neurol 53 50 56 12509847
Hanning RM Sandhu R MacMillan A Moss L Tsuji LJS Nieboer E 2003 Impact on blood Pb levels of maternal and early infant feeding practices of First Nation Cree in the Mushkegowuk Territory of northern Ontario, Canada J Environ Monit 5 241 245 12729262
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Hansen JC 1988. Exposure to Heavy Metals (Hg, Se, Cd & Pb) in Greenlanders. A Review of an Arctic Environmental Study. Aarhus:University of Aarhus.
Hansen JC Jensen TG Tarp U 1991. Changes in blood mercury and lead levels in pregnant women in Greenland 1983–1988. In: Proceedings of the 8th International Congress on Circumpolar Health, 20–25 May 1990, White Horse Yukon, Canada (Postl B, Gilbert P, Goodwill J, Moffatt MEK, Neil JD, Sarsfield PA, et al., eds). Winnipeg:University of Manitoba Press, 605–607.
Hansen JC Kromann N Wulf HC Albøge K 1983 Human exposure to heavy metals in East Greenland. II. Lead Sci Total Environ 26 245 254 6857234
Heidam NZ Christensen J Wåhlin P Skov H 2004 Arctic atmospheric contaminants in NE Greenland: levels, variations, origins, transport, transformations and trends 1990–2001 Sci Total Environ 331 5 28 15325139
Hertz-Picciotto I Croft J 1993 Review of the relation between blood lead and blood pressure Epidemiol Rev 15 352 373 8174662
Johansen P Asmund G Riget F 2001 Lead contamination of seabirds harvested with lead shot—implications to human diet in Greenland Environ Pollut 112 501 504 11291456
Johansen P Asmund G Riget F 2004 High human exposure to lead through consumption of birds hunted with lead shot Environ Pollut 127 125 129 14554002
Johansen P Pars T Bjerregaard P 2000 Lead, cadmium, mercury and selenium intake by Greenlanders from local marine food Sci Total Environ 245 187 194 10682366
Kaufmann RB Staes CJ Matte TD 2003 Deaths related to lead poisoning in the United States, 1979–1998 Environ Res 91 78 84 12584008
Kemp K Palmgren F 2002. Air Quality Monitoring Programme, Annual Summary for 2002. Faglig Rapport fra DMU nr. 450. Copenhagen:Danmarks Miljøundersøgelser.
Lanphear BP Dietrich KN Auninger P Cox C 2000 Cognitive deficits associated with blood lead concentrations < 10 microg/dL in US children and adolescents Public Health Rep 115 521 529 11354334
Lanphear BP Dietrich KN Berger OG 2003 Prevention of lead toxicity in US children Ambul Pediatr 3 27 36 12540251
Larsen EH Andersen NL Møller A Petersen A Mortensen GK Petersen J 2002 Monitoring the content and intake of trace elements from food in Denmark Food Addit Contam 19 33 46 11817374
Lidsky TI Schneider JS 2003 Lead neurotoxicity in children: basic mechanisms and clinical correlates Brain 126 5 19 12477693
Long J Covington C Delaney-Black V Nordstrom B 2002 Allelic variation and environmental lead exposure in urban children AACN Clin Issues 13 550 556 12473917
Milman N Mathiassen B Hansen JC Bohm J 1994 Blood levels of lead, cadmium and mercury in a Greenlandic Inuit hunter population from the Thule district Trace Elem Electrolytes 11 3 8
Nielsen JB Grandjean P Jørgensen PJ 1998 Predictors of blood lead concentrations in the lead-free gasoline era Scand J Work Environ Health 24 153 155 9630064
Odland JØ Perminova I Romanova N Thomassen Y Tsuji LJS Brox J 1999 Elevated blood lead concentrations in children living in isolated communities of the Kola Peninsula, Russia Ecosyst Health 5 75 81
Scheuhammer AM Perrault JA Routhier E Braune BM Campell GD 1998 Elevated lead concentrations in edible portions of game birds harvested with lead shot Environ Pollut 102 251 257
Smith LF Rea E 1995 Low blood levels in northern Ontario—what now? Can J Publ Health 86 373 376
WHO 2000. Safety Evaluation of Certain Food Additives and Contaminants. WHO Food Additives Series 44. Geneva:World Health Organization.
Winneke G Lilienthal H Kramer U 1996 The neurobehavioural toxicology and teratology of lead Arch Toxicol 18 suppl 57 70
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6548ehp0112-00149915531434ResearchArticlesBlood Lead Changes during Pregnancy and Postpartum with Calcium Supplementation Gulson Brian L. 12Mizon Karen J. 1Palmer Jacqueline M. 2Korsch Michael J. 2Taylor Alan J. 3Mahaffey Kathryn R. 41Graduate School of the Environment, Macquarie University, Sydney, New South Wales, Australia2Commonwealth Scientific and Industrial Research Organisation/Division of Exploration and Mining, North Ryde, New South Wales, Australia3Department of Psychology, Macquarie University, Sydney, New South Wales, Australia4U.S. Environmental Protection Agency, Office of Prevention, Pesticides and Toxic Substances, Washington, DC, USAAddress correspondence to B.L. Gulson, Graduate School of the Environment, Macquarie University, Sydney, NSW 2109 Australia. Telephone: 61-2-9850-7983. Fax: 61-2-9850-7972. E-mail:
[email protected] thank M. Salter for phlebotomy, L. Munoz for technical assistance, the participants in this study, English Language Schools in Sydney, B. Jameson and B. Ragan of the National Institute of Environmental Health Sciences (NIEHS) and P. Mushak for their support and encouragement over many years, J. Fouts for reviewing an earlier version of the manuscript, and R. Setright of Blackmores for supply of one of the products.
This research was largely funded by the NIEHS through NO1-ES0252.
The authors declare they have no competing financial interests.
11 2004 27 7 2004 112 15 1499 1507 26 6 2003 27 7 2004 Publication 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. Pregnancy and lactation are times of physiologic stress during which bone turnover is accelerated. Previous studies have demonstrated that there is increased mobilization of lead from the maternal skeleton at this time and that calcium supplementation may have a protective effect. Ten immigrants to Australia were provided with either calcium carbonate or a complex calcium supplement (~ 1 g/day) during pregnancy and for 6 months postpartum. Two immigrant subjects who did not conceive acted as controls. Sampling involved monthly venous blood samples throughout pregnancy and every 2 months postpartum, and quarterly environmental samples and 6-day duplicate diets. The geometric mean blood lead at the time of first sampling was 2.4 μg/dL (range, 1.4–6.5). Increases in blood lead during the third trimester, corrected for hematocrit, compared with the minimum value observed, varied from 10 to 50%, with a geometric mean of 25%. The increases generally occurred at 6–8 months gestation, in contrast with that found for a previous cohort, characterized by very low calcium intakes, where the increases occurred at 3–6 months. Large increases in blood lead concentration were found during the postpartum period compared with those during pregnancy; blood lead concentrations increased by between 30 and 95% (geometric mean 65%; n = 8) from the minimum value observed during late pregnancy. From late pregnancy through postpartum, there were significant increases in the lead isotopic ratios from the minimum value observed during late pregnancy for 3 of 8 subjects (p < 0.01). The observed changes are considered to reflect increases in mobilization of lead from the skeleton despite calcium supplementation. The identical isotopic ratios in maternal and cord blood provide further confirmation of placental transfer of lead. The extra flux released from bone during late pregnancy and postpartum varies from 50 to 380 μg lead (geometric mean, 145 μg lead) compared with 330 μg lead in the previous cohort. For subjects replete in calcium, the delay in increase in blood lead and halving of the extra flux released from bone during late pregnancy and postpartum may provide less lead exposure to the developing fetus and newly born infant. Nevertheless, as shown in several other studies on calcium relationships with bone turnover, calcium supplementation appears to provide limited benefit for lead toxicity during lactation.
bloodbone remodelingcalcium supplementationleadlead isotopespostpartumpregnancy
==== Body
The populations most sensitive to lead exposure from various sources are pregnant women and young children (National Research Council 1993). Ongoing lead exposure not only directly affects health, but also allows the accumulation of lead in tissues such as bone. During pregnancy, the mobilization of bone lead increases, apparently as the bone is resorbed to produce the fetal skeleton (Franklin et al. 1997; Gulson et al. 1997a, 1998b; Manton 1985). Previous studies using only lead concentrations for a limited number of samples (usually three to five) generally showed an increase in blood lead levels during the third trimester, which was attributed to increased bone resorption to meet the calcium requirements of the developing fetus (Hertz-Picciotto et al. 2000; Lagerkvist et al. 1996; Rothenberg et al. 1994; Schell et al. 2000, 2003; Schumacher et al. 1996; Sowers et al. 2002; West et al. 1994).
Recent studies, especially those employing precision stable lead isotope methods, have confirmed the early work of Manton (1985) and demonstrated that extra lead is released from the maternal skeleton during pregnancy and lactation in cynomolgus monkeys (Franklin et al. 1997; O’Flaherty et al. 1998) and in humans (Gulson et al. 1997a, 1998b, 2003; Manton et al. 2003). Furthermore, these studies showed that the lead is transferred to the infant.
Because recent evidence suggests that there are detrimental neurologic effects on young children whose blood lead levels are < 10 μg/dL (Canfield et al. 2003; Lanphear et al. 2000), it is critical to minimize the exposure of the developing fetus and the newborn infant, especially lead from bone.
Results of several cross-sectional studies have indicated that increased levels of calcium during pregnancy have a protective effect, reducing the amount of lead released from bones (Farias et al. 1996; Hernandez-Avila et al. 1996). Calcium carbonate supplementation of 1,200 mg elemental calcium/day gave a modest reduction of 16% in blood lead levels among lactating women with relatively high bone lead burden (Hernandez-Avila et al. 2003). In our longitudinal study of lead mobilization from bone during pregnancy and for 6 months postpartum, we found that both newly arrived migrants to Australia and multigenerational Australian subjects consumed very low amounts of calcium in their diets. Estimations were based on quarterly 6-day duplicate diets and averaged about 400–600 mg calcium/day (Gulson et al. 2001), less than half the intakes recommended by the National Institutes of Health (1994).
Two of the migrant subjects who consumed calcium supplements showed lower amounts of lead mobilized from their skeleton compared with the others on low calcium intakes. In light of this finding and those of the previous studies mentioned above, we undertook a longitudinal investigation in which subjects were provided with calcium supplements during pregnancy and for 6 months postpartum. Our hypothesis was that if calcium supplementation is effective in reducing the mobilization of lead from the maternal skeleton, then there should be little or no change in blood lead or isotopic ratios during pregnancy and postpartum.
Materials and Methods
We employed the protocols refined during previous investigations (Gulson et al. 1995, 1997a, 1998b).
Subjects.
Participants were female immigrants to Australia who were of child-bearing age (19–32 years) and whose skeletal lead isotopic composition, based on our previous investigations and an initial blood sample, was different from that in their current environment. In essence, the lead isotopic composition or “fingerprint” in multigenerational Australian residents is different from that in subjects from most other countries, although these differences are reducing over the past decade because of globalization (Gulson 2000; Gulson et al. 2001). Hence, by monitoring migrant subjects who conceive after arrival in Australia, it is possible to detect changes in isotopic composition and lead during pregnancy and the postpartum period arising from increased mobilization of skeletal lead. The approach of using subjects whose skeletal lead was different from that in their current environment to determine exchange phenomenon was first explored by Manton (1977, 1985) and later reiterated by Rabinowitz (1991).
Subjects were recruited by networking through our previous cohort study (Gulston et al. 1998b), from English language classes, and via limited advertising in ethnic newspapers. Compared with the previous cohort, there are some differences in demographics of the subjects in our present study. For example, in the previous cohort, most subjects were from Eastern Bloc countries, such as the former Soviet Union and former Yugoslavia. With changing migration patterns to Australia, subjects in the present study were from South Asia and the Middle East (Table 1). One subject (subject no. 1226) was approximately 4 months pregnant when recruited but was retained because our previous investigations of people from other countries showed that, in all cases, their isotopic compositions were different from that of long-term Australians. Subject 1208 miscarried during the first trimester of her first pregnancy. Subject 1231 returned to China immediately after the birth of her child, so no postpartum results are available. Two nonpregnant control subjects were matched mainly on country of origin as other parameters such as age, parity, and blood lead concentration were found not to be critical in previous studies (Gulson et al. 1997a, 1998b).
Signed consent forms were obtained from each volunteer. This consent form had been reviewed and approved by the ethics committees of the Central Sydney Area Health Service, Western Sydney Area Health Service, and Macquarie University. As part of the entry requirements into Australia, all subjects were declared medically fit.
Sampling and analysis.
Blood and urine samples were obtained monthly during pregnancy and the postpregnancy period for ≥6 months. The urine samples were collected to back up the blood samples in case isotopic data could not be obtained from the blood samples. Environmental samples of 6-day duplicate diet, drinking water, house dust, gasoline, and ambient air were collected quarterly. High-precision lead isotope ratios and lead concentrations were obtained by thermal ionization mass spectrometry. Details of analytical methods have been published previously (Gulson et al. 1995, 1997a, 1998b).
As in our previous reports (Gulson et al. 1995, 1997a, 1998b), our data are expressed as 206Pb:204Pb ratios for ease of identifying and illustrating changes, especially for readers with limited experience in isotopic methods and for whom significant differences in the third decimal place of a ratio of, say 0.900, may seem unrealistic. Although Manton and colleagues (2003) express results as 206Pb:207Pb, direct comparisons of variations can be made between our data because 206Pb is the numerator in both ratios. For the statistical analyses, both the measured 207Pb:206Pb and 206Pb:204Pb ratios were treated.
Hematocrit was measured in blood samples and creatinine in the urine samples using standard methods.
Calcium supplements.
Because it was not viable to produce a custom supplement for this study, commercially available Australian calcium supplements, which are typically made from overseas components, were administered to the subjects. Details of the supplements are given in Table 2. The supplements were those with the lowest concentrations of lead and were evaluated as suitable in the following investigation.
Approximately 6 months into the present study, we investigated the impact on blood lead of the calcium supplements to be provided to the subjects. This was necessary to satisfy ourselves that we were not “poisoning” the subjects and their developing fetuses because there had been much recent publicity about the potential toxicity of supplements (Boulos and Smolinski 1988; Bourgoin et al. 1993; Capar and Gould 1979; Rogan et al. 1999; Scelfo and Flegal 2000). This publicity occurred despite earlier work from the 1980s indicating that calcium (plus phosphorus) inhibited the uptake of lead from the gastrointestinal tract. Furthermore, Rogan et al. (1999) discovered that vitamin supplements used in the multi-center lead trial were contaminated with lead. Using a cohort of generally younger adults, we showed that an extra intake of 900–1,000 mg calcium/day for 6 months had minimal impact on the blood lead concentration (Gulson et al. 2001). However, we observed changes in the isotopic composition of the blood with a calcium carbonate supplement indicating that lead from the supplement was entering blood, but at this stage we have no explanation as to why there were no changes in blood lead concentration.
Oral and written instructions regarding dosage were given to the subjects. Compliance relied largely on the loyalty established between the cohort coordinator and the subjects to accurately report compliance, and this was checked monthly by tablet count.
A control group taking a placebo or not taking calcium supplements was decided against because, first, although investigated, producing a placebo for such a small group of subjects was not considered viable; second, the much larger cohort in our previous pregnancy investigations had very low calcium intakes, and the present study was one outcome of this; and third, financial constraints given the expense of running such a project.
Data treatment.
After a visual inspection of graphic presentations, we analyzed changes in 206Pb:204Pb (and 207Pb:206Pb) ratios and blood lead concentration over time from the minimum value of blood lead concentration before an increase during late pregnancy using a regression analysis for time series that took into account any autocorrelation of the residuals from the sequences of observations (the autoregression procedure in SPSS 11.0; Chicago, IL, USA). The autocorrelation was taken into account when calculating the significance and magnitude of time effects (Ostrom 1990).
To assess changes in 206Pb:204Pb (and 207Pb:206Pb) ratios and blood lead concentration between pregnancy and postpartum for individual subjects, we used an interrupted time-series analysis procedure (Crosbie 1995). In this analysis, the intercept and slope of the pregnancy (baseline) regression line are jointly compared with those of the regression fitted to the postpartum (treatment) phase, and if the joint test is significant, differences between the two intercepts and two slopes can be considered. This procedure takes account of autocorrelation when fitting the lines and testing the differences, and provides better control of type I error (i.e., an erroneous inference of a significant difference between phases/stages in experiments) than does visual inspection and has acceptable power. It requires measurements at regular intervals, as exemplified by the monthly blood sampling in our investigation.
We calculated the extra amount of lead added to blood during pregnancy and postpartum using a similar area under the curve method that was used for our previous cohort (Gulson et al. 1999). This comparative graphic technique is commonly used for cumulative estimates of lead exposure by integrating blood lead concentration across exposure times in occupational exposure (Chia et al. 1996) or for comparative lead dosings of test animals in the study of such exposure parameters as the relative bioavailability of lead (Casteel et al. 1997). Estimates of extra lead flux mobilized were possible only for migrant subjects whose blood lead concentrations exhibited positive variations during the monitoring period (n = 10). We calculated the extra flux by estimating the total area generated by serial blood lead concentrations versus pregnancy and postpregnancy time and subtracting the estimated background area, the portion of the area representing blood lead over time that would have occurred in the absence of added lead releases. Background area was generated from the lowest blood lead level during late pregnancy, in contrast to our previous study (Gulson et al. 1998b), in which the minimum blood lead level usually, although not always, occurred in the first trimester.
Results
Changes in blood lead during pregnancy and 6 months postpartum.
The data for the present case series have been subdivided into two groups depending on the length of breast-feeding: those who breast-fed for longer periods (Figure 1), in this case ≥3 months, and those who breast-fed for shorter periods, either not at all or for < 1 month (Figure 2).
To account for the well-recognized changes in blood volume during pregnancy (Hytten 1985), increases of which may result in decreases in blood lead concentration, the blood leads can be adjusted for changes in hematocrit. Data for subjects 1212 and 1214 are shown in Figure 3, which illustrate the changes in hematocrit and the hematocrit-corrected and uncorrected blood lead concentrations over time. Hematocrit values throughout pregnancy in the present case series show decreases, little change, or decreases and then increases in late pregnancy (0–2 months before parturition). As shown in Figure 3, the uncorrected and corrected blood lead values follow similar trends and, because of the “return-to-normal” hematocrit values during the postpartum period and the importance of this period for our results, measured blood lead values are shown in the accompanying figures.
Changes in blood lead during pregnancy followed similar U-shaped patterns for the two groups. From early to mid-pregnancy the hematocrit-corrected blood lead concentrations decreased for 8 of the 10 subjects and for the second pregnancy of subject 1208. During late pregnancy, the blood lead concentrations increased in all subjects (Table 3); the increase in blood lead concentration above a minimum value, corrected for hematocrit, ranged from 10 to 55% (geometric mean, 25%), although the values did not exceed those determined in prepregnancy or early pregnancy.
Changes in blood lead from late pregnancy through the 6 months postpartum were variable and did not appear to be related to length of breast-feeding and calcium intakes. The maximum value during postpartum the concentration was almost double the minimum blood lead value during late pregnancy in half the subjects; the percentage increase varied from 30 to 95%. For half the subjects (subjects 1204, 1211, 1213, and 1226), the blood lead increased until the second or third month postpartum and then either leveled or decreased (Figures 1 and 2). Only in subjects 1212 and 1208 was there a continuous linear increase from late pregnancy through postpartum. The increases postpartum in both groups commonly exceeded the values measured in prepregnancy or early pregnancy.
Autoregression analyses of blood lead concentration from the time of minimum value in late pregnancy through postpartum for eight subjects are presented in Table 4. There are significant positive increases for half the subjects and none for the other four (subjects 1211, 1213, 1214, 1226). However, visual inspection of the plots (Figures 1 and 2) shows that for the latter four, there are increases from late pregnancy to the first 1 or 2 months postpartum and, thereafter, either a leveling or decrease in blood lead.
For the interrupted time-series analyses, the overall test of change was significant for five of eight subjects in blood lead concentration during the whole of the pregnancy phase compared with the postpartum phase (Table 5). There were significant differences in the blood lead slopes for half of the eight subjects during pregnancy compared with postpartum, and for six of eight subjects there were significant differences in intercept.
Subject 1225 showed a major increase in blood lead concentration soon after parturition, with the value increasing from 5.6 to about 43 μg/dL. This increase was accompanied by a large decrease in the 206Pb:204Pb ratio toward Australian environmental values (Figure 4).
The blood lead concentrations for the controls showed only small variations and an overall decrease over time (Figure 5).
As is well documented in the literature, the cord blood lead concentrations were lower than those of the last maternal sample taken before parturition. The geometric mean ratio of cord:maternal blood lead was 0.72, with a range from 0.55 to 0.93, and the correlation of cord versus maternal blood lead had an r2 of 0.93.
Changes in isotopic ratios during pregnancy and 6 months postpartum.
As was the case for blood lead concentrations, changes in isotopic ratios varied but not with consistent trends (Figures 6 and 7). In some subjects (1212, 1214, 1225, 1229) it was not possible to estimate any changes in isotopic composition during pregnancy, especially late pregnancy, because of a change in the slope or scatter of the data (Table 3). In the other subjects, any changes during late pregnancy ranged from little difference from the experimental error of ± 0.2% up to 0.8% for subject 1226.
Autoregression analyses for the 206Pb:204Pb (and 207Pb:206Pb) ratio from the time of minimum value in late pregnancy through postpartum for eight subjects are presented in Table 4. The analyses for both ratios give consistent results, except for subject 1229, although those for the 207Pb:206Pb ratio tend to show better correlation than those for the 206Pb:204Pb ratio, probably because of the higher precision and ease of measurement of the 207Pb:206Pb ratio (~ 0.9) compared with the larger 206Pb:204Pb ratio. Four of the eight subjects who were monitored during the 6 postpartum months (1204, 1208, 1213, 1229) showed significant increases in the 206Pb:204Pb ratio, whereas there were minimal changes for the other subjects.
For the interrupted time-series analyses, in no case was there a significant overall test of change for 206Pb:204Pb (and 207Pb:206Pb ratio; not shown in Table 5), nor was there any significant difference in the slopes or intercepts for the isotopic ratios (Table 5).
There were small variations in the isotopic compositions of the control subjects (Figure 5). The ratios for the Bulgarian subject decreased slowly, reflecting an ongoing exchange between her long-term body stores and Australian environmental lead, as we have observed in migrant subjects who did not conceive (Gulson et al. 1997b).
Isotopic ratios in the cord blood samples were similar to those in the maternal blood samples taken before parturition (r2 = 0.98), as we found in our previous studies (Gulson et al. 1998b) and also observed by Manton (1977, 1985). The similarity in isotopic ratios of the maternal and cord blood samples provides confirmation of the placental transfer of lead.
Dietary samples.
Lead concentrations in duplicate diet samples vary considerably both between and within subjects. For example, the geometric mean concentration for all subjects was 7.1 μg/kg with a range of 3.0–20.1 μg/kg (n = 48). In contrast, the range in 206Pb:204Pb ratio was surprisingly less than expected, from 17.79 to 18.81, with a geometric mean ratio of 18.25.
There does not appear to be any relationship between calcium intake and changes in isotopic composition of blood lead during pregnancy and postpartum. For example, subjects 1208 and 1213 exhibited the largest increases in 206Pb:204Pb ratio and had a high compliance with calcium supplements. Likewise, subjects 1204 and 1212 had a high compliance and showed large increases in blood lead concentration postpartum. There was also no relationship between the time of increase in blood lead during pregnancy and calcium intake.
Environmental samples.
Dustfall accumulation ranged from 6 to 94 μg/m2/month with a geometric mean of 17 μg/m2/month (n = 36). The 206Pb:204Pb ratios ranged from 16.50 to 17.85 and were similar to values measured in Sydney air from high-volume air filter samples (Chiaradia et al. 1997); the geometric mean was 17.16.
Lead concentration in fully flushed drinking water ranged from 0.03 to 1.1 μg/dL with a geometric mean 206Pb:204Pb ratio of 16.80; at these very low concentrations, water has no impact on blood lead.
Discussion
Changes in blood lead concentration and isotopic ratios during pregnancy and 6 months postpartum.
Mean increases of approximately 25% in blood lead concentration during late pregnancy for our present case series with calcium supplementation are consistent with increases observed in our previous cohort (Gulson et al. 1998b) with low calcium intakes and in several other studies (Hertz-Picciotto et al. 2000; Lagerkvist et al. 1996; Rothenberg et al. 1994; Schell et al. 2000, 2003; Schumacher et al. 1996; Sowers et al. 2002; West et al. 1994). However, the increases found in the present study occurred later in pregnancy than in our previous cohort (Gulson et al. 1998b) and in many of the other studies (Hertz-Picciotto et al. 2000; Lagerkvist et al. 1996; Rothenberg et al. 1994; Schell et al. 2000, 2003; Schumacher et al. 1996; Sowers et al. 2002; West et al. 1994).
The changes in blood lead during the whole of pregnancy follow a U-shaped curve, as previously observed by Rothenberg et al. (1994), Hertz-Picciotto et al. (2000), Sowers et al. (2002), and Schell et al. (2000, 2003). Apart from the recent study of Manton et al. (2003) and our investigations (Gulson et al. 1995, 1997a, 1998b), only limited numbers of samples were collected in the other studies, usually a maximum of one for each trimester, one at delivery, and one postpartum. In our present case series, the changes in blood lead during pregnancy were obvious whether the blood lead values were corrected or uncorrected for hematocrit. Hematocrit-corrected values did not alter the findings of Hertz-Picciotto et al. (2000) and Schell et al. (2000), but in the case of Schumacher et al. (1996), the decrease in uncorrected blood lead in the first trimester compared with prepregnancy was not observed for the hematocrit-corrected values. Furthermore, blood lead values in the second trimester (~ 20 weeks) showed average increases of approximately 14%, and for the maximum increase observed in the 32-week sampling, the change from the minimum value at 8 weeks was 36%. Sowers et al. (2002) observed increases in blood lead from the 20th week to delivery in African Americans (1.20–1.49 μg/dL; an increase of 24%) and Hispanic Americans (0.99–1.32 μg/dL; an increase of 33%) but a barely detectable decrease in blood lead of 0.03 μg/dL at delivery for Caucasians. Schell et al. (2003) observed smaller increases in blood lead from the second trimester to delivery in whites (1.6–1.8 μg/dL; an increase of 12%) compared with African Americans (1.8–2.5 μg/dL; an increase of 39%).
Four of the eight subjects in the present case series showed linear increases in blood lead concentration from the minimum in late pregnancy through postpartum, but there was no consistency with length of breast-feeding or compliance with calcium supplementation. Manton et al. (2003) also found major increases in blood lead concentration by up to a factor of 3 from a minimum value during pregnancy to postpartum. The magnitude of the increases was attributed partly to breast-or bottle-feeding by the mothers. In contrast to the changes observed in the above investigations, Berglund et al. (2000) could not detect any increase in blood lead during pregnancy, but they found a significant increase in blood lead concentration during lactation.
Extra blood lead and calcium supplementation.
For our present case series, with subjects taking calcium supplements, the increases in blood lead from the minimum value during late pregnancy took place at about 6–8 months in all subjects. In contrast, for the previous cohort (Gulson et al. 1998b), whose daily calcium intake was generally very low, an increase in blood lead from the minimum occurred at 3–4 months for six of the nine subjects. Thus, even though there is an increase of similar magnitude in blood lead from a minimum value during pregnancy in both these cohorts, the fetus in the group receiving calcium supplements would be exposed to considerably lower lead flux than would those whose mothers had a low calcium intake. This is confirmed by the lower mean value of 145 μg lead for the extra flux to blood during late pregnancy through postpartum for the present higher-calcium-intake subjects compared with the mean of 330 μg lead for the low-calcium-intake cohort. In breast-feeding mothers who were consuming close to, or exceeding, what they recommended as the daily calcium intake of 1,000 mg/day, Manton et al. (2003) suggested there was no tendency for increases in blood lead in late pregnancy. However, inspection of their Figure 2 shows that there are increases in blood lead concentration in the last 2 months of pregnancy; increases in blood lead in late pregnancy also occurred for at least four bottle-feeding mothers with varying calcium intakes, consistent with our observations.
We argued that the changes in blood lead concentration and isotopic composition during pregnancy and postpartum in our previous cohort (Gulson et al. 1998b) reflected increased mobilization of lead from the maternal skeleton, probably associated with the low calcium intakes of the subjects (Gulson et al. 1998b). Any changes in blood lead concentration during pregnancy and postpartum are a function of plasma volume, red cell mass, and variations in lead flux from endogenous and exogenous sources. Low blood lead concentrations during the first half of pregnancy have been attributed to hemo-dilution (Rothenberg et al. 1994), which has been associated mainly with an increased plasma volume, calculated by Hytten (1985) to be approximately 1,250 mL for a normal pregnancy. Hytten (1985) showed that once the “plateau” of increased plasma volume had been attained at about week 30 of pregnancy, there was minimal increase in plasma volume and, as expected, little change during postpartum. That is, the dilution effects of increased plasma volume on blood lead concentration should be most marked during the second and early third trimesters. Hytten (1985) also drew attention to changes in red cell mass and the impact of iron supplementation on this. The changes in volume are mirrored in hematocrit, which decreases until third trimester and then increases above normal values until early in postpartum (Figure 3; also see Schell et al. 2000, their Figure 2). During pregnancy, if the flux of lead into blood remains constant, blood lead concentration will decrease as blood volume increases. On the other hand, if blood lead concentrations remain constant or increase during late pregnancy, this suggests that there has been an increase in lead flux into the blood.
The decreases in blood lead from early to mid-pregnancy in our present and previous cohort (Gulson et al. 1998b) are consistent with a hypothesis of reduced flux from the skeleton, as indicated by our estimations of flux. Such a reduction in lead flux was noted by Franklin et al. (1997) in cynomolgus monkeys and in one subject in the recent study of Manton et al. (2003). Although they did not report data from early pregnancy, Manton et al. (2003) hypothesized that in early (to mid) pregnancy, only trabecular bone of presumably low lead content was resorbed, decreasing lead concentrations more than expected from hemo-dilution alone. In late pregnancy, more cortical bone with a presumed higher lead content was resorbed, increasing blood lead concentrations. Such a hypothesis would appear to conflict with measurements of bone lead over the past decades showing that patella or calcaneus (trabecular) bone has higher concentrations than does tibia (cortical) bone (e.g., Hernandez-Avila et al. 2002; Oliveira et al. 2002; Rothenberg et al. 2000) and that the trabecular/cortical bone lead ratio ranges from 1 to 2. Nevertheless, the total body burden of lead in cortical bone is generally higher than in trabecular bone because the proportion of cortical:trabecular bone is about 4:1.
Bone research of relevance to the changes in lead during pregnancy and lactation.
The linear increases in blood lead isotopic ratio, blood lead concentration, and increased lead flux to blood during late pregnancy and postpartum in half the subjects from the present case series and most subjects in our previous cohort (Gulson et al. 1998b) are attributed to increased flux from the maternal skeleton, despite increased calcium intake for the present case series.
Rather than cortical bone providing the major contribution to increased blood lead, as suggested by Manton et al. (2003), it may well be trabecular bone. For example, based on bone X-ray fluorescence measurements, Schütz et al. (1987) and Hu et al. (1989) suggested that trabecular bone exerted the most influence on blood lead and that this was consistent with the higher vascular and turnover rates associated with trabecular compared with cortical bone.
Bone mineral density and biochemical markers of bone turnover and especially resorption during pregnancy and lactation appear to affect trabecular more than cortical bone (Black et al. 2000; Kolthoff et al. 1998; Naylor et al. 2000; Sowers et al. 2000) as a result of having more cancellous (trabecular) bone surfaces available for turnover (Sowers et al. 2000). Even though Sowers et al. (2000) stated that findings of previous studies were inconsistent, there was a convergence toward bone loss during pregnancy and lactation. For example, there are findings of increase at localized bone sites (Drinkwater and Chestnut 1991), no change (Sowers et al. 1991), and bone loss (Aguado et al. 1998; Honda et al. 1998; Kolthoff et al. 1998; Lamke et al. 1977; Laskey et al. 1998; Naylor et al. 2000; Pires et al. 2002; Ritchie et al. 1998; Sowers et al. 1995). Lactation would appear to be associated with a larger decrease in bone density and net bone loss of from 1 to 5% during 3–6 months postpartum compared with pregnancy (Laskey et al. 1998; Pires et al. 2002; Ritchie et al. 1998; Sowers et al. 1995).
Bone research can provide further useful information with respect to the observation in lead pregnancy studies that the most significant increase in blood lead concentration (and significant changes in isotopic ratios) occurs in late pregnancy. For example, Sowers et al. (2000) confirmed that there was increased bone turnover in the third trimester of pregnancy compared with the first trimester, as determined by measurement of the urinary excretion of markers of type I collagen, but suggested that the mechanisms of bone resorption during pregnancy are poorly understood. Furthermore, the data of Black et al. (2000) and Naylor et al. (2000) indicated that there was a dissociation of bone formation and resorption in the first two trimesters and well into the third trimester of pregnancy. They suggested that the gain in bone mineral density at cortical bone sites during pregnancy may result from the redistribution of mineral from trabecular to cortical bone sites and that elevated bone turnover may explain trabecular bone loss during pregnancy. On the other hand, a study of primates using the stable lead isotope method indicated that there was a reduction in first-trimester bone mobilization (Franklin et al. 1997). These conclusions are, however, inconsistent with data from bone mineral density and bone turnover indices demonstrating that bone resorption increased in the first trimester. For example, the bone biopsy data of Purdie et al. (1988) in women at the time of termination of pregnancy at 12–14 weeks of gestation showed that bone resorption predominated in early pregnancy. Likewise, Shahtaheri et al. (1999) reported early bone loss in bone biopsy samples from 15 women in their first trimesters, whereas at term in another 13 women they found new and more numerous (but thinner) trabeculae.
Bone turnover and calcium and lead relationships during pregnancy and lactation.
Changes in blood lead during pregnancy and lactation are inexorably linked with changes in calcium. During pregnancy and lactation, there is increased demand for calcium for transport to the fetus. The maternal response to the demand for calcium theoretically can involve increased absorption of calcium from the intestine, greater calcium conservation by the kidneys, or greater bone turnover (Sowers et al. 2000). There are, however, wide disparities in the literature on both bone/calcium and lead about the efficacy of calcium intakes during pregnancy and lactation.
Several authors consider that bone mineral changes especially during lactation are hormonally regulated and independent of the amount of calcium in the woman’s diet; provision of additional calcium has minimal impact on preventing bone resorption at sites such as the spine and femur (Cross et al. 1995; Kalkwarf 1999; Kolthoff et al. 1998; Laskey et al. 1998; Prentice 2000). For example, Prentice (2000) states there are firm data demonstrating that a low calcium intake during lactation does not lead to impaired lactational performance or to exaggerated bone loss.
In contrast, previous evidence suggested that bone loss during lactation in adolescents may be prevented by adequate dietary calcium intakes (Chan et al. 1987). Later on, Krebs et al. (1997) suggested that bone loss during lactation may be attenuated by a generous dietary ratio of calcium to protein. Specker et al. (1994) concluded that even when dietary intake of calcium exceeded the recommended daily intake, the calcium demands in lactation in humans were preferentially met by increased skeletal resorption of calcium and probably increased renal conservation of calcium, but not by increased intestinal absorption of calcium.
Like the complexity in the bone/calcium literature, the relationships between bone lead, calcium, bone turnover, and reproduction are also controversial. For example, Rothenberg et al. (2000) found calcaneus and tibia lead were directly associated with prenatal third trimester blood lead but only calcaneus lead was associated with postnatal blood lead. They also found that there was no effect of dietary calcium on calcaneus lead despite the more easily mobilized trabecular lead. In a cross-sectional Mexico City study, Hernandez-Avila et al. (1996) also found a significant association between trabecular bone lead (in the patella) and postnatal blood lead and a significant effect of calcium on patella lead.
With respect to calcium intakes, several studies have suggested that dietary calcium may have a protective role against lead by decreasing absorption of lead in the gastrointestinal tract and by decreasing the mobilization of lead from bone stores to blood, especially during periods of high metabolic activity of the bone such as pregnancy, lactation, and menopause (e.g., Farias et al. 1996; Hernandez-Avila et al. 1996, 1997, 2003; Hertz-Picciotto et al. 2000). However, most of these studies were cross-sectional in nature, and dietary calcium was estimated by questionnaire, diary, or recall. Furthermore, the outcomes between studies of similar population groups were sometimes conflicting (e.g., Farias et al. 1996; Hernandez-Avila et al. 1996). In more recent studies in Mexico City, calcium carbonate supplementation of 1,200 mg elemental calcium per day gave a modest reduction of 16% in blood lead levels among lactating women with relatively high bone lead burden (Hernandez-Avila et al. 2003). In an earlier report apparently using the same cohort but with lower numbers, there did not seem to be any benefit from calcium supplementation (Tellez-Rojo et al. 2002). Using a sensitive biomarker of bone resorption, levels of cross-linked N-telopeptides of type I collagen (NTx), Janakiraman et al. (2003) observed that a bedtime 1,200 mg calcium supplement during the third trimester of pregnancy reduced maternal bone resorption by an average of 14%.
Contribution to blood lead from diet and calcium supplements.
In contrast to our previous cohort (Gulson et al. 1998b), where the environmental and dietary contributions to blood lead were considered minimal, conditions were somewhat different in the present case series. For example, even though the 206Pb:204Pb ratios in the dust, air, and water are still lower than observed in the blood of our migrant subjects and would contribute little to blood lead, the ratios in the 6-day duplicate diet increased considerably, especially since our market basket surveys of 1990 when the 206Pb:204Pb ratio was about 17.0 (Gulson et al. 1996). This change has come about with globalization, especially of the food supply. Nevertheless, we do not attribute the increase in blood lead during the last 2 months of pregnancy to diet; otherwise, there should have been obvious effects earlier in the pregnancy. A potential source of the increased lead may come from the calcium supplements. These contribute almost 50% of the daily lead intake, and even though we observed minimal changes in the 6-month trial of a separate cohort taking the same dose and same type of calcium supplements (Gulson et al. 2001), physiologic processes during pregnancy may affect the uptake of lead even in the presence of sufficient calcium. For example, pregnant swine absorb and retain more lead than do nonpregnant swine (Casteel et al. 2001), and there is convincing evidence especially from stable calcium isotope studies, mentioned above, that there is increased intestinal absorption of calcium during pregnancy. An increased absorption of lead from the calcium supplement could partially explain the increase in 206Pb:204Pb ratio observed for three subjects during 6 months postpartum, but there was no such change in the other three subjects for whom we have data. Furthermore, there was no consistency in changes relating to the two different supplements.
The major decrease in 206Pb:204Pb ratio and increase in blood lead for subject 1225 postpartum are remarkable (Figure 4). Fortunately, this subject was not breast-feeding. A sample of the husband’s blood was collected approximately 4 months after detection of the changes in his wife; it had a lead concentration of 23 μg/dL and a 206Pb:204Pb ratio of 16.62, consistent with an exposure similar to that of the wife. Considerable effort was devoted to determine the source of the changes because they indicate a very high acute dose of lead of Australian origin. However, evaluation of the potential sources (diet, traditional medicines, cosmetics, social calendar) indicated that the only explanation for the changes could be attributed to their attendance at an ethnic fair where they consumed foodstuffs different from their regular diet.
In summary, despite the recent studies of Hernandez-Avila et al. (2003) and Janakiraman et al. (2003) pointing to benefits from calcium supplementation during pregnancy and lactation, significant increases in blood lead during late pregnancy and postpartum found in our investigations and those of several other authors appear to indicate that calcium supplementation is ineffective in minimizing the mobilization of lead from the skeleton during lactation—a position consistent with evidence from the calcium literature using bone density and bone turnover index measurements (Prentice 2003). It does appear to offer some protection, however, during pregnancy by delaying the extra lead mobilized from bone and reducing the extra flux. Despite the potential exposure of the infant to lead from breast-feeding, we have shown that the transfer of lead to the infant from breast milk is low, especially at low blood lead concentrations (Gulson et al. 1998a).
Figure 1 Changes in blood lead concentration (not corrected for hematocrit) for subjects who breast-fed for ≥3 months. The length of breast-feeding is noted to the right of the individual lines. Increases in blood lead concentration occurred in late pregnancy compared with our previous cohort (Gulson et al. 1998b), whose calcium intakes were very low. There are significant increases during the late pregnancy–postpartum period.
Figure 2 Changes in blood lead concentration (not corrected for hematocrit) for subjects who breast-fed for ≤1 month. The length of breast-feeding is noted to the right of the individual lines. Increases in blood lead concentration occurred in late pregnancy compared with our previous cohort (Gulson et al. 1998b), whose calcium intakes were very low. There are significant increases during the late pregnancy–postpartum period.
Figure 3 Plot illustrating the changes in hematocrit values (HCt) and uncorrected (PbB) and hematocrit-corrected (HCt Corr) blood lead concentrations for subjects 1212 and 1214. The U-shaped pattern for blood lead concentrations over time persists in the hematocrit-corrected values. The “0 days” scale is offset for subject 1212 and is denoted for all subjects by the breaks in the lines.
Figure 4 Plot of blood lead concentration (PbB) and isotopic ratios for subject 1225, who experienced a major unknown lead exposure soon after parturition (Part).
Figure 5 Plot of blood lead concentration (PbB) and isotopic ratios for subjects 1207 and 1224, who acted as nonpregnant controls.
Figure 6 Changes in isotopic ratio expressed as 206Pb:204Pb for subjects who breast-fed for ≥3 months. The length of breast-feeding is noted to the right of the individual lines.
Figure 7 Changes in isotopic ratio expressed as 206Pb:204Pb for subjects who breast-fed for ≤1 month. The length of breast-feeding is noted to the right of the individual lines.
Table 1 Subject information.
Subject identifier Country of origin Time to conception (months) after arrival No. of children Age (years) Calcium supplement Average compliance (%) during pregnancy Initial Pb (μg/dL) Sex of newborn Breast-feeding
1204 Bulgaria 13 1 31 Carbonate 68 4.0 M > 6 months
1207a Croatia NA 1 26 NA NA 1.0 NA NA
1208b Bosnia 34 1 23 Carbonate 73 1.9 M/F 0
1211c Bangladesh 8 1 25 Carbonate 95 1.6 M 3 months
1212c Turkey 20 1 31 Carbonate 86 (1st 2 months) 2.4 F > 6 months
1213 Lebanon 4 0 32 Complex 71 (1st 6 months) 2.9 M < 2 weeks
1214 Turkey 14 0 25 Complex 52 1.8 F 0
1224a Bulgaria NA 0 31 NA NA 2.4 NA NA
1225c Pakistan 3 0 28 Complex 72 6.5 F 0
1226c,d Iraq 1 0 20 Complex 56 1.4 M > 4
1229 Lebanon 18 0 19 Complex 52 2.3 M < 1 month
1231a,e China 2 0 32 Carbonate 100 (6 months) 1.7 F Unknown
NA, not applicable.
a Nonpregnant controls.
b Gave birth to twins.
c Pregnant when recruited.
d About 4 months pregnant on recruitment
e Subject 1231 returned to China immediately after giving birth.
Table 2 Product information.
Product 1 (complex product)
Product 2
Composition Calcium citrate Calcium phosphate Calcium amino acid Calcium carbonate +vitamin D3
Weight Ca compound (mg) 300 325 200 1,500
Equivalent Ca (mg) 64 126 40 600
Daily total Ca (mg) 920 1,200
Daily dosage 3 times after meals, 1 on retiring Twice daily
206Pb:204Pb 20.1 18.5
Pb [μg/kg (μg/tablet)] 293 (0.4) 940 (1.6)
Daily Pb intake (μg) 2.8 3.2
Table 3 Changes during pregnancy and postpartum.
Percent increase in late pregnancy
Percent increase postpartum
Identifier Country of origin Month PbB increased > minimuma PbB (HCt corr) 206Pb:204Pbb PbB 206Pb:204Pbb Percent increase in PbB:min valuec Extra fluxd (μg)
1204 Bulgaria 8 40 0.17 30 0.33 90 380
1208 Bosnia 8 55 0.11 40 1.22 95 150
1211 Bangladesh 6 10 0.51 50 ND 90 125
1212 Turkey 8 10 ND 55 ND 95 235
1213 Lebanon 8 25 0.28 30 1.65 70 140
1214 Turkey 8 40 ND 10 0.61 40 80
1225 Pakistan 6 50 ND NA NA NA NA
1226 Iraq 8 20 0.80 20 ND 50 200
1229 Lebanon 6 10 ND 40 0.2 30 50
1231 China 4 40 0.33 NS NS NA 165a
Abbreviations: (HCt corr), (hematocrit corrected); min, minimum; NA, not applicable; ND, not able to be estimated because of change in slope (see figures); NS, no samples; PbB, blood lead concentration.
a During pregnancy.
b Relative to experimental error of ± 0.2%.
c Late pregnancy to postpartum.
d Extra lead released from bone during late pregnancy and postpartum.
Table 4 Results for autoregression analyses.
R2-value
p-Value
Subject 206Pb:204Pba 207Pb:206Pb PbB 206Pb:204Pb 207Pb:206Pb PbB
1204 0.87 (10) 0.91 0.63 (9) < 0.001 < 0.001 0.007
1208 0.94 (9) 0.94 0.85 (9) < 0.0001 < 0.00001 < 0.001
1211 0.12 (11) 0.25 0.22 (10) 0.32 0.15 0.38
1212 0.09 (9) 0.23 0.94 (8) 0.46 0.25 0.002
1213 0.91 (7) 0.85 0.40 (6) 0.006 0.01 0.30
1214 0.12 (10) 0.60 0.09 (9) 0.35 0.07 0.37
1226 0.04 (9) 0.16 0.14 (8) 0.56 0.31 0.45
1229 0.76 (8) 0.28 0.77 (7) 0.002 0.21 0.002
Numbers of data points in analysis are given in parentheses.
a Cord blood value incorporated in analysis, except for subject 1208 (no sample available).
Table 5 Results for interrupted time-series analyses.
No. of measures
Sum squares intercept
Sum squares slope
Overall test of change
Subject Pregnancy PP t-Value p-Value t-Value p-Value F-value p-Value
1204 7 7 1.40 0.194 0.45 0.664 0.14 (2,9) 0.87
8 6 4.12 0.003 –3.14 0.012 5.75 (2,9) 0.025
1208 7 6 0.52 0.617 1.41 0.197 1.16 (2,8) 0.36
7 6 4.79 0.001 –3.35 0.010 5.81 (2,8) 0.028
1211 8 7 –1.94 0.081 1.41 0.188 1.28 (2,10) 0.32
8 6 2.50 0.034 –2.15 0.06 4.91 (2,9) 0.036
1212 9 7 2.03 0.068 –1.45 0.175 2.01 (2,11) 0.181
9 6 2.70 0.022 –1.21 0.255 0.83 (2,10) 0.464
1213 9 5 –0.11 0.915 1.39 0.197 1.50 (2,9) 0.274
9 4 4.77 0.001 –4.0 0.004 8.01 (2,8) 0.012
1214 10 7 1.23 0.241 0.22 0.828 3.60 (2,12) 0.060
10 6 –1.21 0.253 1.04 0.320 0.64 (2,11) 0.547
1226 6 8 1.57 0.15 –1.02 0.34 1.05 (2,9) 0.39
6 7 3.94 0.004 –4.39 0.002 10.27(2,8) 0.006
1229 8 5 –0.05 0.964 1.11 0.301 1.09 (2,8) 0.380
8 4 1.32 0.229 –0.76 0.474 1.10 (2,7) 0.385
PP, postpartum. For all subjects, first row is 206Pb:204Pb and second row is PbB. Values in parentheses are degrees of freedom. Cord blood value incorporated in postpartum analysis, except for subject 1208 (no sample available).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7161ehp0112-00150815531435ResearchArticlesProduction of Androgens by Microbial Transformation of Progesterone in Vitro: A Model for Androgen Production in Rivers Receiving Paper Mill Effluent Jenkins Ronald L. 1Wilson Elizabeth M. 234Angus Robert A. 5Howell W. Mike 1Kirk Marion 6Moore Ray 6Nance Marione 1Brown Amber 11Department of Biology, Samford University, Birmingham, Alabama, USA2Laboratories for Reproductive Biology,3Department of Pediatrics, and4Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, North Carolina, USA5Biology Department and6Comprehensive Cancer Center Mass Spectrometry Shared Facility, University of Alabama at Birmingham, Birmingham, Alabama, USAAddress correspondence to R.A. Angus, Biology Department, University of Alabama at Birmingham, Birmingham, AL 35294-1170 USA. Telephone: (205) 934-4799. Fax: (205) 975-6097. E-mail:
[email protected] work was supported in part by the U.S. Environmental Protection Agency through grant R826130 (R.A.A.) and by National Institutes of Health Public Health Service grant HD16910 (E.M.W.).
This report has not been subjected to government agency approval required for peer and policy review and therefore does not necessarily reflect the views of the agency, and thus no official endorsement should be inferred.
The authors declare they have no competing financial interests.
11 2004 22 7 2004 112 15 1508 1511 5 4 2004 22 7 2004 Publication 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 have previously documented the presence of progesterone and androstenedione in the water column and bottom sediments of the Fenholloway River, Taylor County, Florida. This river receives paper mill effluent and contains masculinized female mosquitofish. We hypothesized that plant sterols (e.g., β-sitosterol) derived from the pulping of pine trees are transformed by bacteria into progesterone and subsequently into 17α-hydroxyprogesterone, androstenedione, and other androgens. In this study, we demonstrate that these same androgens can be produced in vitro from the bacterium Mycobacterium smegmatis. In a second part to this study, we reextracted and reanalyzed the sediment from the Fenholloway River and verified the presence of androstadienedione, a Δ1 steroid with androgen activity.
17α-hydroxyprogesteroneandrogen-dependent gene expressionandrostadienedioneandrostenedionebiotransformation of progesteroneenvironmental androgensFenholloway RiverFloridaGambusia holbrookimasculinized mosquitofishMycobacterium smegmatis
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The presence of environmental androgens in biologically effective concentrations was first demonstrated > 20 years ago when a population of the eastern mosquitofish, Gambusia holbrooki, was discovered in which the females were all masculinized (Howell et al. 1980). Since then, other populations of masculinized mosquitofish have been discovered, all living in small coastal streams in Florida that receive paper mill effluent (Bortone and Cody 1999). The presence of androgens in pulp mill effluent does not appear to be unique to Florida. Larsson et al. (2000) recently reported male-biased sex ratios in embryos of the marine live-bearing eelpout, Zoarces viviparous, exposed to paper mill effluent off the coast of Sweden.
In the last 3 years, some of the androgens in a river containing masculinized, paper mill effluent-exposed mosquitofish have been identified. In an earlier study, we used an androgen-dependent gene expression assay to detect, and mass spectrometry to identify, androstenedione (AED) at a concentration of 0.14 nM in the water column of the Fenholloway River (Jenkins et al. 2001). Parks et al. (2001) used a similar androgen-dependent reporter assay and radioimmunoassay to detect an androgenic substance, presumably testosterone, in samples from the Fenholloway River, Taylor County, Florida. In a subsequent study, we (Jenkins et al. 2003) identified higher concentrations of AED (2.4 nM) and its biosynthetic precursor progesterone (155 nM) in sediment samples from one of the sites of the Fenholloway River studied by Parks et al. (2001). Durhan et al. (2002) presented evidence that there are other androgenic compounds, not clearly identified, in the Fenholloway River below the paper mill.
Orlando et al. (2002) noted that the levels of AED in Fenholloway River water did not seem to be sufficient to cause masculinization of the fish, and they hypothesized that the masculinizing effects from paper mill effluent might result from inhibition of the enzyme P450 aromatase. This aromatase is responsible for the biosynthesis of estrogens from androgens such as AED and testosterone (Thompson and Sitteri 1974). If aromatase activity in mosquitofish exposed to paper mill effluent was inhibited, the concentrations of endogenous androgens would increase and potentially cause masculinization. Orlando et al. (2002) showed the aromatase-inactivation hypothesis to be false, finding that brain and ovarian aromatase activity in the mosquitofish in the Fenholloway River was significantly greater than that of controls. The androgenic substances in the Fenholloway River appeared to be increasing aromatase activity in the brain and ovaries of these fish.
After demonstrating the presence of high levels of progesterone in the river sediment, we (Jenkins et al. 2003) hypothesized that some or all of the androgens in paper mill effluent and river sediment were generated by microbial degradation of phytosterols from the mill pulp. To test this hypothesis, in the present study we incubated a common soil bacterium, Mycobacterium smegmatis, with progesterone. Androgenic activity of chemicals in the medium was detected using an androgen receptor transcription assay and was quantified by high-pressure liquid chromatography (HPLC). Some of the androgens found in the microbial incubation study were not previously detected in our analyses of sediment from the Fenholloway River (Jenkins et al. 2003). To determine if these previously unidentified substances were indeed present in the Fenholloway River, in the present study, we reextracted and analyzed the sediment using an androgen receptor transcription assay, HPLC, and liquid chromatography mass spectrometry (LC-MS).
Materials and Methods
Microbial incubations, extraction, and HPLC separation.
Mycobacterium smegmatis (strain ATCC 14468 from Presque Isle Cultures, Erie, PA) were grown in 200 mL Bacto Nutrient broth (3 g beef extract plus 5 g peptone/L; Difco Laboratories, Detroit, MI) at 24–25°C to yield a cell density of 4 × 106/mL. To determine M. smegmatis density, we prepared dilution plates using the pour-plate technique, and we used a Quebec colony counter (Fisher Scientific, Atlanta, GA) for viewing and counting the isolated colonies.
Progesterone (P0130; Sigma Chemical Co., St. Louis, MO) was added to each culture to yield a 1-mM solution. Before progesterone was added and on days 0, 2, 4, 6, 8, 12, 15, 20, and 36 after progesterone was added, triplicate 3-mL samples of the slurried media were extracted in 12 mL 100% methanol (HPLC grade). The vortexed extract was centrifuged (1,000 × g, 10 min) and passed through a 0.3 mL washed C-18 solid-phase extraction cartridge (Varian Instrument Co., Walnut Creek, CA). The eluant was dried under nitrogen gas before fractionation.
Each extract was fractionated on a 4.6 × 15 cm C-18 column (Varian Instrument Co.) using a 35-min HPLC methanol linear gradient with 0.25% O-phosphoric acid and 100% methanol. The gradient increased from 20% methanol at 0 min to 100% methanol at 20–35 min. Ultraviolet detection of column eluants was at 235 nm, and identification was based on co-migration with standards. Quantification of steroids was based on peak area relative to standards, and data are presented as the mean of triplicates (± SE; micromolar) (Jenkins et al. 2003).
Efficiency of steroid extraction.
The percent recovery of extracted steroids in all bacterial sample fractions was determined by sequentially extracting the samples a second and third time in methanol, followed by solid-phase extraction and quantification by HPLC. The percent yield of progesterone, 17α-hydroxyprogesterone (17α-OHP), AED, and androstadienedione (ADD) at each level was 86.7 ± 2.9 % (mean ± SE).
Androgen receptor transcription assays.
The agonist activity of compounds separated from bacterial media extracts by HPLC was determined in transient cotransfection assays using a luciferase reporter gene (He et al. 2001; Jenkins et al. 2001; Kemppainen et al. 1992). Briefly, monkey kidney CV-1 cells (0.42 × 106 cells/6-cm dish) were transfected using the calcium phosphate DNA precipitation method with the human androgen receptor expression vector pCMVhAR (50 or 100 ng/dish) and the luciferase reporter vector under control of the mouse mammary tumor virus promoter (MMTV-luc, 5 μg/dish). Cells were incubated for 24 hr at 37°C with dihydrotestosterone (DHT; 0.01, 0.1, or 1 nM concentrations) or 10 μL additions of HPLC fractions. Representative luciferase activity assays are expressed in optical units relative to the no-hormone control. The MMTV-luc assay performed in CV-1 cells demonstrates human androgen receptor–mediated gene activation (Kemppainen et al. 1992).
Collection, quantification, and purification of androgens from the river sediment.
Sediment was collected from the Fenholloway River on 12 June 2003 where it crosses Highway 361A, east of Perry, Florida, 3.6 km downstream of the paper mill settling ponds. A long-handle 2.8-mm mesh dip net was used to collect samples from the top 20 cm of sediment. Water was allowed to drain thoroughly from the net, leaving only pore water in the sediment sample. Five 50-mL samples of the sediment were immediately mixed with 200 mL 100% methanol (HPLC grade). Within 24 hr the sediment samples were filtered through acid-washed glass wool and vacuum-filtered through 0.8 μm cellulose nitrate membranes (Whatman, Maidstone, UK). The filtrate was passed through methanol-washed solid-phase extraction cartridges (Mega Bond Elut, 6 mL, C-18; Varian Instrument Co.). The eluant was dried under N2 gas. Fractions were reconstituted in methanol for HPLC fractionation and in ethanol for the androgen receptor transcription assay. Steroid quantification was based on peak area in HPLC relative to coeluting standard steroids and was represented as mean of five samples ± SE.
Liquid chromatography–mass spectrometry of river sediment.
We used LC-MS to verify the identity of androgens in the HPLC, C-18 column fractions from the Fenholloway River sediment that induced androgen receptor–mediated transcriptional activity and were preliminarily identified based on co-migration with standards. Individual HPLC fractions were dried under N2 gas and reconstituted in 100% methanol for LC-MS verification on a Shimadzu VP system (Shimadzu, Kyoto, Japan). We used a 10 cm × 2.1 mm, C-8 Aquapore column (Applied Biosystems, Foster City, CA) with a 12 min linear 0–100% methanol gradient in 10 mM ammonium acetate to separate the components. Eluants were passed into an electrospray interface of a PE Sciex API III triple-quadrupole mass spectrometer (PE Sciex, Foster City, CA). Multiple reaction monitoring was used for the final comparison of unknown compounds with standards. In this procedure, the parent ion was selected with the first quadrupole and passed into the second quadrupole containing argon gas. Collision of the parent ion with the argon produced fragment ions. Monitoring of a specific parent ion by the first quadrupole and ion fragments by the third quadrupole constituted the multiple reaction monitoring method. Elution of the selected parent–fragment pair at the same chromatographic retention time as a standard confirmed the identity of the steroids.
Results
In situ production of androgens from progesterone by M. smegmatis.
M. smegmatis incubated with progesterone (1 mM) produced steroidal products that paralleled HPLC standards 17α-OHP, AED, and ADD. Figure 1 illustrates the HPLC chromatograms from 0 days (Figure 1A), 6 days (Figure 1B), and 20 days (Figure 1C) of incubation and the progressive production of 17α-OHP, AED, and ADD. Figure 2 shows the androgen receptor transcription assay for samples removed at day 0 (Figure 2A) and day 6 (Figure 2B), illustrating the accumulation of androgenic components. The sample from day 6 (Figure 2B) contained an unidentified component in the 12-min fraction that stimulated androgen receptor–mediated transcription as well as the steroids in the 19- to 24-min fractions. The 12-min fraction component did not match the elution time of any of our steroid standards and remains unidentified. Figure 3 graphically represents the production of steroids in the incubation medium over the 20-day incubation period. The concentration of 17α-OHP steadily increased from day 0 through day 12. The highest mean concentration of 17α-OHP (23.0 ± 0.7 μM) was detected on day 20. The apparent drop in concentration at day 15 may have resulted from a sampling error. The 12-, 15-, and 20-day mean values do not differ significantly from each other (analysis of variance, p = 0.070). AED concentrations increased from day 0 to day 6, with a maximum mean concentration of 7.2 ± 0.8 μM. AED levels remained steady until day 12 and declined thereafter. ADD concentrations were consistently lower than those of AED, except at day 8, when the mean was maximal at 5.2 ± 0.5 μM (Figure 3).
Verification of androgens from river sediment.
After the tentative identification of AED and ADD from M. smegmatis incubations, sediment from the Fenholloway River was extracted to determine whether these androgens were present. Figure 4 illustrates the androgen receptor transcription assay activity induced by fractions from a gradient HPLC separation of a crude extract of sediment. From the HPLC separation and agonist activity results, fractions containing ADD (22 and 22.5 min), AED (23 min), and progesterone (25 min) were isolated. The verification of AED and progesterone by LC-MS has been previously published (Jenkins et al. 2003).
The principal steroid of the 22- and 22.5-min fractions was verified as ADD by LC-MS and compared with an ADD standard (Figure 5). The parent compound of the sample and standard had a molecular weight of 285 and fragmented to two ions: 285/121 and 285/151. The ratio of the parent ion and two fragment ions (285/121:285/151) was 4.12 for the standard and 4.11 for the sample.
Steroid levels in the Fenholloway River sediment.
The concentrations (mean ± SE) of progesterone, 17α-OHP, AED, and ADD in sediment samples from the Fenholloway River were 150.3 ± 39.9, 11.1 ± 2.2, 4.0 ± 1.0, and 2.6 ± 0.4 nM, respectively. Steroids were quantified from HPLC chromatograms based on the peak area in five replications compared with standard peak areas. These values were similar to previously cited values (Jenkins et al. 2003).
Androgen receptor transactivation by ADD and AED.
Transcriptional activity induced by commercially purified forms of the androgenic microbial products that were verified in the sediment of the Fenholloway River was compared with DHT (Figure 6). The relative potencies of ADD and AED to act as agonists in the mammalian androgen receptor–mediated transcription assay were essentially identical. Agonist activity induced by 1 nM ADD or 1 nM AED was nearly equivalent to the response obtained using 0.01 nM DHT, indicating a 100-fold less potency of ADD and AED relative to DHT.
Discussion
The results of this study are consistent with our hypothesis that steroids detected in the Fenholloway River water and sediment derive from microbial biotransformation of phytosteroids in the waste stream from the wood pulping process. Nagasawa et al. (1969) showed that many genera of microorganisms, including Arthrobacter, Nocardia, Protaminobacter, Serratia, Streptomyces, Mycobacterium, and Microbacterium, contain the enzymes necessary to convert cholesterol and other C-17 sterols to AED and ADD. Other investigators have shown that microbial degradation of plant sterols and cholesterol can produce ADD (Marsheck et al. 1972; Roy et al. 1991). Conner et al. (1976) cultured Mycobacterium species with tall (pine) oil sterols and showed that ADD was the principal product. Thus, the presence of ADD in the Fenholloway River water and sediment could result from the metabolism of phytosterols by a variety of bacterial strains. By slowing the reactions using a cooler incubation temperature (25°C), we were also able to identify the intermediates. Based on the rate of accumulation of the intermediates during the reaction (progesterone > 17α-OHP > AED > ADD), the implied sequence of in vitro production of these androgens from progesterone by a culture of M. smegmatis is shown in Figure 7. Figure 3 illustrates that the pathway continues with a decline of ADD beyond day 8 of incubation, implying the presence of additional reaction steps that metabolize ADD.
Progesterone and AED were recently identified in relatively high concentrations in the sediment of the Fenholloway River at a site that received no inflows other than paper mill effluent (Jenkins et al. 2003). Peck et al. (2004) demonstrated that the sediment of rivers below sewage treatment plants in the United Kingdom are major sinks for steroidal estrogens derived from sewage. Conner et al. (1976) showed that the river sediment below a paper mill contains an abundance of phytosterols. The present study demonstrates that phytosterols have been converted to progesterone via microbial activity and progesterone to the androgenic compounds AED and ADD.
With respect to the masculinization of mosquitofish in the Fenholloway River (Howell et al. 1980), ADD could be a more important androgen than AED, which has been shown to be present in the water column and sediment of the Fenholloway River (Jenkins et al. 2001, 2003). Data in the present study indicate that AED and ADD have similar abilities to induce mammalian androgen receptor–mediated transcription. However, AED is more rapidly aromatized to estrogen. ADD has an inhibitory effect on P450 aromatase (ki = 0.32 mM; kinact = 0.91 × 10−3/sec) and may irreversibly inactivate aromatase by forming enzyme–substrate covalent bonds (Covey and Hood 1982). The 1-androstene prohormones (including ADD) can be converted to 1,4-androstadien-17α-ol-3-one (boldenone). The rates of aromatization of ADD and boldenone to estrogen are about half those of the 4-androstenes (Steele et al. 1977). Because ADD is resistant to aromatization, it would be expected to have a longer and more effective androgenic half-life and thus be a more potent androgen in vivo.
The production of ADD by microbial degradation of phytosteroids may also have more widespread implications than its effects on aquatic wildlife near paper mills. The microbial degradation pathways seen in this study also occur in the intestine of humans and wildlife, where the native bacterial flora could provide the appropriate enzymes for steroid biosynthesis. Escherichia coli has been shown to degrade cholesterol to AED and ADD (Owen et al. 1978). ADD has been isolated from the feces of cattle after subcutaneous injection of progesterone (1 mg/day for 5 days) (Miller et al. 1956). Owen et al. (1978) concluded that progesterone was sequestered by the liver and converted by microbes in the intestines to ADD. It appears that dietary phytosterols may have human health benefits in terms of improving blood lipid profiles (St-Onge and Jones 2003; Vorster et al. 2003) and in reducing the risk of some cancers (Messina and Barnes 1991; Philpotts 1997). If ADD and other Δ1-androgens are generated in the human gut from the degradation of phytosterols to steroidal precursors, other effects on human health may need to be considered.
Figure 1 Reverse-phase, C-18, 36-min gradient HPLC separation of methanol extractions of M. smegmatis cultures containing progesterone (1 mM) after days 0 (A), 6 (B), and 20 (C) of incubation at 24–25°C. Samples were extracted in 80% methanol, vacuum filtered, and separated by C-18 solid-phase extraction. HPLC solvent A was 0.25% O-phosphate, and solvent B was 100% methanol with 20% methanol from 0 to 5 min, linearly increased to 100% methanol from 20 to 36 min. Detection was by absorbance (OD, optical density) at 235 nm, and the full vertical scale represents 1 absorbance unit (106 μV).
Figure 2 Androgen receptor–mediated transcriptional activity (mean optical units ± SE; n = 2) of HPLC fractions from M. smegmatis extractions taken at day 0 (A) and day 6 (B) of incubation. Cotransfection assays were performed in monkey kidney CV-1 cells as described in “Materials and Methods.” Luciferase activity was measured in optical units. Minutes on the abscissa refer to elution time of the HPLC separation. DHT was the positive control.
Figure 3 Steroidal derivatives from the biotransformation of 1 mM progesterone by M. smegmatis from day 0 to day 20 of incubation. See “Materials and Methods” for details of bacterial incubation, sample extraction, and HPLC fractionation. Quantification of steroid levels was based on mean peak areas ± SE (n = 3) compared with standard steroids.
Figure 4 Androgen receptor–mediated transcriptional activity (mean optical units ± SE; n = 2) of gradient HPLC fractions from Fenholloway River sediment. Cotransfection assays were performed in monkey kidney CV-1 cells as described in “Materials and Methods.” Luciferase activity was measured in optical units. Minutes on the abscissa refer to elution time of the HPLC separation. DHT was the positive control.
Figure 5 LC-MS with multiple reaction monitoring chromatograms of parent and ion fragments of ADD standard and Fenholloway River sediment sample. (A) 285/121 fragment ion of 100 pmol ADD standard. (B) 285/151 fragment ion of 100 pmol ADD standard. (C) 285/121 fragment ion of the 22-min HPLC fraction from Fenholloway River sediment. (D) 285/151 fragment of the 22-min HPLC fraction from the Fenholloway River sediment.
Figure 6 Relative effectiveness of DHT, ADD, and AED in the monkey kidney CV-1 androgen receptor–mediated transcription assay. Luciferase activity was measured in optical units, and values shown are mean ± SE.
Figure 7 The sequence of in vitro production of androgens from progesterone by a culture of M. smegmatis.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7192ehp0112-00151215531436ResearchArticlesEvidence for Concurrent Effects of Exposure to Environmental Cadmium and Lead on Hepatic CYP2A6 Phenotype and Renal Function Biomarkers in Nonsmokers Satarug Soisungwan 1Nishijo Muneko 2Ujjin Pailin 3Vanavanitkun Yuvaree 3Baker Jason R. 4Moore Michael R. 151National Research Centre for Environmental Toxicology, University of Queensland, Brisbane, Australia2Department of Public Health, Kanazawa Medical University, Uchinada, Ishikawa, Japan3Department of Laboratory Medicine, Chulalongkorn University, Bangkok, Thailand4Department of Clinical Pharmacology, Flinders University, Adelaide, Australia5Queensland Health Scientific Services, Brisbane, AustraliaAddress correspondence to S. Satarug, National Research Centre for Environmental Toxicology, University of Queensland, 39 Kessels Rd., Coopers Plains, Brisbane, Queensland 4108, Australia. Telephone: 61-7-3000-9195. Fax: 61-7-3274-9003. E-mail:
[email protected] thank all volunteers for their enthusiastic cooperation. We are grateful to C. Itthipanichpong of the Pharmacology Department, Chulalongkorn University, and B. Thititummatada of Ban Bangapi School, Klong-gume, Bangkok, for their advice and support.
The National Research Centre for Environmental Toxicology is funded by Queensland Health, the University of Queensland, Queensland University of Technology, and Griffith University.
The authors declare they have no competing financial interests.
11 2004 28 7 2004 112 15 1512 1518 18 4 2004 28 7 2004 Publication 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 examined the interrelationships between phenotype of hepatic cytochrome P450 2A6 (CYP2A6), nephropathy, and exposure to cadmium and lead in a group of 118 healthy Thai men and women who had never smoked. Their urinary Cd excretion ranged from 0.05 to 2.36 μg/g creatinine, whereas their urinary Pb excretion ranged from 0.1 to 12 μg/g creatinine. Average age and Cd burden of women and men did not differ. Women, however, on average showed a 46% higher urinary Pb excretion (p < 0.001) and lower zinc status, suggested by lower average serum Zn and urinary Zn excretion compared with those in men. Cd-linked nephropathy was detected in both men and women. However, Pb-linked nephropathy was seen only in women, possibly because of higher Pb burden coupled with lower protective factors, notably of Zn (p < 0.001), in women compared with men. In men, Pb burden showed a negative association with CYP2A6 activity (adjusted β= −0.29, p = 0.003), whereas Cd burden showed a positive association with CYP2A6 activity (adjusted β= 0.38, p = 0.001), suggesting opposing effects of Cd and Pb on hepatic CYP2A6 phenotype. The weaker correlation between Cd burden CYP2A6 activity in women despite similarity in Cd burden between men and women is consistent with opposing effects of Pb and Cd on hepatic CYP2A6 phenotypic expression. A positive correlation between Cd-linked nephropathy (urinary N-acetyl-β-d-glucosaminidase excretion) and CYP2A6 activity in men (r = 0.39, p = 0.002) and women (r = 0.37, p = 0.001) suggests that Cd induction of hepatic CYP2A6 expression and Cd-linked nephropathy occurred simultaneously.
β-2-microglobulincadmiumcoumarincytochrome P450 2A6drug-metabolizing enzymeenvironmental exposureleadliver drug metabolismN-acetyl-β-d-glucosaminidasenicotinenicotine C-oxidaseproteinuriazinc
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The metals cadmium and lead are ubiquitous environmental pollutants of increasing worldwide concern because of their renal toxicity and long residence time in the kidney [International Programme on Chemical Safety (IPCS) 1992; Jarup et al. 1998; Madden and Fowler 2000; Satarug et al. 2000]. These metals are absorbed by the body via enteral and pulmonary routes from dietary sources, drinking water, and polluted air (Bhatnagar 2004; IPCS 1992; Galal-Gorchev 1993; Satarug et al. 2004b). Safe levels of dietary intake, known as the provisional tolerable weekly intake (PTWI), have been established for Cd and Pb (World Health Organization 1989). The PTWI for Pb is 25 μg/kg body weight/week, which is approximately three times more tolerated than Cd, with a PTWI of 7 μg/kg body weight/week. Cd accumulates in the liver and kidneys, whereas Pb preferentially accumulates in the bone (IPCS 1992; Satarug et al. 2002). Cd in liver and Pb in bone are mobilizable to the kidney, providing an opportunity for nephrotoxicity with no additional exposure. The manifestations of Cd nephrotoxicity include proteinuria, calciuria, aminoaciduria, glycosuria, and tubular necrosis (IPCS 1992; Jarup et al. 1998).
Chronic exposure to low-level Cd results in 20- to 40-fold higher Cd concentration in kidneys than in the liver. However, although liver Cd levels are much lower than kidney levels, suggestive evidence for liver effects of Cd comes from immunoblotting of postmortem liver samples with anti-peptide antibody preparations against various human drug-metabolizing enzymes of the cytochrome P450 (CYP) superfamily, which revealed correlation between tissue Cd contents and the abundance of certain CYPs (Baker et al. 2001, 2002, 2003). Pb effects on liver drug metabolism were reported in early studies, where diminished phenazone elimination rates in men who had been occupationally exposed to Pb and who had evidence of clinical Pb poisoning were reversed after the patients had been treated with EDTA chelation therapy (Fischbein et al. 1977; Meredith et al. 1977; Moore 2004). A primary effect of Pb that could cause depressed CYP-mediated phenazone metabolism was its reduction of heme bioavailability via Pb induction of hepatic expression of the enzyme heme oxygenase (Moore et al. 1987), which degrades heme (Tenhunen et al. 1968) and Pb inhibition of the enzyme δ-aminolevulinate (ALA) synthase of heme synthesis pathway (Moore et al. 1987). However, although Cd is a potent inducer of heme oxygenase (Alam et al. 1989, 2000; Rosenberg and Kappas 1991) and an inhibitor of ALA synthase (Fujita 1997), as is Pb, Cd also has the ability to induce hepatic and renal cytochrome P450 2A5 (CYP2A5) in mice (Abu-Bakar et al. 2004; Bartosiewicz et al. 2001; Urbenjapol et al. 2001). The ability of Cd to induce the murine CYP2A5, which is an ortholog of human cytochrome P450 2A6 (CYP2A6), is consistent with results obtained from a study in healthy human subjects, which found a positive correlation between Cd burden and accelerated rate of hepatic coumarin metabolism known to be mediated exclusively by CYP2A6 (Satarug et al. 2004a).
CYP2A6 is one of the genetically polymorphic enzymes, known to be expressed predominantly in the liver and to be an exclusive catalyst of a conversion of coumarin to 7-hydroxycoumarin (7-OHC; Oscarson 2001; Pelkonen et al. 2000; Raunio et al. 2001). CYP2A6 metabolizes also the tobacco alkaloid nicotine to cotinine, which is metabolized further by CYP2A6 to 3′-hydroxycotinine (Benowitz et al. 2003; Messina et al. 1997; Nakajima et al. 1996). CYP2A6 is therefore known also as nicotine C-oxidase, and its phenotype, reflected by urinary 3′-hydroxy-cotinine:cotinine ratio, showed a positive correlation with daily cigarette consumption (Benowitz et al. 2003). Other substrates of CYP2A6 include a number of pharmaceuticals (methoxyflurane, halothane, losigamone, valproic acid, letrozole, fadrozole, disulfiram, and tegafur), the caffeine metabolite 17-dimethyl-xanthine, the tobacco-specific nitrosamines, and the tobacco alkaloid nicotine. Individuals with the CYP2A6 gene deficiency were found to be protected from smoking dependence. This has led to the hypothesis that the CYP2A6 genetic polymorphism is a sole determinant of individuals’ smoking dependence. Although conflicting results have been reported, casting doubt on such role of the CYP2A6 genetic polymorphism (Tricker 2003). We hypothesize that CYP2A6 phenotypic variability seen among most healthy individuals is caused by their exposure to environmental substances, which have the ability to induce or suppress the hepatic CYP2A6 gene expression.
Evidence for inducibility of hepatic CYP2A6 comes from studies showing that the metals cobalt and tin, and drugs and compounds of diverse structures such as phenobarbital, rifampicin, clofibrate, pyrazole, thioacetamide, and griseofulvin increase expression of murine CYP2A5, which is an ortholog of human CYP2A6 (Donato et al. 2000; Oscarson 2001; Pelkonen et al. 2000; Raunio et al. 2001). Some of these chemicals were indeed found to induce also CYP2A6 expression in primary culture human hepatocytes (Donato et al. 2000). Previously, we found that hepatic CYP2A6 activity was elevated in individuals who had high Cd burden (Satarug et al. 2004a), assessed by urinary Cd excretion, which is a widely used indicator of long-term Cd exposure (Lauwerys et al. 1994; Mueller et al. 1998). However, in the previous study both smokers and nonsmokers were included, which reflected mixed sources of exposure and heterogeneity in exposure pattern that may also have altered CYP2A6 phenotypic expression. Further, neither Pb exposure, zinc status, nor markers of renal tubular toxicity were determined for the previous subjects. The present study therefore was conducted on another group of subjects who had never smoked, to examine the association between increased hepatic CYP2A6 activity and dietary exposure to Cd. Another purpose was to explore the potential effects of Pb exposure, sex, age, and Zn and iron status, assessed by serum Zn and ferritin levels, on liver CYP2A6 phenotypic variability and renal toxicity.
Materials and Methods
Studied subjects.
This study was approved by the Institutional Ethical Committee on Human Experimentation, Chulalongkorn University Hospital, Faculty of Medicine, Bangkok, Thailand. The studies sample group consisted of 118 individuals (65 women, 53 men), 21–57 years of age. All subjects were non-smokers, and all were Thai nationals who took part in the study after giving informed consent. All subjects lived in residential areas surrounding Bangkok when the study was undertaken. Subjects were students, factory workers, teachers, and laborers. Frequency distribution of various occupations in men and women was similar. None of them had been exposed to Cd in the workplace, and the group was considered to represent the middle-class Thai population in socioeconomic status.
Biologic sample collection.
One 15-mL blood sample was collected by venipuncture from each subject 1 hr after swallowing of one 15-mg coumarin tablet (Venalot, Schaper and Brummer GmbH and Co. KG, Salgitter, Germany) with 300 mL drinking water after overnight fasting. Aliquots of the blood samples collected were subject to routine hematology and clinical biochemistry analysis using the blood analysis automated system of the Chulalongkorn University Hospital. Serum ferritin levels were assayed by an immunoelectrochemiluminescence method (Boehringer Mannheim Elecsys 1010; Boehringer Mannheim and Roche Diagnostics, Bangkok, Thailand). Urine samples were also collected for 3 hr after Venalot administration. The urine samples from each subject were pooled and total urinary volume was recorded. One 5-mL aliquot of urine from each subject was analyzed for creatinine and routine urinalysis (pH, specific gravity, protein, glucose) within the day of collection. The remainder of the urine samples, together with aliquots of whole blood and serum samples, were stored at −80°C for later analysis.
Analysis of biomarkers and metals.
Urine samples were analyzed for 7-OHC, Cd, total protein, β2-microglobulin (β2-MG), and N-acetyl-β-d-glucosaminidase (NAG). For 7-OHC analysis, urine samples were treated with β-glucuronidase (250 U/mL; Sigma-Aldrich, Sydney, Australia) in 1.0 M acetate buffer (pH 5.0) at 37°C for 30 min. Concentrations of 7-OHC in the β-glucuronidase–treated samples were quantified by the high-performance liquid chromatography system with C18-reverse phase column and a fluorescence detector (Walters et al. 1980). Equivalent procedures were conducted for determination of 7-OHC concentration in serum samples.
Cd levels in urine samples were determined by inductively coupled plasma mass spectrometry. The accuracy and precision of our urine metal analysis were assessed by a simultaneous analysis of samples of the urine metal control Lyphochek (Bio-Rad, Sydney, Australia). The level 1 and level 2 urine metal controls refer to the controls with different concentrations of metals. The mean values for Cd and Zn in the level 1 control urine were 6.2 and 713 μg/L, respectively, and the mean values for the same metals in the level 2 control urine were 11.2 and 1,176 μg/L. Our analysis of the level 1 control urine samples (n = 18) gave mean ± SD values of 6.1 ± 0.2 and 662 ± 38 μg/L for Cd and Zn, respectively. The coefficient of variation for the corresponding metal was 2.5 and 5.6%. Our analysis of the level 2 control urine samples (n = 18) gave mean ± SD values of 11.4 ± 0.3 and 1,077 ± 65 μg/L for Cd and Zn. The coefficient of variation for the corresponding metal was 2.5 and 6.0%. A urinary Cd concentration of 0.05 μg/L was assigned to the urine sample found to contain Cd below the detection limit of the method.
Protein in urine samples was determined by turbidimetry after protein precipitation with EDTA and benzethonium chloride using the automated system (Roche/Hitachi 717; Boehringer Mannheim and Roche Diagnostics). Urinary β2-MG was determined by latex immunoagglutination method (LX test, Eiken β2MG-II; Eiken and Shionogi Co., Tokyo, Japan), and urinary NAG was assessed with an enzymatic rate colorimetric assay. Urine β2-MG and NAG assays were conducted with automated systems. Urinary Cd, protein, β2-MG, and NAG were adjusted for creatinine excretion, and both creatinine-adjusted values and total amount of each analyte excreted in 3 hr were used. Similar conclusions were reached with both values. However, in evaluations for effects of Cd exposure by correlation and regression analyses, total amounts of Cd and renal function biomarkers excreted in urine in 3 hr were used because they related more significantly did than creatinine-adjusted figures.
Statistical analysis.
The Kolmogorov-Smirnov goodness of fit test was used to test for conformity to normal distribution of measured and base-10 logarithmically transformed data (see details in “Results”). To identify relationships between variables, the statistical tests used were chi-square testing of cross-tabulated data, Pearson’s correlation test or Spearman’s rank correlation, and multiple regression. We used the Student t-test or Mann-Whitney rank-sum test to determine statistical significance levels for male–female differences in mean values of tested variables. In multiple regression analyses, an indicator of liver effect (CYP2A6 catalytic activity) was entered as a criterion variable, whereas age and urinary Cd, Pb, and Zn excretion were entered as explanatory variables.
Results
Table 1 shows age and blood and urine chemistry profiles of men and women separately. Average age was similar for men (37 years) and women (38 years). Most measured parameters were higher in men than in women: hemoglobin, hematocrit, red blood cell counts, blood urea nitrogen, serum ferritin, serum Zn, and plasma creatinine concentrations (p-values, < 0.001–0.005). Urinary Zn excretion in men was 27% higher than in women (p = 0.001), but men’s and women’s urinary Cd excretion rates were similar. This produces a greater mean urinary Zn:Cd ratio in men than in women (p = 0.001). Average values for serum Cd and Pb were similar in men and women. However, average urinary Pb and urinary total protein excretion rates were higher in women than in men (p < 0.001 for urinary Pb and p = 0.001–0.005 for urinary protein excretion). On average, urinary β2-MG, NAG excretion, and urinary 7-OHC excretion rates were similar in men and women.
The distribution of the logarithmically transformed Cd excretion rates conforms to normal (Kolmogorov-Smirnov goodness of fit test, p = 0.99). The geometric mean, median, and SD values were 54, 55, and 2.3 ng/3 hr, respectively. The lowest and highest urinary Cd excretion rates were 5 and 360 ng/3 hr, respectively. The overall frequency distribution of men and women in Cd-burden groups did not differ (likelihood ratio chi-squared = 2.6, p = 0.46; Figure 1A). The distribution of urinary Pb excretion rates conformed to normal, with mean, median, and SD values of 0.25, 0.23, and 0.18 μg/3 hr, respectively (Kolmogorov-Smirnov goodness of fit test, p = 0.17). The lowest and highest urinary Pb excretion rates were 0.01 and 1.06 μg/3 hr, respectively. The frequency distribution of men and women in Pb-burden groups differed significantly (likelihood ratio chi-squared = 18, p < 0.001; Figure 1B).
The distribution of urinary 7-OHC excretion rates was slightly skewed to the left of normal, with mean, median, and SD values of 5.06, 4.73, and 3.15 mg/3 hr, respectively (Kolmogorov-Smirnov goodness of fit test, p = 0.57). The highest urinary 7-OHC excretion rate was 15 mg/3 hr. Two men and one woman did not excrete 7-OHC, most likely because they lacked the CYP2A6 gene or have a defect in the CYP2A6 gene expression. The overall frequency distribution of men and women in CYP2A6 phenotype groups showed a tendency to be different (likelihood ratio chi-squared = 7.3, p = 0.06; Figure 1C). Of the seven subjects with a very rapid metabolizer phenotype of CYP2A6, six were women.
Table 2 summarizes the relationship between urinary Cd excretion, age, iron store status, and biomarkers of kidney function. We found no correlation between iron store status and Cd burden in men or women, but a strong positive correlation between urinary Cd excretion and age was seen in men (r = 0.47, p < 0.001). The correlation between urinary Cd and age was weaker in women, and it was not statistically significant (r = 0.20, p = 0.06). Both men’s and women’s urinary Cd excretion correlated positively with urinary protein excretion (overall r = 0.25, p = 0.003), but no correlation was seen between urinary Cd and β2-MG excretion in men or women. The correlation between urinary protein and Cd excretion in men and women remained statistically significant after controlling for age (partial r = 0.20, p = 0.02). Urinary NAG excretion rates showed a strong positive correlation with urinary Cd in both men and women (overall r = 0.49, p < 0.001). Controlling for age did not affect the strength of the NAG versus Cd correlation (partial r = 0.48, p < 0.001).
Table 3 shows results of equivalent correlation analysis with Pb exposure, age, and bio-markers of kidney function. Men’s urinary Pb excretion did not correlate with age or with any of the three renal biomarkers tested. Women’s urinary Pb excretion did not correlate with age, but it correlated with all three kidney toxicity markers, with the greatest correlation strength being between urinary Pb and NAG excretion (r = 0.50, p < 0.001), followed by urinary Pb and β2-MG excretion (r = 0.36, p = 0.002) and urinary Pb and protein excretion (r = 0.31, p = 0.01). We undertook partial correlation with controlling for Cd excretion because urinary excretion of the renal function biomarkers protein and NAG showed also significant correlations with urinary Cd (Table 2). The correlation between NAG and urinary Pb excretion was maintained (partial r = 0.39, p = 0.001) after controlling for Cd. The correlations between Pb and urinary protein (partial r = 0.09, p = 0.47) and between Pb and β2-MG excretion rates (partial r = 0.16, p = 0.19) both were lost after controlling for Cd.
Table 4 shows the dose–response relationship between Cd body burden and nephropathy as measured by increases in urinary excretion of NAG. The prevalence of subjects with abnormal urinary excretion of NAG (NAG-nuria) in low, average, above average, and high Cd body burden group was 3, 8, and 23%, respectively. Prevalence of subjects with NAG-nuria differed significantly across the three Cd body burden groups (linear trend chi-squared = 4.4, p = 0.04).
Table 5 shows the correlation matrix for CYP2A6 activity, age, urinary Zn excretion together with Cd, and Pb exposure in men and women, separately. Two men and one woman were not included in this correlation matrix because they did not excrete 7-OHC. Urinary Cd, Zn, and Pb correlated strongly with each other, with r-values of 0.55 and 0.53 in men and of 0.34 and 0.43 in women (p ≤0.001). Thus, partial correlation analysis was undertaken to control for Zn versus urinary 7-OHC excretion. In men, urinary 7-OHC excretion rates showed a positive correlation with urinary Cd (r = 0.32, p = 0.01), whereas it did not show any correlation with age or urinary Zn excretion. In addition, there was a significant inverse correlation between urinary 7-OHC and Pb (r = −0.32, p = 0.001) in men after adjusting for urinary Zn excretion. Multiple regression controlling for age and urinary Zn excretion confirmed a positive association between urinary Cd and 7-OHC excretion (adjusted β= 0.38, t = 2.9, p = 0.006) together with a negative association between urinary Pb and 7-OHC excretion (adjusted β= −0.29, t = 2.2, p = 0.003). As in men, women’s urinary 7-OHC excretion rates showed a positive correlation with urinary Cd (r = 0.21, p = 0.01), whereas it did not show any correlation with age or urinary Zn excretion. However, no correlation between urinary 7-OHC and Pb (r = 0.18, p > 0.05) was seen in women. Of note, women’s urinary Zn excretion showed an inverse correlation with age (r = −0.24, p = 0.01–0.05), but no such correlation was seen in men.
Figure 2 shows a correlation between rates for hepatic CYP2A6-mediated coumarin metabolism and urinary excretion of the marker of renal tubular toxicity NAG in men and women, separately. The Pearson’s r-value for the correlation was 0.39 (p = 0.002) in men and 0.37 (p = 0.001) in women.
Discussion
Age- and Zn-related increment in Cd burden in men versus women.
A lack of an active biochemical process for Cd elimination coupled with renal reabsorption of Cd from the filtrate predicts age-related increment of Cd accumulation in various tissues and organs, notably liver and kidney (IPCS 1992; Jarup et al. 1998, Satarug et al. 2002). Thus, a positive correlation between subjects’ Cd burden and age is expected. Indeed, such expected correlation was observed with greater correlative strength and higher statistical significance in men (r = 0.47, p < 0.001) than in women (r = 0.20, p = 0.06). Body iron store status, however, did not show any correlation with women’s or men’s Cd burden, although a strong influence of body iron store status on Cd burden was shown previously in a group of women who were almost 10 years younger than the women in the present study (Satarug et al. 2004b). Thus, such discrepancy was most likely due to age-related differences in body iron status in women because it is known that women’s iron status is restored to normal at older ages (Custer et al. 1995). As with iron status, Zn is another important determinant of Cd burden and toxicity (Goyer 1997; Madden and Fowler 2000; Satarug et al. 2000). Thus, the correlation between age and Cd burden in women may be insignificant because the women’s Cd burden is more closely related to Zn status than iron status at older ages when their iron status becomes normal. Indeed, women’s urinary Zn excretion showed an inverse correlation with age (r = −0.24, p = 0.01–0.05; Table 5), but no such correlation was seen in men. We saw this as evidence for influence of Zn status on Cd burden. In addition, it provides an explanation for greater strength of the correlation between urinary Cd and Zn in men (r = 0.55, p < 0.001) than in women (r = 0.34, p = 0.001–0.005). A positive correlation between urinary Cd and Zn was expected in view of the fact that most of the Cd excreted in urine is bound to the low-molecular-weight metal-binding protein metallothionein together with Zn (Goyer 1997; Madden and Fowler 2000). Further research to show direct evidence for influence of Zn status on Cd burden and toxicity in women is required, and it should be focused on menopausal women because increased sensitivity to metal toxicity is noted in women (Nishijo et al. 2004; Vahter et al. 2002).
Role of Zn in protection against Pb renal toxicity.
Women in the present study were more exposed to Pb than were men, as reflected by a 46% higher urinary Pb excretion rate in women than in men (p < 0.001; Table 1). Such Pb exposure levels experienced by these women appeared to have produced mild nephropathy, reflected by a significant correlation between urinary Pb and NAG, even after controlling for urinary Cd excretion (partial r = 0.39, p < 0.001). The Pb-linked renal effect seen here is consistent with literature reports (Madden and Fowler 2000) and a recent study where urinary NAG excretion was found to correlate positively with duration of Pb exposure, blood Pb, and nail Pb in policemen who were chronically exposed to the metal in polluted air (Mortada et al. 2001). In animal studies, Zn has consistently been shown to provide protection against Cd and Pb accumulation and toxicity (Furst 2002; Goyer 1997), but there is little research on such a role of Zn in humans. Our finding on the absence of evidence for Pb-linked renal toxicity in men was likely caused by lower Pb exposure levels coupled with higher protective factors in men than in women. Consistent with the protective role of Zn, men in the present study had 36% higher urinary Zn excretion together with higher Zn:Cd and Zn:Pb ratios than did women (p = 0.001–0.005).
Concurrent renal and liver effects of Cd and Pb.
Cd exposure levels experienced by men and women in the present study were sufficient to cause a mild (subclinical) renal toxicity. This was suggested by a positive correlation between urinary Cd and NAG excretion rates in both men and women, after controlling for age (r ~ 0.5, p < 0.001). Renal tubular effects of exposure to low levels of environmental Cd detected in the present study confirmed data from population studies in Belgium (Buchet et al. 1990), China (Jin et al. 2002), Japan (Ikeda et al. 2000; Oo et al. 2000), the United States (Noonan et al. 2002), Sweden (Jarup 2002), and Thailand (Satarug et al. 2004a, 2004b). Further, a Cd-dose–dependent rise in the prevalence of abnormal urinary NAG excretion (NAG-uria) in subjects was shown (Table 4). The probability of having NAG-uria was increased by 20% as Cd burden increased from 0.05–0.26 μg/g creatinine to 1–2.36 μg/g creatinine.
We used rate of coumarin 7-hydroxylation as a marker of Cd and Pb liver effects because the reaction is known to be catalyzed exclusively by CYP2A6, which is expressed predominantly in the liver (Rautio et al. 1992; Satarug et al. 1996, 2004a; Ujjin et al. 2002). Evidence for liver Cd effect was suggested by a positive correlation between CYP2A6 activity and Cd burden seen in men and women, which remained statistically significant after controlling for age (Table 5). Such correlation agreed with the data in a previous study (Satarug et al. 2004a). However, despite overall similarity in Cd burden of subjects in the two sample groups, the strength of correlation between CYP2A6 activity and Cd burden was weaker in the present sample group (r = 0.21 in women and r = 0.32 in men) than in the previous group (r ~ 0.50 in men and women). This result was probably due to the smaller number of subjects combined with higher Pb exposure in the subjects in the present sample group than in the previous sample.
Evidence for Pb being a suppressor of liver CYP2A6 comes from a multiple regression analysis, controlling for Zn excretion and age, which revealed a negative association between urinary Pb and CYP2A6 activity (adjusted β= −0.29, p = 0.003) in men. The Pb suppressive effect on liver CYP2A6 seen here supports findings from previous reports on diminished phenazone metabolism in Pb-exposed workers (Fischbein et al. 1977; Meredith et al. 1977). In contrast with results in men, such inverse correlation between CYP2A6 activity and Pb exposure was not detected in women, although women showed higher urinary Pb excretion than did men. However, a weak correlation between CYP2A6 activity and Cd burden in women appeared consistent with Pb being a suppressor of liver CYP2A6 expression. The phenotype of hepatic CYP2A6 has been shown to be under the influence of liver pathophysiologic state, smoking, and exposure to a number of drugs and environmental factors (Kraul et al. 1991; Pasanen et al. 1997; Satarug et al. 1996, 2004c; Sotaniemi et al. 1995). Further, a recent study in 18 young male African green monkeys showed that exposure to nicotine at daily dose rates between 0.05 and 0.3 mg/kg for 18 days depressed catalytic activity of hepatic CYP2A6-like enzyme (Schoedel et al. 2003). It is therefore conceivable that a lack of an inverse correlation between Pb burden and CYP2A6 activity in women in the present study was more likely due to differences in dietary habits and exposure to the unidentified substances that can increase CYP2A6 expression, thereby offsetting the Pb effect.
Finally, evidence for concurrent effects of Cd in the liver and kidney comes from a positive correlation observed between the marker of kidney effect (urinary NAG excretion) and rate of hepatic CYP2A6-mediated metabolism (Figure 2). Highly statistical significance was observed for both men (p = 0.002) and women (p = 0.001), although the strength of correlation was modest in men (r = 0.39) and in women (r = 0.37). We further note that similar strength of correlation between urinary NAG excretion and CYP2A6 activity in men and women despite a weaker correlation between urinary Cd excretion and CYP2A6 activity in women than in men (Table 5). This may be caused by a very strong correlation between Cd concentrations in liver and kidney and between urinary NAG excretion and Cd concentration in the kidney, consistent with autopsy data, which showed that liver and urinary Cd concentration are closely related (Satarug et al. 2001).
Conclusions
Dietary Cd and Pb exposure at the levels experienced by the subjects in the present study is associated with variation in hepatic CYP2A6 phenotypic expression and signs of mild nephropathy, detectable at the renal Cd concentrations of approximately 1–2 μg/g creatinine, corresponding to renal concentrations ≤50 μg/g kidney cortex. In women, Zn status may be an important determinant of Cd burden and Pb renal toxicity. In men, however, opposing effects of Pb (a suppressor) and Cd (an inducer) on phenotypic expression of hepatic CYP2A6 is observed. Thus, exposure to environmental Cd and possibly Pb may be implicated in the rate of nicotine metabolism because CYP2A6 is known to metabolize up to 90% of nicotine (Oscarson 2001; Pelkonen et al. 2000; Raunio et al. 2001). In addition, Cd and Pb exposure may explain differences among individuals in their reaction to therapy with certain drugs and their ability to handle a variety of environmental chemicals that are metabolized by CYP2A6. Associations between hepatic and renal effects of exposure to environmental Cd and possibly Pb observed in the present study imply that elevated CYP2A6 activity occurs in subjects who show signs of Cd-linked renal toxicity.
Figure 1 Frequency distribution of men and women across (A) Cd-burden groups, (B) Pb-burden groups, and (C) CYP2A6 phenotype groups, classed by percentile ranking. (A) Frequency distribution of men and women across Cd-burden group did not show statistically significant differences (likelihood ratio chi-squared = 2.6, p = 0.46). Cd-burden group was based on percentile ranking of rate of urinary Cd excretion: low, 5–30 ng/3 hr; average, 31–138 ng/3 hr; above average, 144–225 ng/3 hr; high, 235–360 ng/3 hr. (B) Difference in frequency distribution of men and women across Pb-burden group was statistically significant (chi-squared = 18, p < 0.001). Pb-burden group was based on percentile ranking of urinary Pb excretion: low, 0.01–0.14 μg/3 hr; average, 0.15–0.42 μg/3 hr; above average, 0.43–0.53 μg/3 hr; high, 0.54–1.01 μg/3 hr. (C) Null phenotype included three subjects (two men and one woman) who did not excrete any 7-OHC in the urine collected for 3 hr after dosing with 15 mg of coumarin. Frequency distributions of men and women across CYP2A6 phenotype groups tended to be different (likelihood ratio chi-squared = 7.3, p = 0.06). CYP2A6 phenotype group was based on percentile ranking of urinary excretion of 7-OHC in 3 hr after dosing with 15 mg of coumarin: null, 0 mg/3 hr; slow, 0.7–2.2 mg/3 hr; average, 2.5–6.8 mg/3 hr; rapid, 7.0–9.3 mg/3 hr; very rapid, 10.4–15.2 mg/3 hr.
Figure 2 Pearson’s correlation analysis between rate of hepatic CYP2A6-mediated metabolism and the renal tubular toxicity marker NAG. The lines represent a positive correlation between the renal tubular toxicity bio-marker NAG excretion and hepatic CYP2A6 phenotype (urinary 7-OHC excretion after dosing with 15 mg of the probe xenochemical coumarin) in men and women, separately. The Pearson’s correlation coefficient r-values (p-value) for men and for women were 0.39 (p = 0.002) and 0.37 (p = 0.001), respectively.
Table 1 Age, blood, and urine chemistry profiles and assessment of Cd and Pb exposure and markers of liver and kidney effects.
Descriptor Men Women
No. of subjects 53 65
Age (years) 36.7 ± 9.4 (21–57) 38.1 ± 8.3 (23–55)
Body mass index (kg/m2)a 23.1 ± 3.0 22.4 ± 4.5
Hematological profiles
Hemoglobin (g/dL) 14.5 ± 1.0 12.2 ± 1.1**
Hematocrit (%) 43.5 ± 2.7 37.1 ± 2.8**
Red blood cells (×106/μL) 5.3 ± 0.5 4.4 ± 0.4**
White blood cells (×103/μL) 7.4 ± 2.4 7.2 ± 1.9
Serum and urine chemistry profiles
Ferritin (μg/L) 202 ± 161 (14–978) 60 ± 48 (3–197)**
Total protein (g/dL) 7.7 ± 0.5 7.7 ± 0.4
Plasma creatinine (mg/dL) 0.94 ± 0.12 0.66 ± 0.10**
Blood urea nitrogen (mg/dL) 12.6 ± 3.4 11.0 ± 2.5*
Serum Zn (mg/L) 1.38 ± 0.30 1.19 ± 0.16**
Urinary creatinine (mg/mL) 0.75 ± 0.71 0.61 ± 0.47
Urinary Zn excretion (μg/g creatinine) 371 ± 224 272 ± 164*
Cd and Pb exposure indicators
Urinary Cd (μg/g creatinine) 0.48 ± 0.36 (0.05–1.6) 0.54 ± 0.39 (0.09–2.4)
Urinary Zn:Cd (mmol Zn/nmol Cd) 1.91 ± 1.82 (0.5 –13) 1.35 ± 1.14 (0.2–5)*
Urinary Pb (μg/g creatinine) 1.3 ± 1.8 (0.1–12) 2.4 ± 1.1 (0.6–6.8)**
Serum Cd (μg/L) 0.55 ± 0.48 (0.05 –2.5) 0.48 ± 0.44 (0.05–3.2)
Serum Pb (μg/L) 4.2 ± 5.4 (1–28) 3.0 ± 2.2 (1–12)
Kidney toxicity indicatorsb
Protein (mg/g creatinine) 49 ± 47 (0.4 –121) 74 ± 67 (0.4–317)**
β2-MG (μg/g creatinine) 51 ± 121 (0.03–762) 29 ± 38 (0.03–218)
NAG (U/g creatinine) 4.4 ± 2.6 (0.6–15) 4.6 ± 2.2 (0.7–12)
Liver effect indicator
Urinary 7-OHC (mg/3 hr)c 5.0 ± 2.6 (0–11) 5.1 ± 3.5 (0–15)
Values are mean ± SD; numbers in parentheses are ranges.
a Body mass index = body weight/square of height (kg/m2).
b Urine samples were collected for 3 hr after administration of 15 mg of coumarin.
c Two men and one woman did not excrete 7-OHC in urine, most likely due to their lack of the CYP2A6 gene or having a defect in the CYP2A6 gene expression.
* p = 0.001–0.005;
** p < 0.001.
Table 2 Correlation between urinary Cd excretion rates, age, iron store status, and markers of glomerular and tubular functions.
Men
Women
All subjects
Variables r-Value p-Value r-Value p-Value r-Value p-Value
Age 0.47a < 0.001 0.20a 0.06 0.25a 0.003
Iron store status −0.01a 0.46 0.17a 0.09 0.06a 0.26
Kidney function biomarkers
Urine protein 0.23 0.05 0.30 0.01 0.27 0.003
Urine β2-MG 0.20 0.07 0.09 0.23 0.14 0.06
Urine NAG 0.51a < 0.001 0.44a < 0.001 0.49a < 0.001
The r-values are the Spearman’s rank correlation coefficients unless otherwise specified. Significance of the correlation between each pair of variables in row and column is identified by the p-values ≤0.05.
a Pearson’s r-correlation coefficient value.
Table 3 Correlation between urinary Pb excretion rate, age, and markers of glomerular and tubular functions.
Men
Women
All subjects
Variables r-Value p-Value r-Value p-Value r-Value p-Value
Age 0.13a 0.17 −0.14a 0.13 0.02a 0.43
Kidney function biomarkers
Urine protein 0.22 0.06 0.31 0.01 0.27 0.002
Urine β2-MG 0.12 0.19 0.36 0.002 0.21 0.01
Urine NAG 0.08a 0.27 0.50a < 0.001 0.22a 0.007
The r-values are the Spearman’s rank correlation coefficients unless otherwise specified. Significance of the correlation between each pair of variables in row and column is identified by the p-values ≤0.05. The correlation between NAG and urinary Pb excretion was maintained (partial r = 0.39, p = 0.001) after controlling for Cd. The correlations between Pb and urinary protein (partial r = 0.09, p = 0.47) and between Pb and β2-MG excretion rates (partial r = 0.16, p = 0.19) were lost.
a Pearson’s r-correlation coefficient value.
Table 4 Dose–response relationship between Cd exposure and prevalence of NAG-nuria.
Urine NAG (units/g creatinine)
Percentile Urinary Cda < 2.5 3–5 6–7 ≥8 NAG-nuria (%)b
1st–25th 0.05–0.26 10 14 8 1 1/33 (3)
26th–90th 0.27–0.95 15 31 20 6 6/72 (8)
91st–100th 1.00–2.36 2 4 4 3 3/13 (23)
a μg/g creatinine.
b Urinary excretion of NAG ≥8 U/g creatinine, which indicates statistically significant differences in prevalence of subjects across urine–Cd and urine–NAG groups (linear trend chi-squared = 4.4, p = 0.04).
Table 5 Correlation matrix for rate of coumarin 7-hydroxylation, age, markers of renal tubular function, and Cd and Pb exposure.
Variablesa Urinary 7-OHC Urinary Cd Age Urinary Pb Urinary Zn
Male nonsmokers (n = 51)
Urinary Cd 0.32* 1.00
Age 0.09 0.47# 1.00
Urinary Pb −0.32b 0.19 0.13 1.0
Urinary Zn 0.11 0.55# 0.21 0.53# 1.00
Female nonsmokers (n = 64)
Urinary Cd 0.21* 1.0
Age −0.07 0.20 1.0
Urinary Pb 0.18 0.39** −0.14 1.0
Urinary Zn 0.14c 0.34** −0.24* 0.43# 1.0
Numbers are Pearson correlation coefficient (r) values unless otherwise specified.
a The correlation analysis did not include two males and one female who did not excrete 7-OHC in urine, most likely due to a lack of the CYP2A6 gene or having a defect in the CYP2A6 gene expression.
b Partial correlation between urinary 7-OHC and Pb after adjusting for urinary Zn.
c Partial correlation between urinary 7-OHC and Zn, after adjusting for urinary Pb.
* p = 0.01–0.05;
** p = 0.001–0.005;
# p < 0.001.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6848ehp0112-00151915531437ResearchArticlesImpact of the Phytoestrogen Content of Laboratory Animal Feed on the Gene Expression Profile of the Reproductive System in the Immature Female Rat Naciff Jorge M. Overmann Gary J. Torontali Suzanne M. Carr Gregory J. Tiesman Jay P. Daston George P. Miami Valley Laboratories, The Procter and Gamble Company, Cincinnati, Ohio, USAAddress correspondence to J.M. Naciff, The Procter and Gamble Company, Miami Valley Laboratories, P.O. Box 538707 #805, Cincinnati, OH 45253-8707 USA. Telephone: (513) 627-1761. Fax: (513) 627-0323. E-mail:
[email protected] thank C. Ryan and W. Owens for their helpful discussions.
The authors are employed by the Procter and Gamble Company.
11 2004 16 8 2004 112 15 1519 1526 10 11 2003 16 8 2004 Publication 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 effect of the dietary background of phytoestrogens on the outcome of rodent bioassays used to identify and assess the reproductive hazard of endocrine-disrupting chemicals is controversial. Phytoestrogens, including genistein, daidzein, and coumestrol, are fairly abundant in soybeans and alfalfa, common ingredients of laboratory animal diets. These compounds are weak agonists for the estrogen receptor (ER) and, when administered at sufficient doses, elicit an estrogenic response in vivo. In this study, we assessed the potential estrogenic effects of dietary phytoestrogens at the gene expression level, together with traditional biologic end points, using estrogen-responsive tissues of the immature female rat. We compared the gene expression profile of the uterus and ovaries, as a pool, obtained using a uterotrophic assay protocol, from intact prepubertal rats fed a casein-based diet (free from soy and alfalfa) or a regular rodent diet (Purina 5001) containing soy and alfalfa. Estrogenic potency of the phytoestrogen-containing diet was determined by analyzing uterine wet weight gain, luminal epithelial cell height, and gene expression profile in the uterus and ovaries. These were compared with the same parameters evaluated in animals exposed to a low dose of a potent ER agonist [0.1 μg/kg/day 17α-ethynyl estradiol (EE) for 4 days]. Exposure to dietary phytoestrogens or to a low dose of EE did not advance vaginal opening, increase uterine wet weight, or increase luminal epithelial cell height in animals fed either diet. Although there are genes whose expression differs in animals fed the soy/alfalfa-based diet versus the casein diet, those genes are not associated with estrogenic stimulation. The expression of genes well known to be estrogen regulated, such as progesterone receptor, intestinal calcium-binding protein, and complement component 3, is not affected by consumption of the soy/alfalfa-based diet when assessed by microarray or quantitative reverse transcriptase–polymerase chain reaction analysis. Our results indicate that although diet composition has an impact on gene expression in uterus and ovaries, it does not contribute to the effects of an ER agonist.
17α-ethynyl estradiolgene expression profilingimmature rat uterotrophic assaymicroarraysphytoestrogensrodent diet
==== Body
The effect of the dietary background of phytoestrogens on the outcome of rodent bioassays used to identify and assess the reproductive hazard of endocrine-disrupting chemicals is controversial. Phytoestrogens, including genistein, daidzein, and coumestrol, are fairly abundant in soybeans and alfalfa, common ingredients of laboratory animal diets. In fact, soy and alfalfa are commonly used as protein sources in the manufacture of most rodent diets. Some of these ingredients are known to contain endocrine modulators, such as the phytoestrogens genistein and daidzein (abundant in soybeans and its products) and their respective glycosides (genistin and daidzin), and coumestrol (found in alfalfa). These phytoestrogens are able to bind to both estrogen receptor (ER) isoforms, ER-αand ER-β, in vitro (Beck et al. 2003; Casanova et al. 1999). They have a higher affinity for ER-β (Boue et al. 2003), but they activate both ER isoforms, although with less potency than estradiol. Both genistein and daidzein have much weaker affinities than does 17β-estradiol for the rat ERs: genistein binds 3- and 100-fold weaker, and daidzein binds 60- and 1,000-fold weaker to rat ER-β and ER-α, respectively (Boue et al. 2003; Casanova et al. 1999). These two phytoestrogens are able to elicit estrogenic responses in vivo (Boettger-Tong et al. 1998; Brown and Setchell 2001; Degen et al. 2002; Jefferson et al. 2002; Levy et al. 1995; Odum et al. 2001; Thigpen et al. 1997). The selective interaction of phytoestrogens with human ER-αand ER-β is similar in vitro to that described for the rat (Casanova et al. 1999; Kuiper et al. 1998).
Genistein is also known to have other activities, such as inhibition of different enzymes, among them tyrosine kinases (Akiyama et al. 1987), nitric oxide synthase (Duarte et al. 1997), and topoisomerase II (Okura et al. 1988), and decreasing calcium-channel activity in neurons (Potier and Rovira 1999). It also decreases lipid peroxidation (Arora et al. 1998) and diacylglycerol synthesis (Dean et al. 1989). Therefore, the multiple biologic activities of phytoestrogens raise the question of whether they have the potential to influence the outcome and/or interpretation of bioassays used to identify chemicals with estrogenic potential. In particular, questions have been raised about the presence of phytoestrogens in diets fed to animals used in bioassays designed to screen chemicals that may act as weak regulators of ERs and to screen low doses of potent regulators of ERs (Thigpen et al. 1997, 2002). One such bioassay is the uterotrophic assay, designed to evaluate both ER agonists and antagonists.
By using a version of the uterotrophic assay in the immature rat, one of the tier I screening assays recommended for detecting the estrogenic properties of endocrine-disrupting chemicals [Organisation for Economic Co-operation and Development (OECD) 2001; U.S. Environmental Protection Agency (U.S. EPA) 1998], we have identified a set of genes from the uterus and ovaries of prepubertal rats for which expression is regulated by estrogen exposure in a dose-dependent manner and which have the potential to be used as biomarkers for estrogen activity (Naciff et al. 2003). Gene expression changes induced by estrogen stimulation are more sensitive than the classical end points (i.e., uterine weight increase) for evaluating estrogenicity (Naciff et al. 2003). Given that components of the rodent diet commonly used in reproductive toxicology studies include chemicals with known estrogenic activity, understanding the influence of diet and dietary components on estrogen response is an important issue. In this study, we used gene expression profiling to evaluate the effect of two diets with different phytoestrogen content on the transcript profile of two organs that are responsive to estrogen stimulation: the uterus and the ovaries of prepubertal rats.
Materials and Methods
Chemicals.
17α-Ethynyl estradiol (EE) and peanut oil were obtained from Sigma Chemical Company (St. Louis, MO).
Animals and treatments.
Fifteen-day-old female Sprague-Dawley rats were obtained (Charles River VAF/Plus; Charles River Laboratories, Raleigh, NC) in groups of 10 pups per surrogate mother. We chose this rat strain because it is commonly used in reproductive and developmental toxicity studies. The rats were acclimated to the local vivarium conditions (24°C; 12-hr light/12-hr dark cycle) for 5 days and were fed a casein-based diet (soy- and alfalfa-free diet; Purina 5K96, Purina Mills, St. Louis, MO). Starting on post-natal day (PND)20 and during the experimental phase of the protocol, all rats were singly housed in 20 × 32 × 20 cm plastic cages. To test the diet effect, there were two animal groups (n = 20): one group was fed a standard laboratory rodent diet (Purina 5001, Purina Mills), and the other group was maintained on the casein-based diet. The Purina 5001 diet contains phytoestrogens, mostly genistein and daidzein derived from soy and alfalfa, at levels that may have an impact on the gene expression profile (total daidzein + genistein = 0.49 mg/g; Thigpen et al. 1999), particularly in tissues regulated by estrogens such as reproductive tissues. However, those levels are not uterotrophic when evaluated by the traditional end points, uterine weight gain and increase in luminal epithelial cell height. The casein-based diet is essentially phytoestrogen free, consistently containing < 1 ppm aglycone equivalents of genistein, daidzein, and glycitein, and was fed to the four groups of animals from PND16 onward in order to remove any possible effects of the regular rodent diet (Purina 5001) previously fed to the rats by the animal supplier. All the animals were allowed free access to water and specific pelleted commercial diet (Purina 5001 or casein-based 5K96). The experimental protocol was carried out according to Procter and Gamble’s animal care approved protocols, and animals were maintained in accordance with the NIH Guide for the Care and Use of Laboratory Animals (Institute of Laboratory Animal Resources 1996).
Starting on PND20, each diet group was divided into two subgroups of 10 animals. One subgroup from each diet subgroup was dosed by subcutaneous injection with 0.1 μg/kg/day EE in peanut oil. This dose is not sufficient to induce a uterotrophic response in juvenile rats (Kanno et al. 2002; Naciff et al. 2003). Animals received 5 mL/kg body weight of dose solution once a day for 4 days. A 4-day dosing regime was selected to optimize detection of any effect of EE exposure at this low dose, both at the histologic level and at the gene expression level. The dose was administered between 0800 and 0900 hr each day. Controls, fed with the appropriate diet, received 5 mL/kg of peanut oil once a day for 4 days. Doses were administered on a microgram per kilogram body weight basis and adjusted daily for weight changes. Body weight (nearest 1.0 g) and the volume of the dose administered (nearest 0.1 mL) were recorded daily. The exact time of the last dose was recorded, to establish a 24-hr waiting period before tissue collection. The animals were sacrificed by CO2 asphyxiation 24 hr after the last dosing, on PND24. The body of the uterus, cut just above its junction with the cervix, with the ovaries attached, was carefully dissected free of adhering fat and mesentery and was weighed as a whole. Then, the ovaries were dissected free, and the uterine and ovarian wet weight was recorded. Both the uterus and ovaries were placed into RNAlater (50–100 mg/mL of solution; Ambion, Austin, TX) at room temperature.
Histology.
Reproductive tissues from two animals in each dose group were fixed in 10% neutral buffered formalin immediately after weighing and then dehydrated and embedded in paraffin. Serial 4–5 μm cross sections were made through the ovaries, oviducts, and uterine horns, which were stained with hematoxylin and eosin. The evaluation of the morphologic changes induced by the two different diets with or without EE exposure in the uterus was performed as described previously (Naciff et al. 2003).
Expression profiling.
We used 10 μg total RNA, extracted from uterus and ovaries from individual animals (combining only the tissues from the same animal), to prepare biotin-labeled cRNA, as previously described (Naciff et al. 2002, 2003). Labeled cRNA samples were hybridized to the Affymetrix GeneChip Test 3 Array (Affymetrix Inc., Santa Clara, CA) to assess the overall quality of each sample. After determining the target cRNA quality, we selected individual samples of pooled uteri/ovaries from five or six individual females (replicates) from each diet group, from controls, and from EE-treated subgroups (with high quality cRNA) and hybridized them to Affymetrix Rat Genome U34A high-density oligonucleotide microarrays for 16 hr. The microarrays were washed and stained by streptavidin-phycoerythrin to detect bound cRNA. The signal intensity was amplified by second staining with biotin-labeled anti-streptavidin antibody and followed by streptavidin-phycoerythrin staining. Fluorescent images were read using the Hewlett-Packard G2500A gene array scanner (Affymetrix Inc.). Affymetrix image files for the 20 chip hybridizations, and the absolute analysis results of each diet group are available from the authors upon request.
Real-time reverse transcriptase-polymerase chain reaction.
In order to corroborate the changes in gene expression identified by the oligonucleotide microarrays, we used a real-time (kinetic) quantitative reverse transcriptase-polymerase chain reaction (QRT-PCR) approach, as previously described (Naciff et al. 2002). This approach allowed us to evaluate the “basal level” of expression of individual genes in samples derived from animals exposed to the two different diets used in our study, as well as changes induced by low-dose EE exposure (0.1 μg/kg/day). We compared the transcript level of selected genes in samples derived from animals in all experimental groups. To confirm the amplification specificity from each primer pair, the amplified PCR products were size-fractioned by electrophoresis in a 4% agarose gel in Tris borate ethylene diamine tetraacetic acid buffer and photographed after staining with ethidium bromide. Table 1 shows the nucleotide sequences for the primers used to test the indicated gene products. Preliminary experiments were done with each primer pair to determine the overall quality and specificity of the primer design. After QRT-PCR, we observed only the expected products at the correct molecular weight.
Data analysis.
We addressed potential interindividual variability by using independent samples of each experimental group (n = 5 for each set) for analysis. For the uterine/ovarian weight determination, the luminal epithelial cell height, and the gene expression analysis, we compared the data from the animals fed with the casein-based diet with the data from the animals fed the normal rodent diet (Purina 5001). For gene expression analysis, scanned output files of Affymetrix micro-arrays were visually inspected for hybridization artifacts and then analyzed using Affymetrix Microarray Suite (version 5.0) and Data Mining Tool (version 3.0) software, as described by the manufacturer (Affymetrix 2002; Lockhart et al. 1996). Arrays were scaled to an average intensity of 1,500 units and analyzed independently. The Affymetrix Rat Genome U34A microarrays used in this study have 8,740 probe sets corresponding to approximately 7,000 annotated rat genes and 1,740 expressed sequence tags (ESTs).
For each transcript in the diet and dose groups, we conducted pairwise comparisons with vehicle controls fed the casein-based diet, using two-sample t-tests: first, we compared the two diet groups, and then we compared each treatment group with its respective diet control. We then conducted analysis of variance (ANOVA) for general diet and treatment effects on the signal value (which serves as a relative indicator of the level of expression of a transcript) and the log of the signal value. General diet effects were evaluated by ANOVA and a nonparametric test for dose–response trend, the Jonkheere-Terpstra test. Genes for which any of the tests had p ≤ 0.001 was taken as evidence that the expression of those genes was modified by the diet or by EE exposure. For the combined analysis of the two sets (casein-based or Purina 5001 diet), stratified nonparametric tests were conducted that were focused in detecting genes showing a diet response, or where there was a consistent treatment effect versus vehicle for the EE-treated group (0.1 μg kg/day). Here, we used linear models, with terms for both study and treatment effects, on average differences (signal values) and their log transformation, as well as stratified forms of the Wilcoxon-Mann-Whitney nonparametric statistic and a stratified form of the Jonkheere-Terpstra nonparametric statistic for diet response. Fold-change summary values for genes were calculated as a signed ratio of mean signal values (for each diet and EE-treated group compared with the appropriate control). Because fold-change values can become artificially large or undefined when mean signal values approach zero, all the values < 100 were made equal to 100 before calculating the mean signal values that are used in the fold-change calculation. All statistical analyses use the measured signal values, even if they were smaller than 100 units.
Results
Effect of diet on uterine/ovarian and uterine wet weight and uterine luminal epithelial cell height.
Both diets, Purina 5001 and casein-based 5K96, were well tolerated by all the animals. We observed no evidence of overt toxicity and no clinical signs of toxicity. No difference was determined in body weights between animals fed either diet (Table 2). We did not detect premature vaginal opening in any of the animals in either diet group or in animals exposed to EE. There were no differences in wet uterine weight or in absolute and relative uterine weight (Table 2) between the two diet groups, even when the animals were exposed to low doses of EE.
The gross anatomy of the uterus and ovaries of animals fed either diet was identical, and no signs of accumulation of fluid in the uterine lumen were noted in any of the animals. We observed no differences in uterine weight gain (wet weight) or uterine epithelial cell height (Figure 1), and we found no change in the number of uterine glands. The classical morphologic changes induced by estrogen stimulation (hypertrophy of luminal epithelial, stromal, and myometrial cells; thickening of stromal layer; and some stromal inflammatory reaction) were not observed in any of the animals exposed to the two different diets, even when exposed to 0.1 μg kg/day EE (Figure 1).
Effect of diet on gene expression profile of the uterus/ovaries.
In order to compare the gene expression profiles induced by the different diets (different phytoestrogen content) and the EE dose tested, we compared the average value of the signal values, a relative indicator of the level of expression of a transcript, between the two groups of independent controls. We then compared the appropriate diet-control group with the respective EE group (0.1 μg/kg/day), for all the 8,740 transcripts represented on the array.
In comparing the expression profile identified in the uterus/ovaries of animals fed a casein-based diet versus the ones fed a soy/alfalfa-containing diet, we identified the expression of 29 genes that were significantly different (p ≤0.001). A list of those genes, along with their accession numbers, gene symbols, and the average fold changes, is shown in Table 3. The number of genes whose expression is modified by the diet’s composition is relatively small, and the average fold change on the expression of these genes affected by the rodent standard diet, compared with the casein-based diet, is relatively low in the uterus and ovaries. Although robust expression differences for specific genes can be attributed to the composition of the diet, this list does not include genes well known to be estrogen regulated, such as progesterone receptor (PgR), intestinal calcium-binding protein (icabp), and complement component 3 (CC3).
One hypothesis is that if the soy/alfalfa-based diet was not estrogenic on its own, perhaps it would have sufficient potency to measurably enhance the effect of a sub-uterotrophic dose of EE. Although the expression of most genes from the prepubertal uterus/ovaries that respond to estrogen exposure is not altered by the diet composition, there are some that show a variable, nonstatistically significant response. For comparison, we calculated the relative fold change induced by diet for genes that showed a clear dose response to 1–10 μg kg/day EE (Naciff et al. 2003). Presumably, those genes have the potential to represent the response to weak or low levels of estrogen stimulation (expected from the dietary phytoestrogens) and are shown in Table 4. The fold change represents the ratio of the relative expression level of each gene in tissues from animals fed Purina 5001 versus those fed the 5K96 diet (as indicated in Table 3). For comparison, in Table 4 the relative expression level of the same transcripts under EE exposure is also shown. Analyzing the effect of EE exposure on the expression of the same set of genes, comparing the relative expression level of each gene in the tissues from animals exposed to EE versus their respective controls that were fed the same diet, and taking into account that lack of statistical significance for those genes listed, the average (n = 5) response to EE exposure is very similar, if not equal, even for those showing a relative large fold change, regardless of the diet fed to the animals. These results suggest that the response to the diet’s composition is independent from the EE effect at this dose level of exposure. At higher doses of EE, the contribution of the dietary phytoestrogens is considered negligible because EE is a potent ER agonist able to interact with both isoforms of this receptor and with higher affinity than any of the dietary phytoestrogens (Kuiper et al. 1997). The changes induced by exposure to higher EE doses have been reported (Naciff et al. 2003). Corroboration of the microarray results by QRT-PCR for a selected group of genes is shown in Table 5. With the exception of CC3, which is undetectable by microarray analysis of samples derived from the animals exposed to two different diets (Table 5), the expression levels of the other genes are very similar, determined by either QRT-PCR or microarray analysis.
Discussion
Dietary phytoestrogens, such as genistein and daidzein (abundant in soybeans and its products) and their respective glycosides (genistin and daidzin), and coumestrol (found in alfalfa), have been found to have estrogenic properties in both in vitro and in vivo (Beck et al. 2003; Boettger-Tong et al. 1998; Boue et al. 2003; Brown and Setchell 2001; Casanova et al. 1999; Degen et al. 2002; Jefferson et al. 2002; Kanno et al. 2002; Levy et al. 1995; Odum et al. 2001; Thigpen et al. 2002). However, the results of the present study showed that phytoestrogens at concentrations present in a given lot of a commercial rodent diet are not able to elicit an estrogenic response in the reproductive system of the immature rat, judged by classical end points and specific gene expression changes characteristic of estrogen exposure in estrogen-responsive target organs (uterus and ovaries). Although a number of gene expression differences were observed with the two rodent diets tested, Purina 5001 and casein-based diet (relatively high vs. low phytoestrogen content, respectively), they cannot be correlated with estrogenic activity. These gene expression changes are more likely to be caused by nutritional differences between the diets, rather than individual dietary components affecting ER pathways. Also, the traditional end points used to assess estrogenic activity, namely, uterine wet weight gain and hypertrophy of luminal epithelial cell layer, were not affected by the phytoestrogen content of the diet. It has to be stressed that we have previously identified gene expression as being far more sensitive than the classical uterotrophic response in assessing estrogenicity (Naciff et al. 2003).
We also tested whether the consumption of phytoestrogen-containing diets was sufficient to render a subuterotrophic dose regimen of EE active. To do this, we evaluated the number and type of genes whose expression is modified in the uterus/ovaries from animals exposed to 0.1 μg/kg/day EE but fed different diets. There is not a statistically different number of genes affected by components of the diet, and those genes affected by EE are the same, regardless of the diet fed to the animals. More important, the different phytoestrogen content of the diets does not modify—by either increasing or decreasing—the response of the estrogen-sensitive genes from the uterus/ovaries to low doses of a potent ER agonist; their expression changes in the same direction and magnitude as a result of EE regardless of whether the rats were fed the phytoestrogen-containing diet. Table 4 shows the transcripts that we have previously identified as being responsive to estrogen exposure, under the uterotrophic assay protocol (Naciff et al. 2003), with their relative expression level calculated by comparing the two diets. This includes genes that have an extremely robust response to estrogen exposure, such as CC3, PgR, and icabp (Heikaus et al. 2002; Krisinger et al. 1992; L’Horset et al. 1990; Li et al. 2002; Naciff et al. 2003). Thus, we are confident that, despite the potential effect of the phytoestrogens in the Purina 5001 diet, the transcript profile determined in the uterus and ovaries is comparable with the one determined in the animals fed the casein-based diet, and truly reflects the lack of estrogenic activity of the soy/alfalfa-based diet.
Our data corroborate the findings of the OECD (Owens et al. 2003), Wade et al. (2003), and Yamasaki et al. (2002) in the uterotrophic assay. As part of the studies conducted by the OECD validation initiative, it has been established that the phytoestrogen contents of the multiple rodent diets employed by the participant laboratories had no important effect on the sensitivity of the uterotrophic assay (Owens et al. 2003). In independent studies, Wade et al. (2003) and Yamasaki et al. (2002) reached the same conclusions by testing the effect of various phytoestrogen-containing diets in the outcome of their immature uterotrophic assays. Our findings also agree with reports on the effects of phytoestrogens on the reproductive system of other species. Foth and Cline (1998) reported that supplementing the diet of postmenopausal macaques with up to 148 mg of phytoestrogen (from soy) per day for 6 months failed to induce any proliferative effects on endometrial histology, a marker for estrogenic stimulation. Anthony et al. (1996) determined that dietary soybean isoflavones improve cardiovascular risk factors (plasma lipids, lipoproteins, and atherosclerosis) without detectable estrogenic effects in the reproductive system of peripubertal rhesus monkeys. The data presented here establish the fact that the phytoestrogens found in a regular Western diet (compared with traditional Asian diets), exemplified here as the standard rodent diet, do not elicit an estrogenic response at the histologic level or at the gene expression level. Thus, the potential benefits for humans derived from consuming a normal diet (not intentionally enriched with phytoestrogens) are not compromised by undesired estrogenic properties.
These findings demonstrate that the phytoestrogens present in a regular rodent diet do not affect the biologic response to a potent exogenous ER agonist, at the level of tissue architecture or gene expression, in prepubertal rat uterus and ovaries. From the results of the present study, it is clear that in order to elicit an estrogenic response at the gene expression level, the organism has to be exposed to higher concentrations of phytoestrogens, as has been shown in the developing female rat with pure genistein (Jefferson et al. 2002; Naciff et al. 2002). It must be stressed that the route of administration has an impact on the degree of the response; Ashby (2000) has shown that genistein gives a stronger uterotrophic response in the immature mouse when subcutaneously injected than when given orally at equivalent concentrations.
Some of the gene expression changes attributed to the composition of the diet, determined in the present study, may have an impact on the biologic response of the reproductive system (uterus/ovaries), mostly by influencing various pathways, some of which have an effect on sex hormone axis. However, none of these genes was included in the transcript profile determined for estrogens in the immature rat uterus and ovaries (Naciff et al. 2003). For example, rGrb14, the rat homologue of the human growth factor receptor, bound human Grb14 adaptor protein, a direct inhibitor of the activated insulin receptor (Bereziat et al. 2002; Kasus-Jacobi et al. 1998), whose up-regulation may result in modification of the response of the uterus/ovaries to insulin. Another gene whose expression is modified by the composition of the diet is that of the gonadotropin-releasing hormone receptor, which among other activities regulates gametogenic and hormonal functions of the gonads (Kang et al. 2003). The expression of insulin-like growth factor 1 (IGF-1) is up-regulated in the reproductive tissues of animals fed the diet with a relatively high phytoestrogen content (Table 3). IGF-1 is a critical regulator of uterine growth, and locally produced uterine IGF-1 could mediate the effects of estradiol on growth and cellular proliferation (Sato et al. 2002). The expression of the gene encoding steroid 3-α-dehydrogenase is also up-regulated by the soy/alfalfa-based diet. This enzyme, a member of the aldoketo reductase gene superfamily, is an important multifunctional oxidoreductase capable of metabolizing steroid hormones, polycyclic aromatic hydrocarbons, and prostaglandins (Huang and Luu-The 2000). Aquaporin 1 (AQP1) is one of the genes for which expression is down-regulated by a soy/alfalfa-based diet. This gene encodes a protein that is a member of a family of membrane channel proteins which facilitate bulk water transport and possibly other small molecules, the aquaporins. Treatment of adult ovariectomized mice with replacement steroids demonstrates an estrogen-induced shift in AQP1 signals from the myometrium to the uterine stromal vasculature, suggesting a role in uterine fluid inhibition (Richard et al. 2003), one of the physiologic responses of the uterus to estrogen stimulation. However, the relative expression level of AQP1 gene was not determined by Richard et al. (2003). Li et al. (1997) described a stimulatory effect of estradiol at relatively high concentrations (40 μg/kg) in the expression level of an aquaporin gene (AQP-CHIP) in the uterus of immature rats, although this gene was not identified as AQP1. However, the response of AQP1 in the immature uterus of the rat to dietary components is actually a decrease in its expression level, opposite the effect of estrogenic stimulation.
In all, our data indicate that although there is a clear effect of the diet of the gene expression profile of the uterus/ovaries from the immature rats, this effect is subtle and cannot be correlated with the phytoestrogen content of each diet. Most of the gene transcripts represented in the microarray used in this study have an expression level that is very similar in all the animals, regardless of their diet. Further, by analyzing the expression levels of known estrogen-regulated genes (Naciff et al. 2003), we determined that there is not a significant difference in the relative expression level of any of those genes between animals exposed to Purina 5001 or casein-based diets. In addition, we found no significant changes at the transcript level for selected estrogen-regulated genes by QRT-PCR. Thus, we are confident that—despite the potential effect of the phytoestrogens in the diet of animals used in a bioassay designed to evaluate the potential estrogenic activity of a given chemical—the response to the chemical (which could be the transcript profile induced by exposure) is independent of the diet and has the potential to truly reflect estrogenic activity.
Figure 1 Representative uterine transversal sections from equivalent regions of vehicle-treated control immature rats (PND24; A, B, E, F) or animals treated with 0.1 μg/kg/day EE (C, D, G, H) fed with a casein-based diet (5k96; A, C, E, G) or a standard rodent diet (Purina 5001; B, D, F, H). Abbreviations: g, gland; Le, uterine lumen. See “Materials and Methods” for details. The rodent diet (B, D, F, H) containing quantifiable amounts of phytoestrogens did not have an impact on the histologic characteristics of the uterus, compared with tissues obtained from animals fed a relatively phytoestrogen-free casein-based diet (A, C, E, G). Bar = 0.08 mm for A–D; bar = 0.01 mm for E–H.
Table 1 Primers used to verify the array-based gene expression changes induced by the two different diets, by QRT-PCR.
Gene name GenBank accession no.a Forward primer Reverse primer Amplicon size (bp)
Complement component 3 (CC3) M29866 5′-CGTGAGCAGCACAGAAGAGA-3′ 5′-CCAGGTGGTGATGGAATCTT-3′ 204
Progesterone receptor (PgR) L16922 5′-CATGTCAGTGGACAGATGCT-3′ 5′-ACTTCAGACATCATTTCCGG-3′ 428
Intestinal calcium-binding protein (icabp) K00994 5′-ATCCAAACCAGCTGTCCAAG-3′ 5′-TGTCGGAGCTCCTTCTTCTG-3′ 196
11-β -Hydroxylsteroid dehydrogenase type 2 (11β HSD) U22424 5′-ATGGCATTGCCTGACCTTAG-3′ 5′-CTCAGTGCTCGGGGTAGAAG-3′ 194
Vascular α-actin (VaACTIN) X06801 5′-GACACCAGGGAGTGATGGTT-3′ 5′-GTTAGCAAGGTCGGATGCTC-3′ 202
Cyclophilin B AF071225 5′-CAAGCCACTGAAGGATGTCA-3′ 5′-AAAATCAGGCCTGTGGAATG-3′ 239
Cytochrome P450 subfamily XVII (Cyp17) M21208 5′-AAGTGGATCCTGGCTTTCCT-3′ 5′-CAATGCTGGAGTCGACGTTA-3′ 211
AA924771 EST Rattus norvegicus AA924772 5′-TTTGCTGTGCATGGGATTTA-3′ 5′-CCCTGCAGGATGTGAGAAGT-3′ 202
a From GenBank (2004).
Table 2 Diet effect on body, uterine, and ovarian weight and luminal epithelial cell height of the juvenile (PND24) rat.
Casein-based diet (5K96)
Purina 5001 diet
Body weight (g) Ovarian weight (mg) Uterine weight (mg) Epithelial cell height (μm) Body weight (g) Ovarian weight (mg) Uterine weight (mg) Epithelial cell height (μm)
Peanut oil
Mean ± SD (absolute) 68.1 ± 4.8 32.0 ± 2.6 56.1 ± 8.2 13.3 ± 1.3 70.1 ± 4.9 34.8 ± 3.3 59.6 ± 10.8 14.0 ± 2.2
Mean ± SD (relative)a 0.50 ± 0.04 0.58 ± 0.04 0.49 ± 0.07 0.56 ± 0.08
p-Valueb 0.05 0.18 0.41 0.26
0.1 EE (μg/kg/day)
Mean ± SD (absolute) 68.5 ± 5.4 36.2 ± 2.1 61.9 ± 11.2 13.1 ± 1.6 71.1 ± 5.8 37.1 ± 1.8 66.4 ± 13.2 14.5 ± 1.3
Mean ± SD (relative)a 0.52 ± 0.1 0.93 ± 0.1 0.52 ± 0.1 0.93 ± 0.2
p-Valueb 0.05 0.14 0.36 0.18
During the experimental phase, PND20 female rats were fed with a standard laboratory rodent diet (Purina 5001) or with a soy- and alfalfa-free diet (casein-based diet, 5K96) for 5 days (from PND20 to PND24). Epithelial cell height values were obtained from tissue sections from the midregion of each uterine horn, at equivalent areas, and with clear representation of the epithelium lining the lumen along the uterus (as shown in Figure 1). Epithelial cell height was determined by obtaining five measurements from five areas from two animals for each group. These values were used to determine the mean cell height SD for each treatment group, and the corresponding p-value.
a Relative weight (mg/g body weight).
b Two-tailed t-test comparing 5K96 with Purina 5001, in control or treated animals; n = 15 for each diet group (controls) and n = 10 for EE-treated groups.
Table 3 Genes whose expression is modified by exposure to diet in the uterus/ovaries of the immature rat.
GenBank accession no.a Gene name Gene symbol Average fold changeb p-Valuec
X67948 Aquaporin 1 (aquaporin channel forming integral protein) AQP1 1.6 0.000159
U56839 Purinergic receptor P2Y, G-protein coupled 2 P2ry2 1.4 0.000448
AF017756 GSK-3beta interacting protein rAxin Axin 1.4 0.000130
AA859529 Diacylglycerol acyltransferase Dgat 1.3 0.000470
L06096 Inositol 1,4,5-triphosphate receptor 3 Itpr3 1.3 0.000420
U90887 Arginase type II Arg2 1.3 0.000728
U78977 ATPase, class II, type 9A Atp9a 1.3 0.000022
AA892562 EST196365, high homology to nucleolar protein NAP57 and dyskeratosis congenita 1, dyskerin Dkc1 1.3 0.000747
AI639534 ESTs, similar to properdin (factor P) 1.3 0.000446
AI231213 ESTs, high homology to kangai 1 (suppression of tumorigenicity 6), prostate Kai1 1.2 0.000561
D10874 Vacuolar H(+)-transporting ATPase, 1.2 0.000865
X56133 Mitochondrial H+-ATP synthase alpha subunit Atp5a1 −1.1 0.000854
D13417 Transcription factor HES-1 homolog of hairy and enhancer of split 1, (Drosophila) Hes1 −1.2 0.000045
Z71925 Polymerase (RNA) II (DNA directed) polypeptide G Polr2g −1.2 0.000379
AA818487 ESTs, high homology to cyclophilin B Ppib −1.2 0.000253
AI112237 ESTs, moderately similar to JE0384 NADH dehydrogenase −1.2 0.000192
AA818858 Peptidylprolyl isomerase A (cyclophilin A) Ppia −1.3 0.000943
AA686579 ESTs, similar to ubiquitin-like protein SMT3C precursor −1.3 0.000954
U64705 Protein synthesis initiation factor 4AII gene and E3 small nucleolar RNA gene −1.3 0.000405
S69316 GRP94/endoplasmin (5 and 3 regions) −1.3 0.000120
M15481 Insulin-like growth factor 1 IGF-1 −1.3 0.000068
S69315 GRP94/endoplasmin (5 and 3 regions) −1.4 0.000174
D17310 3-alpha-Hydroxysteroid dehydrogenase (3-alpha-HSD) −1.4 0.000397
X67859 Autoantigen or Sjogren syndrome antigen B Ssb −1.4 0.000103
AA685903 ESTs, similar to glucose regulated protein, 94 kDa GRP94 −1.5 0.000878
S68578 Gonadotropin-releasing hormone receptor Grhr −1.5 0.000322
AI009141 EST203592, Rattus norvegicus −1.8 0.000468
AF076619 Growth factor receptor bound protein 14 or molecular adapter rGrb14 (Grb14), an inhibitor of insulin actions Grb14 −2.1 0.000158
a From GenBank (2004).
b The average fold change was determined by comparing the average signal value of the indicated transcripts obtained from the uterus/ovaries from five females fed with the casein-based diet (5K96) versus the average signal value obtained from the same tissues from five females fed the standard rodent diet (Purina 5001).
c Transcripts listed are those showing a robust response to the different diet (p < 0.001) using the stratified form of the Jonkheere-Terpstra nonparametric statistic to identify the diet response.
Table 4 Diet effect on genes whose expression is modified by exposure to of 0.1 μg/kg EE in the uterus/ovaries of the immature rat.
Average fold changeb
GenBank accession no.a Gene name Gene symbol Purina 5001/5K96 EE vs. control, 5K96 EE vs. control, Purina 5001
M29866 Complement component 3 CC3 A 14.7 7.0
Y08358 Eotaxin or small inducible cytokine A11 Scya11 A 2.7 2.9
AI013389 ESTs, similar to calcium-binding protein, intestinal, vitamin D-dependent Calb3 2.0 2.4 1.9
K00994 Intestinal calcium-binding protein icabp 1.1 5.2 2.1
U49062 CD24 antigen Cd24 1.1 1.3 1.2
L14004 Polymeric immunoglobulin receptor pigr 1.5 1.4 1.1
AA859661 ESTs, similar to glutaminyl-peptide cyclotransferase precursor A 2.1 1.7
M57718 Cytochrome P450 IV A1 CYP4A1 A A A
U22424 Hydroxysteroid dehydrogenase, 11-βtype 2 Hsd11b2 1.0 2.2 1.6
L07114 Apolipoprotein B editing protein Apobec1 A A A
S79730 Opioid receptor-like ORL1 receptor Oprl1 1.2 1.3 1.4
M88469 f-Spondin Sponf 1.7 1.7 1.1
X66845 Dynein, cytoplasmic, intermediate chain 1 Dncic1 A A A
L46593 Small proline-rich protein gene Sprr 2.4 2.5 −1.0
L00191 Fibronectin, encoding three mRNAs, exons 1, 2, 3 fn −1.2 1.6 1.2
M22323 Gamma-enteric smooth muscle actin Actg2 1.4 1.5 1.3
D15069 Adrenomedullin Adm 1.9 2.4 1.2
AA893870 EST197673 Rattus norvegicus A 1.7 2.0
AI232078 Transforming growth factor-β (TGF-β) masking protein Ltbp1 −1.1 1.2 1.1
U82612 Fibronectin (fn-1) gene fn-1 1.5 1.6 1.1
X05834 Fibronectin (fn-3) gene fn-3 1.0 1.4 1.2
L00382 Skeletal muscle β-tropomyosin and fibroblast tropomyosin 1 tpm1 1.2 1.7 1.3
AA800908 EST190405 Rattus norvegicus 1.2 1.6 1.4
M25758 Phosphatidylinositol transfer protein Pitpn 1.1 1.3 1.3
AA799773 ESTs, Rattus norvegicus 1.3 1.3 1.2
AA892829 EST, similar to mouse bifunctional 3’-phosphoadenosine (PPS1) PPS1 1.2 1.3 1.1
AB010963 Potassium large conductance calcium-activated channel Kcnmb1 1.2 1.4 1.2
AF083269 Actin-related protein complex 1b Arpc1b 1.0 1.3 −1.0
AA891760 EST195563 Rattus norvegicus A A A
AJ005394 Collagen α1 type V Col5a1 −1.1 1.6 1.2
L11930 Cyclase-associated protein homologue Cap1 1.1 1.3 1.2
X07467 Glucose-6-phosphate dehydrogenase G6pd 1.2 1.2 −1.1
U26310 Tensin Tns 1.2 1.3 1.1
AA891542 EST195345 Rattus norvegicus, similar to mouse heat shock protein hsp40-3 Dnajb5 1.3 1.3 1.1
U44948 Cysteine-rich protein 2 or smooth muscle cell LIM protein (SmLIM) Csrp2 −1.1 −1.6 −1.4
S61868 Ryudocan or heparan sulfate proteoglycan core protein or syndecan-4 SDC4 −1.2 −1.5 −1.1
L41254 Corticosteroid-induced protein or FXYD domain-containing ion transport regulator 4 Fxyd4 −1.1 −1.4 −1.1
AF023087 Nerve growth factor induced factor A, or early growth response 1 Egr1 −1.5 −1.7 1.1
U07181 Lactate dehydrogenase B Ldhb −1.3 −1.2 −1.1
X89225 Solute carrier family 3, member 2 Slc3a2 −1.3 −1.5 −1.3
AF054826 Vesicle-associated membrane protein 5 Vamp5 −1.2 −1.8 −1.2
X75253 Phosphatidylethanolamine binding protein Pbp −1.2 −1.4 −1.1
AA924772 ESTs, similar to metallothionein 3 Mt3 A A A
AA894027 EST197830 Rattus norvegicus A A A
AA894030 EST197833 Rattus norvegicus A A A
AA946532 ESTs, similar to ATP-binding cassette, sub-family D (ALD), member 3 Abcd3 −1.3 −1.4 −1.3
M32754 Inhibin α-subunit Inha −1.3 −1.6 1.0
AA874794 ESTs, similar to nerve growth factor receptor (TNFRSF16) associated protein 1 Ngfrap1 1.0 −1.4 −1.2
M21060 Superoxide dismutase 1, soluble Sod1 −1.2 −1.2 −1.2
X08056 Guanidinoacetate methyltransferase GAMT −1.2 −1.3 −1.0
D00729 δ 3, δ2-enoyl-CoA isomerase −1.2 −1.2 −1.3
U90829 APP-binding protein 1 Appbp1 −1.2 −1.6 −1.1
AI170613 ESTs, similar to heat shock 10 kDa protein 1 Hspe1 −1.2 −1.3 −1.6
D63761 Adrenodoxin reductase Fdxr −1.1 −1.5 −1.1
D78303 Splicing factor YT521-B YT521 −1.1 −1.2 −1.2
L48060 Prolactin receptor Prlr 1.0 −1.3 −1.2
AA849036 ESTs, similar to guanylate cyclase 1, soluble, α-3 Gucy1a3 −1.2 −1.3 −1.1
M33648 Mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase HMGCS2 −1.2 −1.6 −1.3
E05646 Phosphatidylethanolamine binding protein Pbp −1.2 −1.3 −1.2
AA858520 ESTs, similar to follistatin Fst −1.3 −1.5 −1.1
L02842 Follicle-stimulating hormone receptor FSHR A A A
X04229 Glutathione-S-transferase, μ type 1 (Yb1) Gstm1 −1.2 −1.4 −1.2
L23148 Inhibitor of DNA binding 1, helix-loop-helix protein Id1 1.0 −1.1 1.0
D63761 Adrenodoxin reductase Fdxr 1.0 −1.4 −1.2
AF076619 Growth factor receptor bound protein 14 Grb14 −1.5 −1.8 −1.3
M33648 Mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase −1.3 −2.0 −1.6
AA858520 Follistatin Fst −1.3 −1.4 −1.3
X62660 Glutathione transferase subunit 8 −1.2 −1.5 −1.3
AI175776 EST219344 Rattus norvegicus −1.2 −1.7 −1.4
J03914 Glutathione-S-transferase, μ type 2 (Yb2) Gstm2 −1.2 −1.2 −1.1
J02592 Glutathione-S-transferase, μ type 2 (Yb2) Gstm2 −1.1 −1.3 −1.2
S59525 Gonadotropin-releasing hormone receptor grhr −1.1 −1.8 −1.4
M36453 Inhibin α Inha −1.2 −1.6 −1.6
X54793 Heat shock protein 60 (liver) Hsp60 −1.3 −1.3 −1.6
AA858640 ESTs −1.2 −1.4 −1.4
L19998 Minoxidil sulfotransferase PST-1 −1.2 −1.4 −1.4
X78848 Glutathione-S-transferase, αtype (Ya) Gsta1 −1.2 −1.5 −1.4
AF001898 Aldehyde dehydrogenase 1, subfamily A1 Aldh1a1 1.1 −1.5 −1.5
X97754 Hydroxysteroid dehydrogenase 17β, type 1 Hsd17b1 −1.5 −2.3 −1.6
AF000942 Inhibitor of DNA binding 3, dominant negative helix-loop-helix Id3 −1.2 −1.3 −1.1
AI171268 EST217223 Rattus norvegicus, identical to inhibitor of DNA binding 3, dominant negative helix-loop-helix Id3 −1.4 −1.4 −1.1
D84336 Delta-like homolog (Drosophila), a novel member of the epidermal growth factor (EGF)-like family of proteins Dlk1 A A A
S63167 3 β-Hydroxysteroid dehydrogenase isomerase type II HSD3B2 −1.1 −1.4 −1.6
M12492 Type II cAMP-dependent protein kinase regulatory subunit prkar2a −1.3 −1.9 −1.8
S72505 Glutathione S-transferase Yc1 subunit −1.2 −1.5 −1.6
AA874919 Mismatch repair protein Msh2 −1.1 −1.5 −1.1
M14656 Sialoprotein (osteopontin) Spp1 1.0 −3.5 −2.4
X01115 SVS-protein F, or seminal vesicle secretion 5 Svs5 −1.4 −3.9 1.1
M21208 Cytochrome P450, subfamily XVII Cyp17 1.1 −2.4 −2.2
A, absent (undetectable by Microarray Suite 5.0; Affymetrix). Transcripts listed are those previously reported to show a robust response to graded doses of EE in the uterotrophic assay (p < 0.001) (Naciff et al. 2003); n = 5 per group.
a From GenBank (2004).
b The average fold change is the ratio of the relative expression level of each gene in uterus/ovaries from animals fed Purina 5001 versus those fed 5K96 diet (n = 5 per group).
Table 5 Selected gene expression changes verified by QRT-PCR.
CC3 icabp 11β HSD PgR EST AA924772 VaACTIN Cyp17 Cyclo B
Treatment Q M Q M Q M Q M Q M Q M Q M Q M
Vehicle in Purina 5001 vs. 5K96 1.0 A 1.2 1.1 1.2 1.0 1.1 1.1 −1.1 A 1.2 1.1 −1.2 1.1 1.1 1.0
0.1 EE μg vs. control in 5K96 18.7 14.7 3.7 5.2 2.7 2.2 1.8 1.6 −1.5 A 1.1 1.0 −3.5 −2.4 1.0 1.1
0.1 EE μg vs. control in Purina 5001 21.2 7.0 3.3 2.1 2.9 1.6 2.2 1.5 −1.4 A 1.1 1.1 −3.4 −2.2 1.1 1.0
Abbreviations: 11βHSD, 11-β-hydroxylsteroid dehydrogenase type 2 gene; A, absent (undetectable by Microarray Suite 5.0; Affymetrix); cyclo B, cyclophilin B gene; Cyp17, cytochrome P450 subfamily XVII gene; M, microarray-derived fold change; Q, QRT-PCR–derived fold change; VaACTIN, vascular α-actin gene. The relative fold change is the ratio of the relative expression level of each gene in uterus/ovaries from animals fed Purina 5001 versus those fed 5K96 diet. The microarray-derived fold change and the QRT-PCR–derived fold change were determined as described in “Materials and Methods,” using the same amount of total RNA derived from three independent animals, in duplicate. These genes were chosen on the basis of their response to estrogenic stimulation in the uterotrophic assay (Naciff et al. 2003); we also included two control genes, cyclo B and VaACTIN.
==== Refs
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Wade MG Lee A McMahon A Cooke G Curran I 2003 The influence of dietary isoflavone on the uterotrophic response in juvenile rats Food Chem Toxicol 41 1517 1525 12963004
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.6976ehp0112-00152715531438ResearchArticlesRelationship between Composition and Toxicity of Motor Vehicle Emission Samples McDonald Jacob D. 1Eide Ingvar 2Seagrave JeanClare 1Zielinska Barbara 3Whitney Kevin 4Lawson Douglas R. 5Mauderly Joe L. 11Lovelace Respiratory Research Institute, Albuquerque, New Mexico, USA2Statoil Research Centre, Trondheim, Norway3Desert Research Institute, Reno, Nevada, USA4Southwest Research Institute, San Antonio, Texas, USA5National Renewable Energy Laboratory, Golden, Colorado, USAAddress correspondence to J.D. McDonald, Lovelace Respiratory Research Institute, 2425 Ridgecrest Dr. SE, Albuquerque, NM 87108 USA. Telephone: (505) 348-9455. Fax: (505) 348-4980. E-mail:
[email protected]. Johanson and S. Rännar (Umetrics, Umea, Sweden) provided valuable discussions.
This work was supported by the Office of Freedom Car and Vehicle Technologies, U.S. Department of Energy. The views and opinions of the authors do not necessarily reflect those of the U.S. Government or any agency thereof.
The authors declare they have no competing financial interests.
11 2004 15 7 2004 112 15 1527 1538 21 1 2004 14 7 2004 Publication 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 statistical relationship between particle and semivolatile organic chemical constituents in gasoline and diesel vehicle exhaust samples, and toxicity as measured by inflammation and tissue damage in rat lungs and mutagenicity in bacteria. Exhaust samples were collected from “normal” and “high-emitting” gasoline and diesel light-duty vehicles. We employed a combination of principal component analysis (PCA) and partial least-squares regression (PLS; also known as projection to latent structures) to evaluate the relationships between chemical composition of vehicle exhaust and toxicity. The PLS analysis revealed the chemical constituents covarying most strongly with toxicity and produced models predicting the relative toxicity of the samples with good accuracy. The specific nitro-polycyclic aromatic hydrocarbons important for mutagenicity were the same chemicals that have been implicated by decades of bioassay-directed fractionation. These chemicals were not related to lung toxicity, which was associated with organic carbon and select organic compounds that are present in lubricating oil. The results demonstrate the utility of the PCA/PLS approach for evaluating composition–response relationships in complex mixture exposures and also provide a starting point for confirming causality and determining the mechanisms of the lung effects.
diesel exhaustgasoline exhausthopanemutagenicityPAHsparticulate matter health effectsprincipal component analysissemivolatile organic carbonsteranetoxicity of motor vehicle emissions
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Mobile source emissions are important contributors to ambient air pollution and have been associated with cancer-related and non-cancer-related health effects. Recent work has shown that health effects and ambient air pollution increase with proximity to roadways, suggesting that motor vehicle traffic (engine emissions) contributes a large share to ambient health effects (Nicolai et al. 2003; Pearson et al. 2000; van Vliet et al. 1997; Venn et al. 2001). Two interrelated issues pertaining to the health hazards of motor vehicle emissions continue to present serious challenges to manufacturers, regulatory decision makers, toxicologists, and risk assessors. First, it is important to identify the most important contributors to health risk among the myriad physical–chemical species contained in emissions. Second, it is important to be able to estimate changes in health risks that will result from changes in the composition of emissions. Both issues are important for ensuring that the most health-relevant components are controlled and that technologic strategies for meeting emissions regulations reduce rather than increase hazards. The current knowledge base does little to support such judgments because there have been few direct comparisons of the health effects of different types of emissions. Moreover, except for bioassay-directed fractionation schemes that have identified nitro-polycyclic aromatic hydrocarbons as major drivers of bacterial mutagenicity, few approaches have been used to determine the chemical species driving the health hazards of complex emissions.
We have reported the results of studies in which both bacterial mutagenicity (by Ames tests) and lung toxicity assays of inflammation, cytotoxicity, and lung tissue damage (Seagrave et al. 2002) were assessed to rank the toxic potency of motor vehicle exhaust samples of different chemical composition. That work used combined suspensions of particulate material (PM) and vapor-phase semivolatile organic carbon (SVOC) samples collected from a range of in-use (rented from owners) gasoline- and diesel-powered vehicles, including “high-emitting” vehicles. Preliminary studies showed that it was important to include the vapor-phase SVOC because it comprised a large portion of the mass [defined by gravimetric weight as described by Seagrave et al. (2002)] emitted from some vehicles and could contribute substantially to toxicity as evidenced by evaluation of lung inflammatory responses of separated PM and SVOC samples collected from a traffic tunnel (Seagrave et al. 2001). The samples were combined into seven distinct groups of gasoline and diesel-powered vehicles. There was a 5-fold range in the potency of the samples for lung injury, and samples from high-emitting vehicles (both diesel and gasoline powered) had the highest pulmonary toxicity per unit of mass. There was also up to a 10-fold difference in the bacterial mutagenicity among these samples, with no clear difference between the potency of diesel exhaust and gasoline exhaust on the basis of mutations per milligram of sample.
We also reported the results of detailed compositional measurements of the exhaust samples described above (Zielinska et al. 2004). In the present work, we applied multivariate data analysis to determine the relationship between composition and health response. We selected a statistical approach that had been successfully used to determine the key components of organic extracts of diesel exhaust particles causing mutations in bacteria (Eide et al. 2001, 2002; Sjogren et al. 1996) and aryl hydrocarbon receptor induction (Sjogren et al. 1996). The combined principal component analysis (PCA) and partial least-squares regression (PLS; also known as projections to latent structures) approach allows analysis of the similarities and differences in the specific health responses (e.g., mutagenicity vs. lung toxicity) relative to composition. The product of this work is an assessment of the ability of the PLS to “explain” the composition–response relationship and an indication of which chemical compounds in the exhaust samples were most strongly associated with the health response.
Materials and Methods
In this article we summarize only the general approaches used for collection, chemical characterization, and toxicity evaluation of vehicle exhaust samples that have been reported in detail elsewhere (Seagrave et al. 2002; Zielinska et al. 2004). The classifications of vehicle samples, chemical/physical classes measured in these samples, and the toxicologic evaluations conducted (including health response category) are summarized in Table 1.
Emission samples.
Particle and vapor-phase SVOC fractions were collected using filters (for particles) and polyurethane foam/XAD-4 resin traps (for vapor-phase SVOC), respectively, from diluted, fresh emissions from vehicles operated on chassis dynamometers over the unified driving cycle at the Southwest Research Institute (San Antonio, TX, USA) (Whitney 2000, Zielinska et al. 2004). The unified driving cycle is a high-speed, rapid-acceleration test cycle, consisting of a 300-sec cold start phase followed by a 1,135-sec hot stabilized phase, and a 300-sec hot start phase, which is a repeat of the first phase. The maximum speed employed in the cycle was 67.2 mph, with a maximum acceleration of 6.9 mph/sec. Five vehicles or composite groups of vehicles were included: a group of five “normal-emitting” gasoline vehicles (G); a group of three normal-emitting diesel vehicles (D); two “high-emitting” single gasoline vehicles emitting white (WG) or black (BG) smoke; and a single high-emitting diesel vehicle (HD). Specific emission rates for these vehicles are reported elsewhere (Zielinska et al. 2004). All vehicles were in-use light- or medium-duty passenger cars, pickup trucks, or vans, ranging from 1976 to 2000 model years and were tested with fuel and crankcase oil as received (recruited in San Antonio, TX, USA). The normal-emitting groups were sampled while operating both at room temperature and at approximately 30°F (~ −1°C; G30, D30).
Chemical characterization of emission samples.
The chemical composition of the particle and SVOC fractions of each of the seven samples was analyzed at the Desert Research Institute (Reno, NV, USA) as described elsewhere (Zielinska et al. 2004). Analyses included temperature fractions of organic and elemental carbon, elements (metals and associated analytes), inorganic ions (sulfate, nitrate), and speciation of resolvable organic compounds. The temperature fractions were obtained using the IMPROVE thermal carbon analysis technique (Chow et al. 2001), in which eight discrete fractions of carbon are vaporized in a changing temperature and helium/oxygen atmosphere. The temperature fractions are grouped into four “organic” and four “elemental” carbon designations as shown in Table 2. Although these are not explicitly chemical fractions (related to both chemical and physical properties), they provide data on differences among the emission samples and have been used in source apportionment modeling studies to differentiate among motor vehicle and other types of emissions (e.g., Maykut et al. 2003; Watson et al. 2002). The organic species measured focused on components that have been used in previous studies to illustrate differences among motor vehicle and other types of emissions. The classes of organic compounds included polycyclic aromatic hydrocarbons (PAHs), ranging in molecular weight from 128 to 300 Da, a mass range that spans from compounds that are considered to be exclusively gases to species found exclusively in the particle phase. Several subclasses of PAHs, including oxygenated PAHs (oxy-PAHs: ketones, aldehydes, quinones), nitro-PAHs, and sulfur-containing PAHs were measured. Hopanes and steranes, two classes of compounds that are found in lubricating oil (McDonald et al. 2003; Rogge et al. 1993), were also measured. A total of 184 composition variables were measured.
Toxicologic evaluations.
Aliquots of the PM and SVOC extracts (in acetone) were provided (by Desert Research Institute) to the Lovelace Respiratory Research Institute for toxicity testing [description of sample extraction and handling described briefly below and in more detail by Seagrave et al. (2002)]. The PM and SVOC samples were combined (original PM and SVOC were extracted separately) and either mixed with Salmonella culture media (Ames bacterial reverse mutation assay) or instilled into lungs of F344/CRL rats (Charles River Laboratory, Wilmington, MA, USA) over a range of total mass (PM plus SVOC) doses (Seagrave et al. 2002). Responses in rat lungs were evaluated at 4 hr (the cytokine MIP-2) or 24 hr (all other responses) after dosing, as described previously (Seagrave et al. 2002). Lungs were removed and weighed, and then cells, protein, enzymes, and chemical mediators of inflammation were measured in bronchoalveolar lavage collected from the right cardiac, diaphragmatic, and intermediate lobes. The left lung was then fixed and examined by light microscopy for histologic evidence of inflammation and tissue damage. In all, 11 lung response variables were measured. Bacterial mutagenicity was evaluated in Ames tester strains TA98 and TA100, both with and without metabolic activation by a liver micro-some preparation (S9) (Seagrave et al. 2002). Dose–response relationships were analyzed for each variable and for each emission sample, and (toxic) potency factors were derived from these analyses (Seagrave et al. 2002).
Normalization of data for multivariate data analysis.
Data were normalized to weight fraction before statistical analysis by dividing the composition values by the sum of PM and SVOC mass (i.e., composition per unit of total mass). PM was the mass determined on the filter and extracted into solution. As discussed previously (Seagrave et al. 2002), the extraction protocol used to transfer PM into suspension involved agitation, gentle brushing, and sonication in acetone. Analysis of aliquots of the particle extracts was used to measure the recovery of the mass of material in solution compared with the mass weighed on filters before extraction. The recovery for PM was 80–100% for gasoline exhaust samples and 65–70% for diesel exhaust samples. The decreased extraction efficiency for the diesel exhaust samples was likely caused by difficulty in removing elemental carbon from the filters. The SVOC mass was determined by gravimetric analysis of spikes of extracts that were evaporated to dryness to remove the solvent (acetone). Because the compositional data are not reported as weight fractions elsewhere (Zielinska et al. 2004 reported emission rates), we include discussion on the mass composition of the samples and also include the data as Appendix 1 of this report.
Multivariate data analysis (pattern recognition and prediction).
The compositional data were structured in an X-matrix with one row per exhaust sample (i.e., a total of seven rows) and one column per predictor variable (initially, 184 compositional parameters). The mutagenicity and lung toxicity data were structured in a Y-matrix with seven rows and one column per response variable (i.e., a total of 15 responses). Multivariate data analysis was performed with Simca-P 10.0 (Umetrics, Umeå, Sweden). PCA (Jackson 1991) was performed on the X-matrix to evaluate similarities between mixtures and on the Y-matrix to group responses. PLS was used for the regression modeling to correlate the measured responses to the compositional parameters (Wold et al. 1984). PLS was used for the regression modeling because it overcomes the problems of intercorrelated predictor variables and data matrices where the number of variables exceeds the number of samples (Kettaneh-Wold 1992; Kvalheim 1989).
The purpose of PCA is to define “structure,” or patterns, in data that exist in multiple dimensions. Both the PCA and PLS techniques use the same basic data simplification principles by projecting linear planes (or hyperplanes) into a multidimensional grouping of data (Kettaneh-Wold 1992; Kvalheim 1989). A principal component or a PLS component is a straight least-squares regression line (or plane) through the sample points in the multidimensional space (Sjogren et al. 1996). Each component will “explain” a portion of the variance in the data set. Typically, multiple components are required to explain most of the variance. However, it is desirable to have few principal or PLS components relative to the number of samples for optimal confidence in the outcome of the analysis.
The primary difference between PCA and PLS is that PCA is performed on one data matrix (e.g., X or Y) and PLS evaluates both (X and Y) simultaneously to both develop a predictive model (e.g., predict Y from X) and to evaluate relationships between specific X and Y variables (e.g., which chemicals covary with toxicity?). PCA is first used to identify characteristics of data in either the X- or Y-matrix. The principal outcome of this analysis is the identification of data that “cluster” together similarly and thus are assumed to have a systematic relationship. This application of PCA is illustrated in “Results” by the finding that mutagenicity and pulmonary toxicity variables did not cluster together but that variables within each category did cluster together. This indicated that separate PLS models would be needed for mutagenicity and toxicity.
A PCA analysis was first conducted on the 7 × 184 X-matrix to evaluate similarities between samples by “score plots” and on the 7 × 15 Y-matrix to determine groupings (similarities) among response variables by “loading plots.” This grouping was used to segregate the response variables into covarying groups of responses that could be analyzed by PLS. PLS was initially carried out with all 184 predictor variables; however, because of the low number of samples, PLS had to be carried out on subsets and groups of predictor variables, as explained in “Results.”
Before analyses, the data were mean centered and scaled to unit variance as described previously (Jackson 1991; Wold et al. 1984). Data distributions were evaluated and determined to require no further normalization (e.g., log transformations) before analysis. The results of the PLS analysis were evaluated in terms of both goodness of fit (R2, analogous to Pearson correlation coefficient) and goodness of prediction (Q2, determined by cross-validation procedures described in Appendix 2). Each response end point was modeled individually (15 total), and the model results were evaluated by cross-validation procedures. To ascertain that the overall PLS models contained systematic (nonrandom) associations, we validated the models by performing PLS after randomizing (reordering) the values in the Y-matrix as described previously (Eide et al. 2001). This validation procedure is referred to as validation by response permutation (van der Voet 1994). A more detailed description of the validation approach and an example for the validation of one model are included in Appendix 2.
Results
Composition of emission samples.
The mass composition of the emission samples is summarized in Figure 1. These results have been reported elsewhere (Zielinska et al. 2004) but only in units of mass/mile traveled. The normal-emitter and black-smoker gasoline samples were composed primarily of vapor-phase SVOC mass, whereas the others were composed primarily of PM. The PM composition ranged from approximately 20 to 95% organic carbon, with no obvious distinction in the proportion of organic carbon between diesel- and gasoline-powered vehicles. This plot does not portray the differences in specific organic classes; the data for individual chemical species are reported in Appendix 1.
The proportions of elements and PAH compounds among these samples were variable, and there was no clear difference in the classifications of vehicles (e.g., high emitter vs. normal emitter or gasoline vs. diesel) that emit higher proportions (as a weight fraction) of any of these classes. In contrast, the higher-emitting vehicles clearly showed higher proportions of the hopane and sterane compounds (components of lubricating oils).
Principal component analysis.
The loading plot shown in Figure 2 was obtained from PCA on the toxicity data and shows the clustering of the 15 different toxicity measurements according to their similarity in responses to the exhaust samples. The 11 lung toxicity responses clustered together in one group, whereas the four bacterial mutagenicity responses occurred at different spots. This indicated that the lung toxicity responses were associated with similar chemical components and that these components were different from those associated with the mutagenicity responses. As a consequence, regression modeling with PLS was done with the 11 lung toxicity end points simultaneously (it is advantageous to use multiple responses because they will support one another in the model), and the four mutagenicity end points were modeled separately.
PLS analysis of lung toxicity data.
The goal in developing the PLS model was to explain the most variation in the data using the smallest number of PLS components. Initially, PLS was performed with all 184 compositional variables versus the 11 lung toxicity responses. Although it was possible to obtain a PLS model with high R2 and relatively high Q2 with all 184 predictor variables, validation by response permutation showed that the overall PLS model could be due to chance because of the large number of compositional variables relative to the relatively small number of samples. To alleviate this, we performed the PLS analysis after grouping most of the individual compositional variables by chemical class (e.g., hopanes) or subclass (e.g., two-ring PAHs). The number of variables was reduced from 184 to 34, and PLS was carried out with these 34 variables versus the 11 lung toxicity responses. The resulting PLS model performance was acceptable (R2 = 0.95; Q2 = 0.35) with only two PLS components and was hence used only to obtain first-pass indication of which groups of compounds associated (covaried) most strongly with the lung toxicity responses. According to loadings and PLS regression coefficients (not shown), the variables that associated most strongly with the 11 toxicity responses were particulate organic carbon, select thermal fractions of the carbon analysis, and the hopane and sterane classes of compounds.
The 34-variable PLS model was followed by a PLS model in which some compositional variables were ungrouped into their individual compounds (the hopanes and steranes). This gave a final 68-variable X-matrix (Table 2) that performed well (Figure 3 shows performance for each lung toxicity measurement; overall model performance: R2 = 0.93; Q2 = 0.72), accounting for approximately 70% of the variation in the data by just two PLS components (53 and 15% by the first and second PLS components, respectively). Each of the 11 toxicity response PLS models showed satisfactory-to-excellent performance in the validation by permutation tests (results of validations not shown, except the example given in Appendix 2). The model performance indicators for each lung response category (Figure 3) indicated that the model had better predictive capability for direct measures of inflammation (e.g., cell count, histopathology) than for indirect indicators (e.g., MIP-2). An example of the high quality of the model prediction is shown in Figure 4, which illustrates the observed versus predicted response for histologic evidence of lung inflammation.
Once the predictive model was determined, the strength of association (PLS loadings) between the chemical components and the individual lung toxicity responses was evaluated in a loading plot (Figure 5). This plot, analogous to the plot shown in Figure 2 for the 15 toxicity variables, shows the clustering of toxicity and chemical component variables, illustrating the chemical components that were most closely associated (covaried) with lung toxicity. The plot combines the covariance from the two PLS components that were required for the 68-variable model. The chemical variables have been abbreviated or grouped in the plot, and the full names associated with the abbreviations are given in Table 2 (the abbreviations give an indication of the chemical class). The components that had the strongest association with lung toxicity were most of the hopanes, steranes, and particle-phase organic carbon. The hopanes and steranes are compounds that are found in crude oil and are thus emitted as part of the crankcase oil emissions. These compounds are derived from the diagenesis of plant materials (e.g., conversion of phytosterols to steranes). Their characteristic structures have been described elsewhere (e.g., Rogge et al. 1993). The analysis of fuel and crankcase oil collected from the vehicles studied here (reported in Zielinska et al. 2004) showed that the hopanes and steranes were in high concentrations in oil (as expected) and only trace amounts of the steranes were observed in fuel. High-oil-burning vehicles will also show large amounts of particle-phase organic carbon. The most volatile thermal fractions from the carbon analysis along with one elemental carbon temperature fraction and nitrate also covaried with the lung toxicity responses. Other components, namely, the metals and PAHs, had little or no correlation with the lung responses.
PLS analysis of mutagenicity data.
PLS of the mutagenicity data using either the complete set (184) or the first reduced set (34) of chemical variables in the X data matrix was performed without satisfactory results. The 34-variable data set grouped together the chemical components that were known, based on previous studies, to be mutagenic. However, grouping by compound classes did not reveal associations between composition and mutagenicity and did not yield satisfactory performance in the PLS model. A separate strategy for configuring the X data matrix was adapted that ungrouped the individual nitro- and oxy-PAHs known to be direct mutagens and used them in a reduced data set of 23 variables (Table 3). The best model performance (R2 = 0.98; Q2 = 0.73) was obtained with these variables applied to the TA98 and TA100 strains without S9 metabolic activation. Figure 6 shows the observed versus predicted mutagenicity with this PLS model for strain TA100. The models for TA98 and TA100 could explain approximately 60% of the variation with three PLS components. In contrast, PLS models did not perform well for TA98 and TA100 strains with metabolic activation (not shown). This was not surprising because most of the mutagens that have been implicated in engine exhaust are direct acting (e.g., do not require metabolic activation). In addition, the presence of S9 may suppress mutagenicity by inactivating or adsorbing certain mutagens (Shah et al. 1990).
Figure 7 shows the loading plot with combined mutagenicity and chemical variables. Similar to what was expected based on the known mutagenicity of these compounds, the particle-bound higher-molecular-weight nitro-PAH compounds had the highest association with mutagenicity, whereas most of the oxy-PAHs and volatile nitro-PAHs had poor or no association. The similarity between the PLS model associations identified in this study and chemical components that were previously known to drive mutagenicity helped validate the PCA/PLS approach for evaluating composition–response relationships for lung toxicity, for which composition–response relationships were not known in advance.
Discussion
The present study represents a step toward a better understanding of the physical–chemical components of engine emissions presenting the greatest lung health hazards. There is growing recognition of the need to develop a more integrated understanding of the air quality–health relationship (Mauderly 2003; National Research Council 2001), but disentangling the relative roles of air contaminants in complex environmental pollution and source emissions has progressed slowly. Except for the biodirected fractionation approach that identified certain nitro-PAHs as driving bacterial mutagenic responses, there has been little progress in determining the specific species causing the effects of physically and chemically complex combustion emission mixtures. Most epidemiology and toxicology has focused on specific pollutants (e.g., unspeciated PM or nitrogen dioxide) or treated complex emission exposure atmospheres as a single material. Studies comparing the effects of filtered and unfiltered emissions (Maejima et al. 2001) or the effects of the elemental carbon and extractable organic fractions of diesel soot (Nel et al. 2001) are examples of simplified biodirected fractionation but fall far short of testing the roles of the full range of emission species. Epidemiologists commonly employ multivariate analyses involving multiple environmental pollutants, but have data for only a few pollutant species and usually focus on determining the influence of copollutants on estimates of the effects of the single pollutant (or class, e.g., PM) of chief interest (Samet et al. 2000). In a study conceptually more similar to the present study, Wellenius et al. (2003) applied multivariate regression modeling to data from multiple exposures of dogs to concentrated ambient air PM to identify an association between silicon and cardiac effects, but studied only the PM fraction of pollution and did not have data on speciated organic compounds. There are no previous reports of the use of multivariate analyses to disentangle the roles of both the vapor and PM organic phases of engine emissions.
This study, although certainly an over-simplification of environmental exposures to inhaled emissions, demonstrates that PCA/PLS has potential for exploring complex exposure composition–health response associations, given a suitable data set. The utility of this approach in identifying putative causal agents in diesel exhaust samples had been demonstrated but with only a single health response (mutagenicity) and a larger number of samples (Eide et al. 2002). A challenge in applying PLS in the present study was the inclusion of many health responses (15) and composition variables (184) but only seven samples—a very practical situation in view of the limited sample (or exposure) number and diversity typical of environmental studies. The approach worked well largely because of success in grouping covarying composition and response variables and reducing their relationships into a number of principal components smaller than the number of samples. Grouping compositional components by class, however, has the disadvantage of making the often-false assumption that all species within the class are equally toxic per unit of mass or that the proportions among the grouped compounds are similar. This assumption was certainly not true for mutagenicity, in which case total nitro-PAH mass was poorly predictive, but foreknowledge of the mutagenicity of particular species allowed development of a more focused and highly predictive model. In the absence of little previous information on the contributions of individual components, as was the case for lung toxicity, iterative approaches to grouping the composition into classes and disaggregating classes into individual compounds can be used to explore and optimize models. The small number of samples also raised the possibility that apparently meaningful composition–response relationships could reflect random (nonsystematic) statistical associations. The cross-validation and confirmatory steps were critical to developing confidence that the associations portrayed by the models having the best fit and predictive performance were in fact systematic (nonrandom). Overall, the results suggested that PCA/PLS can be useful for identifying composition–response associations for complex exposures even when the number of exposure cases is small. An alternative to grouping and variable selection is hierarchical PLS [described by Wold et al. (1996)], which was used (not shown) to confirm the conclusions of the PCA/PLS results presented in this article.
The use of collected and processed samples, wherein acetone was used to extract species from the collected exhaust material, was a limitation of this study. First, the exhaust collections account for only a portion of the exhaust. Although attempts were made to quantitatively remove 100% of the PM from the filters, only approximately 65–70% of the PM from diesel exhaust samples with high amounts of inorganic carbon could be removed. Although the vapor-phase SVOCs were collected, the samples did not include the most volatile vapor and gas components of the exhaust. In addition, it is known that chemical artifacts can be induced during sample collection and processing (Arey et al. 1988), and it is possible that there were potentially important compositional differences between the collected samples used for this study and the original emissions. However, confidence in the present results derives from the fact that the hopanes and steranes having the strongest associations with toxicity are not formed by artifact, are chemically stable (not prone to decomposition), and are known components of lubricating oil. Thus, although the roles of components that might have been lost during sample processing could not be tested, the components most strongly associated with the lung responses were extremely unlikely to be artifacts.
Instillation of extracted material into the lung has limitations in evaluation of the health hazard of materials that are inhaled in the environment. The instillation of collected non-volatile material could not accurately mimic the particle size–dependent deposition pattern of inhaled PM. The comparative utility of dosing by inhalation and instillation has been reviewed (Driscoll et al. 2000), and although inhalation remains the “gold standard” for hazard assessment, instillation has proven useful for comparing effects among samples and screening for potential cause–effect relationships. Exposure of cultured cells is another alternative to inhalation for comparative toxicity screening, but work preceding the present analysis demonstrated that lung responses to instilled samples and responses of cultured epithelial cells and lung macrophages to the same samples gave quite different sample rankings (Seagrave et al. 2003). Compared with cell culture, lung instillation was considered the more relevant approach for identifying potential public health hazards. In view of the difficulty, cost, and time requirements of conducting inhalation exposures to a wide range of vehicle emissions, the study provided a test of the utility of a practical, albeit limited, approach to identifying chemical composition–toxicity associations warranting closer examination.
However good the models developed from the present sample set might be, caution must be exercised in extrapolating these results broadly to all gasoline and diesel engine emissions. The concordance of the present results for mutagenicity with pre-existing information on the importance of nitrogenated PAHs in different combustion emissions (e.g., Lewtas et al. 1992) suggests that the mutagenicity model might be broadly applicable to normal- and high-emitting gasoline and diesel engines and lends confidence that the lung toxicity results are also likely to be valid beyond this sample set. However, it is clear that lung toxicity was driven largely by the coincident differences in composition and toxicity between the samples from high-emitting and normal-emitting vehicles. The finding that lubricating oil tracers were highly associated with lung toxicity in this sample set does not necessarily mean that oil emissions would be the major determinant of lung toxicity in all engine emissions, and especially among emissions from engines having low oil consumption. The addition of more samples to the analysis, and especially samples differing even more markedly in composition, would bolster confidence in the results and their applicability across a broader spectrum of engine emissions. Regardless, the present results strongly indicate that attention should be given to oil-derived as well as fuel-derived emissions and suggest that as total emissions from fuel combustion continue to fall, oil-derived emissions could contribute relatively more to any residual health hazards.
There is little information on the effects of motor oil in the lung. Subchronic inhalation exposure of rats to high concentrations of aerosolized petroleum oils, including a formulation representing unused motor oil, produced only modest toxicity (Dalbey 2001). It is likely that the toxicity of motor oil increases with use. Zielinska et al. (2004) analyzed the composition of fuel and crankcase oil from the vehicles used in the present study. They reported that diesel fuel was enriched in light and semivolatile PAHs compared with gasoline fuel. In contrast, used oil from the gasoline-powered vehicles in this study was enriched in PAHs, including heavy, particle-phase PAHs, compared with used diesel oil. Lubricating oil in the gasoline vehicles apparently serves as a “sink” for the partitioning of combustion- or fuel-derived components; thus, it is important to consider the time in use of oil in studies of the contribution of oil components to the toxicity of engine emissions. Only one study has investigated the toxicity of used motor oil; Costa and Amdur (1979) reported a 28% increase in pulmonary resistance in guinea pigs exposed to used motor oil, but the variability in the pulmonary measurements rendered the difference from control animals insignificant. Clearly, more work needs to be done to investigate the toxicity of used motor oil as it is emitted in motor vehicle emissions.
A final caveat is that the statistical composition–response associations resulting from this work do not prove causality. There is considerable information indicating that nitro-PAHs cause mutations in bacteria, but there is little information on the effects of hopanes and steranes in the lung. It is possible that these putative agents could have covaried in mass concentration with unknown proximal causal species, rather than actually causing the responses. Although the composition of the samples was determined in detail, the measured mass by organic speciation accounted for only a small percentage (average ~10%) of the total SVOC + PM mass. Additional samples having different toxicity and chemical composition would strengthen the confidence in the observed associations. The causality of specific chemical classes or components of exhaust can be examined in complementary studies, including exposure to inhaled emissions containing different contributions from crankcase oil, “doping” samples with the putative causal agents, and/or progressive fractionation and testing of samples (i.e., bioassay directed fractionation).
The bioassay-directed fractionation approach may be useful for confirming and further evaluating the components that correlate with pulmonary toxicity. However, an important consideration in applying bioassay-directed fractionation for pulmonary toxicity is the much larger effort and cost of the in vivo assays relative to the simpler, less expensive bacterial mutagenicity assays that have been used. As mentioned above, in vitro testing with lung cells ranked the samples quite differently from the in vivo results (Seagrave et al. 2003). Because in vivo toxicity should be more relevant to human health hazard than in vitro results, it appears unlikely that biodirected fractionation for nonmutagenic lung toxicity can be done using in vitro assays.
Conclusions
Despite its several limitations, this study provides important insights into the physical-chemical components of engine emissions that most strongly influence the toxicity of inhaled emissions. We extend the previous conclusion (Seagrave et al. 2002) that high-emitting vehicles contribute disproportionately to the health hazards of engine emissions, to conclude now that crankcase oil–derived, particle-associated organic compounds may contribute strongly to the inflammatory effects of inhaled emissions from high-emitting vehicles. Importantly, the chemicals most closely associated with pulmonary toxicity were different from the chemicals (e.g., nitro-PAHs and oxy-PAHs such as quinones) that were associated with bacterial mutagenicity. This is especially important considering the small amount of information available on chemicals that are associated with pulmonary toxicity. Further work is warranted to confirm the causality of specific classes and compounds, to confirm that oil-derived components are important to the toxicity of inhaled (as well as instilled) emissions, and to determine the relative importance of oil- versus fuel-derived components to the health hazards of emissions from a broader range of normal- and high-emitting vehicles. Moreover, we conclude that the PCA/PLS analytical strategy shows promise for disentangling composition–response associations, even when the exposures are extremely complex, the number of exposures is limited, and multiple responses are measured. In such situations, the success of the approach hinges on the extent to which composition and response variables can be lumped into covarying groups such that predictive models require a number of principal components substantially less than the number of exposures.
Appendix 1 Entire compositional data set presented as weight fraction.
BG WG HD G G30 D D30
Total PM and SVOC
Particle mass 0.288877 0.689480 0.628759 0.162128 0.295583 0.717567 0.693145
SVOC mass 0.711123 0.310520 0.371241 0.837872 0.704417 0.282433 0.306855
Inorganic ions
Chloride 0.000891 0.000661 0.000017 0.001062 0.001106 0.000637 0.000000
Nitrate 0.000294 0.001022 0.000345 0.000273 0.000422 0.000254 0.000585
Sulfate 0.002643 0.002143 0.003073 0.004721 0.013193 0.174678 0.049407
Ammonium 0.001337 0.001251 0.001060 0.004087 0.009031 0.003130 0.001317
Sodium 0.000099 0.000227 0.000516 0.000001 0.000000 0.000185 0.000117
Carbon
Particle organic carbon mass 0.195937 0.657895 0.488888 0.084029 0.100675 0.141816 0.461659
Elemental carbon 0.044261 0.020826 0.130267 0.040269 0.148137 0.348723 0.123590
Total carbon 0.207543 0.569073 0.537674 0.110293 0.232034 0.466659 0.508301
Carbon splits
O1TC 0.087617 0.197504 0.267370 0.031775 0.045685 0.056771 0.099821
O2TC 0.066411 0.297777 0.140208 0.014997 0.018800 0.025543 0.031097
O3TC 0.012191 0.032741 0.033965 0.013365 0.014883 0.029318 0.038484
O4TC 0.009070 0.009965 0.009971 0.010214 0.013101 0.026178 0.031534
OPTC 0.020649 0.119907 0.037376 0.013678 0.008207 0.004008 0.260722
E1TC 0.059643 0.110620 0.035683 0.043630 0.053664 0.196258 0.217269
E2TC 0.001662 0.009845 0.125607 0.007894 0.101237 0.155739 0.123341
E3TC 0.000164 0.000285 0.000121 0.000233 0.000108 0.000068 0.000244
Transition metals
Titanium 0.000008 0.000000 0.000000 0.000016 0.000004 0.000027 0.000033
Vanadium 0.000000 0.000004 0.000002 0.000003 0.000018 0.000012 0.000000
Chromium 0.000088 0.000009 0.000020 0.000051 0.000107 0.000585 0.000473
Manganese 0.000161 0.000008 0.000003 0.000064 0.000086 0.000078 0.000108
Iron mass 0.022771 0.000475 0.000280 0.012046 0.012934 0.011622 0.026206
Cobalt 0.000002 0.000003 0.000006 0.000000 0.000000 0.000005 0.000022
Nickel 0.000054 0.000002 0.000005 0.000029 0.000056 0.000262 0.000311
Copper 0.000352 0.000049 0.000002 0.000119 0.000110 0.000076 0.000113
Zinc 0.002677 0.000536 0.000389 0.001604 0.001206 0.000879 0.001239
Yttrium 0.000006 0.000007 0.000000 0.000002 0.000003 0.000006 0.000011
Zirconium 0.000030 0.000018 0.000000 0.000024 0.000054 0.000010 0.000021
Molybdenum 0.000017 0.000000 0.000000 0.000002 0.000004 0.000000 0.000000
Palladium 0.000022 0.000000 0.000003 0.000001 0.000001 0.000000 0.000007
Silver 0.000021 0.000000 0.000035 0.000009 0.000000 0.000065 0.000000
Cadmium 0.000012 0.000000 0.000000 0.000001 0.000010 0.000001 0.000000
Lanthanum 0.000000 0.000000 0.000000 0.000020 0.000000 0.000022 0.000000
Gold 0.000020 0.000004 0.000025 0.000016 0.000018 0.000020 0.000039
Mercury 0.000002 0.000009 0.000021 0.000002 0.000001 0.000002 0.000000
Other metals
Magnesium 0.000831 0.000449 0.000110 0.000296 0.000133 0.000146 0.000140
Aluminum mass 0.000254 0.000000 0.000000 0.000292 0.000259 0.000522 0.001545
Potassium 0.000069 0.000000 0.000000 0.000146 0.000087 0.000057 0.000027
Calcium 0.001574 0.000304 0.000668 0.001825 0.001369 0.001708 0.002423
Gallium 0.000003 0.000006 0.000018 0.000000 0.000001 0.000001 0.000006
Rubidium 0.000004 0.000008 0.000005 0.000002 0.000003 0.000005 0.000004
Strontium 0.000000 0.000003 0.000008 0.000004 0.000002 0.000005 0.000008
Indium 0.000028 0.000101 0.000107 0.000012 0.000005 0.000027 0.000023
Barium 0.000064 0.000234 0.000000 0.000101 0.000033 0.000249 0.000000
Thallium 0.000000 0.000025 0.000012 0.000005 0.000003 0.000017 0.000013
Lead 0.004843 0.000034 0.000046 0.000264 0.000397 0.000041 0.000036
Uranium 0.000001 0.000007 0.000021 0.000004 0.000000 0.000005 0.000000
Metalloids
Silicon mass 0.005000 0.000951 0.000918 0.007752 0.002787 0.003295 0.004616
Arsenic 0.000000 0.000000 0.000000 0.000001 0.000002 0.000004 0.000000
Tin 0.000050 0.000064 0.000033 0.000032 0.000023 0.000024 0.000047
Antimony 0.000006 0.000031 0.000172 0.000009 0.000004 0.000000 0.000036
Nonmetal elements
Sulfur 0.002354 0.001825 0.001508 0.001716 0.002100 0.028206 0.018411
Chlorine 0.000726 0.000008 0.000016 0.000236 0.000601 0.000000 0.000000
Phosphorous 0.001334 0.000274 0.000152 0.000978 0.000591 0.000139 0.000545
Selenium 0.000001 0.000002 0.000006 0.000003 0.000002 0.000010 0.000008
Bromine 0.000028 0.000004 0.000003 0.000004 0.000001 0.000010 0.000000
Bicyclic or two-ring PAH Organic Compounds
Naphthalene 0.003309 0.003459 0.000626 0.045334 0.003114 0.003792 0.011416
2-Menaphthalene 0.001595 0.002445 0.000997 0.017146 0.002901 0.002079 0.003719
1-Menaphthalene 0.001034 0.002018 0.001099 0.016027 0.002357 0.002054 0.003757
2,6 + 2,7-Dimenaphthalene 0.003518 0.002303 0.000676 0.008528 0.006724 0.001017 0.001218
1,6 + 1,3+1,7-Dimethylnaphthalene 0.004080 0.003833 0.001158 0.012884 0.009602 0.001726 0.002036
2,3 + 1,4+1,5-Dimenaphthalene 0.000883 0.001516 0.000371 0.004195 0.003289 0.000562 0.000709
1,2-Dimethylnaphthalene 0.000401 0.000984 0.000195 0.001914 0.001984 0.000248 0.000252
1-Ethyl-2-methylnaphthalene 0.002846 0.002836 0.000218 0.004541 0.004886 0.000375 0.000480
Biphenyl 0.001059 0.000927 0.000379 0.005099 0.002949 0.000635 0.000976
2-Methylbiphenyl 0.000169 0.000157 0.000102 0.000821 0.000306 0.000167 0.000178
3-Methylbiphenyl 0.000233 0.001011 0.000539 0.005404 0.001538 0.000921 0.001338
4-Methylbiphenyl 0.000104 0.000519 0.000207 0.002470 0.000749 0.000355 0.000613
Bibenzyl 0.000792 0.004328 0.000068 0.006136 0.004549 0.000701 0.001061
α-Trimethylnaphthalene 0.000427 0.001258 0.000304 0.004647 0.002628 0.000666 0.001060
1-Ethyl-2-methylnaphthalene 0.014773 0.003354 0.000028 0.039013 0.033539 0.000881 0.001511
β-Trimethylnaphthalene 0.000285 0.000986 0.000481 0.004565 0.002411 0.000699 0.001057
γ-Trimethylnaphthalene 0.000177 0.000925 0.000336 0.004009 0.001965 0.000616 0.000953
2-Ethyl-1-methylnaphthalene 0.000020 0.000114 0.000009 0.000169 0.000150 0.000014 0.000024
ɛ-Trimethylnaphthalene 0.000111 0.000475 0.000296 0.002788 0.001094 0.000414 0.000658
f-Trimethylnaphthalene 0.000119 0.000455 0.000258 0.002878 0.001180 0.000425 0.000666
2,3,5-Trimethylnaphthalene 0.000172 0.001039 0.000509 0.005509 0.002367 0.000790 0.001268
2,4,5-Trimethylnaphthalene 0.000057 0.000399 0.000054 0.001224 0.000795 0.000092 0.000252
j-Trimethylnaphthalene 0.000048 0.000306 0.000125 0.001293 0.000505 0.000202 0.000303
1,4,5-Trimethylnaphthalene 0.000644 0.000408 0.000065 0.002682 0.002074 0.000288 0.000222
1,2,8-Trimethylnaphthalene 0.000295 0.000213 0.000011 0.001438 0.001124 0.000127 0.000132
Tricyclic or three-ring PAH organic compounds
Acenaphthylene 0.002691 0.004103 0.000115 0.008941 0.025590 0.000486 0.002132
Acenaphthene 0.000280 0.000414 0.000046 0.001136 0.001579 0.000107 0.000242
Fluorene 0.000879 0.003382 0.000238 0.012595 0.008075 0.000760 0.003204
Phenanthrene 0.001121 0.001538 0.000546 0.008017 0.008055 0.000650 0.001958
α-Methylfluorene 0.000187 0.000616 0.000218 0.002117 0.000878 0.000291 0.000519
1-Methylfluorene 0.000126 0.000471 0.000166 0.002054 0.000583 0.000312 0.000509
β-Methylfluorene 0.000062 0.000177 0.000037 0.000477 0.000206 0.000082 0.000117
γ-Methylphenanthrene 0.000159 0.000513 0.000238 0.002131 0.000709 0.000260 0.000519
2-Methylphenanthrene 0.000188 0.000629 0.000238 0.002574 0.000856 0.000351 0.000633
γ-Methylphenanthrene 0.000176 0.000601 0.000135 0.001923 0.000862 0.000196 0.000473
1-Methylphenanthrene 0.000147 0.000491 0.000110 0.001590 0.000521 0.000242 0.000383
3,6-Dimethylphenanthrene 0.000031 0.000135 0.000058 0.000637 0.000145 0.000085 0.000160
α-Dimethylphenanthrene 0.000052 0.000234 0.000071 0.000979 0.000184 0.000123 0.000246
β-Dimethylphenanthrene 0.000029 0.000138 0.000035 0.000632 0.000108 0.000087 0.000162
γ-Dimethylphenanthrene 0.000116 0.000606 0.000125 0.001837 0.000448 0.000227 0.000460
1,7-Dimethylphenanthrene 0.000073 0.000392 0.000057 0.000940 0.000268 0.000103 0.000231
d-Dimethylphenanthrene 0.000024 0.000131 0.000026 0.000567 0.000103 0.000070 0.000145
ɛ-Dimethylphenanthrene 0.000053 0.000353 0.000037 0.000803 0.000248 0.000091 0.000188
Anthracene 0.000228 0.000438 0.000041 0.001107 0.001244 0.000046 0.000258
9-Methylanthracene 0.000028 0.000098 0.000001 0.000129 0.000052 0.000021 0.000029
Retene 0.000010 0.000005 0.000003 0.000032 0.000010 0.000006 0.000008
Tetracyclic or four-ring PAH organic compounds
2,3-Benzofluorene 0.000156 0.001283 0.000003 0.000957 0.001241 0.000013 0.000192
Fluoranthene 0.000454 0.000778 0.000026 0.002844 0.003514 0.000097 0.000688
Pyrene 0.000344 0.000409 0.000058 0.002116 0.001901 0.000045 0.000548
1-Methylfluorene + a-methylfluorene 0.000002 0.000012 0.000001 0.000008 0.000007 0.000001 0.000000
b-Methylpyrene + b-methylfluorene 0.000122 0.000851 0.000009 0.000769 0.000654 0.000031 0.000164
c-Methylpyrene + c-methylfluorene 0.000084 0.000620 0.000002 0.000462 0.000573 0.000009 0.000094
4-Methylpyrene 0.000027 0.000109 0.000010 0.000227 0.000084 0.000006 0.000058
1-Methylpyrene 0.000023 0.000127 0.000006 0.000196 0.000078 0.000003 0.000050
Benzo[c]phenanthrene 0.000032 0.000101 0.000001 0.000207 0.000216 0.000006 0.000052
Benz[a]anthracene 0.000065 0.000173 0.000002 0.000275 0.000293 0.000004 0.000067
7-Methylbenz[a]anthracene 0.000001 0.000002 0.000000 0.000002 0.000003 0.000000 0.000000
Chrysene 0.000093 0.000250 0.000006 0.000849 0.000516 0.000022 0.000227
5+6-Methylchrysene 0.000008 0.000055 0.000001 0.000059 0.000031 0.000027 0.000015
Five-ring PAH organic compounds
Benzo[b+j+k]FL 0.000071 0.000137 0.000003 0.000449 0.000326 0.000008 0.000122
7-Methylbenzo[a]pyrene 0.000001 0.000003 0.000000 0.000007 0.000006 0.000003 0.000002
Benzo[e]pyrene 0.000031 0.000090 0.000002 0.000225 0.000147 0.000003 0.000060
Perylene 0.000010 0.000023 0.000000 0.000010 0.000062 0.000001 0.000002
Benzo[a]pyrene 0.000038 0.000099 0.000001 0.000089 0.000225 0.000028 0.000022
Six-ring PAH organic compounds
Indeno[123-cd]pyrene 0.000035 0.000071 0.000001 0.000204 0.000204 0.000002 0.000056
Benzo[ghi]perylene 0.000104 0.000107 0.000002 0.000276 0.000279 0.000003 0.000074
Dibenz[ah+ac]anthracene 0.000004 0.000015 0.000000 0.000032 0.000024 0.000000 0.000009
Seven-ring PAH organic compounds
Coronene 0.000056 0.000007 0.000001 0.000078 0.000103 0.000002 0.000020
Oxygenated PAH organic compounds
9-Fluorenone 0.007703 0.001677 0.000001 0.012351 0.020705 0.000564 0.000399
Xanthone 0.000000 0.000060 0.000071 0.000008 0.000099 0.000048 0.000002
Acenaphthenequinone 0.000000 0.000018 0.000002 0.000000 0.000000 0.000016 0.000000
Perinaphthenone 0.000000 0.000000 0.000020 0.000000 0.000000 0.000000 0.000000
Anthraquinone 0.000103 0.000466 0.000001 0.000712 0.000507 0.000034 0.000140
9-Anthraldehyde 0.000033 0.000000 0.000035 0.000309 0.000056 0.000112 0.000085
Benz[a]anthracene-7,12-dione 0.000003 0.000050 0.000000 0.000035 0.000055 0.000004 0.000009
Sulfur-containing PAH
Benzonaphthothiopene 0.000005 0.000064 0.000001 0.000091 0.000012 0.000005 0.000024
Nitro PAH organic compounds
1-Nitronaphthalene 0.000012 0.000007 0.000012 0.000029 0.000008 0.000012 0.000013
2-Nitronaphthalene 0.000005 0.000002 0.000002 0.000016 0.000011 0.000004 0.000005
2-Methyl-1-nitronaphthalene 0.000001 0.000001 0.000000 0.000000 0.000001 0.000008 0.000001
α-Methyl-1-nitronaphthalene 0.000001 0.000001 0.000000 0.000001 0.000001 0.000001 0.000000
β-Methyl-1-nitronaphthalene 0.000003 0.000008 0.000003 0.000003 0.000003 0.000005 0.000003
2-Nitrobiphenyl 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
4-Nitrobiphenyl 0.000005 0.000002 0.000012 0.000003 0.000002 0.000003 0.000007
5-Nitroacenaphthene 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
9-Nitroanthracene 0.000000 0.000000 0.000000 0.000001 0.000002 0.000002 0.000002
2-Nitrofluoranthene 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
3-Nitrofluoranthene 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
1-Nitropyrene 0.000001 0.000001 0.000002 0.000001 0.000000 0.000013 0.000010
7-Nitrobenz[a]anthracene 0.000000 0.000001 0.000000 0.000000 0.000000 0.000000 0.000001
6-Nitrochrysene 0.000002 0.000000 0.000001 0.000006 0.000001 0.000002 0.000003
6-Nitrobenzo[a]pyrene 0.000000 0.000000 0.000001 0.000000 0.000000 0.000001 0.000005
Hopane/sterane organic compounds
C27-20S-13β(H),17α(H)-diasterane 0.000010 0.000011 0.000010 0.000002 0.000002 0.000007 0.000004
C27-20R-13β(H),17α(H)-diasterane 0.000007 0.000006 0.000005 0.000002 0.000001 0.000006 0.000002
C27-20S-13α(H),17β(H)-diasterane 0.000003 0.000003 0.000003 0.000001 0.000000 0.000002 0.000001
C27-20R-13α(H),17β(H)-diasterane 0.000004 0.000006 0.000004 0.000001 0.000001 0.000003 0.000001
C28-20S-13β(H),17α(H)-diasterane 0.000005 0.000004 0.000001 0.000001 0.000001 0.000002 0.000001
C27-20S-5α(H),14α(H)-cholestane 0.000019 0.000007 0.000001 0.000001 0.000002 0.000006 0.000003
C27-20R-5α(H),14β(H)-cholestane 0.000019 0.000014 0.000018 0.000002 0.000002 0.000010 0.000005
C27-20S-5α(H),14β(H),17β(H)-cholestane 0.000007 0.000009 0.000012 0.000001 0.000002 0.000005 0.000003
C27-20R-5α(H),14α(H),17α(H)-cholestane 0.000019 0.000022 0.000020 0.000003 0.000004 0.000007 0.000008
C28-20S-5α(H),14α(H),17α(H)-ergostane 0.000003 0.000005 0.000003 0.000001 0.000001 0.000003 0.000002
C28-20R-5α(H),14β(H),17β(H)-ergostane 0.000007 0.000008 0.000010 0.000001 0.000001 0.000007 0.000003
C29-20R-13α(H),17β(H)-diasterane 0.000008 0.000016 0.000023 0.000002 0.000003 0.000006 0.000005
C27-Tetracyclic terpane 0.000010 0.000016 0.000037 0.000005 0.000004 0.000014 0.000007
C28-20R-5α(H),14α(H),17α(H)-ergostane 0.000004 0.000007 0.000008 0.000001 0.000001 0.000004 0.000002
C27-Tetracyclic terpane 0.000003 0.000002 0.000000 0.000001 0.000001 0.000004 0.000003
C28-Tetracyclic terpane 0.000009 0.000007 0.000144 0.000004 0.000004 0.000009 0.000005
C29-20S-5α(H),14α(H),17α(H)-stigmastane 0.000005 0.000012 0.000014 0.000001 0.000002 0.000007 0.000004
C28-Tetracyclic terpane 0.000003 0.000019 0.000001 0.000001 0.000001 0.000005 0.000003
C29-20R-5α(H),14β(H),17β(H)-stigmastane 0.000010 0.000015 0.000021 0.000001 0.000003 0.000008 0.000006
C29-20S-5α(H),14β(H),17β(H)-stigmastane 0.000006 0.000010 0.000013 0.000001 0.000002 0.000006 0.000003
18α(H),21β(H)-22,29,30-trisnorhopane 0.000002 0.000002 0.000009 0.000000 0.000000 0.000001 0.000001
17α(H),18α(H),21β(H)-25,28,30-trisnorhopane 0.000001 0.000002 0.000006 0.000000 0.000000 0.000001 0.000001
C29-20R-5α(H),14α(H),17α(H)-stigmastane 0.000008 0.000015 0.000016 0.000001 0.000003 0.000007 0.000004
17α(H),21β(H)-22,29,30-trisnorhopane 0.000042 0.000083 0.000106 0.000012 0.000016 0.000056 0.000030
17α(H),18α(H),21β(H)-28,30-bisnorhopane 0.0000010 0.0000022 0.0000019 0.0000001 0.0000002 0.0000008 0.0000004
17α(H),21β(H)-30-norhopane 0.0000319 0.0000690 0.0000000 0.0000073 0.0000095 0.0000412 0.0000196
18α(H),21β(H)-30-norneohopane 0.0000040 0.0000099 0.0000027 0.0000006 0.0000003 0.0000030 0.0000031
17α(H),21β(H)-hopane 0.0000029 0.0000066 0.0000076 0.0000003 0.0000009 0.0000030 0.0000014
17β(H),21α(H)-hopane 0.0000020 0.0000023 0.0000081 0.0000002 0.0000007 0.0000023 0.0000014
22S-17α(H),21β(H)-30-homohopane 0.0000146 0.0000345 0.0000514 0.0000027 0.0000046 0.0000187 0.0000088
22R-17α(H),21β(H)-30-homohopane 0.0000090 0.0000258 0.0000351 0.0000018 0.0000035 0.0000129 0.0000067
17β(H),21β(H)-hopane 0.0000031 0.0000123 0.0000040 0.0000004 0.0000010 0.0000043 0.0000019
22S-17α(H),21β(H)-30,31-bishomohopane 0.0000072 0.0000199 0.0000544 0.0000011 0.0000025 0.0000094 0.0000045
22R-17α(H),21β(H)-30,31-bishomohopane 0.0000045 0.0000136 0.0000342 0.0000009 0.0000014 0.0000070 0.0000035
22S-17α(H),21β(H)-30,31,32-trishomohopane 0.0000044 0.0000155 0.0000156 0.0000006 0.0000015 0.0000055 0.0000023
22R-17α(H),21β(H)-30,31,32-trishomohopane 0.0000032 0.0000098 0.0000093 0.0000004 0.0000010 0.0000036 0.0000016
Appendix 2. Evaluation and Validation of PLS Model
Methods for evaluation of statistical modeling results.
The PLS model results were evaluated based on indices of goodness of fit (R2) and prediction capacity (Q2). The R2 goodness of fit coefficient is analogous to the multiple regression correlation coefficient (squared Pearson product-moment correlation between observed and predicted Y-values). Although this index provides insight into the strength of the observed association between model-predicted and observed health outcomes, it is not necessarily a reliable measure of the predictive capacity of models. Q2 is calculated for this purpose. Based on the cross-validation technique described by Wold (1978), Q2 assesses the model’s ability to predict health outcomes for each individual sample when that sample is not used in the PLS model. The Q2 goodness of prediction parameter is similar to R2 in that it is based on the sums of squares of prediction errors. However, unlike the prediction errors used in calculating R2, the prediction errors for Q2 are independent of the prediction itself, rendering Q2 a more reliable index of prediction performance.
Both R2 and Q2 values were calculated for model predictions on the original data matrix as well as for 20 random orderings of the Y (health outcomes) data matrix, while keeping the X-matrix (emissions sample composition) fixed. As the randomly reordered Y-matrix (Yr) changes, the correlation between the original Y-values and reordered Y-values, (i.e., corr[Y1,Yr]), decreases to smaller and smaller values. If there is an underlying systematic (nonrandom) relationship between the Y- and X-matrices, a PLS model constructed on the randomly reordered Y-values (i.e., Yr) would be expected to exhibit predictive power that decreases (decreased Q2) as corr[Y1,Yr] decreases. If this does not occur, and the predictive capability of a model based on random pairings of the health outcome data with their emission sample predictors is as good as the predictive capability of the model based on the observed pairing of health outcome and emissions sample data, there is good evidence that the model is based on chance as opposed to systematic (nonrandom) associations between the health outcome and emission composition. Thus, the test of model plausibility is based upon the examination of relationship between corr[Y1,Yr] and the Q2 associated with reordered Y-values (i.e., Q2[Yr]). If the model is capturing a systematic (nonrandom) relationship, the scatter plot of corr(Y1,Yr) versus Q2[Yr] should exhibit a linear relationship, and the estimated intercept should be near zero. The R2 value may be in the acceptable range even when the variation in the Y-matrix yields an unacceptable Q2, indicating that data exhibit correlations by chance but there are no clear differences in the associations between specific predictor variables and the dependent variables. This illustrates the importance of using the Q2 criterion with permutation testing of these models, which is not always conducted and reported in the literature.
The overriding difficulty in performing the present analysis was the large number of predictor (composition) variables and relatively smaller number of emission samples on which to examine health outcomes. Without some strategy to group (and reduce) the number of composition variables, the identification of systematic (nonrandom) relationships between the composition variables and health outcomes might prove impossible. The strategy for grouping predictor variables focused on composites (sums) of chemical classes or subclasses. This strategy allowed greater interpretive ability because the importance of specific classes of components could be identified. Compounds were also grouped because the lower number of predictor variables improved the model performance (R2, Q2). The problem with grouping compounds in a particular chemical class is that the assumption is made that the individual compounds within that group contribute equally to toxicity. If there are differences among the toxicity of individual components, combining them might mask the effects of the most important components. In addition, grouping compounds masks the differences in the concentrations of individual compounds among the group.
Validation of statistical model, example.
Each iteration of the PLS model was systematically validated as described herein. This began with evaluation of the performance parameters (R2 and Q2, with 1.0 being perfect correlation or goodness of fit or prediction, respectively) of the base model, and was followed by validation by response permutation to ensure that the overall model was robust and not due to chance (random associations). As described, the final model plausibility was based on the relationship between corr[Y1,Yr] and the Q2 associated with reordered response (in this study, response is toxicity) data. Here we give one example (Figure A2-1) of the result from the validation by response permutation for the PLS prediction of lavage protein. Twenty random variations (permutations) of the ordering of the compositional data were modeled by PLS, and the scatter plot of corr(Y1,Yr) versus Q2[Yr] (Figure A2-1) had an intercept near zero, indicating that the base model associations were non-random. An important point that is illustrated by this scatter plot is that each of the reordered Y-matrix models showed acceptable R2, even when there was poor Q2. This is important because models that rely on R2 alone may show correlations between variables that are actually random associations, and this would not be detected without validation using the Q2 criteria.
Figure A2-1 Scatter plot of corr(Y,Yr) versus Q2[Yr]. The near zero y-intercept of the line plotted through the Q2 values confirms that the base model (right) is not due to random statistical associations, by showing that changes in the structure of the data decrease the performance of the model. High R2 for nearly all of the model permutations illustrates the need for the Q2 diagnostic because correlation (R2) occurs under several conditions without good model performance.
Each of the individual models and iterations of the groupings of the compositional variables was evaluated by this permutation of the Y-matrix. Only models where the slope of corr(Y1,Yr) versus Q2[Yr] was near zero were accepted and used to show associations between compositional components and lung or mutagenicity responses.
Figure 1 Composition of engine exhaust samples normalized to weight fraction. Individual components were divided by the concentration of total particle and SVOC mass. The sum of particle and SVOC mass equals 100%.
Figure 2 Loading plot showing the groupings among the 15 toxicity measurements. Measurements grouped together responded similarly to the exhaust samples, and their proximity reflects the degree of similarity of responses. Separation of mutagenicity and lung toxicity groups suggested that they responded to different chemical components.
Figure 3 Goodness of fit (R2) and prediction (Q2) from the PLS model (68 variables and 11 responses).
Figure 4 Observed versus predicted histologic inflammation, showing good predictive performance of the PLS model. R2= 0.97.
Figure 5 Loading plot showing the lung toxicity and 68 chemical components for the first and second PLS components. Proximity to the lung toxicity responses reflects the strength of association (degree of covariance) of individual chemical components to the response. See Table 2 for abbreviations. aCarbon analysis fractions.
Figure 6 Observed versus predicted mutagenicity of TA100 (without S9), showing good predictive performance of the PLS model. R2= 0.98.
Figure 7 Loading plot showing the mutagenicity and chemical components for the first and second PLS components. Proximity to the mutagenicity responses reflects the strength of association (degree of covariance) of individual chemical components to the responses.
Table 1 Summary description of engine samples, chemical measurements, and toxicity measurements.
Exhaust samplesa
Gasoline
G
G30
WG
BG
Diesel
Diesel
D30
HD
Chemical measurements (by class)b
Total particle and SVOC mass
Inorganic ions
Carbon (organic, elemental, thermal fractions)
Transition metals
Other metals
Metalloids
Nonmetal elements
Bicyclic or two-ring PAH organic compounds
Tricyclic or three-ring PAH organic compounds
Tetracyclic or four-ring PAH organic compounds
Five-ring PAH organic compounds
Six-ring PAH organic compounds
Seven-ring PAH organic compounds
Oxygenated PAH organic compounds (including quinones)
Sulfur-containing PAHs
Nitro-PAH organic compounds
Hopane/sterane organic compounds (unique to oil)
Toxicity measurementsc
Lung toxicity
Inflammation potency estimates
Lavage macrophages
Lavage neutrophils
Total lavage leukocytes
Histopathologic inflammation
Macrophage inflammatory protein-2
Cytotoxicity potency estimates
Lactate dehydrogenase
Lavage protein
Histopathologic tissue injury
Parenchymal change potency estimates
Histopathologic structural remodeling
General toxicity potency estimates
Total histopathology
Lung weight as percentage of body weight
Bacterial mutagenicity
Mutagenicity potency estimates
TA98 − S9
TA98 + S9
TA100 − S9
TA100 + S9
a Described by Whitney (2000).
b Described by Zielinska et al. (2004).
c Described by Seagrave et al. (2002).
Table 2 Chemical/physical variables (X-matrix) included in the final PLS model for lung toxicity.
Chemical/physical component Name used for loading plot
Particulate mass PM
Semivolatile organic mass SVOC
Nitrate NO3
Sulfate SO4
Ammonium NH4
Particle organic carbon mass Particle organic carbon
Elemental carbon EC
Total carbon TC
1st carbon thermal fraction O1TC
2nd carbon thermal fraction O2TC
3rd carbon thermal fraction O3TC
4th carbon thermal fraction O4TC
5th carbon thermal fraction OPTC
6th carbon thermal fraction E1TC
7th carbon thermal fraction E2TC
8th carbon thermal fraction E3TC
Transition metals Trans. met
Alkali earth metals Earth met
Metalloids Metalloids
Nonmetal elements Non-met
Group 3A metals 3A metals
Group 4A metals 4A metals
Total PAHs Total PAH
SVOC PAHs SVOC PAH
PM PAHs PM PAH
Two-ring PAHs 2-ring PAH
Three-ring PAHs 3-ring PAH
Four-ring PAHs 4-ring PAH
Five-ring PAHs 5-ring PAH
> Five-ring PAHs > 5-ring PAH
Nitro-PAHs Nitro-PAH
Oxygenated PAHs Oxy-PAH
C27-20S-13β(H),17α(H)-diasterane S1
C27-20R-13β(H),17α(H)-diasterane S2
C27-20S-13α(H),17β(H)-diasterane S3
C27-20R-13α(H),17β(H)-diasterane S4
C28-20S-13β(H),17α(H)-diasterane S5
C27-20S-5α(H),14α(H)-cholestane S6
C27-20R-5α(H),14β(H)-cholestane S7
C27-20S-5α(H),14β(H),17β(H)-cholestane S8
C27-20R-5α(H),14α(H),17α(H)-cholestane S9
C28-20S-5α(H),14α(H),17α(H)-ergostane S10
C28-20R-5α(H),14β(H),17β(H)-ergostane S11
C29-20R-13α(H),17β(H)-diasterane S12
C27-tetracyclic terpane S13
C28-20R-5α(H),14α(H),17α(H)-ergostane S14
C27-tetracyclic terpane II S15
C28-tetracyclic terpane S16
C29-20S-5α(H),14α(H),17α(H)-stigmastane S17
C28-tetracyclic terpane II S18
C29-20R-5α(H),14β(H),17β(H)-stigmastane S19
C29-20S-5α(H),14β(H),17β(H)-stigmastane S20
18α(H),21β(H)-22,29,30-trisnorhopane H1
17α(H),18α(H),21β(H)-25,28,30-trisnorhopane H2
C29-20R-5α(H),14α(H),17α(H)-stigmastane H3
17α(H),21β(H)-22,29,30-trisnorhopane H4
17α(H),18α(H),21β(H)-28,30-bisnorhopane H5
17α(H),21β(H)-30-norhopane H6
18α(H),21β(H)-30-norneohopane H7
17α(H),21β(H)-hopane H8
17β(H),21α(H)-hopane H9
22S-17α(H),21β(H)-30-homohopane H10
22R-17α(H),21β(H)-30-homohopane H11
17β(H),21β(H)-hopane H12
22S-17α(H),21β(H)-30,31-bishomohopane H13
22R-17α(H),21β(H)-30,31-bishomohopane H14
22S-17α(H),21β(H)-30,31,32-trishomohopane H15
22R-17α(H),21β(H)-30,31,32-trishomohopane H16
Table 3 Chemical/physical variables (X-matrix) used for final PLS model of mutagenicity.
Oxygenated PAH organic compounds
9-Fluorenone
Xanthone
Acenaphthenequinone
Perinaphthenone
Anthraquinone
9-Anthraldehyde
Benz[a]anthracene-7,12-dione
Nitro-PAH organic compounds
1-Nitronaphthalene
2-Nitronaphthalene
2-Methyl-1-nitronaphthalene
α-Methyl-1-nitronaphthalene
β-Methyl-1-nitronaphthalene
2-Nitrobiphenyl
4-Nitrobiphenyl
5-Nitroacenaphthene
9-Nitroanthracene
2-Nitrofluoranthene
3-Nitrofluoranthene
1-Nitropyrene
7-Nitrobenz[a]anthracene
6-Nitrochrysene
6-Nitrobenzo[a]pyrene
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7195ehp0112-00153915531439ResearchArticlesLung Cancer in Railroad Workers Exposed to Diesel Exhaust Garshick Eric 12Laden Francine 23Hart Jaime E. 23Rosner Bernard 2Smith Thomas J. 3Dockery Douglas W. 23Speizer Frank E. 231Pulmonary and Critical Care Medicine Section, Medical Service, Veterans Affairs Boston Healthcare System, Boston, Massachusetts, USA2Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA3Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USAAddress correspondence to E. Garshick, Pulmonary and Critical Care Medicine Section, 1400 VFW Parkway, West Roxbury, MA 02132 USA. Telephone: (617) 323-7700 ext. 5536. Fax: (617) 363-5670. E-mail:
[email protected] thank M. Jacobson Canner and H. Guan for programming assistance; E. Larkin and S. Campbell for data management; L. Stayner, National Institute for Occupational Safety and Health (NIOSH), for state of death from Social Security records; D. Gagnon for analysis suggestions; and the Railroad Retirement Board, particularly E. Binkus and A. Alden.
This work was supported by NIOSH grant CCR115818 and National Cancer Institute grant CA79725.
The authors declare they have no competing financial interests.
11 2004 5 8 2004 112 15 1539 1543 20 4 2004 5 8 2004 Publication 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. Diesel exhaust has been suspected to be a lung carcinogen. The assessment of this lung cancer risk has been limited by lack of studies of exposed workers followed for many years. In this study, we assessed lung cancer mortality in 54,973 U.S. railroad workers between 1959 and 1996 (38 years). By 1959, the U.S. railroad industry had largely converted from coal-fired to diesel-powered locomotives. We obtained work histories from the U.S. Railroad Retirement Board, and ascertained mortality using Railroad Retirement Board, Social Security, and Health Care Financing Administration records. Cause of death was obtained from the National Death Index and death certificates. There were 43,593 total deaths including 4,351 lung cancer deaths. Adjusting for a healthy worker survivor effect and age, railroad workers in jobs associated with operating trains had a relative risk of lung cancer mortality of 1.40 (95% confidence interval, 1.30–1.51). Lung cancer mortality did not increase with increasing years of work in these jobs. Lung cancer mortality was elevated in jobs associated with work on trains powered by diesel locomotives. Although a contribution from exposure to coal combustion products before 1959 cannot be excluded, these results suggest that exposure to diesel exhaust contributed to lung cancer mortality in this cohort.
diesel exhaustlung canceroccupational exposure
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Since the 1970s, there has been concern that inhalation of diesel exhaust may cause lung cancer in humans. Diesel exhaust particles are respirable and contain mutagenic and carcinogenic compounds on a carbonaceous core (Schenker 1980). In > 35 studies of workers exposed to freshly generated diesel exhaust, an excess lung cancer risk in the range of 20–50% has been observed (Bhatia et al. 1998; Lipsett and Campleman 1999). However, the nature of the risk between exposure to diesel exhaust and human lung cancer is still being debated (Diesel Epidemiology Working Group 2002). Only limited information is available linking job title to duration and intensity of exposure (Garshick et al. 1987a, 1988; Steenland et al. 1990; Zaebst et al. 1991). Few studies have had occupational histories and follow-up over enough years to assess risk because lung cancer may develop only after many years of exposure and years of latency.
Exposure to high levels of diesel particles has produced lung cancer in rats but has not consistently produced lung cancer in other rodent species (Heinrich et al. 1986; Mauderly et al. 1987). However, lung cancer also has been produced in rats exposed to high levels of fine particles devoid of organics (Borm et al. 2004; Nikula et al. 1995). In these studies, particle clearance mechanisms became overloaded, and pulmonary inflammatory changes were noted. These responses have not been associated with lung cancer in humans. Therefore, the U.S. Environmental Protection Agency (EPA) has described the risk of diesel-exhaust–related lung cancer based on human epidemiologic studies (U.S. EPA 2002).
After World War II, there was a rapid transition by U.S. railroads from steam to diesel locomotives. In 1946, 10% of the locomotives in service were diesel powered, but by 1959 the proportion had increased to 95% diesel (U.S. Department of Labor, Bureau of Labor Statistics 1972). We previously published a retrospective cohort mortality study of lung cancer among 55,407 railroad workers with 10–20 years of work experience in 1959 who had mortality follow-up through 1980. Occupational exposures to diesel were categorized based on an industrial hygiene survey (Woskie et al. 1988a, 1988b). Workers 40–44 years of age, with the greatest likelihood of diesel exposure, had a relative risk (RR) of lung cancer mortality of 1.45 [95% confidence interval (CI), 1.11–1.89] (Garshick et al. 1988). Older workers had a lower lung cancer risk. It was later recognized that the mortality ascertainment was incomplete between 1977 and 1980. Similar results were obtained limiting follow-up through 1976 (Crump 1999; Larkin et al. 2000). Follow-up of this cohort has continued, and in this article we present an assessment of lung cancer mortality risk over a 38-year period (through 1996) with improved mortality information and additional work history data.
Materials and Methods
Population description.
The U.S. Railroad Retirement Board (RRB) has maintained a computerized record of work history since 1959. In 1981, men 40–64 years of age with 10–20 years of railroad service in 1959 were selected for data extraction. Based on job in 1959, we identified 39 job codes with exposure to diesel exhaust characterized during an industrial hygiene survey (Woskie et al. 1988a, 1988b). We sampled 56,208 workers in these job codes, including a) every third engineer (engineers and firemen), b) every third conductor (conductors, brakemen, and hostlers), c) all shop workers (shop supervisors, machinists, and electricians), and d ) a referent group of less-exposed workers (ticket agents, station agents, and signal maintainers, and every fourth clerk). By design, approximately 75% of the workers in the sample were in diesel-exposed jobs and 25% were in low- or no-exposure jobs. The RRB provided a listing of yearly job code, months of railroad service, and mortality information through 1980. The final analytic cohort included 55,407 white males. We later determined that the job codes of shop workers were not specific to work in areas with locomotive exhaust, so diesel exposures of these job codes could not be specified. In addition, it was possible that some of these workers had previous asbestos exposure in steam engine repair shops. Therefore, we considered shop workers separately from the “exposed group” of conductors and engineers. Other workers with potential asbestos exposure were the hostlers (n = 779 in 1959). These workers may have had exposure while driving locomotives in and out of repair facilities during the steam locomotive era. Analyses were conducted with and without these workers based on job in 1959.
Work history and mortality update through 1996.
The Brigham and Women’s Hospital and Veterans Affairs Boston Healthcare System institutional review boards approved the protocol. The RRB provided updated work history information through 1996 for workers meeting the original extraction criteria, and linkage with the original database was possible for 55,016 subjects (99.2%). Updated mortality information was determined from RRB records, the Social Security Death Master (SSDM), and Health Care Financing Administration records. We excluded 43 subjects with < 10 years of railroad service or found to have no job reported on enrollment in 1959, leaving 54,973 subjects.
Cause of death determination.
For subjects known to have died, cause of death [International Classification of Diseases, 9th Revision (ICD-9); World Health Organization (WHO) 1978] was obtained from a search of the National Death Index (NDI) between 1979 (the first year NDI was available) and 1996. For subjects without a valid match and for all others without specific cause of death, efforts were made to obtain a death certificate. State of death was obtained from the SSDM and from a manual search of RRB records. Underlying cause of death was coded using computer packages from the National Center for Health Statistics (1997a, 1997b, 1997c). A nosologist blinded to exposure status coded death certificates not accepted by the program. ICD-8 (8th Revision; WHO 1967) cause of death from the original 1959–1980 database was converted to ICD-9 codes based on three-digit category. To include all possible cases, lung cancer mortality (ICD-9 code 162) was defined by the underlying cause of death or by lung cancer appearing elsewhere on the death certificate or NDI record. This is appropriate because lung cancer is usually rapidly fatal after diagnosis.
Statistical analysis.
To evaluate completeness of mortality ascertainment, standardized mortality ratios (SMRs) were calculated as the ratio of the observed deaths compared with the number of deaths expected based on calendar year and 5-year age-specific mortality rates for U.S. white men. Proportional hazard analyses (SAS, version 8; SAS Institute, Inc., Cary, NC) assessed lung cancer mortality with calendar month as the time axis. Person-time was calculated from 1 January 1959 to date of death or to 31 December 1996, whichever was earlier. Hazard ratios (referred to as RRs) and 95% CIs are presented separately for engineers, conductors, and shop worker groups and for the engineer and conductor groups combined compared with unexposed workers based on job code on entry into the study in 1959. Because years of work in a diesel-exposed job were related to age in 1959 (Table 1), we assessed effect modification using interaction terms with 5-year age groups and exposure-job group in 1959, as in our previous study (Garshick et al. 1988). To account for a healthy worker survivor effect, we included time-varying variables for total years worked and for years off work (usually time after retirement) in survival models. Age was controlled by stratification in 1-year categories. First year of work was available for workers starting in 1947 or later and was estimated based on total months of service for those employed before 1947.
The association of lung cancer mortality with duration of exposure as a surrogate for cumulative dose was assessed as a time-varying covariate, based on yearly job code and service months, starting in 1959 in the combined engineer and conductor groups, and was grouped in 5-year exposure categories. The classification of exposure after 1959 in jobs for which no industrial hygiene sampling was done (4.6% of yearly job codes) was based on similarities in work locations and duties to the original 39 job codes. Models with 5-, 10-, and 15-year exposure lags were assessed, meaning that exposure in the year of death and in the previous 4, 9, and 14 years, respectively, was not included in the calculation of exposure duration. To assess appropriateness of each exposure lag, we also assessed the association of lung cancer mortality with exposure 5, 10, and 15 years before death. An indicator variable was included to account for work in a shop job code between 1959 and 1996.
Results
Cohort mortality.
There were 43,593 deaths between 1959 and 1996 in the 1,364,382 person-years of follow-up. There were 21,639 deaths in the original analysis period (1959–1980) and 21,954 deaths in the additional follow-up period (1981–1996). Of 2,302 deaths from 1959 to 1980 not identified in the original analysis, 98% occurred between 1977 and 1980, the time period with known incomplete mortality ascertainment. Cause of death was defined for 21,116 (98%) deaths from 1959 to 1980 and for 21,670 (99%) deaths from 1981 to 1996. The major causes were circulatory system diseases (ICD-9 codes 390–459, n = 21,779, 50%), malignant neoplasms (ICD-9 codes 140–208, n = 10,558, 24%), and respiratory system diseases (ICD-9 codes 460–519, n = 3,878, 9%). Lung cancer was identified as the underlying cause in 4,021 deaths and as a contributing cause in 330 deaths (4,351 total lung cancer deaths).
The SMR in 1959 was 0.81, consistent with a healthy worker effect (Choi 1992; Li and Sung 1999). Over time, the SMR increased such that by 1967, the yearly SMR had risen to 1.01. Overall, the SMR for all deaths was 1.01, indicating that death ascertainment was effectively complete.
Job histories.
The distribution of lung cancer deaths and years worked by age and job group is presented in Table 1. Career paths within the railroad industry are stable. Over the entire 38 years of follow-up, only 126 workers transferred into engineer or conductor job codes and 284 workers transferred into shop worker job codes. Of unexposed workers in 1959, 97% remained unexposed through the follow-up period. As expected, younger workers at study entry had greater potential for longer years of work (i.e., exposure) compared with older workers.
Total mortality.
Total mortality was elevated for exposed workers, defined as working as either engineers or conductors in 1959, compared with unexposed workers (clerks and signal maintainers; RR = 1.17; 95% CI, 1.14–1.20), adjusting for total years worked, time since last worked, and attained age. The RR of mortality due to circulatory system diseases was 1.13 (95% CI, 1.09–1.16); that due to all respiratory system diseases, including chronic obstructive pulmonary disease (COPD) and allied conditions (ICD-9 490–496), was 1.31 (95% CI, 1.21–1.42); and that due to COPD and allied conditions alone was 1.41 (95% CI, 1.27–1.55). The RR due to lung cancer was 1.40 (95% CI, 1.30–1.51). We explore the excess risk due to lung cancer in further detail below.
Lung cancer mortality.
There was some evidence of effect modification of the lung cancer–diesel exposure relationship by age at entry to the study. Defining exposure by job title in 1959, independently engineers and conductors 40–44, 45–49, 50–54, and 55–59 years of age at study entry had elevated lung cancer risks (Table 2). Among workers 60–64 years of age at entry, the risk was elevated only in the conductor group but not statistically significantly. When the exposed groups were combined, the risk was greatest among the youngest workers (40–44 years of age at study entry) but still significantly elevated for all exposed workers < 60 years of age at study entry. The relative risk for shop workers was elevated only in the 55–59 year age group.
Lung cancer mortality was inversely related to total years worked (RR = 0.97; 95% CI, 0.96–0.98 per year). Relative risk of dying was greatest within the first year after leaving work (RR = 6.14; 95% CI, 5.27–7.14) and decreased with subsequent years, ranging from 2.98 (95% CI, 2.57–3.45) for 2–5 years, 2.74 (95% CI, 2.31–3.26) for 6–10 years, 2.54 (95% CI, 2.05–3.14) for 11–15 years, 2.39 (95% CI, 1.85–3.10) for 16–20 years, and 2.34 (95% CI, 1.72–3.31) for ≥21 years off work. There was no significant effect modification of years on or off work based on diesel exposure (data not shown).
We assessed the relationship between cumulative years of work as a surrogate for diesel exhaust exposure and lung cancer risk, controlling for attained age, any shop work, total years worked, and time since last worked in models without an exposure lag, and with lags of 5, 10, and 15 years (Table 3). Lung cancer mortality was significantly associated with a diesel exhaust exposed job group regardless of the exposure lag model, but risk did not increase with years of exposure. Restriction of the cohort to subjects who survived beyond the last year worked and stratifying on retirement time gave similar results (results not shown).
To assess the significance of exposure in the years before death on lung cancer risk, we included an indicator variable for work in the 5, 10, and 15 years before death in the exposure lag models in Table 3. In the 5 years before death, lung cancer mortality was not significantly elevated compared with unexposed workers (RR = 1.14; 95% CI, 0.85–1.54). RR was 1.26 (95% CI, 1.06–1.50) for exposure within 10 years before death and 1.40 (95% CI, 1.23–1.59) for exposure within 15 years before death. These results suggest that it is appropriate to exclude exposure in the 5 years before death in the assessment of lung cancer mortality. Assuming a 5-year exposure lag before death, lung cancer mortality associated with any exposure after 1959 was 1.40 (95% CI, 1.30–1.51), the same result obtained based on work in an exposed job based on job at entry.
We considered the possibility that lung cancer mortality risk varied based on selection of the reference group. Signal maintainers (n = 3,536 based on job at entry; 259 lung cancer deaths) who worked on the track were considered separately from ticket agents, station agents, and clerks, who worked in offices (n = 10,411 based on job at entry; 704 lung cancer deaths), thus defining a non-office-based, blue-collar control group. Regardless of which comparison group was used, similar lung cancer mortality risks were observed. Excluding the hostlers, potentially exposed to asbestos, from analysis did not change the results.
Discussion
We present a retrospective assessment of lung cancer mortality for 38 years of follow-up in a large cohort of railroad workers, finding elevated risk among engineers, firemen, conductors, and brakemen, job categories identified as diesel exposed. Disregarding exposure in the 5 years before death, the RR for these workers compared with workers without regular work in an exposed job was 1.40 (95% CI, 1.30–1.51). Unlike the original findings of greatest risk in younger workers, lung cancer mortality was elevated to a similar extent regardless of age at entry (in 1959) except in workers 60–64 years of age. Thus, excess risk was not limited to workers with the greatest opportunity for exposure because of their being younger at the start of the diesel era. Finally, there was no evidence of an increased risk with increasing years of work (the exposure surrogate) in a job with exposure to diesel exhaust.
Our observation of lung cancer risk is similar to the risk noted by others in the literature. In > 35 studies of workers with occupational exposure to diesel exhaust, excess risk of lung cancer is consistently elevated by 20–50% (reviewed in Bhatia et al. 1998; Lipsett and Campleman 1999). Most occupational studies rely on a single report of job title to define exposure. In this study, job title was available for each year of follow-up, and jobs with exposure to diesel emissions were defined by an industrial hygiene survey. These results are similar to smoking-adjusted RRs attributable to fine particulate air pollution on lung cancer in prospective population-based cohorts (Dockery et al. 1993; Pope et al. 2002) and risk of lung cancer attributable to vehicle exhausts in urban settings (Nyberg et al. 2000). Effects for cardiovascular and respiratory disease mortality are also consistent with observations reported by population-based studies (Dockery et al. 1993; Pope et al. 2002).
Although we originally reported that lung cancer risk increased with increasing years of work in diesel-exposed jobs (Garshick et al. 1988), subsequent reanalyses of these data, with adjustment for attained age, indicated decreased risk with more years worked (Crump 1999; Health Effects Institute 1999). This inverse association with exposure duration could be explained by a healthy worker survivor effect. Analysis in this updated cohort with longer follow-up also indicates that lung cancer mortality is inversely related to total years worked. The possibility that the healthy worker survivor effect influences the assessment of mortality had not been considered previously. Although methods for controlling for the healthy worker survivor effect have been proposed, it is uncertain whether full adjustment by statistical methods is possible. It was not possible to implement methods suggested by Robins (1987) because there was little change in exposure status, and retirement patterns were stable. Other methods to adjust for healthy worker effects (Arrighi and Hertz-Picciotto 1993, 1994) consider employment status and exposure lag models to exclude recent exposure. With overall employment duration and employment status considered, the relationship between lung cancer risk and years of work in a diesel-exposed job was elevated regardless of exposure duration (Table 3). Restriction of the cohort to subjects who survived beyond the last year worked and stratification on retirement time also gave similar results. We also conducted alternative survival analyses (compared with proportional hazards methodology) employing recently developed techniques in which time to an event is modeled using “first hitting time” methodology (Lee and Whitmore 2003; Lee et al. 2004). Using these methods, there was evidence of a healthy worker survivor effect, with an elevated risk of lung cancer mortality among train crews (Lee et al. 2004).
Exposures before 1959 and changes in exposure patterns could also modify a relationship between years of work and lung cancer mortality. An expectation of increasing risk with years of exposure implicitly assumes that the exposure intensity is approximately constant across years. Diesel locomotive emissions changed throughout the follow-up period. Explicit exposure data are not available, but the first diesel engines (1940s through 1950s) were said to be “smokier” than later locomotives (Woskie et al. 1988b). Cleaner locomotives were introduced in the early 1960s and the 1980s. Although diesel engines are known to produce mainly fine and ultrafine particles, similar information is not available on coal-fired locomotives. Temporal changes in diesel and other combustion-related emissions might contribute to the lack of an exposure–response relationship based on duration of exposure in the train crews.
Because all workers were employed in 1959 and had exposures in the previous 10–20 years, we could not assess whether work exclusively during the diesel or steam locomotive era or with early diesel locomotives differentially influenced mortality. However, in a case–control study using RRB records to determine deaths in 1981–1982, workers > 65 years at death were exposed mainly to steam engine emissions, and younger workers mainly to diesel engine emissions. In the older group, work in diesel-exposed jobs was not associated with lung cancer mortality, whereas the RR was significantly elevated for the younger group. In the present study, the oldest workers (60–64 years of age at study entry) had the fewest years of work after 1959 and the lowest mortality due to lung cancer. These results suggest that introduction of diesel locomotives significantly contributed to lung cancer mortality in the cohort.
Small RRs may be affected by uncontrolled confounding, such as differences in cigarette smoking habits in subjects with and without diesel exposure. In this retrospective cohort, individual data on smoking history are not available. To minimize the possible effect of uncontrolled confounding by smoking, efforts were made to include only workers of similar socioeconomic class, a known correlate of smoking habits (Brackbill et al. 1988; Stellman et al. 1988). Further, estimates were similar when the reference group was restricted to signal maintainers, potentially a more blue-collar unexposed group. Smoking rates vary by birth cohort (Burns et al. 1997). However, all analyses are stratified by age; thus, birth cohort is controlled for.
In our previous case–control study using RRB records (Garshick et al. 1987a), smoking history was obtained from next of kin, and crude and smoking-adjusted effects of exposure were similar. With the distribution of job-specific smoking habits from the case–control study and a survey of 514 white male workers employed by a small railroad in 1982 (Garshick et al. 1987b), we calculated age- and job-specific smoking adjustment factors using Schlesselman and Axelson methods (Axelson and Steenland 1988; Larkin et al. 2000; Schlesselman 1978). These factors, the ratio (diesel exposed:unexposed) of literature-based lung cancer risks weighted by job-specific smoking behavior generally ranged from 1.1 to 1.2 (Larkin et al. 2000). Other investigators have reported similar factors (Blair et al. 1985; Levin et al. 1990; Siemiatycki et al. 1988). Dividing the observed RR for lung cancer for the present study by these factors attenuated the RR to between 1.17 and 1.27. These estimates are consistent with other literature-based smoking-adjusted risks attributable to diesel exhaust, traffic emissions, and air pollution (Dockery et al. 1993; Nyberg et al. 2000; Pope et al. 2002; Steenland et al. 1990). This indirect method is limited in adjusting for smoking by assuming no interaction between diesel exposure and smoking, but there are insufficient data to assess this possibility.
Respiratory disease mortality, including from COPD and allied conditions, was also associated with exposure. The predominant cause of these diseases is cigarette smoking, possibly providing evidence of confounding by smoking in our lung cancer analyses. However, smoking-adjusted cohort studies show that occupational exposures to dusts and fumes are also associated with chronic respiratory symptoms and airflow obstruction (Garshick et al. 1996, 2003; Hnizdo et al. 2002). Studies of workers specifically exposed to diesel exhaust indicate that there is an increase in respiratory symptoms and a reduction in pulmonary function with exposures (U.S. EPA 2002). Controlled studies of human exposures to diesel exhaust and to other fine particles results in pulmonary inflammatory changes (Ghio et al. 2000; Salvi et al. 1999). Therefore, exposure to diesel exhaust from operating trains may in fact lead to an increased risk of chronic respiratory disease mortality, independent of smoking.
Factors other than smoking that might modify the risk of lung cancer seem unlikely to contribute further uncertainty to these results (Alavanja et al. 2001; Henley et al. 2002; Olson et al. 2002). These factors are much less significant than smoking and not expected to be related to exposure. Controlling for the healthy survivor effect by considering the ability to work (years of work) and live into retirement (time off work) in the regression models also may reduce uncontrolled confounding by other lifestyle-related factors and might further address adjustment for smoking behavior. Death certificates were used to identify causes of death. Death certificates may overascertain rather than underascertain primary lung cancer (Bauer and Robbins 1972; Goldman et al. 1983; Jimenez et al. 1975; Kircher et al. 1985; Percy et al. 1981; Rosenblatt et al. 1971). This type of misclassification is likely to be random with respect to exposure and would make the effect of exposure harder to detect.
Conclusion
Lung cancer mortality in workers in diesel-exposed jobs was elevated in this cohort. It is unlikely that this association is explained by uncontrolled confounding. We believe there was no relationship between years of exposure and lung cancer risk because of the healthy worker survivor effect, the lack of information on historical changes in exposure, and the potential contribution of coal combustion products before the transition to diesel. However, these results indicate that the association between diesel exhaust exposure and lung cancer is real. These results along with previous studies of lung cancer and diesel exhaust support current efforts to reduce emissions in both occupational and general environmental settings. Studies designed to provide quantitative exposure estimates are needed to better quantify health risks, including those related to more contemporary diesel engines used in light- and heavy-duty diesel on-road vehicles.
Table 1 Distribution of the cohort by exposure categories, duration of service, and exposure presented by age (years) at baseline (1959).
Characteristic 40–44 (n = 19,794) 45–49 (n = 13,874) 50–54 (n = 9,820) 55–59 (n = 7,216) 60–64 (n = 4,269) Total (n = 54,973)
1959 job groups
Unexposed 4,911 3,132 2,601 1,989 1,314 13,947
Engineersa 4,002 2,834 1,855 1,394 836 10,921
Conductorsb 7,204 5,074 2,898 1,916 971 18,063
Shopc 3,677 2,834 2,466 1,917 1,148 12,042
Lung cancer deaths
Unexposed 292 241 199 138 93 963
Engineers 328 257 189 113 41 928
Conductors 556 475 267 173 80 1,551
Shop 241 220 182 184 82 909
Total 1,417 1,193 837 608 296 4,351
Years of exposure
1959 to retirementd
0 (unexposed) 4,747 3,016 2,530 1,942 1,302 13,537
1–< 5 984 831 689 842 1,115 4,461
5–< 10 1,082 1,005 946 1,694 647 5,374
10–< 15 1,450 1,727 2,670 791 47 6,685
15–< 20 4,519 4,117 451 6 0 9,093
≥20 3,219 267 11 0 0 3,497
Any shop 3,793 2,911 2,523 1,941 1,158 12,326
Duration of employment
Retirement year
Median 1977 1974 1971 1966 1962
IQR 1972–1979 1970–1975 1967–1972 1964–1969 1961–1965
Hire date > 1945 (%) 28.9 22.1 20.5 17.2 13.5
Years of service (mean ± SD) 32.9 ± 6.4 30.5 ± 5.7 27.5 ± 7.8 24.7 ± 4.0 22.0 ± 3.4
IQR, interquartile range.
a Engineers, firemen.
b Conductors, brakemen, hostlers.
c Shop workers.
d Years of work in engineer or conductor group; “any shop” refers to any worker in a shop-worker job between 1959 and retirement.
Table 2 Interaction of 5-year age group (years) and job title at study entry in 1959 and RRs of lung cancer mortality 1959–1996 for engineers, conductors, and shop workers compared to unexposed workers.
40–44 45–49 50–54 55–59 60–64
Unexposed
Cases 292 241 199 138 93
Person-years 148,701 83,949 58,483 36,992 20,072
RR Reference Reference Reference Reference Reference
Engineer
Cases 328 257 189 113 41
Person-years 116,129 72,820 40,190 25,261 13,041
RR (95% CI) 1.59 (1.35–1.86) 1.36 (1.14–1.63) 1.51 (1.24–1.84) 1.27 (0.99–1.63) 0.71 (0.49–1.02)
Conductor
Cases 556 475 267 173 80
Person-years 209,897 130,084 63,127 34,421 14,219
RR (95% CI) 1.43 (1.24–1.65) 1.37 (1.17–1.60) 1.32 (1.10–1.58) 1.38 (1.11–1.73) 1.24 (0.92–1.67)
Shop worker
Cases 241 220 182 184 82
Person-years 109,670 75,286 56,187 36,902 18,952
RR (95% CI) 1.10 (0.93–1.31) 0.99 (0.82–1.18) 0.92 (0.75–1.13) 1.31 (1.05–1.63) 0.88 (0.65–1.18)
Engineers and conductor groups combined
Cases 884 732 456 286 121
Person-years 326,026 202,903 103,317 59,682 27,260
RR (95% CI) 1.49 (1.30–1.70) 1.37 (1.18–1.58) 1.39 (1.18–1.64) 1.34 (1.09–1.64) 0.99 (0.75–1.30)
Models are adjusted for age, years of employment, and time off work as time-dependent covariates.
Table 3 RR of lung cancer mortality based on cumulative years of work in an engineer or conductor job group, adjusting for age and work in any shop category.
Exposure lag Unexposed 0 to < 5 years 5 to < 10 years 10 to < 15 years 15 to < 20 years ≥20 years
None
Cases 922 334 468 665 782 241
Person-years 338,088 171,943 172,565 158,565 166,085 52,493
RR (95% CI) Reference 1.35 (1.17–1.54) 1.43 (1.26–1.60) 1.58 (1.42–1.75) 1.30 (1.17–1.44) 1.24 (1.05–1.45)
5 years
Cases 1,008 391 484 618 707 204
Person-years 480,468 150,143 145,607 126,189 121,884 35,448
RR (95% CI) Reference 1.41 (1.24–1.61) 1.39 (1.23–1.56) 1.51 (1.35–1.68) 1.33 (1.19–1.49) 1.31 (1.10–1.56)
10 years
Cases 1,211 449 479 587 544 142
Person-years 613,828 127,484 119,723 96,675 82,041 19,986
RR (95% CI) Reference 1.49 (1.31–1.69) 1.27 (1.12–1.44) 1.50 (1.33–1.68) 1.29 (1.14–1.46) 1.50 (1.22–1.85)
15 years
Cases 1,532 456 511 499 360 54
Person-years 733,767 105,240 95,345 70,145 48,102 7,141
RR (95% CI) Reference 1.31 (1.15–1.49) 1.30 (1.15–1.47) 1.38 (1.22–1.57) 1.34 (1.15–1.58) 1.40 (1.02–1.92)
Models are adjusted for age, years of employment, and time off work as time-dependent covariates. In the exposure lag models, work in an engineer or conductor job group 5, 10, and 15 years before death is not included as exposure.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7155ehp0112-00154415531440ResearchArticlesIn Vivo Imaging of Activated Estrogen Receptors in Utero by Estrogens and Bisphenol A Lemmen Josephine G. 1*Arends Roel J. 2van der Saag Paul T. 1van der Burg Bart 1**1Hubrecht Laboratory, Netherlands Institute for Developmental Biology, Uppsalalaan, Utrecht, the Netherlands2Department of Pharmacology, NV Organon, Oss, The NetherlandsAddress correspondence to P. van der Saag, Hubrecht Laboratory, Netherlands Institute for Developmental Biology, Uppsalalaan 8, 3584 CT Utrecht, the Netherlands. Telephone: 31-30-2121800. Fax: 31-30-2516464. E-mail:
[email protected]*Current address: Laboratory of Reproductive Biology, Juliane Marie Center for Children, Women and Reproduction, University Hospital of Copenhagen, Copenhagen, Denmark.
**Current address: BioDetection Systems BV, Badhuisweg 3, 1031 CM Amsterdam, the Netherlands.
This study was supported financially by the European Union fifth framework: QLK4-2000-00305, “The Impact of Developmental Exposure to Weak (Environmental) Estrogens on the Incidence of Diseases in Target Organs Later in Life.” It does not necessarily reflect its views and in no way anticipates the commission’s future policy in this area.
The authors declare they have no competing financial interests.
11 2004 21 7 2004 112 15 1544 1549 5 4 2004 21 7 2004 Publication 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. Environmental estrogens are of particular concern when exposure occurs during embryonic development. Although there are good models to study estrogenic activity of chemicals in adult animals, developmental exposure is much more difficult to test. The weak estrogenic activity of the environmental estrogen bisphenol A (BPA) in embryos is controversial. We have recently generated transgenic mice that carry a reporter construct with estrogen-responsive elements coupled to luciferase. We show that, using this in vivo model in combination with the IVIS imaging system, activation of estrogen receptors (ERs) by maternally applied BPA and other estrogens can be detected in living embryos in utero. Eight hours after exposure to 1 mg/kg BPA, ER transactivation could be significantly induced in the embryos. This was more potent than would be estimated from in vitro assays, although its intrinsic activity is still lower than that of diethylstilbestrol and 17β-estradiol dipropionate. On the basis of these results, we conclude that the estrogenic potency of BPA estimated using in vitro assays might underestimate its estrogenic potential in embryos.
bisphenol Aestrogen receptorin uteroin vivoreporter mice
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There is concern about compounds in the environment that could partially mimic the effects of estrogen, which could possibly explain the rising incidence of reproductive abnormalities and certain cancers (Miller and Sharpe 1998). These environmental estrogens are a structurally very diverse group of compounds that can only be identified as environmental estrogens by carrying out functional studies. One compound of concern is bisphenol A (BPA), a monomer component of polycarbonate plastics and epoxy resins. Humans are exposed to BPA when it leaks from plastic packaging and dental appliances (Feldman 1997), and nanomolar concentrations have been measured in human serum (Takeuchi and Tsutsumi 2002). Recently, it was reported that BPA could cause meiotic aneuploidy when female mice were exposed unintentionally through damaged cage material (Hunt et al. 2003). BPA has been found to possess weak estrogenic properties in in vitro assays with an EC50 (median effective concentration) about 10,000 times less than strong estrogens such as 17β-estradiol (E2) and diethylstilbestrol (DES) (Kim et al. 2001; Kuiper et al. 1998; Perez et al. 1998). However, the in vivo estrogenic potential of BPA can vary depending on animal species or strain studied (Milligan et al. 1998; Steinmetz et al. 1997, 1998). In addition, the end point is very important. It was shown that BPA did induce DNA synthesis in vaginal epithelium of Fischer 334 rats but did not in Sprague-Dawley rats, whereas in both strains BPA increased c-fos mRNA expression (Long et al. 2000). Two classical in vivo assays, the rodent uterine wet weight assay and the vaginal cornification assay, have traditionally been used for testing estrogenic activity of compounds. In these assays, BPA has been found to be active (Ashby and Tinwell 1998; Markey et al. 2001; Papaconstantinou et al. 2000) as well as inactive (Coldham et al. 1997; Gould et al. 1998; Mehmood et al 2000; Tinwell et al. 2000). When found active, its potency was four orders of magnitude lower than that of DES, confirming the weak estrogenicity measured in in vitro assays.
It has been proposed that the developing embryo may be much more susceptible to harmful effects of environmental estrogens compared with adult animals (Bigsby et al. 1999; Dencker and Eriksson 1998; McLachlan 2001; Miller 1983). The best-known example of a developmentally active compound is the synthetic estrogen DES, which was prescribed from the 1940s until the 1970s to prevent miscarriages. Children exposed to DES in utero developed abnormalities and cancer of the reproductive tract, whereas these effects were not found in their mothers (Herbst et al. 1971; McLachlan et al. 1975). Structural similarities between DES and BPA are evident, and it has been suggested that prenatal exposure to BPA may cause abnormalities similar to those elicited by DES (vom Saal et al. 1998). Experiments examining the estrogenic effects of BPA on embryos have led to contradictory findings. Although some studies have reported prostate enlargement in offspring of BPA-exposed mice (Howdeshell et al. 1999), others reported no effect (Ashby et al. 1999; Cagen et al. 1999; Nagao et al. 2002; Welshons et al. 1999). Levels of BPA in amniotic fluid at 15–18 weeks of gestation have been shown to be 5-fold higher than serum levels in both pregnant and nonpregnant women, suggesting a possible accumulation of BPA in the early embryo (Ikezuki et al. 2002), although Domoradzki et al. (2003) could not confirm this in animal experiments. Unfortunately, there is no model in which estrogen effects can be determined directly in embryos.
We have developed an approach, using transgenic reporter mice, that allows us to determine direct activation of estrogen receptor (ER) signaling in embryos. For this, we used our recently established transgenic mice model (Lemmen et al. 2004); direct activation of ERs is detected photometrically by measuring luciferase activity, allowing both quantitative and time-course analysis of estrogen target gene activation in vivo. Other estrogen reporter mice that have been generated do not exclude estrogen-response-element (ERE)–independent activation because of the presence of other promoter sequences (Ciana et al. 2001; Nagel et al. 2001; Toda et al. 2004). In our model, activation of the construct via promoter sites other than the EREs is avoided by using only a minimal TATA box in the construct, resulting in low background activity. Although in natural promoters ERE sequences are often found together with other enhancer sequences and other ways of ER transactivation (e.g., via AP1 sites) are possible, we chose a reductionistic approach, with only ERE sequences in the synthetic promoter used. In the present study, we used this model to examine the ability of BPA—in comparison with known strong estrogens DES and 17β-estradiol dipropionate (EP)—to activate endogenous ERs present in mouse embryos (Lemmen et al. 1999). Surprisingly, we found BPA to be more potent in activating embryonic ERs than would be expected on the basis of its in vitro activity.
Materials and Methods
Transgenic animals.
We used transgenic animals carrying a reporter construct that consists of three estrogen-responsive elements (GAGCTTAGGTCACTGTGACCT) upstream of a minimal human E1B TATA promoter sequence (GGGTATATAAT) coupled to luciferase surrounded by chicken β-globin insulator (Chung et al. 1993) sequences (Lemmen et al. 2004). To obtain transgenic embryos, heterozygote transgenic males from line INS3 were mated with wild-type females (F1 from C57Bl/6J × CBA). Heterozygote males were used so that every litter would also contain wild type embryos, which could serve as an internal negative control. Females were checked daily for the presence of a vaginal plug, and when a plug was detected, that day was designated 0.5 day postcoitum (dpc).
Compounds and exposures.
E2, EP, DES, and BPA were all purchased from Sigma-Aldrich (Roosendaal, The Netherlands). For injections, compounds were dissolved in corn oil (Sigma-Aldrich) at a concentration of 10 mg/mL and then diluted in 1:10 steps in corn oil to the required doses (DES, 10–1,000 μg/kg; EP, 10–10,000 μg/kg; BPA, 10–10,000 μg/kg). Compounds or vehicle were injected intraperitoneally in 13.5 dpc pregnant animals. Animal experiments were performed with approval of the Netherlands Academy of Arts and Sciences Animal Ethics Committee. The IVIS imaging experiments were done with additional approval of the Animal Ethics Committee of NV Organon.
In vivo luciferase measurement.
With the Xenogen IVIS imaging system (Xenogen, Alameda, CA, USA), luciferase activity was monitored in living animals 0, 2, 4, 8, and 24 hr after compound injection. Animals were injected subcutaneously with luciferin (150 μL, 30 mg/mL). After 15 min, the animals were placed in a dark imaging chamber under isoflurane anesthesia. Resulting photon emission from the luciferin/luciferase reaction was detected with a CCD (charge-coupled device) camera. The photon image obtained was superimposed on a normal video image of the mouse with Living Image software (Xenogen). We used IGOR software (WaveMetrics Corp., Lake Oswego, OR, USA) to quantify the photon signal over the area encompassing the embryos. For all pregnant animals, this area was kept of equal size.
Luciferase measurement lysates.
Embryos were isolated either at 8 or 24 hr after compound injection and frozen at –80°C. When embryos were isolated from the amniotic membranes, we kept tails separated and stored at –20°C for subsequent DNA isolation and polymerase chain reaction for presence of the transgene, as described previously (Legler et al. 2000). Only transgenic embryos were used for further luciferase measurement. Subsequently, embryos were thawed on ice and lysis buffer [1% (vol/vol) Triton X-100, 2.5 × 10−2 M glycylglycine, 1.5 × 10−2 M MgSO4, 4 × 10−3 M EGTA, and 1 × 10−3 M dithiothreitol (DTT)] was added. Next, samples were sonicated, the lysate centrifuged, and the supernatant collected. Samples (25 μL in duplicate) were analyzed for luciferase enzyme activity in a luminometer (LUMAC/3M BV, Schaesberg, The Netherlands) with injection of 100 μL luciferin substrate as previously described (Lemmen et al. 2004). Luciferase activity was corrected for protein content as measured with the Bradford assay.
In vitro estrogenic activity assay in stable cell lines.
Stable 239HEK cell lines containing human ER-α(hER-α) or hER-βand an estrogen-responsive reporter construct—similar to the one introduced in the transgenic animals, but without the flanking insulator sequences—were used and cultured as previously described (Lemmen et al. 2002). Briefly, cells were plated in 96-well tissue culture plates (NUNC, Life Technologies, Breda, The Netherlands) in medium consisting of phenol red–free Dulbecco’s modified Eagle’s medium/F12 (1:1) medium containing 3 × 10−8 M selenite, 10 μg/mL transferrin, 0.2% (wt/vol) bovine serum albumin, and 5% (wt/vol) dextran-coated charcoal-stripped fetal calf serum. After 24 hr, medium was refreshed and after another 24 hr, the medium was removed and fresh medium containing test compounds (dissolved in ethanol) was added. After 24 hr of incubation, the medium was removed and 50 μL lysis solution [1% (vol/vol) Triton X-100, 2.5 × 10−2 M glycylglycine, 1.5 × 10−2 M magnesium sulfate, 4 × 10−3 M EGTA, and 1 × 10−3 M DTT] was added directly to the cells. Luciferase activity of 25 μL cell lysate was measured with the Luclite Luciferase Reporter gene assay kit (Perkin-Elmer, Brussels, Belgium) according to the manufacturer’s instructions using 25 μL Luclite solution on a Topcount liquid scintillation counter (Perkin-Elmer).
Data analysis and statistics.
We considered one litter as a statistical unit rather than one embryo because we assumed that all embryos in one litter were subject to the same variation of the compound injection and placental transfer. Therefore, the average ± SEM per litter was calculated, and these were then averaged to express the luciferase activity per group. All data were log-transformed and tested for normality with the Shapiro-Wilks test using SPSS 12.0 (SPSS, Chicago, IL, USA). The data were not normally distributed; therefore, we determined significant differences of treatment groups from oil-exposed control using Kruskal-Wallis analysis followed by the Dunn’s posttest using GraphPad Prism 3.02 (GraphPad Software Inc., San Diego, CA, USA). In addition, the presence of a linear trend in the dose response was determined by analysis of variance followed by a posttest for linear trends using GraphPad Prism 3.02. For the IVIS time-course experiments, we performed a Friedman test followed by Dunn’s posttest using GraphPad Prism 3.02.
The in vitro dose–response activation curves obtained with the stable cell lines were fitted using the sigmoidal fit {y = a0 + a1/1 + exp[–(x – a2)/a3]} in Slidewrite Plus for Windows (version 3.0; BIS, Ridderkerk, The Netherlands), which determines the fitting coefficients by an iterative process minimizing the c2 merit function (least squares criterion). The EC50 (median effective concentration) values were calculated by determining the concentration by which 50% of maximum activity was reached using the sigmoidal fit equation. The cell line data shown are the average of at least two independent experiments with each experimental point performed in triplicate. Data are shown as a percentage of maximal induction by E2.
Results
Estrogens activate endogenous ERs in transgenic embryos.
To be able to measure luciferase activity in the transgenic embryos with the IVIS system, it was crucial that wild-type mothers carry the transgenic embryos. If transgenic mothers had been used, strong photon emission would have been generated after the estrogen and luciferin injections, masking the signal emitted from embryos. We chose 13.5–14.5 dpc as the time for exposure because at this time point ERs are expressed in the embryo (Lemmen et al. 1999) and because this is a sensitive time point for disruption of reproductive organs by prenatal estrogen exposure. In nonexposed embryos, we detected no luciferase activity with IVIS and barely any luciferase activity in embryo lysates.
In utero luciferase activity in transgenic embryos was induced dose dependently by DES and EP. When measured 8 hr after exposure, 100 and 1,000 μg/kg DES significantly induced luciferase activity when assessed with the IVIS system (Figure 1) and in embryo lysates ex vivo measured in the luminometer (Figure 2). No plateau levels in luciferase activity were reached, and the profile of induction after DES exposure was similar for both methods used to assess luciferase activity. For EP only, the 10,000 μg/kg dose was able to significantly induce luciferase activity when measured with IVIS after 8 hr (Figure 1). When measured ex vivo in embryo lysates, 1,000 μg/kg EP already significantly induced luciferase activity (Figure 2). Fold induction of luciferase activity of estrogen exposed over controls 8 hr after exposure was, however, lower using IVIS compared with measurements in lysates. For DES doses of 100 and 1,000 μg/kg, induction was 5-fold and 14-fold greater, respectively, when measured with the IVIS system, whereas it was 41-fold and 51-fold greater, respectively, when measured ex vivo on embryo lysates. However, these differences in induction are likely based on a difference in noise rather than in signal. Also, for EP inductions were larger when measured ex vivo than when measured with IVIS (data not shown).
An important advantage of using the IVIS system is that the luciferase induction can be followed in time in a single animal, making it a very useful tool for obtaining information on the kinetics of tissue distribution and gene activation by compounds. With the IVIS measurements, we observed a difference between DES and EP in the kinetics of inducing luciferase activity (Figure 3). DES (1,000 μg/kg) significantly induced luciferase activity, exceeding levels in oil-exposed animals 2 hr after exposure, and this activity peaked 8 hr after exposure (Figure 3). In contrast, only 8 hr after EP exposure (10,000 μg/kg), luciferase activity was significantly above levels in oil-exposed animals, with a peak at 24 hr after exposure (Figure 3). This difference in kinetics thus complicates comparing relative potencies of these estrogens to induce luciferase activity. At 24 hr after estrogen exposure, the embryos were isolated and luciferase activity was measured ex vivo, showing that 100 and 1,000 μg/kg DES and 1,000 and 10,000 μg/kg EP were able to significantly induce luciferase activity compared with oil-exposed controls (Figure 2).
BPA activates ERs in transgenic embryos.
Eight hours after exposure to 10,000 μg/kg BPA, luciferase activity was higher than in oil-exposed animals when measured with IVIS (Figure 4), although this was not statistically significant. To be able to visualize the weak BPA signal, the scale bar of the superimposed video image had to be adjusted compared with Figures 1 and 3. When lysates from embryos sacrificed 24 hr after exposure were measured, no difference between oil- and BPA-exposed animals was found (Figure 2). Because BPA, like DES, may enter the fetal circulation rapidly (Miyakoda et al. 1999; Takahashi and Oishi 2000), other embryos were isolated 8 hr after exposure to 100 and 1,000 μg/kg BPA, EP, and DES or oil. At this time point, luciferase activity was significantly higher after exposure to 1,000 and 10,000 μg/kg BPA compared with oil-exposed animals (Figure 2). Therefore, at least at early time points, BPA is able to transactivate the embryonic ERs and resembles DES rather than EP in its kinetics of luciferase activation.
In vitro potency of estrogens and BPA.
The compounds used for the exposure experiments of transgenic animals were also tested in an in vitro assay to separately assess their potency to activate ER-αor ER-βusing a similar reporter gene as used in the transgenic animals, only without the flanking insulator sequences (Figure 5). All three compounds activated hER-αand hER-βin vitro. The EC50 values for ER-αwere 3.9 × 10−11 M, 8.5 × 10−12 M, and 1.6 × 10−7 M for DES, EP, and BPA, respectively. EC50 values for ER-βwere 3.9 × 10−11 M, 8.5 × 10−12 M, and 1.6 × 10−7 M for DES, EP, and BPA, respectively. In these experiments, BPA was found to be 5,000 times less active than DES in activating ER-αand 1,400 times less active than DES toward ER-β transactivation. EP was 4.6 times more potent than DES in activating hER-αand just as potent as DES in activating hER-β. These results confirm the reported weak estrogenicity of BPA in vitro.
Discussion
We successfully applied our new sensitive estrogen reporter mice to assess the ability of DES, EP, and BPA to activate ER signaling in embryos. In the present study, BPA exposure of pregnant mice induced the estrogen reporter through activation of endogenous ERs in mouse embryos. Hence, the generated in vivo model was successful in detecting estrogenic activity of a suspected environmental estrogen in embryos exposed in utero. In addition, our results show that in utero activation of ERs by BPA, at early time points after exposure, requires much lower doses than extrapolations from in vitro measurements would predict.
Because barely any luciferase activity could be measured in nonexposed embryos, we concluded that either there are no active endogenous estrogens during the life stage tested (13–14.5 dpc), or that our model is not sufficiently sensitive to detect their presence. Very low levels of estrogens have been described to be present in steroid extracts of mouse embryo homogenates as determined in estrogenic activity measurements (Lemmen et al. 2002). These levels may, however, be too low to activate endogenous ERs or are not able to activate ERs in vivo because of such different factors as tissue distribution and inactivation through binding proteins. It is possible that measurements of luciferase activity on dissected organs from embryos would prevent dilution of the luciferase signal below the detection limit. The low sensitivity of our model is also apparent in the high doses of DES and EP needed to be able to show a significant luciferase induction (Figures 1 and 2).
From measurements taken after mice pregnant with transgenic embryos were exposed to DES and EP, it was possible to evaluate the ability of the in vivo model to detect well-known estrogens in embryos exposed in utero. Exposure to DES showed a dose- and time-dependent induction of luciferase activity. The kinetic data obtained with the IVIS system showed that for all DES doses peak activity occurred at 8 hr after exposure. Previous studies using 14C-DES have shown that upon injection of pregnant mice, fetal plasma levels reach a peak after 2 hr and then disappear slowly (McLachlan 1977). The time difference in induction of maximal luciferase activity (i.e., after 8 hr) compared with an expected earlier DES peak in fetal plasma (i.e., after 2 hr) may be additionally due to the time required for transcription and translation of luciferase. Comparing DES with EP, it is evident that EP also shows a dose-dependent increase in luciferase activity. However, the EP-induced peak of luciferase activity was not seen before 24 hr after exposure. The observed time course of EP-induced luciferase activation could be due to a slow transfer to the embryos of EP itself or a relatively slow uptake by target tissues. Because no data are available on the kinetics of placental transfer of EP, only data on placental transfer of E2 can be used for comparison. In rhesus monkeys, placental transfer of 14C-DES and 14C-E2 was similar (Hill et al. 1980). Similar to embryos, exposure of adult transgenic animals to EP induced peak activation of luciferase at 24 hr after exposure (Lemmen et al. 2004), suggesting that a difference in placental transfer is unlikely to explain the delay in activation of luciferase by EP compared with DES. Another explanation for the difference observed between EP and DES exposure in peak luciferase activity could be that EP is initially bound to binding proteins in the serum and uptake by the embryonal target tissues is therefore slower compared with DES, which has much lower affinity to binding proteins (Arnold et al. 1996; Simmons et al. 1994). However, when E2 was tested in adult animals (Lemmen et al. 2004), it did show a peak in luciferase activity at 8 hr rather than at 24 hr; because E2 is bound to binding proteins as is EP, this suggests that the time needed for removal of the propionate groups could explain the difference in kinetics between EP and DES.
In pregnant rats, BPA has been shown to enter the fetal circulation with a peak concentration after 15–20 min (Takahashi and Oishi 2000). When exposing pregnant mice to 100 mg/kg BPA given subcutaneously, BPA was detected 30 min after exposure in fetal sera, liver, brain uterus, and testes (Domoradzki et al. 2003; Shin et al. 2002; Uchida et al. 2002). In the present study, BPA was found to significantly induce luciferase activity at doses of 1,000 and 10,000 μg/kg 8 hr after exposure. The kinetics of luciferase induction by BPA, measured with the IVIS system, resemble the profile of DES. Although the molecular structure of BPA and DES is similar, it remains unknown whether this contributes to the similarity in their kinetics in inducing luciferase activity. Testing more estrogenic compounds with various structures could shed light on this question.
Like DES, BPA showed a transient induction of luciferase activity in embryos; thus, estrogenic potency of BPA is compared with DES rather than with EP. In utero luciferase activation by BPA in transgenic embryos at 8 hr after exposure was significant from oil-exposed controls with 1 mg/kg BPA. Likewise, Nagel et al. (2001) found a significant increase in ER transcriptional activity in the adult uterus after exposure to 1 mg/kg BPA, whereas this dose did not induce a uterine wet weight response. DES was significantly different from oil-exposed controls at a dose of 100 μg/kg (only 10 times less than BPA), which suggests a high in vivo estrogenic potency of BPA. It should be noted that doses of 1 and 10 μg/kg DES induce almost a similar transcriptional activation (Figure 2), and the activation is approximately 20% of maximal activity induced by DES. In vitro, BPA was three to four orders of magnitude less active than DES, consistent with previous reports (Andersen et al. 1999; Kim et al. 2001; Kuiper et al. 1998). Thus, in our hands the relative potency of BPA seems to be higher in utero than in vitro on ER-α, which is the most abundantly expressed ER during embryogenesis (Lemmen et al. 1999). We believe this difference is not due to the use of human ERs in vitro versus the endogenous mouse ERs in utero. It has been shown that human and mouse ER-αhave the same affinity for DES and BPA (Matthews et al. 2000), and this is likely to be the case for ER-βas well. One explanation for a higher estrogenic potency of BPA in utero versus in vitro could be that in vivo BPA is converted to metabolites with enhanced estrogenicity (Ben-Jonathan and Steinmetz 1998; Yoshihara et al. 2004), although others have shown that BPA is mainly metabolized to a less active metabolite BPA monoglucuronide (Domoradzki et al. 2003; Pottenger et al. 2000). Another explanation could be that BPA has a lower affinity for the steroid-binding proteins present in serum, giving it a higher bioavailability than EP, a factor that is not taken into account in the in vitro assay. However, we feel this cannot explain the in vivo versus in vitro potency difference as we compare BPA with DES, and DES does not have a high affinity for binding proteins.
Strain differences in sensitivity to estrogens have been reported. The strain used in this study, C57Bl/6J (B6), has been shown to be more sensitive than CD-1 mice with respect to reduction of testis weight after estrogen exposure (Spearow et al. 1999). Also, for other end points of estrogen exposure, the B6 strain has been shown to be a sensitive strain (Roper et al. 1999; Spearow et al. 2001). In CFLP mice, 0.5 mg/mouse (~ 16.7 mg/kg) BPA was reported to be inactive in the uterine wet weight assay, whereas the other dose tested (5 mg/mouse, ~ 167 mg/kg) was toxic (Coldham et al. 1997). In CD-1 mice, a uterotrophic response was induced by 100 mg/kg BPA (Markey et al. 2001), whereas in B6C3F1 mice, doses between 0.8 and 8 mg/kg could induce uterine wet weight increase (Papaconstantinou et al. 2000). In the present study, a significant induction of luciferase activity in utero was detected after administration of 1 mg/kg BPA to pregnant females. The use of nontransgenic mother animals with a pure B6 background rather than the B6/CBA cross used could further increase the sensitivity of the present model.
In conclusion, we have shown that the mouse model presented here can be used to detect activation of ERs by maternally applied BPA and that other estrogens can be detected in living embryos in utero. BPA was more potent than would be estimated from in vitro assays, although its intrinsic activity is still lower than that of DES and EP. On the other hand, effects on individual embryonic organs might be larger and could be underestimated because we measured total embryo lysates. When considering that nanomolar levels of BPA have been measured in human serum (Takeuchi and Tsutsumi 2002), human amniotic fluid at 15–18 weeks of gestation (Ikezuki et al. 2002), and surface water (Fromme et al. 2003), concern about BPA exposure during embryonic/fetal life seems to be justified. It should be noted, however, that in our model the BPA effect had a more transient nature than did that of the other hormones. If and how this will translate to a biological effect in the exposed embryos should be the target of further investigations using other approaches.
Figure 1 In vivo activation of estrogen-responsive reporter construct by DES and EP in embryos. (A) In vivo activation of estrogen-responsive reporter construct (luciferase) by DES and EP in 13.5 dpc transgenic embryos measured with IVIS; the number of photons produced by the reaction between luciferase and luciferin is depicted in a color image superimposed on a video image of the pregnant animal. (B) Quantification of the signal produced in the embryos after DES and EP exposure. Values shown are mean ± SEM for oil (n = 5 litters), DES 10 μg/kg (n = 4), DES 100 μg/kg (n = 4), DES 1,000 μg/kg (n = 6), EP 10 μg/kg (n = 4), EP 100 μg/kg (n = 2), EP 1,000 μg/kg (n = 5), and EP 10,000 μg/kg (n = 6). Abscissa, dose of DES/EP or oil; ordinate, photons/sec/cm2 measured in an area of the pregnant mouse encompassing the embryos.
*p < 0.05,
#p < 0.001 compared with oil-exposed mean as determined by Kruskal-Wallis analysis followed by the Dunn’s posttest.
Figure 2 Ex vivo measurement of embryo lysates of in utero activated estrogen-responsive reporter construct (luciferase) 8 and 24 hr after exposure to DES (A, B), EP (C, D), and BPA (E, F). Values shown are mean ± SEM for the 24 hr measurements: oil (n = 9 litters) DES 10–1,000 μg/kg (n = 8–9), EP 10–100 μg/kg (n = 5), EP 1,000–10,000 μg/kg (n = 7–8), BPA 10–100 μg/kg (n = 2), and BPA 1,000–10,000 μg/kg (n = 6–8); and for the 8 hr measurements: oil (n = 3), DES 1 μg/kg (n = 2), DES 10 μg/kg (n = 5), DES 100 μg/kg (n = 8), DES 1,000 μg/kg (n = 3), EP 100 μg/kg (n = 3), EP 1,000 μg/kg (n = 5), BPA 100 μg/kg (n = 2), BPA 1,000 μg/kg (n = 8), and BPA 10,000 μg/kg (n = 5). Abscissa, dose of DES, EP, BPA, or oil; ordinate, Luc-units/mg protein as measured on luminometer (LUMAC).
*p < 0.05,
**p < 0.01,
#p < 0.001 compared with oil-exposed mean as determined by Kruskal-Wallis analysis followed by the Dunn’s posttest.
Figure 3 In utero time course of activation of estrogen-responsive reporter construct by DES and EP in embryos. (A) In utero time course of activation of estrogen-responsive reporter construct (luciferase) by DES and EP in transgenic embryos measured with IVIS. The number of photons is depicted in a color image superimposed on a video image of the pregnant animal. (B) Quantification of the signal produced in embryos after DES and EP exposure. Values shown are mean ± SEM for oil (n = 5 litters), DES 10 μg/kg (n = 4), DES 100 μg/kg (n = 4), DES 1,000 μg/kg (n = 6), EP 10 μg/kg (n = 4), EP 100 μg/kg (n = 2), EP 1,000 μg/kg (n = 5), and EP 10,000 μg/kg (n = 6). The 10,000 μg/kg dose was not tested for DES. Abscissa, time in hours after hormone exposure; ordinate, photons/sec/cm2 measured in an area of the pregnant mouse encompassing the embryos.
*p < 0.05,
**p < 0.01,
#p < 0.001 compared with oil-exposed mean as determined by Kruskal-Wallis analysis followed by the Dunn’s posttest.
Figure 4 In utero time course of activation of estrogen-responsive reporter construct by BPA in embryos. (A) In utero time course of activation of estrogen-responsive reporter construct (luciferase) by 10,000 μg/kg BPA in transgenic embryos measured with IVIS. The number of photons is depicted in a color image superimposed on a video image of the pregnant animal. (B) Quantification of the signal produced in embryos after BPA exposure. Values shown are mean ± SEM (n = 5). Abscissa, time in hours after hormone exposure; ordinate, photons/sec/cm2 measured in an area of the pregnant mouse encompassing the embryos. Note that the scale differs from those in Figures 1 and 3.
Figure 5 Transactivation of hER-α and hER-βby DES (A), EP (B), and BPA (C) presented as a percentage of maximal induction by E2. Values shown are mean ± SEM of three independent experiments done in triplicate. Abscissa, log molar concentration of hormone; ordinate, transcriptional activity as percentage of maximal induction by E2 for each ER subtype.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7117ehp0112-00155015531441ResearchArticlesUnderstanding the Spatial Clustering of Severe Acute Respiratory Syndrome (SARS) in Hong Kong Lai P.C. 1Wong C.M. 2Hedley A.J. 2Lo S.V. 3Leung P.Y. 4Kong J. 5Leung G.M. 21Department of Geography, and2Department of Community Medicine, University of Hong Kong, Hong Kong Special Administrative Region, People’s Republic of China3Health Welfare and Food Bureau and4Department of Health, Hong Kong Special Administrative Region, People’s Republic of China5Division of Health Informatics, Hong Kong Hospital Authority, Hong Kong Special Administrative Region, People’s Republic of ChinaAddress correspondence to P.C. Lai, Department of Geography, University of Hong Kong, Pokfulam Rd., Hong Kong. Telephone: 852-2859-2830. Fax: 852-2559-8994. E-mail:
[email protected] thank colleagues in the Department of Health and the Hong Kong Hospital Authority for data collection and processing, A. Mak and K. Chan for cartographic modeling and analyses, and P. Chau for data management.
The Hui-Oi-Chow Trust Fund provided support for the development of the methodologic approach, and the Research Fund for the Control of Infectious Diseases sponsored work on infectious disease epidemiology at the University of Hong Kong.
The authors declare they have no competing financial interests.
11 2004 27 7 2004 112 15 1550 1556 25 3 2004 27 7 2004 Publication 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 applied cartographic and geostatistical methods in analyzing the patterns of disease spread during the 2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong using geographic information system (GIS) technology. We analyzed an integrated database that contained clinical and personal details on all 1,755 patients confirmed to have SARS from 15 February to 22 June 2003. Elementary mapping of disease occurrences in space and time simultaneously revealed the geographic extent of spread throughout the territory. Statistical surfaces created by the kernel method confirmed that SARS cases were highly clustered and identified distinct disease “hot spots.” Contextual analysis of mean and standard deviation of different density classes indicated that the period from day 1 (18 February) through day 16 (6 March) was the prodrome of the epidemic, whereas days 86 (15 May) to 106 (4 June) marked the declining phase of the outbreak. Origin-and-destination plots showed the directional bias and radius of spread of superspreading events. Integration of GIS technology into routine field epidemiologic surveillance can offer a real-time quantitative method for identifying and tracking the geospatial spread of infectious diseases, as our experience with SARS has demonstrated.
geographic information systemsGISSARSsevere acute respiratory syndromespatial distribution
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Since the emergence and rapid spread of the etiologic agent of severe acute respiratory syndrome (SARS)—SARS coronavirus (SARS-CoV)—in late 2002 and during the first 6 months of 2003, great progress has been made in understanding the biology, pathogenesis, and epidemiology of both the disease and the virus (SARS-CoV). Much remains to be done, however, including the development of effective therapeutic interventions and diagnostic tools with high sensitivity and specificity soon after the onset of clinical symptoms. The evaluation of key epidemiologic parameters and the impact of different public health interventions in the various settings that experienced minor or major epidemics is also needed (Affonso et al. 2004; Cui et al. 2003; Lau et al. 2004; Leung et al., in press). In terms of outbreak control on the population level, many questions about “superspreading events” (SSEs) remain to be investigated. Such an SSE was responsible for > 300 cases (out of a total of 1,755) in the Amoy Garden Housing Estate (AMOY) in the Hong Kong epidemic. Moreover, Donnelly et al. (2003) have demonstrated that there were clear geographic concentrations of microclusters of SARS cases where the density of infection varied widely between different districts.
The application of geographic information system (GIS) methods in health and health care is a relatively new approach that started to gain acceptance a decade ago (Higgs and Gould 2001; Meade and Earickson 2000). In particular, a wide variety of cartographic methods have become available for the mapping and analysis of communicable disease data since the defining work of Cliff and Haggett (1988) and Haggett (1994). Advances in new technologies enable the application of GIS to examine spatially related problems from different perspectives. In addition to the descriptive mapping function, GIS possesses capabilities of data manipulation and geostatistical analysis.
In the present study, we applied GIS technology in mapping and visualizing the SARS outbreak in Hong Kong. In this article we focus on cartographic and geostatistical methods in representing and analyzing the patterns of disease spread during the 2003 outbreak. We also address the utility and limitations of GIS as a real-time disease surveillance tool.
Materials and Methods
Data sources.
We used spatial and nonspatial data in this study. Spatial data are geographic in nature and have a physical dimension or location in the real world. These are represented as points, lines, or area symbols, and they form the map base upon which SARS occurrences are depicted. Data on SARS incidence were derived from case-contact interviews that are text based; associated residential address data were first cleaned, checked for completeness and accuracy (e.g., Chinese-English transliteration of building and street names), and then geo-referenced to enable mapping.
We analyzed the SARSID integrated database (coordinated by the Department of Community Medicine, University of Hong Kong, on behalf of the Health, Welfare and Food Bureau—derived from the Hong Kong Hospital Authority eSARS system and the Department of Health’s Master List), which contained details on all patients confirmed to have SARS and admitted to hospitals in Hong Kong throughout the entire epidemic, that is, from 15 February to 22 June 2003. The criteria for inclusion in the SARSID were radiographic evidence of infiltrates consistent with pneumonia, fever ≥38°C or history of such at any time in the past 2 days, and at least two of the following: a) history of chills in the past 2 days; b) cough (new or increased cough) or breathing difficulty; c) general malaise or myalgia; and d) known history of exposure. However, patients were excluded if an alternative diagnosis could fully explain their illness. Moreover, each case classified as confirmed SARS was verified by the Hong Kong Department of Health according to World Health Organization (WHO) guidelines on case definitions (WHO 2003). Eighty-two percent of the 1,755 cases listed as confirmed SARS had either reverse transcription–polymerase chain reaction results positive for SARS-CoV or a 4-fold increase in IgG antibodies in paired sera (at admission and 21 or 28 days after symptom onset). Two questionnaires (case questionnaire and case-contact survey) were administered, mostly through telephone interviews, to all SARS cases confirmed by the Department of Health, initially by four regional field offices and later by a central interviewing team of nurses, to record symptoms at presentation to the hospital and to identify contacts and events of probable significance to transmission.
A total of 1,709 confirmed cases (out of 1,755 total cases) were extracted for the analysis. Forty-six cases (i.e., 2.6% of the total) could not be pinpointed at an exact location because of inconsistencies in the address entries (So 2002).
Geostatistical analyses.
We carried out three levels of analysis: a) an elementary analysis involving simple visual inspection of a geographic phenomenon; b) a cluster analysis attempting the identification of possible “hot spots,” and c) a contextual analysis aiming to explain relationships among geographic phenomena (Bailey and Gatrell 1995; Olson 1976).
At the elementary level, the spread of a disease in a community is revealed through the plotting of disease occurrences at residential addresses of the patients enabled with the address matching function in a GIS. Point by point is the simplest form of mapping disease occurrences without accounting for the magnitude at each location, but the sheer number and spread of points could have impeded effective reading of the event. A map of cumulative counts collapses the numerous observations into circles of varying sizes to signify differences in the magnitude of disease occurrences in the community. The circles are proportionally sized to reflect the number of occurrences at the sites, and geographic clustering of disease infection can then be clearly identified.
We also examined the spread of SARS over time on the basis of point patterns. Each disease occurrence was plotted spatially and the spread or dispersion of disease incidence was examined using nearest neighbor analysis based on the R scale. The nearest neighbor analysis is an accepted spatial statistical analysis used by environmental scientists to study species distribution (Krebs 1989) and by crime analysts to explain the levels of dispersion in crime and disorder data (Eck and Weisburd 1995). The R scale assumes that events will be randomly spaced unless something influences the distribution. Three different patterns are possible: clustered (0 ≤R < 0.8), distributed randomly (0.8 ≤R < 1.8), or with uniform spacing (1.8 ≤R ≤2.149). A contagious process will give rise to a clustered pattern with near-zero R values.
Cluster analysis involves statistical mapping that generalizes the numerous observations into a statistical surface to highlight spatial variation. A 5-day incubation period, consistent with a previous gamma distribution parameter estimation exercise (Leung et al., in press), was used to restructure the data for a time-series study. A statistical surface was created by the kernel method (Bailey and Gatrell 1995) for each day to reveal daily changes of disease hot spots. A kernel size of 300 × 300 m2 was used to reconstruct the territory of Hong Kong into a gridded surface of 208 columns and 151 rows. The kernel size was 300 × 300 m2, and disease occurrences within a bandwidth of 600 m from the kernel were summarized to yield density measures in terms of number of SARS cases per square meter. Each grid was then designated either as urban or suburban based upon land use classification, and its associated density measure was adjusted for the underlying variation in population density (i.e., kernel density × population density × grid cell size/1,000) to yield infection rates per 1,000 population. We adopted the approach by Kafadar (1996) but modified it to account for variation between urban or suburban population densities within a given district in Hong Kong (Table 1). Each urban or suburban grid was considered a homogeneous unit wherein its population density was apportioned according to the proportion of residents in the employed labor force.
We created 12 kernel maps adjusted for population at risk to characterize changes in disease hot spots on 12 prototypical days over 16 weeks in a chronologic sequence. The infection rates, which span across a wide range, were collapsed into 15 classes to reduce the complexity of map representation. Each of the 15 classes was assigned a shade in proportion to the magnitudes, with darker shades representing higher densities of infection. Two kinds of indexes were employed to assess the extent of disease clustering: R scale and Moran’s I coefficient for more highly connected grids of the queen’s case that considers a neighborhood of eight cells in a 3 × 3 matrix. Moran’s I coefficient ranges between −1 and 1 and is interpreted as regionalized or juxtaposition of similar values (0.6 ≤I ≤1 indicating positive spatial autocorrelation), lack of autocorrelation, or the actual arrangement of values as one that we would expect from a random distribution (−0.6 < I < 0.6 indicating no spatial correlation), and either contrasting or tendency for dissimilar values to cluster (−1 ≤I ≤−0.6 indicating negative spatial correlation). Although R scale is a global measure for the spread or dispersion of disease incidence for point data based on nearest neighbor distance (Eck and Weisburd 1995; Krebs 1989; Taylor 1977), Moran’s coefficient measures local spatial autocorrelation for area data (Getis and Ord 1992; Sawada 2001). A comparison of the power evaluation of disease clustering tests has been described by Song and Kulldorff (2003).
For contextual analysis, histograms of the kernel data for 12 prototypical days were drawn to highlight variation in infection rates. Also, we replaced mean and SDs of the classed density data with their natural logarithm functions to accentuate the effect of change between near-zero values; we then graphed the values.
We also established a breakdown of disease occurrences by recognized clusters (e.g., SSEs) for contextual analysis. Three disease clusters each with > 30 observations were extracted: AMOY, Prince of Wales Hospital (PWH), and Lower Ngau Tau Kok Housing Estate (NTKLOW). These data were used to derive origin-and-destination (OD) plots or flow diagrams. Lines were drawn to connect an origin location where the flow started (e.g., index source of infection) with related destinations where the flow ended (e.g., residences of secondary contacts). The OD plots are an established methodology employed by transport professionals and human geographers to examine the extent of spatial interaction and human settlement, as well as the modeling of commodity flows (Batten and Boyce 1986). The flow data themselves can be people, goods, telecommunications, and so on. The lines help to delimit the spatial coverage revealing the extent or degree of spread. SD ellipses centered on the geometric mean of all locations were drawn to provide a summary trend of the dispersion and to examine whether a distribution has a directional bias. The major axis is the direction of maximum spread of the point events, and the minor axis is the direction of minimum spread.
All analyses were carried out using ArcGIS software and its extension modules (Environmental Systems Research Institute, Redlands, CA, USA).
Results
Elementary analysis.
Figure 1 illustrates geographic locations of SARS infection by residential address in Hong Kong. The size of the circle corresponds to the density of cases in a particular location. There was clear clustering of cases in certain districts of the Kowloon peninsula (Kwun Tong, in which AMOY is located) and the New Territories (including Shatin, Tai Po), but Hong Kong Island was relatively spared. Table 2 supports this observation: most affected buildings or apartment blocks had very few cases, whereas seven buildings had > 10 SARS-affected patients.
Cluster analysis.
A series of 12 kernel maps based on date of symptom onset and accounting for a 5-day incubation period of SARS is presented in Figure 2. Each kernel map shows the density of SARS patients adjusted for underlying population density (i.e., SARS infection rate per 1,000 population) on a prototypical day over 16 weeks, with darker zones emphasizing disease hot spots [see also daily animated series by Lai and Chan (2004)]. A few disease hot spots were shown to be developing in the Kowloon peninsula and southeast New Territories (i.e., Ma On Shan and Shatin) by 10 March, which was followed later by a heavy concentration at the AMOY by 28 March. By early April, the AMOY case load began to dissipate and a new hot spot emerged in Tai Po (northeast New Territories). There is clear evidence of varying degrees of clustering as the epidemic progressed over time based on the low R values. The low R values signify substantial degrees of clustering (significant at 99% confidence level), with higher degrees of clustering occurring around the peak of the infection and relatively small divergences from random distribution at the beginning of the outbreak. High Moran’s I coefficients of ≥0.6 indicate that similar values tend to cluster together, which confirms the geospatial clustering and thus infectious nature of the disease, based on rates that were adjusted for the underlying population density. Figure 3 summarizes SARS hot spots in Hong Kong considering cumulative disease occurrences from February through June 2003. The map shows that the urban population was at higher risk of contracting SARS (Moran’s I = 0.78, p < 0.001), having already accounted for variation in population density.
Contextual analysis.
Daily histograms of the number of observations by 15 classes of infection rates, primarily composed of inverse J-shaped curves, show an increased concentration of SARS occurrences toward the end of March (Figure 4). Figure 5 is a logarithmic plot of the mean and SD of the infection rates of the 12 prototypical days representing different stages of the epidemic; values for individual days are presented in Table 3. Pairwise comparisons between each of the prototypical days and day 1 (or the day of indifference) of the epidemic demonstrated no detectable difference between the mean infection rates throughout the epidemic. However, there were statistically significant differences, by the F-test at a 0.01 significance level, in the SDs of the middle 10 prototypical days compared to day 1, suggesting unequal population variances during much of the outbreak. Higher F-values indicate more unequal variance. Given that the SD is a measure of geographic dispersion, we can infer that a larger SD signifies a wider spread of the disease over the territory. The crossover points of the mean and SD curves in Figure 5 indicate, on the one end, the beginning of substantial disease spread across the territory, and on the other end, the subsidence of the epidemic. Therefore, the time from day 1 (18 February) through day 16 (6 March) was the prodrome of the epidemic, whereas days 86 (15 May) through 106 (4 June) marked the declining phase of the outbreak.
OD plots of disease clusters were obtained by linking patients’ places of residence with the likely or probable locations of index cases or environmental sources of infection as defined through contact tracing by public health authorities (Figure 6). PWH is a tertiary teaching hospital and the site of the first SSE and nosocomial cluster in the Hong Kong epidemic, whereas AMOY and NTKLOW were subsequent community SSE clusters that had a strong putative environmental etiology (viz., sewage pipes, building design, and poor environmental hygiene) in addition to human-to-human transmission [Hong Kong Special Administrative Region (HKSAR) 2003; Wong and Hui 2004]. As would be expected because of a large patient catchment area, the PWH cluster was more geographically widespread (as supported by the SD ellipses in Figure 6D) compared with the AMOY cluster (Figure 6B), the sample size of which was one-third larger. The SD ellipses of the PWH cluster (Figure 6D) reveal a northwest–southeast directional trend of disease spread that extends over most of Hong Kong. The AMOY cluster was comparatively more localized, and the map had to be be enlarged to show the standard ellipses that exhibit an almost east–west directional trend of disease transmission (Figure 6B). The NTKLOW cluster (Figure 6F) was the least geographically widespread of the three SSEs, where the very compact spatial distribution must be magnified to visualize details of the SD ellipses.
Figure 7 and Table 4 show low R scores (a measure to inform the extent of disease spread) indicating a high degree of clustering for all three SSEs. The R values were significant at the 0.001 level, confirming that the point patterns exhibited a tendency toward clustering. Figure 7 also shows that block E of AMOY (the epicenter of the AMOY SSE), with a lower R score, exhibited a more compact geospatial arrangement in SARS infection than did other apartment blocks within AMOY. Visitors of ward 8A (the epicenter of the PWH SSE where the index patient of the cluster stayed) of the PWH were found to spread the disease farthest from its source of all the three clusters examined here, as would be expected for such a nosocomial outbreak at a tertiary referral hospital where SARS patients were densely aggregated on the ward but visiting relatives and friends returned home situated in different parts of Hong Kong (and not necessarily from the immediate surrounding neighborhood, given that the hospital is one of only two tertiary referral centers in the territory with a very wide catchment area). The NTKLOW cluster recorded the lowest R score, substantiating earlier observations from Figure 6.
Discussion
Our findings show that GIS methods can be usefully employed during an acute infectious disease outbreak to reveal new geospatial information in addition to standard field epidemiologic analyses. This mapping and cartographic technique can provide visual display of information in both space and time simultaneously. When applied in real time during the onset and evolution of an epidemic, it can monitor and enhance understanding of the transmission dynamics of an infectious agent, thereby facilitating the design, implementation, and evaluation of potential intervention strategies. GIS can offer quantitative and statistical measures along with visualization tools to examine patterns of disease spread with respect to disease clusters. Disease mapping is a first step toward understanding spatial aspects of health-related problems, as particular kinds of information are highlighted in maps. Various cartographic symbolizations (as points, lines, or areal patterns) can show the distribution of diseases. Disease clusters and other associations can then be deduced statistically and visually after examining the disease maps. In Chomsky’s (1965) terms, analyses at the first two levels concern the surface structure of an event, whereas the third level seeks to extract deep structure information. Surface structure information is simple and immediately perceptible to a user, whereas deep structure information is content-specific knowledge needed for problem solving (Nyerges 1991).
In the case of SARS in Hong Kong, our study, first and foremost, demonstrates exceptional spatial clustering of the cases. The kernel method adjusted for population density provided a means of highlighting population at risk, whereas the use of R values and Moran’s coefficients in conjunction with map displays enhanced the analytical context of the point pattern distributions. In fact, such geospatial intelligence gathered from examining statistical surfaces and disease clusters provided the basis for the formulation of our transmission dynamics model (Riley et al. 2003). More specifically, choice of a suitable framework was not straightforward in constructing the transmission dynamics model where a variety of approaches were possible, ranging from a simple deterministic compartmental approach to a spatially explicit, individual-based simulation. Given the data available for Hong Kong, we based our analyses on a stochastic metapopulation compartmental model. A metapopulation approach was appropriate because the incidence of SARS varied substantially by geographical district, as the GIS analyses have shown.
Second, the simultaneous geospatial–temporal approach to modeling the SARS outbreak revealed complementary additional information that would otherwise not be available from the traditional epidemic curve method (a standard public health outbreak investigative approach) in identifying the mode of spread. The daily animated series of kernel maps clearly shows that SARS was a highly localized disease; thus, its route of transmission was unlikely to be through casual contact, as it is for influenza and measles, but more compatible with close contact via heavy respiratory droplets and fomites. This confirms that SARS is only a moderately transmissible condition with a basic reproduction number of about 3 (Riley et al. 2003), in contrast to measles and influenza, which have basic reproduction numbers of about 13 and 5, respectively (Anderson and May 1991; Ferguson et al. 2003). An alternative interpretation of the observed high degree of geospatial clustering would be that SARS was due to an environmental point source outbreak. Indeed, faulty sewage systems and the “chimney effect” is the leading hypothesis explaining the AMOY SSE (especially block E), although some have suggested roof rats as a vector (Ng 2004). Although it is difficult to gauge retrospectively, had the GIS system we implemented in this report been available for near real-time analysis, it would likely have detected the highly unusual clustering of cases in SSEs such as the PWH and AMOY outbreaks much sooner, as they evolved. This in turn could have resulted in more rapid contact tracing and public health intervention, thus perhaps mitigating the extent of spread substantially in the case of person-to-person transmission events and preventing further large-scale environmental point source outbreaks in residential apartment blocks (although it would not have made a difference to AMOY itself given the temporally abrupt and short-lived environmental release of viral particles).
Third, contextual analysis of mean and SD values of different density classes, particularly after logarithmic transformation to accentuate near zero values on a graph, provided a geographic approach to estimating the beginning and subsidence of a large degree of spread of SARS in the community. This is a useful adjunct to the usual biomathematical modeling approach using reproductive numbers at different points in time, representing the average number of infections, excluding SSEs, caused by infected individuals in successive generations at time t throughout the SARS epidemic (Riley et al. 2003).
Fourth, the SD ellipses from the OD analysis, coupled with complementary results from R and Moran’s I values, yielded information on the direction of spread in a disease cluster that can be used to inform contact tracing and the design of quarantine measures. In the case of SARS in China, where entire residential districts were cordoned off for weeks at the height of the outbreak, the selection of such districts for quarantine could have been better informed by these ellipses indicating directional bias and associated physical distance in disease transmission.
There are, however, limitations and caveats to the GIS technique in infectious disease epidemiology and outbreak investigation. Howe (1963) argued that mapping of diseases tended to expose the “where” but not “why there” of the outbreak. Nevertheless, elementary descriptive analysis as an output of disease mapping can be a source of new leads for further exploratory analyses. Map patterns can provide stimuli for generating hypotheses of disease causation (Lloyd and Yu 1994; McKee et al. 2000). Moreover, newer developments that complement traditional mapping functions such as cluster and contextual analyses can be very useful adjunct investigative tools in outbreak control, as our example on SARS in Hong Kong has highlighted.
The completeness and availability of necessary data are another area of potential concern where conventional field epidemiologic data collection forms rarely contain the full range of variables that are required in a GIS analysis. Data consistency and, in particular, the nonstandardization of patient address formats is one such example. Field epidemiologists often relegate certain personal particulars such as residential and work addresses to a lower priority in their data collection procedures, or at least enter the information in a haphazard fashion, rendering GIS analysis very difficult by diminishing the proportion of usable cases for analyses. Similar generic problems that plague the establishment of all information systems must be resolved to enable real-time disease monitoring and surveillance. They include lack of standardization for data capture documents, procedures and protocols for information management, delays in transferring and updating information, and a lack of rapid analysis and audit of databases. The SARS epidemic is a clear signal that Hong Kong needs much greater and sustained investment in health informatics, that is, public health information systems, the skills to use them, and networks to share them.
In summary, integration of GIS technology into routine field epidemiologic surveillance can offer a scientifically rigorous and quantitative method for identification of unusual disease patterns in real time, as our example of SARS has shown. Its potential can be synergistically maximized when linked with clinical databases collecting data at the point of care across the whole population as well as environmental data sources (e.g., meteorologic, transportation, topographical information) to rapidly recognize, locate, and monitor disease outbreaks.
Figure 1 A summary map of SARS-infected cases in Hong Kong (February–June 2003). Data from the SARSID integrated database.
Figure 2 Time sequence of the spatial spread of SARS in Hong Kong (by date of onset with 5-day incubation period and weighted by population density), February–June 2003. Abbreviations: n, number of SARS patients; NA, not computed because of insufficient sample size (n < 25). An animated series is available online (Lai and Chan 2004).
*p < 0.01, which indicates a tendency toward clumping of disease incidence. **p < 0.001, which implies that spatial autocorrelation exists and that similar values on the map tend to cluster together.
Figure 3 SARS hot spots based on cumulative disease occurrences from February through June 2003. Moran’s I = 0.78 (p < 0.001).
Figure 4 Daily histograms of SARS by classes on infection rates. Frequency counts are truncated at 600. Fifteen classes represent different ranges of infection rate per 1,000 population.
Figure 5 A logarithmic plot of mean and SD of infection rates of 12 prototypical days throughout the epidemic.
Figure 6 Extent and trend of spatial spread of known disease clusters. (A) AMOY cluster (n = 335; R = 0.15; p < 0.001); the null hypothesis of a random pattern is rejected and the point patterns exhibit a high tendency toward clustering. (B) SD ellipses for AMOY cluster (ellipse 1: x-length = 869.87, y-length = 2044.20; ellipse 2: x-length = 1739.74, y-length = 4088.40). (C) PWH cluster (n = 212; R = 0.45; p < 0.001); the null hypothesis of a random pattern is rejected, and the point patterns exhibit a tendency toward clustering but a more widespread distribution compared with the others. (D) SD ellipses for PWH cluster (ellipse 1: x-length = 7889.94, y-length = 18541.37; ellipse 2: x-length = 15779.88, y-length = 37082.74). (E) NTKLOW cluster (n = 38; R = 0.22; p < 0.001); the null hypothesis of a random pattern is rejected and the point patterns exhibit a high degree of clustering. (F) SD ellipses for NTKLOW cluster (ellipse 1: x-length = 59.34, y-length = 139.44; ellipse 2: x-length = 118.67, y-length = 278.88).
Figure 7 Spatial clusters of SARS patients (February–June 2003) by nearest neighbor analysis.
Table 1 Urban area and population data of Hong Kong by districts.
18 Districts plus marine Total population Total working populationa Percent urban landb Percent urban allocationc
Central and western 261,884 144,824 29 99.9
Eastern 616,199 314,674 27 99.7
Islands 86,667 43,201 1 97.8
Kowloon City 381,352 185,553 82 99.9
Kwai Tsing 477,092 218,291 44 99.9
Kwun Tong 562,427 226,062 76 99.9
North 298,657 133,767 12 99.1
Sai Kung 327,689 165,219 3 99.8
Shatin 628,634 265,473 17 99.9
Sham Shui Po 353,550 159,861 53 99.9
Southern 290,240 145,086 9 98.9
Tai Po 310,879 145,520 6 99.6
Tsuen Wan 275,527 140,011 8 99.9
Tuen Mun 488,831 210,115 17 99.6
Wan Chai 167,146 93,365 33 99.9
Wong Tai Sin 444,630 200,265 47 99.9
Yau Tsim Mong 282,020 137,765 64 99.9
Yuen Long 449,070 180,198 21 99.1
Marined 5,895 4,629 0 20.7
Total 6,708,389 3,113,879
Data from Hong Kong Census and Statistics Department (2002).
a Sum of employed labor force.
b Total urban areas within each district divided by district area.
c Computed from urban-related occupation in employed labor force, defined as follows: rural-related occupation (includes agriculture and fishing); mining and quarrying; urban-related occupation (includes community, social, and personal services); construction; electricity, gas, and water; financing; insurance, real estates and business services; manufacturing; transport, storage, and communications; wholesale, retail, and import/export trades; restaurants and hotels; unclassified.
d Marine data were not land based and thus were excluded from the study.
Table 2 A frequency breakdown of SARS-infected buildings (February–June 2003).
No. of SARS cases in a building No. of buildings Total no. of SARS cases
136 1 136
47 1 47
46 1 46
43 1 43
20 1 20
18 1 18
11 1 11
10 3 30
9 1 9
8 3 24
7 2 14
6 6 36
5 3 15
4 12 48
3 47 141
2 156 312
1 759 759
Total 1,709
Table 3 Mean and SD of infection rates of 12 prototypical days.
Day 1 (18 Feb) Day 16 (6 Mar) Day 20 (10 Mar) Day 22 (12 Mar) Day 28 (18 Mar) Day 38 (28 Mar) Day 40 (30 Mar) Day 48 (7 Apr) Day 56 (15 Apr) Day 67 (26 Apr) Day 79 (8 May) Day 106 (4 Jun)
No. of patients 1 15 107 120 126 421 276 187 129 67 29 2
Mean 0.020 0.023 0.041 0.046 0.047 0.151 0.108 0.066 0.046 0.031 0.026 0.020
z0.01 = 2.33 0.00000 0.43580 0.75503 0.73118 0.74243 0.22811 0.24205 0.64763 0.70296 0.63733 0.52928 0.09170
SD 0.009 0.023 0.106 0.138 0.137 1.852 1.202 0.279 0.129 0.057 0.047 0.003
F0.01(14,14) = 3.6 1.00 20.00* 503.86* 703.10* 759.91* 102817.04* 38521.60* 2277.52* 873.57* 250.41* 58.85* 2.05
*p < 0.001 indicates that the null hypothesis is rejected and that the SD is significantly different from or greater than that of day 1. z = 2.33 and F(14,14) = 3.6 at the 0.01 level of significance for one-tailed tests.
Table 4 Index of spatial spread by nearest neighbor analysis.
Description R n
AMOY cluster 0.15* 335
AMOY block E residents 0.05* 132
AMOY block E visitors 3
Other block residents 0.06* 181
Other block visitors 5
Visited AMOY shopping mall 14
PWH cluster 0.45* 212
PWH 18
Ward 8A visitors 0.58* 58
Ward 8A patients 0.45* 25
PWH medical workers 0.49* 99
PWH other 12
NTKLOW cluster 0.02* 38
n, number of SARS patients.
*p < 0.001 indicates that the null hypothesis is rejected; a tendency towards clustering exists.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7163ehp0112-00155715531442ResearchArticlesAssessing Ozone-Related Health Impacts under a Changing Climate Knowlton Kim 1Rosenthal Joyce E. 1Hogrefe Christian 2Lynn Barry 3Gaffin Stuart 3Goldberg Richard 3Rosenzweig Cynthia 4Civerolo Kevin 5Ku Jia-Yeong 5Kinney Patrick L. 11Mailman School of Public Health, Columbia University, New York, New York, USA2Atmospheric Sciences Research Center, State University of New York at Albany, Albany, New York, USA3Columbia University Center for Climate Systems Research, New York, New York, USA4National Aeronautics and Space Administration–Goddard Institute for Space Studies, New York, New York, USA5New York State Department of Environmental Conservation, Bureau of Air Research, Albany, New York, USAAddress correspondence to K. Knowlton, Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 60 Haven Ave., B-1, New York, NY 10032 USA. Telephone: (212) 305-3464. Fax: (212) 305-4012. E-mail:
[email protected] thank T. Holloway for her valuable contribution.
This research has been funded by STAR grant R828733 from the U.S. Environmental Protection Agency (EPA). Additional support was provided by National Institute of Environmental Health Sciences Center grant ES09089 and from the National Aeronautics and Space Administration/Goddard Institute for Space Studies Climate Impacts Group.
This research has not been subjected to any U.S. EPA review and therefore does not necessarily reflect the views of the agency, and no official endorsement should be inferred.
The authors declare they have no competing financial interests.
11 2004 16 8 2004 112 15 1557 1563 6 4 2004 16 8 2004 Publication 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. Climate change may increase the frequency and intensity of ozone episodes in future summers in the United States. However, only recently have models become available that can assess the impact of climate change on O3 concentrations and health effects at regional and local scales that are relevant to adaptive planning. We developed and applied an integrated modeling framework to assess potential O3-related health impacts in future decades under a changing climate. The National Aeronautics and Space Administration–Goddard Institute for Space Studies global climate model at 4° × 5° resolution was linked to the Penn State/National Center for Atmospheric Research Mesoscale Model 5 and the Community Multiscale Air Quality atmospheric chemistry model at 36 km horizontal grid resolution to simulate hourly regional meteorology and O3 in five summers of the 2050s decade across the 31-county New York metropolitan region. We assessed changes in O3-related impacts on summer mortality resulting from climate change alone and with climate change superimposed on changes in O3 precursor emissions and population growth. Considering climate change alone, there was a median 4.5% increase in O3-related acute mortality across the 31 counties. Incorporating O3 precursor emission increases along with climate change yielded similar results. When population growth was factored into the projections, absolute impacts increased substantially. Counties with the highest percent increases in projected O3 mortality spread beyond the urban core into less densely populated suburban counties. This modeling framework provides a potentially useful new tool for assessing the health risks of climate change.
air pollutionclimate changeglobal warmingmortalityozone
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A warming climate may result in increased morbidity and mortality related to ozone, an impact that is often overshadowed by concerns about the direct effects of increased heat stress (Githeko and Woodward 2003; Kalkstein and Greene 1997; McMichael et al. 2003). Peak ambient O3 concentrations are typically observed in summer months, when higher temperatures and increased sunlight enhance O3 formation and also lead to increased emissions of biogenic and fugitive anthropogenic hydrocarbons, important precursors of O3 formation. Above 90°F (32°C), a strong positive association has been found between temperature and ground-level O3 production (Patz 2000). Numerous epidemiology studies have reported associations between O3 and hospital admissions or emergency visits for respiratory conditions, diminished lung function, and a variety of other health outcomes (Kinney 1999; Koken et al. 2003). A relatively recent but growing body of literature also has documented acute effects on mortality in large cities, in many cases while controlling for particulate matter and other pollutants (Dominici et al. 2003; Hoek et al. 1997; Moolgavkar et al. 1995; Thurston and Ito 2001; Vedal et al. 2003).
Summer heat waves and high O3 days are current health stressors in the New York metropolitan region, and their impacts may increase under a changing climate (Kinney et al., in press). Warming of 1.4–3.6°C (2.6–6.5°F) by the 2050s has been projected by the Hadley and Canadian global climate models (Columbia Earth Institute 2001). General circulation models (GCMs) such as these typically provide output (e.g., surface temperatures) at resolutions of hundreds of kilometers. Several past studies have assessed health impacts of climate change using GCMs (Anderson HR et al. 2001; Donaldson et al. 2001; Kalkstein and Greene 1997). One study in the United Kingdom used a GCM to examine potential climate impacts on O3-related health effects and concluded that a 10% increase in premature mortality could result by 2020, with a 20% increase possible by 2050 (Anderson HR et al. 2001).
To better assess localized impacts of climate change, models are needed that can project meteorologic parameters at scales of tens of kilometers. One strategy to accomplish this is to link GCM outputs with regional climate models (RCMs). There have been few assessments of health impacts that have used these “down-scaled” models, mostly to assess heat effects rather than air quality impacts (Dessai 2003; McMichael et al. 2003). Other knowledge gaps include potential urban versus rural differentiations in health impacts of climate change, the relative regional impacts of climate-related O3 versus heat impacts, and the relative contributions of various model components to overall uncertainty.
In response to the need for improved methods for assessing potential air pollution health impacts of climate change at regional scales, the New York Climate and Health Project (NYCHP) developed and tested an integrated modeling system in the New York metropolitan region (Kinney et al., in press). The modeling system employed coupled global/regional models to simulate meteorology and air quality in the 2020s, 2050s, and 2080s. The objective of the present report is to assess and compare summer O3-related mortality in the 1990s and 2050s. We analyzed the independent and joint effects of climate change and anthropogenic O3 precursor emission change on summer O3 concentrations and resulting mortality. We also examined the sensitivity of O3-related mortality to a range of modeling assumptions, including population growth and O3 threshold effects.
Materials and Methods
The three-state, 31-county health impact domain for this study is depicted in Figure 1. With New York City at its core, this 33,600-km2 (13,000 mi2) region is presently home to > 21 million people. It has a widely varying landscape and a range of population densities and land uses. In addition to the nation’s largest city, the metropolitan region includes a relatively pristine watershed, the source of New York City’s drinking water; substantial agricultural land in parts of northern New York, Long Island, and central New Jersey; and an estimated 1,600 cities, towns, and villages.
Climate and O3 modeling.
To develop predictions of surface O3 concentrations on a 36-km grid over the region of interest, we linked models for global climate, regional climate, and regional air quality. Global climate modeling was carried out using the Goddard Institute for Space Studies (GISS) GCM (Russell et al. 1995), which produced simulations of hourly climate over the globe at 4° × 5° grid resolution from the 1990s through 2100. Changes in greenhouse gas emissions projections were taken from the A2 scenario of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) (Nakicenovic and Swart 2000). The A2 scenario is characterized by high carbon dioxide emissions (up to 30 gigatons/year), relatively weak environmental concerns, and large population increases (15 billion worldwide by 2100). Analyses of the more environmentally friendly SRES B2 scenario of growth will be the subject of future reports.
Outputs from the GISS GCM were used as inputs to an RCM that was run for the summer seasons (June–August) for five consecutive mid-decadal years (e.g., 1993–1997) in the 1990s and 2050s. Only five summers per decade could be modeled with available computer resources. The mid-decadal years were chosen arbitrarily for these model runs and were meant to be representative of each decade. Regional climate modeling was carried out using the Penn State/National Center for Atmospheric Research Mesoscale Model 5 (MM5) (Grell et al. 1994), which made it possible to simulate climate factors on a 36-km horizontal grid over the New York metropolitan area.
For O3 simulations, we used the Community Multiscale Air Quality (CMAQ) model (Byun and Ching 1999) with the Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE) (Houynoux et al. 2000). The GCM/MM5 linked model provided the meteorologic inputs needed for the air quality simulations at a resolution of 36 km. We compared the GCM/MM5/CMAQ model outputs for the eastern United States in five summers of the 1990s with observations for the same period. The model successfully captured the observed year-to-year and shorter-term temporal variability in O3 as well as the spatial pattern of summer average daily maximum 1-hr O3 levels and the frequency distribution of extreme O3 events (Hogrefe et al. 2004). These O3 simulations did not take into account the effects of possible O3 precursor emission changes from outside of the modeling domain upon future air quality within the study area.
Health impact analysis.
We used a risk assessment framework to assess changes in O3-related mortality in the 2050s compared with the 1990s. Although many other health outcomes have been associated with O3 exposures, we chose to limit the present analysis to acute effects on daily mortality for all internal causes. For each decade, county-level mortality impacts were computed as M = (P/100,000) × B × CRF × E, where M is the estimated number of daily deaths attributable to O3 concentrations; P is the estimated county population during time period of interest; B is the estimated baseline county-level daily mortality rate in June–August (per 100,000 population); CRF is the concentration–response function, which quantifies the magnitude of the proportional change in daily mortality that would be expected in response to a given daily O3 concentration, based on results from the epidemiologic literature; and E is the daily 1-hr maximum O3 concentrations in June–August in each county, interpolated from the GISS/MM5/CMAQ model outputs described above. To estimate the typical June–August summer mortality in each decade, the average daily O3-related mortality across all five summers simulated in each decade was calculated.
County populations in the mid-1990s were estimated from 2000 U.S. Census data (U.S. Census Bureau 2001). These population figures remained constant in the 2050s in base-case calculations aimed at isolating the climate influence. For sensitivity analyses in which population was allowed to grow, 2050s populations were estimated by applying the proportion of the U.S. population that each county comprised in Census 2000 to a set of A2-consistent U.S. growth projections through the year 2100 (Gaffin S, personal communication). This method projected a 53% regional population increase by 2055. This growth rate was assumed to apply equally to all counties; future work will loosen this assumption by tying population growth to results from land use modeling for the region. The population age structure was held constant at Census 2000 conditions.
Average 1990s daily summer mortality rates for each of the 31 counties in the study area were estimated as follows. Annual all-age crude mortality data for all internal causes (International Classification of Diseases, 9th Revision, codes 0–799.9 for years 1990–1998, and International Classification of Diseases, 10th Revision, codes A00–R99 for year 1999) (Anderson RN et al. 2001) were obtained from the U.S. Centers for Disease Control and Prevention (CDC 2004) for each of the 31 counties. A scaling factor of 0.237 (the proportion of annual deaths 1993–1996 that occurred in June–August in the five boroughs of New York City) was applied to the annual mortality rates to adjust them to summer-only seasonal mortality. This was converted to a daily rate. We held baseline mortality rates constant in all analyses. Although mortality rates will undoubtedly change in future in response to changes in the age distribution of the population and in health care, projection of these shifts was beyond the scope of the present study.
CRFs from the epidemiologic literature describe the relationship between changes in daily ambient O3 concentrations and changes in mortality risk. A CRF for O3-related mortality, expressed as relative risk (RR), of 1.056 per 100 ppb increase in daily 1-hr max O3 [95% confidence interval (CI), 1.032–1.081] was used in our base-case analyses, taken from a pooling of seven studies that controlled well for temperature effects using nonlinear functions (Thurston and Ito 2001). Because the RR of mortality associated with an increase in O3 is modeled as an exponential function, the change in RR associated with a change of O3 (ΔO3) is of the form RR = exp (βΔO3) − 1, where β is the pooled Poisson regression slope reported by Thurston and Ito (2001), and ΔO3 is the incremental change in summer-seasonal mean O3 concentrations [adapted from Davis et al. (1997)].
Impact assessments.
We performed two primary mortality assessments and a series of sensitivity analyses. Mortality assessment 1 (M1) estimated future O3 concentrations and associated changes in mortality resulting from climate change alone, where the only changes from 1990s assumptions involve altered A2 greenhouse gas emissions. Mortality assessment 2 (M2) estimated future O3 and mortality under A2 greenhouse gas emission assumptions along with growth in anthropogenic O3 precursor emissions at rates consistent with the A2 scenario. Sensitivity analyses examined alterations in several of the individual assumptions underlying the primary assessments.
M1: A2 climate only.
The objective here was to assess how climate change alone might contribute to changes in summer O3 concentrations and associated mortality in the New York region over the next 50 years, in isolation from other factors. Here, county population totals were held constant at Census 2000 levels through the 2050s (U.S. Census Bureau 2001). Similarly, anthropogenic O3 precursor emissions were held constant at the 1996 county-level U.S. Environmental Protection Agency (EPA) National Emissions Trends inventory; thus, no projected changes in anthropogenic precursor emissions were applied in the CMAQ projections of 2050s summer O3. The base case did allow for temperature-dependent changes in biogenic and mobile source emissions. For mortality estimation, we assumed no threshold for O3 impacts.
M2: A2 climate and precursors.
The objective here was to assess the potential impacts of allowing for changes in both climate and O3 precursor emissions. Anthropogenic O3 precursor emissions from the 1996 inventory were scaled up using A2 growth factors provided by the Center for International Earth Science Information Network (Nakicenovic and Swart 2000). For the 2050s, these scaling factors were oxides of nitrogen (NOx), an increase of 29.5%, and volatile organic compounds (VOCs), an increase of 8%. No detailed county-level or national projections of U.S. emissions taking into account the effects of emissions control programs such as NOx state implementation programs and O3 National Ambient Air Quality Standards (NAAQS) are available at this point for the time horizon of 2050 (U.S. EPA 2004). In lieu of U.S.-specific projections of anthropogenic emissions, we used the emission projections of the IPCC SRES A2 marker scenario generated by the atmospheric stabilization framework socioeconomic model (Pepper et al. 1998; Sankovski et al. 2000).
The IPCC SRES describes various future emissions scenarios based on projections of population, technology change, economic growth, and the like, and these emission factors are superregional in nature (Nakicenovic and Swart 2000). Specifically, all countries in the Organization for Economic Cooperation and Development region OECD90 (including the United States, Canada, and Western Europe) are assumed to have the same emission growth rates. In other words, the IPCC SRES scenarios are not designed to reflect country-specific emission growth. For the IPCC SRES A2 scenario used in this study, the emissions of the O3 precursors NOx/VOCs increase by 125/60% globally and 29/8% for the OECD90 region (including the United States) by the 2050s (Nakicenovic and Swart 2000). The IPCC SRES A2 emission growth for the OECD90 region might be overly pessimistic given enacted or contemplated U.S. emission control programs, whereas by using these emission growth factors for the CMAQ modeling, we maintain internal consistency with the global and regional climate modeling in which the A2 greenhouse gas emissions were used. Therefore, rather than attempting to predict “realistic” air quality in the 2050s, our simulations investigate the overall effect of the A2 scenario, a possible (although pessimistic) trajectory into the future.
All other assumptions (no regional population growth; no threshold concentration for O3-related mortality; CRF value = RR 1.056 per 100 ppb O3) remained the same in mortality assessment M2 as for M1.
Sensitivity analyses.
We performed a variety of sensitivity analyses to evaluate the effects on O3 mortality projections of changing individual modeling assumptions. The baseline assumptions for all of the sensitivity analyses were those described above for M1. The following sensitivity analyses were carried out: S1, population growth, climate change, and O3 precursor emission changes, with the objective to assess the potential impacts of a full set of A2 scenario assumptions; S2, O3 precursor emission changes without climate or population change; S3, climate change only but the existence of an O3 threshold below which no mortality effects occur is assumed; one recent O3–mortality study suggested the existence of a summer threshold (Kim et al. 2004); the regional minimum value (20.3 ppb) from the 31-county average of CMAQ summer 1-hr daily maximum O3 simulations for the 1990s was used as the threshold value.
Results
Primary mortality assessments.
County-specific O3 concentrations and associated mortality estimates under climate change alone (M1) for the 1990s and 2050s are shown in Table 1 and Figure 2. The range of projections for O3 mortality in each county shown in Table 1 is based on calculations that apply the lower and upper confidence limits of the Thurston and Ito (2001) 95% CI in the risk assessment. Increases in estimated summer averaged daily 1-hr maximum O3 concentrations ranged from 0.3 to 4.3 ppb across the 31 counties. The geographic distribution of O3 increases shows greater impacts in coastal counties and in those along the predominant upwind air mass trajectory from the southwest (Figure 2A). As would be expected, the distribution of percent increases in O3-related mortality shows the same geographic pattern (Figure 2B), although the absolute numbers of O3-related deaths (Table 1) are a strong function of underlying county populations. This analysis suggests that the greatest percent increases in summer O3 mortality will occur across the urban core and especially in a ring of suburban counties immediately surrounding the city to the southwest and east (central Long Island). Over the entire region, there was a projected median increase of 4.5% in O3-related deaths.
A different pattern of results is seen under the climate change plus O3 precursor emissions change assumptions represented in M2 (Table 1, Figure 3). Allowing precursor emissions to grow leads to higher O3 increases compared with the climate-only case outside the urban core region, but lower O3 concentrations in the urban core counties (Figure 3A). This reduction in urban O3 likely reflects the nonlinearity of the NOx–O3 relationship (Seinfeld and Pandis 1997), where in some cases increased NOx in urban core areas such as New York City may react with O3, thereby locally lowering O3 concentrations. This effect is called titration.
Estimates of O3-related mortality here show slightly smaller impacts than were estimated under climate change alone (Table 1). The spatial pattern of mortality impacts follows a distribution similar to that of O3 concentrations, but with the added effects of population density (Figure 3B), because areas with relatively low population density coincide with the areas of greatest O3 increases, whereas areas with relatively high population density coincide with counties for which O3 concentrations slightly diminish. Estimated median O3-related summer mortality across the region increased by 4.4% above 1990s estimates.
Sensitivity analyses.
The effects of varying model assumptions of individual O3 impacts are shown in Figure 4, which plots the median, 10th percentile, and 90th percentile of the distribution of county percent changes in mortality under each set of assumptions. The two primary mortality assessments appear as the leftmost two plots on the graph (M1–M2), followed by the series of sensitivity tests S1–S3 in the center. Sensitivity analysis S1 shows that population growth accounts for almost all of the mortality increases in the “full A2” O3 model simulations. In the second sensitivity analysis (S2), which considers the mortality effect of letting only anthropogenic O3 precursor emissions increase in the 2050s, the relative increase in mortality projections fell slightly below the M1 base case, owing to diminished O3 concentrations in the 2050s in the most densely populated urban core areas. The third sensitivity analysis (S3) applies an O3 threshold value of 20 ppb to evaluate regional mortality in both the 1990s versus 2050s and found a slightly greater percent increase in regional summer O3 deaths. This result was driven by a decrease in calculated mortality in the 1990s that was larger than the decrease in the 2050s under the threshold assumption.
Discussion
Results of our analyses illustrate how integrated models can be used to assess potential impacts of climate change at regionally relevant spatial scales, suggesting that, under a variety of assumptions, climate change alone could increase regional summer O3-related mortality by a median 4.5% in the 2050s compared with the 1990s. These assumptions do not include the effect of projected population growth. When a more fully elaborated picture of the likely regional future was evaluated—that is, including population growth and anthropogenic O3 precursor emissions increases—much greater changes in summer mortality are projected: Regional summer O3-related mortality would increase by a median 59.9% in the 2050s compared with the 1990s. These larger impacts are dominated by the growth in population at risk.
The relatively fine spatial resolution afforded by the NYCHP model system projected spatially heterogeneous regional changes in episodic high O3 in coming decades. We applied O3 concentrations that were spatially interpolated from the 36-km model simulations to each of the 31 counties’ geographic centroids in the mortality risk assessment. This enabled us to distinguish “hot spots” in O3 conditions at the county level across the New York metropolitan study area. To describe their geographic distribution, we examined differences in climate-related mortality impacts projected across urban versus suburban counties within the larger New York metropolitan region. With the effects of population and precursor emission growth omitted, the greatest percent increases in summer O3 concentrations and related mortality are projected in the urban core and especially in a ring of suburban counties immediately surrounding the city to the southwest and east (central Long Island). When population and precursor emissions effects are also included, one can discern that areas with relatively low population density coincide with the areas of greatest O3 increases. Mean summer O3 concentrations are lower in the 2050s than in the 1990s in the most highly urbanized counties, assuming anthropogenic precursor emission growth, yet far more people are exposed here and thus mortality still increases. In future analyses from the NYCHP, we will progress to finer spatial resolutions (12 and 4 km) and include temperature-related mortality, to discern locations of vulnerable communities whose health may be most affected by climate change in the next 50 years. Beyond the immediate New York metropolitan region, the projected effects of climate changes on O3 concentrations and related mortality may show different patterns, owing to different overall distributions of urbanization, precursor emissions areas, and population across the eastern United States.
The sensitivity analyses showed that population growth has the largest effect on projections of changing summer O3-related mortality, greater than the isolated effect of climate change alone upon O3 concentrations and related mortality. The application of an O3 threshold leads to slightly greater percent increases in O3-related deaths than does application of the zero-threshold model, because a threshold removes the mortality effect of the days with the lowest O3 in both the 1990s and the 2050s. The 1990s summers had a higher proportion of below-threshold days; thus, the comparative percent increase in 2050s mortality is larger than under a no-threshold model. The effect of increasing O3 precursor emissions in the absence of climate change is to slightly diminish regional mortality, because summer O3 concentrations decrease because of the titration effect in the most densely populated urban counties. These sensitivity results illustrate the impacts of some of the uncertainties inherent in a risk assessment of this kind. Other sources of uncertainty exist that have not been included here, such as alternative modeling approaches for climate and air quality, methods for estimation of baseline summer season mortality rates, the assumption that O3 impacts occur only in summer, and possible modification of the O3–mortality relationship in the future if more households acquire air conditioning. A full uncertainty analysis was beyond the scope of the present report.
This study is the first to apply fully down-scaled global-to-regional climate model outputs to project future-year O3 concentrations for public health impacts assessments. The NYCHP integrates the work of health professionals with the work of air quality modelers and climate scientists and applies a linked model system to project regional mortality for a major metropolitan area of the United States. The daily simulations from the regional meteorology and air quality models at 36-km spatial resolution allowed for estimation of the public health impacts of climate change at local scales potentially useful to health care infrastructure planning. The temporal resolution of the linked model system outputs allowed us to apply CRFs from the epidemiologic literature for acute (daily) exposures and responses. The use of simulated O3 concentrations from the 36-km resolution atmospheric chemistry model yields more detail than that afforded by air monitoring across the 31-county New York metropolitan region. Validation of the fluctuations in surface temperature and O3 concentrations simulated by MM5 and CMAQ showed good agreement with 1990s observations (Hogrefe et al. 2004); hence, the integrated model system presents a useful method for studying regional climate-related changes.
Although a few previous studies have assessed O3 health impacts under climate change assumptions, none have used global-to-regional downscaled climate models to project O3 concentrations for health impacts assessment. Kleinman and Lipfert (1996) considered possible effects of climate change by simply assuming a 2°C temperature increase and evaluating its effect on O3 concentrations and associated mortality in the New York City area. Kalkstein and Greene (1997) were among the first to apply GCM model simulations for the 2020s and 2050s to heat-related mortality projections in 44 large U.S. cities but did not assess air quality impacts. Davis et al. (1997) considered the possible effect that climate-control policies could have on particulate air quality and associated mortality, using two possible scenarios of CO2 emissions but did not apply full GCM or RCM model simulations. Anderson HR et al. (2001) have projected potential O3-related mortality impacts associated with climate change projections for the United Kingdom in the 2020s, 2050s, and 2080s but used a GCM simulation that could not project in detail potential geographic variations in O3 concentrations. Two studies that used RCM simulations of climate change (Dessai 2003; McMichael et al. 2003) projected temperature-related mortality changes but not changes in mortality related to air pollution.
For scenario-based, integrated health risk assessments, there are several sources of uncertainty in estimating future impacts. The climate and air quality models used here introduce uncertainty, yet their simulations can be compared with meteorologic data to find the degree to which the models capture the observations. Furthermore, where changes are being assessed, some model biases are likely to cancel out. The O3 simulations we ran did not take into account, via changed boundary conditions, possible changes in air quality outside of our modeling domain. Recent work by our group suggests that these effects may be of importance in O3 formation equal to those related to more local changes (Hogrefe et al., in press). Mortality rates change in response to many demographic, social, behavioral, and political factors regarding individual and group health and access to health care. The climate–human health relationship within a given geography and population may change over time if populations acclimate and/or adapt to changing conditions. Part of the interdisciplinary process involved in downscaling from global to local impacts involves simplifications in each team’s modeling methods. This simplification introduces its own additional uncertainty to the results that follow. From a health science perspective, using a variety of modeling assumptions and assessing the range of results is one method for expressing uncertainty. By anticipating the range of possible impacts, the range of possibilities suggested by each scenario’s environmental, technologic, demographic, socioeconomic, and political story line can be examined. Uncertainty in the mortality risk estimates was expressed using the 95% CIs from the CRFs extracted from the epidemiologic literature.
These mortality projections do not take into account the possible effects of acclimatization or adaptive measures by the regional population. As a behavioral adaptation, the use of air conditioning could appreciably ameliorate exposures to O3 as well as to heat stress (O’Neill 2003; Rogot et al. 1992), because air-conditioned homes typically have lower outdoor air exchange rates than do residences without air conditioning that rely instead on open windows for ventilation (Janssen et al. 2002). In all likelihood, there will be a lag between periods of increasing environmental stress and behavioral adaptation; thus a “leading edge” of increased mortality before adaptation and/or acclimatization occurs. Furthermore, the increasingly pervasive use of air conditioners will present a potentially damaging positive feedback with climate change. Because these are highly energy-consumptive appliances, more electrical demand will occur on the hottest summer days, generating more airborne emissions from power plants and more urban waste heat from air conditioners. As evidenced during the 2003 eastern U.S. blackout, air conditioning can also sometimes be interrupted on the hottest days, owing to the increased peak demand load, and air conditioning may not really be an appropriate “fix” for adapting to climate change.
We did not consider the impacts of longer-duration O3 events or heat waves upon regional mortality. These will be considered in a separate, future report that compares temperature-related health effects from the GISS GCM versus MM5 RCM model outputs with the O3-related impacts (Knowlton K., unpublished data). The B2 (slower growth) scenario family from the IPCC SRES will also be evaluated as an alternative to A2, and the effects of land use changes will be included in future health impact reports.
Because of the limited scope of the project and available baseline health data, we assessed only mortality impacts in the present study. Because many other health outcomes are known to be associated with O3 exposures (Kinney 1999), our analysis is likely to have yielded underestimates of O3 impacts on health. The CRF used in the O3 mortality analysis (Thurston and Ito 2001) controlled for the effects of temperature upon mortality. Thus, the O3-related mortality estimates should not be confounded by temperature effects. The regional population’s age structure will undoubtedly change in future years, in ways that are difficult to project. The New York region has experienced appreciable immigration in recent decades that is projected to continue, along with a proportional increase in the percentage of people ≥ 65 years of age through the 2020s (U.S. Census Bureau 1996). Changes in age structure could affect the relative increase in future summer mortality in the 2020s and beyond to the 2050s, because the elderly are among those most vulnerable to heat stress and O3 impacts.
With the fourth assessment report of the IPCC scheduled to be released in 2007, there will be increasing emphasis on projecting the health impacts of climate change. The NYCHP linked modeling system may be a useful tool for conducting region-specific risk assessments of health impacts from future climate change and variability. These specific, local results can help bring consideration of the potential human health impacts of climate change into a public forum in those communities that may bear the burden of additional illness and mortality.
Conclusions
The results of the integrated O3 health impacts assessment suggest that changes in climate alone resulting from growth in greenhouse gas emissions could cause a 4.5% increase in the number of summer O3-related deaths across the New York metropolitan region by the 2050s. When the additional effects of O3 precursor emission increases are included, a 4.4% median increase in the number of summer O3-related deaths across the New York metropolitan region is projected for the 2050s. O3 projections for the 2050s show that counties with the highest percent increases in O3 mortality in the 2050s, relative to the 1990s, spread beyond the urban core into less densely populated suburban counties in New Jersey, southern Connecticut, and eastern Long Island. Sensitivity analyses showed that population growth assumptions had a dominant influence over future projections of mortality related to O3.
Figure 1 The 31-county New York metropolitan study area.
Figure 2 Estimated changes in O3 and associated summertime mortality in the 2050s compared with those in the 1990s for M1, where climate change alone drives changes in air quality. (A) Changes in mean 1-hr daily maximum O3 concentrations (ppb). (B) Percent changes in O3-related mortality.
Figure 3 Estimated changes in O3 and associated summertime mortality in the 2050s compared with those in the 1990s for M2, in which we include anthropogenic O3 precursor emission changes along with greenhouse gas emission changes. (A) Changes in mean 1-hr daily maximum O3 concentrations (ppb). (B) Percent changes in O3-related mortality.
Figure 4 Range of projected county-specific percent increases in summer O3-related mortality under mortality assessments (M1, M2) and sensitivity analyses (S1–S3). M1: climate only; M2: climate and anthropogenic emissions; S1: climate, anthropogenic emissions, and population; S2: anthropogenic emissions only; S3: climate only plus minimum threshold.
Table 1 Estimated county-level O3 concentrations and associated mortality in the 1990s and 2050s for M1 (climate only) and M2 (both climate and anthropogenic O3 precursor changes).
1990s
2050s climate (M1)
2050s climate + precursors (M2)
County State O3a O3 mortalityb O3 ΔO3c O3 mortality % Δmortalityd O3 ΔO3 O3 mortality % Δmortality
Fairfield CT 61.3 57 (33–82) 63.3 2.0 59 (34–85) 3.3 64.7 3.3 60 (35–87) 5.5
Litchfield CT 59.4 12 (7–17) 60.4 0.9 12 (7–17) 1.6 62.9 3.5 12 (7–18) 5.9
New Haven CT 62.1 61 (35–88) 64.5 2.5 63 (36–91) 4.0 65.8 3.7 64 (37–93) 6.1
Bergen NJ 49.7 50 (29–72) 51.9 2.2 52 (30–75) 4.5 49.0 −0.7 49 (28–71) −1.5
Essex NJ 52.0 52 (30–75) 54.3 2.3 54 (31–78) 4.5 51.9 −0.1 52 (30–75) −0.2
Hudson NJ 44.1 31 (18–45) 46.2 2.2 33 (19–47) 5.0 41.3 −2.8 29 (17–42) −6.3
Hunterdon NJ 64.3 6 (3–9) 67.2 2.9 6 (4–9) 4.6 68.6 4.3 6 (4–9) 6.8
Mercer NJ 62.6 25 (14–36) 66.9 4.3 26 (15–38) 7.0 66.6 4.0 26 (15–38) 6.5
Middlesex NJ 55.4 41 (23–58) 58.9 3.5 43 (25–62) 6.4 56.8 1.4 42 (24–60) 2.6
Monmouth NJ 54.8 38 (22–54) 58.1 3.3 40 (23–57) 6.2 56.5 1.7 39 (22–56) 3.2
Morris NJ 61.9 26 (15–37) 64.1 2.2 27 (15–39) 3.7 64.9 3.1 27 (16–39) 5.0
Ocean NJ 62.6 55 (31–79) 65.9 3.3 57 (33–83) 5.4 68.6 6.0 60 (34–86) 9.7
Passaic NJ 59.7 33 (19–47) 61.2 1.5 33 (19–48) 2.5 62.0 2.3 34 (19–49) 3.9
Somerset NJ 64.5 17 (10–24) 67.9 3.4 18 (10–25) 5.4 68.7 4.2 18 (10–26) 6.6
Sussex NJ 60.9 7 (4–11) 61.6 0.7 8 (4–11) 1.2 63.7 2.8 8 (4–11) 4.6
Union NJ 52.1 33 (19–47) 54.8 2.7 35 (20–50) 5.3 52.1 0.0 33 (19–47) 0.0
Warren NJ 63.2 7 (4–10) 65.0 1.8 7 (4–11) 2.9 66.8 3.6 8 (4–11) 5.9
Bronx NY 49.7 81 (46–116) 52.1 2.4 85 (49–122) 4.9 48.8 −0.9 79 (45–114) −1.9
Dutchess NY 59.8 17 (10–25) 60.3 0.5 17 (10–25) 0.9 62.8 3.0 18 (10–26) 5.1
Kings NY 44.1 123 (71–176) 46.5 2.4 129 (74–186) 5.6 41.2 −2.9 115 (66–164) −6.6
Nassau NY 56.6 83 (48–119) 60.1 3.4 88 (51–127) 6.2 57.7 1.1 85 (49–122) 1.9
New York NY 44.7 78 (45–113) 46.8 2.1 82 (47–118) 4.8 42.0 −2.7 74 (42–106) −6.1
Orange NY 60.0 20 (11–28) 60.3 0.3 20 (11–28) 0.4 62.6 2.6 20 (12–29) 4.3
Putnam NY 61.0 5 (3–7) 61.9 0.9 5 (3–7) 1.6 63.9 2.9 5 (3–7) 4.8
Queens NY 47.9 120 (69–172) 50.4 2.5 126 (73–181) 5.3 46.1 −1.8 115 (66–166) −3.7
Richmond NY 43.0 21 (12–30) 45.6 2.6 22 (13–32) 6.1 39.9 −3.1 19 (11–28) −7.2
Rockland NY 59.6 16 (9–23) 61.5 1.9 17 (9–24) 3.2 62.1 2.6 17 (10–24) 4.4
Suffolk NY 59.0 84 (48–121) 61.5 2.5 87 (50–126) 4.3 61.5 2.5 87 (50–126) 4.3
Sullivan NY 58.0 6 (3–8) 58.3 0.4 6 (3–9) 0.6 60.9 3.0 6 (4–9) 5.2
Ulster NY 57.6 12 (7–18) 58.1 0.5 12 (7–18) 0.8 60.7 3.1 13 (7–19) 5.4
Westchester NY 59.9 61 (35–88) 62.5 2.6 64 (37–92) 4.3 62.9 3.0 64 (37–93) 5.0
a Mean summer 1-hr daily maximum O3 concentration in ppb.
b Mean summer O3-related mortality typical of decade (95% CI).
c Change in mean summer 1-hr daily maximum O3 concentration, 2050s versus 1990s.
d Percent change in typical summer O3-related mortality, 2050s versus 1990s.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7233ehp0112-00156415531443Environmental MedicineArticleInduced Sputum Assessment in New York City Firefighters Exposed to World Trade Center Dust Fireman Elizabeth M. 123Lerman Yehuda 34Ganor Eliezer 5Greif Joel 13Fireman-Shoresh Sharon 6Lioy Paul J. 7Banauch Gisela I. 89Weiden Michael 810Kelly Kerry J. 8Prezant David J. 891Institute for Pulmonary and Allergic Diseases, and2National Laboratory Service for Interstitial Lung Diseases, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel3Sackler Faculty of Medicine, Tel Aviv University, Tel-Aviv, Israel4National Institute of Occupational and Environmental Health, Raanana, Israel5Department of Geophysics and Planetary Sciences, Tel Aviv University, Tel-Aviv, Israel6Institute of Chemistry, Hebrew University of Jerusalem, Jerusalem, Israel7Environmental and Occupational Health Sciences Institute of New Jersey, New Brunswick, New Jersey, USA8NYC Fire Department Bureau of Health Services, New York, New York, USA9Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA10Pulmonary Division, New York University School of Medicine, New York, New York, USAAddress correspondence to D.J. Prezant, Albert Einstein College of Medicine, Pulmonary Division, Montefiore Medical Center, Centennial 423, East 210th St., Bronx, NY 10467 USA. Telephone: (718) 999-1934. Fax: (718) 999-0174. E-mail:
[email protected] thank E. Eshkol for editorial assistance.
The authors declare they have no competing financial interests.
11 2004 22 9 2004 112 15 1564 1569 6 5 2004 22 9 2004 Publication 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. New York City Firefighters (FDNY-FFs) were exposed to particulate matter and combustion/pyrolysis products during and after the World Trade Center (WTC) collapse. Ten months after the collapse, induced sputum (IS) samples were obtained from 39 highly exposed FDNY-FFs (caught in the dust cloud during the collapse on 11 September 2001) and compared to controls to determine whether a unique pattern of inflammation and particulate matter deposition, compatible with WTC dust, was present. Control subjects were 12 Tel-Aviv, Israel, firefighters (TA-FFs) and 8 Israeli healthcare workers who were not exposed to WTC dust. All controls volunteered for this study, had never smoked, and did not have respiratory illness. IS was processed by conventional methods. Retrieved cells were differentially counted, and metalloproteinase-9 (MMP-9), particle size distribution (PSD), and mineral composition were measured. Differential cell counts of FDNY-FF IS differed from those of health care worker controls (p < 0.05) but not from those of TA-FFs. Percentages of neutrophils and eosinophils increased with greater intensity of WTC exposure (< 10 workdays or ≥ 10 workdays; neutrophils p = 0.046; eosinophils p = 0.038). MMP-9 levels positively correlated to neutrophil counts (p = 0.002; r = 0.449). Particles were larger and more irregularly shaped in FDNY-FFs (1–50 μm; zinc, mercury, gold, tin, silver) than in TA-FFs (1–10 μm; silica, clays). PSD was similar to that of WTC dust samples. In conclusion, IS from highly exposed FDNY-FFs demonstrated inflammation, PSD, and particle composition that was different from nonexposed controls and consistent with WTC dust exposure.
firefightersinflammationinhalation exposureparticulatessputumWorld Trade Center
==== Body
In the aftermath of September 11th, the clouds of dust and smoke that stood for days in place of the World Trade Center’s (WTC) twin towers raised serious health concerns among exposed workers and residents. The Fire Department of New York City (FDNY) operated a continuous rescue/recovery effort from 11 September 2001 through May 2002. Nearly every FDNY firefighter (FDNY-FF) worked at the site during the first weeks, reporting numerous exposures to airborne particulates and products of combustion/pyrolysis [Centers for Disease Control and Prevention (CDC) 2002a] that have since been implicated in the development of “WTC cough,” airways obstruction, and inflammatory bronchial hyperreactivity (Banauch et al. 2003; Feldman et al. 2004; Prezant et al. 2002). Appropriate respiratory protection was not readily available in the first week (CDC 2002b). Firefighters were not the only ones affected. Respiratory symptoms and pulmonary dysfunction has been reported in other WTC rescue workers (Safirstein et al. 2003; Saltzman et al. 2004; Skloot et al. 2004) and in Manhattan residents living near the site. (CDC 2002c; Szema et al. 2004).
Environmental site studies after the collapse reported concentrations of airborne and respirable particulates ranging up to 100 mg/m3 and 1 mg/m3, respectively (CDC 2002a). Analysis of settled WTC dust samples collected 5 and 6 days postcollapse from areas east of the WTC revealed a complex mixture of particulate matter and combustion/pyrolysis products, composed mostly of building debris fibers (e.g., mineral wool, fiberglass, asbestos, wood, paper, cotton) contaminated with polycyclic hydrocarbons (Landrigan et al. 2004; Lioy et al. 2002). More than 90% of the particles in these bulk samples were > 10 μm in diameter and many were fibers with widths < 5 μm and lengths > 10 μm. Further, many were caustic cement particles with a pH of 9–11 (Landrigan et al. 2004; Lioy et al. 2002).
Bronchoalveolar lavage (BAL) recovered significant quantities of fly ash, degraded fibrous glass, and asbestos fibers along with evidence for a significant inflammatory response (70% eosinophils and increased levels of interleukin-5) in one FDNY-FF hospitalized with acute eosinophilic pneumonitis several weeks after WTC exposure (Rom et al. 2002). Although BAL is an important diagnostic tool (Davison et al. 1983; Dodson et al. 1991; Rom et al. 2002), it is an invasive procedure unsuitable for screening or repeated follow-up evaluations after exposure to dusts or combustion/pyrolysis products. In fact, no FDNY-FF has agreed to enroll in a BAL screening program.
Induced sputum (IS) provides a non-invasive alternative method to study respired particulate matter and the lung’s inflammatory response (Fireman et al. 1999a; Maestrelli et al. 1994; Marek et al. 2001; Quirce et al. 2001). Qualitative and quantitative analysis of chemical particles among silica and hard metal workers showed similar patterns when recovered by IS and BAL (Fireman et al. 1999a). IS analyzed by scanning electron microscope (SEM) has demonstrated dust exposures in patients with occupational lung diseases (Cohen et al. 1999; Fireman et al. 2004a; Lerman et al. 2003b; Paris et al. 2002), and IS analyzed by particle size distribution (PSD) has shown significant differences between workers with and without exposure to hazardous dust (Lerman et al. 2003b).
This study is the first to use IS methodology to assess respired particulate matter and the inflammatory response of the lung after exposure to WTC dust. Our objective was to determine if IS collected from highly exposed FDNY-FFs 10 months after the collapse demonstrates a unique pattern of inflammation and particulate matter deposition compatible with WTC dust. Inflammation was assessed by differential cell counts and by measuring metalloproteinase-9 (MMP-9), a cytokine involved in airways inflammation (Montano et al. 2004) and remodeling (Atkinson and Senior 2003). Particulate matter was assessed by size distribution, mineral composition, and by comparison to settled dust samples collected at the WTC site.
Methods
Study population.
Ten months after the WTC collapse, we studied 39 male FDNY-FFs who worked at the WTC, 12 male firefighters who lived in Tel-Aviv, Israel (TA-FF), and 8 male Israeli hospital workers, all apparently free of respiratory disease. Firefighters were recruited from those undergoing medical monitoring during June 2002. The only inclusion criterion was that firefighters must have worked in the WTC dust cloud the morning of 11 September 2001. Current or past tobacco smokers were excluded. IS induction was voluntary, and all were informed about the ongoing research. For FDNY-FFs, cumulative WTC exposure was measured in workdays reported on a self-administered questionnaire, followed by a confirmatory interview before IS induction. The Institutional Review Boards at Tel-Aviv Medical Center and Montefiore Medical Center approved this study.
Sputum induction and processing.
IS was obtained at the FDNY Bureau of Health Services as previously described (Fireman et al. 1999a, 1999b; Lerman et al. 2003b). After pretreatment with a short acting beta-2 agonist, 3% saline was administered by nebulizer (U1 Ultrasonic Nebulizer; Omron HealthCare, Henfield, West Sussex, UK) for up to 20 min while subjects were encouraged to cough and expectorate sputum into a sterile container. Samples were stored at 4°C and processed within 3 hr. All portions with little or no squamous epithelial cells (rich nonsquamous epithelial cell fraction considered to originate from the lower respiratory tract, hereafter referred to as “plugs”) were collected using the selection plug method and processed as previously described (Fireman et al. 1999b; Pin et al. 1992; Popov et al. 1994). Briefly, plugs were selected and treated with dithiothreitol [DTT (Sputalysin); Calbiochem Corp., San Diego, CA, USA]. The cell suspension was filtered through a 52-μm nylon gauze (BNSH Thompson, Scarborough, Ontario, Canada) and the effect of DTT was stopped by diluting the suspension with phosphate-buffered solution to a volume equal to the sputum plus DTT. After centrifugation, the supernatants were deep frozen for consecutive measurement of MMP-9. The pellets were resuspended and cytospinned (Shandon Southern Instruments, Sewickley, PA, USA), and the slides were stained with Giemsa. We counted 200 nonsquamous cells; the results were expressed as a percentage of the total nonsquamous count. Each slide was read by two independent readers.
Differential cell counts.
Samples from the cellular fraction were resuspended and processed to cytospin slides, centrifuged, air-dried, and stained with Giemsa. Differential cell counts were measured by scanning cytospin slides with high power (×500) magnification. We counted 200 nonsquamous cells and expressed cell differentials as percentages of total nonsquamous cell counts.
Measurement of MMP-9 ELISA.
We determined the absolute value of MMP-9 in IS supernatants using an ELISA commercial kit (R&D Systems Inc., Minneapolis, MN, USA). MMP-9 values expressed total levels of active and pro-MMP-9 (0.1–25 ng/mL). We performed spike experiments using 10 and 20 ng/mL pure MMP-9 protein to assess the efficacy of recovery. We found that only 10–12% of protein was equally denatured in the pure protein and in all IS samples by DTT. No other metabolites were measured in the study.
Particle examination.
After separation of the plugs and viscous materials, all fractions of IS were preserved in 10% formalin and stored at 4°C until analysis of mineral particles. We used the samples containing both extracellular and intracellular particles for the SEM analysis; samples were treated with 14% formamide solution and filtered onto a 0.8-μm carbon-coated Nuclepore filter (Millipore Filter Corp., Bedford, MA, USA). Particles with a diameter > 0.4 μm were analyzed by a JEOL 840 SEM (JEOL Ltd., Hertfordshire, UK) equipped with a Link 10,000 energy-dispersive system (EDS; Link Oxford Analytical Instruments, Oxford, UK). The spectrometer of the EDS system separated the elements according to energy rather than wavelength. In addition, we used a petrographic microscope to identify minerals (Fireman et al. 1999a, 1999b). We assessed the size and shape of the particles from the rich cell fraction of the processed plugs with a Cis-100 Analyzer and the analyzer’s video channel (Ankersmid, Yokneam, Israel) (Fireman et al. 1999b) using a PSD method in the range of 0.5–3,600 based on the time of transition (Aharonson et al. 1986; Cohen et al. 1999; Pin et al. 1992) theory where the duration of interaction between beam and particle provides a direct measurement of each particle’s size. A heliumneon laser beam interfered with the intracellular particles, and the signal was recorded. We performed dynamic shape characterization using image analysis techniques. Measurements were performed on 2 drops of a suspension of sputum cells (106 cells/mL) introduced into a quartz cuvette containing stirred water for IS samples and water with glycerol (1:1) for the WTC settled dust sample collected 7 days postcollapse on Cortlandt Street, one block east of the WTC. Each result was an average of three consecutive measurements.
Statistical analysis.
Demographic comparisons between groups were performed by parametric or nonparametric (Kruskal-Wallis) analysis of variance (ANOVA). FDNY-FFs were analyzed according to cumulative exposure days using a continuous scale and dichotomized based on the median (< 10 days vs. 10 days). Groups were compared by independent t-test, Mann-Whitney test, and chi-square test. In addition, group means of all percent cells and particle size, adjusted for MMP9, were compared by a one-way analysis of covariance with the natural logarithm of MMP-9 as covariate. Natural logarithm transformation was applied to MMP-9 because of its skewed distribution (Ln MMP-9). The association between percent parameters was evaluated by Pearson correlation coefficients. For all tests, p-values < 0.05 were considered statistically significant. The data were analyzed using Statistical Package for the Social Sciences (SPSS) for Windows software, Version 11.0 (SPSS, Chicago, IL, USA).
Results
Demographic characteristics.
We found no significant differences in age between the FDNY-FFs and the TA-FFs (37.4 vs. 36.6 years; p = 0.109, respectively) or their respective fire-fighting work tenure (17.5 vs. 13.5 years; p = 0.399, respectively). The average age of the nonfirefighter controls was 41.6 years. All 39 FDNY-FFs were caught in the WTC dust cloud during the morning of 11 September 2001, representing the highest acute exposure group as defined by arrival time (Banauch et al. 2003; Feldman et al. 2004). The period of cumulative WTC work exposure for the FDNY-FF group varied from 1 to 75 days (mean of 20.2 days), with all but 2 of the FDNY-FFs working at the WTC for ≥ 2 days. We found no differences in age or FDNY work years when we separated the group based on the median of cumulative WTC work-site exposure time: < 10 days (n = 23) or 10 days (n = 16).
Differential cell counts and MMP-9 levels in sputum samples. We performed IS differential cell counts in 36 of the 39 FDNY-FFs, 12 of 12 TA-FFs, and 8 of 8 controls. The firefighter groups were significantly different from the nonfirefighter controls, but we found no significant differences between the firefighter groups (Table 1). Differential counts for neutrophils and eosinophils increased with cumulative WTC workday exposure intensity (dichotomized to < 10 or 10 WTC workdays) (Table 2).
We measured MMP-9 levels in IS supernatants retrieved from sample preparations of 25 of 39 FDNY-FFs, 12 of 12 TA-FFs, and 8 of 8 controls. There was a trend for higher levels in the FDNY-FFs vs. the TA-FFs (2.23 ng/mL versus 1.21 ng/mL; p = 0.057), and the combined levels for both groups were significantly higher than for the controls (0.30 ng/mL; p = 0.0001). Independent of exposure group, the levels of MMP-9 were positively correlated to the percentage of neutrophils (r = 0.449, p = 0.002; Figure 1A) and negatively correlated to the percentage of macrophages (r = 0.488, p = 0.001; Figure 1B).
Particle size distribution.
We determined PSD in 35 of 39 FDNY-FF samples (4 samples were eliminated because of contamination before measurement) and 12 of 12 TA-FF samples. Table 3 shows that more of the FDNY-FF samples contained a higher percentage of particles > 2 μm (p = 0.0001) and > 5 μm (p = 0.0001) compared with the TA-FF samples. We found no significant differences in PSD measurements when they were correlated to cumulative WTC exposure (tested by a continuous or a dichotomized 10-day analysis). Most of the particles showed an irregular shape (Figure 2B,D).
We also demonstrated compatibility between the PSD of the settled raw dust samples from Cortlandt Street (located one block east of the WTC) collected 7 days after the collapse with measurements from the IS of FDNY-FFs (n = 39; Figure 3). Both PSD curves showed similar patterns for particles > 4.37 μm in diameter, but the curve for the FDNY-FF IS showed a left shift compared to the settled raw dust material, indicating a higher proportion of small particles in the lung than in bulk samples collected from settled dust. This would be consistent with the fact that the largest dust particles do not efficiently penetrate past the nasopharyngeal region. An inversion between both curves can be observed where they intersect at 7.19 μm.
Chemical and mineralogic analysis of particles.
Chemical and mineralogic analyses were performed on IS samples from 4 FDNY-FFs and 2 TA-FFs randomly chosen from high quality specimens. Chemical analysis of the FDNY-FF samples revealed many elements, for example, titanium, zinc, mercury, gold, tin, and nickel (Table 4), which were present as metal alloys or metal oxides in abundant large particle sizes (range 1–50 μm). The shape varied from irregular (multiple angles) to spherical. In the TA-FF samples, a few smaller particles (1–10 μm) of silica and clays were found that are more typical of normal Tel-Aviv soil than of pollution. Intracellular particles are shown in Figure 4. Figure 5 shows X-ray spectrums of representative particles identifying them as zinc, copper, silver, and mercury.
Discussion
In FDNY-FFs—all highly exposed to WTC dust and combustion/pyrolysis products during the morning of the collapse and nearly all with additional cumulative workday exposures to WTC dust—IS analysis were significantly different from controls, in inflammation (percentages of neutrophils and eosinophils that increased with exposure intensity; increased MMP-9) and particulate matter deposition (a shift in PSD toward larger size particles and chemical/mineral analysis consistent with WTC dust).
It is not surprising to find abnormalities in lung particulate matter and inflammation months after exposure. After chronic occupational exposures, IS has shown increased eosinophil counts in sensitized asthmatics (Maestrelli et al. 1994; Quirce et al. 2001) and in asbestos (Paris et al. 2002), radon (Marek et al. 2001), and uranium (Marek et al. 2001) workers. Asbestos bodies (Paris et al. 2002) and particles with abnormal chemical compositions have been observed in IS years after exposure (Cohen et al. 1999; Fireman et al 1999a, 1999b; Lerman et al. 2003a).
Firefighters are exposed to numerous irritants (e.g., combustion and pyrolysis products, particulate matter), and such exposures have the potential to alter lung permeability (Bergstrom et al. 1997; Burgess et al. 2001). Inflammatory changes have been documented in BAL from nonsmoking firefighters compared with healthy volunteers (Bergstrom et al. 1997; Burgess et al. 2001). In a previous case report (Rom et al. 2002), we found an eosinophilic inflammatory response (70% of total cell count) and increased levels of interleukin-5 in the BAL of an FDNY-FF with acute pneumonitis several weeks after repeated WTC dust exposures. The present study is the first to use IS to characterize differential cell counts, inflammation, particle size deposition, and composition in subjects with WTC exposures. It is also the first to report these findings in firefighters. IS from FDNY-FFs and TA-FFs, all never smokers, had increased percentages of eosinophils and neutrophils compared with healthy nonfirefighter controls. For FDNY-FFs, a significant dose–response relationship was demonstrated, with the proportions of neutrophils and eosinophils increasing as cumulative WTC exposure intensity increased (measured in number of days working at WTC). Neutrophils and eosinophils are important components of the inflammatory cascade responsible for airway inflammation, injury, and remodeling (Azadniv et al. 2001; Lemiere et al. 2001; Woodruff et al. 2001).
The matrix metalloproteinases (MMPs) are a family of zinc- and calcium-dependent endopeptidases with a central role in inflammation and combined ability to degrade components of connective tissue matrices (Murphy and Docherty 1992). MMPs are synthesized and secreted by connective tissue and some hematopoietic cells, and are known to be important mediators of airway inflammation, remodeling, and pulmonary injury (Cataldo et al. 2002; Li et al. 2002). We chose to measure MMP-9 because it a) plays an important role in neutrophil recruitment to the lung (Li et al. 2002); b) is detectable in IS with reliable and reproducible results (Cataldo et al. 2002; Fireman et al. 2004a); and c) is increased in IS from workers exposed to hazardous dust (Fireman et al. 2004b). We found a trend for higher levels of MMP-9 in IS samples from FDNY-FFs than in those from TA-FFs, and both groups were significantly higher than nonfirefighter controls. Moreover, we found a positive correlation between accumulation of neutrophils and MMP-9 levels. In these non-smoking firefighters, the increase in MMP-9 levels provides biochemical evidence for exposure-related immune activation in the lung, complementing the evidence from IS differential cell counts. Persistent inflammation, 10 months after the WTC collapse, is consistent with the clinical findings of new and persistent cough, airway hyperreactivity, and asthma previously reported in FDNY-FFs (Banauch et al. 2003; Feldman et al. 2004; Prezant et al. 2002) and other rescue workers (Safirstein et al. 2003; Saltzman et al. 2004; Skloot et al. 2004) after exposures to WTC dust.
PSD measurements demonstrated significant differences between FDNY-FF and TA-FF IS samples. We found a high load of relatively large particles (1–50 μm in diameter) with irregularly shaped structures in FDNY-FF IS samples that were completely different from the smaller, regular shaped particles found in TA-FF IS samples. Chemical and mineral analyses also demonstrated differences between FDNY-FF and TA-FF IS samples. TA-FF IS showed findings typical of soil contaminants from the Tel-Aviv area. In contrast, a heterogeneous mixture was found in FDNY-FF IS, consistent with exposures to aerosolized building debris and dust and smoke generated by the collapse and fires (Lioy et al. 2002). Mineral particles were seen in macrophages and epithelial cells. The presence of these particles in epithelial cells illustrates the high concentration of respirable particulate matter, overwhelming normal nasopharyngeal filtering, mucociliary clearance, and alveolar macrophage defense systems (Churg 1996). Although asbestos fibers were found in the BAL from the FDNY-FFs with eosinophilic pneumonitis after WTC exposure (Rom et al. 2002), we did not find asbestos fibers in our FDNY-FF IS samples.
Despite our finding significant correlations between inflammation and cumulative WTC exposure and significant differences in PSD between FDNY-FFs and TA-FFs, we could not detect a significant effect of cumulative WTC exposure on PSD. This may be related to other physical factors at the WTC site, such as differences in work location, minute ventilation (related to subject size, physical fitness, experience, and work tasks), specific work task–related exposures, and the use of respiratory protection (minimal during week one and variable thereafter) (CDC 2002b). It may also reflect potential limitations of scoring cumulative exposure only in terms of workdays, unweighted for the above differences and other variables such as environmental conditions. Additionally, selection bias (all FDNY-FF subjects volunteered for this study) may have influenced our current ability to detect the effect of cumulative exposure. However, the strengths of this study outweigh these limitations and include the following: a) all FDNY-FFs had significant WTC dust exposures because they were caught in the dust cloud during the collapse and then continued to work at the WTC site for days thereafter; b) IS provided an assessment of persistent inflammation and cumulative particle deposition because the enormity of the disaster prevented us from collecting IS until 10 months after the collapse; c) IS assessments were not biased toward acute, transient inflammation and particle deposition because at least 1 month had elapsed between collecting IS and the last workday at the WTC site; and d) controls were truly unexposed because they were recruited from outside this region—a necessity because by this time nearly every FDNY-FF and most other rescue workers and many Manhattan residents had reported some level of exposure.
We believe the differences between FDNY-FFs, TA-FFs, and controls demonstrate a unique exposure following the WTC collapse (Tables 3 and 4). This conclusion is supported by similarities between quantitative and qualitative analyses of IS sputum samples and dust samples collected from settled material one block east of the WTC. These dust samples demonstrate a complex mixture of coarse particles and fibers consisting of relatively larger particles (> 90% of dust particle mass was > 10 μm in diameter) (Lioy et al. 2002). Thus, the fact that IS samples from FDNY-FFs had higher amounts of particles > 2.0 μm in diameter than samples from TA-FFs was due to the nature and composition of the sources that dominated FDNY-FF exposure patterns. In 87% of FDNY-FFs, > 20% of the particles found in IS were > 2.0 μm in diameter compared with only 8% of those from TA-FFs. SEM also showed relatively larger and more irregularly shaped particles in FDNY-FFs compared with TA-FFs. Once inhaled, particles > 2.0 μm in diameter are most commonly deposited in the upper airways, causing significant irritation because of their alkaline, caustic nature (Lioy et al. 2002); this explains the increased incidence of upper airway symptoms (nasal congestion/drip, throat irritation, cough, and gastroesophageal reflux) described in highly and moderately exposed FDNY-FFs (Banauch et al. 2003; Feldman et al. 2004; Prezant et al. 2002).
Although helpful, measurements obtained from settled dust samples, which are often enriched with larger particles, are not entirely representative of the particle types or size distribution of aerosolized, potentially respirable dust during the height of exposure. In fact, the majority of particles in FDNY-FF IS samples were < 2.0 μm in diameter: 74% of FDNY-FFs had > 60% of PSD < 2.0 μm in diameter (Figure 2). Compared with settled WTC dust samples, FDNY-FF IS samples demonstrated a distinct leftward shift of the average curve of all PSD measurements toward smaller particles. Particles 2.0 μm in diameter are commonly deposited in the smaller airways. This may explain why post-WTC FDNY-FFs (Banauch et al. 2003; Prezant et al. 2002) have shown an increased incidence of bronchial hyperreactivity, reactive airways dysfunction syndrome, and asthma. Six months after the WTC collapse, methacholine challenge testing demonstrated that highly exposed FDNY rescue workers were 6.8 times more likely to have bronchial hyper-reactivity than moderately exposed and unexposed FDNY controls (Banauch et al. 2003).
Our findings support both the practical utility and scientific usefulness of IS as a non-invasive method for screening and follow-up monitoring of populations exposed to high concentrations of aerosolized particulates following a disaster—natural or man-made. IS is superior to BAL because it is noninvasive and collection can occur at nearly any field location. In contrast to traditional blood and urine biomonitoring, IS directly samples the lung, the specific target organ of interest following an inhalation exposure. For example, blood and urine samples were collected 1 month after the collapse in a different group of FDNY-FFs, and only a few of the 110 chemicals measured showed significant, yet small, differences when WTC-exposed FDNY-FFs were compared with nonexposed FDNY-FFs (Edelman et al. 2003). In contrast, IS directly assesses respired particulate matter and pulmonary inflammation, thereby serving an important complementary role to traditional biomonitoring techniques.
In conclusion, IS from FDNY-FFs caught in the WTC dust cloud during the morning of the collapse showed an influx of inflammatory cells, percentages of neutrophils and eosinophils that increased with exposure intensity, increased MMP-9 levels, a shift in PSD toward larger-size particles, and chemical/mineral analyses consistent with exposure to building debris, smoke, and dust generated by the attack on the WTC. If future population studies demonstrate that IS measures of inflammation or PSD correlate with health outcomes, then IS evaluations would be a valuable addition to medical screening/monitoring programs following inhalation exposures.
Figure 1 Correlation between concentrations of Ln MMP-9 and the percentage of neutrophils (A) and the percentage of macrophages (B) in induced sputum from all samples (n = 56). Values are expressed as the percentage of 200 cells as described in “Methods.”
Figure 2 Size and shape of four different particles found in an FDNY-FF specimen. Images (A and C) and shape of particles analyzed by a Cis-100 analyzer. Images (B and D) and shape of particles analyzed by X-ray spectrum. See “Methods” for details.
Figure 3 Comparison of the size distribution of particles between IS samples (n = 39) from FDNY-FFs and the settled material collected on Cortlandt Street, one block east of the WTC 7 days after the WTC collapse. Measurements were performed as described in “Methods.”
Figure 4 Intracellular phagocytized particles in a Giemsa-stained cytospin preparation from the IS sample of an FDNY-FF exposed to WTC dust. (A) Image showing a single macrophage with intracellular particles and two adjacent lymphocytes. (B) Image showing a mixed cell population with macrophages and intracellular particles. Light microscopy; magnification, ×100.
Figure 5 X-ray spectra of representative particles identified as Cu and Hg and Ag (A) and Zn (B) in FDNY-FF sample 35.
Table 1 IS differential cell counts and MMP-9 levels.
Macrophages (%) Neutrophils (%) Lymphocytes (%) Eosinophils (%) MMP-9 (ng/mL)
FDNY-FF (n = 39) 34.3 ± 15.2 50.7 ± 17 12 ± 6.8 2.8 ± 4 2.2 ± 2.6
TA-FF (n = 12) 35 ± 18.9 44.1 ± 22.9 15.8 ± 9.8 5.4 ± 8.9 1.2 ± 0.99*
Control (n = 8) 64.8 ± 7.9** 29.1 ± 8.9** 5.7 ± 2.2** 0.2 ± 0.4** 0.3 ± 0.09**
Differential counts are expressed as a percentage of 200 cells as described in “Methods.”
* p = 0.057 between levels in FDNY-FFs versus TA-FFs.
** p < 0.05 between cells in FDNY-FFs and TA-FFs versus controls.
Table 2 Differential counts, MMP-9 levels, and particle size distribution in FDNY-FFs analyzed according to cumulative exposure.
Cumulative workdays at the WTC
< 10 days ≥ 10 days p-Value
Duty (years) 15.6 ± 8.3 17.6 ± 8.6 0.78
Macrophages (%) 31.1 ± 13.7 38.4 ± 16.5 0.18
Neutrophils (%) 44.2 ± 16.5 55.7 ± 15.2 0.05
Lymphocytes (%) 11.6 ± 7.0 12.9 ± 6.8 0.59
Eosinophils (%) 1.5 ± 1.9 4.4 ± 5.2 0.04
Particles > 2 μm (%) 32.3 ± 13.7 38.6 ± 17 0.48
Particles > 5 μm (%) 7.8 ± 3.3 9.3 ± 7.1 0.28
MMP-9 (ng/mL) 1.73 ± 0.98 2.7 ± 4.0 0.36
Differential cell counts are expressed as a percentage of 200 cells as described in “Methods.”
Table 3 Particle analysis in IS of FDNY-FFs and TA-FFs.
Measurement FDNY-FF TA-FF p-Value
Percent of particles > 2 μma 34.01 ± 15.5 10.4 ± 5.8 p = 0.0001
Percent of particles > 5 μma 8. 1 ± 15.3 1.6 ± 1.25 p = 0.0001
Percent of samples with > 20% particles > 2 μmb 88.6% (31/35) 8.3% (1/12) p = 0.0001
Percent of samples with > 5% particles > 5 μmb 85.7% (30/35) 0% (0/12) p = 0.0001
a Percentage of total particles present in the sample.
b Percentage of total IS samples measured by a Cis-100 analyzer in each group (n = 35 FDNY-FFs; n = 12 TA-FFs) as described in “Methods.”
Table 4 Mineralogic analysis of IS particles from FDNY-FFs and TA-FFs.
Subject no. Particle size (μm) Frequent elements Type of particles
FDNY-FF 16 1.5–50 Si, SiCa, SiFe Silica
TiFe, Ti Titanium oxide
FeNi, FeCr Stainless steel
Ca Calcite
FDNY-FF 3 1.5–50 Zn Zinc oxide
Ca Calcite
FDNY-FF 26 1.0–10 Si, SiCa Silica
SiFeNi, SiFe Ferrous alloys
FDNY-FF 35 1.0–50 Ca Calcite
MgAlSiCa, AlSiCa, AlSi Clays
Zn Zinc oxide
AgSnCuHg Nonferrous alloys
TA-FF 10 0.8–7.0 FeTi Stainless steel
Si Silica
AlSiCaFe, AlSiFe Clays
AlSiFeTi, AlSi Clays
MgSiAlFe Clays
TA-FF 12 3–10 Fe Ferric oxide
FeCa, FeCr Stainless steel
Analysis was performed as described in “Methods.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7243ehp0112-00157015531444Children's HealthArticlesMaternal and Paternal Risk Factors for Cryptorchidism and Hypospadias: A Case–Control Study in Newborn Boys Pierik Frank H. 12Burdorf Alex 2Deddens James A. 3Juttmann Rikard E. 24Weber Rob F.A. 11Department of Andrology and2Department of Public Health, Erasmus MC, Rotterdam, the Netherlands3Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio, USA4Department of Child Health Care, Rotterdam Homecare Foundation, Rotterdam, the NetherlandsAddress correspondence to F.H. Pierik, Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Dr Molewaterplein 50, NL-3015 GE Rotterdam, The Netherlands. Telephone: 31-10-4087448. Fax: 31-10-4089449. E-mail:
[email protected] Endocrine Modulators Study Group of the European Chemical Industry Council and the Nutricia Research Foundation are acknowledged for financial support. The sponsors of the study had no role in study design, data collection, data interpretation, or reporting.
The authors declare they have no competing financial interests.
11 2004 3 9 2004 112 15 1570 1576 10 5 2004 18 8 2004 Publication 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 is known on environmental risk factors for cryptorchidism and hypospadias, which are among the most frequent congenital abnormalities. The aim of our study was to identify risk factors for cryptorchidism and hypospadias, with a focus on potential endocrine disruptors in parental diet and occupation. In a case–control study nested within a cohort of 8,698 male births, we compared 78 cryptorchidism cases and 56 hypospadias cases with 313 controls. The participation rate was 85% for cases and 68% for controls. Through interviews, information was collected on pregnancy aspects and personal characteristics, lifestyle, occupation, and dietary phytoestrogen intake of both parents. Occupational exposure to potential endocrine disruptors was classified based on self-reported exposure and ratings of occupational hygienists based on job descriptions. Our findings indicate that paternal pesticide exposure was associated with cryptorchidism [odds ratio (OR) = 3.8; 95% confidence interval (95% CI), 1.1–13.4]. Smoking of the father was associated with hypospadias (OR = 3.8; 95% CI, 1.8–8.2). Maternal occupational, dietary, and lifestyle exposures were not associated with either abnormality. Both abnormalities were associated with suboptimal maternal health, a lower maternal education, and a Turkish origin of the parents. Being small for gestational age was a risk factor for hypospadias, and preterm birth was a risk factor for cryptorchidism. Because paternal pesticide exposure was significantly associated with cryptorchidism and paternal smoking was associated with hypospadias in male offspring, paternal exposure should be included in further studies on cryptorchidism and hypospadias risk factors.
childrencryptorchidismendocrine disruptorenvironmentepidemiologyhypospadiasnutritionoccupational exposuretestis
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Cryptorchidism and hypospadias are among the most frequent congenital abnormalities in male births. Cryptorchidism (maldescent of the testis) is observed in 1–5% of full-term male births (Toppari et al. 1996) and is a risk factor for subfertility and testicular cancer. Hypospadias (abnormal location of the orifice of the urethra) is observed in 0.3–0.7% of male births and requires surgical treatment in most cases (Pierik et al. 2002).
In the past two decades, concern has been raised over a possible increase in disorders of the male reproductive tract, including cryptorchidism, hypospadias, testicular cancer, and impaired semen quality. It has been suggested that these disorders are interrelated and share a common etiology during fetal life, described by Skakkebaek and colleagues as the testicular dysgenesis syndrome (TDS) (Sharpe and Skakkebaek 1993; Skakkebaek et al. 2001). Fetal exposure to endocrine disruptors (EDs) with estrogen-like or antiandrogen-like activity has been suggested as a cause for TDS (Sharpe 2003; Sharpe and Skakkebaek 1993). Various groups of chemicals, including pesticides and phthalate esters, have been identified as being weakly estrogenic or antiandrogenic (Sharpe 2003). These chemicals may occur in working environments, drinking water, and food (Toppari et al. 1996). Humans can also be exposed to natural phytoestrogens, through consumption of food products derived from plants (Toppari et al. 1996).
There is only limited evidence that the suggested increase in male urogenital abnormalities in humans can be attributed to exposure to EDs (Sharpe 2003) or environmental chemicals in general. An excess of hypospadias has been reported among newborns in populations living within 2–3 km of landfill sites (Dolk et al. 1998; Elliott et al. 2001). These findings may indicate an effect of chemical wastes, but exposure classification was too crude to differentiate this exposure from confounding factors (Dolk et al. 1998; Elliott et al. 2001). In contrast, no association was observed between hypospadias and occupational exposure to EDs by the mother during pregnancy (Vrijheid et al. 2003). A maternal vegetarian diet during pregnancy has been associated with hypospadias in the offspring, suggesting a role of a higher intake of phytoestrogens (North and Golding 2000). Although several studies have demonstrated male-mediated developmental effects of environmental exposure (Davis et al. 1992; Robaire and Hales 2003), its role in the etiology of cryptorchidism and hypospadias remains unclear.
The aim of the present study was to evaluate the role of maternal and paternal occupational and dietary exposures to potential EDs in the occurrence of cryptorchidism and hypospadias.
Materials and Methods
Design and participants.
We conducted a nested case–control study within a large cohort of newborn boys in the city of Rotterdam. This cohort consisted of newborns who were examined at their first visit to child health care centers (CHCs). In the Netherlands, CHCs are notified of live births within 2 days after registration in the municipal birth register. CHCs invite all parents to participate free of charge in the nationwide preventive child health care program, including growth monitoring and vaccination. From 1 October 1999 to 31 December 2001, 9,146 male births were registered, of which 8,695 boys (95%) were examined by CHC physicians at a median age of 34 days (5th and 95th percentiles, 25 and 105 days, respectively). CHC physicians (n = 30) were trained in a standardized genital examination by a pediatric urologist and a pediatric endocrinologist during a workshop. In addition, all CHC physicians received written instruction on the genital examination procedure. During the course of the study, new CHC physicians were instructed on the standardized examination, and every 6 months a meeting with the CHC physicians, researchers, and expert pediatricians was organized to refresh the CHC physicians on the procedures. Boys were diagnosed as cryptorchid if one or both testes were nonpalpable or if they could not be manipulated to a stable position at the bottom of the scrotum (de Muinck Keizer-Schrama 1987). Hypospadias was defined as a displacement of the urethral meatus from the tip of the glans penis to the ventral side of the phallus, scrotum, or perineum (Pierik et al. 2002). All 91 cases of cryptorchidism (1.1%) and 67 cases of hypospadias (0.8%) that were identified by CHC physicians were eligible for the case–control study, of which four cases had both abnormalities. We selected controls from the 8,541 boys without cryptorchidism or hypospadias if their age was compatible with the observed age range of cases. For statistical power, three times more controls than cases were approached for participation.
Parents of cases and controls were invited to participate in the study, and after written informed consent a research nurse interviewed the mother with a structured questionnaire during a home visit approximately 11 weeks (median) after giving birth (5th and 95th percentiles, 6 and 27 weeks). If present, the father was also interviewed. This study was approved by the institutional review board. The participation rate among mothers was 86% (78 of 91) for cryptorchidism cases, 84% (56 of 67) for hypospadias cases, and 68% (313 of 462) for controls. This participation produced 443 mother–child pairs, including the four boys with both abnormalities. Paternal information was available for 326 of the 443 subjects (74%), in which the paternal information was provided by the biologic father in 91 subjects (28% overall, and 24 and 38% for controls and cases, respectively) and was filled out by the mother because of the father’s absence in 235 subjects (72%). The paternal questionnaire was considered a nonresponse when mothers could not provide core information on the biologic father regarding the country of origin of his parents, date of birth, or occupational history.
Data collection.
A research nurse completed structured questionnaires during interviews with parents. Rotterdam is a multicultural city in which the main groups of immigrants originate from Turkey, Morocco, Surinam, and the Netherlands Antilles, the latter two being Dutch-speaking countries. When necessary because of language problems, a qualified interpreter read the questions aloud from translated written questionnaires in the Turkish or Moroccan-Arabic language. The maternal questionnaire gathered information on personal characteristics, health, pregnancy aspects, diet, and occupational history. The paternal questionnaire collected data on personal characteristics, health, and occupation. Personal characteristics were age, height, weight, education, country of origin, and lifestyle factors such as smoking habits and alcohol use during the past 12 months. Education level was defined as low (≤ 9 years), intermediate (10–14 years), or high (≥ 15 years). The country of origin of the mother and father was based on the country of birth of their parents (i.e., the newborn’s grandparents) as defined by Statistics Netherlands (Keij 2000). The country of origin assigned to foreigners (defined as someone with at least one parent born abroad) is that of the mother if both parents are born abroad; otherwise, it is the country of birth of the parent that was born abroad (Keij 2000).
Self-perceived general health was measured with a four-point ordinal scale and dichotomized into good health versus less than good health (Ware et al. 1996). Information was also collected on time to pregnancy (in months), parity, weeks of gestation, birth weight (grams), folic acid supplements, contraceptive pill use before the last pregnancy, and whether the pregnancy was induced by assisted reproduction technologies (ART). Infants were defined as small for gestational age (SGA) when their birth weight was more than two standard deviations below the reference value for their gestational age (Usher and McLean 1969). Preterm delivery was defined as a birth before 35 weeks of gestation (10th percentile).
We ascertained dietary patterns during the first 6 months of pregnancy. One general question distinguished vegetarian diets and diets rich in vegetables, fruits, meat, or fish. A phytoestrogen-specific food questionnaire was developed to differentiate categories of exposure based on a semiquantitative estimation of the intake of food products containing isoflavonoids and lignans, which are considered the most important naturally occurring phytoestrogens. The questionaire was developed for this study by TNO Food and Nutrition Research (Zeist, the Netherlands; Brants 1999). For the questionnaire, food products were selected that may contribute to isoflavonoid or lignan intake based on previous research (Brants 1999). For soy consumption, all known soy products were selected except soy oil and soy sauce, because they contain little or no biologically active isoflavonoids. Lignan-containing products were selected for their contribution to the total lignan intake, which was estimated on the basis of their lignan contents (according to food-constituent tables) and the use of the product in the general population (including nonusers) or in the group of users (Brants 1999). We also considered the feeding patterns in Surinam, Turkish, or Moroccan culture (the main groups of immigrants in Rotterdam). We quantified the average daily intake of phytoestrogens (based on consumption per week) by multiplying frequency of use by portion size by the concentration of phytoestrogens according to food-constituent tables (Brants 1999). The intakes per product were added up to the total intake of lignans and isoflavonoids to allow differentiation of subjects with high, intermediate, and low intake, based on tertiles.
We derived occupational exposure from generic questions on paid employment (yes/no) and jobs held in the year before delivery. The focus was on chemicals that may have endocrine activity (Van Tongeren et al. 2002) or that have previously been described as male reproductive toxicants (Tielemans et al. 1999a). For the few parents with multiple jobs, the job with most working hours was selected at the time of the first trimester (for mothers) or around fertilization (for fathers). Parents without a job were considered as having no occupational exposures. Additional questions were asked about job title, type of business, name of employer, and activities in the job. A checklist was used for self-reported exposure (yes/no) to ionizing radiation, physical exposures, and classes of chemical substances that have been linked to human reproductive impairment, such as solvents, pesticides, and heavy metals (Tielemans et al. 1999a). Subjects were classified as being exposed to solvents when reporting contact in their job to industrial cleaning products (degreasers), paints, printing inks, glues, or industrial cleaning products (Tielemans et al. 1999b).
We also assessed occupational exposure by applying a job-exposure matrix (JEM) for potential EDs (Van Tongeren et al. 2002). The JEM was based on the judgment of occupational hygienists who estimated for particular jobs the exposure to seven categories of potential EDs (e.g., pesticides and polychlorinated organic compounds) (Van Tongeren et al. 2002). A person in a particular job was assigned “probable exposure = yes” if the experts judged that it was probable that a reasonable proportion of workers had some exposure. An overall classification of “probable exposure to potential EDs = yes” was given to a job if at least one of the seven exposure categories was scored as “yes.”
Statistics.
The agreement between self-reported exposure and exposure classification derived from the JEM was determined by the weighted Cohen’s κ. A κvalue < 0.4 was considered poor agreement, 0.4–0.6 moderate agreement, and > 0.6 good agreement (Landis and Koch 1977).
We computed frequency counts, crude odds ratios (ORs), and 95% confidence intervals (95% CIs) for all potential risk factors. Continuous risk factors were categorized into three or four categories for ease of interpretation. Trends were assessed by a chi-square test for trends in 2 × 3 or 2 × 4 tables. Logistic regression analysis with stepwise forward selection on univariate risk factors was used to arrive at a multivariable model for either outcome, with a significance level of 0.05 for retained variables. In addition, exposure variables of interest were also included in a multivariable model when this factor was statistically significantly associated with either cryptorchidism or hypospadias in the univariate analysis and the factor caused a change by ≥ 15% in the coefficient of other risk factors in the model. Interactions of all variables were also tested for significance. The 95% CIs around the ORs were derived from the individual Wald’s statistics, except for variables with cell frequencies of five or fewer, in which case likelihood-based confidence intervals are given. Because information on fathers was not collected on all children, we performed separate analyses for those with mother information and those with mother and father information. Regression analyses were performed using PROC LOGISTIC in SAS (version 8.2; SAS Institute, Cary, NC, USA).
Results
The general characteristics of the study population are shown in Tables 1 and 2. Table 1 presents the risk factors for cryptorchidism and hypospadias related to the mother and pregnancy. Significant risk factors were related to intrauterine growth (low birth weight and SGA for hypospadias, preterm delivery for cryptorchidism). Mothers with better general health, higher education, and larger height showed less risk of having offspring with either abnormality. These individual characteristics were strongly interrelated. Boys born from mothers of Turkish origin had increased risks for cryptorchidism and hypospadias. Compared with a Dutch origin, a Turkish origin was strongly associated with suboptimal maternal health, a lower education level, and lower maternal height. Dietary phytoestrogens and maternal occupational exposure to potential EDs did not significantly alter the risk of either abnormality.
Table 2 presents paternal risk factors. Paternal age, education, and country of origin were associated with cryptorchidism and hypospadias. Smoking among fathers was associated with hypospadias (OR = 3.4). ORs for cryptorchidism in offspring were elevated for self-reported solvent exposure (OR = 2.0) and pesticide exposure according to the JEM (OR = 4.5). Self-reported exposure to pesticides also gave an increased risk (OR = 2.8) of borderline significance (p = 0.08). Paternal self-reported solvent exposure (OR = 2.4) was also associated with hypospadias. Self-reported exposure to heavy metals, anesthetics, and other JEM categories was not significantly associated with the outcomes.
The exposure prevalence in men was significantly higher than in women. Among men, the prevalence of self-reported exposure was 23.0% (n = 75) for solvents, 10.2% (n = 33) for heavy metals, 4.6% (n = 15) for pesticides, and 1.9% (n = 6) for anesthetics, and 31% were exposed to at least one of these categories. The single largest group reporting pesticide exposure were workers in greenhouses involved in cultivation of vegetables (n = 3) or flowers (n = 3). The JEM identified paternal ED exposure in 12.0% of the fathers. In the JEM, pesticide exposure (n = 14) was assigned primarily to greenhouse workers in flowers (n = 7) or vegetables (n = 6). Among couples, maternal and paternal exposures to pesticides were associated for self-reports and the JEM (Spearman rank correlation, 0.18 and 0.21, respectively). The agreement between pesticide exposures based on self-reports and the JEM was moderate (κ= 0.54; 95% CI, 0.36–0.71). Age, education level, smoking, and country of origin within couples were strongly correlated (Spearman correlation coefficients > 0.50).
Tables 3 and 4 present the multivariate models with maternal and paternal risk factors for cryptorchidism and hypospadias, respectively. The final models on maternal risk factors (Tables 3 and 4) provide no evidence for an association between maternal dietary and environmental exposure and the occurrence of both outcomes while adjusting for other risk factors. When manually added to the final multivariate models, the risk estimates for occupational exposures and dietary phytoestrogens were very similar to their effects in the univariate analyses in Table 1 (< 15% change in coefficient), although the confidence intervals were somewhat larger.
A preterm delivery and a low education level were the strongest risk factors for cryptorchidism in the maternal multivariate model, together with an interaction between country of origin and mother’s age at delivery. Among Turkish mothers ≥ 30 years of age, an increased risk of cryptorchidism in newborns was observed compared with younger Turkish and with Dutch mothers. When taking into account also the characteristics of the father (Table 3), the only paternal risk factor associated with cryptorchidism was probable occupational exposure to pesticides (OR = 3.8). Although not selected by the stepwise forward selection, manual addition of self-reported exposure to solvents produced a similar effect as in the univariate analysis (OR = 1.9; 95% CI, 0.9–3.9), but the influence of probable exposure to EDs was substantially smaller (OR = 1.3; 95% CI, 0.5–3.3) than when analyzed univariately.
The important maternal risk factors for hypospadias were SGA birth and health status of the mother (Table 4). Again, Turkish origin was associated with an increased risk for hypospadias (OR = 3.0), but no interaction with age was identified. When also taking into account the characteristics of the father, current smoking of the father was a strong risk factor (OR = 3.8). The risk for self-reported exposure to solvents among fathers was elevated (OR = 2.0) but of borderline significance (p = 0.09). This risk factor was included because it influenced the risk estimates of time to pregnancy, because of the moderate association between time to pregnancy and solvent exposure. When manually entered into the multivariate model, the risk estimates for maternal and paternal occupational exposures and dietary phytoestrogens (that were not selected by the stepwise procedure) were very similar to their univariate effects, except for a reduced risk associated with self-reported maternal exposure to pesticides (OR = 1.1; 95% CI, 0.2–6.2) and an increased risk associated with lignan intake of 4–6 g/day (OR = 1.5; 95% CI, 0.6–3.5) and < 4 g/day (OR = 1.7; 95% CI, 0.7–3.9).
Discussion
This study reports the novel findings that paternal pesticide exposure is a risk factor for cryptorchidism and that paternal smoking is associated with hypospadias in the offspring. A strength of the study is that the results are based on a case–control study nested within a large birth cohort in the general population of Rotterdam. Because 95% of all consecutive newborn boys in Rotterdam were prospectively subjected to a standardized examination of the external genitalia, bias in case identification by exposure is unlikely. The prevalence of 1.1% cryptorchidism and 0.8% hypospadias in our population has been described elsewhere and is within the range reported by comparable studies (Pierik FH et al., unpublished data; Pierik et al. 2002). A good accuracy of the diagnosis of both abnormalities by CHC physicians is expected because of the standardized and systematic examination of a birth cohort. A high accuracy (88% verification) of the hypospadias diagnosis by CHC physicians has been demonstrated previously (Pierik et al. 2002), whereas the accuracy of cryptorchidism diagnosis was not assessed. Because the case status was assessed prospectively before data on determinants were collected, the misclassification by CHC physicians is probably nondifferential, which would bias the results toward unity in our analyses. Resources were insufficient to have CHC physicians report the exact location of the urethral opening and the left and right testis for the nearly 9,000 subjects. Another strength of the present study is that both maternal and paternal determinants were included. A weakness of the study is that the paternal determinants were missing for 26% (n = 116) of the subjects, and in the subjects with paternal information, the paternal determinants were presented by the fathers themselves in only 28% (n = 91). Differential misclassification between mothers and fathers on self-reported paternal exposure to solvents cannot be ruled out because fathers and mothers reported a paternal exposure prevalence of 31 and 20%, respectively. However, the hypospadias risk for paternal solvent exposure reported by the father (OR = 1.9; 95% CI, 0.6–6.2) or mother (OR = 2.5; 95% CI, 1.0–6.2) was comparable in size, although the 95% CI was wider in these smaller subsets. For other paternal occupational exposures and lifestyle factors, such as smoking and alcohol use, no differences were observed between reporting mothers and fathers.
The multivariate analyses suggest an important role of paternal smoking and occupational exposures. Paternal smoking was significantly associated with hypospadias (OR = 3.8; Table 4). Paternal smoking has previously been associated with the occurrence of single and multiple birth defects (Zhang et al. 1992), but not specifically with hypospadias. Paternal smoking could have an effect through passive exposure of the mother, but this is unlikely because active smoking by the mother was not a risk factor. We cannot exclude that mothers have underreported their smoking. When mothers of cases under-report their own smoking more than that of their partner, paternal smoking may partly be a spurious risk factor.
After correction for other significant risk factors, paternal pesticide exposure based on the JEM was significantly associated with cryptorchidism (OR = 3.8; Table 3), and self-reported paternal solvent exposure was borderline associated with hypospadias (OR = 2.0; Table 4). The exposure classifications of solvents and pesticides were too broad to allow identification of specific (groups of) chemical agents to be held responsible for the increased risks of either anomaly. Because parents of cryptorchidism and hypospadias cases may have been more concerned with and knowledgeable about environmental risk factors than were parents of controls, differential reporting between cases and controls may have occurred. However, several reasons argue against information bias explaining the observed associations. First, the increased cryptorchidism risk for self-reported pesticide exposure (OR = 2.8) was confirmed by the independent JEM-based pesticide exposure (OR = 4.5). Unfortunately, no JEM judgment was available to validate self-reported solvent exposure. Second, parents were not informed about potential risk factors or the JEM classification. Third, the agreement between self-reported and JEM exposures was not different between cases and controls.
The JEM was developed for a study on occupational risk factors for hypospadias, with a focus on EDs (Van Tongeren et al. 2002; Vrijheid et al. 2003). The interexpert agreement among the industrial hygienists developing the JEM was good for pesticides (κ= 0.77) (Van Tongeren et al. 2002). Although the JEM may misclassify occupational exposures, nondifferential misclassification leads to attenuation of the ORs when both the outcome and the determinant are dichotomous variables (Chen 1989; Greenland 1980), and cannot explain the observed association between cryptorchidism and JEM-based pesticide exposure.
Some studies have reported on the association between occupational exposure and birth defects. Paternal solvent exposure has been associated with cleft palate, neural tube defects, and preterm birth (Kristensen et al. 1993; Olshan et al. 1991). A study among gardener and farmer families applying pesticides reported an increased risk of cryptorchidism and hypospadias in their offspring (Kristensen et al. 1997) but could not distinguish paternal from maternal exposure. Another study observed an increased risk of cryptorchidism in sons of female gardeners and farmers but not in sons of men working in farming or gardening (Weidner et al. 1998). Neither paternal nor maternal occupation was associated with hypospadias. Because exposure assessment was limited to job title, limited information was available on the role of specific occupational exposures, such as pesticide use (Weidner et al. 1998).
It remains to be established whether the associations between external agents and cryptorchidism and hypospadias are causal or based on confounding (e.g., by unknown but related occupational risk factors). Several plausible biologic mechanisms that could mediate the observed effects of paternal smoking and occupational exposure on the offspring have, however, been described. There is growing human evidence that paternal environmental factors around the time of fertilization play a role after fertilization. More than 100 chemicals, including pesticides and solvents, have been related to male-mediated adverse reproductive outcomes (Davis et al. 1992). Animal studies provide extensive evidence for male-mediated developmental effects (i.e., spontaneous abortions, growth retardation, malformations, and behavioral abnormalities) of environmental agents (Robaire and Hales 2003). Several modes of action of chemicals have been shown, the most likely being genetic (e.g., germline DNA modification) or epigenetic (e.g., DNA repair, chromatin structure, apoptosis) effects on germ cells, whereas exposure of the oocyte or embryo to contaminated seminal fluid could also play a role (Davis et al. 1992; Robaire and Hales 2003). A study in mice demonstrated that environmental pollution resulted in DNA mutations that were inherited by the offspring, primarily through the paternal germline (Somers et al. 2002).
On the basis of the xenoestrogen hypothesis (Sharpe 2003), we anticipated that maternal exposure to EDs during fetal life could be a causal pathway leading to cryptorchidism and hypospadias. As of yet, few human data are available to confirm or refute this hypothesis. We did not find an association between maternal occupational exposure and either abnormality, perhaps due to the small proportion of exposed mothers. A previous study reported a maternal vegetarian diet as a risk factor for hypospadias and suggested a higher phytoestrogen intake as explanation (North and Golding 2000). We specifically assessed dietary phytoestrogen intake, which was not a significant risk factor for hypospadias or cryptorchidism. However, the nutrition data may suffer from inaccuracies because nutrition was assessed only once, whereas considerable intraindividual variation has been described with food-frequency questionnaires (Goldbohm et al. 1995). The findings in our case–control study suggest an association between cryptorchidism and hypospadias and lower socioeconomic status, as reflected in low education level and suboptimal general health status of both parents. The effect of socioeconomic status may be confounded by selection bias, especially because of differential response between cases and controls. For the impact of education to be spurious, this would require approximately a 2-fold higher response among parents of cases than of controls in subjects with a low education.
A similar differential response bias may have contributed to the observed effect of Turkish origin on cryptorchidism and hypospadias. Based on the nationalities of all 8,695 examined boys, Moroccan, Turkish, and other minorities were underrepresented by about 40–50% among controls. To exclude confounding by country of origin, we repeated the regression analysis in Dutch subjects only, which did not yield significantly different results, although standard errors increased because of a smaller sample. Among Dutch subjects paternal exposure to pesticides has a similar effect (OR = 3.4; 95% CI, 0.3–43.0) on cryptorchidism but failed to reach the level of conventional significance. Paternal smoking (OR = 6.5; 95% CI, 2.0–21.7) and self-reported paternal exposure to solvents (OR = 3.3; 95% CI, 1.2–9.5) remained significant risk factors for hypospadias among Dutch subjects.
Previous studies have reported ethnic variations in the occurrence of cryptorchidism and hypospadias (Chia et al. 2003; Fredell et al. 2002). Familial aggregation has been described for both abnormalities, supporting the importance of genetic factors (Fredell et al. 2002; Weidner et al. 1999). The association between Turkish origin and cryptorchidism and hypospadias may be the result of a genetic or environmental factor among Turkish people that predisposes toward these abnormalities. A higher maternal age was a significant risk factor within the Turkish minority, but not in the overall group of non-Turkish origin. We cannot exclude the possibility that the response may have been different with age among Turks.
In the multifactorial models without adding paternal risk factors, preterm delivery was associated with cryptorchidism (OR = 3.1; Table 3), and being SGA was associated with hypospadias (OR = 7.3; Table 4). These associations are well known from previous studies (Weidner et al. 1999). Some authors point to reduced placental function as underlying etiology for low birth weight, cryptorchidism, and hypospadias (Fredell et al. 1998).
Some earlier studies looking at large groups of cases have reported ORs ranging from 1.1 to 1.9 for low birth order and a higher maternal age as risk factors for cryptorchidism or hypospadias cases (Akre et al. 1999; Biggs et al. 2002; Kallen 2002; Møller and Skakkebaek 1997), although others did not observe these excess risks (Berkowitz et al. 1995; Jones et al. 1998). Birth order and parental age were not significantly related to cryptorchidism or hypospadias in our study, which may be because of the relatively small effect and limited population size.
Our observation that a longer time to pregnancy was associated with hypospadias (Table 4) may be explained by familial aggregation of hypospadias (Fredell et al. 2002) and its association with subfertility (Skakkebaek et al. 2001). Previous studies have reported a higher incidence of hypospadias in boys born after intracytoplasmic sperm injection (Ericson and Kallen 2001; Wennerholm et al. 2000), which may be explained by a lower birth weight that occurs more frequently after ART. In our study, the frequency of ART was too low to evaluate its association with hypospadias or cryptorchidism.
This study suggests that paternal environmental exposures may increase the risk of cryptorchidism and hypospadias in newborn boys, which may indicate an effect on the paternal germline. Cryptorchidism was associated with paternal exposure to pesticides, and hypospadias was more frequent in fathers that were active smokers. The pregnancy-related risk factors of low birth weight and SGA birth for hypospadias and preterm delivery for cryptorchidism have consistently been found in previous studies (Weidner et al. 1999). Future studies on environmental risk factors for cryptorchidism and hypospadias should not only focus on maternal exposure during fetal life but also include the paternal pathway to substantiate whether the observed associations are causal.
Table 1 Univariate analysis of the association between maternal risk factors and the occurrence of cryptorchidism and hypospadias in a case–control study among 443 mother–child pairs.
Cryptorchidism (n = 78)
Hypospadias (n = 56)
Variable Controls Cases OR (95% CI) Cases OR (95% CI)
Age at delivery (years)
< 25 48 14 1.0 9 1.0
25–30 80 20 0.9 (0.4–1.9) 17 1.1 (0.5–2.7)
30–35 111 29 0.9 (0.4–1.8) 19 0.9 (0.4 –2.2)
≥35 70 15 0.7 (0.3–1.7) 11 0.8 (0.3–2.2)
Height (cm)
< 160 41 16 1.0** 16 1.0**
160–165 65 25 1.0 (0.5–2.1) 10 0.4* (0.2–1.0)
165–170 95 14 0.4* (0.2–0.9) 12 0.3* (0.1–0.8)
≥170 111 23 0.5 (0.3–1.1) 18 0.4* (0.2–0.9)
Education level
Low 65 27 1.0 21 1.0
Intermediate 154 37 0.6 (0.3–1.0) 23 0.5* (0.2–0.9)
High 94 14 0.4* (0.2–0.7) 12 0.4* (0.2–0.9)
Country of origin
Netherlands 170 34 1.0 28 1.0
Morocco 21 8 1.9 (0.8–4.7) 3 0.9 (0.2–2.7)
Turkey 18 15 4.2* (1.9–9.1) 8 2.7* (1.0–6.6)
Surinam 35 8 1.1 (0.5–2.7) 5 0.9 (0.3–2.2)
Other 69 13 0.9 (0.5–1.9) 12 1.1 (0.5–2.2)
Good general health
Yes 291 66 1.0 43 1.0
No 22 12 2.4* (1.1–5.1) 13 4.0* (1.9–8.5)
Current smoker
Yes 71 22 1.3 (0.8–2.3) 18 1.6 (0.9–3.0)
No 242 56 1.0 38 1.0
ART
Yes 14 4 1.2 (0.3–3.3) 3 1.2 (0.3–3.9)
No 299 74 1.0 53 1.0
Time to pregnancy
0 months 96 26 1.0 13 1.0
1–3 months 113 21 0.7 (0.4–1.3) 24 1.6 (0.8–3.3)
≥4 months 91 26 1.1 (0.6–2.0) 15 1.2 (0.6–2.7)
Birth weight (g)
< 3,000 57 19 1.5 (0.7–3.0) 21 4.1* (1.7–9.8)
3,000–3,500 106 26 1.1 (0.6–2.1) 15 1.6 (0.6–3.8)
3,500–3,750 58 11 0.8 (0.4–1.9) 9 1.7 (0.6–4.7)
≥3,750 88 20 1.0 8 1.0**
SGA
Yes 7 2 1.2 (0.2–4.9) 6 5.5* (1.8–17.1)
No 302 74 1.0 47 1.0
Premature birth
Yes 25 14 2.5* (1.2–5.1) 8 1.9 (0.8–4.5)
No 288 64 1.0 48 1.0
Primiparous
Yes 162 44 1.2 (0.7–2.0) 25 0.8 (0.4–1.3)
No 151 34 1.0 31 1.0
Folic acid supplements in pregnancy
Yes 179 35 0.6 (0.4–1.0) 32 1.0 (0.6–1.8)
No 134 43 1.0 24 1.0
Vegetable-rich diet
Yes 125 24 0.7 (0.4–1.1) 17 0.7 (0.4–1.2)
No 186 54 1.0 39 1.0
Soy protein intake
≥20 g/day 51 8 0.6 (0.3–1.3) 9 1.0 (0.5–2.2)
> 0–20 g/day 41 12 1.1 (0.6–2.3) 8 1.1 (0.5–2.5)
0 g/day 221 58 1.0 39 1.0
Lignan intake
≥6 g/day 115 23 0.7 (0.4–1.3) 22 1.0 (0.5–2.1)
4–6 g/day 119 31 0.9 (0.5–1.6) 19 0.8 (0.4–1.8)
< 4 g/day 79 24 1.0 15 1.0
Paid employment
Yes 213 46 0.7 (0.4–1.1) 31 0.6 (0.3–1.0)
No 100 32 1.0 25 1.0
Probable exposure to EDs (JEM)
Yes 24 6 1.0 (0.4–2.6) 3 0.7 (0.2–2.0)
No 289 72 1.0 53 1.0
Probable exposure to pesticides (JEM)
Yes 7 2 1.2 (0.2–4.9) 2 1.6 (0.2–6.9)
No 306 76 1.0 54 1.0
Self-reported exposure to pesticides
Yes 4 2 2.0 (0.3–10.6) 1 1.4 (0.1–9.7)
No 309 76 1.0 55 1.0
Self reported exposure to solvents
Yes 32 6 0.7 (0.3–1.8) 9 1.7 (0.8–3.8)
No 281 72 1.0 47 1.0
* p < 0.05.
** **Significant trends were observed for maternal height with cryptorchidism and hypospadias (OR = 0.67 and 0.52 per 10 cm height increase, respectively) and birth weight and hypospadias (OR = 0.91 per 100 g of body weight increase).
Table 2 Univariate analysis of the association between paternal risk factors and the occurrence of cryptorchidism and hypospadias in a case–control study among 326 father–child pairs.
Cryptorchidism (n = 50)
Hypospadias (n = 41)
Variable Controls Cases OR (95% CI) Cases OR (95% CI)
Age (years)
< 25 19 10 1.0 5 1.0
25–30 43 6 0.3* (0.1–0.8) 12 1.1 (0.3–3.4)
30–35 64 19 0.6 (0.2–1.4) 8 0.5 (0.1–1.6)
> 35 109 15 0.3* (0.1–0.7) 16 0.6 (0.2–1.7)
Height (cm)
< 175 59 14 1.0 12 1.0
175–180 42 9 0.9 (0.4–2.3) 5 0.6 (0.1–1.8)
180–185 48 12 1.1 (0.5–2.5) 11 1.1 (0.5–2.8)
> 185 82 15 0.8 (0.4–1.7) 14 0.8 (0.4–2.0)
Educational level
Low 59 19 1.0 12 1.0
Intermediate 89 13 0.5* (0.2–1.0) 25 1.4 (0.6–4.0)
High 85 18 0.7 (0.3–1.4) 5 0.3* (0.1–0.9)
Country of origin
Netherlands 127 26 1.0 25 1.0
Morocco 16 6 1.8 (0.6–4.9) 2 0.6 (0.1–2.4)
Turkey 16 11 3.4* (1.4–8.1) 7 2.2 (0.8–5.8)
Surinam 31 2 0.3 (0.1–1.1) 3 0.5 (0.1–1.5)
Other 46 5 0.5 (0.2–1.4) 5 0.6 (0.2–1.4)
Good general health
Yes 205 39 1.0 34 1.0
No 30 10 1.8 (0.8–3.9) 7 1.4 (0.6–3.5)
Current smoker
Yes 98 22 1.2 (0.6–2.1) 29 3.4* (1.7–7.0)
No 138 27 1.0 12 1.0
Paid employment
Yes 209 41 0.7 (0.3–1.6) 37 1.0 (0.3–3.6)
No 27 8 1.0 5 1.0
Probable exposure to potential EDs (JEM)
Yes 38 13 1.8 (0.9–3.8) 10 1.6 (0.7–3.6)
No 198 37 1.0 32 1.0
Probable exposure to pesticides (JEM)
Yes 7 6 4.5* (1.4–13.9) 1 0.8 (0.3–3.6)
No 229 44 1.0 41 1.0
Self-reported exposure to pesticides
Yes 9 5 2.8 (0.8–8.5) 1 0.6 (0.0–3.4)
No 227 45 1.0 41 1.0
Self-reported exposure to solvents
Yes 45 16 2.0* (1.0–3.9) 15 2.4* (1.2–4.8)
No 191 34 1.0 27 1.0
* p < 0.05.
Table 3 Multivariate models of the association between maternal and paternal risk factors and the occurrence of cryptorchidism in a case–control study.
Risk factors OR (95% CI)
Maternal risk factors (n = 443)
Education level (low vs. intermediate/high) 1.9* (1.0–3.4)
Premature birth ( > 2 weeks) 3.1* (1.5–6.6)
Interaction age at delivery and country of origin:
Non-Turkish mothers < 30 years of age 1.0
Turkish mothers < 30 years of age 2.0 (0.7–5.6)
Non-Turkish mothers ≥ 30 years of age 0.8 (0.5–1.5)
Turkish mothers ≥ 30 years of age 16.3* (3.3–81.2)
Maternal and paternal risk factors (n = 326)
Good general health of mother (no vs. yesa) 3.8* (1.5–9.8)
Vegetable-rich diet of mother (yes vs. noa) 0.4* (0.2–0.9)
Probable exposure to pesticides of father (JEM) 3.8* (1.1–13.4)
Interaction age at delivery and country of origin:
Non-Turkish mothers < 30 years of age 1.0
Turkish mothers < 30 years of age 1.6 (0.5–5.6)
Non-Turkish mothers ≥ 30 years of age 1.0 (0.5–2.0)
Turkish mothers ≥ 30 years of age 8.8* (1.2–63.2)
a Reference.
* p < 0.05.
Table 4 Multivariate models of the association between maternal and paternal risk factors and the occurrence of hypospadias in a case–control study.
Risk factors OR (95% CI)
Maternal risk factors (n = 443)
Education level (low vs. intermediate/high) 2.0* (1.1–3.9)
SGA (yes vs. no) 4.2* (1.2–14.7)
Turkish origin of mother (vs. non-Turkish) 3.0* (1.2–7.7)
Good general health (no vs. yesa) 3.6* (1.6–8.1)
Maternal and paternal risk factors (n = 326)
SGA (yes vs. noa) 7.3* (1.7–31.4)
Current smoker, father (yes vs. noa) 3.8* (1.8–8.2)
Self-reported exposure to solvents of father 2.0 (0.9–4.6)
Time to pregnancy
0 months 1.0
1–3 months 3.9* (1.3–11.5)
≥4 months 3.4* (1.1–10.3)
a Reference.
* p < 0.05.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7053ehp0112-00157715531445Children's HealthArticlesGeographic Analysis of Blood Lead Levels in New York State Children Born 1994–1997 Haley Valerie B. Talbot Thomas O. Bureau of Environmental and Occupational Epidemiology, New York State Department of Health, Troy, New York, USAAddress correspondence to V. Haley, Bureau of Environmental and Occupational Epidemiology, New York State Department of Health, 547 River St., Room 200, Troy, NY 12180-2216 USA. Telephone: (518) 402-7990. Fax: (518) 402-7959. E-mail:
[email protected] thank A. Iasonos, S. Forand, J. Bowers, J. Camadine, and F. Boscoe for their help managing and mapping the data.
The authors declare they have no competing financial interests.
11 2004 18 8 2004 112 15 1577 1582 24 2 2004 18 8 2004 Publication 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 examined the geographic distribution of the blood lead levels (BLLs) of 677,112 children born between 1994 and 1997 in New York State and screened before 2 years of age. Five percent of the children screened had BLLs higher than the current Centers for Disease Control and Prevention action level of 10 μg/dL. Rates were higher in upstate cities than in the New York City area. We modeled the relationship between BLLs and housing and socioeconomic characteristics at the ZIP code level. Older housing stock, a lower proportion of high school graduates, and a higher percentage of births to African-American mothers were the community characteristics most associated with elevated BLLs. Although the prevalence of children with elevated BLLs declined 44% between those born in 1994 and those born in 1997, the rate of improvement may be slowing down. Lead remains an environmental health problem in inner-city neighborhoods, particularly in upstate New York. We identified areas having a high prevalence of children with elevated BLLs. These communities can be targeted for educational and remediation programs. The model locates areas with a higher or lower prevalence of elevated BLLs than expected. These communities can be studied further at the individual level to better characterize the factors that contribute to these differences.
geographic information systemshousinglead poisoningNew York Statesimultaneous autoregressionsocioeconomic statusspatial autocorrelation
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Although the rate of childhood lead poisoning has been decreasing because of the reduction in exposure to environmental lead sources and increased education, lead poisoning is still considered to be one of the most prevalent and preventable childhood health problems in New York State. New York State has the largest proportion (43%) and largest number (3.3 million) of housing units built before 1950 of any state (U.S. Census Bureau 2002). Because lead paint was not banned nationally until 1978 (U.S. Consumer Product Safety Commission 1977), older houses may still contain layers of lead paint that contaminate indoor dust when the paint deteriorates or spreads during renovation. Lead is also present in soil as a result of historical deposition from automobile exhaust, deteriorated paint, lead arsenate pesticide, and industrial and incinerator emissions. Other sources of lead exposure include plumbing, ceramicware, traditional remedies, and lead brought into the home from occupational and recreational exposures [Centers for Disease Control and Prevention (CDC) 1997].
Exposure to lead is significant because it damages the central nervous system and impairs learning and behavior even at low levels (Canfield et al. 2003). Young children are more vulnerable than adults for several reasons. They may ingest contaminated dust and soil as a result of normal mouthing activity. They also take in more lead as a proportion of body mass and absorb more lead than do adults (Mushak 1992). The toxicity of lead depends on the dose, the duration of exposure, and the developmental and nutritional susceptibility of the child (American Academy of Pediatrics 1998). The cost of the health effects of lead exposure is estimated to be $43.4 billion each year in the United States, much more than costs of other childhood diseases of environmental origin (Landrigan et al. 2002).
Locating areas with a high prevalence of children with elevated (blood lead levels) BLLs is important for identifying communities that could be targeted with educational and remediation programs. In this study, we examined the geographic distribution of elevated BLLs in New York State children born between 1994 and 1997. We also considered time trends. We used spatial regression to identify community characteristics associated with areas that exhibit a high prevalence of elevated lead values. The regression model identified areas that have higher rates of elevated BLLs than expected, after taking into account significant community characteristics. These areas can be examined more carefully to better characterize the local factors that contribute to lead exposure.
Previous studies have shown that elevated BLLs have been associated with housing and sociodemographic characteristics, including older housing stock, lower housing value, a higher proportion of children living below the poverty level, a lower proportion of high school graduates, a lower proportion of owner-occupied housing, a higher proportion of vacant housing, a higher proportion of households headed by a female, a higher proportion of minority births, a higher population density, and industrialization (Bailey et al. 1994; Griffith et al. 1998; Lanphear et al. 1998; Litaker et al. 2000; Miranda et al. 2002; Sargent et al. 1995, 1997; Talbot et al. 1998). In addition, studies have shown that children’s lead levels tend to be higher in the summer months (e.g., Yiin et al. 2000). These studies have several limitations that our analysis seeks to overcome.
Some of the earlier studies used only surrogate markers of BLLs (Bailey et al. 1994; Sargent et al. 1995). Other studies used data for only a small geographic area or time period (Griffith et al. 1998; Lanphear et al. 1998; Litaker et al. 2000; Miranda et al. 2002; Sargent et al. 1997), making it difficult to understand patterns over time in wider geographic areas. The present study is the largest population-based study in the literature to date. It includes New York City, the largest urban area in the country, with a wide spectrum of demographics, and considers a large proportion of children screened. The previously published study of New York State children (Talbot et al. 1998) used data for children born over a 2-year period, whereas the present study used data for children born over a 4-year period. This enabled us to perform the analysis at a finer geographic scale in some areas and to detect trends over time. We also sought to control for spatial autocorrelation that is inherent in childhood lead data (Griffith et al. 1998), to better estimate the variance in the data. Finally, our study only focused on children < 2 years of age. Other investigators studied children < 4–6 years of age. Younger children have different behavioral risk factors than do older children and tend to have higher BLLs (Brody et al. 1994; Griffith et al. 1998).
Materials and Methods
The New York State Department of Health (NYSDOH) Laboratory Reporting System provided data on the BLLs of children born between 1994 and 1997 and screened before 2 years of age. New York State requires all health care providers to screen the blood of all 1- and 2-year-olds for lead (NYSDOH 2001). Data include the name, address, and date of birth of the child, the name and telephone number of a parent or guardian, the date of the test, the blood lead value, and the method by which the blood sample was obtained (finger-stick or venipuncture). More than 80 New York State certified laboratories reported results, but approximately 75% of the reports came from just 10 labs. For tests that were reported at below the detection limit, the average detection limit was 3 μg/dL. For the 34% of children who were screened more than once, we used the highest BLL done by venipuncture because these samples are less susceptible to environmental contamination than are fingerstick samples (Parsons et al. 1997). We used the highest fingerstick measure if no venipuncture measures were available for the child. In the final data set, 65% of the test results were from venipuncture, 31% were from fingerstick, and 4% had no method recorded.
The NYSDOH Bureau of Vital Statistics provided the number of births by race and ZIP code in New York State from 1994 to 1997. We estimated the screening rates as the number of children tested divided by the number of births.
We chose U.S. Postal Service ZIP codes, which are used for mail delivery, as the geographic level of analysis because they are readily available with the childhood lead data. There are 1,598 ZIP codes in New York State, with populations ranging from 0 to > 100,000. In cases where the child had a missing or invalid ZIP code, we used address-matching software to assign a ZIP code. To obtain valid ZIP codes in cases where no street or town information was available for the child, we matched the parent or guardian’s phone number to digital phone directories. We could not determine valid ZIP codes for 3% of the children screened for lead and for 0.2% of the birth records obtained from vital statistics files. These records were excluded from the analysis.
On the basis of a review of previous published work (Bailey et al. 1994; Griffith et al. 1998; Lanphear et al. 1998; Litaker et al. 2000; Miranda et al. 2002; Sargent et al. 1995, 1997; Talbot et al. 1998; Yiin et al. 2000), we selected the following variables for further study: the percentage of homes built before 1940 and 1950, the percentage of adults ≥25 years of age who did not receive a high school diploma, the percentage of children living below the poverty level, the percentage of vacant housing units, the percentage of the population that rents a home, the percentage of children screened in summer (July–September), population density, and the percentage of African-American births. We used the percentage of children < 5 years of age living below the poverty level rather than median household income because median household income does not focus specifically on households with young children, and once a household reaches a certain threshold income level, further increases in income may not lead to improved living conditions and reduced exposures. The percentage of results obtained by venipuncture was not included as a variable because it is related to the outcome measure; venipuncture is the recommended confirmatory test for an initial high BLL. Several sources provided the socioeconomic data because not all of the Census 2000 data were available at the time of analysis. Age of housing data for 1999 were obtained from Claritas, Inc. (Ithaca, NY). We obtained the socioeconomic data at the census block and block group level (U.S. Census Bureau 1991, 1992, 2001) because 1990 Census ZIP code population estimates and 1990 Census ZIP code boundaries did not conform to the more current and accurate ZIP code boundaries used in this study. We used a geographic information system (MapInfo Professional version 7.5; MapInfo, Inc., Troy, NY) to apportion the socioeconomic data based on population and the location of the block centroids. We estimated the proportion of African-American children in each ZIP code using the mother’s race from the birth certificate because it contained more complete race information than did the lead database.
Mapping data in small geographic areas such as ZIP codes often produces rates that vary widely when the population is small or the health outcome under analysis is rare. To obtain more stable numbers for the spatial analysis, we combined data for children born between 1994 and 1997 and then merged ZIP codes with < 100 children screened. To achieve the most homogeneous merged areas, we selected the adjacent ZIP code that had the closest expected percentage of elevated BLLs based on a regression model developed earlier (Talbot et al. 1998). This reduced the number of ZIP codes used in the analysis to 952.
The CDC defines an elevated BLL as ≥10 μg/dL (CDC 1997). We chose the percentage of children with elevated BLLs in each ZIP code group to be the dependent variable. There are many advantages of using this measure rather than the mean of the BLLs: Laboratories report results with different detection limits, extreme results may be due to child-specific traits such as pica, and we wanted to estimate the number of children with elevated BLLs in a community who will need follow-up blood lead screening and education. Log transformation of the dependent variable normalized the distribution.
We first used multiple linear regression to describe the relationship between children’s BLLs and the housing and socioeconomic variables. We analyzed the residual autocorrelation in the model using SpaceStat software (version 1.91; TerraSeer, Inc., Ann Arbor, MI). Spatial autocorrelation was expected because, according to Tobler’s first law of geography (Tobler 1970), “everything is related to everything else, but near things are more related than distant things.” Neighboring ZIP code groups may be similar because neighborhoods may cross ZIP code group boundaries and because children may visit or use services in nearby areas. Environmental and housing characteristics, secondary occupational exposures, and behavioral factors may also be similar in neighboring ZIP code areas. If two areas are very similar to each other, they provide less information to a model than do dissimilar areas. This causes the estimated standard deviations to be biased downward and results in reports of stronger levels of significance.
We then developed a simultaneous autoregressive model (SAR), as described by Anselin (1988). In this model, the predicted values are adjusted by the information in the surrounding ZIP codes. The SAR model can be written Y = Xβ+ ρW(Y – Xβ) + ɛ. Here, Y, X, β, and ɛhave the same interpretation as in the linear regression model. The scalar ρ estimates the strength of the autocorrelation effect and can be interpreted similarly to the correlation coefficient: the closer its value is to 1, the stronger the positive autocorrelation between ZIP codes. W is the row-normalized weight matrix that describes the spatial structure of the data.
Results
Table 1 summarizes statistics for the blood lead database by year. The final data set contained 677,112 children with blood lead tests, which represented 63.4% of children < 2 years of age born in 1994–1997. The screening rate was 65.6% if the lead records and births with missing ZIP codes were included. The percentage of children screened remained relatively constant during the study period, although it varied geographically. We compared the screening rates in poor inner-city neighborhoods with those of the rest of the state. The poor neighborhoods were defined as the upper fifth percentile of the variable “percentage of children living below the poverty level,” and they were located in certain ZIP codes in New York City, Buffalo, Rochester, Syracuse, Schenectady, and Albany. Eighty percent of the children were screened in poor inner-city neighborhoods, compared with 61% in other areas. The prevalence of children with elevated BLLs declined 44%, from 6.9% for children born in 1994 to 3.9% for children born in 1997. However, the rate of decline slowed in the last 2 years.
The geographic distribution of the prevalence of elevated BLLs is shown in Figure 1. Most of the ZIP code areas had a small percentage of children with elevated BLLs. However, the upstate cities of Buffalo, Rochester, Syracuse, Albany, and Schenectady had some ZIP codes in which > 20% of the children screened had elevated BLLs. No ZIP code in New York City had > 15% of the children with elevated BLLs.
The linear regression model is summarized in Table 2. Because the effects of the independent variables in New York City were muted compared with the effects in the rest of the state, and none of the variables studied explained this difference, we developed a two-regime model. This model calculates separate parameters for New York City and for the rest of the state (upstate/Long Island) and presents one correlation coefficient for the whole model. Age of housing was the best predictor of the amount of children with elevated BLLs. The coefficient of determination (r2) for percentage of houses built before 1940 was 10% stronger than the coefficient for percentage of houses built before 1950, so we used the former. The poverty and education variables were highly correlated (r = 0.8). The effect of education was stronger in New York City, and the effect of poverty was stronger upstate. We chose to use only the education variable for consistency between models, because it was more linear than was the poverty variable and less correlated with the other variables in the model. The percentage of children born to African-American mothers remained significant after adjusting for age of housing and education. The other variables tested did not contribute strongly in both the New York City and upstate/Long Island regions. We excluded these variables because we wanted to create a parsimonious model and compare the same variables for each region.
The corresponding spatial error model is presented Table 2. When developing the spatial error model, four different weight matrices were tested: first-order neighbors, second-order neighbors, inverse distance to 25 km, and inverse distance squared to 25 km. First-order neighbors were chosen for the final model because they resulted in the best fit. There was a moderate amount of positive autocorrelation: ρ= 0.46. Modeling this correlation in adjacent ZIP codes widened the confidence intervals around the parameter estimates by 14% on average, and reduced the r2 by 21%. Spatial autocorrelation accounted for 11% of the variability in the data. The effects of housing age, education, and race were still stronger in the upstate/Long Island model than in the New York City model. The slopes of the parameter estimates were all within 20% of the multiple linear regression slopes.
The uncorrelated residuals for the SAR model are mapped in Figure 2. Areas where more children have elevated BLLs than the model predicts have positive residuals and are shown in orange. For example, there are some underpredicted ZIP code areas on the north fork of Long Island, the Hudson Valley, and eastern New York State, and there are some overpredicted ZIP codes (shown in blue) in the Bronx and the middle of Long Island, and western New York State.
Figure 3 contains conditional effect plots to facilitate interpretation of the slopes. These plots show the predicted value of elevated BLLs versus each of the variables used in the model while holding the other variables at their means and assuming an average autocorrelation of zero. They show that the effect of all the variables used in the model is weaker in New York City. We looked at the rates of rescreening to further examine reasons for the difference between New York City and the rest of the state. The CDC (1997) recommends that children with BLLs of 10–19 μg/dL be retested within 3 months and that those with higher BLLs be rescreened even sooner. Adherence to this recommendation was investigated by examining the first high test of all children up to 1.75 years of age so that their test results would be observed before 2 years of age; 27% of those in the 10–19 μg/dL range were retested within 3 months, and 62% of the ≥20 μg/dL range were retested within 3 months. The results were similar for New York City and the rest of New York State and thus do not contribute to the differences in these two areas of the state.
Discussion
BLLs varied widely across the state. The age of housing, education level, and percentage of African-American births in a community were related to BLLs. These variables described a large portion of the variation in the data; the coefficient of determination we observed (r2 = 0.52) is very strong for an ecologic analysis.
The significance of housing, education, and race was consistent with other studies we reviewed. All researchers who tested it found age of housing to be significant. All researchers included a measure of socioeconomic status such as income or educational attainment in their final models. Educational attainment is often thought to be the most stable indicator of socioeconomic standing over the course of a lifetime, and it better captures persons not in the labor force such as homemakers. All the studies but one found that a higher proportion of African-American births was associated with elevated BLLs after controlling for housing and poverty. African Americans may have higher BLLs because of a lower calcium intake (Mahaffey et al. 1986) or poorer housing conditions (Lanphear et al. 1996). Sargent et al. (1997) found that the percentage of recent immigrants from other countries predicted elevated BLLs rather than percentage of African Americans, perhaps because the recent immigrants lived in worse neighborhoods, were less knowledgeable about lead hazards, or were exposed before immigration.
Some additional variables that other researchers included in their models were not appropriate for New York State because of large differences between New York City and the rest of the state. For example, we did not find housing tenure to be significant in New York City because renter-occupied housing is more common and may not relate to socioeconomic status in New York City as it does in the upstate/Long Island region. We found that the proportion of vacant homes, unadjusted by other census variables, did not capture the difference in how vacancy can exist in both impoverished neighborhoods and vacation areas. We found no association with population density. This is contrary to the findings of Lanphear et al. (1998), who reviewed data for Monroe County, New York, and those of Griffith et al. (1998), who looked at data for the city of Syracuse, New York. Within small geographic areas such as individual cities or counties, population density may be associated with other socioeconomic variables such as poverty. Across larger geographic areas, these associations become more complicated. We found that population density is not correlated with poverty in New York State because there are both rural and inner-city poor areas. The lack of association we found with population density is, however, consistent with earlier studies that looked at statewide data for New York (Talbot et al. 1998) and statewide data for Massachusetts (Sargent et al. 1995, 1997).
The percentage of children screened was positively associated with elevated BLLs in New York City and upstate cities but not in the rest of the state. We did not use screening rates in the model because the relationship between screening rate and BLLs often depends on the levels of environmental exposure and medical practices, which vary across the state. For example, in newer suburban areas where there is limited lead exposure, there would be a low percentage of children with elevated BLLs, regardless of the screening rate. In poor inner-city areas, where public assistance programs are successful at screening high-risk children, increasing the percentage screened to include those less at risk would decrease the percentage of children screened with elevated BLLs.
Although New York State regulation calls for universal screening of children at 1 and 2 years of age, we found that only 66% of New York State children born between 1994 and 1997 were screened before 2 years of age. However, this is the highest screening rate that we found in the literature. Surveys of pediatricians investigated reasons for non-compliance. In California, the physicians who did not test all patients tended to have less factual knowledge, less recent training, fewer minority patients, and less financial incentive (Ferguson and Lieu 1997). Nationwide, the noncompliant physicians did not commonly see children with elevated BLLs, and they believed that the benefits of screening did not outweigh the costs (Campbell et al. 1996). The U.S. General Accounting Office (GAO 1999) found that children who receive Medicaid are not being adequately screened. Although we did not relate our lead data to Medicaid data, we found that the screening rates were higher in poor inner-city neighborhoods, both in New York City and upstate. A further investigation of who is screened will require linking the lead records to birth certificates to obtain information on the race and educational attainment of the parents, and to real property records to obtain information on age and condition of housing.
The lower prevalence of elevated BLLs in New York City compared with upstate areas is surprising given that New York City is among the oldest and most densely populated areas of the state. New York City has a higher percentage of older housing stock and minority births and a lower percentage of high school graduates than does the rest of New York State. Based on these characteristics, some New York City neighborhoods would be expected to have some of the highest prevalence rates of elevated BLLs in the state. This was not the case, however, and none of the variables examined in this analysis could explain these findings. Several possibilities for the difference exist. New York City banned the residential use of lead paint in 1960, 18 years earlier than the rest of the state and nation (NYCDOH 1998), and began a lead poisoning prevention program earlier than did other areas (NYCDOH 2002). New York City also has a large number of public housing units. Regulations regarding the removal and abatement of lead-based paint in public housing have historically been stricter than for privately owned housing used by the poor (Chisholm et al. 1985). There may have been differences in abatement policies in New York State. Studies comparing blood lead data in two counties in New England found that children screened in an area with a historically weak abatement policy were more likely to have elevated BLLs than were children in an area that began actively enforcing an abatement policy 20 years before (Bailey et al. 1998; Sargent et al. 1999). Mielke (1999) speculates that the lower BLLs in Manhattan are due to the lack of exposed soil in this “concrete jungle.” This could explain Manhattan, but not the lower percentages in the entire region. Some residential areas in New York City are not very different in terms of green space from residential areas in upstate cities that have a much higher percentage of children with elevated BLLs. Water quality could also explain some of the difference. The New York City Water Department has been at the forefront of reducing lead in the water supply by reducing corrosivity (New York State Joint Legislative Commission 1992).
There are several possible limitations to this study. First, the children screened may not have represented the population. This could bias the parameter estimates. Additional bias could exist because of measurement error. For example, we analyzed the data at the ZIP code level, rather than using census tracks or census block groups that contain more homogeneous groups of people. We did not use census areas because it would be difficult and time-consuming to accurately assign the hundreds of thousands of children in our study to the correct census areas. Last, bias could be caused by missing variables, as follows.
Research suggests that soil lead exposure from both leaded gasoline and degraded lead paint is an important exposure pathway (Mielke and Reagan 1998). In Syracuse, New York, soil lead levels were associated with BLLs when areas were aggregated to the size of census tracts (Johnson and Bretsch 2002). Unfortunately, soil lead data are available only for a limited number of sites in New York State and thus cannot be incorporated into a statewide model. Mielke et al. (1997) explained that age of housing tends to overestimate the exposure of children in small towns because soil lead concentrations are low in old towns with low traffic flow. Historical land use can play an important role in the distribution of lead contamination in soil. For example, Eckel et al. (2001, 2002) located secondary lead smelters that were no longer operational and found high soil lead concentrations near the plants. These sites represent only a small fraction of industrial and commercial properties where lead was used. Housing condition is an important factor because children are more likely exposed to lead in old houses with deteriorating paint compared with well-maintained houses of the same age. Housing condition assessments may vary by town and are performed only from the outside of houses and on an irregular basis. There are also several unmeasured interventions that could have affected BLLs. For example, in select neighborhoods in eight New York State counties, the Healthy Neighborhoods Program went door to door to assess homes for the presence of lead paint hazards, provide educational materials, and ensure that children were appropriately screened. However, because this program covered such a small geographic area, it was not included in this analysis. The levels of funding for many housing programs that seek to reduce lead paint hazards also vary across the state. Because the types of incentives and reporting for these programs are so variable, we did not attempt to include them in the model. Air monitoring data were not included in the study because lead is monitored at only a small number of sites in New York State. The U.S. Environmental Protection Agency (EPA) National-Scale Air Toxics Assessment dispersion model was not used because it may not be reliable at the ZIP code level (U.S. EPA 2001).
Conclusions
The dramatic 44% decline in elevated BLLs between children born in 1994 and 1997 is likely caused by greater public awareness, remediation and replacement of older homes, and the phaseout of lead from gasoline. The New York State data indicate that the rate of decline in the prevalence of elevated BLLs may be slowing. Similar declines have been observed nationwide. According to the National Health and Nutrition Examination Surveys, the percentage of children 1 to 5 years of age with elevated BLLs declined 50% between the 1991–1994 survey and the 1999–2000 survey (Meyer et al. 2003).
Lead remains an environmental health problem in inner-city neighborhoods, particularly in upstate New York. Older housing stock, a lower proportion of high school graduates, and a higher percentage of births to African-American mothers were the community characteristics most associated with elevated BLLs. Lead poisoning prevention resources should be targeted at the communities that we predicted to have a high prevalence or number of children with elevated BLLs.
We also identified areas with a higher or lower percentage of elevated BLLs than expected based on the model. These areas may have unique housing characteristics, other sources of environmental exposure, or differences in lead screening and remediation programs. These areas can be studied at the individual level to answer these remaining questions.
Figure 1 Prevalence of elevated BLLs of children born in 1994–1997 and screened before 2 years of age, by ZIP code groups containing at least 100 children screened.
Figure 2 Residuals from SAR, by ZIP code groups containing at least 100 children screened.
Figure 3 Conditional effect plots for the upstate/Long Island and New York City models. (A) Effect of age of housing. (B) Effect of education. (C) Effect of race. Mean value for percentage of homes built before 1940, 32%; percentage African American, 10%; percentage with no high school diploma, 23%.
Table 1 Summary children screened and those with elevated BLLs by year of birth.
1994 1995 1996 1997 Total
No. (%) screened 169,395 (61.2) 172,986 (64.0) 171,890 (65.3) 162,841 (63.4) 677,112 (63.4)
No. (%) ≥10 μg/dL Pb 11,753 (6.9) 9,605 (5.6) 7,630 (4.4) 6,313 (3.9) 35,301 (5.2)
Table 2 Parameter estimates (SEs) for two-regime linear regression and spatial error models.
Variablea Linear regression model Spatial error model
New York City regime
Intercept 0.8147 (0.0960) 0.8650 (0.1232)
Percent built < 1940 0.0110 (0.0017) 0.0102 (0.0018)
Percent without high school diploma 0.0059 (0.0024) 0.0060 (0.0030)
Percent African American 0.0069 (0.0012) 0.0060 (0.0014)
Upstate/Long Island regime
Intercept 0.2926 (0.0414) 0.3675 (0.0521)
Percent built < 1940 0.0244 (0.0010) 0.0233 (0.0011)
Percent without high school diploma 0.0182 (0.0022) 0.0153 (0.0023)
Percent African American 0.0091 (0.0011) 0.0108 (0.0013)
ρ NA 0.4649 (0.0383)
r2 0.63 0.52b
NA, not applicable.
a Dependent variable Y is ln(% elevated BLLs + 1) for both models.
b Pseudo r2 (Buse adjustment) used for spatial error model.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/ehp.7157ehp0112-00158315531446Children's HealthWorkgroup ReportThe Relationship between Housing and Health: Children at Risk Breysse Patrick 1Farr Nick 2Galke Warren 3Lanphear Bruce 4Morley Rebecca 3Bergofsky Linda 51Department of Environmental Health Sciences, Johns Hopkins University, Baltimore, Maryland, USA2Housing Consultant, Columbia, Maryland, USA3National Center for Healthy Housing, Columbia, Maryland, USA4Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA5U.S. Department of Health and Human Services, Washington, DC, USAAddress correspondence to R. Morley, National Center for Healthy Housing, 10227 Wincopin Circle, Suite 100, Columbia, MD 21044 USA. Telephone: (410) 772-2774. Fax: (410) 715-2310. E-mail:
[email protected] thank the following individuals for their contributions to the workshop: P. Eggleston, Johns Hopkins University School of Medicine; D. Dockery, Harvard School of Public Health; T. Buckley, Johns Hopkins University Bloomberg School of Public Health; P. Gergen, Center for Primary Care and Research, Agency for Health Care Policy and Research, U.S. Department of Health and Human Services; L. Wallace, U.S. Environmental Protection Agency; T. Matte, R. Jackson, and M.J. Brown, National Center for Environmental Health, Centers for Disease Control and Prevention (CDC); K. Dietrich, University of Cincinnati College of Medicine; R.M. Whyatt, Mailman School of Public Health, Columbia University; K. Yolton and K.J. Phelan, Cincinnati Children’s Hospital Medical Center; J.A. Paulson, George Washington University School of Medicine; C. Branche, M. Jackson, and D. Sleet, National Center for Injury Prevention and Control, CDC; D. Jacobs and E. Taylor, Office of Healthy Homes and Lead Hazard Control, U.S. Department of Housing and Urban Development; E. Tohn, ERT Associates; K. Stein, Office of Sen. Jack Reed, RI; J. Sharfstein, Committee on Government Reform, Office of Rep. Henry Waxman, CA; D. Ryan, Alliance for Healthy Homes.
This workshop was supported by grants R13/CCR322017-01 and EHLP017/03 from the CDC National Center for Injury Prevention and Control and National Center for Environmental Health. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the CDC.
The authors declare they have no competing financial interests.
11 2004 18 8 2004 112 15 1583 1588 5 4 2004 18 8 2004 Publication 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 November 2002, the National Center for Healthy Housing convened a 2-day workshop to review the state of knowledge in the field of healthy housing. The workshop, supported with funds from the U.S. Centers for Disease Control and Prevention’s National Center for Injury Prevention and Control and National Center for Environmental Health, was unique in that it focused solely on the effect of housing on children’s health and the translation of research findings into practical activities in home construction, rehabilitation, and maintenance. Participants included experts and practitioners representing the health, housing, and environmental arenas. Presentations by subject-matter experts covered four key areas: asthma, neurotoxicants, injury, and translational research. Panel discussions followed the presentations, which generated robust dialogue on potential future research opportunities and overall policy gaps. Lack of consensus on standard measurements, incomplete understanding about the interaction of home hazards, inadequate research on the effectiveness of interventions, and insufficient political support limit current efforts to achieve healthy housing. However, change is forthcoming and achievable.
asthmachildhood exposureenvironmental toxicantshealthy housinglead poisoning
==== Body
Because children spend as much as 80–90% of their time indoors, the possible origins of many of the health risks they face can be traced to homes, schools, and other indoor environments (U.S. EPA 2002). Prevention of environmental disease among children has important social and economic benefits. Landrigan et al. (2002) recently estimated that the total annual costs for environmentally attributable childhood diseases in the United States—lead poisoning, asthma, cancer, and developmental disabilities—is $54.9 billion.
The probable relationship between housing and health inequality, particularly within urban inner-city neighborhoods, has been acknowledged for some time. In 1938, the American Public Health Association (APHA 1938) addressed housing and health in Basic Principles of Healthful Housing. In 1971, APHA identified knowledge gaps with respect to housing and health, including the need “to understand and assess better the relative effects on humans of the various stresses that may exist in housing and its environment” (APHA 1971). A renewed appreciation for the housing and health connection led the U.S. Department of Housing and Urban Development (HUD) to implement its Healthy Homes Initiative in 1999.
Measuring the direct impact of housing quality on health was a difficult task in 1938, and this challenge is still with us today. In a recent study of the impact of housing on health, investigators estimated that indices of urban residential quality explained up to 25% of the variability in health status in Japan (Takano and Nakamura 2001). Housing quality remains an important component of health disparities in America and around the world.
The home environment, in particular, represents an important source of fetal and early childhood exposures to biologic, chemical, and physical agents, as well as a strategic opportunity for intervention (Krieger and Higgins 2002). Many studies have linked housing-related factors and health, and we have learned much over the past decades about how to make homes healthier places to live (Saegert et al. 2003). Studies suggest that an integrated approach to housing and health needs to be developed (e.g., Matte and Jacobs 2000).
In November 2002, the National Center for Healthy Housing, a national nonprofit organization dedicated to eliminating residential health hazards to children while preserving affordable housing, convened a 2-day workshop to review the state of knowledge and to help promote the paradigm shift to a healthy housing approach. The workshop was supported with funds from the National Center for Injury Prevention and Control and National Center for Environmental Health of the U.S. Centers for Disease Control and Prevention (CDC). This forum was unique in that it focused solely on housing and the translation of research findings into practical activities to improve health through improved home construction, rehabilitation, and maintenance. The purpose of this article is to present a summary of The Relationship between Housing and Health: Children at Risk Workshop, held 7–8 November 2002 in Annapolis, Maryland.
This workshop had four objectives: a) to identify what is known and unknown about the relationship between children’s health and the residential environment; b) to identify current “best practices” to address residential health hazards, particularly those that can be readily applied in construction, rehabilitation, and maintenance; c) to promote the development of a research agenda concerning residential health hazards and practical housing interventions; and d) to identify policy and market options to promote healthy and affordable housing for the nation’s children.
The agenda was divided into four sessions. The first three dealt with housing factors associated with a) childhood asthma and other respiratory diseases, b) neurodevelopmental and behavioral problems, and c) unintentional injuries. The final session focused on how to implement healthy housing programs. During the conference, each session began with two or more invited presentations, a total of 15 presentations for all four sessions, followed by a panel discussion.
This format provided an opportunity for professionals in different topic areas to learn about causes, mechanisms, effects, remediation, and prevention for topics other than their own specialties. It also enabled exploration of possible application of learning from one area to others—identifying commonalities, overarching concepts, and ways to influence policy and guidelines.
The 60 participants included mainly administrators, managers, researchers, and technical experts from major universities and state and federal agencies related to health, housing, and the environment. Participants also included public health professionals, academics, and physicians specializing in allergens, neurotoxicants, pesticides, airborne pollutants, and injury prevention and control. Other participants represented health and housing nongovernmental organizations, state and federal legislative staff, and a few representatives of related industries (pharmaceuticals, construction).
Residential Determinants of Asthma and Other Respiratory Conditions
Current state of knowledge.
Asthma, a chronic inflammatory condition of the lung airways, is the most common chronic disease among children today. Although asthma is thought to be caused by one or more mechanisms, there is general agreement that asthma is associated with airway inflammation and hyperresponsiveness. Both environmental and genetic factors are thought to play a role in asthma initiation and exacerbation. In a sensitized individual, small amounts of allergen can result in a large inflammatory response.
It has been estimated that approximately 80% of asthma in children is allergic asthma [Institute of Medicine (IOM) 2000]. Asthma rates are higher in children who are sensitized to allergens (Kattan et al. 1997; Lau et al. 2000; Nelson et al. 1999). Unlike allergic asthma, in which specific immunoglobulin E antibody responses occur, some individuals have nonallergic asthma that is also characterized by inflammation and airway hyper-responsiveness. It is important to note that once asthma has been established, a variety of exposures, including allergens, can trigger an asthma attack or exacerbate symptoms. Asthma often resolves as a child grows up; this happens for about half the children with asthma. However, the person will still have abnormal lung function later in life. In addition, asthma can recur in adulthood. One study suggests that asthma recurs in adults in approximately 20% of the cases (Blair 1997).
Chronic exposure to allergens in the indoor environment from mold, pets, mice and rats, cockroaches, and dust mites is associated with asthma. Indoor moisture sustains mold, pests, dust mites, and bacteria. There appear to be different patterns of sensitization due to varying allergens in different indoor settings. For inner-city home environments, exposure to cockroaches, mice, and rats is also related to asthma and allergic morbidity. Rosenstreich et al. (1997), for example, reported a cockroach sensitization rate of 36% in inner-city asthmatic children.
Indoor air pollutants have also been associated with the development and exacerbation of asthma. Because of cost and other practical limitations, there are relatively few data on personal exposure to common indoor pollutants. One of the most common indoor air pollutants is environmental tobacco smoke (ETS). In a recent review of indoor air quality and asthma, the IOM concluded that there is sufficient evidence to associate ETS exposure with the development and exacerbation of asthma (IOM 2000). According the IOM review panel, there is suggestive evidence that nitrogen dioxide can exacerbate asthma, but there is inadequate evidence to support an association with the development of asthma (IOM 2000). The volatile organic compound formaldehyde has received research and public attention, but its role in the development or exacerbation of asthma is not clear.
Three outdoor pollutants—ozone, sulfur dioxide, and fine particles—are also known to exacerbate asthma. Indoors, SO2 and O3 are readily adsorbed onto surfaces and taken out of the air. Thus, there seems to be less of an indoor problem with these chemicals. The relationship between in-home particulate matter exposure and asthma is not well researched. Additional research is also needed on the combined effects of indoor and outdoor pollutants.
Best practices to address housing-related asthma risk factors.
Because exposure to allergens has been identified as a major source of airway inflammation, asthma control efforts have focused on allergy avoidance. Accordingly, the basic strategy to alleviate respiratory symptoms is to determine to which allergens a person is sensitive and then follow a set of steps to avoid those specific allergens. Because half of asthmatics have multiple (three or more) sensitivities (Eggleston 2000; Huss et al. 2001), several actions may have to be undertaken over long periods of time to control their symptoms.
Efforts to control asthma require consistent application of various measures. Practical issues, including the cost of equipment and the time involved to keep a home sufficiently clean, can affect the patient’s ability to carry out recommended steps. Factors affecting behavior change need to be understood and incorporated into the design of interventions. Interventions should be promoted that are most likely to give positive results (i.e., reduced attacks, for example) and encourage continued compliance. Examples include the Master Home Environmentalist program (MHEP)—a community-based program that focuses on indoor sources of pollution, pesticides, and moisture—as well as programs aimed at helping builders construct healthy homes, such as that of the American Lung Association and the Asthma Regional Council. The MHEP is operated by the American Lung Association of Washington State and has spread to four other states. Trainees can use the Home Environmental Assessment List questionnaire to assess the home environment and negotiate an action plan with the family (Leung et al. 1997).
Knowledge gaps and research needs.
The complex mixture of allergens in the home setting presents several challenges. There could be interaction effects among allergens to cause sensitization and attacks and worsen the condition. Presently, there is no indicator of total allergen burden, and if it is developed, it would have to correlate well with asthma development and exacerbation.
Within the home, there are several measurement issues related to allergens. For example, the relationship between a surface allergen sample and the inhaled dose is not known. Additionally, the location of sample measurements can give different levels, so there is need to give attention to sample site selection.
Other research needs include the following: a) Interactions and synergy among allergens, mixtures, and multiple risk factors are not well understood or documented. b) The dose–response relationship between mold and the development of specific disease states is not adequately documented. c) Additional research is needed to document the optimal and feasible behavior change strategies required to maintain respiratory health and improve compliance with proven interventions. d) The relative benefits of various interventions such as rehabilitation and renovation versus intensive cleaning have not been measured.
Environmental Neurotoxicants in the Home
Current state of knowledge.
The most significant neurotoxicants found in residential settings are lead, pesticides, and ETS (Jordaan et al. 1999; Lanphear et al. 2000; Whyatt et al. 2002). Lead toxicity affects the brain and neurodevelopmental processes, and its effects are irreversible. Lead paint dust is the primary source of exposure in homes, rather than pieces of lead-based paint or lead in soil. In the United States, 17% of low-income children have blood lead levels > 10 μg/dL, the level that is considered elevated by the CDC. Recent research suggests that there is probably no lower-level threshold of blood lead (Bellinger and Needleman 2003; Canfield et al. 2003). The biologic mechanisms involved in lead poisoning are not yet well understood (Silbergeld 1992), although it is known that lead disrupts processes regulated by calcium and changes synapse formation (Bressler et al. 1999). Even at quite low levels (2.5–10 μg), deleterious effects of lead can be detected (Canfield et al. 2003). An inverse relationship between blood lead concentration and arithmetic and reading scores was observed for children with blood lead concentrations < 5 μg/dL (Lanphear et al. 2000).
Smoking cigarettes during pregnancy has effects on fetal development and the baby’s health and development after birth. Estimates of women who smoke during pregnancy range from 25 to 44%. A mother’s smoking habits are closely associated with a child’s exposure, probably because the child is with the mother for more time and in closer proximity. Children’s blood cotinine levels have been shown to increase with increasing numbers of smokers in the household (Jordaan et al. 1999).
Exposure to tobacco smoke during and after pregnancy is associated with prematurity, low birth weight, low Apgar scores, poor growth of infants, and dysfunctional behavior (Bauman et al. 1991; Eskenazi and Trupin 1995; Fergusson et al. 1993; Williams et al. 1998). Currently, evidence related to child development and behavior is stronger for prenatal than for postnatal exposure. Recent research using blood cotinine levels as an indicator of exposure to ETS shows a robust inverse relationship between postnatal cotinine levels and cognitive scores (math and reading) in 6- to 16-year-old children. The relationship remains statistically significant after controlling for various characteristics (Lanphear et al. 2000).
The mode of action of most pesticides is to be neurotoxic to pests. It is reasonable to assume, therefore, that they will also have neurotoxic effects on humans. There is a growing body of evidence suggesting that public exposure to cholinesterase-inhibiting pesticides (organophosphates and carbamates) is a health concern (Whyatt et al. 2002). The impact of organophosphate and carbamate exposure on children has not been extensively researched, particularly with respect to neuro-behavioral testing.
In inner-city home environments, indoor exposures to some pesticide toxicants can be frequent and at high levels due to cockroach and rodent problems. Whyatt et al. (2002) recently reported on the pesticide use of inner-city residents in New York City. This study documented widespread pesticide use, and in the case of diazinon, the exposure for some women may have exceeded healthy levels based on the U.S. Environmental Protection Agency (EPA) reference dose. Eighty-four percent of the women questioned as a part of this study reported that pest control measures were used in the home during pregnancy. Not surprisingly, a number of organophosphate (both chlorpyrifos and diazinon) and carbamate (propoxur) pesticides were detected in air samples, maternal blood, and cord blood samples (Perera et al. 2003).
Best practices to address exposure to housing-related neurotoxicants.
The steps needed to prevent childhood exposures to neurotoxicants are founded in core public health practice. They include identifying sources of exposure, defining unacceptable levels of exposure, developing and testing interventions, and, finally, implementing effective policies and screening programs. Intervention strategies include (in increasing order of effectiveness and cost) education, enforcement, and engineering controls with an emphasis on primary prevention. It is difficult to address detrimental effects on neurodevelopment when children are exposed to multiple neurotoxicants at the same time. It is common for inner-city children to be exposed to lead, ETS, and pesticides both prenatally and postnatally. To be effective, intervention efforts should address these multiple exposures at the same time.
In the case of childhood lead exposure, there is an extensive body of literature documenting the impact that various methods of lead hazard control have on dust and blood lead levels (Galke et al. 2001; Haynes et al. 2002; Niemuth et al. 1998; Staes and Rinehart 1995). Niemuth et al. (1998) summarized the literature from 1980 through 1998 and included both trials and observational studies. Generally, the studies report successful reductions in dust lead levels and in blood lead levels when initially > 20 μg/dL. To date, the published data on the effectiveness of specific lead hazard control treatments have been too limited to draw conclusions about the relative effectiveness of specific lead hazard control approaches (e.g., window replacement, paint stabilization).
Although pesticide use in the home is common, designing and implementing intervention strategies are difficult because of the lack of basic toxicity testing information specific to neurodevelopmental effects. Without adequate toxicity and human exposure data, it is not possible to target control strategies. Basic toxicity testing for neurodevelopmental effects as well as aftermarket health and exposure surveillance should be mandated. Further, we need to shift programs away from screening of children to screening of homes. This will require the development of health-based screening guidelines similar to those developed for lead.
Besides the elimination of ETS, three methods of control—air filtration, ventilation, and smoke containment—could be used to reduce the presence of tobacco smoke in the home environment. Although it is clear that eliminating smoking (either by quitting or smoking outside and away from children) will reduce ETS exposure (Johansson et al. 2004; Wakefield et al. 2000), there is little research on the efficacy of active engineering control methods (i.e., ventilation and/or filtration in the home). A controlled pilot study of high-efficiency particulate air-carbon potassium zeolite filters has shown that they are able to reduce nicotine in the air (Aligne CA et al., unpublished data). Unintended negative consequences of interventions must be considered. For example, it is not clear whether air purifiers that generate O3 present a health problem that might mitigate their effectiveness. Nonetheless, education and smoking cessation programs are the most commonly used interventions for ETS exposure. Smokers must be educated about the impact of ETS on children and the need to avoid smoking in their presence. In addition, educational efforts should continue to target pregnant mothers to prevent prenatal exposure. Further investigation is also needed to understand why some families take steps to reduce ETS exposure and others do not.
Knowledge gaps and research needs.
Although there may be limitations, laboratory studies are needed to increase knowledge about the active toxicants found in complex mixtures present in the home environment, such as house dust and ETS. Basic laboratory studies can provide early indications about the likely effects of environmental toxicants such as pesticides and ETS because they are typically cheaper and faster than epidemiology studies. However, issues inherent in studying animal models and extrapolating to human beings underscore the need for epidemiology studies.
Consistent outcome measures across studies, such as what to measure and agreed-upon cutoff points, are needed. Also, interactions and synergistic effects of multiple toxicant exposures on neurodevelopment need additional research. Finally, premarket neurotoxicity testing of home pesticides and postmarket surveillance will help fill knowledge gaps.
Several measurement considerations were also raised during the workshop. These included appropriateness of measures of exposure and dose, ability to detect differences and interpret them, and comparability across studies. Scientific agreement on the best measures of specific exposures and effects would make it easier to compare results of different studies and to perform meta-analyses. Precise measurement of actual exposures in the home environment would improve the ability to find effects and draw clear conclusions. Additionally, there is a need to develop new and less costly techniques to measure and analyze pesticide exposures.
There is insufficient evidence that educational programs and other interventions about ETS and pesticides result in less exposure by pregnant women and after a child is born. Besides reported behavior, additional measures of the behavior effects of toxicants need to be developed.
Unintentional Injury of Children in the Home
Current state of knowledge.
Between 1985 and 1997, home injuries accounted for almost two-thirds of all fatal unintentional injuries occurring to U.S. children and adolescents, with mean residential death rates for children and adolescents varying markedly by age, race, and geographic location.
Data from the National Hospital Ambulatory Medical Care Survey for 1993–1999 [National Center for Health Statistics (NCHS) 1993–1999] for children < 20 years of age show that injuries accounted for 11 million visits to the emergency department (ED). Injuries occurring in the home accounted for four million of these visits. Children in the youngest age groups had significantly higher ED visit rates, similar to rates for fatal injuries. Residential injuries leading to ED visits were highest for children 1–4 years of age. In addition, males had higher ED visit rates than did females (Phelan et al., in press). For children < 1 year of age, 93.5% of all deaths due to injury occurred in the home. That proportion declined progressively with age through adolescence, falling to 38% for 15- to 19-year-olds (Nagaraja J et al., unpublished data; Pollock et al. 1988). Similar results were found in a recent study reporting that 80–90% of injury deaths among children < 5 years of age occur in the home, compared with 80% for 5- to 9-year-olds and 60% for 10- to 14-year-olds (Lanphear B et al., unpublished data).
The fatality rate for residential injuries has declined by about 25% since 1985. Despite the decline, residential injury death rates are substantially higher for African-American children than for other race groups. Unlike injury death rates, nonfatal residential injury rates by race were similar (Nagaraja J et al., unpublished data).
Injuries of differing severity may be associated with different risk factors. The different injury severity outcomes must be examined separately to define the risk factors that describe the linkage between children’s homes and the injuries they might suffer.
Falls are the leading type of residential injury for children; they account for an estimated 3 million visits to the ED. The primary residential hazards associated with falls are lack of safety devices such as grab bars, safety gates, or window guards; structural defects in the home; and insufficient lighting on stairs and other areas (Battelle Memorial Institute 2001). Risk of death from fire is higher in the South and Southeast than in other regions of the United States, and children < 5 years of age and the elderly are at higher risk than other age groups. Scalds, nonfire burns, and poisoning injuries occur among children fairly often. Burns account for about 185,000 ED visits annually for children < 20 years of age (Phelan et al., in press), and 95% occur among children < 5 years of age (CDC 2002). Infants and toddlers are at higher risk of accidental poisoning requiring an ED visit than are children 5–19 years of age (Phelan et al., in press).
Although large national sources of injury data provide some key insights into injury rates, there is a need to improve injury epidemiology and surveillance to collect data on injuries that do not result in hospital visits. In addition, better data are needed to evaluate the determinants of injury rate variability in order to design effective intervention strategies.
Best practices to address housing-related injury risk factors.
The most important home safety actions documented in the literature are reduction of the temperature of hot water heaters to 120°F to prevent scald burns; use of stair fences; installation of window guards, especially in high-rise buildings and upper stories of homes and apartment buildings; installation and maintenance of smoke alarms; installation and use of cabinet locks; and segregation and locking away of poisons.
Smoke alarms are a key means to prevent injury or death due to home fires. When homes have functioning smoke alarms, there is a 50–80% reduction in injury and death due to residential fires (CDC 2002). A pilot program for comprehensive residential fire prevention showed promising results (Jackson 2002). Components of the program included installation of smoke alarms, educational activities, and cooperation among local health departments, fire departments, community organizations, and the media. The smoke alarm program found that 85% of alarms were operational when follow-up was done. This program approach might be a useful model for prevention of other types of residential injuries, but it should be evaluated for effectiveness.
Knowledge gaps and research needs.
There is a need for better data on residential injuries of children and for meaningful measures of injury outcomes. Questions on injuries and injury prevention might be included in household surveys to better capture injuries that do not result in hospital ED visits. Overall surveillance of home injuries must be improved, as well as the capability to identify, measure, and report on new and emerging home hazards. Higher-quality information should be obtained from existing sources of home injury data, such as fire departments and fire marshals, to facilitate development of prevention activities.
Behavior change is a challenging aspect of injury prevention. Researchers and policy-makers need to understand what motivates and enables parents to take injury prevention measures in the home and sustain such behavior over time. Concerted efforts should also be made to tap the vested social and economic interests of employers and insurers in reducing injuries.
Translating Healthy Homes Research into Action
Understanding the relationships between residential environmental hazards and children’s health problems is a necessary precedent to preventing those problems. Equally important is an understanding of how to translate that knowledge into preventive action by developing and promoting the most cost-effective techniques to assess and control hazards. To that end, workshop participants suggested several action steps to facilitate the pursuit and dissemination of translational research.
Increasing funding for research and evaluation of demonstration projects to determine how best to assess and control hazards.
At present, the HUD Healthy Homes Initiative is the predominant source of funding dedicated to understanding how to prevent diseases and injuries associated with housing hazards. Because of the small appropriation and the structure of the program, HUD funds only 2-year projects, which makes it challenging to develop definitive conclusions. Congress should provide HUD with a longer-term authorization and mission, and it should direct other agencies such as the U.S. EPA, CDC, and National Institute of Environmental Health Sciences to fund long-term studies, including randomized control studies. Those studies should be developed and managed by collaborations that include medical and public health schools, research-oriented housing organizations, and members of those communities most affected by the issues.
Additional long-term funding will be available only if the interested scientific and advocacy communities make a major effort to provide the potential funders and key legislators with existing evidence that diseases can be prevented and money saved in the long run by supporting substantial expansion of the healthy housing effort.
Enforcing existing hazard elimination and control regulations and considering enacting new regulations.
Federal, state, and local legislation, regulations, and guidelines already exist that could materially reduce residential hazards. The existing comprehensive lead laws, regulations, and guidelines set the example. Most housing codes aim to prevent excess moisture intrusion and pest infestation and require ventilation. Just as code requirements for smoke alarms and stair and window guards reduce unintentional injuries, effective enforcement of those codes could sharply reduce such allergens as cockroaches, mold, and rat and mouse dander.
For the most part, these requirements and their enforcement are aimed at individual hazards. Relatively minor changes in the codes could increase their effectiveness across hazards. Focused cross training of sanitary and building code inspectors using a common assessment protocol could increase efficiency and thoroughness in identifying hazards as well as help reinforce any educational program. A common assessment tool would also enable researchers to compare assessment results with any health conditions reported.
Additionally, government agencies should promote basic healthier housing construction standards for housing receiving government funding. The government should evaluate the cost and effectiveness of these standards in increasing durability, decreasing maintenance and energy costs, and lowering costs associated with children’s exposure to hazards. The results of the research and evaluations described above should be completed and the results disseminated widely to build a consensus and affect policy change. For example, government agencies and nongovernmental organizations could sponsor conferences to enable medical organizations, congressional staff, and foundation representatives to collaborate and discuss the results of research and evaluations.
Pursuing market-based approaches to eliminate or control hazards.
Ideally, the market consisting of informed homebuyers and renters would induce landlords, homebuilders, renovators, and remodelers to make housing safe and healthy. However, in some cases current buyers and renters value aesthetics and lower costs over health issues that they do not understand, and builders are unlikely to change their plans voluntarily, particularly if the change adds even marginally to costs. In many cases, individuals with limited means are forced to choose between healthy housing and affordable housing and are therefore not in the position to influence market forces.
Public education programs featuring the dangers of unsafe and unhealthy housing should be mounted to stimulate demand for healthy market rate housing. This is especially true in low-income housing where the stakeholders (i.e., tenants) lack the political capital needed to stimulate change.
Builders are motivated primarily by decreasing the time and lowering the cost required for construction. It can and should be possible to demonstrate that moisture-resistant housing can be built for the same price and within the same time frame as housing that is constructed otherwise. Moreover, healthy housing is likely to reduce buyer complaints and lawsuits. Experimental programs to train New England builders that simple moisture resistance techniques are practical and inexpensive were well received (Tohn 2002). Demonstrating that safe and healthy housing may mean lower maintenance costs and healthier residents should motivate owners, housing finance agencies, and even banks to include healthy housing techniques in their specifications and underwriting standards.
Fear of liability induced property owners to accept lead safety standards. Publicity about illness apparently caused by mold has threatened the availability of insurance in parts of the country. So far, few if any lawsuits have been brought against rental property owners for exposure to allergens or pollutants, presumably because control of those hazards is considered to be the resident’s responsibility. As the relationship between structural conditions that cause excess moisture and disease becomes clearer, such lawsuits may become more common. Insurers and lenders could include moisture resistance measures in their underwriting guidelines. Additionally, insurers could consider rate discounts for properties that follow housing guidelines.
Persuading medical and policy organizations that eliminating or controlling hazards in housing should be given high priority.
The medical establishment naturally focuses on treating diseases such as asthma, lead poisoning, and cancer with drugs rather than eliminating or reducing exposure to the hazards that contribute to the diseases. Because the hazard of lead in household dust is so clearly the primary cause of childhood lead poisoning, the medical community has shifted its emphasis to a primary prevention approach as opposed to treatment. Asthma and cancer are much more complex diseases, and the relationship between disease and allergens, pests, pollutants, pesticide residue, and other chemical and biologic hazards in housing is less clearly understood. Primary prevention approaches to a wide spectrum of housing hazards need to be both developed and implemented. Brunekreef et al. (1989) suggest that failure to control dampness, mold, and moisture in homes and the associated pests, bacteria, and dust mites has the same large impact on children’s health as does ETS.
Research proposed in this workshop summary is critical to convincing the medical profession, Congress, government agencies, foundations, and other stakeholders that they should also focus on housing if these diseases are to be prevented. Because research and demonstrations show that assessing and treating hazards in housing can reduce the incidence and severity of disease, a concerted effort must be mounted to disseminate the data and conclusions to medical professionals, policymakers, and funders.
Conclusions
Four major themes emerged from the expert presentations and panel discussions: a) Although all of the mechanisms are not yet well studied and described, the built environment, including residential housing, is an agent of health (or illness) for children. b) The body of research around lead toxicity can serve as a model for analysis and exploration for other environmental hazards. c) Studies that can establish linkages among the residential environment, children’s health status, and interventions face ethical and practical constraints, which may limit the range of options available. d) Social determinants influence who is at risk for exposure or injury, how they react to those substances or risk factors, and the efficacy of interventions.
Participants identified four global research gaps and policy issues requiring further consideration and resources: a) Home hazard measurement techniques and standards have not been developed for all hazards. Until better measurements of the direct impact of housing quality on health are developed, the relationship will be undervalued. b) Translational research is lacking. The efficacy of interventions has not been sufficiently demonstrated through rigorous, long-term studies. c) Interactions and synergies among hazards are presumed to exist but are not well documented or understood. d) A broader coalition of researchers, policy-makers, appropriators, and advocates must be engaged to fill data gaps, support needed research, and pursue policy change.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00864PerspectivesCorrespondenceResidential Tetrachloroethylene Exposure: Response Hudnell H. Kenneth U.S. Environmental Protection Agency, Office of Research and Development, National Health and Effects, Research Laboratory, Neurotoxicology Division, E-mail:
[email protected] Judith S. State of New York, Office of the Attorney General, Division of Public Advocacy, Environmental Protection Bureau, E-mail:
[email protected] letter was reviewed by the National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the agency.
The authors declare they have no competing financial interests.
11 2004 112 15 A864 A865 Publication 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 are grateful for the opportunity to respond to Storm and Mazor’s comments about our study “Apartment Residents’ and Day Care Workers’ Exposures to Tetrachloroethylene and Deficits in Visual Contrast Sensitivity” (Schreiber et al. 2002). We investigated potential relationships between environmental exposure to the dry-cleaning solvent perchloroethylene (perc, or tetrachloroethylene) and effects on visual function in two exposed populations (17 residents, including 4 children, in two apartment buildings and 9 adults working at a day care center) and age- and sex-matched control groups (n = 25 and n = 9, respectively). Mean airborne perc concentrations were 778 and 2,150 μg/m3 in the apartments and the day care center, respectively, levels well above the background range of < 1.6–22 μg/m3 [New York State Department of Health (NYSDOH) 1997]. Perc concentrations in biological samples were also elevated (Schreiber et al. 2002). We assessed visual function using tests of near acuity, near visual contrast sensitivity (VCS; a sensitive indicator of neurologic function), and color discrimination. Visual acuity did not differ between groups, but VCS scores from both the apartment residents and the day care workers were depressed across the spatial frequency spectrum, similar to results obtained in other solvent exposure studies (Broadwell et al. 1995; Campagna et al. 1995; Castillo et al. 2001; Donoghue et al. 1995; Frenette et al. 1991; Hudnell et al. 1996a; Mergler 1995; Mergler et al. 1991). We concluded that
Although the similar VCS deficits in both the residential study and day care investigation were apparently associated with chronic low-level environmental perc exposures, methodologic limitations preclude a definitive attribution of causation.
It is unlikely that age differences caused the group differences in VCS, as Storm and Mazor suggested. The exposed and control participants in the residential study were matched for age within 2 years, and the group means were within 1 year of each other. The mean age of the four exposed children was about 6 months greater than that of the six controls. The day care workers and controls were matched within 1 year of age, and the group means were within 6 months of each other. Such small age differences were highly unlikely to account for the VCS deficit.
Storm and Mazor reported that one exposed child was developmentally delayed and one had an attention deficit disorder, and they suggested that this may have caused the group difference in VCS. However, they did not provide comparable data for the control children, who were family members of NYSDOH employees. The same assessment of potentially confounding factors should have been applied to both groups. Furthermore, they cited a previously published article (Hudnell et al. 1996b) when suggesting that the VCS deficits in the exposed children may have been due to developmental delays. That article actually reported an association between perinatal exposure to airborne neurotoxicants and developmental delay in VCS (Hudnell et al. 1996b). We felt that it was inappropriate to exclude children from study participation because of conditions that may have been caused by perc exposure.
As noted by Storm and Mazor, “sample sizes were not sufficient to support statistical analysis of VCS stratified by age (i.e., child, adult)” in the residential study. It is not surprising that when they reduced the sample size to 13 pairs by excluding all children, the p-value increased from < 0.001 to 0.16, even though 7 of the 13 exposed adults had VCS scores in the lower 12th percentile of control scores.
We took several steps to minimize the influence of potentially confounding factors on VCS. A standard operating procedure and luminance control ensured test consistency. The exclusion criteria—failing to attentively complete the VCS test (one control resident excluded), having Snellen acuity worse than 20:70 (two eyes from exposed residents excluded, perhaps due to cataracts), and observing strabismus or other ocular anomalies (one control resident excluded)—were applied to both groups. None of the participants reported having an illness that might affect neurologic function. In the day care investigation, all participants were healthy females, and eight of nine were 21–29 years of age, thereby further reducing the potential for confounding. The observation of similar reductions in the VCS spatial-frequency profiles of the residential and day care exposed cohorts supported our conclusion that the effects may have been due to perc exposure.
Storm and colleagues recently conducted a study of apartment residents potentially exposed to perc and reported normal VCS in the exposed cohort (NYSDOH 1999, 2003). However, two factors limited comparability to our study (Schreiber et al. 2002). First, they measured far, rather than near, VCS. Near and far VCS do not provide comparable data due to differences in illumination, near and far visual acuity, and the visual field size of the test stimuli. Second, the mean airborne perc concentration was 34 μg/m3 in their study (NYSDOH 2003), 1–2 orders of magnitude lower than in our studies. These differences precluded an attempt to verify the VCS effects reported in our article (Schreiber et al. 2002). We stand by our methodologic procedures, results, and conclusions.
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References
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Mergler D 1995. Behavioral neurophysiology: quantitative measures of sensory toxicity. Neurotoxicology: Approaches and Methods (Chang LW, Slikker W, eds). San Diego:Academic Press, 727–736.
NYSDOH 1997. Tetrachloroethene Ambient Air Criteria Document. Final Report. Albany, NY:New York State Department of Health.
NYSDOH 1999. Improving Human Health Risk Assessment for Tetrachloroethene by Using Biomarkers and Neuro-behavioral Testing in Diverse Residential Populations. Albany, NY:New York State Department of Health. Available: http://cfpub2.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/977/report/0 [accessed 24 August 2004].
NYSDOH 2003. Progress Report: Improving Human Health Risk Assessment for Tetrachloroethene by Using Biomarkers and Neurobehavioral Testing in Diverse Residential Populations. Albany, NY:New York State Department of Health. Available: http://cfpub2.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/977/report/2003 [accessed 24 August 2004].
Schreiber JS Hudnell HK Geller AM House DE Aldous KM Force MS 2002 Apartment residents’ and day care workers’ exposures to tetrachloroethylene and deficits in visual contrast sensitivity Environ Health Perspect 110 655 664 12117642
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00870EnvironewsForumChildren’s Health: Methylmercury and Children’s Heart Function Tibbetts John 11 2004 112 15 A870 A870 Publication 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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Pregnant women who consume significant amounts of seafood may have a new reason to take precautions against methylmercury, the most hazardous form of mercury: a recent study suggests that when expectant women consume fish containing high levels of the toxicant, their children’s future cardiovascular health may be jeopardized.
Fish and shellfish are the main sources of exposure to methylmercury for most Americans. Methylmercury tends to accumulate the most in large predatory species such as yellowfin tuna, shark, swordfish, and marlin. Other commonly eaten species can accumulate intermediate levels of methylmercury. Fish with the lowest mercury content include cod, flounder, salmon, herring, and smaller tuna species that Americans buy canned.
In 1986, researchers led by Harvard environmental epidemiologist Philippe Grandjean and Faroese Hospital System chief physician Pal Weihe began a long-term study of mothers in the Faroe Islands and their children. The Faroese are among the world’s leading seafood consumers per capita, with the average islander eating 2.4 ounces of fish per day. This diet exposes them to increased amounts of methylmercury.
Over a 21-month period, the researchers gathered a cohort of 1,022 women giving birth in the Faroe Islands. They tested mercury concentrations in the children by analyzing cord blood samples at birth and blood and hair samples taken at ages 7 and 14 years. They also measured the mercury in each woman’s hair by taking a sample at the time of parturition.
In one of the latest papers to come from this study, published in the February 2004 issue of the Journal of Pediatrics, Grandjean and his colleagues report that mercury which passed from mother to child in utero, first measured in cord blood, produced long-lasting harm to the child’s neurologic mechanism that regulates heart function, as measured by heart rate variability. At higher mercury exposures, children were less capable of maintaining normal heart rate variability, which is a risk factor for development of heart disease. The decrease in heart rate variability at increasing mercury exposures was the steepest in the low range of mercury exposures, around the U.S. Environmental Protection Agency exposure limit. When the exposures increased above twice that limit, the effect was not as clear.
Very little is known about the impact of heart rate variability in children, except that children with congenital heart disease also have lower heart rate variability. Grandjean says, “The mercury-associated changes in the Faroe Islands study persisted at least to age fourteen, and it’s possible that they are permanent. In adults, decreased heart rate variability is a known risk factor for heart disease mortality.”
Alan Stern, an adjunct associate professor of public health at the University of Medicine and Dentistry of New Jersey, points out that because this effect is likely the result of developmental changes in the children’s neurologic systems, it may be a sentinel for other neurophysiological disturbances. The developing brain is particularly vulnerable to methylmercury, and brain damage incurred during development is likely to be permanent.
However, Gary Myers, a pediatric neurologist who studies mercury exposure at the University of Rochester in New York, says that the Faroese are unusual in their diet of whale meat, which is especially high in concentrations of mercury and other toxicants. Therefore, he says, this study cannot be generalized to the United States and other countries with populations that do not consume whale meat.
But many Faroese do not eat whale, says Grandjean, and its availability varies seasonally and among communities. He says mercury associations found at low-exposure levels are more likely to be related to other kinds of seafood with high mercury concentrations. Still, he cautions that scientific conclusions should not be based on a single study. Moreover, consumers should not be scared away from eating seafood, but should instead be wary of fish with elevated mercury concentrations, particularly large predatory species.
Skipping a beat. New data from studies of children in the Faroe Islands exposed in utero to methylmercury show long-lasting effects on the neurological mechanism that controls heart rate variability.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00873EnvironewsForumEHPnet: American Heart Association Dooley Erin E. 11 2004 112 15 A873 A873 Publication 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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Many diseases fall under the umbrella of “cardiovascular disease,” including high blood pressure, cardiac arrhythmia, and congenital heart disease. These conditions affect young and old alike, as well as every ethnic group. In the United States, heart disease is the leading cause of death for both men and women. In 2004 alone, it is expected to cost the U.S. economy over $238 billion in health care services, medications, and lost working hours. Since 1924 the American Heart Association (AHA) has been working to raise funds for heart disease research and to generate awareness among the general public about the seriousness of these diseases. The AHA website located at http://www.heart.org/ disseminates information about heart disease and the association’s wide range of public information and continuing education programs.
A menu bar down the left-hand column of the homepage includes a pull-down listing of 10 diseases and conditions, which can then be selected for in-depth information. For example, the page on high blood pressure provides facts on the disease, ways to keep it in check, how to sign up for the AHA’s monthly e-mail list service, and more. Visitors can also find information on risk factors, how a condition can affect health, and information geared toward medical professionals.
Another option on the menu bar leads to information on heart disease specifically in children. Along with a basic overview of childhood heart disease, this section includes in-depth pages on topics including DiGeorge syndrome, Kawasaki disease, and exercise for children. Also accessible from the children’s section is HeartPower!, a free, downloadable curriculum devised to help teachers in grades pre-kindergarten through 8 teach their students about healthier lifestyles.
The Healthy Lifestyle link from the homepage leads to information on topics ranging from diet and nutrition to heart disease in women. Various pages provide tips on creating healthy eating habits, developing and maintaining food plans, reducing cholesterol, and keeping fit. The Health Tools portion of this section has a cardiovascular disease risk assessment tool; a family health history tree; and online logs for tracking blood glucose, blood pressure, cholesterol, and exercise.
The Publications section provides links to lists of AHA-produced consumer and patient education materials, many of which can be ordered for free. Visitors will also find a listing of AHA cookbooks (with sample recipes posted online) and other books on heart health. The AHA publishes five journals that also can be accessed online, as can a variety of other scientific publications, including Scientific Statements from the AHA, performance measures, and clinical data standards. The online Heart and Stroke Encyclopedia is available from the homepage as its own section.
Visitors to the homepage will find information on current events, and can search by zip code for nearby events and information on local AHA chapters. Many of the site’s pages are available in Spanish, and can be accessed through the En Español link on the homepage.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0087415531421EnvironewsNIEHS NewsNational Toxicology Program: Landmarks and the Road Ahead McGovern Victoria 11 2004 112 15 A874 A878 Publication 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 National Toxicology Program (NTP), a cross-agency unit of the Department of Health and Human Services (DHHS), is one of the focal points for government efforts aimed at generating, collecting, and coordinating data used for guiding public health decisions. Now 25 years old, the NTP is in the middle of a strategic planning effort to define how it will integrate new technologies with classical toxicological approaches to continue providing, according to the NTP’s motto, “good science for good decisions.”
In its current form, the NTP integrates activities from the NIEHS, the National Institute for Occupational Safety and Health (NIOSH), and the Food and Drug Administration (FDA) National Center for Toxicological Research (NCTR). Its mission is to evaluate agents of public health concern by developing and applying tools of modern toxicology and molecular biology, a mission it achieves through the use of several strategies. It designs studies on potential toxicants and works with outside groups and government labs to carry them out. It reviews and evaluates what’s missing in understanding environmentally induced diseases. It then seeks to fill those gaps by collaborating and cooperating with federal agencies and with other domestic and foreign toxicology and public health organizations, and by carrying out research itself on human exposure and toxicity at laboratories housed at NIOSH and the NIEHS. It supports grants, contracts, and interagency agreements made through the NIEHS Division of Extramural Research and Training, and it supports the activities of three centers.
These activities have brought immense gains in knowledge for the scientific community. But there is more work still to do. Since the fall of 2003, the NTP has been engaged in developing and seeking public comment on a “roadmap” to guide the program’s route forward over the next decade. The roadmap seeks to build on recent technological advances that will allow toxicology to evolve from a largely observational science to one that is more predictive, and thus in many ways more protective of public health.
The Journey Begins
The NTP’s roots reach to the late 1960s, when concerns over the effects of environmental chemicals on human health began to grow among scientists, policy makers, public health officials, and the general public. Books like Silent Spring began to describe the relationship between humankind and the rest of the world as dangerously out of balance. The growth of the environmental movement in this decade led to greater public awareness of natural resources and the potentially harmful effects of environmental pollution. As law makers and the public became increasingly concerned about unforeseen effects of chemicals all around us—particularly those used in manufacturing, agriculture, and household products such as cleaners—new scientific and regulatory approaches were enacted to better understand these agents and decrease or eliminate human exposure to harmful agents.
Through the 1960s and 1970s, new agencies were formed and responsibilities within existing agencies were realigned in efforts to understand, classify, and/or regulate compounds of concern. The Toxicology Information Program at the National Library of Medicine began gathering toxicological information and compiling data banks to allow searching and comparison of the assembled material. The NIEHS, which was established in 1966 and became a full NIH institute in 1969, started a laboratory focused specifically on neurotoxicology in 1977, and in the same year hosted one of the first conferences on environmental estrogens. The Environmental Protection Agency (EPA) was founded in 1970 to pull environmental and health protection activities from across the government into one administrative unit. The FDA took on licensing authority for new biological therapeutic agents but transferred its responsibility for oversight of potentially hazardous materials and other dangers in home items (such as toys, furniture, clothing, and household chemical products) to the Consumer Product Safety Commission (CPSC), which was established in 1972.
In November 1978, Joseph Califano, secretary of the Department of Health, Education, and Welfare (the predecessor of today’s DHHS), established the NTP as a cooperative and coordinating effort between agencies involved in public health. The program was seated at the NIEHS and placed under the leadership of then–NIEHS director David Rall. Kenneth Olden assumed directorship of both the NIEHS and the NTP in 1991. Despite this link, the NTP was and remains independent of the institute and of NIH.
The new program’s charge included coordinating toxicological testing programs within public health agencies; strengthening the scientific basis of toxicology; developing and validating new assays and improved testing methods; and providing information about potentially toxic chemicals to health, regulatory, and research agencies across the government, to the scientific and medical communities, and to the general public.
Strengthening the Scientific Basis of Toxicology
Among its earliest activities, the NTP began collecting data from work going on across federal agencies; by 1980 it had generated a database comparing the results gained from a variety of widely used genotoxicity assays. By the end of that year, the program had issued its first Report on Carcinogens (ROC), a scientific and public health document identifying substances, mixtures, and circumstances of exposure that may lead to human cancers. The ROC has been updated periodically since then, and the eleventh edition is scheduled for release late in 2004.
In 1983, the NTP first developed and began using five standard categories to summarize the strengths of experimental data produced by its own laboratories and those of other agencies and industry in studies of chemical or physical agents. Four categories are based on a scale of confidence that ranges from “clear evidence” to “no evidence” of harm; a fifth judges that evidence amounts to an “inadequate study.” The standardization helped make the program’s long-term toxicity reports—which can form the basis for policy recommendations on acceptable exposure levels and use of chemicals—more consistent across the range of agents and exposures studied. So, for example, when the State of California passed Proposition 65, the Safe Drinking Water and Toxic Enforcement Act of 1986, NTP data from the ROC were available to help the state set standards for discharge of potentially harmful agents into drinking water. By 1987, the NTP’s standardized categorization had been used to classify conclusions from earlier federal studies of potential hazards, allowing the older information to be integrated into current data sets.
In a paper in the 22 May 1987 issue of Science, the NTP published the first comprehensive evaluation of genotoxicity assays, laying the groundwork for a more systematic approach to developing and validating new in vitro assays. At the same time, the program developed a battery of tests for assessing chemically induced immunotoxicity. As the 1980s drew to a close, the NTP began developing transgenic mouse models for toxicology and carcinogenicity testing, a new approach that would allow easier detection of end points such as tumors. (The NTP has continued to investigate the development of transgenic animals as research tools, and in 2003 launched a new technical report series to convey the findings from transgenic model systems.)
Revving Up in the 1990s
The 1990s saw the NTP embark on a series of new directions. One new effort, the Predictive-Toxicology Evaluation Project, was launched in 1990. Designed to use chemical structures for direct prediction of bioassay outcomes, the project published its first results in the October 1996 issue of EHP Supplements. Another new focus evolved in 1992, when the FDA and the NIEHS formed an interagency agreement to coordinate and jointly fund a phototoxicology research facility at the NCTR in Jefferson, Arkansas. Existing laboratory space at that facility was renovated in 1998 and 1999 to form a dedicated NTP Center for Phototoxicology. The center works to address the carcinogenic potential of chemicals when they are exposed to light or applied to photo-treated skin.
The NIH Revitalization Act of 1993 required that the NIH focus more on developing methods that lessen the use of research animals, reduce pain and distress in animals used, or avoid using animals altogether. In 1997 Olden put together a cross-agency panel, the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM), to implement the act’s requirements. The committee was made permanent in 2000 with the passage of the ICCVAM Authorization Act. The NTP Interagency Center for the Evaluation of Alternative Toxicological Methods was founded in 1998 at the NIEHS to provide support for ICCVAM’s activities by holding workshops and meetings, and by generating reports. Through their efforts, the first alternative test acceptable to the EPA, the FDA, and the Occupational Safety and Health Administration, one for allergic contact dermatitis, was established, significantly decreasing the number of animals tested for this end point.
Through the 1990s, emerging public interests led the NTP in new pursuits. With the passage of the 1994 Dietary Supplement Health and Education Act, the NTP began looking at the potential for toxicity and carcinogenicity of selected dietary supplements, medicinal herbs, and the compounds within them. The ROC also saw some changes. The report was put on a biennial publication schedule in 1993. The NTP’s nominations process, by which federal and nongovernmental entities can recommend agents for testing, was made clearer and more open to public scrutiny. In addition, new criteria allowing broader consideration of mechanistic data were added to the ROC.
A third NTP center, the Center for the Evaluation of Risks to Human Reproduction, was established at the NIEHS in 1998. The center provides scientifically based uniform assessments of the potential for reproductive and developmental damage from human exposure to certain toxicants. Expert panels convened by the center evaluate evidence of reproductive toxicity, determine patterns of chemical use and human exposure, and develop scientific consensus around tested agents’ capacity to harm humans.
NTP workshops, conferences, and reports in recent years have covered a broad range of emerging and continuing toxicological issues. The program has addressed exposures of concern such as methylmercury, acrylamide, and genetically modified foods. Researchers have developed and validated technical methods for assessing human exposures, performing dermal exposure studies in animals, and detecting endocrine disruptors. And the program has brought researchers together to build scientific consensus on issues including how thyroid hormone affects reproduction, approaches to selecting agents to study in transgenic mouse models, and assessment of acute systemic toxicity.
Establishing a Roadmap for the Future
In 2003, the NTP began to look at how it could better position its activities to take advantage of new technologies and address a broader range of toxicological end points. “The NTP has been in existence for twenty-five years,” says Christopher Portier, associate director of the program. “It was time to look at what we’re doing and decide if it’s still the right thing.”
In fall 2003, the NTP’s advisory Board of Scientific Counselors (BSC) developed and approved a vision statement: “to support the evolution of toxicology from a predominantly observational science at the level of disease-specific models to a predominantly predictive science focused upon a broad inclusion of target-specific, mechanism-based, biological observations.” Three workgroups—one from the NIEHS, one representing all of the federal agencies served by the NTP, and one that was a subcommittee of the BSC—then contributed comments on what it would take to make the vision a reality. Their comments were incorporated into a revised statement, which the NTP released to the public in late 2003. At the same time, the NTP announced a year-long process to develop and seek public comment on a “roadmap” for achieving the vision.
The NTP’s effort is not a part of, but rather complements, NIH’s recently completed Roadmap for Medical Research. The latter roadmap was aimed at identifying critical gaps and opportunities in biomedical research that do not fit clearly into the mission of any of the member institutes.
The process of developing the NTP roadmap has been very open: the vision and roadmap were discussed at a public meeting at the NIH’s Bethesda campus in January 2004; at a meeting of the NTP’s Scientific Advisory Committee on Alternative Toxicological Methods held in March; at a meeting of leading toxicologists held the same month at the conclusion of the Society of Toxicology’s annual meeting; at the June meeting of the BSC; and at a retreat with advisors, stakeholders, and federal agency staff in August.
Those commenting were asked to address what information the NTP should be producing and what technical capacities it should have at different points over the next 10 years; how refinement and replacement of classical toxicological studies with mechanism-based assays will affect the evaluation of public health hazards; how the NTP can best be structured to provide this information and ensure its optimal utilization in the protection of public health; and what resources and length of time will be needed to realize this vision.
Early on, says Mark Toraason, a NIOSH representative to the interagency committee providing input on the roadmap, the vision focused solely on turning toxicology on its head by moving it from an observational science to a predictive science. But because of perceived limitations, the vision did not get immediate buy-in from all stakeholders. “It was like, ‘Wait a minute, it may be NTP that generates that data, but it’s other government agencies that use it. The other government agencies should be indicating what they need, and NTP should meet those needs,’” says Toraason. “There has been a meeting of the minds there, and the roadmap has been modified with the recognition put in that the needs of the other government agencies can continue to be met as the vision is attained.”
Short-term goals (to be accomplished in the next five years) include a number of workshops to set priorities and working strategies. Basic study design, from choice of model organism strain and species to duration of experiments, will be re-evaluated, and gaps where current study designs or the current knowledge base fall short will be addressed. NTP staff and advisors will interact with policy makers to build better tools for analyzing risks and setting standards. Proof-of-principle studies will be conducted with new and revamped assays, and new data infrastructure and data management tools will be built. Work toward improving the NTP’s use of high-throughput screening will include cataloguing approaches being used in the public domain and applying these assays to more than 500 agents that have already gone through the full two-year bioassay that is the NTP’s current gold standard.
Long-term goals (to be accomplished over the next 10 years) are ambitious—validating the use of new kinds of assays for regulatory decision making, prioritizing compounds for toxicity testing, evaluating human relevance, and addressing specific diseases. In the long run, the NTP aims to develop and validate a battery of predictive tests and a systematic understanding of the metabolism of toxicants. The plan also calls for banking tissue samples from NTP studies and panels of tested agents, which would be available to the extramural research community by 2010.
Wheels of Change
With rapidly developing technologies creating new opportunities for doing better science faster, application of high-throughput approaches—including whole-genome analysis and work that builds on genomic data—is a critical part of the roadmap. These new approaches show great promise for solving a central problem: the sheer volume of chemicals that must be characterized and tested. Various estimates put the number of chemicals in commerce in the tens of thousands; Portier says such estimates are almost certainly an understatement. “We can’t possibly test everything,” he says. EPA scientist and Society of Toxicology president Linda Birnbaum puts it another way: “We can’t keep doing more and more and more tests on more and more and more chemicals. We have to be able to test smarter, not necessarily more.”
“I think we can double, triple, quadruple, tenfold the number of things we look at if we’re intelligent about how we approach it,” Portier says. He explains using an example from the world of industry: “In developing a new drug, the pharmaceutical industry scans a very large number of chemical entities in some very simple assays. From that, they choose a smaller set that they test in medium-throughput assays, and from that they choose one or two that they test in animals. That’s the direction I want to see us going.”
But moving in new directions isn’t easy, and incorporating new technologies into the NTP’s suite of well-accepted assays provides one of the biggest challenges. “One of the problems with developing totally new methods is when you do something new, it has to be replicated, which means that the expertise and the work have to be going on in a number of different laboratories,” says George Daston, a research fellow at Procter and Gamble and member of the BSC.
Validation of the assays developed around new technologies will be critical. “The technology is quite enticing and it’s razzle-dazzle, but we always have to be able to bring it back to the reality of biology and what it means for the whole organism, and especially what it means for human risk,” says Samuel Cohen, a professor at the University of Nebraska Medical Center in Omaha and BSC member. “This needs to be an evolutionary process rather than a revolutionary process.”
Reduction, refinement, and replacement of animal use may be one of the areas where this evolution first appears. Currently the NTP favors the two-year animal bioassay as its assay of choice. Yet adding more analyses to the experiments currently being done could help, says Cohen: “There is a considerable amount that can be gained, not only from the animals during the two-year process, but also in the preliminary studies—the four-week and thirteen-week studies that are done ahead of time—that could be of more predictive value, so that we didn’t have to do as much in the two-year study ultimately, if at all.”
All for One and One for All
Data sharing will help bring stakeholders together. The NTP intends to eventually make its chronic assay database and other databases publicly available, which Toraason says will help with communication, not just to the public but also across federal agencies. “In the past it could be difficult to get information from NTP at times,” he explains. “You needed a reliable contact and had to know who knew what and so on. Now their website contains a wealth of searchable information, and the intent is to increase the amount of information that is publicly available.”
The extramural research community will have access to all these public data and can expect to play new roles if the roadmap is put in place, but it’s not yet clear how they will be involved. “We talked about a large number of possibilities,” Portier says, “from providing the extramural community with NTP tissues from NTP studies to do their own research, to providing them with arrays of chemicals if someone has their own high-throughput facility, to setting up centers for doing toxicological screenings toward mechanisms, and a number of other different possibilities.”
The work of extramural scientists will be needed and appreciated. “We don’t have all the answers, we don’t know how to analyze all the data, we don’t know what it all means, and I’m not sure I have the staff on hand to do all that,” Portier says. “The only way it’s going to actually happen is if the public has access to the data. It’s going to be scientists at universities who come out and get NIEHS grants to analyze our data and give us some interpretation tools that really lead that effort.”
James Popp, a BSC member and cofounder of the consulting group Stratoxon, has worked in academe, government, and industry. He says there’s a much greater openness among those three realms to talking and working together than he’s seen in a long time. “There’s been this mind-set change, and I think that’s important to opening up these dialogues,” he says. “We’re in an era of very rapid technological changes, and so it’s a matter of capturing that opportunity of mind-set changes, technology changes, and new advancements in basic knowledge of biology.”
This greater accord between sectors raises some concerns, though, according to Jennifer Sass, a senior scientist for the Natural Resources Defense Council, an environmental advocacy group. “The collegial familiarity that’s being set up leads to a lot of questions,” she says. “The problem is that industry clearly sees a triangle including government, academia, and themselves, and there is no public interest, no public watchdog groups, in the picture.”
Other opinions vary. “NTP has done a good job of informing just about everybody in the scientific community about what they’re doing. They have listened to the criticisms, and have expanded the vision accordingly,” Toraason says.
Still, the public does need to be informed, says Popp, and that will take better efforts at communications. “There are multiple audiences,” he explains. “Obviously, there’s the political audience, but it’s more than that—it’s a broader societal audience that needs to be addressed, and the general public does not have a good grasp of basic scientific principles in toxicology or anything else. So there’s a very large educational process that needs to go along with the scientific work in the roadmap.”
The Road Ahead
The roadmap sets an ambitious course for the next decade. But is it achievable? “[The NTP has] to recognize that what they put out is a vision, very broad and somewhat all-encompassing,” says Michael Holsapple, executive director of the Health and Environmental Sciences Institute of the International Life Sciences Institute, an industry-funded nonprofit that works to enhance the scientific basis for public health decision making. Holsapple recommends “partnering, being judicious, and recognizing that their map is a good first step.” But in reality, he says, “when the rubber hits the road, they can’t do it all.”
Portier sees it differently. “Partnerships will be an important part of the initiatives outlined in the roadmap,” he says. “While the NTP has the budget necessary to address all of the aspects of the roadmap, it will require a concerted effort from all of the NTP stakeholders to develop the tools that will allow us to understand and use this new information in addressing public health priorities.”
Everything could change with the arrival of a new director. Olden has announced he will leave his positions as NTP and NIEHS director in the near future, though he will continue to serve until his successor is appointed. “If the new director comes in and doesn’t like [our plan], then we sit down and think about where we’re going to go and how we’re going to get there based upon his or her vision of what the NTP should be doing,” Portier says. “But the advice we’ve gotten is still excellent advice, and it will help to chart whatever course we eventually take.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00877EnvironewsNIEHS NewsHeadliners: Maternal Nutrition and Child Cancer: Mother’s Pre-pregnancy Diet May Influence Child Cancer Risk Booker Susan M. 11 2004 112 15 A877 A877 Publication 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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Jensen CD, Block G, Buffler P, Ma X, Selvin S, Month S. 2004. Maternal dietary risk factors in childhood acute lymphoblastic leukemia (United States). Cancer Causes Control 15(6):559–570.
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer (with 2,400 cases diagnosed each year in those under age 20) and the second most common cause of mortality in children aged 1–14. Recent research has confirmed that ALL can originate in utero. New findings from the NIEHS-funded Northern California Childhood Leukemia Study (NCCLS) show that the disease may originate even earlier—in the foods a woman eats before she even becomes pregnant.
The effect of maternal diet on child leukemia risk has not been rigorously studied; the few studies that have been done have focused on specific dietary factors, and the results have been mixed. The NCCLS is a population-based case–control study of risk factors for child leukemia, including maternal diet. It is the first study to capture mothers’ overall dietary patterns and relate them to child leukemia.
Researchers compared 138 mothers of children diagnosed with ALL with a control group of 138 mothers whose children did not have cancer. All the mothers completed a questionnaire pertaining to their diet in the 12 months prior to pregnancy. The researchers chose this period as a more accurate reflection of each woman’s typical diet, compared to pregnancy, when diet can vary with the degree of nausea experienced. The questionnaire asked about 76 food items, plus use of vitamins, certain reduced-fat foods, and cooking fat.
The researchers found that the more vegetables, fruits, and proteins a woman ate, the lower the risk of her child having leukemia. Of the vegetables and fruits, carrots and cantaloupe showed the highest inverse effect, perhaps because of these foods’ high carotenoid content. String beans and peas also correlated inversely with ALL risk. Among the proteins, beef and beans—both sources of the antioxidant glutathione—showed the highest inverse effect. Use of vitamin supplements was not significantly linked to leukemia risk.
The authors stress that dietary factors work together, and no one food should be singled out in attributing risk or benefit. Further, a cause-and-effect relationship can not be concluded from this study. However, they write, it remains prudent for women who are pregnant or think they may become pregnant to eat a diet rich in fruits and vegetables.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0087915540280EnvironewsNIEHS NewsBeyond the Bench: Problems along the New “Silk Road” Hricko Andrea M. 11 2004 112 15 A879 A879 Publication 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 inexpensive sweater you buy in New Jersey, the bargain dresser you buy in Wisconsin, and the low-cost toys you buy in Ohio all have one thing in common: most likely they were made in Asia, and chances are they entered the United States through a Southern California port. These consumer goods may be easy on the pocketbook, and thriving international trade may be good for the economy, but the environment is seeing some serious impacts. Today the NIEHS-funded Southern California Environmental Health Sciences Center (SCEHSC) is working to raise awareness about these impacts. The center is a partnership between investigators at the University of Southern California and the University of California, Los Angeles.
The adjacent twin ports of Los Angeles and Long Beach have become the world’s third largest port complex, flooding the region with imported cargo containers, which are transported by big-rig diesel trucks and by ships and freight trains operating on low-grade fuel. The booming trade has made the ports the region’s number-one source of air pollution. And the ports keep growing, creating a need for expanded freeways and larger intermodal facilities where containers are moved from trucks onto trains.
Emissions from foreign-flagged ships (nearly all ships entering the two ports) are currently unregulated, constituting a major concern for residents in the port communities. Residents along truck-congested freeways and rail routes are also impacted. “We have homes and parks right next to the rail yard,” says Angelo Logan, director of the grassroots organization East Yard Communities for Environmental Justice, “and sometimes locomotives idle here for hours.”
The SCEHSC, spurred by community concerns raised at a 2001 Town Meeting it sponsored, has been dedicating significant attention to these global trade impacts. Through the center’s Community Outreach and Education Program, center investigators and outreach staff are raising awareness of the need to reduce trade-related air pollution and better protect residents from the surge in such pollution.
Center investigators outreach staff have present ed data to elected officials on air pollution’s respiratory effects on children. Among these are the results of the 10-year longitudinal Children’s Health Study (directed by SCEHSC director John Peters), which shows a larger propor tion of children in more polluted communities suffering lung function deficits, with lifelong health significance. Staff have also testified on the health effects of exposure to diesel exhaust at hearings to ban idling of diesel trucks at the ports, to triple the capacity of a freeway that goes through dozens of low-income, primarily Latino communities, and to construct huge trucking distribution centers in Riverside, one of the country’s most polluted cities.
Center member Ed Avol serves as an appointee to the Mayor of Los Angeles’ special task force on port emissions, which was convened in October 2004. The center recently published a policy brief looking at the need to place a higher priority on health when key development decisions are made—for example, in regards to expansion of ports, rail freeways, or warehouses. And center member John Froines and outreach director Andrea Hricko discussed the impacts of global trade on local communities at the November 2003 roundtable discussion “Globalization, International Trade, and Environmental Health,” sponsored by the Institute Medicine of the National Academies.
Center staff are also working directly with community members, for example by taking high school and college students on tours of the ports and teaching them how to take ultrafine particle measurements. The center is also cosponsoring a Town Meeting in February 2005 with a theme of “Local and Regional Health Impacts of the Ports and Global Trade.”
The high price of cheap goods. Container ships bring inexpensive goods from around the world into the Port of Los Angeles, but the pollution caused by transportation of these goods by ship, truck, and train has human health costs.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0088015531422EnvironewsFocusEnvironmental Cardiology: Getting to the Heart of the Matter Weinhold Bob 11 2004 112 15 A880 A887 Publication 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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Cardiovascular disease (CVD) is the leading killer in many developed countries, and is soon expected to be the leading killer in all countries. A number of factors have raised CVD to this unsavory stature, among them lack of exercise, poor diet, and smoking. But evidence has slowly been building to indicate that exposures to chemicals and other environmental substances also can have a profound impact on heart health. The link between environmental agents and CVD was once considered tenuous by much of the medical and scientific establishment. But after watching the evidence accumulate over the years, with a surge in the past five years, more and more scientists, doctors, and organizations are acknowledging the importance of a field that some are calling environmental cardiology.
One group that is beginning to embrace environmental cardiology is the American Heart Association (AHA), an 80-year-old organization that has traditionally focused on risk factors such as poor diet and lack of exercise as some of the most important contributors to CVD. In the 1 June 2004 issue of Circulation, an expert panel of 11 researchers and physicians published an AHA Scientific Statement that concluded that air pollutants, one of the major environmental exposure sources under investigation by environmental cardiologists, pose a “serious public health problem” for CVD. This is the first official AHA acknowledgment of such links.
The group’s decision was based on the breadth and depth of the accumulating information. “There was no single major study that prompted the writing of this paper,” says Sidney Smith, past president of the AHA and a professor of medicine at the University of North Carolina at Chapel Hill. “It was the gathering body of evidence that connected air pollution with cardiovascular diseases, extending well beyond cigarette smoke.”
The AHA paper was a very positive development in the eyes of some of the researchers who have been involved in the field for many years. “That’s pretty amazing,” says C. Arden Pope III, an environmental epidemiologist at Brigham Young University. “It’s taken the research out of the fringes and made it part of the mainstream.”
Less than two months after the release of the AHA statement, the U.S. Environmental Protection Agency (EPA) gave a clue to how seriously it takes this issue, awarding the largest scientific research grant in its history, $30 million, to study links between air pollution and CVD. The research team will be headed by associate environmental and occupational health professor Joel Kaufman of the University of Washington, and includes scientists from nine other universities and medical centers.
A few other government agencies, such as the National Heart, Lung, and Blood Institute (NHLBI), have also begun to address the links between environmental agents and CVD, as have advocacy organizations such as the American Lung Association and the Natural Resources Defense Council. And the NIEHS, one of the original players in the environmental cardiology arena, has ramped up its efforts to explore this area of research.
There is still a ways to go before environmental cardiology is fully embraced as a medical paradigm. Many major public health organizations, such as the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), have yet to fold this concept into their prevention efforts in any significant way. And there is very little trickle-down into the typical doctor–patient relationship.
Nonetheless, environmental cardiology shows signs of increasingly becoming a factor in research, public policy discussion, and pollutant regulation, as its presence spreads into journals, conferences, textbooks, e-mail discussion groups, and continuing medical education courses. Even The Weather Channel is getting into the act with a new feature that advises viewers on daily levels of pollutants that can affect heart health.
A Heavy Burden for Hearts Worldwide
The most basic facts about CVD haven’t been available for very long. The early decades of the 1900s, when physicians were just beginning to form groups to address heart diseases, were a time of “almost unbelievable ignorance” about these conditions, according to the AHA website.
That has changed, spurred in large part by the huge impact CVD has on people. Heart conditions such as heart attack and congestive heart failure are the leading killer in the United States, and stroke is third, according to the CDC’s Deaths: Preliminary Data for 2002, released in February 2004. Combined, these two categories of CVD alone account for about 35% of all U.S. deaths, compared to 23% for cancers. Other serious health problems that fall into the CVD classification include aortic aneurysms, high blood pressure, and congenital cardiovascular defects.
CVD deaths had been declining sharply in the United States over the past few decades, but that curve has flattened out in recent years. Death rates for heart diseases (responsible for nearly 696,000 U.S. deaths in 2002) declined about 3% from 2001 to 2002, as did death rates for stroke (responsible for about 163,000 U.S. deaths in 2002). But death rates attributed to high blood pressure (responsible for about 20,000 U.S. deaths in 2002) rose about 3%, continuing a steady rise over the prior 20 years.
Other industrialized nations have seen similar patterns. The WHO says that CVD accounts for about one-third of global deaths, killing about 16.7 million people each year. Patterns in developing countries are quickly emulating those in developed countries, thanks to the imported western lifestyle and reductions in infectious disease deaths and other acute causes of death. The WHO estimates that CVD will be the leading killer in developing countries by 2010.
But there are huge variations from country to country. In 36 countries tracked by the AHA, CVD death rates differ dramatically, with rates in some of the most-affected countries, such as the Russian Federation, Bulgaria, and Romania, more than five times higher than in some of the least-affected countries, such as France, Japan, and Australia. Variations in factors such as diet, exercise, smoking, health care quality and availability, and pollution likely play a role in these differences.
Within a country, there also can be huge variations. In the United States, the CVD rate in the least-affected state, Minnesota, is less than 60% the rate in the most-affected state, Mississippi, according to the AHA’s Heart Disease and Stroke Statistics—2004 Update. And the gap has been widening. Minnesota had a 27% decline from 1990 to 2000, while Mississippi saw a 12% decline.
Race and ethnicity are significant risk determinants. Black women in the United States are 2.5 times as likely as Asian and Pacific Islander women to die of diseases of the heart, and a similar ratio of 2.25 to 1 holds for black men, according to CDC statistics published in Women and Heart Disease and Men and Heart Disease. Death rates for American Indian, Alaska Native, Hispanic, and white men and women fall in between these two extremes.
Similar disparities exist for stroke, with the death rate for black men and women more than twice that of the least-affected groups—Hispanics, American Indians, and Alaska Natives—according to the CDC’s 2003 publication Atlas of Stroke Mortality: Racial, Ethnic, and Geographic Disparities in the United States.
The elderly tend to be most vulnerable to CVD, and the problem is expected to worsen in many countries as populations age. But sudden cardiac-related deaths have increased dramatically among people under age 35, according to the CDC’s 2003 report A Public Health Action Plan to Prevent Heart Disease and Stroke. And CVD is the third-leading cause of death for children under 15, according to the AHA.
Deaths aren’t the only consideration. Chronic diseases, which the CDC says affect more than 90 million people in the United States alone, are often due to CVD. Diseases of the heart, high blood pressure, and stroke underlie about one of every five U.S. chronic disease cases.
CVD also includes cardiac birth defects. Among structural birth defects, cardiovascular malformations are the most common among live births, affecting 1 baby in 125, according to the March of Dimes. They are also the leading cause of birth defect–related infant deaths.
There is also growing evidence that prenatal exposures to some environmental pollutants, such as solvents, pesticides, and dioxins, may result in subtle functional abnormalities that show up as disease in adulthood. In the area of CVD, this hypothesis—although important—is only just beginning to receive much attention.
The Role of the Environment: A Change of Heart
Until the past few years, the huge worldwide toll of CVD had been attributed primarily to lifestyle factors such as poor diet, lack of exercise, lack of medical care, smoking, and exposure to secondhand smoke. A large worldwide study published 11 September 2004 in The Lancet found that nine lifestyle and biological factors accounted for 90% of the risk for one major form of CVD, heart attack, with two factors—cigarette smoking and an abnormal ratio of blood lipids—accounting for two-thirds of that risk.
But public health officials still haven’t been able to explain the cause behind a significant number of CVD deaths, says Aruni Bhatnagar, a professor of medicine and project leader of the University of Louisville’s Center of Environmental Cardiology, in a February 2004 article in the American Journal of Physiology—Heart and Circulatory Physiology.
Clues about environmental influences began to show up decades ago. For instance, beer containing elevated concentrations of cobalt (used for quite a few years around the world to retain the beer’s head) were found in the 1960s to contribute to cardiomyopathy in some drinkers. Studies by the National Toxicology Program have examined links between numerous environmental agents and cardiovascular effects for several decades, says NIEHS chemist June Dunnick. And a 1985 study of chain saw exhaust published in the European Journal of Respiratory Diseases found an increase in carboxyhemoglobin, which makes it harder for the blood to carry oxygen and can lead to serious nerve damage.
By 1994, a number of published studies had found links between smoking and CVD. Evidence against smoking and secondhand smoke continues to grow, and among the chemical exposures with potential CVD links, tobacco smoke is the most worrisome so far, says William Farland, acting deputy assistant administrator for science in the EPA Office of Research and Development.
A few researchers saw some of these early clues about various chemicals and shifted their work into the environmental realm. Eliseo Guallar, an assistant professor of epidemiology at The Johns Hopkins University, studied nutritional aspects of fish oils for several years, but saw no conclusive evidence of benefits. Then he saw a study on mercury in fish, and the light went on. “For a long time, it never occurred to me that, when you eat fish, you eat contaminants,” he said. “I read this and I said, ‘Of course!’” He shifted his research, and published a study in the 28 November 2002 New England Journal of Medicine that found that mercury canceled out the benefits of fish oils and contributed to an increased risk of heart attack.
Others were following a parallel path. In 2000, University of Louisville researchers coined the term “environmental cardiology,” Bhatnagar says, and established the Center of Environmental Cardiology in the university’s Health Sciences Center. Wayne Cascio, chief of the Division of Cardiology at East Carolina University’s Brody School of Medicine, says he came up with the same term of “environmental cardiology” on his own, and Harvard’s John Godleski, an associate professor of environmental health who has published eight related papers in recent years, uses the term as well.
Others, such as Farland, have heard the term and think it’s appropriate, especially for bringing cardiologists into the field, but aren’t using it routinely. Thomas Burke, a professor of health policy and management at the Johns Hopkins Bloomberg School of Public Health, uses a long version, “environmental influences on cardiovascular health,” but likes “environmental cardiology.” Guallar thinks the term “cardiology” is a little too clinically oriented, and uses the term “cardiovascular disease epidemiology.”
Whatever term is used, by 2002 the notion of environmental cardiology was no longer considered outlandish after other epidemiological studies, and some studies of biological processes, found numerous links between different chemicals and CVD. That year, the NIEHS, the EPA, NHLBI, the AHA, and St. Jude Medical convened a workshop in Durham, North Carolina, and scores of participants discussed the links between environmental agents and CVD. Environmental cardiology even began to creep into the public consciousness with the Food and Drug Administration’s 2004 ban of dietary supplements containing ephedrine alkaloids, based on evidence that the substances are linked to heart attack and stroke.
Fingering the Villains
Given the evidence so far, fine particulates appear to be one of the primary environmental villains linked with CVD, and are drawing much of the research interest. One of the first major developments was publication of findings from the Six Cities Study in the 9 December 1993 New England Journal of Medicine. The study, by Harvard environmental epidemiologist Douglas Dockery, Pope, and six others, found a significant link between ambient urban air pollution, especially fine particulates, and increased deaths caused by cardiopulmonary disease, along with increases in lung cancer. In January 2004, Circulation published a study by Pope and others covering more than half a million people over 16 years, which found that fine particulates were more strongly linked with deaths from cardiovascular causes than with deaths from respiratory causes. The pattern of cardiovascular deaths was consistent with evidence that mechanistic pathways linking exposure and death included pulmonary and systemic inflammation, accelerated atherosclerosis (hardening of the arteries), and changes in cardiac autonomic function (as measured by changes in heart rate variability).
In addition to particulates, dozens of other substances have been identified as playing some role in CVD. The other five EPA “criteria” air pollutants (ozone, carbon monoxide, nitrogen oxides, sulfur dioxide, and lead) have some evidence linking them with CVD, as do at least 17 of the 87 drinking water contaminants monitored by the EPA. At least 8 of the 116 contaminants in people tracked so far in the CDC’s ongoing biomonitoring project have CVD links. Pat Mastin, chief of the NIEHS Cellular, Organ, and Systems Pathobiology Branch, points out that several occupational exposures also have been associated with CVD, including exposures to vinyl chloride (used to produce polyvinyl chloride and industrial solvents), carbon monoxide (a common exhaust gas), and allylamine (used in ion exchange resins, pharmaceuticals, and water-soluble polymers).
According to Mastin, arsenic has been linked in Asia with a condition known as black foot disease, so named because of the gangrene caused by severe disease in the blood vessels. In addition, he says, areas of the United States have high drinking water concentrations of arsenic, and there is evidence for a link between arsenic exposure and ischemic heart disease and hypertension in such areas. And there are more than 50 other substances, including many heavy metals, solvents, and a few pesticides, that have been implicated in CVD by other sources.
The research conducted so far has found many tangible indicators of cardiovascular system effects, including atherosclerosis; vasoconstriction; and changes in heart rate variability, blood pressure, coagulation, platelet activation, endothelial cells, and the clotting protein fibrinogen. Those changes have been linked with serious outcomes such as ischemic heart disease, congestive heart failure, acute myocardial infarction, malignant ventricular arrhythmias, plaque vulnerability, acute thrombosis, stroke, and hypertension.
The difficult job of figuring out exactly how various chemicals cause these problems has just begun. Many pathways are under investigation. On the top of the list for some is the systemic inflammation caused by some chemicals. “In the end, it’s all going to be tied to inflammation,” Cascio says.
Pope, who agrees inflammation is a key element, has another prime suspect. “There’s simply a lot of evidence that there’s a role for [effects on] autonomic function,” he says.
Cellular changes, such as alterations in ion channel function, cell proliferation, signal transduction pathways, and cell signaling, also are under scrutiny. For instance, research by Armando Meyer and colleagues published in the February 2004 EHP found that the insecticide chlorpyrifos affects cell signaling cascades critical to cardiac homeostasis.
Problems may be caused not just by a chemical, but by its metabolites. In a study reported in the September 2004 issue of Toxicological Sciences, Dunnick and NIEHS colleague Abraham Nyska found that bis(2-chloroethoxy)methane caused mitochondrial damage in hearts in a rodent model system. They hypothesized that the thiodiglycolic acid, a metabolite of bis(2-chloroethoxy)methane as well as many other chemicals, causes this chemical-related mitochondrial damage and heart toxicity. Dunnick and Nyska observed a biphasic response: initial damage to myocytes was repaired in an apparent temporary adaptive response that the animals were no longer able to launch as they aged.
Other areas of concern include genetic variations and expression, gene polymorphisms, oxidative stress, protein expression, and post-translational modifications. In addition, many researchers suspect indirect links with the immune, pulmonary, and neurological systems.
Seeking the Heart of the Matter
Millions of dollars are being pumped into environmental cardiology research. The largest single award is the $30 million granted in July 2004 to the University of Washington–led team, which will focus on the effects of fine particulates. The 10-year study will evaluate about 8,700 people in six states, representing a variety of ethnic groups, for clinical and subclinical effects such as heart attack, stroke, and atherosclerosis.
The University of Louisville Center of Environmental Cardiology was awarded a five-year $7 million NIEHS grant in 2003 for studies on the effects of aldehydes, which are found throughout the environment, making up a large part of typical urban air pollution and also showing up in food and drinking water. The NIEHS also awarded $3 million in 2003 to environmental epidemiologist Ralph Delfino at the University of California, Irvine, and his colleagues to study the effects of fine particulates in the elderly. The study, expected to conclude in 2007, will investigate a range of CVD effects, in part through intensive personal monitoring, and will address seasonal and geographic variations. The EPA and the NIEHS awarded a dozen grants in September 2004, totaling about $4 million, that focus on a variety of links between particulates and CVD. The topics range from acute effects of particulates on the autonomic nervous system to chronic effects of particulates on atherosclerosis, Mastin says.
The Health Effects Institute, funded by the EPA and industry, is backing several related studies, says institute senior scientist Geoffrey Sunshine. A University of Rochester study looking at the effects of particulates on CVD is expected to be published by November 2004. Dockery is looking at whether particulate exposures may make internal cardiac defibrillators fire more frequently. And Annette Peters, head of the Institute of Epidemiology at Germany’s government-funded GSF–National Research Center for Environment and Health, and her colleagues are investigating the effects of fine particulates on nonfatal myocardial infarction. Many other related studies are being conducted around the world.
Karen Kuehl, a professor of pediatrics and a cardiologist at Children’s National Medical Center in Washington, D.C., has participated in a number of studies of environmental influences on cardiovascular birth defects, but laments the limited funding available, possibly due to the minor influence infants have on the budget process. “Babies don’t vote,” she says with a short laugh. Nonetheless, the NIEHS has begun to make this area more of a priority, and issued the program announcement “Environmentally Induced Cardiovascular Malformations” in 2002. Mastin, whose branch administers this program, says research funded under these grants is examining how prenatal exposures influence the risk of cardiovascular birth defects.
Related information may also eventually come out of the chronic disease tracking efforts of the CDC’s Environmental Public Health Tracking Program, says Burke, who heads one arm of the CDC effort. The fledgling program, begun in 2002, is designed to eventually provide extensive data documenting links between the presence of environmental agents, exposures, and ensuing diseases, including CVD.
From a policy perspective, the AHA is continuing to develop official policy on the links between pollutants and CVD. It’s also advocating and supporting further research to help it determine whether pollutants will rise to the level of an actual risk factor in its perception, meaning that it is a significant, independent contributing factor to CVD. The evidence isn’t quite there yet, according to Smith.
Although the links between chemical exposures and CVD are becoming more widely recognized, few public health agencies have responded yet. The WHO is focusing most of its efforts on preventing CVD caused by factors such as diet, exercise, and smoking, which it says account for 75% of CVD, though it acknowledges that pollutants other than cigarette smoke are a cause for concern. In the United States, the CDC updated its Public Health Action Plan to Prevent Heart Disease and Stroke in 2003, but the plan still pays scant attention to environmental contaminants, though it acknowledges pollutants are an issue. NHLBI, too, acknowledges the issue of chemical exposures, says George Sopko, a cardiologist at that institute, but has not given it much weight yet. Few, if any, state health programs address environmental cardiology in any significant way, according to several state health officials.
Riding the Learning Curve
The venues for professionals interested in learning more about the links between environmental agents and CVD are expanding. One journal that focuses extensively on related issues of cardiovascular toxicities of drugs, novel therapies, and environmental pollutants is Cardiovascular Toxicology, which began publishing in 2001. Among the general journals that have published related studies are EHP, JAMA, the New England Journal of Medicine, Circulation, Inhalation Toxicology, Toxicologic Sciences, Epidemiology, and the American Journal of Epidemiology.
The 2004 reference book Netter’s Cardiology has a chapter on the topic, authored by Cascio, and the issue is increasingly appearing on the agenda of conferences run by organizations such as the AHA, the American Thoracic Society, the International Society of Environmental Epidemiology, the NIEHS, and the EPA. Discussions on the latest developments routinely occur on the NIH e-mail group EnviroHeart (http://list.nih.gov/archives/enviroheart.html).
Interested doctors may soon be able to learn more through continuing medical education programs. The EPA is working on a certification program to educate doctors about ozone and respiratory effects, says agency environmental health scientist Susan Stone, and the agency anticipates following that up with a program on particulates that will cover cardiovascular effects.
At the moment, though, doctors have no accepted medical treatments that are known to be effective in reducing the effects of chemicals on CVD, says Robert D. Brook, who is lead author of the AHA Scientific Statement, an assistant professor of medicine at the University of Michigan, and a practicing physician. Instead, concerned people should avoid exposures to the extent possible, he says.
One tool to help people avoid air pollution exposures is the EPA’s AIRNow website (http://www.epa.gov/airnow/), Stone says. Daily reports on local particulate and ozone levels can help people decide whether to limit activity and thus, exposure. That idea will be incorporated into a Weather Channel feature called “Air Aware” that the EPA is collaborating on, which is scheduled for launch in mid-autumn 2004.
The EPA also is finalizing an educational poster designed to be hung in doctors’ offices and elsewhere, which is expected to be released in November 2004. It devotes about half its space to the effects of a few common pollutants on the cardiovascular system. The other half addresses respiratory effects. In 2003 the agency released an educational brochure on particulates that folded in some information on cardiovascular effects. The brochure has been distributed by some state and local air agencies, and is available to the public through the EPA’s National Center for Environmental Publications.
Reaching a Regulatory Threshold
Given the relatively early stages of wide-scale research into the links between chemicals and CVD, it likely will be some time before any regulations change. “I wish new science were incorporated more rapidly, but I’m not kidding myself that the EPA is going to hop to it on this one,” says Gina Solomon, a senior scientist with the Natural Resources Defense Council and a practicing physician.
Greg Dana, vice president of environmental affairs with the Alliance of Automobile Manufacturers, hopes she is correct. He says that vehicle pollution, which accounts for a large part of the environmental load of particulates and other air pollutants, has already been addressed enough. “There’s a pretty big onslaught of rules and regulations in coming years that will take care of a lot of emissions,” he says. “Hopefully we’ve addressed whatever concerns are being raised.”
Dana points to a number of emissions regulations set to phase in over the next several years. Federal “Tier 2” regulations set by the Clean Air Act Amendments of 1990 will phase in between 2004 and 2009, and will reduce car and light truck emissions by 80% over today’s cars. Beginning in model year 2007, Tier 2 regulations will reduce heavy-duty vehicle nitrogen oxide emissions by 90% and particulate emissions by 95%. Both of these rules also contain provisions to remove sulfur from gasoline and diesel fuel. In addition, “maximum achievable control technology” standards are now final for 110 industry categories covering almost all business sectors in the country to control air toxics. “These are three of the biggest [rules], but there are others that will reduce emissions even more,” Dana says.
If additional regulations are adopted, they might address concentrations of allowable exposure, time period of exposure, and vulnerable populations, says Farland. He notes the EPA is already incorporating some CVD concerns into discussions about the next-generation fine particulate standard under consideration (which will be published as a proposal by 31 March 2005 and finalized by 20 December 2005). Guallar also notes that environmental cardiology research might shift cost–benefit calculations used in assessing new regulations.
Although the regulatory outlook is unpredictable, and much of the science and medicine is in its early stages, the rapidly expanding evidence appears to be carrying the newly recognized field of environmental cardiology into ever-widening areas of influence. Says Pope, “It’s remarkable what’s happened in the last five to six years. I think we’re making a lot of progress.”
Leading Causes of Death for All Males and Females United States, 2001
Smoking gun. In study after study, tobacco smoke is one of the pollutants most directly linked with CVD.
Chemicals and cardiology. Research studies have linked certain pesticides with CVD, including some effects in children.
Death Rates for Total Cardiovascular Disease, Coronary Heart Disease, Stroke, and Total Deaths in Selected Countries (most recent year available)
A diet for disease? New research will look at links between CVD and chemical contaminants in food and water.
Particularly offensive. Fine particulate air pollution is being investigated as a contributor to effects such as heart attack, stroke, and atherosclerosis, especially in the elderly.
Estimated Direct and Indirect Costs of Cardiovascular Disease and Stroke
Children,Youth, and Cardiovascular Disease
Non-Hispanic Whites Non-Hispanic Blacks Mexican Americans
Diseases and Risk Factors Total Population Total Males Total Females Males Females Males Females Males Females
Congenital Defects
Mortality 2001 (all ages) 4.1 K 2.1 K 1.9 K 1.8 K 1.5 K 0.4 K 0.3 K — —
Mortality 2001 (< age 15) 2.1 K 1.1 K 1.0 K — — — — — —
Tobacco
Prevalence grades 9–12:
Current tobacco use 2001 — 38.5% 29.5% — — — — — —
Current cigar use 2001 — 22.1% 8.5% — — — — — —
Smokeless tobacco use 2001 — 14.8% 1.9% — — — — — —
High school students:
Used tobacco in last 30 days — — 43.4% 32.3% 21.6% 17.4% 31.5% 27.2%
Blood Cholesterol
Ages 4–19:
Mean total cholesterol mg/dL 165 — — 162 166 168 171 163 165
Ages 4–19:
Mean HDL cholesterol mg/dL — — — 48 50 55 56 51 52
Ages 12–19:
Mean LDL cholesterol mg/dL — — — 91 100 99 102 93 92
Physical Inactivity
Prevalence 2001 grades 9–12:
Vigorous activity last 7 days — — — 73.7% 59.8% 72.4% 47.8% 68.8% 52.4%
Moderate activity last 7 days — — — 29.8% 24.7% 23.7% 16.5% 25.9% 18.5%
Overweight
Prevalence 2001:
Preschool children ages 2–5 >10% — — 10% 8% 11%
Children ages 6–11 3.8 M (15.3%) 2.0 M (16.0%) 1.8 M (14.5%) 11.9% 12.0% 17.6% 22.1% 27.3% 19.6%
Adolescents ages 12–19 5.0 M (15.5%) 2.6 M (15.5%) 2.4 M (15.5%) 13.0% 12.2% 20.5% 25.7% 27.5% 19.4%
Students grades 9–12 — — — 12.4% 5.3% 17.5% 14.6% 21.3% 8.8%
Note: K = thousands; M = millions; mg/dL = milligrams per deciliter; (−) = data not available. Overweight in children is body mass index (BMI) at 95th percentile of the Centers for Disease Control and Prevention 2000 growth chart. Death rates are age-adjusted per 100,000 population, based on the 2000 U.S. standard.
Source: AHA. Heart Disease and Stroke Statistics—2004 Update. Dallas,TX: American Heart Association; 2003.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0088815531423EnvironewsSpheres of InfluenceCoal Poised for a Comeback? Fields Scott 11 2004 112 15 A888 A891 Publication 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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Coal could be called energy’s comeback kid: sometimes forgotten, perhaps underappreciated, but always available for one more shot at the big time. It is one of humankind’s original sources of energy, and is used worldwide for cooking, heating, forging steel, and making electricity. In the United States, coal’s role today is limited almost exclusively to electricity generation; for the last decade or so, even that use has stagnated. For years new power facilities that relied on coal were spurned in favor of natural gas, as American electric companies were wooed by the cleaner-burning fossil fuel and its easier-to-site and cheaper-to-build power plants. Still, because so many coal-fired plants were built before the natural gas craze, coal accounts for over 50% of our annual electric generation. And now many energy experts say coal is poised to once again play a prominent role in the United States.
Coal does have an appeal. For one thing, there’s plenty of it. It’s located here in the United States, a comfort to those worried about the political and security hazards of overdependence on imported energy. It’s cheap. And its price is stable, at least compared to natural gas.
But coal can be ugly, too. If left unchecked with inadequate emissions control, it can emit ash (which has been linked to human cancers and genotoxic effects in some animal studies), sulfur dioxide (which contributes to acid rain), carbon dioxide (CO2; the chief culprit behind global warming), nitrogen oxides (NOx; which can produce smog and low-lying ozone), and mercury (linked to disorders in the kidneys and the nervous, digestive, and respiratory systems). Mining coal can also be a messy business, carving scars into the Earth, releasing clouds of dust, leaving behind sources of acidic water that can persist decades after a mine closes, and requiring dams—“impoundments” in industry lingo—that sometimes break and ravage miles of waterways.
In coming years, however, what’s right about coal will almost certainly overpower what’s wrong, says Richard Gendreau, a senior market consultant for R.W. Beck, a Framingham, Massachusetts, management consulting and engineering company. And what’s wrong, he says, will be made better by new technologies and more vigorous application of existing technologies. “The ultimate driver on all of this,” he says, “is that ninety-five percent of our fossil energy reserves—the amount of fossil energy that we have within our boundaries that we can rely on for energy and economic security, as well as national security—is coal.”
The Saudi Arabia of Coal
Sometimes called the “Saudi Arabia of coal,” the United States has enough known reserves of coal—some 250–300 billion tons—to last at least 250 years, according to the Washington, D.C.–based National Mining Association. This coal, the association estimates, represents about one-quarter of the world’s known reserves and contains energy equivalent to all of the Earth’s known oil reserves. And that may be just the tip of the iceberg, says Connie Holmes, a senior economist and director of international policy for the association. If you consider sources that haven’t yet been discovered or are too impractical to extract, there is an estimated 2 trillion tons of coal in the United States. So if exploration and extraction technologies improve, there is a great deal more coal that could be recovered.
Although Gendreau and many other energy pundits predict a markedly growing role for coal in American electric production over the next two decades, current coal production is relatively stable, says Holmes. This year, she says, American coal mines will produce about 1.1 billion tons of coal. “That has been pretty much the case, give or take ten million tons, since 1996,” she explains. “There has not been an appreciable increase in production overall, but there has definitely been a shift in the market.”
In the past few years, U.S. steel producers have used far less coal, as foreign steel makers have surpassed American companies and domestic production has slacked off. Coal exports have also plummeted as cheap sources of coal have surfaced in Asia and South America. Holmes says these decreases have been offset by a resurgence in coal used to make electricity—coal use by utilities has gone up by about 120 million tons. In its Annual Energy Outlook 2004, the Energy Information Administration of the Department of Energy projects that the United States will use about 1.5% more coal each year between 2002 and 2025. But according to the National Mining Association, past estimates by this agency have fallen short. Predictions made in the 1990s for coal use in 2010, for example, were reached by 2000.
Although plentiful and available, coal had until recently fallen out of favor with U.S. utilities for new electric generation, Gendreau says. The deregulation of natural gas, partially in 1978 and then completely with the Natural Gas Wellhead Decontrol Act of 1989, drove down the cost of natural gas. Carter-era conservation programs, along with the completion of ongoing nuclear and coal projects, resulted in excess electric-generating capacity in most parts of the country throughout the 1980s and into the 1990s.
During this period, Holmes adds, utility managers were coasting on this excess capacity. They were uncertain about upcoming fuel markets and regulatory climates, and were steered by the Clinton administration, which encouraged natural gas use and discouraged coal use. As a result, through the 1980s and early 1990s there were few new coal power plants built as the nation’s thirst for electric power continued.
The Advent of Natural Gas
“All of a sudden people realized in the early to mid nineties, ‘Oh my god, we’re running out of electric power,’” Gendreau says. At that time, natural gas was cheap, costing about $2–3 per million British thermal units (Btus). And new highly efficient gas turbine technologies allowed much greater power yields from burning gas.
These technologies made it possible for investment-shy utilities and the rapidly emerging nonutility generators—which produce power for the wholesale market—to build 500- to 750-megawatt modern, efficient, gas-powered electricity plants (by comparison, a modern coal-fired plant is typically in the 400- to 1,000-megawatt range, although larger units have been built). Not only were these plants less of an investment than a larger coal plant, they made it easier to meet environmental regulations because they naturally burn cleanly, without the controls required for coal plants. This also made them more attractive to citizens and environmental groups.
It was also much faster to build a gas-fired plant, Gendreau says. Just the permitting process for a coal plant takes two to three years, compared to about one year for a gas plant. Building a coal plant takes four to five years, compared to about two years for gas. Simply put, a natural gas plant does little more than send gas to a turbine; the gas is already in the form that is used. Coal, on the other hand, must be processed before use as a fuel, and there are side effects that must be managed. As a result, coal plants are more complicated. That’s also why they have to be larger—to take advantage of economies of scale.
Since the late 1990s, however, natural gas prices have doubled. “With natural gas prices over six dollars per million Btus, that makes coal much more attractive,” says Ned Helme, executive director of the Center for Clean Air Policy, a nonprofit organization in Washington, D.C. In fact, American utilities and nonutility generators have proposed constructing somewhere in the area of 100 new coal-fired plants, for an additional generating capacity of more than 57 gigawatts, according to the Department of Energy’s National Energy Technology Laboratory.
Can Coal Really Cope?
But surrendering to coal’s skin-deep charms is the easy—and wrong—way out, says Jeff Deyette, an energy analyst for the Union of Concerned Scientists, a Cambridge, Massachusetts–based environmental organization. Even cleaner coal-fired plants aren’t clean enough, he says. Technologies to control coal’s by-products, especially CO2, are inadequate or unproven. And liberating coal always traumatizes the earth that surrounds it. A better approach, he says, would be to emphasize conservation and renewables, such as solar, biomass, geothermal, and wind. By 2020, Deyette says, renewables could deliver 20% of the nation’s electricity. The steps to this goal are described in the Union of Concerned Scientists’ 2001 report Clean Energy Blueprint: A Smarter National Energy Policy for Today and the Future.
“Left to their own devices, utilities will choose coal and natural gas because these are things that they’re comfortable with and because they don’t really have to account for all of the negative impacts of those fuels,” Deyette says. Although utilities must abide by emissions regulations, they don’t have to pay for the environmental costs of releasing NOx, ash, mercury, CO2, and other pollutants.
So how does one get the utilities to switch? “We’ve tried voluntary measures in the past,” Deyette says. “I think it’s time we move toward placing a requirement on utilities to increase renewables.” Currently 16 states—including Texas, Minnesota, and Wisconsin—have programs encouraging or requiring their utilities to invest in renewables.
Others counter that technical and political barriers prevent renewables from even keeping pace with the additional electricity the country requires each year, let alone making a dent in the total energy budget. Gendreau says, “There is no credible way to do this over such a short period. Even with incentives, renewables face many challenges and will remain a relatively small, but important and growing, part of our generation mix.”
Further, he says, it’s important that the cost impact to the rate payer be taken into consideration. “When dealing with such a critical component of our economy—as electricity certainly is—you can’t just impose such an extreme requirement, as laudable as it may be, and hope that somehow it happens,” he says.
A Cleaner Coal Plant
Deyette says if the utilities are going to stick with coal, they should at least adopt more innovative technologies. But of the 100 or so proposed coal plants, only a couple deviate from the basic type—in which pulverized coal is burned while airborne in a furnace—that dominates the industry. “Almost all of the new coal plant proposals are, in fact, these older-generation technologies,” he says.
Others question this line of reasoning. “These technologies do represent advances as evidenced by the fact that emissions from coal utilities have been reduced by over thirty percent in the past two decades while electricity generated from coal has increased by approximately sixty-five percent,” says Paul Oakley, executive director of the Washington, D.C.–based Coalition for Affordable and Reliable Energy, an organization that represents the energy interests of companies and other organizations.
“Most of the plants . . . that are being proposed right now certainly utilize conventional power plant technologies, but it’s not necessarily grandma and grandpa’s technology,” Oakley says. “Just because they’re not new technologies doesn’t mean that they’re not advanced technologies. And they certainly emit fewer pollutants than the power plants we were seeing built twenty or twenty-five years ago.”
According to Gendreau, almost all modern power plants remove 99% or more of ash emissions and up to 95% of the sulfur, depending on the coal type and sulfur content. Power plants—new or old, although many older plants have been grandfathered to allow lower environmental standards—can be equipped with flue gas desulfurization equipment, commonly called “scrubbers,” which use a chemical reaction to convert sulfur dioxide from exhaust gas to a solid by-product. Plants can tame ash and other particulates with electrostatic precipitators or fabric filters. So-called selective catalytic reduction equipment can reduce NOx emissions by exposing exhaust gas to a catalyst that triggers the NOx to break down into nitrogen and water vapor. Utilities are testing newer technologies, such as activated carbon injection, to reduce mercury emissions.
But none of these methods reduce CO2 emissions. Gendreau says the only real method of reducing CO2 from coal burning is to burn the fuel more efficiently. That’s why “any new coal plant needs to be a coal gasification plant where the carbon can be captured and stored,” says Antonia Herzog, a legislative advocate for the Natural Resources Defense Council, a New York–based environmental organization. “The gasification plants are significantly better for even just conventional pollution.”
In coal gasification, solid coal is converted into a synthetic gas that is primarily carbon monoxide and hydrogen. Integrated gasification combined cycle (IGCC) technology is used to drive two types of turbines. The synthetic gas is combusted to drive gas turbines, which provide 60–70% of the power, and heat from the exhaust gas drives steam turbines, providing the rest of the power. Although IGCC units are somewhat cleaner in most respects when compared to a conventional pulverized-coal plant that is equipped with scrubbers, says Herbert Kosstrin, a senior director for R.W. Beck, where these units may excel is in capturing mercury and CO2 at less cost than in conventional plants.
It’s All About the Money
In spite of these apparent advantages, full-blown IGCC plants are rare in the United States. And only a few of the proposed plants are proposed to be IGCC. “Until there are even more stringent regulations with emissions, you’re going to see conventional [plants built],” says Bruce Miller, director of the Pennsylvania State University Center for Fuel Utilization. “As the regulations become more stringent, you’ll see more gasification plants.”
As with most business decisions, it’s all about the money, Kosstrin says. As it becomes more expensive to pollute, as IGCC plants are proven to be more efficient, and as improved gasification systems are developed (the government is channeling hundreds of millions of dollars into clean-coal research, much of it centered on gasification), utilities will switch, he says.
That said, utilities are notoriously slow to accept new technologies, says J. Davitt McAteer, who was chief of the Department of Labor Mine Safety and Health Administration during the Clinton administration and who is now a special consultant on Appalachian affairs at Wheeling Jesuit University in West Virginia. “The industry continues in the old way to do business,” he says. “You’re talking about people who are constitutionally opposed to change and chance taking and ideas and concepts. It is going to take a sea change to shift both the utilities and the coal industry into a new mind-set.”
But unless this sea change is a reexamination of nuclear power or a massive change in the way Americans use energy, Gendreau says, the country is going to markedly increase its coal consumption. “When you look at all of the factors and consider all of the available options, you realize that no matter what you do, even as efficient as [Americans] are today, that you’re going to need more electricity—and there’s only one place to get it, and that’s coal,” he says.
“Coal will be part of the energy future of the United States,” Helme agrees, “but it’s critical that we address environmental issues now because we’re building that next fleet of power plants over the next twenty years. For the most part, we’re going to replace much of this by 2030, and what we replace it with is the whole game in terms of the climate issue.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0089215531424EnvironewsInnovationsUnderwater Logging: Submarine Rediscovers Lost Wood Tenenbaum David J. 11 2004 112 15 A892 A895 Publication 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 global market for industrial wood products (including wood and paper) is a $400 billion industry, according to From Forests to Floorboards: Trends in Industrial Roundwood Production and Consumption, a 2001 report from the World Resources Institute. The report notes that real prices for timber rose 30% between 1975 and 1996, an increase that author Emily Matthews says indicates that demand is growing faster than supply. Worldwide demand for wood products is expected to grow steadily, according to the Food and Agriculture Organization of the United Nations. As the world’s thirst for wood grows and the resulting deforestation contributes to a wide range of environmental problems, one enterprising group has gone to a surprising location to search for more sustainable wood supplies—under the water.
A great amount of timber sank during log drives or was flooded during the construction of hydroelectric dams around the world. Although under water, the trees may be as good as new. One obvious—but dangerous and expensive—way to retrieve this “rediscovered wood” is to hire divers to run underwater saws. A second solution, uprooting the trees with a chain, mucks up the water and disrupts aquatic ecosystems.
Now Triton Logging, a firm in British Columbia, has come up with a third alternative: the Sawfish. This remotely piloted submarine—named for a relative of the shark that has a beak like a giant hedge trimmer—sports a long, electric-powered chain saw. Triton president Chris Godsall, who has a master’s degree in business and sustainability, had worked salvaging individual sunken logs when he realized there was more to gain by salvaging whole drowned forests. The Sawfish, he says, represents “an arranged marriage of marine and logging technologies” that may offer a sustainable way to reduce the environmental impacts of logging and the attendant road building.
Underwater Logging: Tough, but not Impossible
Underwater logging is possible because many submerged trees and logs are barely affected by their decades of submersion. Lake and river water is often too cold and too deficient in oxygen for decay organisms to survive. (Ironically, the above-water portion of trees often must be discarded due to degradation by sunlight and microorganisms.)
Studies of logs raised from Lake Superior show slight color changes, but “the properties are virtually the same as modern timber,” says Terry Mace, who has studied underwater log retrieval for the Wisconsin Department of Natural Resources. And although sugars have leached from the Lake Superior logs, this effectively seasons the wood, making it highly desirable for use in musical instruments.
It’s hard to pinpoint how many trees are available for underwater logging. Some underwater logs were sunk or otherwise lost during log drives on rivers, but the majority came from forests submerged during the building of dams. The number of large dams—those more than 15 meters high—has increased nearly sevenfold since 1950, reported the World Resources Institute in World Resources 2000–2001. And while dam building has decreased sharply in developed countries due to environmental considerations and a lack of good sites, it does continue elsewhere. Godsall estimates that about 35,000 square kilometers of forest worldwide have already been submerged by dams. In British Columbia alone, he says, about 20 million trees lay underwater.
Although all that submerged timber seems like a waste, Godsall says the schedule and economics of dam building are to blame—the trees are considered expendable, and the costs of removing them are too high. Further, he says, “if you were to clear two hundred square miles of forest, where would it go? Could you cut it economically? Cost–benefit analyses done time and time again, [in Canada], in the States, in Russia, in Brazil, or Southeast Asia focus on [generation of] electricity, not logs, and the result is flooded forests.”
One Sharp Fish
The Sawfish is 6 feet high, nearly 12 feet long, and 6 feet wide; it weighs 7,700 pounds. The craft is tethered to a cable carrying electric power, video feeds, and control circuits. A sonar system and eight onboard video cameras allow the sub to “fly very easily through the lake, without touching the lake floor,” says Godsall.
Staying off the bottom reduces the amount of silt that gets suspended in the water, Godsall says. He adds, “We don’t think we can sell a wood product that has some environmental benefits for terrestrial forests while fouling the aquatic environment. And we don’t like turbidity, which interferes with our visibility.”
The operator works in a control booth on a barge, directing the robot to the base of a standing tree. A hydraulically powered grapple (driven by vegetable oil, not hydraulic fluid) grabs the tree, and the sub screws a large air bladder to the trunk and inflates it. After the Sawfish saws the trunk with its 40-horsepower electric chain saw, the bladder lifts the tree to the surface. Workers then remove the bladder and the tree’s limbs. In three hours, the Sawfish can cut 37 trees.
The logs are then sent to a conventional lumber mill for processing. Although the drowned trees contain more moisture than living trees, the lumber can be air- or kiln-dried with little trouble, according to Triton.
Triton is progressing toward certification under the Rediscovered Wood underwater salvage standards established by SmartWood, a nonprofit environmental program of the Rainforest Alliance that began assessing the environmental, social, and economic impact of forestry operations in 1989. SmartWood assessors evaluate the negative and positive effects of an operation on the environment: what types of fluids and chemicals are used in the machine (in case there is a hose break), whether the operation creates a disturbance at the lake bottom, whether sediment is being disturbed, whether there is shoreline erosion where the logs are being removed, and whether the waterway is improving or worsening because of the operation.
The assessor then scores each criterion within the principal standards. A weak performance will result in conditions that must be met by the company before certification is granted. SmartWood also requests monitoring by the company of the environmental impact that the salvage operation has, and will ask to see the results of the monitoring during annual on-site audits, which are a requirement of certification.
Cost versus Benefits
In logging, as in all natural resource industries, the cost of raw materials is critical, and the success of underwater wood will probably depend on economics, says Eugene Wengert, a forest products industry consultant and retired professor of forest ecology and management from the University of Wisconsin–Madison. The question, he says, is whether those trees are cheaper to cut than fresh ones. “Logging and sawmilling are not done because we really love it,” he says. “They are done to make a profit.”
Companies such as Timeless Timber of Ashland, Wisconsin, recover logs that sank during log drives on rivers as much as a century ago. The process of raising these river logs is expensive, so the company charges a premium for its wood, limiting its market to customers who appreciate the wood’s historic and environmental value.
But Godsall maintains that the Sawfish is not necessarily more expensive than normal cutting. “There should not be a premium on owning a thing you believe in,” he says. “Ninety-nine percent of everything that comes out of [a submerged] forest goes into established markets [at normal market prices].” At the same time, flooded forests can also contain some premium wood, he adds.
Triton is entering the regular market for so-called saw logs (logs large enough to mill into lumber), Godsall says. The company’s present output, mainly strong, desirable Douglas fir, is sold to mills making lumber for flooring, furniture, and construction. In August 2003 the first Sawfish began cutting trees in Lois Lake, British Columbia, a dam impoundment built in the 1930s to power a sawmill. A second Sawfish is under construction.
Given the enormous amount of flooded forestland, Triton plans to both use the Sawfish itself and sell the remote-controlled loggers to other companies at a cost of US$1 million and up. “There are millions and millions of trees underwater in our own backyard, and we’re addressing those with our logging operations,” says Godsall. “But there are underwater forests all around the world that are out of our reach.”
The Environmental Payoff
Drowned logs, sunken trees, and wood from building demolition are all considered rediscovered wood. The environmental promise of using rediscovered wood is to reduce the impact of logging and the attendant road building on forests.
Roads allow an influx of invasive species, and they increase erosion and runoff to surface waters. And roads are very common in forests. According to a 2000 report from the National Center for Policy Analysis titled Banning Roads, Burning Forests, the National Forest System has over 383,000 miles of roads—eight times the mileage of the interstate highway system—on its 192 million acres. Most of these roads were built for timber harvesting, but have since been adopted by recreational forest visitors.
But the environmental benefits of recovering drowned trees are difficult to compare to the standard of being “sustainable” because the trees are not replaced, even though other trees are being drowned by newer dams. Underwater logging can pose an environmental hazard if silt on the drowned logs is distributed into the water. Unlike sawing, the past practice of yanking standing trees from lake beds can pollute the water with sediment, blocking the light needed by aquatic plants.
Some in the forest products industry question the need for underwater logging, noting that forests have been expanding for about a century in the United States. Wengert argues that conventional logging itself may actually be sustainable. “No way are we running out of wood,” he says. “We may be running out of some species, but since 1909, the supply of wood has been increasing [in the United States]. Most forests are sustainable, if you look widely enough. If you look at one county, maybe they will cut half the county. But if you look at a big enough area, [those trees are replaced elsewhere].” Unless it’s developed, he says, forestland continues as forestland.
Perhaps, but Godsall argues that sustainable forestry is still a goal, not a reality, in the industry. “The industry has changed tremendously in the last ten years,” he says. “We have seen a huge capacity building in forestry companies regarding environmental sustainability and responsible engagement [in social issues related to forestry]. But that capacity to understand the issues is not always converted into a collective approach to sustainable forestry.”
SmartWood’s William Timpano, who monitors the movement of certified wood through the production process so manufacturers can place SmartWood’s Rediscovered Wood logo on their products, points out another benefit. “Since this volume of timber is not natural,” he says, “taking the timber out may make for better fish habitat or increase the presence of naturally occurring aquatic fauna.”
With underwater logging, every acre of drowned trees that is chain sawed in a hydroelectric reservoir should translate into an acre of forest that’s left standing. And that, in turn, could translate into significant environmental benefits for the world.
Catch of the day. The Sawfish in action (above), felling a submerged tree. The crew (left) stands in front of timber recovered from Lois Lake, British Columbia, which was flooded to create a reservoir.
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Suggested Reading
Matthews E 2001. From forests to floorboards: trends in industrial roundwood production and consumption. In: EarthTrends: The Environmental Information Portal [online database]. Washington, DC: World Resources Institute. Available: http://earthtrends.wri.org/features/view_feature.cfm?theme=9&fid=6 [accessed 7 October 2004].
Rainforest Alliance SmartWood homepage. New York, NY: Rainforest Alliance. Available: http://www.rainforest-alliance.org/programs/forestry/smartwood/index.html [accessed 7 October 2004].
Zhu S Tomberlin D Buongiorno J 1998. Global Forest Products Consumption, Production, Trade and Prices: Global Forest Products Model Projections to 2010. Global Forest Products Outlook Study. Working Paper No. GFPOS/WP/01. Rome, Italy: United Nations Food and Agriculture Organization. Available: http://www.fao.org/DOCREP/003/X1607E/X1607E00.htm [accessed 7 October 2004].
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00897EnvironewsScience SelectionsThe Safety of Xenoestrogens Eubanks Mary 11 2004 112 15 A897 A897 Publication 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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Challenging the Genomic Model of Effects
Current thinking holds that environmental estrogens cause endocrine disruption when these steroid mimics enter the cell’s nucleus and turn genes on or off, or up or down, by binding to DNA. According to this genomic–nuclear pathway model, many xenoestrogens are viewed as harmless to humans and wildlife because exposure to high levels of chemical is necessary before there is a change in gene expression. However, the genomic model assumes a long, multistep process of macromolecular synthesis; it does not fully account for empirical evidence that the signal response to some hormones is so fast it must be initiated outside the cell via membrane receptors connected to fast-acting molecules. An alternative membrane-initiated hypothesis is just beginning to be addressed and tested. This month, Nataliya N. Bulayeva and Cheryl S. Watson of the University of Texas Medical Branch report experimental evidence that challenges the prevailing genomic paradigm of endocrine disruption [EHP
112:1481–1487].
For their experiments, the scientists employed a prolactinoma cell line derived from a rat pituitary gland that has been a model experimental cell line for over 30 years. A subline of these cells that exhibits fast responses and is sensitive to estrogens at small doses provides a good test system for the study of nongenomic responses to estrogenic compounds. This system allows researchers to investigate questions about mechanisms by measuring functional end points of estrogen action, such as prolactin secretion, which increases upon exposure to estrogen.
Extracellular signal–regulated protein kinases, or ERKs, belong to a large class of enzymes involved in cell signaling pathways that generate signals to multiple end points. They are good indicators of non-genomic estrogenic activity because ERK activation is mediated by phosphorylation, a signal from outside the cell. When ERKs are activated by exposure to estrogens or compounds that mimic estrogens, the cell’s medium turns yellow. The yellow product, which correlates with the amount of phosphorylated ERK identified by an antibody, can be measured so precisely that small changes in levels of phosphorylated ERK can be detected.
Bulayeva and Watson compared ERK activation by the most potent endogenous estrogen—estradiol—with activation by three major classes of xenoestrogens: organochlorine pesticides (endosulfan, dieldrin, and DDE, a DDT metabolite), detergents used in plastics manufacturing (p-nonylphenol and bisphenol A), and coumestrol (a phytoestrogen present in alfalfa sprouts, soybeans, and sunflower seeds/oil). The responses were measured at different concentrations over a 3- to 30-minute time course. Affected points along the activation pathway were subsequently investigated by the addition of specific inhibitors to each pathway participant.
The results showed that every xenoestrogen tested, except bisphenol A, exhibited strong ERK activation. Unexpectedly, individual compounds produced the effect at different times and concentrations specific to the particular compounds. Also, individual compounds were found to trigger specific pathways within the nongenomic signaling network leading to different end points. Coumestrol, endosulfan, and p-nonylphenol had an effect at extremely low picomolar levels, as low as estradiol. The authors concluded, “These very low effective doses for xenoestrogens demonstrate that many environmental contamination levels previously thought to be subtoxic may very well exert significant signal- and endocrine-disruptive effects, discernable only when the appropriate mechanism is assayed.”
This study sheds new light on the conundrum of why exposure to concentrations of environmental estrogens deemed safe by the genomic model exhibit well-documented harmful effects on wildlife. It raises new concerns about the effect of xenoestrogens on human health and important questions about the adequacy of current environmental protection policy and regulations based on the genomic model.
New model explains differences. Researchers compared ERK activation by three classes of environmental estrogens found in pesticides, plastics, and plants to that of estradiol. Results showed that, unlike previously believed, not all xenoestrogens act the same.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00899AnnouncementsNIEHS Extramural UpdateSusceptibility and Population Health Branch Editor’s note: This is the fourth in a series of articles describing the four extramural program branches at the NIEHS.
11 2004 112 15 A899 A899 Publication 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 Susceptibility and Population Health Branch (SPHB) plans and administers extramural programs that study the complex interplay of all factors in the environment. The overarching theme of susceptibility, broadly determined by genetic, behavioral, and sociocultural factors, is present in all SPHB programs. The hallmark of the SPHB is the creation of unique research programs that brings together diverse groups of people—scientists of many disciplines, community and advocacy members, and health care professionals—to work on research that translates basic science into effects on human populations and unique interventions to protect public health. The program areas of science education and community outreach are represented, as well, in order to disseminate scientific research to the public. Involving and informing the general public is key to the success of the SPHB programs.
The SPHB manages a variety of center programs, including the NIEHS Core Centers, along with other projects in a number of subject areas:
NIEHS Core Centers—These centers provide infrastructural support to universities in order to promote multidisciplinary research and outreach in the environmental health sciences.
Environmental Genome Project—This project comprises discovery and resequencing of single-nucleotide polymorphisms, the Comparative Mouse Genomics Centers Consortium, and programs in molecular epidemiology and bioethics.
Centers for Children’s Environmental Health and Disease Prevention Research—These centers explore topics such as asthma, autism, learning, growth, and development.
Environmental justice/community-based participatory research—This program area covers issues particular to subgroups of the population who may be disproportionately exposed, who may be disadvantaged, or who may experience disparities in disease prevalence.
Breast Cancer and the Environment Research Centers—These centers conduct research on development of the mammary gland across the life span and effects of environmental exposures that may impact development.
Centers for Oceans and Human Health—These centers conduct research in marine sciences that will lead to improved public health.
SPHB Staff
Gwen Collman, PhD—CHIEF [email protected]
NIEHS Core Centers
Kimberly Gray, PhD—PROGRAM ADMINISTRATOR [email protected]
Epidemiology, exposure assessment, child health
Elizabeth Maull, PhD—PROGRAM ADMINISTRATOR [email protected]
Botanical research, breast cancer
Kimberly McAllister, PhD—PROGRAM ADMINISTRATOR [email protected]
Genetic susceptibility, molecular epidemiology
Les Reinlib, PhD—PROGRAM ADMINISTRATOR [email protected]
Breast cancer, carcinogenesis, DNA repair
Shobha Srinivasan, PhD—PROGRAM ADMINISTRATOR [email protected]
Health disparities, community-based research
Fred Tyson, PhD—PROGRAM ADMINISTRATOR [email protected]
Oceans, health disparities, genomics, Advanced Research Cooperation in Environmental Health
Liam O’Fallon, MA—PROGRAM ANALYST [email protected]
Primary and secondary school science education, community outreach and education
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00900AnnouncementsFellowships, Grants, & AwardsFellowships, Grants, & Awards 11 2004 112 15 A900 A901 Publication 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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Obesity and the Built Environment
In the United States, obesity and overweight have risen to an epidemic rate during the past 20 years and are among the most important health challenges of our time. Obesity in adults is associated with increased risk of a number of diseases and metabolic abnormalities; most of the conditions associated with obesity are also associated with increasing age. Overweight among children aged 6–19 years currently stands at 15%. Risk factors for heart disease, such as high cholesterol and high blood pressure, occur with increased frequency in overweight children compared to children with a healthy weight; type 2 diabetes mellitus, previously considered an adult disease, is increasingly observed in overweight children. Obesity coupled with impaired mobility can easily lead to an increase in functional limitations and a decrease in the quality of life.
This request for applications (RFA) will support projects that delineate the significance and impact of the built environment on overweight and obesity by enhancing our understanding of the roles played by city and regional planning, housing, transportation, media, access to healthy foods, and availability of public and green spaces (e.g., playgrounds, walking paths) as determinants of physical activity and nutritious dietary practices. This initiative will support studies in two specific areas related to the built environment and obesity: 1) understanding the role of the built environment in causing/exacerbating obesity and related comorbidities, and 2) developing, implementing, and evaluating prevention/intervention strategies that influence parameters of the built environment in order to reduce the prevalence of overweight, obesity, and comorbidities.
The built environment encompasses all buildings, spaces, and products that are created or modified by people. It includes homes, schools, workplaces, parks/recreation areas, greenways, business areas, and transportation systems. It extends overhead in the form of electric transmission lines, underground in the form of waste disposal sites and subway trains, and across the country in the form of highways. It includes land-use planning and policies that impact our communities in urban, rural, and suburban areas.
Currently, there is insufficient research that delineates the influence of the built environment on nutritional factors and physical activity. One intended outcome of this program is the development of models of health-promoting communities that provide access to a wide variety of healthful foods and physical activity patterns, and how this impacts overweight and obesity.
These community-based environmental interventions would serve as models for management and prevention of overweight, obesity, and comorbidities across other communities in need of change. Of particular interest are studies conducted in vulnerable populations (such as children, the aging population, low-socioeconomic-status [SES] communities, racial/ethnic minorities, and persons with disabilities that require use of assistive mobility devices such as wheelchairs and prostheses).
Many educational programs targeted to individuals to effect changes in obesity and weight gain have not been successful. However, environmental changes that reinforce factors supporting healthy lifestyles and reduce barriers to healthy lifestyles may serve to diminish health disparities. Access to healthy foods and opportunities to impact physical activity are factors that have been considered at both the community and individual levels. Approaches that modify the environment to promote healthful eating practices, increase physical activity, and decrease sedentary behaviors offer the potential for safe and effective programs for obesity prevention that could be widely disseminated.
Because of the wide range of issues to be addressed and the diversity of communities, this RFA requires interdisciplinary partnerships. These teams must consist, at a minimum, of a scientist with expertise in health research, a clinical specialist, and an expert on planning, design, or transportation. Other scientists and experts as well as those with scientific expertise in diseases and conditions that are comorbidities of obesity should be considered to complement, but will not substitute for, those mentioned above. Partnerships with state and local health departments working in the area of obesity prevention and control through promotion of improved physical activity and nutrition may also be considered.
Investigators applying to this RFA may propose collaborations with organizations/institutions such as schools; supermarkets; restaurants; religious organizations; recreation facilities; industry, governmental, public health, or community groups; worksites; and so forth, to develop approaches that could potentially be translated into larger-scale prevention/intervention programs.
Research on the connections between the built environment and health has shown that the burden of illness has been greater on lower-SES strata and minority/vulnerable populations, including persons who have impaired mobility. Increased environmental pollutants have resulted in lifestyle changes, especially with regard to diet and physical activity. For example, the increased pollution in Alaskan waters has led to some Native American communities substituting their traditional diets with more foods bought at the grocery stores. This also has had a negative impact on their traditional fishing and hunting activities. Lower-SES communities usually have limited access to quality housing and live in neighborhoods that do not facilitate outdoor activities and provide an uneven mix of healthy and unhealthy food outlet choices such as few supermarkets but many low-cost restaurants. Inequities in construction and maintenance of low-income housing——especially for racial/ethnic minorities, older persons, and immigrants—have resulted in insufficient housing, poor-quality housing, overcrowding, and higher levels of population density and health problems. Also, studies have consistently shown an association between a deteriorated physical environment and higher rates of crime, making neighborhoods less safe for walking and in some cases resulting in greater social isolation.
Most studies to date have solely focused their efforts in the urban environment, while there are several unattended issues in rural areas. For example, greater pesticide use in rural areas has resulted in people remaining indoors for longer periods of time and decreasing non-motorized travel. One of the consequences of motorized ways of living in rural areas has been decreased levels of physical activity and concomitant increases in obesity.
Though the built environment is one of our most important habitats, current research in the area has focused mainly on the challenges of balanced transportation, urban sprawl, air pollution due to increased traffic, and the diminishing natural environment. However, more concerted research is needed to identify mechanisms by which the built environment adversely and positively impacts health, and to develop appropriate interventions to reduce or eliminate harmful health effects.
Some recent research explores the effect of improved built environments or sustainable communities on physical activity, asthma, obesity, cardiovascular disease, lung cancer mortality, and mental health. The President’s Council on Sustainable Development in 1993 (Executive Order 12852) offered a working definition for sustainable communities as “healthy communities where natural and historic resources are preserved, jobs are available, sprawl is contained, neighborhoods are secure, education is life-long, transportation and health care are accessible, and all citizens have opportunities to improve the quality of their lives.” While much research indicates the negative health impact of a poor built environment, there is very limited research on the health benefits of promoting sustainable communities to reduce overweight, obesity, and related comorbidities. The sparse research on sustainable communities suggests that diligent planning is needed to create an environment that is conducive to the mental and physical well-being of humans as well as the natural environment.
This RFA will support both R21 and R01 mechanisms. To understand the wide variety of issues relating to the built environment and obesity, studies that combine qualitative and quantitative methodologies are strongly encouraged. Studies can be conducted at the micro (individual/familial), meso (interpersonal and community), and macro (societal, policy) levels. Intervention projects must incorporate multilevel approaches. The Community Guide developed by the Community Preventive Services Task Force (appointed by the director of the Centers for Disease Control and Prevention) provides recommendations regarding population-based interventions for physical activity (see http://www.thecommunityguide.org/pa/default.htm). Intervention projects supported by this RFA must build upon and advance these recommendations and should not duplicate the Task Force efforts. Studies should develop and include new and improved objective measures that link the built environment to biology wherever appropriate, such as serum glucose, endothelium-dependent dilation, cholesterol, insulin, energy balance, oxidative stress, body composition, and lipid profile.
The Surgeon General’s Report on overweight and obesity (http://www.surgeongeneral.gov/topics/obesity/calltoaction/CalltoAction.pdf.) indicates that environmental modifications offer the best opportunity for the treatment and prevention of obesity. Given this, multisite and multilevel environmental interventions including schools, worksites, communities, and a variety of larger social settings would be appropriate. Single site–based interventions have been shown to be effective in improving diet and physical activity levels of children for a short period of time through changes in school lunch and vending machine contents and levels of physical activity. Thus, the most effective strategy would be studies that address both energy intake and expenditure and combine different levels and settings. Across all settings, interventions submitted in response to this RFA may address food access and availability, opportunities for physical activity, and the policies that connect energy intake and expenditure to the built environment.
Evaluation is an essential component for all intervention studies, and proposed studies should include both process and outcome measures. The latter could also include appropriate biological measures. While it is important that both quantitative and qualitative measures are included, wherever possible the adoption of objective measures is encouraged.
The R21 mechanism (see http://grants.nih.gov/grants/guide/pa-files/PA-03-107.html) is limited to two years of funding and is intended to encourage new exploratory/developmental research projects by providing support for the early stages of their development. For example, such pilot/exploratory projects could assess the feasibility of a novel area of investigation or a new experimental system that has the potential to enhance health-related research. Applications for R21 awards should describe projects distinct from those supported through the traditional R01 mechanism. For example, long-term projects or projects designed to increase knowledge in a well-established area will not be considered for R21 awards. Applications submitted under the R21 mechanism should be exploratory and novel and may include development of new methods and measurements. Under these provisions, these R21 proposals should evaluate the validity and reliability of the measures and when appropriate also ensure that they are linguistically and culturally relevant.
Some areas of interest for R21 submissions include but are not limited to the following: 1) at the macro level, the development and validation of reliable measures/indicators for assessing the means by which the built environment impacts food availability/access and physical activity to promote healthy behaviors and lifestyles and reduce obesity and related comorbidities; 2) at the meso level, the development of measures of neighborhood characteristics that promote healthy lifestyles and mediate the effect of the built environment on overweight, obesity, and related comorbidities; 3) at the micro level, the development of valid and reliable measures for behaviors that moderate obesity such as physical activity, nutrient intake, etc.; it is also important that these measures be linguistically and culturally validated; 4) feasibility studies to identify and quantify risk factors and variables related to the built environment that mediate and moderate built environment health effects for public health interventions and outcomes to reduce overweight, obesity, and related comorbidities; 5) pilot studies that assess the impact of state, local, and institutional policy changes in the built environment on overweight, obesity, and related comorbidities (these could be an evaluation of natural experiments such as comparing differently designed communities, or examining before-and-after events, such as physical activity and driving levels before and after a new transit system is opened and nutritional changes with the removal of unhealthy foods from schools); 6) studies to identify environmental and social conditions of people’s work and living choices that may influence physical activity and dietary intake; for example, whether healthy people choose to live in healthy environments or healthy environments differentially attract healthy people; 7) evaluate the health impact assessment instrument as an appropriate and useful tool for local and state health officials to provide information to decision makers about the health consequences of proposed built environment projects and policies; or 8) studies, such as the above examples, that include or focus on obesity concerns and needs of persons who have impaired mobility and use assistive devices such as wheelchairs and lower-limb prostheses.
This RFA uses just-in-time concepts. It also uses the modular budgeting as well as the nonmodular budgeting 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. Otherwise, follow the instructions for nonmodular budget research grant applications. If you are submitting an R21 application, use the modular budget format. This program does not require cost sharing as defined in the current NIH Grants Policy Statement at http://grants.nih.gov/grants/policy/nihgps_2003/NIHGPS_Part2.htm.
The participating institutes intend to commit approximately $5 million in fiscal year 2005 to fund 10–12 new grants in response to this RFA. R01 applications may request a project period of up to five years and a budget for direct costs of up to $400,000 per year, excluding fiscal and administrative costs (http://grants.nih.gov/grants/guide/notice-files/NOT-OD-04-040.html). R21 applications may request a project period of up to two years with a combined budget for direct costs of up to $275,000 for the two-year period. For example, requests can be $100,000 in the first year and $175,000 in the second year.
Letters of intent must be received by 17 November 2004. Applications must be received by 17 December 2004. The complete version of this RFA is available online at http://grants.nih.gov/grants/guide/rfa-files/RFA-ES-04-003.html.
Contact: Shobha Srinivasan, NIEHS, PO Box 12233, MD EC-21, 111 T.W. Alexander Dr, Research Triangle Park, NC 27709 USA, 919-541-2506, fax: 919-316-4606, e-mail: [email protected]; Deborah H. Olster, Office of Behavioral and Social Sciences Research, NIH, Bldg 1, Rm 256, One Center Dr, Bethesda, MD 20892-0183 USA, 301-451-4286, fax: 301-402-1150, e-mail: [email protected]; Harold W. Kohl, III, CDC, National Center for Chronic Disease Prevention and Health Promotion, 4770 Buford Hwy NE, MS K-46, Atlanta, GA 30341-3717 USA, 770-488-5481, fax: 770-488-5473, e-mail: [email protected]; Andrew Dannenberg, CDC, National Center for Environmental Health, 4770 Buford Hwy NE, MS F-30, Atlanta, GA 30341 USA, 770-488-7108, e-mail: [email protected]; Gilman D. Grave, NICHD, 6100 Executive Blvd, Ste 4B-11, Bethesda, MD 20892-7510 USA, 301-496-5593, fax: 301-480-9791, e-mail: [email protected]; Louise C. Masse, NCI, 6130 Executive Blvd, MSC 7335, Executive Plaza North, Rm 4076, Bethesda, MD 20892-7335 USA, 301-435-3961, fax: 301-480-2087, e-mail: [email protected]. Reference: RFA No. RFA-ES-04-003
Novel Approaches to Enhance Animal Stem Cell Research
The purpose of this program announcement (PA) is to encourage research to enhance animal stem cells as model biological systems. Innovative approaches to isolate, characterize, and identify totipotent and multipotent stem cells from nonhuman biomedical research animal models, as well as to generate reagents and techniques to characterize and separate those stem cells from other cell types, is encouraged. Embryonic and other stem cells are valuable biomedical research models for the study of biological and disease processes and the creation of disease models. In addition, these cells hold promise as model systems for development of therapeutics and of replacement tissues.
Thus far, embryonic stem cells have been isolated from some biomedically important nonhuman research models. In addition, stem cells with a more restricted potential have been characterized from post-embryonic tissue types. However, research is needed to provide for a full array of totipotent and multipotent stem cells from nonhuman biomedical research animal models, as well as to provide the research tools to identify, characterize, and purify those cells.
This PA supports the isolation and characterization of embryonic and other multipotent stem cells in a variety of nonhuman animal species. Examples of research areas appropriate to this PA include, but are not limited to, projects to 1) expand the number of nonhuman animal model systems in which embryonic stem cells are available; 2) identify, isolate, culture, and characterize multipotent stem cell populations derived from nonhuman embryonic stem cells; 3) identify, isolate, culture, and characterize multipotent stem cells from postfetal tissue types; 4) generate and use panels of markers for stem cell attributes common across species for use in characterization and isolation of stem cells in a range of animal species or tissues; and 5) create universal methods of culture to maintain the undifferentiated state of embryonic or other characterized multipotential stem cells across nonhuman animal species.
Projects supported by the National Center for Research Resources under this PA are intended to generate research tools, reagents, or stem cells of utility to research on a broad range of tissue or cell types and of interest to more than one categorical or disease-oriented NIH institute or center. Projects that will focus on research on tissues or disease processes specific to the mission of an institute or center should be directed to the respective facility.
This PA will use the NIH R01 and R21 award mechanisms. As an applicant, you will be solely responsible for planning, directing, and executing the proposed project. R21 applications should meet the requirements for this mechanism as recently redefined in PA-03-107, available at http://grants.nih.gov/grants/guide/pa-files/PA-03-107.html. In brief, by using the R21 mechanism, the NIH seeks to foster the introduction of novel scientific ideas, model systems, tools, agents, targets, and technologies that have the potential to substantially advance biomedical research. These studies may involve considerable risk but may lead to breakthroughs, developments, or applications that could have a major impact on a field of biomedical, behavioral, or clinical research.
Applications for R21 awards should describe projects distinct from those supported through the traditional R01 mechanism. For example, long-term projects or projects designed to increase knowledge in a well-established area will not be considered for R21 awards. Applications submitted under this mechanism should be exploratory and novel. These studies should break new ground or extend previous discoveries toward new directions or applications.
Applications for R21 awards may request a project period of up to two years with a combined budget for direct costs of up to $275,000 for the two-year period. The request should be tailored to the needs of the project. Normally, no more than $200,000 may be requested in any single year.
This PA uses just-in-time concepts. It also uses the modular budgeting as well as the nonmodular budgeting 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 budgeting format. Otherwise, follow the instructions for nonmodular budgeting research grant applications. This program does not require cost sharing as defined in the current NIH Grants Policy Statement at http://grants.nih.gov/grants/policy/nihgps_2003/NIHGPS_Part2.htm.
Applications must be prepared using the PHS 398 research grant application instructions and forms (rev. 5/2001). Applications must have a Dun and Bradstreet Data Universal Numbering System (DUNS) number as the Universal Identifier when applying for federal grants or cooperative agreements. The DUNS number can be obtained by calling 1-866-705-5711 or through the website at http://www.dunandbradstreet.com/. The DUNS number should be entered on line 11 of the face page of the PHS 398 form. The PHS 398 document is available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance, contact GrantsInfo by calling 301-435-0714 or e-mailing [email protected].
Applications submitted in response to this PA will be accepted at the standard application deadlines, which are available at http://grants.nih.gov/grants/dates.htm. Application deadlines are also indicated in the PHS 398 application kit.
Contact: For the complete listing of contacts, please consult the full PA, available online at http://grants1.nih.gov/grants/guide/pa-files/PA-04-125.html. Reference: PA No. PA-04-126
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0858a15531410PerspectivesEditorialEditorial: Environment and Health: Capacity Building for the Future Northridge Mary E. Editor-In-Chief, American Journal of Public Health, New York, New York, E-mail:
[email protected] Siaka Editor-in-Chief, Mali Médical, Faculté de Médecine, de Pharmacie et d’Odontostomatologie, Bamako, Mali, E-mail:
[email protected] Thomas J. Editor-in-Chief, EHP, Research Triangle Park, North Carolina, E-mail:
[email protected]’s note: We are pleased to provide this issue’s editorial in French as well as in English.
We thank our sponsoring organizations—the American Public Health Association, the Mali Médical, and especially the U.S. National Institute of Environmental Sciences—for their essential support of this partnering initiative.
Mary E. Northridge is an associate professor of sociomedical sciences at Mailman School of Public Health, Columbia University.
Siaka Sidibe is a radiologist at Hôpital du Point G in Bamako, Mali.
Thomas J. Goehl is a branch chief in the Division of Research Coordination, Planning, & Translation.
11 2004 112 15 A858 A860 Publication 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 October 2002, the World Health Organization (WHO) sponsored a workshop in Geneva, Switzerland, to address the problems faced by medical journals in the developing world regarding their efforts to provide critical, timely health information to local health practitioners and research scientists. Of major concern is the unavailability of the results of medical research from developing nations, which is published in international journals, to those who are living and working in developing nations. If scientific knowledge is to be used to improve public health and the environment, then it must be accessible to the local health professionals who need it most. A specific outcome of this workshop was the creation of the Forum for African Medical Editors (FAME) with 12 inaugural African medical editors, both anglophone and francophone. While the WHO is a major sponsor of FAME, other participating organizations, institutions, journals, and associations offer various forms of assistance.
In September 2003, the U.S. National Institutes of Health (NIH)— specifically, the National Library of Medicine (NLM), the Fogarty International Center, and the National Institute of Environmental Health Sciences (NIEHS)—sponsored a meeting in London at the offices of the British Medical Journal (BMJ). The primary objective was to discuss the partnership of four sub-Saharan African medical journals with five Northern Hemisphere medical journals as a mechanism to enhance the quality and credibility of the African journals and thereby attract high-level research.
Identified steps needed to enhance the quality of the African journals included providing training for editors in improving sustainability and publishing regularity, improving the peer-review process by identifying experienced reviewers willing to serve and mentoring new reviewers, and offering local researchers guidance in preparing research papers for publication. Ideas for improving the credibility of the African journals included having respected scientists from multiple countries serve on the journals’ editorial boards, earning inclusion in major indexing databases such as the NLM’s MEDLINE, and exploring ways to share journal content, for example, by copublishing peer-reviewed research articles of high importance to the people in the area served by regional journals. This latter approach would have the added benefit for researchers and health practitioners in developed countries of making important regional research results more available in the international literature.
In May 2004, a contract was awarded to the Council of Science Editors to manage the funds for a pilot project intended to build the capacity of the four sub-Saharan African journals as per the thoughts generated at the London meeting. The African journals were selected because all of their editors are founding members of FAME and thus are committed to enhancing the capacities of their journals as well as other sub-Saharan medical journals. In addition, the African journals are published in countries that have active NIH-sponsored research and are also part of the communication network developed by the NLM for the Multilateral Initiative on Malaria. The Northern Hemisphere journals were selected on the basis of their missions and commitment to advancing public health and the environment in developing regions of the world. The following four journal partnerships have been established: a) African Health Sciences with BMJ; b) Ghana Medical Journal with The Lancet; c) Malawi Medical Journal with the Journal of the American Medical Association; and d) Mali Médical with Environmental Health Perspectives (EHP) and the American Journal of Public Health. The last partnership—ours—is the only one involving two Northern Hemisphere journals and the only one involving a francophone journal.
If scientific knowledge is to be used to improve public health and the environment, then it must be accessible to the local health professionals who need it most.
In September 2004, the three of us met in Research Triangle Park, North Carolina, to begin working toward the successful completion of our contract tasks, which were as follows:
To identify the equipment and facility needs of the Mali Médical and then provide computer hardware and software to the publishing offices, along with initial training for editorial office personnel
To identify the editorial needs of the Mali Médical through mutual site visits by the partnering editors-in-chief to observe editorial and publishing practices
To provide author/reviewer training via workshops, emphasizing international standards for writing and systematic approaches for reviewers, open to all FAME members at scheduled scientific/medical meetings in Africa
To provide training and support for a managing editor/business manager in establishing business plans for effective, sustainable publishing operations through technical consultation and through a workshop in Africa open to all FAME members
To develop and maintain a website that would permit online publication of the Mali Médical
To establish internships for representatives of the Mali Médical at the editorial offices of EHP and the American Journal of Public Health
To commission four systematic reviews on topics relevant to sub-Saharan Africa to be published in partnering African journals in both English and French.
Over the next several years, we plan to evaluate the success of our capacity-building initiative using the following indicators: progress toward indexing the Mali Médical in MEDLINE, numbers of articles submitted and published, numbers and effectiveness of local peer reviewers, and timeliness of publication. If our journals and sponsoring organizations are to fulfill our common missions of working to improve the public’s health and achieving equity in health status for all, then our nascent partnership is a viable means toward this end. Our collective hope is that all three journals will better realize their potential to serve as vehicles for progressive change through increased understanding, collaboration, insight, and connections between the environment and health in the developed and developing world. Look for regular updates from us published simultaneously in all three journals in both English and French. We aim to hold one another accountable in fulfilling our assigned tasks and enlisting other partners in our rewarding struggle to find creative and practical solutions that eliminate past and present health inequalities and protect the environment for future generations.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0862a15531413PerspectivesCorrespondenceAre Maternal Thyroid Autoantibodies Generated by PCBs the Missing Link to Impaired Development of the Brain? Koppe Janna G. ECOBABY Society, Amsterdam, the Netherlands, E-mail:
[email protected] author declares she has no competing financial interests.
11 2004 112 15 A862 A862 Publication 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 her interesting review addressing endocrine disruption and the developing brain, Colborn (2004) asked rightly for special attention to the role of a disruption of thyroid hormones and thyroid hormone metabolism, which negatively influence early development of the fetal brain. As mechanisms of action, chemicals such as polychlorinated biphenyls (PCBs) were discussed in a dose-related way; the higher the exposure level of the mother, the more problems of brain development will be found in the baby (Colborn 2004). However, this is not always true. Patandin et al. (1999) found a four-point decline in IQ at 4 years of age in relation to maternal PCB levels in the Netherlands. In a follow-up study of Faroese children at 7 years of age, Grandjean et al. (1997) found no relation of PCBs with cognitive impairment; the levels of PCBs were almost 4 times higher in the Faroese population than in the Dutch population (Longnecker et al. 2003).
One explanation of the missing link might be that effects of PCBs are not directly toxic but instead are toxic through immunomodulatory mechanisms in the mother. In a comment on the impact of maternal PCB and dioxin exposure on the neonate’s thyroid hormone status, Vulsma (2000) noted that PCBs affect the generation of autoantibodies against thyroid tissue [e.g., thyroid peroxidase antibodies (TPO-Ab)]. In a study in Slovakia, Langer et al. (1998) described an increase in TPO-Ab in relation to PCB exposure. These antibodies do pass through the placenta.
An important risk factor for impaired infant development is a low free thyroxine (fT4) concentration in early pregnancy; particularly at risk are the mothers with low fT4 and high TPO-Ab titers. These anti-bodies are found in 10% of (euthyroid) women at 12 weeks’ gestation in the Netherlands (Pop et al. 1995, 1999). To my knowledge, none of the studies on effects of PCBs in human pregnancy have reported data on maternal TPO-Ab titers.
If the findings reported by Colborn (2004) can be explained by autoimmune processes that cause low fT4 in the mother and negatively affect her developing baby, then it seems more logical that prenatal PCB exposure is related to developmental impairment instead of the amount of PCBs transferred by breast milk after birth.
I agree with Colborn (2004) that all women who plan to become pregnant should be evaluated for thyroid hormone status.
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References
Colborn T 2004 Neurodevelopment and endocrine disruption Environ Health Perspect 112 944 949 15198913
Grandjean P Weihe P White RF Debes F Araki S Yokoyama K 1997 Cognitive deficit in 7-year-old children with prenatal exposure to methylmercury Neurotoxicol Teratol 19 417 428 9392777
Langer P Tajtakova M Forodr G Kocan A Bohov P Michalek J 1998 Increased thyroid volume and prevalence of thyroid disorders in an area heavily polluted by polychlorinated biphenyls Eur J Endocrinol 139 402 409 9820616
Longnecker MP Wolff MS Gladen BC Brock JW Grandjean P Jacobson JL 2003 Comparison of polychlorinated biphenyl levels across studies of human neurodevelopment Environ Health Perspect 111 65 70 12515680
Patandin S Lanting CI Mulder PG Boersma ER Sauer PJ Weisgla-Kuperus N 1999 Effects of environmental exposure to polychlorinated biphenyls and dioxins on cognitive abilities in Dutch children at 42 months of age J Pediatr 134 33 41 9880446
Pop VJ de Vries E van Baar AL Waelkens JJ de Rooy HA Horsten M 1995 Maternal thyroid peroxidase antibodies during pregnancy: a marker of impaired child development? J Clin Endocrinol Metab 80 3561 3566 8530599
Pop VJ Kuijpens JL van Baar AL Verkerk G van Son MM de Vijlder JJ 1999 Low maternal free thyroxine concentrations during early pregnancy are associated with impaired psychomotor development in infancy Clin Endocrinol 50 149 155
Vulsma T 2000 Impact of exposure to maternal PCBs and dioxins on the neonate’s thyroid hormone status Epidemiology 11 239 241 10784237
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0862bPerspectivesCorrespondenceMaternal Thyroid Autoantibodies: Colborn’s Response Colborn Theo The Endocrine Disruption Exchange, Paonia, Colorado, E-mail:
[email protected] author declares she has no competing financial interests.
11 2004 112 15 A862 A862 Publication 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 thank Koppe for raising the question of the significance of the presence of increased thyroid peroxidase antibodies (TPO-Ab) during neurodevelopment or even later in life. I have wondered for years why medical practitioners and laboratories do not routinely quantify TPO-Ab in blood screening for thyroid disorders. High priority should be given to learning more about the relationship between the combination of high TPO-Ab and low free thyroxine (fT4), and impaired IQ and psychomotor development and the possible role of foreign substances such as polychlorinated biphenyls (PCBs) in these changes. Although the value of routine antithyroglobin antibody (TG-Ab) testing is being questioned, in future epidemiologic studies looking at the role of PCBs in neurodevelopment perhaps TG-Ab should be included in the design as well. It might prove enlightening to also routinely test for TG-Ab at several research/medical institutions to continue to explore this immune connection with the thyroid economy. Also, perhaps it is time to explore the nutritional state (protein consumption, quality and quantity of serum proteins) of the mother and her unborn child during gestation, which might contribute to the conflicting findings among the various cohort studies about the role of PCBs in neurodevelopment. In the meantime, until more is understood about neurodevelopmental impairment, I would like to take this opportunity to reinforce the need to routinely test all pregnant women and those planning to become pregnant for fT4, free triiodothyronine, thyroid-stimulating hormone, and TPO-Ab. Information such as this would allow for intervention, if needed, to prevent irreversible brain damage.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0862c15531411PerspectivesCorrespondenceUpdate of Residential Tetrachloroethylene Exposure and Decreases in Visual Contrast Sensitivity Storm Jan E. Mazor Kimberly A. New York State Department of Health, Center for Environmental Health, Troy, New York, E-mail:
[email protected] authors declare they have no competing financial interests.
11 2004 112 15 A862 A864 Publication 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
In “Apartment Residents’ and Day Care Workers’ Exposures to Tetrachloroethylene and Deficits in Visual Contrast Sensitivity,” Schreiber et al. (2002) reported significantly lower visual contrast sensitivity (VCS) in apartment residents exposed to tetrachloroethylene (perchloroethylene, or perc) compared to unexposed “matched” control subjects. The authors stated that the VCS deficit may “represent a long-lasting, adverse alteration in neurobehavioral function” caused by chronic, environmental perc exposures, although they cautioned that methodologic limitations preclude definitive attribution of causation.
Residential data reported by Schreiber et al. (2002) were originally collected by the New York State Department of Health (NYSDOH) as a pilot project to support development of a larger study (NYSDOH, unpublished data). Residents exposed to perc included in the study were 13 adults from six households (20–72 years of age) and 4 children from three households (6–13 years of age) located in two buildings. Continued research by the NYSDOH and others (Farrar et al. 2001; NYSDOH 2004) suggests that confounding factors may influence VCS test performance of children in this and other studies. Consequently, we would like to update the findings of the residential study described by Schreiber et al. (2002).
In the analyses described by Schreiber et al. (2002), VCS of all perc-exposed adult and child residents and unexposed matched controls were compared using analysis of variance and SAS software (version 8.2; SAS Institute, Cary, NC). Matched pair, exposure (perc exposed, unexposed), and spatial frequency (cycles per degree) were independent variables; VCS was the dependent variable. The authors reported a significant effect of exposure on VCS (F = 19.38; df = 1,144; p < 0.001). Sample sizes were not sufficient to support statistical analysis of VCS stratified by age (i.e., child, adult); VCS data were available for only four children. However, review of individual VCS functions suggested that the significant VCS deficit was likely to be attributable to the four children in the exposed group. VCS functions of the exposed children were therefore carefully examined with respect to VCS functions for their matched controls and with respect to information about the children available from parental questionnaires.
Individual VCS functions for each exposed child were lower than his/her matched control (Figure 1A). Although perc exposure may have influenced VCS of these children, other factors could have contributed to their poor performance. For example, conditions such as developmental delay (DD) and attention deficit disorder (ADD) are known to be associated with decreased VCS (Farrar et al. 2001; Hudnell et al. 1996). One of the exposed children was characterized as having psychologist-diagnosed DD, and another exposed child was characterized as having physician-diagnosed ADD (Table 1). These two children performed poorly on the VCS but similar to unexposed children with similar diagnoses examined in a recently completed NYSDOH study (Figure 1B) (NYSDOH 2004). Also, another perc-exposed child was characterized as being forgetful at school, although not specifically as developmentally or learning disabled. (Questionnaires administered to residents of dry-cleaner buildings are part of NYSDOH records for the residential study; questionnaires were not completed for controls.) It is therefore possible that the perc exposure–VCS association reported by Schreiber et al. (2002) may have been confounded by the presence of these conditions.
In studies now being conducted by the NYSDOH and as reported by Scharre et al. (1990), 5- and 6-year-old children perform variably on the VCS test; sometimes they perform well, and sometimes they are inattentive and unable to perform. Two exposed children included in the residential study were 6 years of age. The matched control for one of these was 8 years of age, and the matched control for the other was the average of a 5-year-old and 7-year-old. Thus, although VCS was poor in perc-exposed child residents compared to others not exposed to perc, this may have been partly due to differences between groups in factors other than perc exposure (e.g., age).
In an exploratory analysis, VCS was evaluated only among adult participants in the residential study. When VCS of perc-exposed adult residents and unexposed adult control subjects were analyzed alone, excluding the four child pairs, a significant effect of perc exposure was not observed (F = 2.04; df = 1,108; p = 0.16). The sample size was small (n = 13) and consequently the statistical power was limited; however, the results suggest that VCS was not significantly decreased in perc-exposed adult residents.
Clearly, the possible effect of perc on VCS in adults, and especially in children, should continue to be explored. However, as illustrated here and discussed by Swinker and Burke (2002) and Hudnell and Shoemaker (2002), the possible influence of factors other than perc exposure on VCS should also be considered. These factors include age and the presence of learning disabilities or developmental delay in children, as illustrated here, as well as conditions such as diabetes, high blood pressure, glaucoma, and cataracts, in adults (Bodis-Wollner and Camisa 1980).
Figure 1 Individual VCS functions of children. (A) VCS functions of perc-exposed child residents (E9, E10, E14, E17) and matched controls (C9, C10, C14, C17) included by Schreiber et al. (2002) and the NYSDOH (2000). (B) Individual VCS functions of children characterized as having DD or ADD included by Schreiber et al. (2002; E10, E14) and examined in the NYSDOH study (NYSDOH 2004; P1, P2). The gray band reflects the normal adult range (90% confidence limits) reported for the Functional Acuity Contrast Test, F.A.C.T 101 (Stereo Optical Co., Inc., Chicago IL).
Table 1 Child residents and matched controls in the VCS studies.
Exposed
Matched control
ID Age DD or ADD ID Age DD or ADD
Childrena
E9 8 – C9 9 –
E10 6 X C10 8 –
E14 12 X C14 12 –
E17 6 – C17 5,7 –
Childrenb
P1 8 X
P2 10 X
aChildren shown in Figure 1A (NYSDOH, unpublished data; Schreiber et al. 2002).
bChildren shown in Figure 1B; E10 and E14 from Schreiber et al. (2002) and P1 and P2 examined in the NYSDOH study (NYSDOH 2004).
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References
Bodis-Wollner I Camisa JM 1980. Contrast sensitivity measurement in clinical diagnosis. In: Neuro-ophthalmology, Vol 1 (Lessell S, Van Dalen JTW, eds). Amsterdam, the Netherlands:Excerpta Medica, 373–401.
Farrar R Call M Maples WC 2001 A comparison of the visual symptoms between ADD/ADHD and normal children Optometry 72 441 451 11486939
Hudnell HK Shoemaker RC 2002 Visual contrast sensitivity: response [Letter] Environ Health Perspect 110 A121 A123
Hudnell HK Skalik D Otto D House D Subri P Sram R 1996 Visual contrast sensitivity deficits in Bohemian children Neurotoxicology 17 615 628 9086482
NYSDOH (New York State Department of Health) 2004. Pumpkin Patch Day Care Center Follow-up Evaluation. Troy NY:Center for Environmental Health, Bureau of Toxic Substance Assessment.
Scharre JE Cotter SA Block SS Kelley SA 1990 Normative contrast sensitivity data for young children Opt Vis Sci 67 826 832
Schreiber JS Hudnell HK Geller AM House DE Aldous KM 2002 Apartment residents’ and day care workers’ exposures to tetrachloroethylene and deficits in visual contrast sensitivity Environ Health Perspect 110 655 664 12117642
Swinker M Burke WA 2002 Visual contrast sensitivity as a diagnostic tool [Letter] Environ Health Perspect 110 A120 A121 11882486
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0865a15531417PerspectivesCorrespondenceMore Recent Studies on Fragrances Smith Ladd W. Research Institute for Fragrance Materials, Inc., Woodcliff Lake, New Jersey, E-mail:
[email protected] author is employed by the Research Institute for Fragrance Materials; he declares that the RIFM publishes its work in the peer-reviewed literature under the guidance of an independent scientific panel and receives support from the private sector.
11 2004 112 15 A865 A865 Publication 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 response to Curtis (2004), I would like to cite more recent studies by researchers at the Research Institute for Fragrance Materials, Inc. (RIFM) that address the health and environmental effects of fragrances.
The RIFM strives to be the international leader for the safe use of fragrance ingredients (Balk et al. 2004; Bickers et al. 2003a; Cadby et al. 2002b; Smith 2003) and has ongoing research programs in the areas of fragrance allergy, human health effects (Cadby et al. 2002a; Bickers et al. 2003b), respiratory safety (Isola et al. 2004; Smith et al. 2004), and environmental impact (Salvito et al. 2002, 2004). The RIFM’s comprehensive, logical, and documented research methods are modeled after the National Academy of Sciences’ (NRC) Elements of Risk Assessment and Risk Management (NRC 1994).
Research is prioritized (Ford et al. 2000) and designed using information in the RIFM proprietary database (RIFM 2004) and according to the needs of the scientific community and the general public. The database provides a clearinghouse for human health and environmental studies, as well as basic material information, and is maintained by continuously monitoring journals, government reports, company-sponsored research, and available literature to enable analysis of documented conclusions.
All available information pertaining to the safety of fragrance materials, study protocols, and results are reviewed by an independent international panel of scientific and medical experts from the fields of toxicology, dermatology, pathology, and environmental science. Research results and safety evaluations are published in peer-reviewed scientific journals and presented at professional meetings.
In addition, the RIFM accepts proposals for sponsored scientific research and will work jointly with interested third parties to further knowledge on health and environmental issues.
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References
Balk F Blok H Salvito DT 2004. Recent studies conducted by the Research Institute for Fragrance Materials in support of the risk assessment process. In: The Handbook of Environmental Chemistry, Vol 3X (Rimkus G, ed). New York:Springer Verlag, 311–331.
Bickers DR Calow P Greim HA Hanifin JM Rogers AE Saurat JH 2003a The safety assessment of fragrance materials Regul Toxicol Pharmacol 37 218 273 12726755
Bickers D Calow P Greim H Hanifin JM Rogers AE Saurat JH 2003b A toxicologic and dermatologic assessment of linalool and related esters when used as fragrance ingredients Food Chem Toxicol 41 7 919 942 12804649
Cadby PA Troy WR Vey MGH 2002a Consumer exposure to fragrance ingredients: providing estimates for safety evaluation Regul Toxicol Pharmacol 36 3 246 252 12473409
Cadby PA Troy WR Middleton JD Vey MGH 2002b Fragrances: are they safe? Flavour Fragr J 17 472 477
Curtis L 2004 Toxicity of fragrances [Letter] Environ Health Perspect 112 A461 15175191
Ford RA Domeyer B Easterday O Maier K Middleton J 2000 Criteria for development of a database for safety evaluation of fragrance materials Regul Toxicol Pharmacol 31 166 181 10854123
Isola DA Rogers RE Ansari R Smith LW 2004 Exposure characterization from a surrogate fine fragrance [Abstract] Toxicologist 78 S-1 107
National Research Council (NRC) 1994. Science and Judgment in Risk Assessment. Washington, DC:National Academy Press.
RIFM (Research Institute for Frangrance Materials) 2004. RIFM Database. Available: http://www.rifm.org/members_rifm.htm [accessed 30 August 2004].
Salvito DT Senna RJ Federle TW 2002 A framework for prioritizing fragrance materials for aquatic risk assessment Environ Toxicol Chem 21 6 1301 1308 12069318
Salvito DT Vey MGH Senna RJ 2004 Fragrance materials and their environmental impact Flavour Fragr J 19 105 108
Smith LW 2003 The scientific basis for sound decisions on fragrance material use [Editorial] Regul Toxicol Pharmacol 37 172
Smith LW Rogers RE Black MS Isola DA 2004 Exposure characterizations of three fragranced products [Abstract] Toxicol Appl Pharmacol 197 3 189
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0865b15531416PerspectivesCorrespondencePesticides and Organic Agriculture DiMatteo Katherine Organic Trade Association, Greenfield, Massachusetts, E-mail:
[email protected] author declares she has no competing financial interests.
11 2004 112 15 A865 A865 Publication 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 read with horror the article “Pesticides and Parkinson Disease” by Renee Twombly (2004) in which she implied that rotenone is “often used in organic gardening and farming.” She went on to describe the effects of rotenone and the even more harmful effects of pyridaben, which is far more toxic than rotenone, both of which are used in conventional agriculture.
To set the record straight, rotenone is not commonly used in organic agriculture. Rotenone that has been naturally derived is listed as a “restricted substance” by the Organic Materials Review Institute (OMRI 2004) and may be used only in special circumstances with designated limitations. Meanwhile, rotenone’s synergist, piperonyl butoxide, is prohibited from use in organic agriculture.
The premise of organic agriculture is to fortify the soil through wholesome, nontoxic means, thereby strengthening the ability of plants to defy diseases and pests.
It is the hope of the hardworking pioneers in the organic movement that the instance of Parkinson disease, cancer, and many environmentally related illnesses will diminish exponentially with the conversion of acreage to organic cultivation.
Editor’s response: As DiMatteo implies, rotenone is, or should be, used only as a last resort in organic gardening and farming. It should be noted, however, that this pesticide is commonly marketed and sold under the rubric “organic gardening supplies.”
==== Refs
References
OMRI 2004. OMRI Homepage. Eugene, OR:Organic Materials Review Institute. Available: http://www.omri.org [accessed 22 September 2004].
Twombly R 2004 Pesticides and Parkinson disease Environ Health Perspect 112 A548
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0865c15531415PerspectivesCorrespondenceAgricultural Task Not Predictive of Children’s Exposure to OP Pesticides Fenske Richard A. Kissel John C. Shirai Jeffry H. Curl Cynthia L. Galvin Kit Department of Environmental and Occupational Health Sciences, School of Public Health and Community Medicine, University of Washington, Seattle, Washington, E-mail:
[email protected] authors declare they have no competing financial interests.
11 2004 112 15 A865 A866 Publication 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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Coronado et al. (2004) reported that the agricultural task of plant thinning by adults was associated with higher urinary pesticide metabolite concentrations in children. Their analysis was based on data from a 1999 study of farmworkers in the Yakima Valley of Washington State (Curl et al. 2002; Thompson et al. 2003). Their conclusion was based on a finding that one of the three dimethyl dialkylphosphate (DAP) metabolites of the organophosphorus (OP) pesticides—dimethylthiophosphate (DMTP)—was more frequently detected among children living in the same household with adult farmworkers who reported having thinned plants compared with children living in the same household with farmworkers who did not report thinning (92% vs. 81%, respectively).
We examined the same data set to determine if the actual urinary pesticide metabolite concentrations, rather than simply the frequency of metabolite detection, differed between these groups of children. We used log-transformed data and the independent t-test (equal variance assumption) to determine differences between geometric mean metabolite concentrations. We found no significant differences between children of thinners versus non-thinners for any of the three DAP metabolites. Geometric mean values for DMTP were 6.13 μg/L for 136 children of thinners and 5.27 μg/L for 75 children of non-thinners (p = 0.41). Furthermore, we did not find a significant difference between these groups for the sum of the dimethyl DAP metabolites (geometric means of 0.097 vs. 0.083 μmol/L; p = 0.33).
Coronado et al. (2004) also suggested that children of thinners may receive higher exposures than children of pesticide handlers (mixers, loaders, applicators). We compared the children of thinners to children of handlers, excluding the 28 children for whom the corresponding adult farmworker reported both thinning and handling. No differences were observed between these groups for any of the three DAP metabolites. Geometric mean values for DMTP were 6.47 and 6.05 μg/L, respectively (p = 0.81), and 0.10 and 0.096 μmol/L, respectively, for the sum of the dimethyl DAP metabolites (p = 0.78). It is not surprising that the child population in this study exhibited high frequencies of detection of the DAP metabolites. The most recent study by the Centers for Disease Control and Prevention (Barr et al. 2004) found that 87% of U.S. children 6–11 years of age had one or more of the dimethyl DAP metabolites detected in their urine.
We conclude from our analysis of this data set that a) children living in households with thinners did not exhibit higher OP pesticide exposures than children living in households with workers in other agricultural task categories; and b) OP pesticide exposures did not differ between children of thinners and children of pesticide handlers. We further conclude that frequency of detection is an inadequate exposure metric for urinary pesticide metabolites that are detected with high frequency, and that its use independent of metabolite concentration data can prove misleading. We recommend that future analyses of children’s pesticide exposure focus on measured metabolite concentrations rather than the simple presence or absence of metabolites in biological samples.
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References
Barr DB Bravo R Weerasekera G Caltabiano LM Whitehead RD Jr Olsson AO 2004 Concentrations of dialkyl phosphate metabolites of organophosphorus pesticides in the U.S. population Environ Health Perspect 112 186 200 14754573
Coronado GD Thompson B Strong L Griffith WC Islas I 2004 Agricultural task and exposure to organophosphate pesticides among farmworkers Environ Health Perspect 112 142 147 14754567
Curl CL Fenske RA Kissel JC Shirai JH Moate TF Griffith W 2002 Evaluation of take-home organophosphorus pesticide exposure among agricultural workers and their children Environ Health Perspect 110 A787 792 12460819
Thompson B Coronado GD Grossman JE Puschel K Solomon CC Islas I 2003 Pesticide take-home pathway among children of agricultural workers: study design, methods, and baseline findings J Occup Environ Med 45 42 53 12553178
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0866a15531418PerspectivesCorrespondenceChildren’s Exposure to OP Pesticides: Response to Fenske et al. Coronado Gloria D. Thompson Beti Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, E-mail:
[email protected] William C. Department of Environmental and Occupational Health Sciences, School of Public Health and Community Medicine, University of Washington, Seattle, WashingtonThe authors declare they have no competing financial interests.
11 2004 112 15 A866 A866 Publication 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
In our article (Coronado et al. 2004), we reported a higher proportion of urine samples containing detectable levels of the organophosphate (OP) pesticide urinary metabolite dimethylthiophosphate (DMTP) from children of farmworkers who reported having thinned plants, compared with urine samples from children of non-thinners. We reported the detection frequency for individual dimethyl metabolites, not a composite score for the detection of multiple dimethyl metabolites. We thank Fenske et al. for their additional analyses showing slightly higher, though not significant, concentrations of urinary DMTP in children of thinners versus non-thinners.
We knew that assessing detection frequencies would provide only a preliminary view of a more complex pattern of exposure; thus, we specifically stated in the “Methods” section of our paper that the analysis was exploratory in nature. We examined job task as a factor possibly associated with high exposure to pesticides because job task is closely linked with regulatory policy. We understood that if substantial differences in the percentage of detectable samples existed between groups further exploration would be warranted.
This type of analysis follows the logic put forth by others in the field of exposure assessment. For example, Fenske et al. highlight that Barr et al. (2004)—in the same issue of EHP in which our article was published—provided detection frequencies of OP pesticide urinary metabolites in older children (6–11 years of age) from the general population. Barr et al. reported a detection frequency for DMTP of 69%, with a limit of detection of 0.18 μg/L. In our study we matched children (2–6 years of age) with an adult agricultural worker in the same home. Among the children matched to farmworkers who reported thinning, we observed a detection frequency for urinary DMTP of 92%, with a limit of detection of 1.1 μg/L.
We agree with Fenske et al. that a more in-depth analysis is warranted and thank them for their interest and recommendations.
==== Refs
References
Coronado GD Thompson B Strong L Griffith WC Islas I 2004 Agricultural task and exposure to organophosphate pesticides among farmworkers Environ Health Perspect 112 142 147 14754567
Barr DB Bravo R Weerasekera G Caltabiano LM Whitehead RD Jr Olsson AO 2004 Concentrations of dialkyl phosphate metabolites of organophosphorus pesticides in the U.S. population Environ Health Perspect 112 186 200 14754573
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0866b15531418PerspectivesCorrespondenceOlden’s Contributions Nussbaum Rudi H. Professor Emeritus, Physics and Environmental Sciences, Portland State University, Portland, Oregon, E-mail:
[email protected] author declares he has no competing financial interests.
11 2004 112 15 A866 A867 Publication 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 read with mixed feelings of approval and sadness your editorial about the end of tenure for Kenneth Olden as director of the National Institute of Environmental Health Sciences (NIEHS) (Brown et al. 2004). I have been greatly impressed with Olden’s significant contributions to broadening the scope of public health sciences, as reflected in the evolution of EHP into an exemplary, innovative, and internationally highly respected journal on environmental health sciences.
Specifically, the following sentence in the editorial was grist for my mill:
From early on he showed awareness and understanding of a fact that had often been ignored by others in research administration—that local communities have the collective ability to identify environmental health problems but often lack the time, means, and research expertise to effectively resolve these problems.
We have just published a report describing a unique community–physician–scientist cooperative research effort without support from any public agency that has been—at least for a small number of survivors of this group of Hanford, Washington, “downwinders”—of great significance for their experiencing a sense of empowerment and, at least to some degree, of “justice” through the process of scientific validation (Nussbaum et al. 2004).
It seems that the efforts of our alliance might well have fallen within the boundaries of projects that Olden’s initiatives could have supported: to provide and link communities with appropriate research resources.
I fear that Olden’s departure will be a great loss for the NIEHS and that it will be very difficult to find a replacement for him with an equally bold vision and willingness to take risks in innovative leadership.
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References
Brown D Thigpen Tart KG Goehl TJ 2004 Olden times: looking back on a career at the NIEHS Environ Health Perspect 112 A598 A599 15289171
Nussbaum RH Hoover PP Grossman CM Nussbaum FD 2004 Community-based participatory health survey of Hanford, WA, downwinders: a model for citizen empowerment Soc Nat Resour 17 547 559
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0871aEnvironewsForumAsbestos: Showdown in El Dorado Renner Rebecca 11 2004 112 15 A871 A871 Publication 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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Taken at face value, northern California’s El Dorado County has a lot going for it—the dramatic Sierra Nevada foothills scenery, lots of room for spacious new houses, and its short distance from Sacramento. That’s why the population has nearly sextupled since 1960, according to U.S. Census figures. But the construction that is transforming this once rural area has also dug up a health risk—thin needles of amphibole asbestos, a particularly hazardous form of the mineral.
Naturally occurring amphibole asbestos is not a problem when left underground. But development can unearth the mineral, increasing the risk of exposure. In November 2003 the U.S. Environmental Protection Agency (EPA) found that over 25% of 153 soil samples collected from the local high school contained more than 1% asbestos by weight, a level that, if disturbed, could pose a threat to public health. As a result, the agency determined that additional investigations are required. This fall, officials from the EPA Superfund program plan to simulate the activities of school kids playing sports and use personal monitors to measure exposures associated with these activities at several locations including the high school, according to Superfund senior science advisor Richard Troast.
The monitoring comes in the wake of another town’s recently publicized experience: occupational and environmental exposure to amphibole asbestos in the small mining town of Libby, Montana, resulting in deaths and other widespread health effects. There are lessons from Libby that apply to El Dorado, according to many asbestos experts. People with no occupational exposure, such as women who handled their miner husbands’ work clothes, can have asbestos-related problems. Further, ambient air monitoring may not reflect actual asbestos exposure—individuals can dramatically heighten their exposure by kicking up fibers on the ground. So an accurate estimate of exposure requires personal air monitoring during specific activities.
In the April 2004 issue of Occupational and Environmental Medicine, a group of British researchers led by J. Corbett McDonald reported their study of 406 Libby miners, in which they calculated that occupational exposure resulted in a 14% increase in mortality from all asbestos-related causes. They also estimated that environmental exposure for 50 years would lead to a 3.2% mortality increase and called attention to the potential health risk in northern California. Extrapolation is fraught with uncertainty, but for El Dorado’s population of 160,000, such a mortality increase could translate into thousands of deaths.
A 2002 EPA peer consultation unanimously agreed that for mesothelioma, a rare cancer of the lining of the lung, the carcinogenic potency of amphibole fibers is a minimum of two orders of magnitude greater than for chrysotile asbestos fibers (most likely because amphibole fibers persist longer in the body). Forms of amphibole asbestos also can cause noncancer diseases in proportion to exposure, according to researchers led by Lucy Peipins of the Agency for Toxic Substances and Disease Registry. They conducted a clinical study in which about 6,700 Libby residents had chest X rays. In the November 2003 issue of EHP, the researchers reported 29 exposure pathways related to work, recreation, and household activities. The prevalence of pleural abnormalities increased with the number of exposure pathways, ranging from 6.7% for those who reported no apparent exposures to 34.6% for those who reported 12 pathways.
Some data already indicate that exposures in El Dorado are high enough to be harmful. When concerned citizens requested analysis of four deceased pets, the animals’ lung fiber burdens revealed concentrations of amphibole fibers higher than those found in goats from Corsica, where episodic environmental exposure to amphibole asbestos is clearly associated with human mesothelioma, says pathologist Jerrold Abraham of Upstate Medical University. Working collaboratively, pathologist Bruce Case of McGill University confirmed these findings in an independent analysis. A summary of the study findings is available online at http://www.asbestos.net/.
What happens next in El Dorado will depend on the exposure monitoring data, says Troast. Some experts are braced for the worst. Says Case, “The situation in El Dorado has the potential to be the most important source of environmental asbestos-related mesothelioma ever in the United States.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0871bEnvironewsForumThe Beat Dooley Erin E. 11 2004 112 15 A871 A873 Publication 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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Jakarta’s New Monorail
Jakarta has begun a monorail project to combat the air pollution and massive traffic jams that plague the city of nearly 9 million people and 5 million vehicles. The monorail system, still in the planning phases, is expected to consist of two lines, one serving the Indonesian capital’s central business district and the other running several miles through the city’s outer areas. The city has also reserved two lanes of the city’s main thoroughfare for bus traffic only.
Jakarta’s air quality is ranked among the worst in the world. Studies have found that the sixth leading cause of death in Indonesia is inflammation of the respiratory tract, which is closely linked with poor air quality.
U.S. Climate Changes
In August 2004, the Bush administration delivered a report to Congress acknowledging that emissions of greenhouse gases are the best explanation for the global warming trend of the past 30 years. The report, which is available on the Internet at http://www.climatescience.gov/, was prepared by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research, which are made up of federal representatives. The report also cited specific risks to farmers: increased carbon dioxide emissions stimulate the growth of invasive weeds, while reducing the nutritional value of certain grasses. Some industry groups dispute the new report, stating the science behind it is flawed.
LA’s Shipshape Terminal
The Port of Los Angeles is home to the world’s first container terminal using Alternative Maritime Power (AMP) technology. Although this technology has been used by the U.S. Navy since World War II, the shipping industry has resisted adopting it due to cost and time issues. AMP technology provides a dock with electricity that is converted to a ship-compatible voltage. Ships plug in to the dock instead of running their diesel engines during loading and unloading, cutting emissions of smog-forming nitrogen oxides by 1 ton and particulate matter by 87 pounds per day.
China Shipping Lines, which leases the new berth, has agreed to retrofit 11 of its ships for the new power source. The first vessel plugged in to the updated berth in June 2004.
Rain Theft in China
With some agricultural areas of China in the grip of an extended drought, cities have turned to rain-making technology to extract precious water from the skies. Now neighboring cities in Henan province are accusing one another of an unusual crime: rain theft. In July 2004, Zhoukou city officials claimed that rain makers in Pingdingshan overseeded clouds so that the latter city enjoyed rainfall that should have been Zhoukou’s. City officials want the courts to set up laws for “cloud farming,” although scientists believe the technology is not yet proven enough to regulate.
China is one of the world’s leading users of rain-making technology, which involves seeding cumulus clouds with dry ice or silver iodide to prompt precipitation. The Chinese government has set aside approximately US$50 million for nationwide weather management systems. Many local and provincial governments have set up “weather modification” bureaus charged with cloud seeding.
AHA Links Pollution to Heart Disease
In the 1 June 2004 issue of Circulation, the American Heart Association made its first firm policy statement linking heart disease and long-term exposure to air pollution. The statement, written by University of Michigan researchers, is based on an extensive literature review. It cites particulate matter such as that generated by traffic as especially dangerous. The statement also points to a clear association between secondhand tobacco smoke and heart disease. Lead author Robert Brook called the link between air pollution and heart disease “a serious public health problem” because of the large number of people affected and because exposure occurs over a lifetime.
Seaweed Attacks DDT
An international research team funded by the Royal Thai government has found that applying powdered seaweed to soil contaminated with the pesticide DDT can accelerate the breakdown of the contaminant. DDT was widely used from its introduction in the 1940s until it was banned in the United States in 1972. It is still used for mosquito control in some countries where malaria is prevalent. The researchers, whose work appeared in the June 2004 issue of the Journal of Chemical Technology and Biotechnology, found that the optimal proportion of 0.5% seaweed by weight resulted in 80% of the DDT degrading within six weeks. The sodium in the seaweed loosens the soil, allowing microorganisms to reach and attack the DDT.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0872aEnvironewsForumBioinformatics: Literature Searchlight Holton W. Conard 11 2004 112 15 A872 A872 Publication 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 time and cost required to bring a new drug to market can exceed 10 years and $800 million, according to the Tufts Center for the Study of Drug Development. Now researchers at etexx Biopharmaceuticals in Dallas, Texas, are using software that they say will slash this time and cost. How? By mining the medical literature for hints on new uses for drugs already approved by the Food and Drug Administration.
Repurposing existing drugs can offer substantial payoffs for both pharmaceutical companies and the public. For example, bupropion has been licensed separately as an antismoking drug (Zyban) and an antidepressant (Wellbutrin); Rogaine, now used to treat hair loss, was originally developed as a treatment for high blood pressure.
The software was created at The University of Texas (UT) Southwestern Medical Center by biochemistry professor Harold Garner and colleagues, and has been licensed to etexx, which Garner founded. Known as IRIDESCENT (for Implicit Relationship IDEntification by in-Silico Construction of an Entity-Based Network from Text), the program allows full-scale automated analysis of records in MEDLINE, the National Library of Medicine’s bibliographic database. Eventually the software could be used with other online resources such as the Physicians’ Desk Reference and even internal documents from pharmaceutical and biotech companies.
The software analyzes MEDLINE abstracts to identify and evaluate statistical relationships among biomedical terms such as names of genes, phenotypes, drugs, and diseases. The program compares how often sets of these terms appear in texts relative to random probability. It can identify and compare over 300,000 different biomedical terms along with their spelling variations, synonyms, and acronyms. A network of these “co-mentions” is created and then analyzed by a statistical program to find indirect or implicit connections.
IRIDESCENT then scores the objects for relevance, significance, and interest, allowing the researcher to inspect the resulting connections to trigger hypotheses on new uses for existing drugs. The team showed the value of this approach by validating in several lab trials a connection between the drug Thorazine, used to treat psychotic disorders, and a reduction in the progression of cardiac hypertrophy, or enlargement of the heart, which the program had predicted. The results were published 12 February 2004 in Bioinformatics.
Besides conducting its own lab research on potential repurposed drugs, etexx will help pharmaceutical and genomics organizations sort through existing data and generate hypotheses from high-throughput data processes such as microarrays or proteomics mass spectroscopy analysis. “Everyone is trying to find ways to develop drugs cheaply,” says Stephen Johnston, director of the UT Southwestern Center for Biomedical Inventions, which develops new drugs and procedures. “Garner has had very promising preliminary results.”
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0872bEnvironewsForumEnvironmental Justice: Young Hearts Suffer in Poorer Countries Potera Carol 11 2004 112 15 A872 A872 Publication 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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Cardiovascular disease (CVD) is a well-known killer of older people in affluent countries. In developing countries, however, the disease is striking a younger age group. In India, South Africa, Brazil, and the Russian republic of Tatarstan, people aged 35–65 die from CVD significantly more often than counterparts in the United States, according to a report released recently by Columbia University’s Earth Institute.
CVD is on the rise in developing nations for the same reasons that made it a killer in the west: a rise in cigarette smoking, a higher-fat diet, and lack of physical exercise. The CVD death rate in developing areas is reminiscent of that experienced in the United States in the 1950s and 1960s before effective public health measures, like warnings about the dangers of smoking and treatment for hypertension, became common. But such measures “have not yet occurred in developing countries, and treatment is often unavailable,” says epidemiologist Stephen Leeder of the University of Sydney in Australia, who coordinated the project while a visiting fellow at Columbia University.
Leeder’s team combined available death rate and workforce data from five representative middle- and low-income regions to estimate the economic impact of CVD on society. Their report, released in April 2004, reveals a silent epidemic affecting both women and men of working age.
In Tatarstan, CVD deaths among men aged 35–64 have soared 70% in just 20 years. Among women aged 15–34, four times more die from CVD than from pregnancy-related problems—a surprise, given that CVD is rarely considered a woman’s disease in developing nations. China’s CVD death rate currently mirrors that of the United States but is expected to be twice the U.S. rate by 2030, when half of the 9 million projected Chinese CVD deaths will be among people aged 35–64. In Brazilians aged 35–44, the male death rate from CVD is 30% higher and the female death rate 75% higher than for the same age group in the United States. In South Africa, CVD ranks third as cause of death in women and sixth for men.
The report’s title, A Race Against Time, refers to a 20-year window of opportunity to tackle the problem. “Younger people can be educated about lifestyle changes and treated with drugs,” says Leeder. If action is not taken now, the health costs in 20 years as these people reach end-stage disease “will be stupendous,” he warns.
As world health organizations struggle to finance treatments for infectious diseases such as malaria and AIDS, the report reminds us that “we need to pay attention to chronic diseases like heart disease,” says Daniel Fox, president of the Milbank Memorial Fund, which works with decision makers to bring the best available evidence to bear on health care and public health policy. The report is “analytically tight,” says Fox, and suggests that the economic and social impact of heart disease in the next generation may dwarf that of communicable diseases.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0896aEnvironewsScience SelectionsThe Big Picture Alderson Laura 11 2004 112 15 A896 A896 Publication 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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Mapping SARS in Hong Kong
Epidemiologists have long used maps to track the spread of disease, and in the past decade, geographic information system (GIS) technology has added powerful new tools that help reveal far more than simply the “where” and “when” of epidemics. Now P.C. Lai of the University of Hong Kong and colleagues show how GIS technology can be used during an acute infectious disease outbreak to reveal crucial real-time, quantitative information, such as the direction of superspreading events (in which one person infects more than the typical three or fewer others) and distinct disease hot spots [EHP
112:1550–1556]. Reaching beyond typical descriptive mapping, this study demonstrates the rich depth of GIS capabilities in analyzing patterns of disease spread from various perspectives.
The global outbreak of severe acute respiratory syndrome (SARS) in late 2002 and into 2003 ultimately accounted for more than 8,000 cases in 29 countries, according to the World Health Organization. About 20% of the cases were in Hong Kong. Lai and colleagues applied geostatistical methods to analyze the spread of SARS in Hong Kong during this time period.
The investigators analyzed an integrated database that contained clinical and personal details on the 1,755 Hong Kong patients confirmed to have had SARS. They plotted patient residence addresses using a GIS to research such aspects as the superspreading event responsible for more than 300 cases in the Amoy Gardens housing development and microclusters of SARS cases (where the density of infection varied widely between districts).
The geostatistical analysis was conducted at three levels: elementary (visual inspection of geographical phenomena), cluster analysis to identify hot spots, and contextual analysis to explain relationships between geographical phenomena. Among the methods the researchers applied were nearest neighbor analysis, which discerns nonrandom distribution of cases and is often used by scientists studying species distribution. For another analysis, they used the kernel mathematical method to create a series of statistical “surfaces” to reveal daily changes in disease hot spots.
Elementary analysis revealed the spread of the disease: a clear clustering of cases in certain districts of the Kowloon peninsula, where Amoy Gardens is located, and in Hong Kong’s New Territories region. Next, cluster analysis produced a series of 12 kernel maps based on date of symptom onset. These maps showed the density of SARS patients (adjusted for underlying population density) on typical days representing different stages of the 16-week outbreak; this demonstrated the development and dissipation of disease hot spots over the course of events. Another sophisticated analysis produced a map that summarized SARS hot spots by infection rate per 1,000 population, indicating that the urban population was at the highest risk.
With contextual analysis, the researchers developed origin-and-destination plots for three superspreading event clusters: Prince of Wales Hospital, Amoy Gardens, and Lower Ngau Tau Kok Housing Estate. The Prince of Wales Hospital cluster showed a northwest–southwest trend of disease spread that extended over most of Hong Kong (visitors to SARS patients at Prince of Wales Hospital spread the disease as they returned home, the authors observed). The Amoy Gardens cluster was comparatively more localized, while the Lower Ngau Tau Kok cluster was the most contained of the three.
The authors cautioned of limitations in applying GIS technology to infectious disease epidemiology and outbreak investigation, among them the occasional lack and unavailability of the necessary data. Still, the authors wrote, “integration of GIS technology into routine field epidemiologic surveillance can offer a scientifically rigorous and quantitative method for identification of unusual disease patterns in real time.” When linked with point-of-care databases and other sources of environmental data (including meteorological, transportation, and topographical information), such geospatial intelligence has the potential to rapidly recognize, locate, and monitor disease outbreaks.
Bird’s-eye view of SARS. Using GIS technology, researchers have mapped how SARS spread in Hong Kong to help predict patterns of future infectious disease epidemics.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0896bEnvironewsScience SelectionsA Hazard in Utero? Josephson Julian 11 2004 112 15 A896 A897 Publication 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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Bisphenol A More Potent than Expected
Environmental estrogens are a structurally diverse group of chemicals that partially mimic the effects of endogenous estrogens. Scientists believe the wide use of environmental estrogens such as bisphenol A (BPA), a component of epoxy resins and polycarbonate plastics, may help explain the rising incidence of birth defects and certain cancers. It is further believed that the developing embryo is more vulnerable to the effects of environmental estrogens than adult animals, but until now it has been difficult to determine these effects directly in embryos. In this issue, Josephine G. Lemmen of the Netherlands Institute for Developmental Biology and colleagues investigate the use of a new transgenic mouse model to study such effects [EHP
112:1544–1549].
During the late 1990s, it was first suggested that prenatal exposure to BPA might cause reproductive abnormalities; experimental data, however, led to contradictory findings. For instance, one 1999 study showed prostate enlargement in offspring of BPA-exposed mice, but other studies reported no effect. Although BPA concentrations in amniotic fluid at 15–18 weeks’ gestation have been shown to be five times those in the serum of pregnant and nonpregnant women—suggesting possible accumulation in the embryo—this could not be confirmed through animal experiments.
Lemmen and her colleagues recently developed a transgenic mouse in which estrogen-responsive elements are coupled with the reporter enzyme luciferase. Direct activation of estrogenic receptors (ERs) is detected photometrically by measuring luciferase activity, which allows quantitative and time-course analysis of target gene activation in vivo. The C57Bl/6J mouse strain used in this model had previously been shown to be especially sensitive to the effects of estrogen exposure.
This model was used to evaluate the ability of BPA to activate endogenous ERs in mouse embryos, as compared with the strong estrogens diethylstilbestrol (DES) and 17β-estradiol dipropionate (EP). Exposure of pregnant mice to varying dosages of all three estrogens activated the endogenous ERs in their embryos. Exposure to DES and EP showed a dose- and time-dependent induction of luciferase activity. For all DES exposures, peak activity was seen at 8 hours after exposure. For EP, peak activity was seen at 24 hours after exposure.
Like DES, BPA showed a transient induction of luciferase activity. Surprisingly, though, BPA was found to be more potent in vivo than would have been expected on the basis of its activity in vitro. In utero luciferase activation by BPA in transgenic embryos at 8 hours after exposure was significant at a dosage as low as 1 milligram BPA per kilogram body weight, compared with controls.
One possible explanation for a higher potency in utero may be that in vivo BPA is converted to metabolites with enhanced estrogenicity, as some previous studies have suggested. Yet another explanation could be that BPA has a lower affinity with steroid-binding proteins present in serum, which gives it a greater bioavailability than, say, EP. However, these explanations cannot account for the in vivo versus in vitro potency of BPA as compared with DES, because neither has a high affinity to binding proteins.
Although BPA’s intrinsic activity is lower than that of DES or EP, it still was more potent in vivo than would be estimated from in vitro assays. Effects on individual embryonic organs have not yet been evaluated and could possibly provide even more sensitive end points than whole embryos. Although the Lemmen study model showed that the effects of BPA did not persist like those of the other estrogens, its biological effects in exposed embryos should continue to be assessed, perhaps with other types of models.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0914aAnnouncementsBook ReviewHeal the Ocean: Solutions for Saving Our Seas Sandifer Paul A. Paul Sandifer is a member of the U.S. Commission on Ocean Policy, a former director of the South Carolina Department of Natural Resources, and currently senior scientist for the National Centers for Coastal Ocean Science within NOAA’s National Ocean Service. He is located at the Hollings Marine Laboratory in Charleston, South Carolina.11 2004 112 15 A914 A914 Publication 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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By Rod Fujita
Gabriola Island, BC, Canada:New Society Publishsers, 2003. 227 pp. ISBN: 0-86571-500-9, $16.95 paper
In only 198 pages of text, Rod Fujita’s Heal the Ocean: Solutions for Saving Our Seas covers major threats to the health of the coast, nearshore waters, coral reefs, the continental shelf, and the deep sea. Fujita writes convincingly and from personal experience about the sickening of our coastal waters and the planetary ocean, and he blames such impacts squarely on humans. Part diagnosis, part prescription, part lecture, and part pep talk, Heal the Ocean is never boring, although occasionally irritating. However, this is not a balanced view of the issues or a scientific review, but an advocacy piece, and a good one based on Fujita’s extensive experience as a senior scientist at Environmental Defense.
Fujita argues that there are major and growing threats to the health of our coastal and ocean waters and that society must take steps now to stave off potentially catastrophic impacts in the marine environment. Similar findings and concerns have been trumpeted loudly and repeatedly at high levels in the United States recently in the reports and numerous public statements of two independent bodies, the Pew Ocean Commission and the U.S. Commission on Ocean Policy. Never before have there been such broad-based and clear calls for action to address humans’ impacts on marine ecosystems and for the development of a strong ocean stewardship ethic in the American public.
Relying heavily on West Coast examples and issues where Environmental Defense played a lead role, Fujita briefly but thoughtfully describes several major environmental issues, including the “dolphin-safe tuna” and sound-in-the-sea controversies, among others. He also takes a useful look at new threats to deep ocean vent communities and related environments.
A recurring theme is that the most practical way to address many of the ocean’s ills is to create a nationwide network of marine reserves. Fujita discusses the very human processes and divergent opinions involved in developing several protected areas, including the Bonaire Marine Park, the Florida Keys Marine Sanctuary, the Florida Bay Restoration effort, and the Northwest Hawaiian Islands coral reef reserve. Although “marine reserves” or “marine protected areas” may connote different things to different people, Fujita argues persuasively, though not exclusively, for no-take reserves and that a system of carefully chosen and completely protected areas would provide substantial benefits for a broad range of marine species and habitat types. These assertions would have benefited from a more comprehensive review of the scientific literature.
Although many actors appear in Fujita’s drama, his principal antagonists in the battle for the future of the oceans appear to be the organized environmental community—which he sees as the appropriate group to decide what is sustainable and what is not—and fisheries, especially fishery managers. A glaring weakness is that the book provides no perspective from the fishery community, particularly managers, despite the major criticisms Fujita levels at them. In my opinion, the United States has many highly principled, scientifically grounded, and courageous fishery managers who are dedicated to managing sustainable - fisheries and recovery of overexploited populations. Unfortunately, it appears Fujita never met any of them.
Yet the strengths of Fujita’s book lie not in his recitation of an ever-expanding catalog of injuries or the casting of blame, but rather in his message of hope based on some practical suggestions and the optimistic view (which I share) that the American people can become educated to the types and extent of problems and then take necessary actions. Besides marine reserves, he suggests ideas about molding and mobilizing public opinion, managing mining in the deep sea to prevent some environmental impacts, managing fisheries more sustainably, and using renewable resources such as wind, tidal, and thermal energy to - produce electrical power, at least at small scales.
Those wishing to stimulate thinking about the very real problems facing our ocean environments and potential solutions will find Fujita’s - interesting and eminently readable book a very good place to start. But serious students of these topics will need much more information.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0914bAnnouncementsNew BooksNew Books 11 2004 112 15 A914 A914 Publication 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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A Scientist Audits the Earth
Stuart L. Pimm
Piscataway, NJ:Rutgers University Press, 2004. 304 pp. ISBN: 0-8135-3540-9, $19.95
Academia to Biotechnology: Career Changes at any Stage
Jeffrey Gimble
Burlington, MA:Elsevier, 2004. 186 pp. ISBN: 0-12-284151-4, $34.95
Acid Rain Science and Politics in Japan: A History of Knowledge and Action toward Sustainability
Ken Wilkening
Cambridge, MA:MIT Press, 2004. 328 pp. ISBN: 0-262-73166-5, $19
Cells, Tissues, and Disease: Principles of General Pathology, 2nd ed.
Guido Majno, Isabelle Joris
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/txg.7345ehp0112-00158915598610ToxicogenomicsArticlesPhenotypic Anchoring of Gene Expression Changes during Estrogen-Induced Uterine Growth Moggs Jonathan G. 1Tinwell Helen 1Spurway Tracey 1Chang Hur-Song 2*Pate Ian 1Lim Fei Ling 1Moore David J. 1Soames Anthony 1Stuckey Ruth 1Currie Richard 1Zhu Tong 2Kimber Ian 1Ashby John 1Orphanides George 11Syngenta Central Toxicology Laboratory, Alderley Park, Cheshire, United Kingdom2Syngenta Biotechnology Inc., Research Triangle Park, North Carolina, USAAddress correspondence to G. Orphanides, Syngenta CTL, Alderley Park, Cheshire, SK10 4TJ, UK. Telephone: 44-1625-510803. Fax: 44-1625-585715. E-mail:
[email protected]*Present address: Diversa Corporation, 4955 Directors Place, San Diego, CA 92121 USA.
Supplemental data is available online (http://ehp.niehs.nih.gov/txg/members/2004/7345/supplemental.pdf)
We thank M.G. Parker, D.G. Deavall, N. Wallis, and T. Barlow for critical comments on the manuscript; P. Lefevre and J. Odum for technical assistance; and I. Kupershmidt and E. Hunter (Silicon Genetics) for advice on statistical analysis of microarray data.
This work was partially supported by the UK Food Standards Agency.
The authors declare they have no competing financial interests.
11 2004 7 10 2004 112 16 1589 1606 22 6 2004 7 10 2004 Publication 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. A major challenge in the emerging field of toxicogenomics is to define the relationships between chemically induced changes in gene expression and alterations in conventional toxicologic parameters such as clinical chemistry and histopathology. We have explored these relationships in detail using the rodent uterotrophic assay as a model system. Gene expression levels, uterine weights, and histologic parameters were analyzed 1, 2, 4, 8, 24, 48, and 72 hr after exposure to the reference physiologic estrogen 17β-estradiol (E2). A multistep analysis method, involving unsupervised hierarchical clustering followed by supervised gene ontology–driven clustering, was used to define the transcriptional program associated with E2-induced uterine growth and to identify groups of genes that may drive specific histologic changes in the uterus. This revealed that uterine growth and maturation are preceded and accompanied by a complex, multistage molecular program. The program begins with the induction of genes involved in transcriptional regulation and signal transduction and is followed, sequentially, by the regulation of genes involved in protein biosynthesis, cell proliferation, and epithelial cell differentiation. Furthermore, we have identified genes with common molecular functions that may drive fluid uptake, coordinated cell division, and remodeling of luminal epithelial cells. These data define the mechanism by which an estrogen induces organ growth and tissue maturation, and demonstrate that comparison of temporal changes in gene expression and conventional toxicology end points can facilitate the phenotypic anchoring of toxicogenomic data.
estrogengene expressionmicroarrayphenotypic anchoringuterus
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Gene expression profiling, used within the existing framework of toxicologic assessment, promises to advance significantly the mechanistic understanding and prediction of adverse effects. To benefit fully from the opportunities offered by gene expression profiling, we must first understand the relationships between changes in gene expression and alterations in traditional toxicology parameters. The process by which gene expression changes are linked to changes in phenotype has been termed “phenotypic anchoring” (Cunningham et al. 2003; Paules 2003; Schmidt 2003). This approach has been used successfully to identify groups of genes whose expression correlates with specific pathologic changes during griseofulvin-induced chronic liver injury (Gant et al. 2003), renal toxicity (Amin et al. 2004), furan-mediated hepatotoxicity (Hamadeh et al. 2004), and aceta-minophen-induced hepatotoxicity (Heinloth et al. 2004). In the present study we used phenotypic anchoring, in conjunction with gene ontology analysis, to define the transcriptional program associated with the response of the rodent uterus to a reference estrogen and to identify groups of genes that may drive specific histologic changes.
The immature mouse uterus is a major estrogen-responsive organ and forms the basis for a reference assay of estrogenic activity of chemicals (Owens and Ashby 2002). The physiologic response of the uterus to exogenous estrogens has been documented in detail (Clark and Mani 1994). The immature mouse uterus is sensitive to elevations in endogenous levels of 17β -estradiol (E2) that occur during puberty. E2 releases the immature uterus from quiescence and promotes cell proliferation and differentiation. The initial effects of E2 are rapid (4–6 hr) and involve the uptake of fluid resulting from hyperemia and vasodilation of uterine capillaries, which causes the uterus to swell. This phenomenon is termed “water imbibition” and increases the availability of substrates and ions required for growth. Another early event is an increase in overall levels of mRNA and protein synthesis. The uterus then enters a proliferative phase that is responsible, at least in part, for the large increase in uterine weight that occurs 16–30 hr after E2 exposure. Later responses mimic the changes in uterine physiology that accompany the onset of puberty and include alterations in the surface of the luminal epithelia.
Although the events described above have been characterized at the physiologic level, little is known about how E2, acting through the estrogen receptors ER-αand ER-β, coordinates at the molecular level the myriad cellular processes involved, despite significant progress in elucidating the molecular mechanisms by which ERs regulate gene expression in vitro (Hall et al. 2001; McKenna and O’Malley 2002; Metivier et al. 2003; Moggs and Orphanides 2001; Moggs et al. 2003; Tremblay and Giguere 2002). Our data reveal the transcriptional program associated with E2-induced uterine growth. We show that E2 induces a tightly coordinated transcriptional program that regulates successive and interlinked cellular processes during the uterotrophic response. Moreover, by comparing changes in gene expression with alterations in uterine weight and histology, we have identified classes of genes that may drive specific histologic changes in the uterus, including fluid uptake, coordinated cell division, and remodeling of the luminal epithelial cell layer in preparation for embryo implantation. Our data also provide novel insights into how E2 initiates paracrine signaling events, recruits immune and inflammatory cells, increases mRNA and protein synthesis, and suppresses apoptosis.
These data describe, at an unprecedented level of detail, how E2 induces organ growth and maturation and provide a paradigm for understanding the mechanisms of action of other nuclear receptors. Furthermore, this study demonstrates that analysis of the temporal associations between a chemically induced transcriptional program and the accompanying histologic changes can provide valuable insight into the relationships between gene expression changes and phenotypic alterations.
Materials and Methods
Animals
Female Alpk:ApfCD-1 mice (19–20 days old), weighing no more than 14 g on arrival in the laboratory, were obtained from a barriered animal breeding unit (AstraZeneca, Macclesfield, Cheshire, UK). The animals were housed five per cage in solid-bottom cages and allowed to acclimatize for 24 hr. They were allowed RM1 diet (Rat and Mouse No. 1; Special Diet Services Ltd., Witham, Essex, UK) and water ad libitum for the duration of the study. All animal experimentation described in this article was conducted in accord with accepted standards (local and national regulations) of humane animal care. Group sizes of 10 animals were used for the first two of the three replicate studies. Five animals per group were used in the third replicate study.
Uterotrophic Assays
The mice were given a single subcutaneous injection of E2 (400 μg/kg) or arachis oil (AO; vehicle control) using a dosing volume of 5 mL/kg body weight. A single dose of E2 was used to avoid the complex transcriptional program that may result from the standard uterotrophic assay exposure regime (i.e., repeated administration on 3 consecutive days; Odum et al. 1997). The relatively high dose level of 400 μg/kg was chosen to ensure a sustained and significant increase in blotted uterine weight during the 72-hr sampling period (Supplemental Data, Figure 1). No overt toxicity was observed during the 72-hr exposure to E2 (400 μg/kg). All animals were terminated at the appropriate time using an overdose of halothane (Concord Pharmaceuticals Ltd., Essex, UK) followed by cervical dislocation. Vaginal opening was recorded, and the uterus was then removed, trimmed free of fat, gently blotted, and weighed. Blotted uterine weights were analyzed by covariance with terminal body weights (SAS Institute Inc. 1999). Half of each left uterine horn was fixed in 10% formol saline and processed to paraffin wax for histologic analysis (Odum et al. 1997). The mean thickness of the endometrial and epithelial cell layers, indicators of cellular hypertrophy, were calculated based on the assessment of 10 locations on hemotoxylin- and eosin-stained longitudinal uterine sections for each animal. All hypertrophy data were assessed for statistical significance by analysis of variance (ANOVA). The remainder of the uterus was snap frozen in liquid nitrogen and stored at −70°C for RNA extraction.
Mitotic Index
The total number of mitotic figures in each uterus section was counted, noting the tissue location, and the area of the section was measured using a KS400 image analysis system (Imaging Associates, Bicester, UK). The number of mitotic figures per square millimeter was calculated, and the frequency after administration of E2 was compared with the frequency seen after the administration of AO using an appropriate statistical procedure. The number of mitoses per square millimeter was considered by a fixed-effects ANOVA allowing for treatment, time, and the treatment by time interaction. Analyses were carried out using the MIXED procedure in SAS, version 8.2 (SAS Institute Inc. 1999). Contrasts within the treatment by time interaction provided estimates of differences in E2 and control response at each time point. These were compared statistically using a two-sided Student t-test based on the error mean square in the ANOVA.
Transcript Profiling and Data Analysis
Three independent biologic replicates of the entire time course study for E2-treated and time-matched AO-treated groups of animals were used to generate transcript profiling data and for subsequent statistical analysis. To minimize the effect of any interanimal variability, total RNA was isolated from the pooled uteri for each treatment group (n = 10 in the first two studies; reduced to n = 5 for the last study because of highly similar transcriptional responses being obtained in replicate studies 1 and 2) using RNeasy Midi kits (Qiagen Ltd., Crawley, West Sussex, UK). Biotin-labeled complementary RNAs were synthesized using the Enzo Bioarray HighYield RNA transcript labeling kit and hybridized to Affymetrix murine U74-Av2 GeneChips as described previously (Zhu et al. 2001) and in the Affymetrix GeneChip expression analysis technical manual (Affymetrix, Inc. 2002). Probe arrays were scanned and the intensities were averaged using Microarray Analysis Suite 5.0 (Affymetrix, High Wycombe, UK). The mean signal intensity of each array was globally scaled to a target signal value of 500. To select E2-responsive genes, each gene was subjected to a mixed-model ANOVA allowing for treatment, time, and the treatment by time interaction as fixed effects and replicate study as a random effect. The use of mixed ANOVA models for the analysis of differential gene expression in microarray experiments has been previously described (Churchill 2004; Cui and Churchill 2003). Analyses were carried out using the MIXED procedure in SAS, version 8.2 (SAS Institute Inc. 1999). Contrasts within the treatment by time interaction provided estimates of differences in E2 and control response at each time point. These were compared statistically using a two-sided Student t-test based on the error mean square in the ANOVA [Supplemental Data, Table 1 (http://ehp.niehs.nih.gov/txg/members/2004/7345/supplemental.pdf)]. Data for genes exhibiting significant changes in expression (p < 0.01, two-sided) at one or more time points were then exported into GeneSpring 6.0 (SiliconGenetics, Redwood City, CA, USA), and a data transformation (values < 0.01 set to 0.01) and per-chip normalization (to the 50th percentile) were applied. Genes that did not have a Present detection call (Affymetrix) in any of the 14 treatment groups were removed from further analysis. Ratios of changes in gene expression were then calculated by normalizing each E2-treated sample to its corresponding time-matched vehicle (AO)-treated control. GeneChip data sets for the three independent biologic replicates were interpreted in log of ratio analysis mode, with biologic replicates being selected as a noncontinuous parameter. A total of 3,538 E2-responsive genes exhibiting a minimum of 1.5-fold up- or down-regulation in at least one time point were then subjected to gene tree–based hierarchical clustering (Pearson correlation). To identify genes that function in specific biologic pathways, these 3,538 genes were further filtered using functional annotations derived from the NetAffx database‚ Analysis Center (Liu et al. 2003; http://www.affymetrix.com/analysis/index.affx), together with manual annotations from published literature, before hierarchical clustering using GeneSpring. Gene names used in this article (see Appendix) were derived by homology searching of nucleotide sequence databases (BLASTn; http://www.ncbi.nih.gov/BLAST/) using Affymetrix probe target sequences and the interrogation of NetAffx (Liu et al. 2003) database. All genes described in the figures and text showed statistically significant alterations in expression in all three replicate studies. MIAME (Minimum Information About a Microarray Experiment)-compliant microarray data for the three independent replicate studies are available as supplementary information and have been submitted to the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/).
Quantitative Real-Time Polymerase Chain Reaction
Uterine RNA was isolated and purified from all E2-treated and time-matched vehicle control groups (each consisting of pooled uteri) in all three replicate time course studies using the Qiagen RNeasy Midi kit (Qiagen). Before reverse transcription, RNA was treated with Dnase I (DNA-free kit; Ambion, Huntington, UK) to remove any contaminating genomic DNA. For each pool, 2 μg total RNA was reverse transcribed in a 25-μL reaction using SuperScript II (Invitrogen, Paisley, UK) and oligo-dT primer according to the manufacturer’s instructions. Polymerase chain reaction (PCR; 25 μL) containing 2 μL first-strand cDNA (1:10 dilution), 12.5 μL of SYBR Green PCR Master Mix (Applied Biosystems, Warrington, UK), and 0.3 μM each of forward and reverse primers were run for 40 amplification cycles in an ABI PRISM 7700 Sequence Detection System (Applied Biosystems). Cycling conditions were 50°C for 2 min, 9°C for 10 min, 95°C for 15 sec, and 60°C for 1 min. All reactions were run in triplicate. Real-time (RT) PCR primers for FOS (5′-CTGTGGCCTCCCTGGATTTG-3′and 5′-TGAGAAGGGGCAGGGTGAAG-3′), LTF (5′-CGGGGGCCTTCAGACCATC-3′and 5′-CTAAAGTGACAGCAGGG AGTG-3′), and the control gene RPB1 (5′ - GTTCTGGACCCCATTTTTGATAGGC-3′ and 5′-CAGGGGACTGGCAGGGTAACAA-3′) were designed using Primer Express software (version 1.5; Applied Biosystems) to generate amplicons within their corresponding Affymetrix probe set target sequences.
Results
Histologic Changes and Increases in Uterine Weight
Our aim was to identify the genes and molecular networks associated with the uterotrophic response and to define the relationships between gene expression changes and histologic alterations. To this end, we gave immature female mice a single subcutaneous injection of E 2 (400 μg/kg) or vehicle and used DNA microarrays to measure uterine gene expression profiles at seven different times (1, 2, 4, 8, 24, 48, and 72 hr) after exposure. To facilitate the phenotypic anchoring of expression changes, we also measured blotted uterine weights and determined the average heights of the luminal epithelium and stromal endometrium for each animal. Three independent replicate experiments were carried out to allow a rigorous statistical analysis of the gene expression data (see “Materials and Methods”). We chose to use a single dose of E2 to avoid the complex transcriptional program that may result from the standard uterotrophic assay exposure regime in which test compound is dosed by repeated administration on 3 consecutive days (Odum et al. 1997). This dose induced a sustained increase in blotted uterine weight that was similar in the three replicate experiments (Figure 1A). In each replicate experiment, a significant increase (p < 0.01) in uterine weight was observed 4 hr after exposure to E2 and reached maximal levels between 24 and 72 hr (Figure 1A).
Histologic analysis of uterine sections revealed the cellular changes associated with the increase in uterine weight between 1 and 72 hr (Figure 2A). Consistent with previous reports (Clark and Mani 1994), the weight increase that occurred within 4 hr of exposure (Figure 1A) was associated with thickening of the stromal endometrium (Figure 2B) resulting from the uptake of fluid. The larger increase in uterine weight that occurred between 8 and 24 hr was due to hypertrophy and cell proliferation (Kaye et al. 1971; Quarmby and Korach 1984), which caused an increase in thickness of the luminal epithelium between 8 and 24 hr (Figure 2C). We conclude that the single dose of E 2 used induced a conventional uterotrophic response. Furthermore, the expression profiles of two classical E2-responsive genes, lactotransferrin (LTF ; Liu and Teng 1992) and the proto-oncogene C-FOS (Weisz and Bresciani 1988), demonstrate that E2 elicited a robust transcriptional response that was similar in the three replicate experiments (Figure 1B).
Multistep Method for Analysis of Gene Expression Changes
Uterine RNA from the seven time points for each of the E2-treated and time-matched vehicle control groups was analyzed using Affymetrix MG-U74Av2 GeneChips. A total of 42 microarray data sets were collected for the three replicate experiments. We used a multistep method to analyze the microarray gene expression data (Figure 3A). First, data were filtered and subjected to statistical analyses to identify the 3,538 genes with altered expression in E2-treated mice (p < 0.01 and > 1.5-fold) during at least one time point (see “Materials and Methods”). Unsupervised hierarchical clustering was then used to group these genes into co-regulated clusters (Quackenbush 2002; Figure 3B), revealing a complex multistage transcriptional response to E2 in the uterus (gene clusters A–I in Figure 3B). To gain an overview of the predominant molecular functions and biologic pathways that were regulated at the transcriptional level during the uterotrophic response to E2, we interrogated the 3,538 E2-responsive genes using the GOStat gene ontology mining tool (http://gostat.wehi.edu.au) (Beissbarth and Speed 2004). This approach revealed that E2 targets predominantly genes involved in protein metabolism, cell cycle, cell proliferation, DNA replication, RNA metabolism, mRNA transcription, and blood vessel development [Supplemental Data, Table 2 (http://ehp.niehs.nih.gov/txg/members/2004/7345/supplemental.pdf)]. Next, we used a supervised clustering approach using customized gene ontology definitions (see “Materials and Methods”) to identify gene functions that were predominant in each co-regulated cluster in Figure 3B. This revealed that E2 regulates each class of gene during a narrow window of time and suggests that E2 induces uterine growth and maturation by regulating successively the activities of different biologic pathways (described below). Finally, we analyzed the temporal associations between the gene expression program and alterations in uterine weight and histology to anchor the gene expression changes to alterations in uterine phenotype. These associations are described below.
Phase 1: Rapid Induction of Transcriptional Regulators and Signaling Components by E2
The first 4 hr of the uterotrophic response is characterized by the influx into the uterus of fluid that provides the nutrients and ions required for growth (Clark and Mani 1994). This leads to decompaction of stromal cells (Figure 4A) and thickening of the stromal endometrial layer at 4 hr (Figure 2B). This first phase of the uterotrophic response is accompanied by the rapid and transient regulation of genes encoding components of intra- and inter-cellular signaling pathways (Figure 4B) and sequence-specific transcriptional regulators (Figure 4C). Most of these genes show maximal expression between 1 and 4 hr, suggesting that the transcriptional effects of E 2, mediated via ER- αand ER-β, are amplified rapidly through the induction or modulation of multiple transcriptional and nontranscriptional signaling pathways.
Signaling Genes
The signaling genes rapidly up-regulated by E2 function in a broad array of signal transduction pathways (Figure 4B). These genes include protein kinases (AKT, MEK1, PIM3), growth factors (VEGF, PLGF), GTPases (RHOC, RAB11A, DEXRAS1), cytokine signaling proteins (MCP1, SOCS1, SOCS3, WSB1, IL17R), and a Wnt signaling factor (WNT4). Several E 2 -induced genes may act to attenuate initial signaling events (e.g., the protein phosphatase MKP1 negatively modulates MAP kinase activity). Strikingly, many of the signaling genes induced within 4 hr of E2 exposure have roles in the regulation of vascular permeability in other tissues, suggesting that they may be involved directly in initiating the influx of fluid into the uterus at this time (Figure 4B). These genes include angiogenic/vascular cell growth factors (VEGF, PLGF, ADM, ANGPT2, TGFB2), vasoactive serine proteases (KLK2, KLK6, KLK9, KLK22), and vascular endothelial receptors (IL17R, BDKRB1, ENG, GNA13). Furthermore, the vascular growth factor receptors TIE1 and TIE2 are rapidly down-regulated in response to E2 (Figure 4B), which may serve to attenuate the uptake of fluid after 4 hr. Collectively, these genes shed light on the mechanism by which E2 promotes fluid uptake in the uterus and provide a clear link between gene expression changes and histologic changes occurring at this time.
Transcriptional Regulators
The sequence-specific transcription factors induced during the first 4 hr of the response can be divided into four main classes (Figure 4C). The first contains members of the Jun, Fos, and ATF subgroups of transcription factors (C-FOS, FOSB, C-JUN, JUNB, ATF3, ATF4, ATF5) that form AP-1 dimers implicated in the regulation of cell proliferation and survival (Shaulian and Karin 2001). The second class contains genes that control cell differentiation during the development of a number of tissues (SOX11, SOX18, HEY1, CART1, PRX2, SMAD7, ID1). The early induction of members of this class suggests that E2 deploys a diverse range of gene expression networks to control cell growth and differentiation in the uterus. The third class contains two genes that encode co-regulators for nuclear receptors (RIP140, NCOR2), suggesting that these may act to modulate ER-mediated responses to E2 in the uterus. The fourth class of genes encodes presumed transcriptional regulators of unknown function (e.g., GIF).
We conclude that the initial response to E2 serves to a) modulate the activities of intra- and intercellular signaling pathways that, among other functions, promote vascular permeability and fluid uptake and b) up-regulate the expression levels of transcription factors that promote growth and differentiation. These early gene expression changes facilitate the amplification of the originating hormonal signal and set into motion the series of events that result in uterine growth and differentiation.
Phase 2: Coordinated Induction of Genes Required for mRNA and Protein Synthesis
No increase in uterine weight or obvious changes in uterine histology occur between 4 and 8 hr (Figures 1 and 2). Nevertheless, our data reveal that this phase is associated with the induction of a large cluster of genes (Figure 5). Most are induced 2 hr after E2 administration, reach maximal expression at 4 or 8 hr, and return to control or subcontrol levels by 48 hr (Figure 5B). Most of these genes play roles in mRNA and protein synthesis, demonstrating that the bulk of transcriptional activity occurring at this time functions to increase the capacity of the uterus for new protein synthesis. This is consistent with earlier observations that exposure to E2 results in a rapid increase in the mRNA and protein content of the uterus (Clark and Mani 1994). Our data define the molecular basis for these prior observations and identify the genes targeted by ERs to induce these effects.
In a broad sense, protein synthesis includes the interlinked processes of transcription, mRNA processing, mRNA export into the cytoplasm, protein translation, and protein folding (Orphanides and Reinberg 2002, and references therein; Figure 5G). Our data reveal the coordinated induction of genes involved in each of these processes (Figure 5A–F). These genes include a) components of the RNAP II general transcription machinery (RPB8, RPB10, TAF10; Figure 5A); b) transcription termination and polyadenylation factors (NSAP1; Figure 5A); c) mRNA splicing factors (SFPQ, U2AF1, RNPS1; Figure 5A); d) mRNA export proteins (NXF1; Figure 5C); e) protein translation factors (EIF1A, EIF2A, EIF2B, EIF3; ribosomal proteins RPL11, RPL12, RPL20, RPL52, RPS18b, and tRNA synthetases VALRS, GLURS, PHERS; Figure 5D), and f ) protein folding factors (FKBP4, CCT3, CCT6a, CCT7, CCT8; Figure 5E). The down-regulation of several genes associated with transcriptional repression (HDA1, TGIF, MAD4, EZH1) and mRNA degradation (AUH; Figure 5B) may also contribute to the general elevation of mRNA synthesis. We also note a concurrent increase in the expression of components of the ubiquitin–proteasome proteolytic pathway (PAD1, SUG1; Figure 5F) and genes whose products are required for the nuclear import and export of proteins (IMPORTINα2, IMPORTINα3, RAE1, G3BP2; Figure 5C), indicating that E2 additionally elevates proteasome levels and nuclear-cytoplasmic protein transport activity at this time. We conclude that E2 is able to increase protein synthesis activity in the uterus by altering the expression of genes involved in all aspects of the protein biosynthesis pathway.
Therefore, during the first two phases of the transcriptional program, E2 induces the expression of a battery of sequence-specific transcriptional regulators (phase 1; Figure 4C) and then induces the expression of genes in the protein synthesis pathway (phase 2; Figure 5). It appears, therefore, that, during phase 1, E2 specifies the gene expression networks that will be active, and then ensures during phase 2 that these networks have sufficient mRNA and protein synthesis capacity to operate. In addition the increased expression of components of the RNA and protein synthesis machinery is likely to be a prerequisite for proliferation in the uterus because cells must increase their mass before division to provide sufficient cellular components required for survival of the daughter cells (Norbury and Nurse 1992). Consistent with this, we note that induction of protein synthesis components immediately precedes the up-regulation of genes required for proliferation (Figure 6; see below). An additional function of the increased uterine capacity for protein synthesis may be to facilitate the production of the abundant cytoarchitectural and secreted proteins induced at the end of the uterotrophic response (see below).
Phase 3: Coordinated Regulation of Genes Controlling Chromosome Replication and the Cell Cycle
The next phase in the uterotrophic response occurs between 8 and 24 hr and involves an approximate doubling in uterine weight (Figure 1A) and a large increase in the thickness of the luminal epithelium (Figures 2C, 6A). A quantitative histologic analysis of mitotic figures in the uterine cells (“Materials and Methods”) revealed a clear and statistically significant (p < 0.01) increase with E 2 at 24 hr, whereas no E2-dependent increase was observed at 8, 48, or 72 hr (Table 1, Figure 6A). These observations are consistent with previous studies showing that most cells in the immature rodent uterus are stimulated to leave their quiescent state and divide synchronously under the influence of E2 (Kaye et al. 1971; Quarmby and Korach 1984).
We found that genes required for the replication of chromosomal DNA (PCNA, FEN1, CDC6, MCM2, MCM3, MCM4, MCM5, ORC1, ORC6, RRM1, RRM2) and genes required for postreplicative phases of the cell division cycle (e.g., CCNB1, PLK1) are coordinately induced and reach maximal expression levels between 8 and 24 hr (Figure 6B), consistent with the timing of the histologic changes observed in Figure 6A. Genes required for maintaining genome integrity (CHK1, CKS1, GEMININ) and the epigenetic status of newly replicated DNA (CAF-1 p60, AHCY) are also up-regulated at 8 and/or 24 hr (Figure 6B). It is striking that after their induction during the proliferative phase (8–24 hr), the expression levels of most genes that regulate chromosome replication and cell division are reduced to levels well below those of control animals (Figure 6B). This suggests that mechanisms exist for the active repression of these genes to prevent further rounds of proliferation. Declining E2 levels in mice 48 hr after a single subcutaneous injection may also contribute to the cessation of proliferation. Together, these data provide a molecular explanation for the changes in uterine weight and histology that occur between 8 and 24 hr (Figures 1A, 2, and 6A) and support the assertion that the early increase in weight seen at 4 hr is due to fluid uptake. Furthermore, these gene expression changes demonstrate that cell proliferation is restricted to a narrow window of time between 8 and 24 hr by the coordinated regulation of chromosome replication and cell division genes.
Regulation of Cell Division
Our data also provide insight into the mechanisms by which E2 releases cells of the immature uterus from quiescence and promotes cell division. The E2-induced expression profile of E2F1, a key transcriptional regulator of DNA replication genes (Ohtani 1999), closely parallels the induction of the chromosome replication genes (Figure 6B), consistent with the proposal that E2F1 regulates the expression of components of the DNA replication fork in human breast cancer cell lines exposed to E2 (Lobenhofer et al. 2002). Our data indicate that release from quiescence also involves the E2-induced down-regulation of genes that maintain cells in a growth-arrested state (KIP1, CCNG2, CCNG1). The principle way in which mitogens induce proliferation of quiescent cells involves a reduction in levels of the Kip1 protein, which inhibits the activities of cyclin–cdk complexes and induces cell cycle arrest (Olashaw and Pledger 2002). We found that KIP1 was down-regulated within 1 hr of E2 exposure and remains repressed over a period of at least 24 hr, only reaching control levels when cell proliferation has ceased (Figure 6C). Furthermore, E2 may promote degradation of the Kip1 protein via the induction of CDC34 (Figure 6C), a gene that has been implicated in the ubiquitin-mediated degradation of Kip1 (Koepp et al. 1999). These data suggest that E2 promotes cell proliferation by coordinately reducing Kip1 mRNA and protein levels. It is not clear whether KIP1 is a direct or indirect target of the activated ERs. However, KIP1 gene expression is controlled by ras-mediated PI3K signaling pathways (Olashaw and Pledger 2002), components of which are up-regulated rapidly in response to E2 (e.g., DEXRAS1, RASSF1; Figure 4B).
Suppression of Apoptosis
E2 protects against apoptosis in a number of tissues, including brain, testes, and uterus (Thompson 1994). Although the anti-apoptotic activity of estrogen in the uterus is thought to play a crucial role in the maintenance of uterine homeostasis, the mechanistic basis for this action has not been defined. Our data reveal that E2 induces the expression of anti-apoptotic genes (BAG2, BAG3, DAD1) while simultaneously down-regulating the expression of pro-apoptotic genes (CASP2, NIX; Figure 6D). Thus, apoptosis appears to be suppressed through transcriptional mechanisms during E2-induced uterine growth. Consistent with these observations, E2 also induces the apoptotic regulators BCL2 and BAG1 in cultured breast cancer cells (Perillo et al. 2000; Soulez and Parker 2001). It will be important to determine whether estrogens elicit similar changes in the expression of apoptosis-regulating genes in other tissues.
Phase 4: Induction of Genes Involved in Uterine Cell Differentiation and Defense Responses
The period from 24 to 72 hr after E 2 exposure is associated with remodeling of the luminal epithelial cell layer, including the formation of secretory epithelial cells and a glycocalyx layer consisting of glycoproteins (Paria et al. 2003; Weitlauf 1994). These changes result in the formation of a highly differentiated epithelial layer that is primed for cell recognition and adhesion events necessary for embryo attachment and implantation.
Changes in Cytoarchitecture
The final phase of the uterotrophic response coincides with the induction of a battery of genes involved in the cytoarchitectural remodeling of proliferating uterine cells, thus providing a further link between phenotypic and gene expression changes (Figure 7A). These genes encode components of desmosomes (DSG2), gap junctions (CX26), tight junctions (CLDN4, CLDN7), the cornified envelope (SPRR1A, 2A, 2B, 2E, 2F, 2G, 2I, 2J), intermediate filaments (KRT19), and a variety of cell-surface and extracellular-matrix glycoproteins (SPP1, BGP1, BGP2, MUC1, TROP2, CLU). The latter class of genes is likely to contribute to the formation of the glycocalyx layer present on differentiated uterine epithelium (Weitlauf 1994). The concomitant E2-dependent induction of a number of enzymes required for carbohydrate metabolism (MAN2B1, GALNT3) may provide the increase in sugar metabolism necessary for the production of these glycoproteins. E2 also induces genes encoding ion channels that regulate the balance of Na+ absorption and Cl− secretion across the endometrial epithelium to maintain a luminal fluid microenvironment suitable for implantation (CFTR, CLCA3, MAT8; Figure 7A).
Defense Responses
A number of genes involved in host defense processes or detoxification are first regulated between 24 and 72 hr (Figure 7B). We speculate that the products of these genes may provide an environment that is protective of, and facilitates, embryo implantation and development. These include genes encoding lysosomal enzymes (LYZP, LYZM, CTSH CTSL, CTSS, LGMN), genes involved in detoxification and clearance of xenobiotics (GSTO1, GSTT2, UGT1A1), and genes involved in immune and inflammatory responses (CD14, MX1, PIGR). The up-regulation of genes encoding chemoattractant cytokines (Figure 7C) for infiltrating eosinophils (EOTAXIN) and monocytes (MCP1/3) is consistent with previous observations of immune cell infiltration into the uterus (Gouon-Evans and Pollard 2001, and references therein). Another E2-regulated defense response may be provided by the induction of LTF (Liu and Teng 1992), an iron-binding protein with bacteriostatic activity (Singh et al. 2002). Our data reveal the induction of two additional iron metabolism genes at this time (CP, LCN2; Figure 7E; Kaplan 2002), suggesting a role for iron homeostasis in the uterotrophic response to E2.
Several components of the complement system are also induced 48–72 hr after exposure to E 2. These include C1QA, C1QB, C1QC, C2, C3, C4, CFH, and CFI (Figure 7D). Although many complement components have been identified in female reproductive epithelium, only C3 has previously been established as an E2-responsive gene (Sundstrom et al. 1989). In addition to participating in immune and inflammatory responses and host resistance, there is increasing evidence that complement functions in tissue remodeling and organ regeneration (Mastellos and Lambris 2002). Intriguingly, complement also influences mammalian reproduction and particularly the integrity of maternofetal interfaces during pregnancy (Caucheteux et al. 2003; Mastellos and Lambris 2002). Therefore, it is possible that the complement system may play a noninflammatory role in the uterotrophic response.
Evidence for a Transcriptional Cascade in the Uterus
It is striking that many different induction profiles can be seen in the genes regulated by E2: some genes are induced within 1 hr of exposure, whereas others are not induced until 48 hr (Figure 3B). The induction of a large number of sequence-specific transcription factors during the first phase of the response suggests that a transcriptional cascade may operate in the uterus, with the products of genes induced at the beginning of the program regulating the transcription of those toward the end. The regulation of the SPRR genes provides evidence for the existence of such a cascade (Figure 8). The mouse SPRR genes are located in a tandem array at the same chromosomal locus, and their transcription is regulated by the AP-1 and Ets transcription factors (Patel et al. 2003; Figure 8A). Eight members of the SPRR gene family are induced between 4 and 72 hr, with maximal induction occurring between 24 and 48 hr (Figure 8B). Intriguingly, the mRNAs encoding Ets2 and components of AP-1 (c-Jun, JunB, c-Fos, FosB, and Atf3, Atf4, Atf5) are maximally induced during the first phase of the uterotrophic response, between 1 and 4 hr (Figure 8B). We speculate, therefore, that a transcriptional cascade operates, in which ER-αor ER-βinduces the expression of Ets2 and AP-1 components, which in turn regulate the transcription of the SPRR genes (Figure 8C). Alternatively, it is possible that ER-αor ER-βcooperates with Ets2 and AP-1 to regulate the expression of the SPRR genes. In this way, transcription of the SPRR genes would not begin until sufficient levels of Ets2 and AP-1 were present. Consistent with this model, feed-forward loops (in which a transcriptional regulator controls a second transcription factor that then functions in concert with the initial regulator on a common downstream target gene) are emerging as common mechanisms in eukaryotes for transcriptional networks (Lee et al. 2002). It is likely that analysis of the regulatory regions of other E2-responsive genes during the uterotrophic response will suggest the existence of additional transcriptional networks.
Discussion
Our data describe at an unprecedented level of detail the molecular events that initiate and drive uterine physiologic changes upon exposure to the sex steroid hormone E 2 in the immature mouse uterus. Gene expression profiling reveals that E2 induces a multistage and tightly coordinated transcriptional program that regulates successive and functionally interlinked cellular processes during the uterotrophic response (Figure 9). The temporal patterns of gene expression we have identified for E2 are consistent with, and extend, those reported recently for the uterotrophic response of immature, ovariectomized mice after exposure to 17α-ethynylestradiol (Fertuck et al. 2003), in which concordant temporal responses were seen for genes involved in several functional categories in Figure 9. These include RNA and protein metabolism, cell cycle regulation, immune responses, and complement components. Furthermore, many of the genes regulated by exogenous E2 in our study are also differentially regulated in response to endogenous hormones (Tan et al. 2003).
Comparison of gene expression changes with alterations in uterine weight and histologic alterations, and analysis of gene expression data according to gene function allowed us to implicate specific groups of genes in driving water imbibition in the stromal endothelium, synchronous cell proliferation, and cytoarchitectural changes associated with luminal epithelial cell differentiation. These data thus provide a detailed mechanistic view of the relationships between the uterotrophic response and the underlying transcriptional program. Furthermore, this work demonstrates that comparison of temporal changes in gene expression and conventional toxicology parameters (uterine weight and histologic changes) can provide an understanding of the relationships between gene expression patterns and phenotypic change.
E2 can regulate transcription through a combination of at least two distinct signaling pathways: a) via activation of the nuclear transcription factors ER-αand ER-β(Hall et al. 2001; McKenna and O’Malley 2002; Moggs and Orphanides 2001; Tremblay and Giguere 2002) and b) via extranuclear or “nongenomic” signaling events (Falkenstein et al. 2000; Hammes 2003; Moggs et al. 2003). The transcriptional responses to E2 that we have defined here are likely to involve a combination of direct gene regulation by nuclear ERs and indirect gene regulation via extranuclear signaling pathways. Although the uterus of the immature mouse expresses both ER subtypes (αand β) at comparable levels (Weihua et al. 2000), recent transcript profiling studies using ovariectomized ER-knockout mice revealed a predominant role for ER-αin the regulation of estrogen-responsive genes in the uterus (Hewitt et al. 2003; Watanabe et al. 2003) consistent with the observation that only a partial uterotrophic response occurs in ER-αknockout mice (Lubahn et al. 1993). Therefore, it is likely that most E2-responsive genes we have identified are regulated by ER-α. However, identification of the direct gene targets for each ER subtype will ultimately require the development of methods for measuring the occupancy of receptor subtypes at promoters in vivo. Nevertheless, our temporal analysis of E2-responsive genes provides novel insights into the transcriptional cascades that are initiated through E2-responsive transcription factors.
The molecular events described here for the reference natural estrogen E2 provide the basis for understanding how other estrogenic chemicals, including synthetic estrogens and phytoestrogens, induce their effects (Moggs et al. 2004). Increasing attention is being paid to the use of gene expression changes in the uterus for the identification of surrogate markers for short-term rodent estrogenicity assays (Naciff et al. 2002, 2003; Owens and Ashby 2002; Watanabe et al. 2002), and our data reveal a large number of novel candidate marker genes. The insights provided by these data, into how an ER ligand coordinates transcriptional regulatory networks that result in proliferation and differentiation in a complex organ, provide a paradigm for understanding the modes of action of other nuclear receptors.
Supplementary Material
Supplemental Figures and Tables Figure 1 Uterotrophic response to a single dose of E2. (A) Uterine blotted weight. Data for replicate studies A and B are mean ± SD from 10 immature female mice in each treatment group. Five animals per group were used in replicate study C. (B) Temporal expression profile of the estrogen-responsive genes complement component C3 and C-FOS. Quantitative RT-PCR analysis of FOS and LTF gene expression from three independent time-course studies (A–C) and comparison with microarray data. Each RT-PCR data point represents a fold value, obtained using the comparative Ct (threshold cycle) method, for E2-induced change in gene expression relative to time-matched vehicle controls. The fold induction value is relative to the endogenous control gene RPB1 and to treatment, that is, estrogen/untreated. Microarray data are ratios (E2:time-matched vehicle control) of normalized Affymetrix GeneChip signal intensities (see Figure 3A and “Materials and Methods”).
*p < 0.05;
**p < 0.01.
Figure 2 Histologic analysis of uterotrophic response to a single dose of E2. (A) Panels show longitudinal 0.3-μm–thick paraffin sections of uteri stained with hematoxylin and eosin; bar = 50 μm. Luminal space (L), luminal epithelium (LE), stromal endothelium (SE), and glandular epithelium (GE) are indicated. (B) Height of stromal endothelial cell layer. (C) Height of luminal epithelial cell layer. Data in B and C are mean ± SD from 10 immature female mice in each treatment group. Solid bars, E2; open bars, AO.
*p < 0.05;
**p < 0.01.
Figure 3 (A) Experimental strategy for phenotypic anchoring of E2-responsive genes during uterotrophic response. Three independent biologic replicate studies were performed in which we analyzed seven different time points for E2-treated animals and the equivalent time points for vehicle-treated animals. (B) Staged transcriptional response of the immature mouse uterus to E2. Gene tree generated by hierarchical clustering of 3,538 E2-responsive genes showing clusters (labeled A–I) of temporally co-regulated genes. The genes clustered in groups A–I are further annotated using gene ontology analyses in Figures 4–7. The color scale indicates the mean fold change of E2-induced gene expression relative to time-matched AO-treated control samples (based on the three independent studies shown in Figure 1A).
Figure 4 Phase 1: rapid induction of transcriptional regulators and signaling components by E2. (A) Water imbibition and increased vascular activity in stromal endothelium (SE) 2 and 4 hr after a single dose of E2. Longitudinal 0.3-μm–thick paraffin sections of uteri stained with hematoxylin and eosin are shown. Scale bar, 50 μm. (B) Coordinate expression of genes encoding signaling components. Genes marked with a red circle have functions associated with altered vascular permeability and may drive the water imbibition seen at this time. (C) Coordinate expression of genes encoding transcription factors. Detailed quantitative data for genes encoding AP-1 transcription factors are shown in Figure 8B. Gene trees were generated by supervised hierarchical clustering; genes with related functions were selected from clusters of temporally co-regulated E2-responsive genes (Figure 3B) using universal gene ontology descriptions. The color scale for fold change in expression is identical to that used in Figure 3B. Data derived from independent Affymetrix probe sets are shown for GIF and SOX18. See Appendix for gene nomenclature and Affymetrix probe sets.
Figure 5 Phase 2: coordinated induction of genes required for mRNA and protein synthesis. Coordinated expression of genes involved in (A) RNA synthesis, (B) transcriptional repression, (C) nuclear import/export, (D) protein translation, (E) protein folding, and (F) protein degradation. Gene trees were generated as described in Figure 4. Data derived from independent Affymetrix probe sets are shown for eIF1A, eRF1, and CCT3. See Appendix for gene nomenclature and Affymetrix probe sets. (G) Schematic overview of RNA and protein synthesis in eukaryotes, showing machinery involved in each step of the process. Reprinted from Orphanides and Reinberg (2002), with permission from Elsevier.
Figure 6 Phase 3: coordinated regulation of genes controlling chromosome replication and the cell cycle. (A) Thickening of luminal (LE) and glandular epithelium (GE) and increased number of mitotic cells (indicated by arrowheads) between 8 and 24 hr after a single dose of E2. Longitudinal 0.3-μm–thick paraffin sections of uteri were stained with hematoxylin and eosin. Scale bar, 50 μm. Coordinated expression of genes involved in (B) chromosome replication and cell division, (C) cell-cycle regulation, (D) and apoptosis. Gene trees were generated as described in Figure 4. Data derived from independent Affymetrix probe sets are shown for MCM3. See Appendix for gene nomenclature and Affymetrix probe sets.
Figure 7 Phase 4: induction of genes involved in uterine cell differentiation and defense responses. (A) Cytoarchitecture; (B) defense responses; (C) chemoattractant cytokines; (D) complement; and (E) iron homeostasis. Gene trees were generated as described in Figure 4. Data derived from independent Affymetrix probe sets are shown for SPRR2A, CD133, TROP2, BGP1, CLU, KRT19, and CFH. Detailed quantitative data for the SPRR gene family are shown in Figure 8B. See Appendix for gene nomenclature and Affymetrix probe sets.
Figure 8 Evidence for a transcriptional regulatory network during the uterotrophic response. (A) Organization of mouse SPRR genomic locus that is coordinately regulated by the transcription factors AP-1 and Ets. (B) E2-induced expression (mean + SD) of genes encoding AP-1 and Ets transcription factors temporally precedes the coordinate regulation of the tandem array of SPRR genes. (C) Feed-forward model for an ER-dependent transcriptional cascade in the uterus. Transcriptional regulators are represented by blue circles. Gene promoters are represented by white rectangles. See Appendix for gene nomenclature and Affymetrix probe sets.
Figure 9 Summary of the transcriptional program associated with E2-induced uterine growth showing the successive regulation of genes with distinct molecular functions.
Table 1 Quantitative histologic analysis of mitotic figures in uterine cells after exposure to E2 for 8, 24, 48, and 72 hr.a
Mitosis/mm2 (mean ± SD)
Time (hr) AO (5 mL) E2 (400 μg)
8 1.36 ± 1.81 0.51 ± 0.41
24 3.86 ± 5.05 25.15 ± 6.37**
48 3.81 ± 0.83 3.46 ± 3.26
72 3.88 ± 2.28 1.67 ± 1.77
Quantitative mitotic index data were derived from four animals per group.
a Data were assessed for statistical significance using ANOVA and a two-sided Student t-test (see “Materials and Methods”).
** p < 0.01.
Appendix. Gene nomenclature and Affymetrix probe sets for Figures 4–8.a
Gene symbol Affymetrix Probe Set Gene description
Figure 4B —Signaling components
IL17R 99992_at interleukin 17 receptor
RAP1 160822_at Rap1, GTPase-activating protein 1
DEXRAS1 99032_at RAS, dexamethasone-induced 1
MKP1 104598_at dual specificity phosphatase 1
WNT4 103238_at wingless-related MMTV integration site 4
IGFBP10 92777_at cysteine rich protein 61
PIP92 99109_at immediate early response 2
PIM3 96841_at similar to serine/threonine-protein kinase pim-3
ARHU 96747_at ras homolog gene family, member U
CISH3 162206_f_at cytokine inducible SH2-containing protein 3
NAB2 100962_at Ngfi-A binding protein 2
SOCS3 92232_at cytokine inducible SH2-containing protein 3
EPLG2 98407_at ligand for receptor tyrosine kinase ELK
IL17R 99991_at interleukin 17 receptor
CDKN1A 98067_at cyclin-dependent kinase inhibitor 1A (P21)
CDKN1A 94881_at cyclin-dependent kinase inhibitor 1A (P21)
WSB1 98946_at WD-40-repeat-containing protein with a SOCS box
VEGF 103520_at vascular endothelial growth factor A
GADD45 102292_at growth arrest and DNA-damage-inducible 45
SYT 99610_at synovial sarcoma translocation, chromosome 18
SOCS1 92832_at cytokine inducible SH2-containing protein 1
GADD45g 101979_at growth arrest and DNA-damage-inducible 45 gamma
GLY96 94384_at immediate early response 3
MAPKAP2 160353_i_at MAP kinase-activated protein kinase 2
KLK22 101289_f_at epidermal growth factor binding protein type 1
TROB 99532_at tob family
RGS3 160747_at regulator of G-protein signaling 3
GNA13 100514_at guanine nucleotide binding protein, alpha 13
RAB11A 96238_at RAB11a, member RAS oncogene family
PLGF 92909_at placental growth factor
BDKRB1 101748_at bradykinin B1 subtype receptor
CF3 97689_at coagulation factor III
PDK4 102049_at pyruvate dehydrogenase kinase, isoenzyme 4
HERPUD1 95057_at homocysteine-inducible, endoplasmic reticulum stress-inducible, ubiquitin-like domain member 1
MYD116 160463_at myeloid differentiation primary response gene 116
NORE1 102028_at Ras association (RalGDS/AF-6) domain family 5
NET1A 94223_at neuroepithelial cell transforming gene 1
GEM 92534_at GTP binding protein (gene overexpressed in skeletal muscle)
SNRK 97429_at SNF related kinase
ALASH 93500_at aminolevulinic acid synthase 1
NTTP1 161171_at dual specificity phosphatase 8
MAPKAP2 95721_at MAP kinase-activated protein kinase 2
MEK1 92585_at mitogen activated protein kinase kinase 1
RGSr 94378_at regulator of G-protein signaling 16
RASSF1 102379_at Ras association (RalGDS/AF-6) domain family 1
NGEF 93178_at neuronal guanine nucleotide exchange factor
C-KIT 99956_at kit oncogene
NOTCH1 97497_at Notch gene homolog 1
BTG3 96146_at B-cell translocation gene 3
PC4 160092_at interferon-related developmental regulator 1
SGK 97890_at serum/glucocorticoid regulated kinase
ADM 102798_at adrenomedullin
ANGPT2 92210_at angiopoietin 2
UBQLN1 95601_at ubiquilin 1
THBS1 160469_at thrombospondin
ROCK2 98504_at rho-associated coiled-coil forming kinase 2
SNK 92310_at serum-inducible kinase
MAP2K3 93315_at mitogen activated protein kinase kinase 3
ENG 100134_at endoglin
PTDSR 95486_at phosphatidylserine receptor
SWIP2 160296_at WD-40-repeat-containing protein with a SOCS box
AKT 100970_at thymoma viral proto-oncogene 1
RHOC 96056_at ras homolog gene family, member C
TGFB2 93300_at transforming growth factor, beta 2
EPCR 98018_at protein C receptor, endothelial
KLK6 100061_f_at kallikrein 6
GALN 100407_at galanin
NEDD4B 103907_at neural precursor cell expressed, developmentally down-regulated gene 4-like
KLK22 95775_f_at kallikrein 22
KLK9 94716_f_at kallikrein 9
MCP1 102736_at platelet-derived growth factor-inducible protein JE
TIE1 99936_at tyrosine kinase receptor 1
RAMP1 104680_at receptor (calcitonin) activity modifying protein 1
PGF 97769_at prostaglandin F receptor
PDGFαRA 95079_at platelet derived growth factor receptor, alpha polypeptide
OB-RGRP 93600_at leptin receptor
ERK1 101834_at mitogen activated protein kinase 3
GRB7 103095_at growth factor receptor bound protein 7
ADCY6 102321_at adenylate cyclase 6
TIE1 161184_f_at tyrosine kinase receptor 1
GNAI1 104412_at guanine nucleotide binding protein, alpha inhibiting 1
ADCY7 103392_at adenylate cyclase 7
TIE2 102720_at endothelial-specific receptor tyrosine kinase
GPCR26 100435_at endothelial differentiation, lysophosphatidic acid G-protein-coupled receptor, 2
Figure 4C—Transcription factors
GIF 99603_g_at TGFB inducible early growth response
GIF 99602_at TGFB inducible early growth response
ETS2 94246_at E26 avian leukemia oncogene 2, 3’ domain
ID1 100050_at inhibitor of DNA binding 1
SMAD7 92216_at MAD homolog 7
C-JUN 100130_at Jun oncogene
BRF2 160273_at zinc finger protein 36, C3H type-like 2
IRF8 98002_at interferon concensus sequence binding protein
AGP/EBP 92925_at CCAAT/enhancer binding protein (C/EBP), beta
C-FOS 160901_at c-fos oncogene
KROX24 98579_at zinc finger protein Krox-24
FOSB 103990_at FBJ osteosarcoma oncogene B
NR4A1 102371_at N10 nuclear hormonal binding receptor
SOX18 161025_f_at SRY-box containing gene 18
SOX18 104408_s_at SRY-box containing gene 18
KROX20 102661_at Early growth response 2
ESG 104623_at transducin-like enhancer of split 3, homolog of Drosophila E(spl)
FOG 97974_at zinc finger protein, multitype 1
NCOR2 95129_at nuclear receptor co-repressor 2
SOX11 101631_at SRY-box containing gene 11
C/EBP 94466_f_at CCAAT/enhancer binding protein alpha (C/EBP), related sequence 1
PRX2 103327_at paired related homeobox 2
ATF4 100599_at activating transcription factor 4
STAT5B 100422_i_at signal transducer and activation of transcription 5A
HEY1 95671_at hairy/enhancer-of-split related with YRPW motif 1
ATF5 103006_at activating transcription factor 5
C/EBP 98447_at CCAAT/enhancer binding protein
RIP140 103288_at nuclear receptor interacting protein 1
CRTR1 103761_at Tcfcp2-related transcriptional repressor 1
MEF2A 93852_at myocyte enhancer factor 2A
TIS11 92830_s_at zinc finger protein 36
STAT5B 100423_f_at signal transducer and activation of transcription 5A
ATF3 104155_f_at activating transcription factor 3
CART1 100005_at TNF receptor associated factor 4
JUNB 102362_i_at transcription factor junB
Figure 5A—RNA synthesis
SFPQ 99621_s_at splicing factor proline/glutamine rich (polypyrimidine tract binding protein associated)
U2AF1 97486_at U2 small nuclear ribonucleoprotein auxiliary factor (U2AF), 35 kDa
RBMXP1 160192_at RNA binding motif protein, X chromosome retrogene
DDX21 94361_at DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 21 (RNA helicase II/Gu)
DDX3 101542_f_at DEAD (aspartate-glutamate-alanine-aspartate) box polypeptide 3
NSAP1 94985_at NS1-associated protein 1
MKI67 bp 93342_at Mki67 (FHA domain) interacting nucleolar phosphoprotein
ELAVL1 94001_at ELAV (embryonic lethal, abnormal vision, Drosophila)-like 1 (Hu antigen R)
PSP1 103393_at paraspeckle protein 1
SRP20 101003_at splicing factor, arginine/serine-rich 3 (SRp20)
JKTBP 96084_at heterogeneous nuclear ribonucleoprotein D-like
RPA2 92225_f_at RNA polymerase 1–2 (128 kDa subunit)
RALY 98511_at hnRNP-associated with lethal yellow
SFRS10 95791_s_at splicing factor, arginine/serine-rich 10
FBL 160503_at fibrillarin
SNRPA1 101506_at small nuclear ribonucleoprotein polypeptide A’
TASR 98048_at neural-salient serine/arginine-rich
RPB10 93551_at RNA polymerase II subunit 10
AUF1 94303_at heterogeneous nuclear ribonucleoprotein D
HRMT1L2 96696_at heterogeneous nuclear ribonucleoproteins methyltransferase-like 2
CGI-110 95714_at pre-mRNA branch site protein p14
SMN 103620_s_at survival motor neuron
RPB8 97254_at RNA binding motif protein
RNPS1 93518_at ribonucleic acid binding protein S1
NCL 160521_at nucleolin
RPA1 93620_at RNA polymerase 1–4 (194 kDa subunit)
HNRPA2B1 93118_at heterogeneous nuclear ribonucleoprotein A2/B1
SNRPD1 100577_at small nuclear ribonucleoprotein D1
H/ALAsnRNP 97824_at nucleolar protein family A, member 2
TAF10 103910_at TAFII30
DDX24 99096_at DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 13 (RNA helicase A)
Figure 5B
MAD4 99024_at Max dimerization protein 4
EZH1 100486_at enhancer of zeste homolog 1 (Drosophila)
HDA1 104376_at histone deacetylase 5
AUH 96650_at AU RNA binding protein/enoyl-coenzyme A hydratase
TGIF 101502_at TG interacting factor
Figure 5C—Nuclear import/export
POM121 96174_at nuclear pore membrane protein 121
NXF1 101079_at nuclear RNA export factor 1 homolog (S. cerevisiae)
IMPORTINa3 96010_at karyopherin (importin) alpha 3
RAE1 160466_at RNA export 1 homolog (S. pombe)
IMPORTINa2 92790_at karyopherin (importin) alpha 2
G3BP2 94913_at Ras-GTPase-activating protein (GAP120) SH3-domain binding protein 2
Figure 5D—Protein translation
eIF3S7 99101_at eukaryotic translation initiation factor 3, subunit 7 (zeta, 66/67kDa)
eIF2B 160365_at eukaryotic translation initiation factor 2, subunit 2 (beta, 38kDa)
eIF3S4 96883_at eukaryotic translation initiation factor 3, subunit 4 (delta, 44kDa)
EBNA1-bp2 96297_at EBNA1 binding protein 2
GLNRS 96628_at glutamyl-prolyl-tRNA synthetase
NAT1 100535_at eukaryotic translation initiation factor 4, gamma 2
eIF3S9 93973_at eukaryotic translation initiation factor 3, subunit 9
RPS18b 95159_at ribosomal protein S18b
VALRS 97894_at valyl-tRNA synthetase 2
RPL12 160431_at mitochondrial ribosomal protein L12
eIF1A 93058_at eukaryotic translation initiation factor 1A
eRF1 160451_at translation releasing factor eRF1
eIF1A 103708_at eukaryotic translation initiation factor 1A
eIF6 94826_at integrin beta 4 binding protein
eRF1 98608_at translation releasing factor eRF1
RPL20 94875_at mitochondrial ribosomal protein L20
PHERS 94494_at phenylalanine-tRNA synthetase-like
ASNS 95133_at asparagine synthetase
eIF3S10 94250_at eukaryotic translation initiation factor 3
NOP56 95109_at nucleolar protein 5A
eIF2AS1 94253_at eukaryotic translation initiation factor 2A
RRS1 96778_at regulator for ribosome resistance homolog (S. cerevisiae)
eRF1 96755_at translation releasing factor eRF1
eRF1 96754_s_at translation releasing factor eRF1
SUI1 92855_at suppressor of initiator codon mutations, related sequence 1 (S. cerevisiae)
RPL11 98876_at mitochondrial ribosomal protein L11
RPL52 97443_at mitochondrial ribosomal protein L52
Figure 5E—Protein folding
CCT3 98153_at chaperonin subunit 3 (gamma)
FKBP4 92808_f_at FK506 binding protein 4 (59 kDa)
CCT7 160562_at chaperonin subunit 7 (eta)
PPID 97445_at peptidylprolyl isomerase D (cyclophilin D)
CCT10 92829_at heat shock 10 kDa protein 1 (chaperonin 10)
CCT8 160102_at chaperonin subunit 8 (theta)
CCT6A 162279_f_at chaperonin subunit 6a (zeta)
CCT3 161238_f_at chaperonin subunit 3 (gamma)
Figure 5F—Protein degradation
PAD1 97274_at 26S proteasome-associated pad1 homolog
PSMB5 101558_s_at proteasome (prosome, macropain) subunit, beta type 5
PSMD4 94302_at proteasome (prosome, macropain) 26S subunit, non-ATPase, 4
PSMB3 94025_at proteasome (prosome, macropain) subunit, beta type 3
SUG1 160534_at protease (prosome, macropain) 26S subunit, ATPase 5
PSMB6 101992_at proteasome (prosome, macropain) subunit, beta type 6
PSMB2 94219_at proteasome (prosome, macropain) subunit, beta type 2
Figure 6B—DNA replication and cell division
SAKB 98996_at serine/threonine kinase 18
RRM2 102001_at ribonucleotide reductase M2
CAF1 p60 100890_at chromatin assembly factor, p60 subunit
ORC6 95712_at origin recognition complex, subunit 6-like (S. cerevisiae)
PCNA 101065_at proliferating cell nuclear antigen
MCM2 93112_at mini chromosome maintenance deficient 2
CDC6 103821_at cell division cycle 6 homolog (S. cerevisiae)
MCM4 93041_at mini chromosome maintenance deficient 4 homolog
MCM3 160496_s_at mini chromosome maintenance deficient (S. cerevisiae)
MCM3 100062_at mini chromosome maintenance deficient (S. cerevisiae)
TOPB1 103071_at topoisomerase (DNA) II binding protein
CHK1 103064_at checkpoint kinase 1 homolog (S. pombe)
MCM5 100156_at mini chromosome maintenance deficient 5
CKS1 97468_at CDC28 protein kinase 1
ORC1 92458_at origin recognition complex, subunit 1-like (S. cerevisiae)
RRM1 100612_at ribonucleotide reductase M1
FEN1 97327_at flap structure specific endonuclease 1
GEMININ 160069_at geminin
E2F1 102963_at E2F transcription factor 1
PLK1 93099_f_at polo-like kinase homolog (Drosophila)
CCNB1 160159_at cyclin B1, related sequence 1
Figure 6C—Cell-cycle regulators
CCND1 94232_at cyclin D1
CDC34 94048_at cell division cycle 34 homolog
KIP2 95471_at cyclin-dependent kinase inhibitor 1C (P57)
CCNG2 98478_at cyclin G2
KIP1 161010_r_at cyclin-dependent kinase inhibitor (p27)
CCNI 94819_f_at cyclin I
Figure 6D—Apoptosis
CASP2 99049_at caspase 2
NIX 96255_at BCL2/adenovirus E1B 19 kDa-interacting protein 3-like
APR3 160271_at apoptosis related protein APR3
TNFSF12 93917_at tumor necrosis factor (ligand) superfamily, member 12
PDCD4 103029_at programmed cell death 4
MIAP2 102734_at baculoviral IAP repeat-containing 3
MTD 98031_at Bcl-2-related ovarian killer protein
SDNSF 97451_at neural stem cell derived neuronal survival protein
DAD1 96008_at defender against Apoptotic Death 1
AAC11 101035_at apoptosis inhibitor 5
BAG3 96167_at Bcl2-associated athanogene 3
BAG2 161129_r_at similar to BAG-family molecular chaperone regulator-2
Figure 7A—Cytoarchitecture
MDEG2 99910_at amiloride-sensitive cation channel 1, neuronal (degenerin)
MAT8 103059_at FXYD domain-containing ion transport regulator 3
CLCA3 162287_r_at chloride channel calcium activated 3
CD133 93389_at prominin
CD133 93390_g_at prominin
PIGF 104725_at ras-like protein
DSG2 104480_at desmoglein 2
MAN2B1 99562_at mannosidase 2, alpha B1
CLDN4 101410_at claudin 4
CLDN7 99561_f_at claudin 7
SPRR2E 100723_f_at small proline-rich protein 2E
SPRR2J 101755_f_at small proline-rich protein 2J
SPRR2A 101025_f_at small proline-rich protein 2A
TROP2 103648_at tumor-associated calcium signal transducer 2
SPRR2I 95794_f_at small proline-rich protein 2I
SPRR2C 101761_f_at small proline-rich protein 2C
SPRR2A 101024_i_at small proline-rich protein 2A
LRG 97420_at leucine-rich alpha-2-glycoprotein
TROP2 160651_at tumor-associated calcium signal transducer 2
SPRR2G 101754_f_at small proline-rich protein 2G
SPRR2F 94120_s_at small proline-rich protein 2F
BGP1 102805_at CEA-related cell adhesion molecule 1
BGP1 102804_at CEA-related cell adhesion molecule 1
BGP1 102806_g_at CEA-related cell adhesion molecule 1
BGP2 101908_s_at CEA-related cell adhesion molecule 2
CX26 98423_at connexin 26
MUC1 102918_at mucin 1, transmembrane
SPP1 97519_at secreted phosphoprotein 1
CLU 161294_f_at clusterin
CLU 95286_at clusterin
CFTR 94757_at cystic fibrosis transmembrane conductance regulator homolog
KRT19 92550_at keratin complex 1, acidic, gene 19
KRT19 102121_f_at keratin complex 1, acidic, gene 19
SPRR1A 160909_at small proline-rich protein 1A
GALNT3 162313_f_at UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 3
Figure 7B—Defense responses
PLGR 99926_at polyimmunoglobulin receptor
CTSL 101963_at cathepsin L
LAMP1 100136_at lysosomal membrane glycoprotein 2
CTSS 98543_at cathepsin S
GSTO1 97819_at glutathione S-transferase omega 1
GSTT2 104603_at glutathione S-transferase, theta 2
CTSH 94834_at cathepsin H
UGT1A1 99580_s_at UDP glycosyltransferase 1 family, polypeptide A6
CD14 98088_at CD14 antigen
LGALS3 95706_at lectin, galactose binding, soluble 3
PGLYRP 104099_at peptidoglycan recognition protein
LGMN 93261_at legumain
GARG16 100981_at interferon-induced protein with tetratricopeptide repeats
H2Q1 99378_f_at MHC beta-2-microglobulin
ISGFG3 103634_at interferon dependent positive acting transcription factor 3 gamma
H2D1 101886_f_at histocompatibility 2, D region locus 1
LYZP 101753_s_at P lysozyme structural
LYZM 100611_at lysozyme M
MLGP85 101389_at scavenger receptor class B, member 2
H2D1 97540_f_at histocompatibility 2, D region locus 1
CD68 103016_s_at CD68 antigen
LY6A 93078_at lymphocyte antigen 6 complex, locus A
MX1 98417_at myxovirus (influenza virus) resistance 1
Figure 7C—Chemoattractant cytokines
MCP3 94761_at monocyte chemoattractant protein 3
MCP1 102736_at platelet-derived growth factor-inducible protein JE
EOTAXIN 92742_at small inducible cytokine a11
Figure 7D—Complement
CFI 99927_at complement component factor i
C3 93497_at complement component 3
CFH-related 92291_f_at complement component factor-related
C2 103673_at complement component 2 (within H-2S)
CFH-related 101853_f_at complement component factor h
C1QA 98562_at complement component 1, q subcomponent, alpha polypeptide
C1QB 96020_at complement component 1, q subcomponent, beta polypeptide
C4 103033_at complement component 4 (within H-2S)
C1QC 92223_at complement component 1, q subcomponent, c polypeptide
CFH-related 94743_f_at complement component factor-related
Figure 7E—Iron homeostasis
CP 92851_at ceruloplasmin
LTF 101115_at lactotransferrin
LCN2 160564_at lipocalin 2/24p3 gene.
Figure 8B
ETS2 94246_at E26 avian leukemia oncogene 2, 3’ domain
ATF3 104155_f_at activating transcription factor 3
JUN 100130_at Jun oncogene
JUNB 102362_i_at transcription factor junB
FOS 160901_at c-fos oncogene
FOSB 103990_at FBJ osteosarcoma oncogene B
ATF5 103006_at activating transcription factor 5
ATF4 100599_at activating transcription factor 4
SPRR2I 95794_f_at small proline-rich protein 2I
SPRR2C 101761_f_at small proline-rich protein 2C
SPRR2G 101754_f_at small proline-rich protein 2G
SPRR2J 101755_f_at small proline-rich protein 2J
SPRR2A 101025_f_at small proline-rich protein 2A
SPRR2F 94120_s_at small proline-rich protein 2F
SPRR2E 100723_f_at small proline-rich protein 2E
SPRR1A 160909_at small proline-rich protein 1A
a Gene annotations were derived by interrogation of the NetAffx (Liu et al. 2003) database; http://www.affymetrix.com/analysis/index.affx and by homology searching of nucleotide sequence databases (BLASTn; http://www.ncbi.nih.gov/BLAST/) using Affymetrix probe target sequences.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/txg.7204ehp0112-00160715598611ToxicogenomicsArticlesPrediction of Toxicant-Specific Gene Expression Signatures after Chemotherapeutic Treatment of Breast Cell Lines Troester Melissa A. 1*Hoadley Katherine A. 2*Parker Joel S. 3Perou Charles M. 141Department of Pathology and Laboratory Medicine and2Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA3Constella Health Sciences, Durham, North Carolina, USA4Department of Genetics and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USAAddress correspondence to C.M. Perou, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Campus Box 7295, Chapel Hill, NC 27599-7295 USA. Telephone: (919) 843-5740. Fax: (919) 843-5718. E-mail: cperou@med. unc.edu*These authors contributed equally to the work.
Supplemental data is available online (http://ehp.niehs.nih.gov/txg/members/2004/7204/supplemental.pdf).
All microarray raw data tables are available at the UNC Microarray Database (https://genome.unc. edu/) and have been deposited in the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE1647 (submitted by C. Perou).
This work was supported by National Institute of Environmental Health Sciences (NIEHS) grant (5-U19-ES11391-03). M.A.T. was supported by NIEHS Individual National Research Service Award (NRSA) 5F32ES012374 and Institutional NRSA in Environmental Pathology 2T32ES07017. K.A.H. was supported by National Institutes of Health Institutional NRSA in Genetics T32GM07092.
The authors declare they have no competing financial interest.
11 2004 14 9 2004 112 16 1607 1613 26 4 2004 14 9 2004 Publication 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. Global gene expression profiling has demonstrated that the predominant cellular response to a range of toxicants is a general stress response. This stereotyped environmental stress response commonly includes repression of protein synthesis and cell-cycle–regulated genes and induction of DNA damage and oxidative stress–responsive genes. Our laboratory recently characterized the general stress response of breast cell lines derived from basal-like and luminal epithelium after treatment with doxorubicin (DOX) or 5-fluorouracil (5FU) and showed that each cell type has a distinct response. However, we expected that some of the expression changes induced by DOX and 5FU would be unique to each compound and might reflect the underlying mechanisms of action of these agents. Therefore, we employed supervised analyses (significance analysis of microarrays) to identify genes that showed differential expression between DOX-treated and 5FU-treated cell lines. We then used cross-validation analyses and identified genes that afforded high predictive accuracy in classifying samples into the two treatment classes. To test whether these gene lists had good predictive accuracy in an independent data set, we treated our panel of cell lines with etoposide, a compound mechanistically similar to DOX. We demonstrated that using expression patterns of 100 genes we were able to obtain 100% predictive accuracy in classifying the etoposide samples as being more similar in expression to DOX-treated than to 5FU-treated samples. These analyses also showed that toxicant-specific gene expression patterns, similar to general stress responses, vary according to cell type.
breast cancerclass predictiondoxorubicinetoposide5-fluorouracilgene expressionmicroarrays
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A stereotyped environmental stress response to a wide range of stressors and toxicants was first demonstrated in yeast (Gasch et al. 2000) and has subsequently been observed in a variety of mammalian cell models (Heinloth et al. 2003; Morgan et al. 2002; Murray et al. 2004; Park et al. 2002; Sesto et al. 2002; Weigel et al. 2002). We have previously used DNA microarray experiments to characterize the transcriptional responses of four breast cell lines to the chemotherapeutics doxorubicin (DOX) and 5-fluorouracil (5FU); these cell lines included two human telomerase reverse transcriptase (hTERT–immortalized human mammary epithelial (HME) cell lines and two tumor-derived cell lines of luminal epithelial origin (MCF-7 and ZR-75-1). A general stress response was shown to predominate when these cells were treated with DOX and 5FU (Troester et al. 2004). All four cell lines repressed genes involved in cell growth and induced DNA-damage response and xenobiotic metabolism genes, but there were differences in the general stress responses depending upon the cell type of origin of the cell line.
The mechanisms of action of DOX and 5FU are distinct. DOX is a topoisomerase IIA (TOP2A) poison. TOP2A is a nuclear enzyme that transiently breaks and rejoins the phosphodiester backbone of both strands of the double helix. As such, it is vital for DNA replication, chromosome segregation, and maintenance of chromosome structure. In previous studies (Tewey et al. 1984), DOX formed a stable ternary complex with DNA and TOP2A, thereby inhibiting the normal function of the enzyme. The complexed enzyme is unable to religate DNA so complex formation increases DNA strand breaks. TOP2A is highly expressed during S-phase, but TOP2A poisoning causes cell-cycle arrest in G2-M. The commonly used chemotherapeutic 5FU has several known mechanisms of action that distinguish it from DOX. 5FU covalently binds to thymidylate synthase, preventing de novo production of thymidine. It also incorporates into DNA and RNA (Longley et al. 2003; Pizzorno et al. 2000). The importance of each of these 5FU-mediated disruptions in cellular metabolism varies across cell lines and patients, but current studies emphasize the role of thymidylate synthase inhibition (Banerjee et al. 2002; Longley et al. 2003; Peters et al. 2002). Thymidylate synthase is highly expressed during S-phase, and its inhibition is thought to cause cell-cycle arrest in S-phase.
Using microarrays, it is often possible to identify unique patterns associated with specific toxicants in addition to common patterns of response. We used our panel of treated breast cell lines (Troester et al. 2004) to identify toxicant-specific expression signatures for DOX and 5FU. Cell lines derived from breast basal-like and luminal epithelium exhibited distinct toxicant-specific patterns of response. Using two statistical methods for class prediction, we then identified sets of genes that distinguished DOX- and 5FU-treated cells and used these lists to predict the mechanism of etoposide (ETOP), a drug that is mechanistically similar to DOX.
Materials and Methods
Cells and Cell Culture Conditions
ME16C and HME-CC cells, two basal-like hTERT-immortalized HME cell lines described by Torester et al. (2004), were gifts from J.W. Shay at the University of Texas Southwestern Medical Center at Dallas (Dallas, TX) and C. Counter at Duke University Medical Center (Durham, NC), respectively. ME16C cells and HME-CC cells were maintained in mammary epithelial growth media (Cambrex Bio Science Walkersville Inc., Walkersville, MD). MCF-7 cells (a gift from F. Tamanoi, University of California at Los Angeles) and ZR-75-1 cells (American Type Culture Collection, Manassas, VA) were maintained in RPMI 1640 with l-glutamine (GIBCO, Carlsbad, CA) supplemented with 10% fetal bovine serum (Sigma Chemical Co., St. Louis, MO) and 50 U/mL penicillin and 50 U/mL streptomycin (GIBCO). All cell lines were tested for mycoplasma by the University of North Carolina at Chapel Hill Tissue Culture Facility before experiments were conducted and at regular intervals thereafter. Cells were maintained at 37°C and 5% carbon dioxide.
Cytotoxicity Assay
A mitochondrial dye conversion assay (Cell Titer 96; Promega Corp., Madison, WI) was used to measure cell viability after treatment. This assay was conducted according to manufacturer’s instructions, with modification as follows. Briefly, 5,000 cells were seeded per well of a 96-well plate. Cells were allowed to adhere overnight, and then media were replaced with fresh media containing a range of drug doses (DOX, 0–1 μM; ETOP, 0–500 μM; 5FU, 0–10 mM). After 36 hr of drug treatment, 15 μL of tetrazolium dye solution were added, and cells were incubated for 1 hr at 37°C before adding stop solution. Dye conversion products were solubilized in a humidified chamber overnight, and absorbance was measured at 570 nm (minus background absorbance at 650 nm). The 50% inhibitory concentration (IC50) for 36 hr of treatment with each drug in each cell line was estimated using nonlinear regression (SAS Statistical Software, version 8; SAS Institute Inc., Cary, NC) as described previously (Troester et al. 2004).
Collection of mRNA for Microarray Experiments
Cell lines were grown in 150-mm dishes to 70–80% confluence and then treated for 12, 24, or 36 hr with toxicant at the IC50 concentration. The cells were harvested by scraping, and cell lysates were enriched for mRNA using a Micro-FastTrack kit (Invitrogen Corp., Carlsbad, CA). The reference RNA was generated by harvesting mRNA from each cell line at 80% confluence and pooling four such harvests (i.e., four MCF-7 harvests were pooled and served as reference mRNA for all MCF-7 experiments).
Microarray Experiments
To synthesize labeled cDNA, reverse transcription reactions were carried out using 3 μg of mRNA as described previously (Perou et al. 2000; Troester et al. 2004). Briefly, 5FU, DOX, ETOP, and vehicle controls were labeled with Cy5–dUTP, and the pooled cell line control was labeled with Cy3–dUTP. The Cy3- and Cy5-labeled samples were combined and hybridized overnight at 65°C to a custom oligonucleotide microarray created in the University of North Carolina at Chapel Hill Genomics Core Facility. Arrays were spotted with Compugen (Jamesburg, NJ) human oligos representing approximately 22,000 genes. Two replicate arrays for each sample were selected for subsequent analysis. All micro-array raw data tables are available at the UNC Microarray Database (https://genome.unc.edu/) and have been deposited in the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE1647 (submitted by C. Perou).
Significance Analysis of Microarrays
Genes that were significantly up- or down-regulated were identified using Significance Analysis of Microarrays (SAM; Tusher et al. 2001). For the SAM analysis, data were excluded for genes that did not have mean intensity greater than twice the median background for both the red and green channel in at least 70% of the experiments. The log2 of the median red intensity over median green intensity was calculated for each gene. Missing data were imputed using the SAM Add-In for Excel (Microsoft Corp., Redmond, WA) plug-in with 100 permutations and k-nearest neighbors (KNN) with k = 10. For each cell line, 12-, 24-, and 36-hr DOX–treated arrays were coded as one class and were compared the 12-, 24-, and 36-hr 5FU–treated arrays using a two-class, unpaired SAM. Delta values were adjusted to obtain the largest gene list with a false discovery rate < 5%. The effects of adding media would be present in the signatures of both compounds and would not be identified as significantly associated with either toxicant. However, because DOX was solubilized in water and ETOP and 5FU were solubilized in dimethyl sulfoxide (DMSO), we also collected mRNA from each cell line treated with DMSO only for 12, 24, or 36 hr (data not shown). We compared these DMSO-treated samples with sham (media only) samples for these same time points using SAM. The lowest false discovery rate obtained was 15.3% (15 genes with 2.29 false significant), which shows that the toxicant-specific changes we detected are highly unlikely to reflect changes induced by vehicle.
Class Prediction
The number of genes needed to distinguish DOX and 5FU samples were identified using 10-fold cross-validation (CV) analysis using Prediction Analysis of Microarrays (PAM) and a KNN classifier. The KNN metric uses the Euclidian distance to determine the similarity of a sample to its k nearest sample neighbors. To select genes for the KNN method, we used a gene selection method that was first described by Dudoit and Fridlyand (2002); the KNN genes were identified in the training set according to the ratio of between-group to within-group sums of squares (Dudoit and Fridlyand 2002). The n top-ranked genes were used for each round of CV. The size of the gene subset was increased for subsequent rounds of CV. The set of n top-ranked genes that gave the highest average prediction accuracy during CV was also determined and reported. Gene selection using PAM was completed as described previously (Tibshirani et al. 2002). Genes were selected that yielded the greatest predictive accuracy in classifying DOX versus 5FU using a 10-fold CV analysis.
For class prediction, we performed a 10-fold CV analysis to iteratively optimize the list of genes and to determine prediction accuracies. Each round of CV would begin by splitting the samples into a training set (90% of the samples) and a test set (10% left-out samples), with gene selection and training being performed on the 90% and then used to predict the status of the withheld 10%. This was repeated 10 times, each time using a different 10% subset and a different gene set. Our reported prediction accuracies are the average of these iterative cycles of prediction for the optimized model. The results of all CV analyses using PAM and KNN are presented in Supplemental Data, Tables 1–4 (http://ehp.niehs.nih.gov/txg/members/2004/7204/supplemental.pdf). To independently assess the validity of these gene lists, we used them to predict class for ETOP samples; this analysis is independent because the ETOP samples were not used to train the predictor. The prediction accuracy for the ETOP samples are shown in Tables 2 and 3. For the four-class model, two samples were misclassified with PAM (HME-CC 12 hr and ME16C 12 hr) and two samples were misclassified with KNN (MCF-7 12 hr and ZR-75-1 36 hr) yielding the reported prediction accuracies of 75%.
Clustering of Toxicant-Specific Responses
Once gene lists were identified for the toxicant-specific responses of each cell line, hierarchical clustering analysis was conducted using the program Cluster (version 2.0; http://rana.lbl.gov/EisenSoftware.htm) to perform uncentered, average-linkage clustering; the data were visualized using Treeview (http://rana.lbl.gov/EisenSoftware.htm; Eisen and Brown 1999; Eisen et al. 1998). The gene lists generated with SAM for the luminal lines (MCF-7 and ZR-75-1) were combined into a nonredundant list, and data for these genes were compiled for all MCF-7 and ZR-75-1 samples. Similarly, the gene lists for the two basal-like lines (ME16C and HME-CC) were combined into a non-redundant list, and data for these genes were compiled for all HME-hTERT samples. For clustering and displaying results, data were excluded for genes that did not have mean intensity greater than twice the median background for both the red and green channels in at least 80% (Figures 1 and 2) or 70% (Figure 3) of the experiments. Supplemental Data, Figures 1–3 are (http://ehp.niehs.nih.gov/txg/members/2004/7204/supplemental.pdf) the complete cluster diagrams that correspond to Figures 1–3.
Results
Toxicant-Specific Transcriptional Responses
To investigate the toxicant-specific responses of four breast cell lines treated with chemotherapeutics, we collected mRNA from MCF-7, ZR-75-1, ME16C, and HME-CC cell lines after treating with DOX and 5FU at doses that produced similar levels of toxicity (IC50) across all four lines.
The IC50 was estimated from mitochondrial dye conversion assay results after 36 hr treatments with 5FU and DOX. The IC50 values and their 95% confidence intervals are shown in Table 1. For DOX and 5FU, the doses selected are consistent with physiologic doses expected in patients receiving treatment with DOX (Gewirtz 1999) or 5FU (Peters et al. 1993; Terret et al. 2000). This experimental design was aimed at defining the steady-state transcriptional response of these cell lines to toxicants and on defining chemotherapeutic-specific responses that were consistent over time. By combining 12-, 24-, and 36-hr–treated experiments into a single class for all supervised analyses, we identified genes that had a consistent pattern of expression across all three time points. These genes are the most likely to be consistent with in vivo experiments or patient samples, where it is difficult to assess how long a tissue sample has been exposed to a toxic agent. Although we did not specifically search for temporal variation in our SAM analyses, some temporal variation in gene expression can be observed in the clusters.
Toxicant-Specific Responses in Luminal Cell Lines
A large list of genes was identified for MCF-7 (974 genes with 44.7 false significant) and for ZR-75-1 (883 genes with 41.6 false significant) when supervised analyses were conducted to compare DOX-versus 5FU-treated samples. Hierarchical clustering analysis of the MCF-7 and ZR-75-1 experiments using the combined and nonredundant gene lists showed distinct responses for each toxicant (Figure 1 and Supplemental Data, Figure 1). The primary dendrogram branches for DOX-treated and 5FU-treated experiments were subdivided into MCF-7 and ZR-75-1 branches (Figure 1B); this suggests that most variation in these genes is attributable to the toxicant, but that cell lines also contribute to the variation. A total of 191 genes (77 down-regulated and 114 up-regulated) appeared on the SAM lists for both MCF-7 and ZR-75-1. However, there are many more genes that show qualitative similarity in the toxicant-specific responses of MCF-7 and ZR-75-1 cells (Figure 1 and Supplemental Data, Figure 1) than are captured using the strict SAM analysis. Figure 1D shows a cluster of genes that is up-regulated in MCF-7 cells after DOX treatment but is down-regulated in ZR-75-1 cells after both treatments; thymidylate synthase (TYMS) is included in this cluster. Recent studies have shown that thymidylate synthase, the target of 5FU, binds p53 mRNA and regulates the expression of p53 at the translational level (Chu et al. 1999; Ju et al. 1999). This is relevant because p53 expression is slightly induced by DOX in MCF-7 cells but not in ZR-75-1 cells or by 5FU treatment in either cell line (Figure 1E).
The gene set in Figure 1E also shows that several other genes had slightly higher expression in MCF-7 cells treated with DOX, and that these genes were typically repressed in ZR-75-1 cells. For example, the mismatch repair gene mutL homolog 1 (MLH1) was unchanged by DOX, and N-methylpurine-DNA glycosylase (MPG), a base excision repair gene, was repressed by 5FU. Both DOX and 5FU can cause DNA damage, but differences in the profiles of damage induced by each compound may account for differently regulated repair enzymes. Cyclin E1 (CCNE1) was also slightly induced in DOX-treated MCF-7 cells, as has been shown in previous studies (Arooz et al. 2000). CCNE1 and v-myb myeloblastosis viral oncogene homolog avian-like 2 (MYBL2) are important genes involved in the G1–S transition and are transcriptional targets of E2F (Yasui et al. 2003).
Figure 1F shows that ZR-75-1 cells have a unique response to DOX compared with MCF-7 cells and 5FU-treated cells. In concordance with increased E-cadherin (CDH1) expression shown in this cluster, an increase in (CDH1) mRNA (and CDH1–mediated cell–cell adhesion) has been shown previously in another breast cancer cell line after treatment with DOX (Yang et al. 1999). Cyclin G2 (CCNG2) was also induced in ZR-75-1 cells treated with DOX. This cyclin is inducible by DNA damage in a p53-independent manner (Bates et al. 1996).
Figure 1C and G shows clusters of genes that are induced by 5FU in both cell lines and either unchanged or only modestly changed in DOX-treated lines. For example, inhibitor of DNA binding 3 (ID3) (Figure 1C) and ID1 (Figure 1G) were strongly induced only in the 5FU-treated samples. The Id proteins control cellular differentiation and cell-cycle progression by preventing transcription factors from binding DNA (Norton et al. 1998). These proteins target basic helix–loop–helix proteins that regulate cell-type–specific and cell-cycle–regulatory gene expression (Lassar et al. 1994); however, the role of these proteins in the response to 5FU is not known.
Toxicant-Specific Responses in Basal-Like Cell Lines
A smaller list of toxicant-specific genes was identified for ME16C (76 genes with 3.7 false significant) and HME-CC (193 genes with 8.6 false significant) cells when SAM was used to compare DOX-treated with 5FU-treated samples. Hierarchical clustering using the combined and nonredundant gene lists for these two cell lines showed that there were distinct responses by toxicant (Figure 2 and Supplemental Data, Figure 2). However, the primary dendrogram branch for 5FU-treated basal-like cell lines also included two early time points for DOX-treated ME16C (Figure 2B). The 12-hr ME16C time point has many gene expression changes in response to treatment (Troester et al. 2004), but this time point does not exhibit the same toxicant-specific signature as do the 24- and 36-hr time points. These temporal differences likely account for the grouping of toxicant-specific signatures in Figure 2. As we have also seen in our previous study of the general stress response of these cell lines, the temporal response to these two toxicants varies by cell line.
Figure 2C shows a cluster of genes that is up-regulated in DOX-treated basal-like cell lines but down-regulated in 5FU-treated basal-like cells. These genes differ in both magnitude and direction of change. A number of these genes play a role in mediating DNA repair, including ubiquitin-conjugating enzyme E2A (UBE2A), which is a member of the RAD6 pathway that uses ubiquitin conjugation to control DNA damage–induced mutagenesis (Stelter and Ulrich 2003). Similarly, DNA polymerase delta is known to repair single-strand DNA interruptions produced during the process of base excision repair (Ho and Satoh 2003). Cell division cycle 25B (CDC25B), an important regulator of mitosis, is also found in this cluster.
The cluster in Figure 2D contains several mitochondrial genes (indicated in red). The altered expression of mitochondrial genes might be expected based on a recent study that demonstrated that anthracyclines, such as DOX, impair cellular respiration (Souid et al. 2003). Figure 2E consists of a set of genes that is clearly enriched for ribosomal proteins. Disruption of protein biosynthesis has been associated with alterations in the cell cycle and cell growth (Ruggero and Pandolfi 2003). Five ribosomal proteins are highlighted in red, and AL110170 is a hypothetical protein with 65% homology to ribosomal protein L22. The genes for these proteins are induced in the DOX-treated HME-CC cell line after 36 hr but are repressed in the ME16C cells at this and all other time points assayed.
Class Prediction and Sample Classification for ETOP-Treated Samples
Having identified a number of genes that distinguish DOX- from 5FU-treated breast cell lines using SAM, we next performed class prediction analyses to assess whether these differences could be used to classify an independent data set collected using the same four cell lines. Because SAM does not perform sample classification, we used 10-fold CV with PAM (Tibshirani et al. 2002) and a KNN metric based upon the work of Dudoit and Fridlyand (2002). CV was implemented to optimize the number of neighbors (k) and the number of genes for KNN, and to optimize the shrinkage parameter (Δ) for PAM. Parameters were selected that generated the highest CV accuracy (internal validation) when distinguishing the DOX- and 5FU-treated samples. Then, using the optimized models, we made predictions on a test set of ETOP-treated samples (external validation). (Note that because CV excludes samples and the final model using the optimized parameters does not, the Δ-value selected during CV with PAM may correspond to a different number of genes during prediction. However, the number of genes selected in CV is held constant for the KNN-based prediction.)
We expected that because ETOP and DOX both inhibit TOP2A, their resulting transcriptional profiles should be similar. Therefore, we considered ETOP samples correctly classified if they were classified as DOX. In a two-class analysis (DOX vs. 5FU), we obtained a high degree of CV accuracy (80–98%) during training and a high degree of predictive accuracy (100%) in assigning the ETOP experiments as more similar to DOX than 5FU (Table 2). However, when we attempted to further subclassify the DOX and 5FU samples according to cell-type (basal-like–DOX vs. basal-like–5FU vs. luminal-DOX vs. luminal-5FU), our CV (76–80%) and prediction (75%) accuracies were diminished (Table 3). The errors in four-class prediction occurred in the 12-hr basal-like samples. This is not surprising based on our clustering results in Figure 2, where the early time points in one of the basal-like cell lines appeared distinct from later points.
To visualize the expression differences from the two-class DOX versus 5FU predictor using Euclidian KNN, we took these samples and the 100 gene set shown to be 98% accurate in prediction and performed hierarchical clustering analysis (Figure 3 and Supplemental Data, Figure 3). The similarities between the ETOP and DOX samples were observable across this gene set. This analysis showed two separate dendrogram branches in Figure 3B, with one branch containing all of the 5FU samples and the other containing the ETOP and DOX samples. Some of the genes identified in the earlier supervised analysis were recapitulated in this predictive gene set. Notably, ID3 appears in Figure 3C and p53 appears in Figure 3E. An interesting cluster of genes that was more strongly induced in DOX and ETOP samples appears in Figure 3D, which includes cathepsin L (CTSL) and cystatin C (CST3). The activity of the cysteine protease CTSL is regulated by the cystatins (a family of cysteine proteinase inhibitors), and their imbalance is associated with increased invasiveness and development of the malignant cell phenotype (Kos and Lah 1998).
Discussion
Most changes that occur in gene expression after treatment with either DOX or 5FU are indicative of a general stress response (Troester et al. 2004). However, in the work presented here, we were interested in identifying the toxicant-specific transcriptional responses to DOX and 5FU in breast epithelial cell lines. We conducted several different supervised analyses to identify genes that distinguished between DOX and 5FU and were able to define toxicant-specific profiles. Using SAM, we found that each cell type (basal-like or luminal-derived) and each cell line had unique responses to DOX and 5FU. Similar to our previous observations for general stress responses (Troester et al. 2004), we found that the luminal cell lines responded to treatment by regulating a large number of genes, whereas the basal-like cell lines had many fewer expression changes in response to treatment. In addition the basal-like cell lines showed greater temporal variation in expression than did the luminal lines. Some of the genes that comprised the general stress signature for each cell type were also found to have toxicant-specific expression in our supervised analyses. This occurred in cases where both DOX and 5FU induced or repressed gene expression relative to shams, but where one treatment induced a change with greater magnitude. For example, the expression of CST3 was induced more strongly byTOP2A inhibitors than by 5FU (Figure 3D) but was induced in both treatments relative to sham (Troester et al. 2004). Thus, CST3 is a general stress response gene with a toxicant-specific gene expression signature.
Toxicant-specific expression responses in our data were corroborated by published reports with these drugs in the same or similar cell lines. For example, impaired cellular respiration after DOX treatment has been previously reported (Souid et al. 2003), and in our data, mitochondrial gene expression was altered (Figure 2D). Earlier studies have shown that 5FU’s target protein thymidylate synthase can bind p53 (Chu et al. 1999; Ju et al. 1999), and we show that p53 mRNA levels are reduced in our 5FU-treated cells. Thus, many of the gene expression changes that we identified recapitulated previous findings. However, a number of significant changes that were not anticipated based on the literature were identified and likely have functional importance. For example, the induction of ID1 and ID3 has not previously been reported for 5FU. The importance of the Id proteins has only recently begun to be investigated (Norton et al. 1998); our findings suggest that these pathways may be responsive to toxicant treatment and warrant further investigation.
In addition to characterizing the toxicant-specific changes by cell line and cell type, we used toxicant-specific gene lists to make predictions on a third toxicant (ETOP) that is believed to have a similar mechanism of action as one of the training toxicants (DOX). Successfully classifying similar compounds establishes that observed transcriptional responses reflect an underlying mode of action. Using as few as 100 genes, we were able to classify ETOP samples as being similar to DOX treated samples with 100% predictive accuracy. This predictive accuracy was reduced to 75% when we attempted to further subclassify the DOX and 5FU samples according to cell type of origin. However, considering that with a four-class model, the likelihood of correctly classifying samples by chance is only 25% (compared with 50% for a two-class model), the four-class model still performs very well. The samples that were misclassified included the early time points in basal-like cell lines, which is consistent with our previous findings that the basal-like cell lines have a distinct expression profiles at 12 hr compared with their 24- and 36-hr time points (Troester et al. 2004).
We have used computational analyses to demonstrate that distinct transcriptional patterns can be identified for mechanistically dissimilar compounds and that toxicants with similar mechanisms can be classified accordingly. We selected two compounds with distinct mechanisms to train our model and a test compound with a mechanism similar to one of the training compounds. These kinds of mechanistic analyses are critical for predictive toxicology using gene arrays. Many studies in the field of toxicogenomics are aimed at populating databases with expression data for diverse toxicants with known mechanisms of action (Hamadeh et al. 2002). These databases can then be used to infer mechanism of action for new compounds. Our data show that this approach is feasible and identifies many new genes and pathways that are important in the response to these toxicants.
Supplementary Material
Supplemental Figures and Tables Figure 1 Gene expression patterns for genes that distinguish between DOX- and 5FU-treated luminal cells (MCF-7 and ZR-75-1). Hierarchical clustering analysis was conducted using 13 DOX-treated and 13 5FU-treated samples. Data from the union of the genes identified by SAM for MCF-7 and ZR-75-1 were identified and combined into a nonredundant list, and the compressed cluster is shown in A (complete cluster is available in Supplemental Data, Figure 1). Colored bars on right side of A illustrate the location of clusters shown in C–G. The dendrogram in B shows that the samples clustered into two groups according to treatment (DOX experiments labeled in red, 5FU experiments labeled in blue), but within each treatment branch, cell line–specific branches are also identifiable. Gene names and accession numbers are from Unigene (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigene). Gene names and accession numbers highlighted in red are discussed in text.
Figure 2 Gene expression patterns for genes that distinguish between DOX- and 5FU-treated basal-like cells (ME16C and HME-CC). Hierarchical clustering analysis was conducted using 13 DOX-treated and 12 5FU-treated samples. Data from the union of the genes identified by SAM for ME16C and HME-CC were identified and combined into a nonredundant list, and the compressed cluster is shown in A (complete cluster available in Supplemental Data, Figure 2). Colored bars in A illustrate the location of clusters shown in C–E. The dendrogram in B shows that the samples clustered into two groups according to treatment (DOX experiments labeled in red, 5FU experiments labeled in blue); however, there early time points for DOX-treated ME16C samples clustered with the 5FU-treated samples. Gene names and accession numbers are from Unigene (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigene). Gene names and accession numbers highlighted in red are discussed in text.
Figure 3 Gene expression patterns for genes selected for a two-class (DOX vs. 5FU) predictive model. Hierarchical clustering analysis was conducted using 26 DOX-treated, 25 5FU-treated samples, and 8 ETOP-treated samples. Data from the genes identified using a KNN classifier for DOX-treated versus 5FU-treated experiments are displayed in the compressed cluster shown in A (complete cluster available in Supplemental Data, Figure 3). Colored bars in A illustrate the location of clusters shown in C–E. The dendrogram in B shows that the samples clustered into two groups according to treatment (DOX experiments labeled in red, 5FU experiments labeled in blue and ETOP experiments labeled in orange. Gene names and accession numbers are from Unigene (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigene). Gene names and accession numbers highlighted in red are discussed in text.
Table 1 Estimated IC50 for 5FU, DOX, and ETOP based on mitochondrial dye conversion assay.a,b
Cell line IC50c Treatment dosec
5FU MCF-7 0.34 (0.13–0.55) 0.3
ZR-75-1 3.3 (2.8–3.7) 3.0
ME16C 0.064 (0.055–0.074) 0.06
HME-CC 0.011 (0.009–0.013) 0.01
DOX MCF-7 0.86 (0.74–0.97) 0.9
ZR-75-1 0.43 (0.37–0.50) 0.4
ME16C 0.52 (0.49–0.54) 0.5
HME-CC 0.16 (0.14–0.18) 0.2
ETOP MCF-7 35 (30–40) 40
ZR-75-1 26 (8.6–43) 30
ME16C 21 (18–23) 20
HME-CC 6.1 (5.6–6.7) 10
a Values in parentheses represent 95% confidence intervals.
b Partially adapted from Troester et al. (2004); IC50 values for 5FU and DOX were previously reported.
c Doses for 5FU are in millimolar (mM); those for DOX and ETOP, micromolar (μM).
Table 2 Two-class CV and prediction accuracy for ETOP samples.
CV accuracy
Prediction accuracy
Method PAM KNNa PAM KNNa
No. 2,460 (2.75)b 100 279 (2.75)b 100
Accuracy 80% 98% 100% 100%
a k = 11.
b Δ-Value is shown in parentheses.
Table 3 Four-class CV and prediction accuracy for ETOP samples.
CV accuracy
Prediction accuracy
Method PAM KNNa PAM KNNa
No. 652 (3.5)b 100 465 (3.5)b 100
Accuracy 76% 80% 75% 75%
a k = 9.
b Δ-Value is shown in parentheses.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/txg.7105ehp0112-00161415598612ToxicogenomicsArticlesThe TAO-Gen Algorithm for Identifying Gene Interaction Networks with Application to SOS Repair in E. coli Yamanaka Takeharu Toyoshiba Hiroyoshi Sone Hideko Parham Frederick M. Portier Christopher J. 1Laboratory of Computational Biology and Risk Analysis, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USAAddress correspondence to C.J. Portier, Laboratory of Computational Biology and Risk Analysis, NIEHS, P.O. Box 12233, MD A3-06, 111 Alexander Dr., Research Triangle Park, NC 27709. Telephone: (919) 541-3802. Fax: (919) 541-3647. E-mail:
[email protected] would like to thank T. Darden, S. Roel, and N. Walker for helpful comments and advices.
The authors declare they have no competing financial interests.
11 2004 21 7 2004 112 16 1614 1621 19 3 2004 21 7 2004 Publication 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. One major unresolved issue in the analysis of gene expression data is the identification and quantification of gene regulatory networks. Several methods have been proposed for identifying gene regulatory networks, but these methods predominantly focus on the use of multiple pairwise comparisons to identify the network structure. In this article, we describe a method for analyzing gene expression data to determine a regulatory structure consistent with an observed set of expression profiles. Unlike other methods this method goes beyond pairwise evaluations by using likelihood-based statistical methods to obtain the network that is most consistent with the complete data set. The proposed algorithm performs accurately for moderate-sized networks with most errors being minor additions of linkages. However, the analysis also indicates that sample sizes may need to be increased to uniquely identify even moderate-sized networks. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data where each gene in the network is over-expressed using plasmids inserts.
gene networksmicroarrayBayesian model selectionSOS repairtoxicogenomics
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Gene expression microarrays (gene chips) have revolutionized biology by generating vast amounts of data roughly quantifying the level of mRNA expression for thousands of genes in a single sample. The analysis of these data is extraordinarily complex, resulting in a shift in biology from predominantly qualitative evaluations to quantitative approaches. With microarray technologies, scientists are forming global views of the structural and dynamic changes in genome activity during different phases in a cell’s development and following exposure to external stimulants such as environmental agents or growth factors. These views describe the molecular working of a complex information processing system: the living cell. Numerous methods have already been proposed for the analysis of gene expression data. The most commonly used methods rely on clustering (Eisen et al. 1995; Tamayo et al. 1999), significance testing (Kerr et al. 2000) and sequence motif identification (Pilpel et al. 2001). These methods do not readily reproduce gene expression networks but are more focused on the fundamental linkage between pairs of genes. Other investigators have proposed methods to identify gene regulatory networks using Boolean networks (Akutsu et al. 2000) where each gene has one of only two states (on and off), regression methods (Gardner et al. 2003), Bayesian network models (Friedman et al. 2000; Hartemink et al. 2002) and other methods (Johnson et al. 2004).
The use of genomics data in the evaluation of health hazards and risks has received considerable attention focusing on priority setting (Pesch et al. 2004), biomarker identification (Toraason et al. 2004), hazard identification (Suter et al. 2004), and dose–response analysis (Schonwalder and Olden 2003; Simmons and Portier 2002; Waters et al. 2003). If genomics is to play a direct role in dose–response assessment, there will be a need for methods that provide a direct, quantitative assessment of changes in gene expression as a function of dose and changes in toxicity as a function of changes in gene expression. Developing and modeling gene interaction networks can be quantitative and provide direct dose–response data for use in risk assessment. They also are an excellent means of identifying agents that provide identical changes in expression across a broad spectrum of genes and help link agents on the basis of similar mechanistic changes.
Bayesian networks are well suited for inferring genetic interactions because of their ability to model causal influence between genes linked as a network and because they are an effective method for modeling the joint density of all variables in a system. However, the approaches suggested to date have generally focused on conversion of gene expression data to discrete states and have avoided the use of formal statistical methods for quantifying the joint density of the resulting parameters.
In this article we describe a method for inferring an “optimal” gene interaction network from microarray-based gene expression data. Unlike other network identification methods, the analytical approach presented here uses the actual measured observations on gene expression (rather than discretized data) and incorporates prior distributions for all parameters in the gene interaction network model. The method encompasses model selection theory from Bayesian regression to find gene network structures suitable for given data sets. Computer simulations presented in this article demonstrate that the proposed method is capable of identifying networks, given the sample size is sufficiently large. For small networks the limited number of replicates used for most microarray studies available today are adequate; for larger networks other options are discussed.
Materials and Methods
Figure 1 illustrates the general structure of a four gene regulatory system where the linkage between expression of gene i and expression of its parents (indirect regulators to gene i) is described by weighting the function wi (ηi), where the subscript i denotes that this weighting function pertains to the control of gene i expression by all genes linked to it and ηi denotes the vector of parameters defining the functional relationship. Let N be a directed acyclic graph which consists of p vertices (genes). Each edge is also assumed to include information about the linkage between genes (i.e., activation, as in the case for the linkage between expression of gene 1 and expression of gene 4, or suppression, expression of genes 3 and 4). In essence, N is a discrete random variable that takes on any of the different acyclic network structures that are possible for a set of p genes. Define Xi to be the random variable corresponding to the measured relative level of gene expression (the expression level of a target gene for an “exposed” group to the expression level of the same gene in a “control” group) for gene Gi, 1 ≤ i ≤ p. For a given network, N = n, and for each Xi, define the conditional density function, fXi(Xi|pan(Xi), ηi) where pan(Xi) denotes the set of vertices corresponding to the parents of expression for gene i in the network n with parameters ηi. All networks in the support space for N are assumed to satisfy the Markov property where expression of gene i is independent of all genes not included in pan(Xi). Application of the Markov property and imposition of the acyclic restriction allow decomposition of the joint density function into
where η = (η1, η2, . . . ηp) is the set of all parameters in the network.
Gene expression data, for the purposes of this analysis, can be expressed as a p by m matrix of the form x = [xik]i
= 1,2,…p,
k
= 1,2…m where m is the number of observations (samples analyzed for gene expression) taken for each gene and xi = [xik]k
= 1,2 …
m is the vector of all observations of expression for gene i. The observed gene expression levels for the parent set for gene i in vector notation is pan(xi) = [xijk]j
= 1,2,…
pi,
k=1,2 …
m where pi is the number of parents for gene i. Similarly define the random vector X. Then, conditional on the parameters and the model, the likelihood of the data, x, is given by
The goal of our analysis is the identification of the “best” network structure using gene expression data. Our criterion for the best network is defined as the network, n*, from the set of all acyclic networks that maximizes the posterior likelihood of the network,
The posterior probability Pr(N = n|x) is given by
where Pr(N = n) and fηi(ηi) are derived from the prior distributions of N and ηi respectively, and the ηi are assumed independent.
Several methods are available for assigning prior information to the distribution of countable networks for a given set of genes. One approach, which is used here, is to assume no prior knowledge by choosing N to be uniformly distributed (equal probability) over the space of all possible acyclic networks. By this assumption the solution to Equation 3 is identical to finding the maximum of the log of the product term in Equation 4 over the parameter space; that is the solution to Equation 3 is identical to
This equation is similar to the maximum likelihood estimator in classical statistical theory, but weighted over the prior densities for the parameters in the model. A clear benefit of this approach is that one does not need to estimate the model parameters while finding the best network because the integration removes those parameters from the final solution. A possible criticism of this approach is that the assumption of a uniform prior for network structure fails to completely exploit the prior knowledge of which networks are of greatest interest. This is most certainly true, but in light of our limited understanding of gene interaction networks, this appears to be a reasonable choice for a first step in network identification. When available, prior knowledge can be incorporated into this algorithm or modified algorithms to limit the space of networks to be searched; this is the solution to a different problem and will be discussed in a subsequent report.
Many possible weighting functions wi(ηi) can be used to relate the relative level of expression of gene i to the relative levels of expression of its parents. The analysis presented here uses a log-linear model
where the notation ij refers to the jth parent of gene i
βi = [βiji]1×pi, and ɛi is a random variable with mean 0. From a mechanistic basis, using a model linear in the logarithms of the expression levels is equivalent to approximating the full nonlinear system by equations in power-law form (Kikuchi et al. 2003; Voit and Radivoyevitch 2000).
Given prior distributions for the ɛ’s and the β’s for all genes, the Markov-Chain Monte-Carlo (MCMC) method developed by Hastings (Hastings 1970) makes it possible to estimate a solution to Equation 5 and identify the “best” network. It is possible, under further restrictions, to obtain a closed form solution to the argument in Equation 5. The advantage of this approach in the framework of this article is that the entire network space can be searched exhaustively to find the best network for small networks like the ones in our simulation studies.
As is common in Bayesian linear regression theory (Gelman et al. 1995), we assume that ɛi|σi2 ~ Normal(0, σi2), βi|σi2 ~ Normal(bi, σi2Ai−1) and σi2 ~ Gamma(v0/2, v1/2), v0, v1 ≈ 0. These priors do not assume additional or specific information (in Bayesian parlance these are uninformative priors) and thus would be applicable for many cases. Simple algebra then results in:
where Γ is the gamma function, Ai = ln[pan(xi)]ln[pan(xi)]T and Bi = ln[xi]ln[pan(xi)]T
Ai−1. Given N = n, this equation allows for the direct calculation of Pr(N = n|x). This formula is specific to these priors, but similar formulae might be derived for other cases.
Any single gene in a p = 4 gene network has 8 possible sets of parents (no parents, 3 single parents, 3 double parents, all other genes), hence the total number of networks including cyclic networks would be 84 = 4,096 networks of which 543 are acyclic. As p increases, the total number of networks increases as the squared power of p(2p(p−1)) resulting in a very large network space to evaluate for larger networks (e.g., ~ 4 × 10469 for a 40-gene network). Many different types of searching algorithm could be used to limit the number of networks to be evaluated for Equation 6; through trial and error, the following modified simulated annealing algorithm (Press et al. 1989) appears to work. We will refer to this method as the TAO-Gen (Theoretical Algorithm for identifying Optimal GENe interaction networks) algorithm.
The TAO-Gen algorithm has 7 basic steps:
Search conditions: Restrict to ζ< p, the maximum number of parents for any one gene and calculate the value of Equation 6 for all ∑ζi=0
p−1Ci parent combinations, where pCi is the binomial coefficient (When p is relatively small, ζ = p−1 can be chosen and the entire network space is evaluated in this step. When p is even moderately large (> 10), assuming ζ = 4 or 5 will substantially reduce the computational burden). Specify a number t (0 ≤ t ≤ 1) governing the probability of local versus global switching in step 4 (t = 0 implies only global switching, t = 1 implies only local switching).
For the initial step k = 0, randomly select an order in which genes enter the network Gk = (Gk1
Gk2
Gk3Gkp) and build a starting network choosing the parents for each gene that maximize Equation 6 while keeping the network acyclic (i.e., choose the parents for Gk1 that are optimal first, then parents for Gk2 that are optimal, etc.)
Calculate the posterior likelihood (Equation 4) for this network and denote it Lk.
Generate a uniform random number u1 ε uniform (0,1) to determine the type of permutation. if u1 < t, the permutation occurs between two randomly chosen genes, j and l, switching the two genes for the next permutation Gk+1,j = Gk,l and Gk+1,l = Gk,j). Otherwise, make the second half of the set of genes, starting from randomly chosen gene j, appear first in the order (Gk|1,1 = Gk,j|1, Gk|1,2 = Gk,j|2,…,
Gk+1,m−j+1 = Gk,1,…,
Gk
+1,m = Gk,j). Thus form a new gene order, Gk+1.
Calculate a new posterior likelihood of the network Lk+1 associated with the order Gk+ 1 , as in steps 2 and 3. If Lk+1 > Lk, then keep Gk+1. Otherwise generate a uniform random number u2 ε uniform (0,1)and if u2 ≤ Lk+1/Lk, keep Gk+1 else set Gk+1 = Gk.
Return to step 4 and iterate.
Choose the network with the highest posterior probability from the sequence (G0, G1,…).
This algorithm combines aspects of the Metropolis algorithm used for Markov-Chain Monte-Carlo sampling (Hastings 1970), with the simulated annealing algorithm used for optimization (Press et al. 1989). In essence it represents a new form of genetic algorithm aimed at networks in which mutations occur in each cycle as either base-pair switches or large translocations. It may be possible under certain fixed conditions to analytically determine the degree to which the TAO-Gen algorithm reduces the number of networks to be evaluated and the efficiency with which it finds the correct solution. This is left as a separate exercise; instead, simulation studies were used to address these issues as discussed in “Results.”
Gene Expression Data Set
Gardner et al. (2003) developed a gene-regulatory network for a nine-gene subnetwork of the SOS pathway in Escherichia coli. The nine genes (all gene names and locators, in parentheses following gene name, are from the EcoGene database (http://bmb.med.miami.edu/EcoGene/EcoWeb) they focused on were the principal mediators of the SOS response, recA (recombinase gene A, locator EC10823) and lexA (lambda excision gene A, locator EC10533); genes with known involvement in the SOS response, ssb (single strand binding gene, locator EC10976), recF (recombinase gene F, locator EC10828), dinI (damage inducible gene I, locator EC12670), umuDC (UV mutator gene, locator EC11057); and three sigma factor genes whose function in SOS response is not clearly identified, rpoD (RNA polymerase factor subunit D, locator EC10896), rpoH (RNA polymerase factor subunit H, locator EC10897), and rpoS (RNA polymerase factor subunit S, locator EC10510). To quantify the subnetwork, they applied a set of nine transcriptional perturbations to E. coli cells in which each perturbation overexpressed a different one of the nine genes in the SOS network. Using an arabinose-controlled episomal expression plasmid, they grew the cells in batch cultures for 5.5 hr after the addition of arabinose, then measured relative change in message for their nine target genes using quantitative real-time polymerase chain reaction. In addition to the nine perturbed cultures, they also produced two additional cultures, one in which a double plasmid (lexA/recA) was incorporated into the cells and another in which 0.75 μg/mL of mitomycin C (MMC) was added to the culture to stimulate gene expression of recA. The resulting data set with 11 samples of relative changes in gene expression for the nine target genes is given in Table S1 in Gardner et al. (2003). In addition to the nine target genes, the nine plasmid constructs were added to the modeling as fixed stimulators of each of their respective genes to mimic changes in gene expression induced by insertion of the ten plasmid constructs. A separate stimulation by MMC was also included but with links to all genes in the network to determine if the predominant linkage to recA assumed by Gardner et al. (2003) was evident in the data. The exact model linking genes for sample k (k = 1,2, …11) is given by
where βiji is as described previously, Iik is an indicator variable equal to 1 if gene i has an inserted plasmid in sample k and is equal to 0 otherwise, αi is the magnitude of increase in gene expression induced in the ith gene by the plasmid when it is present, Mk is the relative change (relative to the standard of 0.5 μg/mL) in MMC exposure for sample k, and γi is the magnitude of change in gene expression for gene i as a function of the relative change in MMC.
Simulation Results
Data were simulated for a given network by sampling from the assumed error distributions and priors for a given model situation. To simulate a network, genes highest on the parental list were simulated first and the simulated values were used to simulate daughters, etc. Different starting points and different priors were used to estimate parameters in both the simulated data and the SOS data; these had no impact on the final results provided the priors chosen were uninformative.
Results
The TAO-Gen algorithm was applied to real time PCR data on nine genes (recA, lexA, ssb, recF, dinI, umuDC, rpoD, rpoH, and rpoS) from the SOS pathway in E. coli as described above. Data consisted of 11 separate relative changes in gene expression: 9 samples for which a plasmid was inserted for one of the nine genes, a single construct for a combination of two genes (lexA and recA), and a modification of the culture (1.5 × increase in mitomycin C) in wild-type cells. Figure 2 illustrates the optimal gene interaction network identified by the TAO-Gen algorithm for these data. It is generally believed that the SOS regulon in E. coli is predominantly under the control of the products of the genes lexA and recA. Figure 3 illustrates a literature-based linkage map between genes in the SOS response for the repair of DNA damage. When genotoxins, such as ultraviolet radiation and MMC, damage DNA base nucleotides, the replication process is activated and a region of single-stranded DNA (ssDNA) is formed. RecA (the product of recA) coats ssDNA, signaling the SOS response. RecA/ssDNA stimulates degradation of LexA (the products of lexA), which is a repressor of RecA in the normal repair process. This inactivation of LexA affects other genes involved directly in SOS response, such as dinI, and downstream genes involved in DNA replication, cell division and mutagenesis, such as rpoS (Beuning 2004; Janion 2001; Lindner 2004; Lusetti 2002; McKenzie 2000; Rangarajan 2002). The results from the TAO-Gen algorithm are given in Figure 2 and support this role for LexA with significant repressor activity on umuDC, dinI and ssb. In contrast, RecA, the gene product of recA, is expected to serve as an activator of the SOS regulon. Figure 2 indicates that recA serves as a central node in the regulation of genes in the SOS pathway, showing significant activation of lexA, recF, umuDC, rpoH and ssb and significant repression of rpoD. There are four remaining significant linkages: ssb and rpoS repress and activate rpoD, respectively, and recF activates umuDC and rpoH activates ssb. Table 1 provides summary information on the parameter estimates estimated by treating the identified network (Figure 2) as known and quantifying the linkages between genes by the method of Toyoshiba et al. (2004). With the exception of the plasmid-induced change in recF, all linkages in Figure 2 are statistically significant (p < 0.05).
An indicator variable was used to separate data with and without plasmid insertion for each gene. For all nine genes, plasmid inserts increased mRNA levels ranging from a nonsignificant (p = 0.31) 1.06-fold increase for recF to a significant (p < 0.01) 28-fold increase for rpoH. Changes in the level of MMC had significant effects on eight of the nine genes, the sole exception being lexA, which did not appear to be directly affected by changes in MMC. This finding is in contrast to what was believed to be the presumed transcriptional target of MMC, recA. It was previously suggested that all other MMC-induced changes in transcription are mediated through recA. In this analysis the largest impacts of MMC on transcription were for rpoH and rpoS (an ~12.3-fold increase in activity for each doubling of the MMC level) followed by effects on recA, dinI and umuDC (approximately a 1.9-fold increase in activity for each doubling of MMC level).
Our best network (Figure 2) and the literature-based network (Figure 3) support the notion that the activation of the SOS system is through activation of recA. Increases in recA result in activation of umuDC and ssb, critical components in the activation of repair of single-strand DNA damage. An increase in recA also induces an increase in lexA, which serves to suppress the activity induced by recA in umuDC and ssb. rpoH appears to serve as an independent activator of ssb with signaling from recA and possibly other genes not included in the network. Finally, while rpoS and rpoD seem to be linked to the network, they appear to be under control of other genes in the network rather than exerting control over the SOS response. Recent articles hypothesized possible roles for roles for RpoS, LexA and RecA in global stress gene regulation, but clear conclusions are not yet available (Gerard et al. 1999; Gill et al. 2000).
With such a small number of samples (11) relative to the number of genes involved (9), it is likely that the resulting model is overly sensitive to any one data set. To evaluate this, we applied the TAO-Gen analysis to 11 data sets in which one sample from the original data was eliminated. Generally, removing a sample resulted in deletion of a connection rather than inclusion of new connections. Removing the dinI plasmid insert had no impact on the resulting network; removing the double plasmid insert only added a single additional connection between rpoH and rpoS; and removing the MMC sample (no plasmid insert) removed only one linkage (rpoH-rpoS). All other sample removals resulted in two to five changes in the network with no more than one additional linkage in any case. Three linkages (recA to lexA, lexA to umuDC and recF to umuDC) remained unchanged for all sample deletions; all others were simply eliminated once or twice for specific sample deletions with the exceptions of recA to rpoH, which was removed in four sample deletions, and rpoS to rpoD which was removed in one sample deletion and switched direction for three sample deletions. All additional linkages (there were six sample deletions with one additional linkage in each case) included at least one of the stationary phase regulators (rpoH, rpoS, rpod), suggesting the linkage between this class of genes and the SOS pathway may be too distant to quantify. Generally, with the exception of linkages to and between the stationary phase regulators, the model was fairly stable across deletions of single samples from the data set.
Discussion
The network presented in Figure 2 is substantially smaller than that proposed by Gardner et al. (2003) Using their NIR (network identification by multiple regression) algorithm, they identified a network with 45 linkages (excluding changes due to MMC or the plasmids) compared with our network with only 13 gene linkages. There are significant differences between the NIR and TAO-Gen algorithms that directly impact affect these findings. In the NIR algorithm, parents for each gene are discovered independently of the other genes by finding the five parents that maximize the usual likelihood of the data given the model. The choice of five parents is somewhat arbitrary, and the use of the data multiple times for each gene overstates the information available. In addition each gene is allowed to be a parent of itself, creating a singularity in the model that results in most other parents having no significant impact on any given gene expression level. Of the 36 linkages (six parents were chosen for recF) identified by the NIR algorithm, all nine genes have significant linkages with themselves as parents. Of the remaining 27 linkages, only 9 are significant (p < 0.05 by a Wald test) as follows: ssb activates recA and recF, recA suppresses lexA and rpoH, dinI activates recA, umuDC and rpoS, rpoH suppresses rpoD, and rpoS suppresses recF. The TAO-Gen algorithm, in contrast, restricts the network to acyclic linkages and uses the full likelihood (all of the data simultaneously) to find the best network. Of the 9 significant linkages identified by the NIR algorithm, the TAO-Gen algorithm identified only the suppression of lexA and rpoH by recA. The significant findings by the NIR algorithm do not identify recA as a key controlling gene in the network whereas the TAO-Gen algorithm does.
Mathematically the data obtained by Gardner et al. (2003) does not have sufficient statistical support to identify a cyclical network. The data required to estimate parameters in a cyclical network must contain observations at different time points to estimate the dynamic characteristics of a cyclic network. To directly compare the Gardner et al. network to the one shown in Figure 2, the Gardner et al. network was made acyclic by removing the linkages for genes as their own parents and by removing the linkage between dinI and lexA. When the Bayesian estimation algorithm was applied (Toyoshiba et al. 2004), the posterior log-likelihood for this model had a mean value of 329.2 compared with 354.7 from the model identified by the TAO-Gen algorithm, suggesting a considerably better fit of the model in Figure 2 to the data. Using the “known model” suggested by Gardner et al. (2003), the resulting mean of the posterior log-likelihood was 311.0, also suggesting a serious lack of fit.
So is the model presented in Figure 2 a better representation of the gene interaction network for the SOS pathway in E. coli? The resulting network has identified the significant gene linkages seen in the data. It correctly identifies recA as playing the major role in control of this pathway and provides estimates of the steady-state linkage between these genes. The interpretation of the values estimated for the parameters linking genes in Figure 2 does not preclude that the network could be dynamic with substantial feedback; such a possibility is likely. But given the data available, this network identifies the key linkages that exist as the network changes from one steady-state to another. What this means can be explained by example. The activation of recF by recA has a mean value of 0.393. This implies that, if the steady-state expression of recA doubles, then the steady-state expression of recF would fold increase by the exponential of 0.393 × ln(2) or 1.32-fold. Singular changes in any gene in the network can easily be used to calculate new steady-state conditions for the network.
Illustrating that one can achieve a network from a given data set does not assess the reliability of a new algorithm. A better method is to evaluate the probability of choosing the correct network using data from a known network. Monte Carlo simulation was used to generate 100,000 artificial gene expression arrays from the network in Figure 1 using four different sets of model parameters as defined in Table 2. When the algorithm is applied to these data, the resulting optimal network is identical to the network shown in Figure 1 in all four cases. This illustrates that the algorithm is consistent for extremely large data sets. To assess the behavior of the algorithm for small samples, the four sets of 100,000 artificial arrays were subdivided into 1,000 data sets of 100 arrays, 2,000 data sets of 50 arrays, 4,000 data sets of 25 arrays, and 10,000 data sets of 10 arrays. For each data set, the algorithm was applied and an optimal network chosen; the results appear in Table 2.
There are 543 possible acyclic networks that can arise from a combination of four genes. Table 2 summarizes the frequency (from 543 total networks) seen for various network structures (column 3 is the correct structure). For example, with 100 arrays in the sample, the correct network is chosen 922/100 = 92% of the time for parameter set A (row 1 of Table 2). Generally, with 100 replicate arrays, the search algorithm is better than 92% effective in finding the right network. The most common error in finding an array for this sample size is to add an additional linkage between gene 2 and gene 4 (column 8 in Table 2, 1–8%). When the sample size is halved to 50 arrays, accuracy drops to between 86 and 93%, with the same additional linkage being the most common mistake (2–9%). With only 25 arrays, accuracy is still between 70 and 80%, with most of the errors occurring for the same additional linkage (4–8%), single deletions of linkages (3–4%), or reversals of individual linkages (2–3%). Replicate samples consisting of just 10 arrays surprisingly find the correct network 32–38% of the time, with 30–40% of the errors being additional linkages, single linkage removal, or single linkage reversals. The simulations suggest the algorithm generally detects networks having very close topologies to the correct one even if the sample number is severely diminished.
As noted in “Materials and Methods,” the algorithm being used to find the best network is intended as an approximation for using the posterior likelihood to identify the best network. In the last four columns of Table 2, the correct network has the best posterior likelihood in every case for which it is the optimal network. In addition the algorithm works well at placing the correct network into the top three networks, ranging from about 99% for samples involving 100 arrays to 58% for samples consisting of 10 arrays. These simulations suggest that the best directed acyclic network does not necessarily mean that all the links are real or that they are causal. Conversely, they do suggest that the limitations inherent to small sample sizes could be reduced by considering not only the best network, but several of the best networks and using other resources, such as knowledge of the existing pathways, to decide which makes the most sense.
These results were expanded to look at an eight-gene network, effectively a combination of two four-gene networks similar to that in Figure 1, where gene 2 activates gene 5 and gene 3 activates gene 8 (Figure 4). In this case it is computationally impossible to conduct the exhaustive search as in the four-gene case because the number of acyclic networks is approximately 78 × 1013. Instead, 1,000 data sets were randomly generated for each sample case (100, 50, 25, 10) and the TAO-Gen algorithm was applied to identify a best network for each data set. Table 3 shows the numbers of connections detected by the algorithm, where the rows and columns correspond to parents and child genes, respectively. For example, the algorithm detected the incorrect path from gene 1 to gene 2 only three times in 1,000 data sets with 100 samples. The red elements show the true connections. For 100 replicate samples (microarrays), the TAO-Gen algorithm identified the correct network in 95% of the cases. As before, the deviations from the correct model were all cases of adding an additional linkage or removing a single linkage. As the sample size dropped to 50, 25 and 10, the correct network was identified 76, 30 and 1% of the time, respectively. Even though the performance in finding the fully correct network became poor, the linkages in the correct network were generally properly identified with high frequency, again indicating that the cases where the network was incorrect generally involved single or double alterations in the pathways of the network. The simulation using eight genes accentuates the importance of study design and prior knowledge about gene linkages in trying to find the best network to explain the data.
Many issues remain to be studied. It is unclear whether the TAO-Gen algorithm works better or worse than other algorithms in identifying gene interaction networks. The main problem arises because other algorithms have not used computer simulations to examine model specifity to directly address this issue. Also, the use of acyclic models to develop gene interaction networks is somewhat limited. A fully dynamic model using time-dependent differential equations could be used with the TAO-Gen algorithm provided multitime point data were available; the method would simply need to link models across time as suggested elsewhere (Toyoshiba et al. 2004) or use dynamic Bayesian networks. Here we assume samples are independent; in time-course data, that would not necessarily be the case and the error structure between samples would need to be altered (in Equation 4 and subsequent derivations) to account for the longitudinal nature of such data. In any case the analysis would certainly require more data than are generally available. Perhaps the biggest advantage of using a Bayesian-linked analysis algorithm would occur when prior knowledge, based on known biologic linkages such as those derived from bioinformatic evaluations of transcription sequences, is used to limit the range of networks to be explored. The TAO-Gen algorithm could work in these situations but would need to be modified to use a prior different than the uniform prior used in this case.
Conclusion
In this article we have presented the TAO-Gen algorithm for identifying gene interaction networks. The algorithm was applied to data on the SOS pathway in E. coli to identify gene linkages. The resulting network is shown to be superior to a network derived by the NIR algorithm in (Gardner et al. 2003) both biologically and statistically. Unlike the NIR algorithm, this algorithm identified a statistically significant role of recA in controlling the SOS pathway; the linkages from recA in the NIR-derived network were generally not significant. To demonstrate the accuracy of the algorithm for varying sample sizes, a simulation study was performed. It was found that for moderate-size networks the algorithm performs accurately, with most errors being minor additions or deletions of a single linkage. However, the simulations do suggest that sample sizes need to be increased if large networks are to be identified and quantified using gene expression data.
Figure 1 A simple gene interaction network consisting of four genes.
Figure 2 Network linkages of key genes in the SOS response in E. coli as identified by the TAO-Gen algorithm.
Figure 3 A literature-based linkage map between genes in the SOS response in E. coli. The map represents inducible genes/proteins in the SOS response for repair from DNA damage. Black lines indicate pathways in the normal repair process and red lines with arrows activation/induction due to an exposure to damaging agents. Recombination and repair, DNA damage–inducible protein, nucleotide excision repair, error-prone repair, and stationary-phase regulator have family molecules in each box. Green circles are genes used for the analysis.
Figure 4 A hypothetical eight gene network used for the Monte-Carlo simulations in Table 3. The numbers attached to the arrows show linear parameters, where positive numbers correspond to up-regulations and negative numbers down-regulations.
Table 1 Estimated means, standard deviations and percentage above 0 for all interactions in SOS response genes for E. coli identified as linked by the TAO-Gen algorithm (see Figure 2).
From To Type Mean SD % < 0
recA lexA Activate 0.435 0.065 0.00
ssb Activate 0.137 0.056 0.99
recF Activate 0.393 0.161 0.93
umuDC Activate 0.365 0.129 0.42
rpoD Repress −0.356 0.091 99.97
rpoH Activate 0.193 0.093 2.06
lexA ssb Repress −0.158 0.065 98.86
dinI Repress −0.287 0.156 96.61
umuDC Repress −0.550 0.169 99.85
ssb rpoD Repress −0.077 0.029 99.46
recF umuDC Activate 0.512 0.204 0.81
rpoH ssb Activate 0.031 0.012 0.55
rpoS rpoD Activate 0.496 0.108 0.02
Plasmid insert recA Activate 0.458 0.080 0.00
lexA Activate 0.396 0.041 0.00
ssb Activate 2.443 0.039 0.00
recF Activate 0.062 0.130 30.95
dinI Activate 1.188 0.110 0.00
umuDC Activate 1.007 0.093 0.00
rpoD Activate 1.409 0.069 0.00
rpoH Activate 3.319 0.074 0.00
rpoS Activate 0.513 0.100 0.00
MMC recA Activate 0.979 0.282 0.06
ssb Activate 0.479 0.108 0.05
recF Activate 0.637 0.345 3.28
dinI Activate 0.896 0.282 0.07
umuDC Activate 0.969 0.252 0.05
rpoD Activate 0.460 0.221 2.12
rpoH Activate 1.233 0.204 0.00
rpoS Activate 1.255 0.248 0.00
Table 2 Results from 100,000 Monte Carlo simulations of four hypothetical four-gene networks (A, B, C, D)a describing the ability of the TAO-Gen algorithm to specify the correct network.
Frequency (%) of resulting optimal network structure
Rank (%) of the posterior likelihood for the true network over all possible 543 acyclic networks
Sample size True model 1 2 3 4–10
100 arrays A 922 (92) 0 (0) 0 (0) 0 (0) 0 (0) 68 (7) 0 (0) 922 (92) 52 (5) 10 (1) 16 (2)
1,000 sims B 977 (98) 0 (0) 0 (0) 0 (0) 0 (0) 6 (1) 0 (0) 977 (98) 17 (2) 4 (0.4) 2 (0.2)
C 929 (93) 0 (0) 0 (0) 0 (0) 0 (0) 71 (7) 0 (0) 929 (93) 50 (5) 8 (1) 13 (1)
D 980 (98) 0 (0) 0 (0) 0 (0) 0 (0) 6 (1) 0 (0) 980 (98) 13 (1) 5 (0.5) 2 (0.2)
50 arrays A 1,716 (86) 4 (0.2) 3 (0.2) 6 (0.3) 4 (0.2) 165 (8) 0 (0) 1,716 (87) 157 (8) 34 (2) 70 (4)
2,000 sims B 1,841 (92) 8 (0.4) 0 (0) 4 (0.2) 8 (0.4) 41 (2) 0 (0) 1,841 (92) 82 (4) 20 (1) 55 (3)
C 1,745 (87) 6 (0.3) 4 (0.2) 3 (0.2) 6 (0.3) 175 (9) 0 (0) 1,745 (88) 128 (6) 41 (2) 62 (3)
D 1,860 (93) 4 (0.2) 0 (0) 2 (0.1) 0 (0) 46 (2) 0 (0) 1,860 (93) 68 (3) 30 (2) 42 (2)
25 arrays A 2,920 (73) 76 (2) 72 (2) 56 (1) 77 (2) 328 (8) 3 (0.1) 2,920 (73) 423 (10) 112 (3) 387 (10)
4,000 sims B 3,179 (80) 92 (2) 55 (1) 48 (1) 47 (1) 192 (5) 8 (0.2) 3,179 (79) 348 (9) 133 (3) 249 (6)
C 2,891 (72) 60 (1) 100 (2) 56 (1) 76 (2) 296 (7) 4 (0.1) 2,891 (72) 404 (10) 114 (3) 444 (11)
D 3,086 (77) 76 (2) 96 (2) 48 (1) 48 (1) 164 (4) 8 (0.2) 3,086 (77) 328 (8) 149 (4) 365 (9)
10 arrays A 3,198 (32) 909 (9) 741 (7) 230 (2) 149 (2) 328 (3) 497 (5) 3,198 (32) 1,027 (10) 781 (8) 2,389 (24)
10,000 sims B 3,768 (38) 1,002 (10) 1,051 (10) 220 (2) 309 (3) 378 (4) 567 (6) 3,768 (38) 966 (10) 821 (8) 2,519 (25)
C 3,177 (32) 892 (9) 691 (7) 230 (2) 151 (2) 398 (4) 457 (5) 3,177 (32) 1,232 (12) 769 (8) 2,347 (23)
D 3,768 (38) 1,052 (10) 1,031 (10) 280 (3) 259 (3) 538 (5) 477 (5) 3,768 (38) 1,146 (11) 871 (9) 2,371 (24)
a (A) β14 = 2.0, β13 = 0.8, β23 = 0.8, β34 = −1.3, σ1 = σ2 = σ3 = σ4 = 1.0
(B) β14 = 2.0, β13 = 0.8, β23 = 0.8, β34 = −5.0, σ1 = σ2 = σ3 = σ4 = 1.0
(C) β14 = 2.0, β13 = 0.8, β23 = 0.8, β34 = −1.3, σ1 = σ2 = σ3 = σ4 = 1/3
(D) β14 = 2.0, β13 = 0.8, β23 = 0.8, β34 = −5.0, σ1 = σ2 = σ3 = σ4 = 1/3
Table 3 Number (percent) of linkages between two genes identified by the TAO-Gen algorithm in 1,000 Monte Carlo simulations of the hypothetical eight-gene network shown in Figure 3.
From gene number To cell number
1 2 3 4 5 6 7 8
100 Chips 1 —— 3 (0.3) 1,000 (100)a 1,000 (100)a 4 (0.4) 1 (0.1) 4 (0.4) 5 (0.5)
2 0 (0) —— 999 (99.9)a 9 (0.9) 1,000 (100)a 1 (0.1) 3 (0.3) 7 (0.7)
3 0 (0) 1 (0.1) —— 1,000 (100)a 0 (0) 0 (0) 0 (0) 1,000 (100)a
4 0 (0) 0 (0) 0 (0) —— 0 (0) 0 (0) 0 (0) 0 (0)
5 0 (0) 0 (0) 0 (0) 3 (0.3) —— 0 (0) 1,000 (100)a 999 (99.9)a
6 2 (0) 0 (0) 2 (0.2) 2 (0.2) 2 (0.2) —— 1,000 (100)a 8 (0.8)
7 0 (0) 0 (0) 0 (0) 1 (0.1) 0 (0) 0 (0) —— 1,000 (100)a
8 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) ——
50 Chips 1 —— 4 (0.4) 980 (98)a 1,000 (100)a 23 (2.3) 11 (1.1) 23 (2.3) 8 (0.8)
2 8 (0.8) —— 977 (97.7)a 19 (1.9) 989 (98.9)a 6 (0.6) 13 (1.3) 24 (2.4)
3 14 (1.4) 2 (0.2) —— 995 (99.5)a 3 (0.3) 3 (0.3) 9 (0.9) 1,000 (100)a
4 0 (0) 0 (0) 5 (0.5) —— 0 (0) 0 (0) 1 (0.1) 0 (0)
5 2 (0.2) 9 (0.9) 14 (1.4) 7 (0.7) —— 4 (0.4) 991 (99.1)a 973 (97.3)a
6 10 (1) 4 (0.4) 15 (1.5) 13 (1.3) 15 (1.5) —— 989 (98.9)a 11 (1.1)
7 1 (0.1) 0 (0) 0 (0) 7 (0.7) 7 (0.7) 2 (0.2) —— 998 (99.8)a
8 0 (0) 0 (0) 0 (0) 5 (0.5) 0 (0) 0 (0) 2 (0.2) ——
25 Chips 1 —— 33 (3.3) 832 (83.2)a 960 (96)a 26 (2.6) 18 (1.8) 26 (2.6) 50 (5)
2 20 (2) —— 751 (75.1)a 63 (6.3) 912 (91.2)a 14 (1.4) 57 (5.7) 94 (9.4)
3 37 (3.7) 46 (4.6) —— 933 (93.3)a 10 (1) 5 (0.5) 46 (4.6) 962 (96.2)
4 1 (0.1) 0 (0) 63 (6.3) —— 2 (0.2) 0 (0) 2 (0.2) 11 (1.1)
5 5 (0.5) 50 (5) 59 (5.9) 34 (3.4) —— 9 (0.9) 905 (90.5)a 811 (81.1)
6 9 (0.9) 10 (1) 19 (1.9) 38 (3.8) 64 (6.4) —— 857 (85.7)a 69 (6.9)
7 2 (0.2) 0 (0) 21 (2.1) 24 (2.4) 60 (6) 19 (1.9) —— 964 (96.4)
8 2 (0.2) 0 (0) 13 (1.3) 9 (0.9) 0 (0) 0 (0) 33 (3.3) ——
10 Chips 1 —— 51 (5.1) 516 (51.6)a 702 (70.2)a 63 (6.3) 30 (3) 73 (7.3) 141 (14.1)
2 49 (4.9) —— 335 (33.5)a 155 (15.5) 590 (59)a 35 (3.5) 171 (17.1) 166 (16.6)
3 73 (7.3) 84 (8.4) —— 596 (59.6)a 67 (6.7) 16 (1.6) 126 (12.6) 641 (64.1)a
4 23 (2.3) 15 (1.5) 227 (22.7) —— 11 (1.1) 8 (0.8) 22 (2.2) 71 (7.1)
5 16 (1.6) 106 (10.6) 79 (7.9) 87 (8.7) —— 33 (3.3) 519 (51.9)a 375 (37.5)a
6 35 (3.5) 30 (3) 73 (7.3) 93 (9.3) 95 (9.5) —— 408 (40.8)a 187 (18.7)
7 9 (0.9) 18 (1.8) 74 (7.4) 79 (7.9) 168 (16.8) 51 (5.1) —— 693 (69.3)a
8 3 (0.3) 2 (0.2) 68 (6.8) 51 (5.1) 24 (2.4) 8 (0.8) 135 (13.5) ——
a Linkage that exists in the original simulated model.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/txg.7109ehp0112-00162215598613ToxicogenomicsArticlesUsing Decision Forest to Classify Prostate Cancer Samples on the Basis of SELDI-TOF MS Data: Assessing Chance Correlation and Prediction Confidence Tong Weida 1Xie Qian 2Hong Huixiao 2Fang Hong 2Shi Leming 1Perkins Roger 2Petricoin Emanuel F. 31Center for Toxicoinformatics, Division of Biometry and Risk Assessment, and2Bioinformatics Group, National Center for Toxicological Research, Jefferson, Arkansas, USACenter for Biologics Evaluation and Research, U.S. Food and Drug Administration, Bethesda, Maryland, USAAddress correspondence to W. Tong, Center for Toxicoinformatics, Division of Biometry and Risk Assessment, NCTR, 3900 NCTR Rd., HFT020, Jefferson, AK 72079 USA. Telephone: (870) 543-7142. Fax: (870) 543-7662. E-mail:
[email protected] authors declare they have no competing financial interests.
11 2004 5 8 2004 112 16 1622 1627 22 3 2004 5 8 2004 Publication 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. Class prediction using “omics” data is playing an increasing role in toxicogenomics, diagnosis/prognosis, and risk assessment. These data are usually noisy and represented by relatively few samples and a very large number of predictor variables (e.g., genes of DNA microarray data or m/z peaks of mass spectrometry data). These characteristics manifest the importance of assessing potential random correlation and overfitting of noise for a classification model based on omics data. We present a novel classification method, decision forest (DF), for class prediction using omics data. DF combines the results of multiple heterogeneous but comparable decision tree (DT) models to produce a consensus prediction. The method is less prone to overfitting of noise and chance correlation. A DF model was developed to predict presence of prostate cancer using a proteomic data set generated from surface-enhanced laser deposition/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The degree of chance correlation and prediction confidence of the model was rigorously assessed by extensive cross-validation and randomization testing. Comparison of model prediction with imposed random correlation demonstrated biologic relevance of the model and the reduction of overfitting in DF. Furthermore, two confidence levels (high and low confidences) were assigned to each prediction, where most misclassifications were associated with the low-confidence region. For the high-confidence prediction, the model achieved 99.2% sensitivity and 98.2% specificity. The model also identified a list of significant peaks that could be useful for biomarker identification. DF should be equally applicable to other omics data such as gene expression data or metabolomic data. The DF algorithm is available upon request.
bioinformaticschance correlationclass predictionclassificationdecision forestprediction confidenceprostate cancerproteomicsSELDI-TOF
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Recent technologic advances in the fields of “omics,” including toxicogenomics, hold great promise for the understanding of the molecular basis of health and disease, and toxicity. Prospective further advances could significantly enhance our capability to study toxicology and improve clinical protocols for early detection of various types of cancer, disease states, and treatment outcomes. Classification methods, because of their power to unravel patterns in biologically complex data, have become one of the most important bioinformatics approaches investigated for use with omics data. Classification uses supervised learning techniques (Tong et al. 2003b) to fit the samples into the predefined categories based on patterns of omics profiles or predictor variables (e.g., gene expressions in DNA microarray). The fitted model is then validated using either a cross-validation method or an external test set. Once validated, the model could be used for prediction of unknown samples.
A number of classification methods have been applied to microarray gene expression data (Ben-Dor et al. 2000; Simon et al. 2003; Slonim 2002), including artificial neural networks (Khan et al. 2001), K-nearest neighbor (Olshen and Jain 2002), Decision Tree (DT; Zhang et al. 2001), and support vector machines (SVMs; Brown et al. 2000). Some of the same methods have been applied similarly to proteomic data generated from surface-enhanced laser deposition/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for molecular diagnostics (Adam et al. 2002; Ball et al. 2002). For example, Petricoin et al. (2002a, 2002b) developed classification models for early detection of ovarian and prostate cancers (PCAs) on the basis of SELDI-TOF MS data using a genetic algorithm–based SVM.
Omics data present challenges for most classification methods because a) the number of predictor variables normally far exceeds the sample size and b) most data are unfortunately very noisy. Consequently, optimizing a classification model inherently risks overfitting the noise, a result that is difficult to overcome for most classification methods (Slonim 2002). Furthermore, many existing classification methods require predetermination of a set of predictor variables, thereby introducing additional complexity and bias that could adversely affect both model fitting and validation (Ambroise and McLachlan 2002).
In this article a novel classification method, Decision Forest (DF), is proposed for developing classification models using omics data. A DF model is developed by combining multiple distinct but comparable DT models to achieve a more robust and better prediction (Tong et al. 2003a). DF does not require predetermination of predictor variables before model development and is less prone to overfitting of noise. Developing a statistically sound model that fits the data is straightforward with most classification methods, but assuring that the model can accurately classify unknown samples with a known degree of certainty poses a significant challenge. In DF, an extensive cross-validation and randomization testing procedure was implemented, which provides two critical measures to assess a fitted model’s ability to predict unknown samples, the confidence level of predictions and the degree of chance correlation. DF is demonstrated in an application to distinguish PCA samples from normal samples on the basis of a SELDI-TOF MS data set. The results indicate that the reported DF model could be useful for early detection of PCA.
Materials and Methods
Proteomics Data Set
A proteomic data set reported by Adam et al. (2002) is used in this study. The data set consists of SELDI-TOF MS spectra for 326 samples, which is generated using the IMAC-3 chip (Ciphergen Biosystems, Inc., Fremont, CA). Of 326 serum samples used, 167 samples were from the PCA patients, 77 from the patients with benign prostatic hyperplasia (BPH), and 82 from healthy individuals. The samples were subsequently divided into two classes for this study, cancer samples (167 PCA samples) versus noncancer samples (159 samples including both BPH and healthy individuals) (Qu et al. 2002). Each sample was characterized by 779 peaks of a spectrum. These peaks were determined in the mass range of 2,000–40,000 Da and provided by the original authors (Adam et al. 2002) for this study. All these peaks were used as predictor variables without preselection to develop the DF model.
Decision Tree
A DT model was developed using a variant of the classification and regression tree (CART) method (Breiman et al. 1995), which consists of two steps—tree construction and tree pruning (Clark and Pregibon 1997). In the tree construction process the algorithm identifies the best predictor variables that divide the sample in the parent node into two child nodes. The split maximizes the homogeneity of the sample population in each child node (e.g., one node is dominated by the cancer samples, and the other is populated with the noncancer samples). Then, the child nodes become parent nodes for further splits, and splitting continues until samples in each node are either in one classification category or cannot be split further to improve the quality of the DT model. To avoid overfitting the training data, the tree is then cut down to a desired size using tree cost-complexity pruning (Clark and Pregibon 1997). In the end of the process, each terminal node contains a certain percentage of cancer samples. This percentage specifies the probability of a sample to be the cancer sample. In this study the cutoff 0.5 was used to distinguish cancer samples from noncancer samples. If a terminal node contains the proportion of cancer sample (p) > 50% (i.e., p > 0.5), all the samples in this terminal are designated as cancer samples and p is the probability value assigned to the entire sample in this terminal node. Similarly, samples are noncancer if the probability is < 0.5.
Decision Forest
DF is a consensus modeling technique, where the results of multiple DT models are combined to produce a more accurate prediction than any of the individual independent DT models. Because combining several identical DT models produces no gain, the rationale behind DF is to develop multiple DT models that are heterogeneous with comparable quality. “Heterogeneity” emphasizes each DT model’s unique contribution to the combined prediction, which is accomplished by developing each DT model based on a distinct set of predictor variables. “Comparable quality” ensures each DT model’s equal weight in combining prediction, which requires each DT model having similar accuracy of prediction. Thus, the development of a DF model consists of three steps (Tong et al. 2003a): a) develop a DT model, b) develop the next DT model based on only the predictor variables that are not used in the previous DT model(s), and c) repeat the first two steps until no additional DT models can be developed. In this process the misclassification rate for each DT model is controlled at a fixed level (3–5%) to ensure the comparable quality of individual DT models. The same classification call in DT is used for determining a sample’s classification based on the mean probability value of all DT models used in DF.
Randomization Test for Chance Correlation
Because proteomic data usually contain a large number of predictor variables with a relatively small number of samples, it is possible that the patterns identified by a classification model could be simply due to chance. Thus, we used a randomization testing to assess the degree of chance correlation. In this method the predefined classification of the samples was randomly scrambled to generate 2,000 pseudo-data sets (Good 1994). The DF models were developed for each pseudo-data set, and the results were then compared with the DF model from the real data set to determine the degree of chance correlation.
Model Validation
A common approach for assessing the predictivity of a classification model is to randomly split the available samples into a training set and a test set. The predictivity of a fitted model using all the samples is estimated based on the prediction accuracy for the test set. Arguably, the cross-validation method could be considered as an extension of this external validation procedure and might offer an unbiased way to assess the predictivity of a model from a statistical point of view (Hawkins et al. 2003). In this procedure a fraction of samples in the data set are excluded and then predicted by the model produced using the remaining samples. When each sample is left out one at a time, and the process repeated for each sample, this is known as leave-one-out cross-validation (LOO). If the data set is randomly divided into n groups with approximately equal numbers of samples, and the process is carried out for each group, the procedure is called leave-n-out cross-validation (LNO). Because LOO gives a minimal perturbation to the data set and therefore might not detect overfitting of a model, the leave-10-out cross-validation (L10O) is commonly used for classification models.
It is important to point out that the LNO results vary for each run because the partition of the data set is changing in a random manner (except for the LOO procedure). The variation increases as the number of left-out samples increases (i.e., n decreases with n > 1). Care must be taken when interpreting the results derived from only one pass through an LNO process, which could lead to a conclusion that might not represent the true predictivity of the fitted model due to chance. Rather, the mean of many passes through the LNO process should well approximate the predictivity of the fitted model. In this study an extensive L10O procedure was implemented in DF, where the L10O process was repeated 2,000 times using randomly divided data sets in each run. The choice of 2,000 runs is based on our previous experience of where reliable statistics can be reached (Tong et al. 2003a). In this validation process a total of 20,000 pairs of training and test sets were generated, and each sample was predicted by 2,000 different models. The results derived from this process provide an unbiased statistic for evaluating the predictivity of a fitted model.
Results
DF was applied to the proteomic data set for distinguishing cancer from noncancer. The fitted DF model for the data set contains four DT models, each of them having the comparable misclassifications ranging from 12 to 14 (i.e., 3.7–4.3% error rate; Table 1). The misclassification is significantly reduced as the number of DT models to be combined increases to form a DF model (Figure 1). The four-tree DF model gave 100% classification accuracy. However, it is important to note that a statistically sound fitted model provides limited indication of whether the identified pattern is biologically relevant or is solely due to chance. Neither does such a fitting result provide validation of the model’s capability for predicting unknown samples that were not included in the training set used for model development. It is important to carry out a rigorous validation procedure to determine the fitted model with respect to the degree of chance correlation and the level of confidence for predicting unknown samples.
Assessment of Chance Correlation
We compared the predictive accuracy for the left-out samples in the 2,000 L10O runs of the real data set (total of 20,000 pairs of training and test sets) with those derived from the L10O run for each of the 2,000 pseudo-data sets (total of 20,000 pairs of training and test sets). The distributions of the prediction accuracy of every pair for both real and pseudo-data sets are plotted in Figure 2. The distribution of prediction accuracy of the real data set centers around 95%, whereas the pseudo-data sets are near 50%. The real data set has a much narrower distribution compared with the pseudo-data sets, indicating that the training models generated from the L10O procedure for the real data set give consistent and high prediction accuracy with their corresponding test sets. In contrast the prediction results of each pair of training and test sets in the L10O process for the pseudo-data sets varied widely, implying a large variability of signal:noise ratio among these training models. Importantly, there is no overlap between two distributions, indicating that a statistically and biologically relevant DF model could be developed using the real data set.
Assessment of Prediction Confidence
DF assigned a probability value for each prediction, where samples with the probability value ≥ 0.5 were designated as cancer samples, whereas others were designated as normal samples. Figure 3 provides two sets of information derived from the 2,000 L10O runs over 10 equal probability intervals between 0 and 1: a) the number of left-out samples predicted in each bin and b) the misclassification rate in each bin. Analysis shows that the 0.7–1.0 interval has a concordance of 99.2% for the cancer samples (0.8% false positives), whereas the 0.0–0.3 interval has a concordance of 98.2% for the noncancer sample (1.8% false negatives). These two probability ranges accounted for 79.7% of all left-out samples. The vast majority of misclassifications occur in the 0.3–0.7 probability range, where the average prediction accuracy was only 78.9% but which, fortunately, accounted for only 20.3% of total of left-out samples. Therefore, we defined both the predicted probability ranges of 0.0–0.3 and 0.7–1.0 as the high-confidence (HC) region, whereas the predicted probability range of 0.3–0.7 was considered the low-confidence (LC) region.
Comparison of DF with DT
Table 2 summarizes the statistical results of the 2,000 L10O runs for both DF and DT. Overall, the DF model increases prediction accuracy by about 5% compared with the DT model, from 89.4 to 94.7%. In the HC region, the DF model increases prediction accuracy compared with the DT model by 8% from 90.7 to 98.7%, compared with 15% from 63.8 to 78.9% in the LC region.
Biomarker Identification
In addition to development a predictive model for proteomic diagnostics, identification of potential biomarkers is another important use of the SELDI-TOF MS technology (Diamandis 2003). Each DT model in DF determines a sample’s classification through a series of rules based on selection of predictor variables. Thus, it is expected that the DF-selected variables could be useful as a starting point for biomarker identification.
There were two lists of model-selected variables derived from DF, one used in fitting (the fitting-variable list; Table 1) and the other used by at least one of the models in the 2,000-L10O process (the L10O-variable list). The L10O-variable list contained 323 unique variables, which actually included all variables in the fitting-variable list. Given that the sample population is different among the models in the 2,000 L10O runs, the number of models selecting a particular variable should tend to increase in direct proportion to the biologic relevance of the variable. There were 46 variables that were selected > 10,000 times in the 2,000-L10O process (Table 3), including all 12 m/z peaks identified by Qu et al. (2002) using boosted decision stump feature selection based on a slightly larger data set. The two-group t-test results indicated that 32 of 46 high-frequency variables have p-values < 0.001 (Table 3). Selection of 23 variables from Table 3 that were used in both fitting and L10O with p < 0.001 appears a reasonable approach to choosing a set of proteins for biomarker identification.
Discussion
We developed a classification model for early detection of PCA on the basis of SELDI-TOF MS data using DF. DF is an ensemble method, where each prediction is a mean value of all the DT models combined to construct the DF model. The idea of combining multiple DT models implicitly assumes that a single DT model could not completely represent important functional relationships between predictor variables (m/z peaks in this study) and the associated outcome variables (PCA in this study), and thus different DT models are able to capture different aspects of the relationship for prediction. Given a certain degree of noise always present in omics data, optimizing a DT model inherently risks overfitting the noise. DF minimizes overfitting by maximizing the difference among individual DT models. The difference is achieved by constructing each individual DT model using a distinct set of predictor variables. Noise cancellation and corresponding signal enhancement are apparent when comparing the results from DF and DT. DF outperforms DT in all statistical measures in the 2,000 L10O runs. Whether DT performs better than other similar classification techniques depends on the application domain and the effectiveness of the particular implementation. However, Lim and Loh (1999) compared 22 DT methods with nine statistical algorithms and two artificial neural network approaches across 32 data sets and found no statistical difference among the methods evaluated. Thus, the better performance of DF than DT implies that the unique ensemble technique embedded in DF could also be superior to some other classification techniques for class prediction using omics data.
Combining multiple DT models to produce a single model has been investigated for many years (Bunn 1987, 1988; Clemen 1989; Zhang et al. 2003). Evaluating different ways for developing individual DT models to be combined has been a major focus, which have all been reported to improve ensemble predictive accuracy. One approach is to grow individual DT models based on different portions of samples randomly selected from the training set using resampling techniques. However, resampling using a substantial portion of samples (e.g., 90%) tends to result in individual DT models that are highly correlated, whereas using a less substantial portion of samples (e.g., 70%) tends to result in individual DT models of lower quality. Either high-correlated or lower-quality individual DT models can reduce the combining benefit that might otherwise be realized. The individual DT models can also be generated using more robust statistical resampling approaches such as bagging (Breiman 1996) and boosting (Freund and Schapire 1996). However, it is understood that boosting that uses a function of performance to weight incorrect predictions is inherently at risk of overfitting the noise associated with the data, which could result in a worse prediction from an ensemble model (Freund and Schapire 1996). Another approach to choosing an ensemble of DT models centers on random selection of predictor variables (Amit and Geman 1997). One popular algorithm, random forests, has been demonstrated to be more robust than a boosting method (Breiman 1999). However, in an example of classification of naive in vitro drug treatment sample based on gene expression data, Gunther et al. (2003) showed reduced prediction accuracy of random forests (83.3%) compared with DT (88.9%).
It is important to note that the aforementioned techniques rely on random selection of either samples or predictor variables to generate individual DT models. In each repeat the individual DT models of the ensemble are different; thus, the biologic interpretation of the ensemble is not straightforward. Furthermore, these methods need to grow a large number of individual DT models (> 400) and could be computationally expensive. In contrast the difference in individual DT models is maximized in DF such that a best ensemble is usually realized by combining only a few DT models (i.e., four or five). Importantly, because DF is reproducible, the variable relationships are constant in their interpretability for biologic relevance.
Omics data such as we stress in this article normally have a limited number of samples and a large number of predictor variables. Furthermore, the noise associating with both categorical dependent variables and predictor variables is usually unknown. It is consequently imperative to verify that the fitted model is not a chance correlation. To assess the degree of chance correlation of the PCA model, we computed a null distribution of prediction with 2,000 L10O runs based on 2,000 pseudo-data sets derived from a randomization test. The null hypothesis was tested by comparing the null distribution with the DF predictions in 2,000 L10O runs using the actual training data set. The degree of chance correlation in the predictive model can be estimated from the overlap of the two distributions (Figure 2). Generally speaking, a data set with an unbalanced sample population, small sample size, and/or low signal:noise ratio would tend to produce a model with distribution overlapping the null distribution. For the PCA model, the distributions are spaced far apart with no overlap, indicating that the model is biologically relevant.
A model fitted to omics data has minimal utility unless it can be generalized to predict unknown samples. The ability to generalize the model is an essential requirement for diagnostics and prognostics in medical settings and/or risk assessment in regulation. Commonly, test samples are used to verify the performance of a fitted model. Such external validation, while providing a sense of real-world application, must incorporate assurance that samples set aside for validation are representative. Setting aside only a small number of samples might not provide the ability to fully assess the predictivity of a fitted model, which in turn could result in the loss of valuable additional data that might improve the model. Besides, one rarely enjoys the luxury of setting aside a sufficient number of samples for use in external validation in omics research because in most cases data sets contain barely enough samples to create a statistically robust model in the first place. Therefore, an extensive L10O procedure is embedded in DF that can provide an unbiased and rigorous way to assess the fitted model’s predictivity within the available samples’ domain without the loss of samples set aside for a test set.
A model’s ability to predict unknown sample’s is directly dependent on the nature of the training set. In other words, predictive accuracy for different unknown samples varies according to how well the training set represents the given samples. Therefore, it is critical to be able to estimate the degree of confidence for each prediction, which could be difficult to derive from the external validation. In DF the information derived from the extensive L10O process permits assessment of the confidence level for each prediction. For the PCA model the confidence level for predicting unknown samples was assessed based on the distribution of accuracy over the prediction probability range for the left-out samples in the 2,000 L10O runs. We found that the sensitivity and specificity of the model were 99.2 and 98.2% in the HC region, respectively, with an overall concordance of 98.7%. In contrast, a much lower prediction confidence of 78.9% was obtained in the LC region, indicating that these predictions need to be further verified by additional methods. Generally, the number of samples within the HC region compared with the LC region depends on the signal:noise ratio in the data set. For noisy data, more unknown samples will be predicted in the LC region and could be as high as 40–50% (results not shown). For the PCA data set some 80% of the left-out samples predicted in the 2,000 L10O runs were in the HC region, indicating that the data set has a high signal:noise ratio.
A number of classification methods reported in the literature require selection of the relevant or informative predictor variables before modeling is actually performed. This is necessary because the method could be susceptible to noise without this procedure, and the computational cost is prohibitive for iterative variable selection during cross-validation. Although these are otherwise effective methods, they could produce what is called “selection bias” (Simon et al. 2003). Selection bias occurs when the model’s predictive performance is assessed using cross-validation where only the preselected variables are included. Because of selection bias, cross-validation could significantly overstate prediction accuracy (Ambroise and McLachlan 2002), and external validation becomes mandatory to assess a model’s predictivity. In contrast, model development and variable selection are integral in DF. DF avoids the selection bias during cross-validation because the model is developed at each repeat by selecting the variables from the entire set of predictor variables. The cross-validation thereby provides a realistic assessment of the predictivity of a fitted model. Given the trend of ever decreasing computation expense, carrying out exhaustive cross-validation is increasingly attractive, particularly when scarce sample data can be used for training as opposed to external testing. Of course, external validation is still strongly recommended when the amount of data suffices, in which case the cross-validation process will still enhance the rigor of the validation.
Figure 1 Plot of misclassifications versus the number of DT models to be combined in DF.
Figure 2 Prediction distribution in the 2,000-L10O process: real data set (A) and 2,000 pseudo-data set (B) generated from a randomization test.
Figure 3 Distribution of true/false predictions for the left-out samples over 10 equal-probability bins in the 2,000-L10O process.
Table 1 Summary of the four DT models combined for developing the DF model (n = number of misclassifications).
DT model 1 (n = 12) DT model 2 (n = 13) DT model 3 (n = 14) DT model 4 (n = 14)
Variables (m/z peaks) used in each DT model 9,656 8,067 6,542 7,692
8,446 8,356 7,934 6,756
5,074 5,457 7,195 9,593
6,797 2,144 4,497 9,456
8,291 7,885 4,080 5,978
9,720 7,024 6,199 3,780
3,486 7,771 7,481 2,794
4,191 3,897 5,586 7,844
4,653 4,757 6,099 5,113
6,890 7,070 28,143
2,014 24,400 2,982
9,149 2,887 6,443
7,054 7,820
4,475 4,580
4,537
7,409
7,054
Table 2 Comparison of statistics between DF and DT models in prediction of the left-out samples in the 2,000 L10O runs.
Prediction accuracy DF (%) DT (%)
Overall accuracy 94.7 89.4
Accuracy in HC region 98.7 90.7
Accuracy in LC region 78.9 63.8
Table 3 List of m/z peaks used more than 10,000 times in the 2,000-L10O process, where 23 peaks are used in fitting with p < 0.001.
m/z Peaks (Da) Frequency p-Value
7,934a 30,203 < 0.001
9,149a 26,482 < 0.001
7,984b 25,171 < 0.001
8,296a 24,793 < 0.001
3,897a 23,754 < 0.001
9,720a,c 22,630 < 0.001
7,776a 21,723 0.003
7,024a,c 21,718 < 0.001
5,074a 20,800 < 0.001
8,446a 20,620 < 0.001
9,656a,c 20,479 < 0.001
6,542a,c 20,219 < 0.001
8,067a,c 20,058 < 0.001
7,692a 19,982 0.004
6,797a,c 19,587 < 0.001
8,356a,c 19,429 < 0.001
7,054a 19,333 0.010
6,099a 19,265 0.004
5,586a 18,103 < 0.001
7,820a,c 17,918 0.359
6,756a 17,668 < 0.001
9,593a 17,615 < 0.001
7,844a 17,611 0.089
4,191a 17,387 < 0.001
3,486a 17,290 < 0.001
4,451b 17,041 0.459
4,079a,c 16,790 0.020
9,456a 16,767 < 0.001
4,653a 16,674 0.002
7,195a 15,832 < 0.001
7,885a,c 15,388 < 0.001
8,277b 15,388 < 0.001
6,072b 15,093 < 0.001
3,963b,c 14,434 < 0.001
3,780a 14,139 0.014
4,291b 13,540 < 0.001
4,102b 13,294 0.001
4,858b 13,076 0.003
6,949b,c 12,555 < 0.001
3,280b 11,808 < 0.001
6,991b,c 11,281 0.122
2,144a 11,110 < 0.001
9,100b 10,578 < 0.001
7,652b 10,159 0.005
5,457a 10,139 < 0.001
6,914b 10,073 < 0.001
a Used in fitting.
b Not used in fitting.
c Reported by Qu et al. (2002).
==== Refs
References
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/txg.7296ehp0112-00162815598614ToxicogenomicsArticlesRenal Toxicogenomic Response to Chronic Uranyl Nitrate Insult in Mice Taulan Magali 12Paquet François 1Maubert Christophe 1Delissen Olivia 1Demaille Jacques 2Romey Marie-Catherine 21Institut de Radioprotection et de Sûreté Nucléaire, Laboratoire de Radiotoxicologie Expérimentale, Pierrelatte, France2Institut de Génétique Humaine, Laboratoire de Génétique Moléculaire et Chromosomique, Montpellier, FranceAddress correspondence to M-C. Romey, Laboratoire de Génétique Moléculaire et Chromosomique, Institut de Génétique Humaine, 141, Route de la Cardonille, 34396 Montpellier Cedex 05, France. Telephone: 33 4 67 41 53 64. Fax: 33 4 67 41 53 65. E-mail:
[email protected] thank M. Claraz-Donnadieu, F. Petitot, and S. Frelon for helpful discussions.
The authors declare they have no competing financial interests.
11 2004 15 10 2004 112 16 1628 1635 28 5 2004 14 10 2004 Publication 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 nephrotoxicity of uranium has been established through numerous animal studies, relatively little is known about the effects of long-term environmental uranium exposure. Using a combination of conventional biochemical studies and serial analysis of gene expression (SAGE), we examined the renal responses to uranyl nitrate (UN) chronic exposure. Renal uranium levels were significantly increased 4 months after ingestion of uranium in drinking water. Creatinine levels in serum were slightly but significantly increased compared with those in controls. Although no further significant differences in other parameters were noted, substantial molecular changes were observed in toxicogenomic profiles. UN induced dramatic alterations in expression levels of more than 200 genes, mainly up-regulated, including oxidative-response–related genes, genes encoding for cellular metabolism, ribosomal proteins, signal transduction, and solute transporters. Seven differentially expressed transcripts were confirmed by real-time quantitative polymerase chain reaction. In addition, significantly increased peroxide levels support the implication of oxidative stress in UN toxicant response. This report highlights the potential of SAGE for the discovery of novel toxicant-induced gene expression alterations. Here, we present, for the first time, a comprehensive view of renal molecular events after uranium long-term exposure.
drinking watergene expression profileslong-term exposuremiceSAGEtoxicogenomicsuranyl nitrate
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Uranium, the heaviest of the naturally occurring elements, is widely present in the environment as a result of leaching from natural deposits, release in mill tailings, emissions from the nuclear industry, the combustion of coal and other fuels, and the use of phosphate fertilizers and weapons that contain uranium. Thus, uranium is found in various chemical forms and different levels in all soils, rocks, sea, and bedrock (Bosshard et al. 1992; Kurttio et al. 2002; Moss et al. 1983). It is also found in both food and drinking water. The wide range of levels of uranium in drinking water, together with the observation of consistently higher levels in certain community water supplies, has raised concerns regarding the potential hazard of such sources of uranium to human health.
Many isolated studies conducted on the mechanisms for the toxic effects of uranium at moderate to high acute doses on experimental animals have shown that the major health effect of uranium is chemical kidney toxicity rather than a radiation hazard (Lin et al. 1993; Miller et al. 1998, 2002). In addition only a few studies have attempted to characterize the effects of chronic exposure to uranium through drinking water (Gilman et al. 1998a, 1998b, 1998c; Kurttio et al. 2002; McDonald-Taylor et al. 1997; Zamora et al. 1998). Although chronic uranium exposure in humans has been clearly associated with increasing urinary glucose, alkaline phosphatase, and β2-microglobulin supporting proximal tubule alterations, the urinary albumin levels, which are indicators of glomerular function, are conflicting (Kurttio et al. 2002; Zamora et al. 1998). Although both functional and histologic damage to the proximal tubules resulting from acute uranium exposure has been clearly demonstrated (Schramm et al. 2002; Sun et al. 2002), little is known about the effect of long-term environmental uranium exposure in both humans and animals (Gilman et al. 1998a, 1998b, 1998c; Kultima et al. 2002; Mao et al. 1995; McDonald-Taylor et al. 1997; Zamora et al. 1998).
Toxicogenomics is presently used to evaluate risk assessment of environmental toxicants through the identification of gene expression networks, as well as to evaluate toxicant-induced gene expression as a biomarker to assess human exposure. Several researchers are currently combining the identification of gene expression patterns representative of adverse outcomes with traditional biochemical parameter measures to categorize and classify toxic responses through direct comparison in exposed and control samples. The use of oligonucleotide-based or cDNA microarrays for understanding the biochemical processes associated with environmental chemical exposures has proven successful in recent experiments on human health risk assessment for several toxicants (Andrew et al. 2003; Bartosiewicz et al. 2001).
Because the risk assessment and establishment of exposure limits for uranium in drinking water are of considerable importance in various areas, including Finland, we used for the first time the SAGE (serial analysis of gene expression) approach to identify gene expression profiles associated with this hazard exposure. Because toxicogenomics provides increased confidence in extrapolation of hazards observed in animals studies to likely hazards in humans, we examined renal molecular effects of chronic exposure to uranium in mice.
Materials and Methods
Animals
The C57BL/6J mouse was chosen because of the current state of knowledge about this transcriptome and numerous databases such as Mouse SAGE Site (http://mouse.biomed.cas.cz/sage/). This animal model should help improve the overall quality of SAGE gene expression data. Experiments were performed with 16 male C57BL/6J mice, weighing 25–30 g (Harlan, Gannat, France) at the beginning of the study. The mice were randomly divided into three groups: one control group (group 0, six animals) and two uranyl nitrate (UN)-treated mice (groups 1 and 2, six and four animals, respectively). Exposed groups 1 and 2 received UN mineral water at concentrations of 80 or 160 mg UN/L of water, respectively, approximately 3- or 6-fold higher than levels found in bedrock of southern Finland (Juntunen 1991). Uranium in water, given to control mice, was determined to be < 0.002 mg/L uranium. Body weights were measured weekly. Food intake and fluid consumption data were recorded. After 4 months of treatment all animals were euthanized by exsanguination using cardiac puncture. Urine and blood were collected for each group. The kidneys were either embedded in Epon for morphologic examination or snap-frozen in liquid nitrogen and then stored at −70°C until further study.
Assessment of Renal Function Parameters
Uranium contents were determined in samples of kidney using a kinetic phosphorescence analyzer (KPA; Ejnik et al. 2000). Serum creatinine and urea levels and urinary concentrations of glucose and γ-glutamyl transpeptidase (γ-GT) were measured by routine methods.
RNA Isolation
Total RNAs, extracted from renal tissue using the RNA isolation mini kit (Qiagen, Courtaboeuf, France), were pooled for SAGE or used individually for real-time reverse transcriptase polymerase chain reaction (RT-PCR) analyses. The amount of total RNA was determined using a fluorescent nucleic acid stain (RiboGreen RNA Quantitation Kit; Molecular Probes, Montluçon, France). The quality of the RNA was evaluated by measuring the 260:280-nm ratios and confirmed by visualization of intact 18S and 28S RNA bands after agarose gel electrophoresis.
Analysis of Gene Expression
Production of kidney library.
Kidney libraries were generated from 50 μg of total RNA using the I SAGE kit (Invitrogen Corp., Cergy Pontoise, France) following the manufacturer’s instructions (Invitrogen Corporation 2004), adapted from initial description (SAGE 2004; Velculescu et al. 1995). Because of budgetary restrictions, SAGE was performed for only control [UN(−)] and 80 mg UN/L–treated mice [UN(+)], that is, groups 0 and 1, respectively.
Tag quantification.
Concatemer sequences were analyzed by using SAGE software (version 4.0; Invitrogen), which automatically detects and counts tags from sequence files. SAGE software excludes replicate ditags from the tag sequence catalog because the probability of any two tags being coupled in the same ditag is small, even for abundant transcripts. For tag identification, the tag list of each library was matched against a mouse tag database extracted by SAGE software from GenBank (http://www.ncbi.nlm.nih.gov/). Usually, SAGE tag sequences matched more than one transcript. The average p-value computed by the SAGE software, based on a Monte Carlo analysis (Zhang et al. 1997), serves as ranking parameter to produce a list of differentially expressed genes. SAGE data for the libraries described here are available at Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo); accession nos. GSM24256 and GSM24257).
Real-Time RT-PCR
Total RNAs (1 μg) from UN(−) and UN(+) renal tissue (extracted as described above) were used to generate cDNA using Moloney Murine Leukemia Virus Reverse Transcriptase (Invitrogen) according the manufacturer’s conditions. Primers and probes specifically designed for selected cDNA using Primer Express software, version 2.0 (PE; Applied Biosystems, Courtaboeuf, France), are listed in Table 1. The ABI PRISM 7000 Sequence Detection System was used for detected real-time RT-PCR products with the SYBR Green I assay, according to recommendations of the manufacturer (PE; Applied Biosystems). For two cases in which we encountered difficulties with the SYBR Green I assay, we used TaqMan probe assays (Applied Biosystems) (Table 1). Each PCR reaction was optimized to ensure that a single band of the appropriate size was amplified and that no bands corresponding to genomic DNA amplification or primer–dimer pairs were present. The PCR cycling conditions were performed for all samples as follows: 50°C, 2 min for AmpErase UNG (Applied Biosystems) incubation; 95°C, 10 min for AmpliTaq Gold (Applied Biosystems) activation; and 40 cycles for the melting (95°C, 15 sec) and annealing/extension (60°C for 1 min) steps. PCR reactions for each template were done in triplicate in 96-well plates. The comparative threshold cycle (Ct) method using Primer Express software, version 2.0, was used to determine relative quantitation of gene expression for each gene compared with the hypoxanthine guanine phosphoribosyl transferase control (listed in Table 1).
Hydrogen Peroxide Assay
To determine the impact of UN on the oxidative balance status, hydrogen peroxide levels were determined using a PeroxiDetect kit (Sigma, Lyon, France). Briefly, kidney samples from different groups (0, 1, and 2) were homogenized in the indicated phosphate buffer on ice, then centrifuged at 15,000×g for 15 min at 4°C. Supernatant samples (100 μL) were incubated for 30 min with 1 mL of aqueous peroxide color reagent (aqueous solution containing 100 mM sorbitol and 125 μM xylenol orange) and 10 μL of ferrous ammonium sulfate reagent (25 mM ferrous ammonium sulfate in 2.5 M sulfuric acid), and the hydrogen peroxide level was measured by the absorbance at 560 nm.
Results
General Observations
To examine the general parameters, we performed gross end-point analysis such as body and organ weight changes and histologic observations, as well as the dosage of uranium content in renal tissue and biochemical markers. No significant dose-related effects were observed on body weight gain, food intake, or water consumption. Because the concentrations of UN in the drinking water remained constant throughout the study, it is natural to assume that the measurement of UN per kilogram body weight decreased with age.
Gross pathologic examination was performed in all animals, and the histopathologic analysis did not identify any significant differences between control and exposed groups. We observed a significant dose-dependent increase in renal uranium tissue levels in groups 1 and 2 compared with control mice, using KPA. Compared with controls, there were no significant differences in kidney weights in any dose group (Table 2). Serum creatinine levels appeared to increase in dose-independent manner with UN treatment, and groups 1 and 2 showed creatinine levels significantly higher than those of controls.
Genes Responding to Toxic UN Exposure
We investigated the transcriptomic response that underlies the induction of the metal-elicited molecular modification in C57/Bl6J mice. SAGE was used to determine the global gene expression profile in UN toxicity. This approach allows an analysis of gene expression by the sequencing of approximately 21,000 transcripts from kidney libraries of the groups 0 and 1, which represent 5,252 and 4,069 unique tags, respectively.
We validated the quality of both libraries by comparing both with previous data on the kidney (Chabardes-Garonne et al. 2003; El-Meanawy et al. 2000; Virlon et al. 1999). For example, known markers for proximal tubules [kidney androgen-regulated protein (kap)] and thick ascending limbs [uromodulin (Umod)] were evidenced in both libraries. As expected, a large fraction of the most abundant tags matched with widely expressed mitochondrial genes or ribosomal proteins such as ribosomal proteins P1 and S26. Because the kidney mass consists predominantly of proximal tubules, a significant fraction of tags are mapped to genes known to be expressed in proximal tubular epithelial cells. Particularly, the most abundant transcripts in normal kidney were kap and glutathione peroxidase 3 (GPx3), in agreement with previous data (El-Meanawy et al. 2000).
Tags that are significantly up- or down-regulated in the UN RNA library are listed in Table 3 with their frequency and their relevant accession number. We considered only the transcripts with a significant expression change (p < 0.05). Considering the large number of sequenced tags, the number of genes expressed in kidney was evaluated by excluding tags matching mitochondrial sequences, tags with multiple matches, and nonreliable matches. Tags were arbitrarily separated in categories according to gene function. As illustrated in Table 3, most of these changes involved up-regulation. SAGE analysis revealed the expression changes of genes related to lipid metabolism [crystalline, zeta (Cryz); phosphatidic acid phosphatase type 2c (PPap2c)], carbohydrate metabolism [phosphoglycerate kinase 1 (Pgk1); sorbitol dehydrogenase 1 (Sdh1)], and amino acid metabolism (glutamate dehydrogenase (Glud); ornithine decarboxylase, structural (Odc). The UN-induced transcripts consisted mainly of genes encoding proteins associated with protein biosynthesis [ribosomal protein S25 (Rps25); S26 (Rps26); large, P1 (Rplp1); L19 (Rpl19)], protein folding [heat-shock 10 kDa protein 1 (chaperonin 1) (Hspe1)], and proteolysis [kallikrein 5 (Klk5), protein C (Proc)]. Many genes involved in signaling were up-regulated, such as hormonal receptors [growth hormone receptor (Ghr), cholecystokinin A receptor (Cckar)]. Chronic exposure to UN also increased the expression of a number of genes related to oxidative process and detoxification. Among these is cytochrome P450 (Cyp4b1), which catalyzes the oxidation of a wide variety of substrates, including endogenous lipids and xenobiotics (Heng et al. 1997). Other relevant enzymes under- or overexpressed include thioredoxin, mitochondrial (Txn2); superoxide dismutase 1, soluble (Sod1); and thioether S-methyltransferase (Temt). We also mainly observed up-regulation of genes related with ion transporters including solute carrier family 34 (sodium phosphate), member 1 (Slc34a1, NaPi-II); and with electron transporters such as ATPase inhibitor, and cytochrome c oxidase, subunit IVa (Cox4a); subunit VIIIa (Cox8a); and subunit XVII assembly protein homolog (Cox 17 ). Finally, expression levels of several genes, in the category related to stress/apoptosis [Bcl2 associated athanogene 1 (Bag1); nerve growth factor receptor (TNFRSF16) associated protein 1 (Ngfrap1)]; immunity (Ia-associated invariant chain (Ii)]; and translationally regulated transcripts (21 kDa) (Trt, Tpt1, Tctp, Umod) were changed.
Real-Time Quantitative PCR Analyses
To validate our SAGE data, we conducted real-time quantitative PCR analyses to verify the differential expression of seven selected genes (Figure 1). kap was chosen because of its high abundance level in the normal and contaminated kidney. Solute carrier family 34 [sodium phosphate, NaPi] member 1 (slc34a1, NaPi-II)], Sod1, Finkel-Biskis-Reilly murine sarcoma virus ubiquitously expressed (Fau), and translationally regulated transcript (Trt or Tctp) were chosen because they were increased in our data. Umod and ornithine decarboxylase structural (Odc) were chosen because their expression levels were decreased in the present study as well as in ischemic acute renal failure (ARF) or UN-induced chronic renal failure, respectively (Fleck et al. 2003). Using real-time PCR analyses, Kap, NaPi-II, Sod, Fau, and Tctp were confirmed to be significantly increased whereas Odc and Umod were decreased in chronic exposure to UN. In summary PCR analysis confirmed the accuracy of the differences in expression levels observed in our SAGE analysis for group 1. Moreover, using real-time PCR for group 2, we observed that the expression of the selected transcripts were altered in the same direction compared with group 1, that is, increased or decreased. We noted dose-dependent increases in Tctp mRNA level at the highest concentration, and the observed decrease of Odc mRNA levels was more moderate for group 2.
Peroxide Level Measurement
To evaluate whether the variations in both Sod and Gpx transcripts may reflect a potential oxidative stress, we examined the production of H2O2. The concentration of H2O2 in the kidney was found to be significantly higher in groups 1 and 2 compared with the control group (4.06 ± 0.06 and 4.39 ± 0.11 vs. 3.3 ± 0.02 nmol peroxide/mL) (Figure 2). Long-term UN exposure clearly caused the production of H2O2 levels in UN groups 1 and 2, in dose-dependent fashion.
Discussion
Human exposures to metals such as uranium in both occupational and environmental settings are common occurrences. Uranium exposures are a growing concern in our society. Classically, toxicologists assess potential chronic adverse health outcomes resulting from chemical exposures by using gross end points such as body or organ weight changes and histopathologic observations. However, analysis of histologic or biochemical markers often does not provide information about the mechanisms involved in toxicant response. The study of molecular mechanisms of toxicant action might provide information crucial to the understanding of their potential adverse effects on human health. Recent technologies such as SAGE facilitate studies that add insight into the cellular response to chemical exposure. In environmental monitoring, SAGE could not only provide a method for quickly categorizing chemicals and assigning a mode of toxic action but also allow more sensitive end points to address specifically gene expression pattern.
Results reported here identify > 200 genes from approximately 21,000 tags sequenced, for which the expression in kidney changed significantly after UN long-term exposure. Most of these tags represent distinct transcripts; however, some tags, especially those detected only once, may result from PCR or sequencing errors (Velculescu et al. 1997; Zhang et al. 1997). Using classical end-point examination, including histologic appearance of the kidney and clinical and biochemical parameters, we observed that the UN doses used in this study produced only a slight alteration in serum creatinine levels and a significant but nonlinear increase of intrarenal uranium content. The dose-independent induction of the serum creatinine may be attributable, as already reported (Amin et al. 2004), to the fact that this parameter, like serum urea, traditionally used as indices of changes in glomerular filtration rate, is a relatively insensitive marker of glomerular injury. Taken together, these data suggest that the glomerular filtration rate remains relatively normal in mice after UN chronic exposure. Because the degree of renal injury appeared to be minimal in the strain of mouse used in the present study, further work will be needed to correlate the renal toxicity with the chronic uranium treatment, in dose- and time-dependent manner.
At the molecular level we observed that UN induced changes in expression profiles for oxidative response–related genes and genes encoding for ribosomal proteins, cellular metabolism, signal transduction, and solute transporters. Altered expression of these genes likely reflects an altered protein product (not determined in the present study).
Oxidative Stress Response
Reactive oxygen species (ROS) are produced by the metabolism of O2 in all aerobic cells and are essential for normal cellular signaling functions. However, oxidative stress can occur as a result of either increased ROS generation or depressed antioxidant system, or both. Of them, SOD, catalase, and GPx constitute the main components of the antioxidant defense system. These antioxidants protect the cell against cytotoxic ROS such as superoxide anions, hydrogen peroxide, and hydroxyl radicals. The measurement of peroxides in biologic systems is one of the factors allowing the determination of the degree of certain free radicals present in specific tissues. Recently, Jung et al. (2003) suggested that H2O2 produced by arsenite might activate growth factor receptor by increasing its tyrosine phosphorylation. These data indicated that H2O2 might be a pivotal mediator of the tumor-promoting activity of arsenite (Jung 2003). In the present study we observed that UN induces dose-dependent production of H2O2. We also observed an increase in Cu,Zn-SOD mRNA levels in the kidney. SOD is an enzyme responsible for dismutation of highly reactive superoxide radicals to H2O2. Moreover, GPx, which scavenges H2O2 and lipid peroxides, had its gene expression level increased, potentially induced by the high concentrations of H2O2. Induction of oxidative balance perturbation has been previously described in UN-induced ARF (Schramm et al. 2002). In addition, it has also been reported that some toxicants such as cadmium and arsenic are able to induce an increase in H2O2 levels after acute exposure (Ercal et al. 2001). Taken together, these data suggest that UN induces oxidative stress. Exploring this point seems of interest in evaluating the risks of UN long-term exposures.
Involvement of Genes Encoding Ion Transporters
The proximal tubule (especially the S3 segment) and the outer medullary thick ascending limb suffer the most severe injury after toxic and ischemic insult (Kwon et al. 2000; Sun et al. 2000). Although basolateral transport of sodium among the entire nephron and collecting ducts occurs via the active Na-K-ATPase pump, the active absorption is mediated by the Na+-dependent inorganic phosphate co-transporters (NaPi-II). In contrast to a previous study (Park et al. 1997) showing that chronic exposure to cadmium impairs the Pi transport capacity, probably by reducing the effective number of NaPi co-transporter units, we found that UN long-term exposure induces an increase of NaPi-II mRNA levels. As already suggested (Levi et al. 1994; Loghman-Adham 1997), this increase in NaPi-II is probably the result of an increase in Vmax by a transporter-shuttling mechanism, which is sensitive to disruptors of microtubule integrity. In addition, as previously reported (Moz et al. 1999) in hypophosphatemia studies, our observations suggest that UN chronic exposure could enhance the renal translational machinery. Further experiments, for example, examining the in vivo rates of NaPi-II synthesis, should allow clarification of whether UN-like hypophosphatemia affects NaPi-II translation. Moreover, Na-K-ATPase expression levels are down-regulated after UN long-term ingestion. This observation is consistent with previous work, after ischemic injury, that also shows a decreased Na-K-ATPase mRNA transcription (Kwon et al. 2000). The potential significance of this observation is that urine volume might be increased because of decreased Na+ reabsorption. Unfortunately, urine volumes were not recorded throughout the experiments, and the monitoring of the water consumption did not reveal any change in differently treated groups compared with controls. Thus, the role of these proteins in response to UN exposure remains unclear and warrants additional investigation.
Involvement of Protein Biosynthesis–Related Genes
Interestingly, many ribosomal subunits and other factors involved in protein synthesis (elongation factor) were induced upon UN treatment. Ribosomal proteins are major component of ribosomes that catalyze protein biosynthesis in the cytoplasm of cells. Under normal growth conditions, ribosomal proteins are synthesized stoichiometrically, in relation with ribosomal RNA, to produce an equimolar supply of ribosomal components. However, regulation of the transcriptional activity of the genes encoding for ribosomal protein in differentiated human tissues appears to be less concertedly regulated than previously reported (Bortoluzzi et al. 2001). Recent progress in ribosome research provides growing evidence that ribosomal proteins can also have a function during various cellular processes such as replication, transcription, RNA processing, DNA repair, and even inflammation; all these functions are independent of their own involvement in the protein biosynthesis (Wool 1996; Yamamoto 2000). In the present work, up-regulation of transcripts for several ribosomal proteins such as RPL13a, RPL19, RPL30, RPLP1, RPS24, and RPS26 has been observed. This latter has been described as a marker to differentiate either ozone or ultraviolet B radiation environmental stresses in plants (Brosché and Strid 1999). Whereas RPS4, RPL19, and RPS18 have been involved in regulation of the development (Wool 1996), RPL13A, RPS18, and RPS24 have been associated in the maturation of mucosal epithelia (Kasai et al. 2003). Moreover, the latter was markedly decreased in colorectal cancer (Kasai et al. 2003). Taken together, these observations may suggest that UN induce a perturbation in protein synthesis and offer a new putative way of investigation on cellular proliferation study after chronic UN exposure.
Others Genes of Interest
ODC, described as the rate-limiting enzyme of polyamine biosynthesis and a marker of G1 phase, is down-regulated in long-term UN exposure. Recently, Fleck et al. (2003) also observed a decrease in Odc expression levels 10 weeks after a single injection of UN. Kramer et al. (2001) have showed that a depletion of polyamine pool, through inhibition of ODC, causes p21-mediated G1 cell cycle arrest, followed by development of a senescence-like phenotype and loss of cellular proliferative capacity. Thus, the decrease in Odc mRNA levels might be related to an arrest of the cell cycle after UN treatment. However, these data are inconsistent with the observed increase in protein biosynthesis–related genes. It has been previously reported that mammalian ODC protein has a very short half-life; its control is under negative feedback regulation by the polyamines, and its degradation is dependent on 26S proteasome complex (Hascilowicz et al. 2002). Interestingly, we noted an increase in proteasome subunit (Psma7) mRNA expression levels. Nevertheless, further study with added dimensions of time and doses may clarify the observed modest Odc mRNA expression levels for the group 2 and allow a best evaluation of uranium chronic exposure impact on its expression. Of particular interest, Umod (Tamm-Horsfall protein) was decreased in the present study. This protein is one of the most abundant in the renal tubule (Bachmann et al 1990). Moreover, expression levels of UMOD have been previously reported to decrease in ischemic-induced ARF (Yoshida et al. 2002). Unexpectedly, in previous work performed in our laboratory, we showed that its expression level was increased in UN-induced ARF. In addition, an up-regulation of Umod has been observed in the progression of nephrolithiasis (Katsuma et al. 2002). However, the role of this protein remains unclear and requires additional investigation. Finally, and perhaps more interestingly, TCTP, a cytoplasmic protein usually found in both normal and tumor cell lines, is overexpressed after UN long-term ingestion. It was identified as an antiapoptotic protein (Li et al. 2001). TCTP is associated with components of the translational machinery, the elongation factors implicated in tumor formation (Cans et al. 2003). Interestingly, we observed dose-dependent increases in Tctp mRNA levels using RT-PCR analysis. Further investigations are necessary to evaluate the implication of this protein in potentially adverse health effects.
In summary, by using SAGE, we elegantly demonstrated that UN chronic exposure induces changes in expression profiles. The present report provides the first evidence that UN alters the expression of numerous genes including those encoding for oxidative-stress–related proteins, ribosomal proteins, solute transporters, and genes involved in cellular metabolism or signal transduction (Figure 3). Although these molecular changes, resulting from a subclinical toxicity, do not systematically lead to kidney failure or overt illness, our results might constitute a determining step in the identification of sensitive biomarkers to prevent the development of a UN-induced renal injury. Moreover, although studying human biology is ideal, such studies are neither feasible nor ethical. Thus, the vast majority of current biomedical research is conducted using mice and rats. However, we must keep in mind that extrapolation to humans might have some bias because humans can be exposed to many compounds simultaneously, often on a chronic or intermittent basis. Thus, the use of throughput genomic approaches after long-term exposure to mixtures of toxicants might help in the assessment of interactions such as additivity, synergism, or antagonism. The comparison of gene expression profiles could help to identify putative new sensitive biomarkers of chronic nephrotoxicity and then evaluate the impact of environmental toxic contaminants on human health.
Figure 1 Confirmation of SAGE data by real-time RT-PCR analysis. The variation of the amplification of the expression in groups 1 and 2 [UN(−)/UN(+)] is plotted. PCR analyses were performed on cDNA from UN(−) or UN(+) tissues.
Figure 2 Measurement of hydrogen peroxide already formed in kidney tissue. An increase in H2O2 level was induced by UN in a dose-dependent manner. Data shown represent means ± SE of three independent experiments (n = 4).
*p < 0.05 versus control.
Figure 3 Cellular pathways triggered in response to UN long-term exposure. Some genes or molecules, which present an altered expression level after uranium ingestion, emphasize the implication of these cellular processes in UN long-term exposure (in parentheses).
Table 1 SYBR Green and TaqMan primer sequences used for RT-PCR reactions.
Gene symbola Gene namea Accession no.a Primer 5′→3′ sequence or assay ID Amplicon size (bp)
Primers using SYBR Green detection
Hprt hypoxanthine phosphoribosyl transferase NM_013556 Forward 5′-TTGCTGACCTGCTGGATTAC-3′ b 112
Reverse 5′-CCCGTTGACTGATCATTACA-3′
Sod1 superoxide dismutase 1 XM_128337 Forward 5′-TGGTGGTCCATGAGAAACAA-3′ 75
Reverse 5′-TCCCAGCATTTCCAGTCTTT-3′
Odc ornithine decarboxylase, structural NM_013614 Forward 5′-TTGCCACTGATGATTCCAAA-3′ 129
Reverse 5′-CATGGAAGCTCACACCAATG-3′
Fau Finkel-Biskis-Reilly murine sarcoma virus NM_007990 Forward 5′-GCTGGGAGGTAAAGTTCACG-3′ 125
Reverse 5′-TGTACTGCATTCGCCTCTTG-3′
Tctp translationally regulated transcript NM_009429 Forward 5′-CCGGGAGATCGCGGAC-3′ 92
Reverse 5′-TTCCACCGATGAGCGAGTC-3′
Primers using TaqMan technology
Hprt hypoxanthine phosphoribosyl transferase NM_013556 Mm00446968m1c
Kap kidney androgen regulated protein NM_010594 Mm00495104m1
NaPi-II solute carrier family 34, member 1 NM_011392 Mm00441450m1
Umod uromodulin NM_009470 Mm00447649m1
a From Applied Biosystems (http://myscience.appliedbiosystems.com/cdsEntry/Form/gene_expression_keyword.jsp).
b Primer 5′→3′ sequence.
c Assay ID – Applied Biosystems.
Table 2 Physiologic parameters in serum and urine and uranium amount in control group 0 and contaminated groups 1 and 2 after 4 months of daily contamination (mean ± SE).
Group
Parameter 0 1 2
Exposure (mg UN/L) 0 80 160
Kidney
Weight (g) 0.47 ± 0.01 0.46 ± 0.01 0.47 ± 0.02
Uranium amount (μg/g) 0.16 ± 0.04 0.35 ± 0.02* 1.05 ± 0.21*
Serum
Urea (mg/dL) 59 ± 5 57 ± 5 54 ± 7
Creatinine (mg/dL) 0.12 ± 0.02 0.23 ± 0.02* 0.25 ± 0.02*
Urine
Glucose (g/L) 0.08 ± 0.03 0.08 ± 0.03 0.04 ± 0.01
γ -GT (U/L) 86 ± 44 94 ± 42 119 ± 66
*p < 0.05 versus control; n = 4.
Table 3 List of tags with significant variations in expression level induced by UN long-term ingestion (p < 0.05), their frequency, and their relevant accession number.
Tag sequence Count
Gene namea Accession no.a Regulationb Gene symbola
UN(−) UN(+)
Apoptosis
GCTGCCAGGG 11 4 Bcl2-associated athanogene 1 NM_009736 − Bag1
GAAAGCAATG 0 6 nerve growth factor receptor (TNFRSF16) associated protein 1 NM_009750 + Ngfrap1
TGCCTTACTT 3 8 programmed cell death 6 NM_011051 + Pdcd6
Amino acid metabolism
CGTATCTGTA 4 10 D-amino acid oxidase NM_010018 + Dao1
CAGTTACAAA 1 6 glutamate dehydrogenase NM_008133 + Glud
TTTTACCTGC 0 8 glycine amidinotransferase (L-arginine:glycine amidinotransferase) NM_025961 + Gatn
CTACCACTGC 4 12 fumarylacetoacetate hydrolase NM_010176 + Fah
ATACTAACGT 40 24 ornithine decarboxylase, structural NM_013614 − Odc
AACAGAAAGT 1 8 phenylalanine hydroxylase NM_008777 + Pah
Carbohydrate metabolism
GCAAACAAGA 11 18 isocitrate dehydrogenase 2 (NADP+), mitochondrial NM_173011 + Idh2
GTGCCATATT 12 26 isocitrate dehydrogenase 2 (NADP+), mitochondrial NM_173011 + Idh2
/CCAAATAAAA 17 31 lactate dehydrogenase 1, A chain NM_010699 + Ldh1
TGATATGAGC 33 12 lactate dehydrogenase 2, B chain NM_008492 − Ldh2
TTGTTAGTGC 70 89 malate dehydrogenase, soluble NM_008492 + Mor2
GCAATCTGAT 17 31 phosphoglycerate kinase 1 NM_008828 + Pgk1
GCCCAGACCT 25 41 sorbitol dehydrogenase 1 NM_146126 + Sdh1
GCTTGTGACG 1 8 transaldolase 1 NM_011528 + Taldo1
Cell adhesion
CTCTGACTTA 3 8 basigin NM_009768 + Bsg
GAGACTAGCA 4 10 transmembrane 4 superfamily member 8 NM_019793 + Tm4sf8
Immunity and defense
Immunity
GTTCAAGTGA 4 12 Ia-associated invariant chain NM_010545 + Ii
TATCCTGAAT 14 2 lymphocyte antigen 6 complex, locus A NM_010738 − Ly6a
TTTTATGTTT 12 20 tumor necrosis factor, alpha-induced protein 1 (endothelial) NM_009395 + Tnfaip1
TATACATCCA 43 26 uromodulin NM_009470 − Umod
TGGGTTGTCT 151 174 translationally regulated transcript (21 kDa) NM_009429 + Trt, Tpt1, Tctp
Antioxidant and free radical removal
CTATCCTCTC 297 341 glutathione peroxidase 3 NM_008161 + Gpx3
CAGCTTCGAA 12 2 glutathione S-transferase, theta 2 NM_010361 − Gstt2
AGAAACAAGA 7 18 superoxide dismutase 1, soluble XM_128337 + Sod1
TTGCTTCTAT 20 8 thioether S-methyltransferase NM_009349 − Temt
CATCAGCCTC 7 0 thioredoxin, mitochondrial NM_019913 − Txn2
Lipid fatty acid and steroid metabolism
TCTCCTTAGC 0 10 ATP-binding cassette, subfamily D (ALD), member 3 NM_008991 + Abcd3
TTAAGACCTG 9 18 crystallin, zeta NM_009968 + CryZ
TATAATAAAC 0 8 cytochrome P450, 2d9 NM_080006 + Cyp2d9
TGTGTGGAAT 14 20 cytochrome P450, subfamily IV B, polypeptide 1 NM_007823 + Cyp4b1
GGAGGGTGTG 4 10 phosphatidic acid phosphatase type 2c NM_015817 + Ppap2c
Protein metabolism and modification
Protein folding
CCTCCCTTTT 4 14 heat shock 10 kDa protein 1 (chaperonin 10) NM_008303 + Hspe1
Protein biosynthesis
GATGTGGCTG 7 22 eukaryotic translation elongation factor 1 beta 2 NM_018796 + Esf1b2
TCACCCAATA 36 49 eukaryotic translation elongation factor 2 NM_007907 + Eef2
CTAATAAAGC 18 43 Finkel-Biskis-Reilly murine sarcoma virus (FBR-MuSV) ubiquitously expressed (fox derived) NM_007990 + Fau
TGTCATCTAG 7 14 laminin receptor 1 (67 kDa, ribosomal protein SA) NM_011029 + Lamr1
TGCTGGGATG 6 16 mitochondrial ribosomal protein S12 NM_011885 + Mrps12
AGGTCGGGTG 7 14 ribosomal protein L13a + Rpl13a
TGGATCAGTC 47 66 ribosomal protein L19 NM_009078 + Rpl19
CCAGAACAGA 7 20 ribosomal protein L30 NM_009078 + Rpl30
GGCTTCGGTC 48 68 ribosomal protein, large, P1 NM_018853 + Rplp1
GTGAAACTAA 36 45 ribosomal protein S4, X-linked NM_009094 + Rps4x
CTGGGCGTGT 3 8 ribosomal protein S15 NM_009091 + Rps15
GTGGGCGTGT 0 6 ribosomal protein S15 NM_009091 + Rps15
CAGAACCCAC 0 6 ribosomal protein S18 NM_138946 + Rps18
GCCTTTATGA 4 10 ribosomal protein S24 NM_011297 + Rps24
AACAGGTTCA 11 18 ribosomal protein S25 NM_024266 + Rps25
TAAAGAGGCC 18 29 ribosomal protein S26 NM_013765 + Rps26
Proteolysis
GGTTAAATGT 1 8 cathepsin L NM_009984 + Ctsl
CAGCAAAAAA 33 41 kallikrein 5 NM_008456 + Klk5
GAGAGTGTGA 6 14 kidney-derived aspartic protease-like protein NM_008437 + Kdap
CAGAATGGAA 14 29 peptidase 4 NM_008820 + Pep4
AGGCGGGATC 3 8 proteasome (prosome, macropain) subunit, alpha type 7 NM_011969 + Psma7
CAACAAACAT 3 10 protein C NM_008934 + Proc
GTAAGCAAAA 22 43 ubiquitin B NM_011664 + Ubb
Signal transduction system, receptor
TGGGACTCAC 4 14 cholecystokinin A receptor NM_009827 + Cckar
AGAAAAAAAA 7 14 ciliary neurotrophic factor receptor NM_016673 + Cntfr
TGATTTTTGT 1 10 disabled homolog 2 (Drosophila) NM_023118 + Dab2
GGGCAAGCCA 4 14 estrogen-related receptor, alpha NM_007953 + Esrra
CATACGCATA 7 16 growth hormone receptor NM_010284 + Ghr
TTAAGAGGGA 12 0 transducer of ErbB-2.1 NM_009427 − Tob1
Transport
Electron transport
GCTTTGAATG 20 35 ATPase inhibitor NM_007512 + Atpi
CCAGTCCTGG 12 24 ATP synthase, H+ transporting, mitochondrial F0 complex, subunit c (subunit 9), isoform 1 NM_007506 + Atp5g1
GTTCTTTCGT 3 8 ATP synthase, H+ transporting, mitochondrial F0 complex, subunit c (subunit 9), isoform 2 NM_026468 + Atp5g2
GCCGAGCATA 6 16 ATP synthase, H+ transporting, mitochondrial F0 complex, subunit f, isoform 2 NM_020582 + Atp5j2
GATAGATAAT 3 8 ATP synthase, H+ transporting, mitochondrial F1 complex, alpha subunit, isoform 1 NM_007505 + Atp5a1
CTAATAAAAG 33 45 cytochrome c oxidase, subunit IVa NM_009941 + Cox4a
TATTGGCTCT 53 74 cytochrome c oxidase, subunit VIIIa NM_007750 + Cox8a
AGGGCACTGG 3 8 cytochrome c oxidase, subunit XVII assembly protein homolog AV158262 + Cox17
CAGAATGTGC 3 8 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 2 NM_010885 + Ndufa2
TTATGAAATG 15 24 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 1 NM_019443 + Ndufa1
ACTGCTTTTC 1 10 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 7 NM_023202 + Ndufa7
Ion transport
TTCTAGCATA 28 10 ATPase, Na+/K+ transporting, beta 1 polypeptide NM_009721 − Atp1b1
CTAGGTACTG 48 91 solute carrier family 34 (sodium phosphate), member 1 NM_011392 + Slc34a1
ACAAATTATG 1 8 voltage-dependent anion channel 2 NM_011695 + Vdac2
Lipid fatty acid transport
GCTCTGATAC 0 8 sterol carrier protein 2, liver NM_138508 + Scp2
Others
TGCTTTTACG 7 20 6-pyruvoyl-tetrahydropterin synthase/dimerization cofactor of hepatocyte nuclear factor 1 alpha (TCF1) NM_025273 + Pcbd
ATTACGGTGG 7 18 aldo-keto reductase family 1, member A4 (aldehyde reductase) NM_021473 + Akr1a4
AAGACCTATG 12 2 diazepam binding inhibitor NM_007830 − Dbi
CTCCTGCAGC 15 29 esterase 10 NM_016903 + Es10
ATCTGACTCC 3 10 hemoglobin Y, beta-like embryonic chain NM_008221 + Hbb
TAAAGCAAAA 20 43 H2B histone family, member S NM_023422 + Hist1h2bc
GACTTCACGC 155 182 kidney androgen-regulated protein NM_010594 + Kap
GCACGAGCGT 7 0 low density lipoprotein receptor-related protein 2 XM_130363 − Lrp2
TGCTGTGACC 9 16 membrane-associated protein 17 pending NM_026018 + Map17-p
TGTGCTTCCC 4 12 neural precursor cell expressed, developmentally down-regulated gene 8 NM_008683 + Nedd8
TGAGCGCTGC 15 24 PDZ domain containing 1 NM_021517 + Pdzk1
GGGGAGGGGG 7 0 pre B-cell leukemia transcription factor 2 NM_017463 − Pbx2
GGCTGGGGGC 3 10 profilin 1 NM_011072 + Pfn1
AAGTAAAGCG 6 12 SEC61, gamma subunit (S. cerevisiae) NM_011343 + Sec61g
CAGCCTGAGC 4 10 selenoprotein R NM_013759 + Sepr
TTTCCAGGTG 1 8 selenoprotein W, muscle 1 NM_009156 + Sepw1
a From Applied Biosystems (http://myscience.appliedbiosystems.com/cdsEntry/Form/gene_expression_keyword.jsp.).
b +, up-regulation; −, down-regulation.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences 10.1289/txg.7253ehp0112-00163615598615ToxicogenomicsArticlesSubchronic Exposure to TCDD, PeCDF, PCB126, and PCB153: Effect on Hepatic Gene Expression Vezina Chad M. 1Walker Nigel J. 2Olson James R. 31University of Wisconsin–Madison, School of Pharmacy, Madison, Wisconsin, USA2National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA3University at Buffalo, Department of Pharmacology and Toxicology, Buffalo, New York, USAAddress correspondence to J. Olson, University at Buffalo, Department of Pharmacology and Toxicology, 102 Farber Hall, 3435 Main Street, Buffalo, NY 14214 USA. Telephone: (716) 829-2319. Fax: (716) 829-2800. E-mail:
[email protected] for this study were provided to us by the National Toxicology Program as part of a series of chronic 2-year rat bioassays examining the relative potencies for carcinogenicity of individual and mixtures of dioxin-like compounds.
These studies were supported in part by National Institute of Environmental Health Sciences ES09440, the University at Buffalo, and the Environment and Society Institute, University at Buffalo.
The authors declare they have no competing financial interests.
11 2004 22 9 2004 112 16 1636 1644 13 5 2004 22 9 2004 Publication 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 employed DNA microarray to identify unique hepatic gene expression patterns associated with subchronic exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and other halogenated aromatic hydrocarbons (HAHs). Female Harlan Sprague-Dawley rats were exposed for 13 weeks to toxicologically equivalent doses of four different HAHs based on the toxic equivalency factor of each chemical: TCDD (100 ng/kg/day), 2,3,4,7,8-pentachlorodibenzofuran (PeCDF; 200 ng/kg/day), 3,3′,4,4′,5-pentachlorobiphenyl (PCB126; 1,000 ng/kg/day), or 2,2′,4,4′,5,5′-hexachlorobiphenyl (PCB153; 1,000 μg/kg/day). Global gene expression profiles for each exposure, which account for 8,799 gene probe sets contained on Affymetrix RGU34A GeneChips, were compared by principal components analysis. The aryl hydrocarbon receptor (AhR) ligands TCDD, PeCDF, and PCB126 produced very similar global gene expression profiles that were unique from the nonAhR ligand PCB153, underscoring the extensive impact of AhR activation and/or the resulting hepatic injury on global gene expression in female rat liver. Many genes were co-expressed during the 13-week TCDD, PeCDF, or PCB126 exposures, including classical AhR-regulated genes and some genes not previously characterized as being AhR regulated, such as carcinoembryonic-cell adhesion molecule 4 (C-CAM4) and adenylate cyclase-associated protein 2 (CAP2). Real-time reverse-transcriptase polymerase chain reaction confirmed the increased expression of these genes in TCDD-, PeCDF-, and PCB126-exposed rats as well as the up- or down-regulation of several other novel dioxin-responsive genes. In summary, DNA microarray successfully identified dioxin-responsive genes expressed after exposure to AhR ligands (TCDD, PeCDF, PCB126) but not after exposure to the non-AhR ligand PCB153. Together, these findings may help to elucidate some of the fundamental features of dioxin toxicity and may further clarify the biologic role of the AhR signaling pathway.
AhRHAHlivermicroarrayPCBTCDD
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2,3,7,8-Tetrachlorodibenzo-p-dioxin (dioxin, TCDD) is a persistent environmental contaminant, a human and rodent carcinogen, and the most potent ligand for the aryl hydrocarbon receptor gene (AhR) (Fingerhut et al. 1991; Gu et al. 2000; Kociba et al. 1978; McGregor et al. 1998). The AhR gene also displays affinity for structurally related xenobiotics, including polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and coplanar polychlorinated biphenyls (PCBs) (Denison et al. 2002). Ligand binding and activation of AhR induces nuclear localization and heterodimerization with the AhR nuclear transporter (ARNT) protein (Whitlock 1999). This activated heterodimer binds to cognate cis-acting sequences [dioxin response elements (DREs)], located in the 5′-regulatory region of target genes.
A specific subgroup of genes are activated by an AhR-dependent mechanism during dioxin exposure, including (but not limited to) cytochrome P450 (CYP)1A1 CYP1A1, CYP1A2, CYP1B1, aldehyde dehydrogenase (ADH), NADPH-quinone-oxidoreductase (NQO1), glutathione S-transferase (GST) Ya (GSTA1), and UDP-glucuronosyltransferase 1A1 (UGT1A1) (Manjunath and Dufresne 1988; Mimura and Fujii-Kuriyama 2003; Schrenk 1998; Sutter and Greenlee 1992). AhR-dependent transcription is required for dioxin toxicity (Bunger et al. 2003), but it is unclear how activation of AhR-dependent genes produces the multiplicity of toxic responses characteristic of dioxin exposure.
As an attempt to characterize AhR-dependent genes and signaling pathways responsible for subchronic dioxin toxicity, the present study evaluated differential hepatic gene expression in female Harlan Sprague-Dawley (SD) rats exposed subchronically (13 weeks) to toxicologically equivalent doses of the AhR ligands TCDD (100 ng/kg/day), 2,3,4,7,8-pentachloro-dibenzofuran (PeCDF; 200 ng/kg/day), 3,3′,4,4′,5-pentachlorobiphenyl (PCB126; 1,000 ng/kg/day), or the non-AhR ligand 2, 2′, 4, 4′, 5, 5′-hexachlorobiphenyl (PCB153; 1,000 μg/kg/day). This gene expression study was performed in conjunction with a cancer bioassay conducted by the National Toxicology Program (NTP), which included interim sacrifices (13, 30, and 52 weeks) to investigate tissue dosimetry, histopathology, and other biochemical and molecular responses throughout the 2-year study.
Subchronic TCDD exposure is associated with numerous toxic responses, and many of these responses may be AhR dependent. Kociba et al. (1976) showed previously that SD rats exposed to high levels of TCDD (1 μg/kg/day) for 13 weeks were subject to mortality, chloracne, thymic atrophy, and a “wasting syndrome” characterized by rapid weight loss and fat redistribution. Antioxidant enzyme expression was enhanced in rats exposed to lower doses of TCDD for 13 weeks (10–46 ng/kg/day; Hassoun et al. 2003), and the hepatotoxic biomarkers serum bilirubin and alkaline phosphatase were elevated in rats exposed to intermediate doses for the same exposure period (100 ng/kg/day TCDD; Kociba et al. 1976). Rats exposed to TCDD for 13 weeks (100 ng/kg/day) also developed cachexia, hepatic hypertrophy, and altered hepatic foci (Kociba et al. 1976), whereas chronic exposure (104 weeks) at this dose resulted in porphyria and cancer of the liver, lung, and oral mucosa (Kociba et al. 1978; NTP 2004a). Although AhR activation likely contributes to the many toxicologic effects produced by subchronic and chronic TCDD exposures, little is known about AhR and non-AhR signaling mechanisms mediating these effects.
The TCDD, PeCDF, and PCB126 exposure doses used in this study were carcinogenic to female SD rats, but tumors and other hepatotoxic effects were not evident until several months after the 13-week interim sacrifice (NTP 2004a, 2004b, 2004c). Thus, evaluation of differential gene expression patterns after 13-week exposures to subchronic halogenated aromatic hydrocarbons (HAHs) may yield important clues about the mechanisms by which these chemicals produce their chronic toxicologic effects, including cancer. Although there have been several attempts to evaluate TCDD-dependent gene expression in vitro and in vivo (Fisher et al. 2004; Frueh et al. 2001; Martinez et al. 2002; Puga et al. 2000), to our knowledge, this is the first of such to characterize gene expression during long-term, subchronic exposure to carcinogenic doses of TCDD and other dioxin-like chemicals.
Materials and Methods
Sample Procurement
Tissues for this study were provided by the NTP (NTP 2004a, 2004b, 2004c) as part of a 2-year bioassay for relative carcinogenic potencies of dioxin-like chemicals. Female Harlan SD rats were exposed 5 days a week by oral gavage to toxicologically equivalent doses of TCDD [toxic equivalency factor (TEF) = 1.0; 3, 10, 22, 46, 100 ng/kg/day], PeCDF (TEF = 0.5; 6, 20, 44, 92, 200 ng/kg/day), PCB126 (TEF = 0.1; 10, 30, 100, 175, 300, 550, 1,000 ng/kg/day), PCB153 (TEF = 0, N/A; 10, 100, 300, 1,000 μg/kg/day), or corn oil:acetone (99:1; vehicle control). Toxicologic dose equivalence was based on the current World Health Organization TEF recommendations (Van den Berg et al. 1998). Subgroups of rats were sacrificed at 14, 31, and 53 weeks (corresponding to 13, 30, or 52 weeks of exposure), and target organs were removed, flash frozen in liquid nitrogen, and stored at −70°C for mechanistic studies.
RNA Isolation and Hybridization
The present study used liver from female rats exposed to vehicle control or the highest dose of each compound for 13 weeks to ensure that hepatic gene expression was evaluated in the context of carcinogenic exposure doses for TCDD, PeCDF, and PCB126. Frozen hepatic tissue was disrupted by homogenization with a rotor stator homogenizer, and total RNA was isolated with Qiagen RNeasy columns Qiagen Inc., Valencia, CA). There were a total of six rats in each exposure group. Three pools of RNA were created from each exposure group (n = 2 rats per pool), similar to the experimental design of Yechoor et al. (2002). Pooled total RNA was further purified using the Qiagen poly(A) RNA isolation kit. RNA integrity was assessed by the Agilent Bioanalyzer 2100 (Agilent Technologies, Palo Alto, CA). This study employed high-quality RNA that displayed two distinct, sharp peaks and a 28S/18S ribosomal RNA ratio greater than 1. Poly(A) RNA was transformed into labeled cRNA by the Roswell Park Cancer Institute Microarray and Genomics Core Facility (Buffalo, NY). cRNA from each pool was fragmented and its quality evaluated with Affymetrix GeneChip Test3 arrays (Santa Clara, CA) by comparing 3′:5′ signal ratios of housekeeping genes. High-quality cRNA (3′:5′ signal ratio near 1) was subsequently hybridized to Affymetrix RGU34A GeneChips, and chips were scanned with the Affymetrix 428 scanner.
Data Analysis
Cell intensity files (.CEL) files were generated with Affymetrix Microarray Suite (MAS) 5.0 software (Affymetrix) and probe-level data were background subtracted and normalized, and gene expression was summarized using the MAS 5.0 algorithm included in the Bioconductor Affy package for R, version 1.6.1 (Ihaka and Gentleman 1996). Gene expression data from n = 3 GeneChips in each exposure group were averaged, and changes in gene expression were calculated as the average change versus gene expression for the n = 3 GeneChips from the vehicle-treated control group. Cluster analysis was performed with TIGR Microarray Experiment Viewer (Saeed et al. 2003). The gene expression profiles associated with TCDD, PeCDF, PCB126, and PCB153 exposures were assessed by principal components analysis (PCA) with the covariance value distance metric (Raychaudhuri et al. 2000) to evaluate relationships between exposure groups. Genes co-expressed during various exposure conditions were identified by Pavlidis template matching (PTM; Pavlidis and Noble 2001). For each PTM analysis, gene expression profile templates were constructed by designating relative gene expression ratios for each exposure condition. Gene expression data were filtered for genes that matched each template based on the Pearson correlation (R ≥ 0.9). Template matching genes were subjected to Euclidean distance hierarchical clustering. Genes were annotated with GenBank accession numbers by Affymetrix MAS 5.0 and TIGR Resourcerer gene annotation tool (Tsai et al. 2001), and official gene names were provided by the Rat Genome Database (http://rgd.mcw.edu/). Expressed sequence tags without annotation were filtered from PTM outputs, thus restricting gene sets to annotated genes. Promoters of selected genes were mapped for DREs using MatInspector Professional (Quant et al. 1995). Quantitative gene expression estimates obtained by microarray analysis were validated by two-step real-time reverse-transcriptase polymerase chain reaction (RT-PCR) for selected genes.
Real-Time RT-PCR Validation of Gene Expression
Reverse transcriptase reactions (80 μL) contained 20 μg total RNA, 0.5 mM dNTP mix, and 15 ng/μL random primers, 1 × first-strand buffer, 10 mM dithiothreitol, 27 U Rnasin RNase inhibitor (Promega, Madison, WI), and 800 U superscript reverse transcriptase (Invitrogen). A mixture containing total RNA, dNTPs, and random primers was heated to 65°C for 5 min to denature the RNA and then immediately placed on ice. The remaining components of the reaction mixture were then added to the RNA, and cDNA synthesis was performed at 42°C for 60 min. Reactions were terminated by heating to 70°C for 10 min.
PCR primers were selected from GenBank database sequences with Primer3 software (Rozen and Skaletsky 2000). Primer sequences were between 20 and 22 bp, contained at least one 3′-GC clamp, displayed a maximal Tm (melting temperature) difference of 1°C, a maximal poly-X value of 3, maximal 3′-complementarity of 2, and a Tm between 60 and 62°C. Nonspecific mispriming was managed by a mispriming threshold of 10.0 in the rodent mispriming library. The primer sequences used in the present study are shown in Table 1.
Real-time PCR was performed with the SYBR Green PCR kit from Applied Biosystems (Foster City, CA) according to the manufacturer’s instructions. PCR reactions (25 μL) contained diluted cDNA, 1 × SYBR Green buffer, 3 mM MgCl2, 0.2 mM dNTP mix, 0.2 μM left and right primers, 10 nM fluorescein, and 1.1 U of Amplitaq Gold polymerase. The reaction was initiated by incubation at 95°C for 10 min and followed by 40 cycles of denaturation at 95°C for 15 sec and primer annealing/extension at 60°C for 1 min. Sample fluorescence was evaluated during the annealing/extension step. Upon completion of thermocycling, the specificity of each reaction was evaluated by melting analysis. Samples were heated to 95°C for 2 min and cooled to 55°C. The temperature was maintained at 55°C for 15 sec to analyze sample fluorescence, and the temperature was increased by increments of 0.5°C followed by 15 sec of fluorescence analysis for a total of 80 cycles.
The efficiency of each primer set was validated over a range of cDNA concentrations. Primer pairs that demonstrated reaction efficiencies between 85 and 103%, concentration vs. fluorescence slope factors between −1.3 and −1.7, and concentration versus fluorescence correlation coefficients between 0.98 and 1.0 were accepted for further use. After primer validation, PCR reactions were performed with a single cDNA concentration, and the threshold cycle (Ct) was determined for each reaction. The difference (ΔCt) between the threshold cycle for the target gene and endogenous control gene (18S RNA) was calculated for each sample, and the 18S normalized relative expression of each gene was calculated by the comparative method according to the Applied Biosystems’ User Bulletin No. 2 for the ABI Prism Sequence Detection System (Applied Biosystems), as described by Imasato et al. (2002).
Results
Dosimetry and Liver Pathology from the NTP Cancer Bioassay
Tissue dosimetry and liver pathology for each 13-week HAH exposure are detailed in Table 2. The dosimetry values indicate that the AhR ligands (TCDD, PeCDF, PCB126) exhibit more pronounced hepatic accumulation, relative to administered dose, than does the non-AhR ligand PCB153. It is important to note that the exposure to PCB153 is in micrograms per kilogram per day (μg/kg/day), whereas the level of PCB153 in the liver is in units of nanograms per gram of liver (ng/g liver), supporting the greatly reduced relative hepatic accumulation of PCB153. Preferential hepatic accumulation of TCDD, PeCDF, and PCB126 is consistent with CYP1A2 serving as a sequestering protein for AhR ligands (Van den Berg et al. 1994). There was a statistically significant increase in the incidence of hepatic hypertrophy for all exposures compared with the respective vehicle control animals. Liver hypertrophy was mild to moderate among female rats from the TCDD exposure group, minimal to mild in the PCB126 exposure group, and minimal in rats exposed to PeCDF or PCB153. A limited and non-significant number of animals exposed to TCDD for 13 weeks displayed multinucleated hepatocytes.
There was an increased incidence and severity of liver hypertrophy for the TCDD and PCB126 exposures relative to the other AhR ligand, PeCDF. PTM was employed to identify genes associated with the more pronounced hepatic hypertrophy in the TCDD and PCB126 groups relative to the PeCDF group (Figure 1). We were unable to identify genes that were selectively repressed, but we did identify a limited subset of genes selectively induced by TCDD and PCB126 compared with PeCDF. These genes were functionally classified as neurotransmitter and endocrine signaling genes.
Effects of HAH Exposure on Global Hepatic Gene Expression
TCDD, PeCDF, and PCB126 activate AhR, whereas PCB153 displays little or no binding affinity for AhR. PCA was employed to determine whether the relationship between global gene expression profiles for each chemical would be related to their AhR affinities (Figure 2). The principal components for TCDD, PeCDF, and PCB126 exposures were spatially co-localized along the x-axis of the PCA plot (PeCDF and PCB126 were localized to the same quadrant, whereas TCDD was proximally located in the adjacent quadrant). The principal component for the PCB153 exposure was unique from those of AhR ligands.
Because the global gene expression profile associated with PCB153 exposure differed from that of TCDD, PeCDF, and PCB126, PTM was employed to identify genes that were selectively induced or repressed by PCB153 (Figure 3). PCB153 does not bind to AhR, and genes activated or repressed during PCB153 exposure are likely regulated by an AhR-independent mechanism. Liver from PCB153-exposed rats exhibited a unique class of differentially expressed genes compared with rats exposed to AhR ligands, with CYP2B1 being the most up-regulated by PCB153. PCB153 exposure also produced the differential expression of proinflammatory genes interleukin 2 (IL2) and interleukin 1 (IL1) and myxovirus (influenza virus) resistance (MX1) and apoptosis-related genes B-cell leukemia/lymphoma 2 (BCL-2) and Wee1 tyrasine kinase (WEE1).
Global hepatic gene expression profiles of animals exposed to PeCDF more closely resembled those of PCB126-exposed animals than of TCDD-exposed animals, indicating that PeCDF and PCB126 may co-regulate a unique group of genes that are not differentially expressed relative to TCDD exposure. This subgroup of genes may be activated by an AhR-independent mechanism unique to PeCDF and PCB126. PTM was used to identify genes that were selectively activated by PeCDF and PCB126 compared with TCDD (Figure 4). A total of 29 different genes were identified, all of which were mutually induced by PeCDF and PCB126. These genes included those coding for metabolic enzymes (cytochrome P450 15-beta gene, CYP2C39, NADH dehydrogenase) and oxidative stress response genes [catalase (CAT), cytochrome c oxidase (COX)].
PTM was also employed to identify genes co-expressed during TCDD, PeCDF, and PCB126 exposures because differential expression of these genes may be associated with AhR-mediated pathology (Figure 5). Many of the TCDD-inducible genes identified by PTM represented classic dioxin-inducible genes, including CYP1A1, CYP1B1, CYP1A2, NAD(P)H-menadione oxidoreductase, immunoglobulin M, and UDP glycosyltransferase 1 family, polypeptide A1. Although cytochrome c oxidase has been associated with TCDD induction, COX8H represents a novel dioxin-inducible isoform of this gene. Carcinoembryonic-cell adhesion molecule 4 (C-CAM4) and adenylate cyclase–associated protein 2 (CAP2) were also induced during TCDD, PeCDF, and PCB126 exposures. Several genes were coordinately down-regulated by TCDD, PeCDF, and PCB126 but not by PCB153, including epidermal growth factor and mitochondrial thioesterase.
C-CAM4 and CAP2 were highly induced by all three AhR ligands but have not been linked to AhR-mediated transcriptional regulation. The enhanced expression of C-CAM4 and CAP2 was verified by real-time RT-PCR (Table 3). To determine whether C-CAM4 and CAP2 are direct AhR target genes, the 5′-regulatory sequences of these genes were modeled by MatInspector Professional to identify potential cis-acting DRE sequences within these gene promoters. Neither gene promoter contained DRE consensus sequences.
The expression of several additional genes was also verified by real-time RT-PCR (Table 3). In general, there was relatively good association between the up- or down-regulation of genes according to Affymetrix GeneChip analysis and real-time RT-PCR methods. CYP1B1, C-CAM4, and CAP2 were consistently increased in livers of rats exposed to AhR ligands (TCDD, PeCDF, PCB126), whereas livers of PCB153-exposed rats exhibited little or no change. CYP3A9 and serine proteinase inhibitor, clade A (SERPIN7A) were down-regulated by exposure to AhR ligands, whereas somatostatin was down-regulated by exposure to AhR ligands and PCB153. Real-time RT-PCR also suggests that carboxylesterase 3 (CES3) was down-regulated by AhR ligands.
Discussion
The TEF classification scheme has been used for many years to facilitate risk assessment for individual congeners and mixtures of dioxin-like PCDDs, PCDFs, and PCBs (Becher et al. 1998; Finley et al. 2003; Flesch-Janys et al. 1998; Van den Berg et al. 1998). However, there is some question about whether TEF values are predictive of long-term toxicologic end points, including cancer (Safe 1994). The present study evaluated hepatic gene expression during a 13-week interim sacrifice from a 2-year chronic toxicity and carcinogenicity study of TCDD, PeCDF, PCB126, and PCB153 in female Harlan SD rats. The TCDD, PeCDF, and PCB126 exposures from this study produced cholangiocarcinoma, hepatocellular adenoma, toxic hepatopathy, multinucleated hepatocytes, diffuse fatty change, and liver pigmentation after 2 years of exposure (NTP 2004a, 2004b, 2004c). Liver tumor incidence was not equivalent among AhR ligands but instead was higher in animals exposed to TCDD and PCB126 compared with animals exposed to PeCDF at toxicologically equivalent doses. Similar results were seen for the incidence and severity of hepatic hypertrophy at 13 weeks, where PeCDF produced less hypertrophy than TCDD and PCB126 (Table 2). The 13-week PeCDF exposure also induced substantially less CYP1B1 mRNA compared with TCDD and PCB126, as shown by RT-PCR (Table 3). Thus, it is possible that enhanced AhR activation by TCDD and PCB126 may be responsible for the increased liver pathology of TCDD- and PCB126-exposed rats compared with PeCDF-exposed rats. Consistent with these findings, our laboratory previously reported more CYP1-mediated ethoxyresorufin-O-deethylase activity in liver microsomes from TCDD- and PCB126-exposed female SD rats compared with PeCDF-exposed rats after 13 weeks of exposure (Shubert et al. 2002). Thus, the 200 ng/kg/day PeCDF exposure is less effective in activating AhR-dependent gene expression and less toxic regarding hepatic hypertrophy and carcinogenicity compared with 100 ng/kg/day TCDD and 1,000 ng/kg/day PCB126 exposures.
An alternative possibility of the enhanced toxicity of TCDD and PCB126 exposures compared with PeCDF is that these chemicals activate a unique subgroup of genes that is not activated by PeCDF, thus enhancing toxicity. In support of this possibility, DNA microarray analysis revealed a subset of differentially expressed genes in TCDD- and PCB126-exposed rat liver that were relatively unchanged during PeCDF exposure (Figure 1). These genes were functionally related to neurotransmitter and neuroendocrine signaling. Activation of neuroendocrine signaling by TCDD has been demonstrated in other tissues, including monkey hypothalamus and rodent pituitary and adrenal (Pitt et al. 2000; Shridhar et al. 2001). Subchronic exposure to TCDD and PCB126 may therefore stimulate hepatic neuroendocrine signaling. Activation of hepatic neuroendocrine cells by these chemicals may also be a symptom or contributor to chemical-induced liver hypertrophy, which was substantially less in PeCDF-exposed rats.
AhR activation by PCDDs, PCDFs, and coplanar PCBs is a hallmark of exposure to dioxin-like chemicals and is likely implicated in their toxicity. AhR activation has also been attributed to the direct activation of some dioxin-responsive genes (Mimura and Fujii-Kuriyama 2003; Schrenk 1998; Sutter and Greenlee 1992). However, there is little knowledge regarding how many genes are activated or repressed in an AhR-dependent mechanism during sub-chronic exposure to AhR ligands. The present study used PCA to address relationships between exposure to traditional AhR ligands (TCDD, PeCDF, and PCB126) and the non-AhR ligand PCB153. PCA has been previously employed to compare genomic profiles of toxicants (Heijne et al. 2003), identify common molecular effects among potential drug candidates (Shi et al. 2000), and predict treatment prognosis (Ringberg et al. 2001). PCA revealed a unique association among hepatic gene expression profiles produced by exposure to dioxin-like toxicants, where global gene expression profiles for rats exposed to PeCDF and PCB126 were very similar and closely related to the gene expression profile for TCDD exposure (Figure 2). The global gene expression profile for exposure to the noncoplanar PCB153, however, was substantially different from that of the dioxin-like AhR ligands. Prominent differences between PCB153-mediated and AhR-ligand–mediated gene expression suggest that subchronic exposure to TCDD, PeCDF, and PCB126 has an extensive impact on global hepatic gene expression that involves many genes. Furthermore, it is important to note that the expression or repression of the genes examined may result from direct regulation by the AhR, events further downstream from a direct regulation of some gene by the AhR, and/or a response to the tissue injury resulting from the genes directly or indirectly regulated by the dioxin-like chemicals via the AhR.
PCB153 is the most prevalent PCB congener in biologic tissues (Kimbrough 1995; Krogaenas et al. 1998; Safe 1994). It is also extraordinarily persistent and its half-life may exceed 100 years in marine sediments (Jonsson et al. 2003). PCB153 exposure has been associated with various biologic effects including developmental toxicity (Kuchenhoff et al. 1999) and induction of CYP2B1 and other phenobarbital-responsive genes (Connor et al. 1995). Although PCB153 does not bind to AhR and produced minimal hepatic hypertrophy after 13 weeks of subchronic exposure, PCB153 did promote differential expression of several biomarker genes for liver injury (Figure 3). Thus, gene expression profiling may be a more sensitive gauge of PCB153 toxicity than standard histology, and this hypothesis will be tested with low-dose PCB153 exposures in future experiments. PCB153 activates an acquired immune response in mice (Smialowicz et al. 1997). PCB153 exposure in this study was associated with differential gene expression of proinflammatory cytokines, including IL1, IL2, and the immune response gene MX1. PCB153 exposure decreased expression of the apoptotic genes BCL-2 and WEE1. BCL-2 and WEE1 are responsive to p53 and are down-regulated during apoptosis (Bishay et al. 2000; Leach et al. 1998). PCB153 exposure selectively enhanced expression of cAMP response element modulator (CREM) protein. CREM gene activation is a signature response to liver regeneration after hepatocyte injury. CREM mRNA is increased after partial hepatectomy in wild-type mice and liver regeneration is inhibited in CREM(−/−) mice (Servillo et al. 1998). The identification of these and other PCB153-responsive genes in the present study provides new targets for future mechanistic studies of PCB153 toxicity.
The mutual AhR binding affinity of TCDD, PeCDF, and PCB126 is likely responsible for strong similarity between global gene expression profiles produced by these chemicals. However, PCA also revealed that gene expression profiles associated with PCB126 and PeCDF exposures were more closely related to each other than to TCDD. On the basis of this finding, it is possible that PeCDF and PCB126 activate a unique group of genes not activated during TCDD exposure.
We identified a limited subset of genes activated by PeCDF and PCB126 but not TCDD (Figure 4). Induction of CAT, cytochrome b5 (CYB5), and COX oxidatative stress response genes (Poulsen et al. 2000) suggests that PeCDF and PCB126 exposures are capable of inducing oxidative stress. PeCDF and PCB126 also induced growth arrest and DNA-damage-inducible 45 (Gadd45) expression, a DNA-damage–inducible gene product (Sheikh et al. 2000). Induction of Gadd45 during PeCDF and PCB126 exposures may indicate oxidative DNA damage in liver from animals exposed to these toxicants. Oxidative stress was previously reported during AhR activation (Dalton et al. 2002). Interestingly, however, oxidative stress response genes were not activated in livers from animals exposed to TCDD in the present study. It is interesting that PeCDF produced less hypertrophy than did TCDD, yet was more effective in activating the expression of oxidative stress response genes. These data may indicate that PeCDF and PCB126 exposures promote oxidative stress through a unique, AhR-independent mechanism and/or that the responses are secondary to liver injury produced during the subchronic exposure.
AhR activation plays a critical role in many end points of TCDD toxicity [Agency for Toxic Substances and Disease Registry (ATSDR) 1998; Hahn 2002; Mimura and Fujii-Kuriyama 2003; Van den Berg et al. 1998]. PCA from the present study suggests that subchronic exposure to AhR ligands causes differential expression of numerous genes. Although a few AhR target genes have been identified in previous studies, many members of the AhR gene battery remain unknown. Genomic and proteomic approaches provide valuable opportunities to elucidate additional genes involved in AhR signal transduction and hepatotoxic responses to dioxin-like chemicals.
PTM revealed genes specifically induced or repressed by AhR ligands TCDD, PeCDF, and PCB126 but not by PCB153 (Figure 5). Many of these genes were previously classified as being dioxin responsive, which validated the efficacy of the PTM approach and further verified RNA sample integrity. PTM also revealed several genes, including C-CAM4, CAP2, SERPIN7A, CES3, and expressed sequence tags (ESTs) not yet associated with the AhR signaling pathway (Table 3).
C-CAM4 represents a novel dioxin-responsive gene. In the present study, C-CAM4 was selectively induced in rats exposed to AhR ligands, but not in rats exposed to PCB153, and was among the genes most highly up-regulated by TCDD exposure (Table 3, Figure 5). C-CAM4 does not contain a promoter-based DRE sequence, which suggests that it may not be regulated directly by AhR.
C-CAM4 is a recent addition to the carcinoembryonic antigen (CEA) family of cell adhesion molecules (Earley et al. 1996). CEA immunoglobulins demonstrate important roles in growth and differentiation. Although most soluble CEA molecules are unable to mediate intercellular associations, C-CAM4 actively promotes cell adhesion (Lin et al. 1998). C-CAM1 is a secretory paralog of C-CAM4, is expressed during hepatocyte differentiation (Cheung et al. 1993; Thompson et al. 1993), and is selectively down-regulated in hepatocellular carcinoma (Cheung et al. 1993; Thompson et al. 1993). TCDD has been associated with hepatocellular carcinoma in female Spartan SD rats (Kociba et al. 1978) and with hepatocellular adenoma and cholangiocarcinoma in a recent NTP study of female Harlan SD rats (NTP 2004a). The NTP study also found evidence of other hepatotoxic responses after 2 years of chronic exposure to TCDD, including hepatocyte hypertrophy, multinucleated hepatocyte, diffuse fatty change, bile duct hyperplasia, bile duct cyst, oval cell hyperplasia, necrosis, pigmentation, inflammation, nodular hyperplasia, portal fibrosis, cholangiofibrosis, and toxic hepatopathy (NTP 2004a). No major evidence for hepatotoxicity was observed after 13 weeks of exposure to TCDD, but livers from female rats exposed for this time period were hypertrophic. The increased expression of secreted C-CAM4 in these rats may suggests that TCDD initiates a cellular transition from membrane-bound C-CAM1 expression in normal tissue to secreted C-CAM4 expression during hypertrophy and neoplastic transformation. Although C-CAM4 may be a potential marker for disrupted cell differentiation in livers of animals exposed to dioxin-like toxicants, it will be important in future studies to investigate protein expression in conjunction with gene expression
Like C-CAM4, CAP2 is a novel dioxin-responsive gene that exhibits dynamic activation in the presence of AhR ligands (Table 3, Figure 5). The CAP2 promoter is also devoid of XRE sequences. CAP2 mRNA is expressed at moderate to low levels in normal rat liver tissue (Swiston et al. 1995) but is markedly induced by TCDD and related compounds. It is unclear whether CAP2 activity is implicated in the preneoplastic hepatotoxic effects of dioxin in rats, but a yeast homolog of CAP2 associates with the actin cytoskeleton (Lila and Drubin 1997), is responsible for the posttranslational posttranslational processing of Ras, and serves as an effector for Ras-dependent activation of adenylyl cyclase (Shima et al. 1997). Ras expression was increased in altered hepatic foci from a diethyl nitrosamine/TCDD tumor initiation and promotion study (Sills et al. 1994). A Ras-related mechanism suppressed CYP1A1 expression, potentially serving as a negative feedback mechanism that rectifies CYP1A1 levels in the presence of TCDD (Reiners et al. 1997). Although the AhR pathway may indeed have cross talk with the adenylyl cyclase pathway, the specific details of these interactions are unclear. CAP2 may play an important role in this signaling cross talk and warrants further characterization. As with C-CAM4, future studies will investigate protein expression in conjunction with the expression of this gene.
This study represents one of the first attempts to characterize hepatic gene expression in the context of subchronic exposure to carcinogenic doses of dioxin-like chemicals. DNA microarrays and/or RT-PCR successfully identified novel dioxin-responsive genes that were either up-or down-regulated after exposure to AhR ligands (TCDD, PeCDF, PCB126) but not after exposure to the non-AhR ligand PCB153. Future studies are needed to assess the species- and tissue-specific expression of these genes and their respective functional proteins to establish whether these differentially expressed genes may be a response to and/or lead to the carcinogenic and/or noncarcinogenic effects of these compounds in humans and laboratory animals. Together, these findings may help to elucidate some of the fundamental features of dioxin toxicity and may further clarify the biologic role of the enigmatic AhR signaling pathway.
Figure 1 Hepatic genes differentially expressed during TCDD and PCB126 exposures but not during PeCDF exposure. Acc. no., GenBank accession number. PTM was used to identify genes co-expressed after exposure to TCDD and PCB126 but not PeCDF for 13 weeks. Matching genes conformed to a template where the relative expression ratios for toxicant versus vehicle exposure were PeCDF = 0.2, TCDD = 1, and PCB126 = 1 (r
2 = 0.9). The PTM output was further refined to include only those genes differentially expressed ≥2-fold in livers of rats exposed to TCDD and PCB126 compared with vehicle control animals. (A) PTM diagram showing co-expressed genes. The color key indicates the magnitude of change. (B) Average fold changes for n = 3 Affymetrix GeneChips (each chip represents pooled RNA from two animals). Accession numbers and gene names are from GenBank (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=nucleotide).
Figure 2 Relationship between global hepatic gene expressions resulting from TCDD, PeCDF, PCB126, or PCB153 exposures. The global hepatic gene expression profiles for rats exposed to each toxicant were analyzed by PCA. The principal components (X, Y, Z) for the gene expression of rats exposed to AhR ligands TCDD, PeCDF, and PCB126 were localized near a single PCA quadrant, whereas the principal component for the nonAhR ligand PCB153 was located in a distant quadrant.
Figure 3 Hepatic genes differentially expressed during PCB153 exposure but not during exposure to PCDD, PeCDF, PCB126. Acc. no., GenBank accession number. PTM was used to identify differentially expressed genes in livers of rats exposed to PCB153 compared with rats exposed to AhR ligands. Matching genes conformed to a template where the relative expression ratios for toxicant versus vehicle exposure were TCDD = 0, PeCDF = 0, PCB126 = 0, and PCB153 = 1 (r
2 = 0.9). The PTM output was further refined to include only those genes differentially expressed ≥2-fold in livers of rats exposed to PCB153 compared with vehicle control animals. (A) PTM diagram showing genes differentially expressed during PCB153 exposure. The color key indicates the magnitude of change. (B) Average fold changes for n = 3 Affymetrix GeneChips (each chip represents pooled RNA from two animals). Accession numbers and gene names are from GenBank (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=nucleotide).
Figure 4 Hepatic genes activated or repressed during PeCDF and PCB126 exposure but not during TCDD exposure. PTM was used to identify genes co-expressed after exposure to PeCDF and PCB126 but not TCDD for 13 weeks. Acc. no., GenBank accession number. Matching genes conformed to a template where the relative expression ratios for toxicant versus vehicle exposure were TCDD = 0.2, PeCDF = 1, and PCB126 = 1 (r
2 = 0.9). The PTM output was further refined to include only those genes differentially expressed ≥2-fold in livers of rats exposed to PeCDF and PCB126 compared with vehicle control animals. (A) PTM diagram showing co-expressed genes. The color key indicates the magnitude of change. (B) Average fold changes for n = 3 Affymetrix GeneChips (each chip represents pooled RNA from two animals). Accession numbers and gene names are from GenBank (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=nucleotide)
Figure 5 Identification of novel dioxin-responsive genes. Gene expression data were clustered by PTM to identify co-expressed genes in livers of rats exposed to AhR ligands TCDD, PeCDF, and PCB126 for 13 weeks. Acc. no., GenBank accession number. Matching genes conformed to a template where the relative expression ratios for toxicant versus vehicle exposure were TCDD = 0.8, PeCDF = 0.8, PCB126 = 0.8, and PCB153 = 0.1 (R
2 = 0.9). Each row represents a separate gene; each column specifies the toxicant treatment (n = 3 replicate arrays per toxicant). The PTM output was further refined to include only genes differentially expressed ≥2-fold in livers of rats exposed to PeCDF and PCB126 compared with control animals. The color key indicates the magnitude of change. Italicized genes were previously shown to respond to dioxin. Accession numbers and gene names are from GenBank (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=nucleotide).
Table 1 Primer sequences for real-time RT-PCR.
GenBank accession no.a Gene Primer sequences (5′ to 3′)
U09540 CYP1B1 CGTCTGATGCTTTCAGCAAAGG
GCAGGCTTTCCAACTAAGCCAG
U23055 C-CAM4 CTCGTCTCCTCAGAGGGCAGATTC
ACAGCGTCTACGGTGACTTGGG
Al145367 CAP2 ATCACCGTCGATAACTGCAAG
CCCATTACCTGGATCTGAATG
U46118 CYP3A9 CCACCAGCATGAAAGACATC
GTCCTGTGGGTTGTTAAGGG
M63991 SERPIN7A TCTGGCTCTAGCACCCAAAC
GATCAAATGCTGGAAGCCC
M25890 SST CAGAACTGCTGTCTGAGCCC
AGCTCCAGCCTCATCTCGTC
X65296 CES3 GGCCATTTCTGAGAGTGGTGTG
GCAGGCAATGAACCATAACAGC
V01270 Ribosomal 18S RNA GAGCGAAAGCATTTGCCAAG
GGCATCGTTTATGGTCGGAA
a From GenBank (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db_nucleotide)
Table 2 Summary of the 13-week dosimetry and liver pathology data from the NTP cancer bioassay.a
TCDD
PeCDF
PCB126
PCB153
Control 100 ng/kg/day Control 200 ng/kg/day Control 1,000 ng/kg/day Control 1,000 μg/kg/day
Liver levels (ng/g) BLOQ 18.3 ± 0.8 BLOQ 132.9 ± 47.4 BLOQ 412.6 ± 73.5 BLOQ 9,250 ± 2,061
Liver hypertrophy 1 (1.0) 10* (2.3) 0 7* (1.0) 0 10* (1.7) 0 9* (1.2)
Multinucleated hepatocytes 0 3 (1.0) 0 0 0 0 0 0
Diffuse fatty change 0 2 (1.0) 0 2 (1.0) 0 0 0 0
Pigmentation 0 0 0 2 (1.0) 0 0 0 0
Abbreviations: BLOQ, below level of quantification; N/A, not applicable.
a Dosimetry values are the mean ±SD of 10 rats per group. Values for each pathologic end point represent the incidence or number of rats (of 10) that exhibit the response. The mean severity score is given in parentheses (1, minimal; 2, mild; 3, moderate; 4, marked).
* Statistically significant (p < 0.05) increase in the incidence of a given pathologic response relative to the respective control group.
Table 3 Differential gene expression in liver from rats exposed subchronically (13 weeks) to HAHs.
TCDD PeCDF PCB126 PCB153
CYP1B1
RT-PCR 256 (236.3 to 277.3) 59.7 (48.3 to 73.8) 106.4 (98.2 to 115.3) −1.2 (−1.6 to 1)
Microarray 676.3 (470.8 to 881.8) 493.3 (310.1 to 676.5) 317.1 (41.1 to 593.1) 2.1 (1 to 3.2)
C-CAM4
RT-PCR 4.1 (3.8 to 4.4) 2.5 (2.3 to 2.7) 3.5 (2.6 to 4.7) −1.1 (−1.3 to 1)
Microarray 133.4 (112.1 to 154.7) 37.4 (25.5 to 49.3) 25.1 (4 to 46.2) 1.1 (−0.7 to 1.5)
CAP2
RT-PCR 168.9 (147.9 to 192.9) 20.2 (17.8 to 22.9) 119.4 (104 to 137.2) −1.3 (−1.5 to −1.1)
Microarray 53.6 (31.1 to 76.1) 10.9 (4 to 17.8) 7.6 (2 to 13.2) 1.1 (−0.8 to 1.4)
CYP3A9
RT-PCR −1024 (−1201.8 to −872.5) −4.2 (−5 to −3.5) −28.5 (−30.2 to −26.9) −1.8 (−2.1 to −1.5)
Microarray −35.4 (−53.4 to −17.4) −2.3 (−3.3 to −1.3) −4.5 (−5.8 to −2.2) 1.6 (0.8 to 2.4)
SERPIN7A
RT-PCR −26.6 (−30.2 to −23.4) −29.9 (−32 to −27.9) −8.4 (−12 to −5.9) −1.2 (−1.5 to 1)
Microarray −11.5 (−17.2 to −5.8) −2.7 (−3.6 to −1.8) −4 (−4.9 to −3.1) 1.6 (1.3 to 1.9)
SST
RT-PCR −36.8 (−46.4 to −29.1) −1.6 (−2 to −1.2) −1.5 (−1.6 to −1.4) −3 (−3.5 to −2.5)
Microarray −8 (−10.5 to −5.5) −8.3 (−11.1 to −5.5) −6.4 (−9.4 to −3.4) −9.5 (−10.1 to −8.9)
CES3
RT-PCR −4.8 (−5.2 to −4.4) −2.8 (−2.5 to −3.1) −1.4 (−1.5 to −1.3) 2.4 (2.1 to 2.6)
Microarray −8.1 (−9.9 to −6.3) −2.8 (−3.8 to −1.8) −4 (−4.4 to −3.6) 1.3 (1 to 1.6)
The average changes in hepatic mRNA for TCDD, PeCDF, PCB126, or PCB153 exposures compared with vehicle exposure were determined by real-time RT-PCR to validate microarray results. Each value represents the average (± 1 standard deviation) of three independent RNA pools (RT-PCR) or three independent GeneChips (microarray). Real-time RT-PCR values were normalized to 18S ribosomal RNA as described in “Materials and Methods.”
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Sills RC Goldsworthy TL Sleight SD 1994 Tumor-promoting effects of 2,3,7,8-tetrachlorodibenzo-p -dioxin and phenobarbital in initiated weanling Sprague-Dawley rats: a quantitative, phenotypic, and ras p21 protein study Toxicol Pathol 22 270 281 7817118
Smialowicz RJ DeVito MJ Riddle MM Williams WC Birnbaum LS 1997 Opposite effects of 2,2’,4,4’,5,5’-hexachlorobiphenyl and 2,3,7,8-tetrachloro-dibenzo-p -dioxin on the antibody response to sheep erythrocytes in mice Fundam Appl Toxicol 37 141 149 9242587
Sutter TR Greenlee WF 1992 Classification of members of the Ah gene battery Chemosphere 25 223 226
Swiston J Hubberstey A Yu G Young D 1995 Differential expression of CAP and CAP2 in adult rat tissues Gene 165 273 277 8522189
Thompson NL Panzica MA Hull G Lin S-H Curran TR Ruppuso PA 1993 Spatiotemporal expression of two cell-cell adhesion molecule 105 isoforms during liver development Cell Growth Differ 4 257 268 8494790
Tsai J Sultana R Lee Y Pertea G Karamycheva S Antonescu V 2001 Resourcerer: a database for annotating and linking microarray resources within and across species Genome Biol 2 0002.1 0002.4
Yechoor VK Patti M-E Saccone R Kahn CR 2002 Coordinated patterns of gene expression for substrate and energy metabolism in skeletal muscle of diabetic mice Proc Natl Acad Sci USA 99 10587 10592 12149437
Van den Berg M de Jongh J Poiger H Olson JR 1994 The toxicokinetics and metabolism of polychlorinated dibenzo-p -dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) and their relevance for toxicity CRC Crit Rev Toxicol 24 1 74
Van den Berg M Birnbaum L Bosveld ATC Brunstrom B Cook P Feeley M 1998 Toxic equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for humans and wildlife Environ Health Perspect 106 775 791 9831538
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0092615598605PerspectivesEditorialGuest Editorial: Systems Biology, the Second Time Around DeLisi Charles Bioinformatics Program, Boston University, Boston, MA, E-mail:
[email protected] DeLisi is Arthur Metcalf Professor of Science and Engineering and chair of the All-University Doctoral Program in Bioinformatics at Boston University. From 1990–2000 he was dean of the College of Engineering. Before moving to Boston he was professor and chair of Biomathematical Sciences at the Mount Sinai School of Medicine (1987–1990), director of the Department of Energy’s Health and Environmental Research Programs (1985–1987), and chief of Theoretical Immunology and Mathematical Biology at the National Institutes of Health. He has authored or co-authored more than 200 research papers in biophysical chemistry, genomics, and immunology and is the recipient of numerous awards including the Presidential Citizens Medal from President Clinton for his role in initiating the Human Genome Project.
11 2004 112 16 A926 A927 Publication 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 T.S. Eliot (Eliot 1963) wrote “. . . the end of all our exploring Will be to arrive where we started And know the place for the first time,” he was probably not thinking explicitly of science, but as science is deeply imbedded in the human condition, we should not be surprised that these words ring true.
Systems biology as a quantitative science dates at least to Hermann von Helmholtz, the 19th century German physicist whose studies of metabolism led to the first law of thermodynamics. Helmholtz explored human physiology in its entirety, making fundamental contributions to audition, vision, the conduction of the nervous impulse and, perhaps most important in so far as systems biology is concerned, physiologic energy balance. Our understanding of physiologic systems has of course evolved substantially during the past 150 years, and today sophisticated, if domain-specific, mathematical models are used to simulate, plan, and interpret experiments in numerous branches of biomedicine including endocrinology, cardiovascular physiology, immunology, neurophysiology, and the cognitive sciences. Moreover, with the completion of the first phase of the visible human project, which provides high-resolution MR (magnetic resonance) and CT (computed tomography) imaging scans of male and female anatomies, we can seriously contemplate coupling organ-level models that integrate anatomical, biophysical, and physiologic data to produce a computer-based virtual human.
Molecules are not currently the building blocks of useful organ-level models. Instead, the cell is modeled at low resolution, if not as a black box. For example, a model of the humoral immune response might include B-cell trafficking, stimulation by antigen, and regulation by T cells. The dynamics of helper and suppressor T cells and their interaction with antigen-presenting cells could be modeled as a separate subsystem, or module, whose output served as input to the B-cell module. The response of B cells to antigen would then be modeled using experimentally determined rate constants for antigen–receptor interaction to obtain receptor occupancy, and a phenomenologic function determined experimentally would relate occupancy and T-cell state to antibody secretion and B-cell proliferation rate.
The levels of depth that would not be modeled explicitly are apparent. The antigen–receptor rate constants could themselves in principle be calculated in terms of the detailed atomic-level structures of the antigen and immunoglobulin receptors, using long-and short-range force fields determined by quantum chemical calculations and thermodynamic measurements. Such calculations, even if crystal structures were available and the force fields were known precisely, would need to take into account conformational rearrangements in surface side chains, some backbone adaptation, and solvent restructuring. Such calculations are currently too difficult to perform routinely with even moderate precision.
Similarly, one could in principle model by any number of methods—physical chemical, probabilistic, etc.—the signaling pathways leading from receptor occupancy to gene activation, with all the various post-translational modifications and their dependence on the state of the cell, terminating in the modulation of sets of genes combinatorially regulated by sets of transcription factors. But the information required is currently far too sketchy for detailed cell-based models to be useful inputs to organ-and tissue-level models. The advantages of including such deeper-level models explicitly would be a) the connection they may provide between the (dynamic) state of the cell’s surface and the gene–protein–metabolite network topology in the interior of the cell, thus providing an entrée to a global-integrated model; b) their ability to integrate cell physiology with cell anatomy—just as a virtual human would integrate anatomy and physiology at the organ level; and c) the foundation they would provide for deep design; that is, for rational molecular manipulations aimed at production of prespecified phenotypes.
Although historical and global perspectives remind us that we are not in an entirely new place, profound changes have occurred in recent years—changes that are driving a fundamental shift in the culture and content of the life sciences. One such change is, of course, genomic decoding—work that has only just begun. The next 5–10 years will see the production of complete lists of parts of eukaryotic cells, and the next 15–20 years will see reasonably complete wiring diagrams. But—a worn analogy not withstanding—understanding a cell from its list of parts is far more complex than understanding a Boeing 747 airplane or many other complex systems. The cell is not hard wired, therefore a “wiring diagram” only provides, after much analysis, a combinatorially rich repertoire of circuit modules, particular subsets of which are selected by particular environments. And because a cell’s environment is in fugue, the problem of systems biology is understanding the rules of subset selection, and connecting recurrent functional modules to phenotype.
There are many ways to carry out such a program at various levels of spatial and temporal resolution. The level selected depends on experimental or clinical goals. But regardless of the approach used, connecting the genomic revolution and a biology that would understand the cell as a hierarchical system of environmentally selected functional modules is a long-term program. Along the way, as our understanding deepens and as our models attain broader phenomenologic coverage, we can expect to attain a greatly accelerated understanding of evolutionary and developmental biology and greater precision in identifying drug targets and individualizing therapies.
While genomics—and I use the word canonically—does not in itself enable a cell systems biology, it is providing the tools and data that embolden us to begin thinking and working seriously toward that goal. But it is doing much more. It has married the two most powerful technologies of the 20th century—computer science and molecular biology. Computer science is the sine qua non for postgenomic biology, and the dexterity with which its leaders have responded to the biological challenge is one of the great stories in the sociology of science. Nevertheless, the fundamental cultural challenge remains with the biology community itself . The pace of progress will continue to be rate limited by the ability of our universities to educate a new generation of biologists. Not an easy task for organizations that—for some good and some not so good reasons—remain instinctively conservative, even as they sow the seeds of revolution.
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References
Eliot TS Collected Poems, 1909–1962. 1963. New York:Harcourt Brace Jovanovich.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0093015598606EnvironewsForumGenomics: The Year of the Rat Hood Ernie 11 2004 112 16 A930 A930 Publication 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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After two years of intensive efforts by an international consortium of researchers, the Brown Norway rat (Rattus norvegicus) joins the human and the mouse as the third mammalian genomic sequence to be completed. The achievement is expected to yield important new knowledge about mammalian evolution and human disease processes, and should also contribute significantly to progress in toxicogenomics.
The project, funded primarily by the National Human Genome Research Institute (NHGRI) and the National Heart, Lung, and Blood Institute (NHLBI), was conducted by the Rat Genome Sequencing Project Consortium. The Human Genome Sequencing Center at Baylor College of Medicine led the collaboration, assembled the genome, and coordinated the data and resource contributions of a large network of academic and private research centers. Next, an international team comprising more than 20 groups in six countries analyzed the results vis-à-vis the human and mouse genomes. The sequence was published in the 1 April 2004 issue of Nature, along with more than 30 papers analyzing the results relative to the human and mouse genomes published simultaneously in the April 2004 issue of Genome Research.
The Brown Norway rat has long been one of the primary models employed in biomedical, toxicological, and pharmaceutical research. “A large number of human diseases are mimicked in the rat model, and having the genome sequence lets us easily walk between what we understand in the physiology and biology in the rat, and translate that to a better understanding of human biology and disease processes,” says Susan Old, associate director of the Clinical and Molecular Disease Program in the NHLBI Division of Heart and Vascular Diseases and a project officer for the sequencing initiative. She adds, “Our hope is to improve the health of the individual by better understanding of the mechanism of disease, and to develop better therapeutics and diagnostics. We are going to be able to do that a lot more effectively than we had been able to previously.”
The availability of the rat genome sequence should also have a profound impact on toxicogenomics. “At the present time, when we generate expression profiles that are associated with a particular disease or genotype, or in response to a toxicant, we still have difficulty putting together the story of what genes and what pathways are being affected, because a lot of the [genes] represented by features on the DNA micro-arrays have still not been identified,” says Helmut Zarbl, a toxicologist at the Fred Hutchinson Cancer Research Center in Seattle. Knowing the locations and identities of all of the genes in the rat genome will aid toxicogenomicists in their efforts to accurately characterize their functions—“to actually put the story together and come up with the predictive toxicology we’re looking for,” says Zarbl.
Zarbl’s group uses rat models to search for genes associated with human breast cancer. He says having the sequence in hand will advance his work as well as toxicogenomics studies of many other complex diseases strongly suspected to be linked to gene–environment interactions. “By being able to map some of these complex diseases in the rat model and find the causal genes,” he says, “we can then very quickly go to human studies using comparative sequence analysis to formulate hypotheses about what genes are involved in human disease.”
According to Michael Waters, assistant director of database development at the NIEHS-based National Center for Toxicogenomics (NCT), having the sequence will enhance work being conducted in a variety of “omics” areas. “We at the NCT are using microarray technology, but we’re also using proteomics, and we want to be able to use metabolomics to understand how toxicants operate, what their mode of action is, what some of the biomarkers are that would indicate when a toxic outcome is likely to occur, [and] . . . predict those effects at earlier times and lower doses,” he says. Waters says the rat genome information provides the genetic scaffold for scientists to link expression profiles to a genome that is relevant to toxicology.
With the rat, mouse, and human genome sequences now completed, comparative genomics—identifying the essential functional and structural components of the human genome by comparing it with the genomes of other organisms—is positioned for rapid acceleration. “Every model organism has its advantages and disadvantages, and the more of them that we have to do experiments in, the more quickly we’ll be able to find genes and genotypes associated with specific phenotypes, relate these genotypes back to the human genome, and find causes of human diseases,” says Zarbl.
In August 2004, NHGRI announced that it has added 18 new model organisms to its sequencing pipeline, including the orangutan, the African savannah elephant, the rabbit, and the domestic cat. Other groups are working on sequencing the dog, the cow, the macaque, and several nonmammalian species. Many of these projects are expected to be completed within a few years.
Waters anticipates that the flood of additional genomes will be extremely valuable to toxicogenomics in two specific ways. First, he says, the value of existing databases will be enhanced, with the cross-species genomic information contributing to chemical risk assessment in the ecological domain, as well as in human health. Second, we could possibly learn far more about basic biological function in terms of phylogenetic relationships.
Three’s company. The Brown Norway rat joins the human and the mouse as the third mammal to have its genome fully sequenced.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0093115625737EnvironewsForumMolecular Biology: Getting to the Core of Antimicrobials Susman Ed 11 2004 112 16 A931 A931 Publication 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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Much research on host defenses against infection has concentrated on the amino acid sequences of antimicrobial peptides in the belief that the order of the acids and their replication reflect how they work against aberrant cells. Now researchers at the University of California, Los Angeles, (UCLA) suggest that the shape the sequences are arranged in may be a critical part of how these peptides work. A new report indicates that host defense systems across the spectrum of life rely on a universal core structure integral to many natural antimicrobial peptides. This core motif may play a key role in preventing or limiting infection, an insight that could accelerate a major advancement in antimicrobial drug development.
“It has been generally accepted that there is a wide diversity in amino acid sequences and sources of antimicrobial peptides,” says study co-investigator Michael Yeaman, a professor of medicine at the David Geffen School of Medicine at UCLA. “But there hasn’t been as much insight into the similarities that might exist that link all of these diverse groups of molecules.”
The gamma (γ)-core motif—so called because it resembles the Greek letter—may be that missing link, providing a key ingredient in the signature of antimicrobial peptides. Yeaman and coauthor Nannette Yount, a molecular biologist at the Los Angeles Biomedical Research Institute, say the γ-core alone can have antimicrobial activity, but also appears to provide a scaffold on which critical modules are configured to create molecules that hunt down microbial pathogens and destroy them in diverse tissue contexts without injury to the host.
The duo studied the amino acid sequences and three-dimensional structures of over 500 antimicrobial peptides, and found the γ-core structure in molecules as diverse as pea defensins, fruit fly drosomycin, pig protegrin, and human hepcidin. Such molecules share the multidimensional signature of antimicrobial peptides. In a paper published 11 May 2004 in Proceedings of the National Academy of Sciences, the authors wrote, “This striking multidimensional signature is conserved among disulfide-containing antimicrobial peptides spanning biological kingdoms, and it transcends motifs previously limited to defined peptide subclasses.”
But the sequence, composition, and biochemistry of the amino acids that make up the signature still play a major role, says Yeaman. “We feel that some of the universality identified here may have been missed previously because to identify this signature, we had to look at amino acid sequences in both forward and reverse orientation, and that is not typically done,” he says. “The broad conservation of the multidimensional signature identified may have been missed if we only performed amino acid sequence searches and alignments in a conventional way.”
There are other critical aspects of the γ-core motif as well, Yeaman says. “The amino acid sequence is configured in three-dimensional space so that the γ-core has certain characteristics. For example, electrostatic charge tends to be placed in one part of this motif and hydrophobicity in another; disulfide linkages are also conserved. These hallmark features of the γ-core motif rely on both composition and three-dimensional structure.”
Yeaman and Yount are now translating the motif into peptide mimetics and small molecules, and are designing so-called modular anti-infectives with customized payloads of drugs that attach to the γ-core motif. These compounds are at different stages of development—some are in the design phase, while some have been tested and proven to have antimicrobial efficacy. Still others are being optimized based on data generated in the lab as well as in initial ex vivo studies. “We are trying to develop entirely new types of ‘smart’ antibiotics that recognize and act against harmful microbes, particularly those that have become resistant to most all conventional drugs,” Yeaman says.
The work has captured the attention of researchers in the drug development industry. “It’s the structure that defines the signature,” says Steve Projan, vice president of biological technologies at Wyeth Research in Cambridge, Massachusetts, and a consultant to the American Society for Microbiology, based in Washington, D.C. “That structure may be more important than sequence of amino acids. Even if the amino acids are different, it is the overall structure that defines the activity of the molecule.” However, Projan admits, “I’ll be skeptical about the impact of this work until we have a molecule that works by [these] rules and a molecule that also works in an infection model.”
Yeaman suggests that learning how nature has evolved antimicrobial agents may allow scientists to use the γ-core motif or mimetics thereof as the scaffold that will guide the right peptide or molecule to the right target. “Nature has done much of the designing,” he says. “We are capitalizing on the experiments that nature has performed over millions of years [and] trying to integrate the results of that process in new antibiotics.”
Common threads. The γ-core motif, visualized in red above, is seen in antimicrobial peptides from a breadth of organisms, including (left to right) the scorpion, the human, Aspergillus, the mussel, and the buckeye tree. The motif appears to provide a scaffold upon which disease-fighting molecules are configured.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0093215625738EnvironewsForumInfectious Disease: Tackling Innate Immunity Potera Carol 11 2004 112 16 A932 A932 Publication 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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According to the 1999 World Health Organization report Removing Obstacles to Healthy Development, infectious diseases cause one-third of all human deaths worldwide. These diseases also cost the livestock industry billions of dollars yearly, according to figures from the U.S. Department of Agriculture National Center for Animal Health Surveillance. Infectious diseases are currently fought largely with vaccines (which generate so-called adaptive immunity) and antibiotics. But adaptive immunity can take months to acquire, and overuse of antibiotics may promote resistance in bacteria. If researchers with a Canadian project called Functional Pathogenomics of Mucosal Immunity (FPMI) can unlock the genetic mechanisms behind another branch of immunity—innate immunity—they may have the key to faster-acting, more effective medicines by harnessing the body’s rapid-response agents. Indeed, project scientists recently identified a highly promising peptide candidate for future immunotherapies.
FPMI is funded by Genome Canada, a nonprofit corporation dedicated to advancing genomics and proteomics to improve human and animal health. The three-year project involves groups at the University of Saskatchewan, the University of British Columbia, Simon Fraser University, and the Vancouver firm Inimex Pharmaceuticals.
“The unique strength of FMPI is the application of animal and human models of infection to study evolutionarily conserved host responses,” says microbiologist Vivek Kapur, co-director of the Biomedical Genomics Center at the University of Minnesota. Because the mechanisms of the innate immune system are not well understood, this comparative genomics approach to study host–pathogen interactions may lead to new immunotherapeutics to prevent infections, he adds.
Innate immunity appears highly conserved in evolution, suggesting that similar events occur in different species. Innate immunity is relatively nonspecific and acts rapidly to block pathogens at the point that they enter the body: the mucous membranes of the respiratory, digestive, and reproductive tracts. Agents produced by the innate immune system—such as cytokines, chemokines, and natural host defense peptides—act immediately in response to infection.
The researchers use microarrays to watch gene activity in humans and animals following exposure to six bacteria and three viruses associated with hospital-acquired infections, food poisoning, and livestock illnesses. “If we can show that the same genetic processes happen in cows, chickens, and humans, that gives us a great deal of confidence that we’re on the right path [to understanding the mechanism involved],” says project co-leader Lorne Babiuk, director of the Vaccine and Infectious Disease Organization at the University of Saskatchewan.
The data generated by the thousands of microarray experiments are processed by bioinformaticists headed by Fiona Brinkman, an assistant professor of molecular biology and biochemistry at Simon Fraser University. The team’s sophisticated software system, called ArrayPipe, “allows researchers from distant geographic regions to work together and view each others’ analyses,” says Brinkman. The software is available in an “open source” format that makes it very flexible and easy to customize. ArrayPipe can be downloaded for free at http://www.pathogenomics.ca/arraypipe/.
The genes related to innate immunity encode disease-fighting substances, which not only kill pathogens, but also produce inflammation. Although some inflammation is necessary to kill pathogens, it can escalate to undesirable conditions such as septic shock. One goal of the FPMI researchers is to find ways to induce desirable disease-fighting responses, yet quell undesirable ones related to inflammation.
A major breakthrough came when researchers in the laboratory of FPMI co-leader Bob Hancock, who is director of the Centre for Microbial Diseases and Immunity Research at the University of British Columbia, showed that the natural host defense peptide LL-37 cures infections as it suppresses inflammation. In a report published 15 March 2004 in The Journal of Immunology, Hancock and colleagues write that LL-37 up-regulates genes linked with the inflammation that kills microbes, but down-regulates those linked with the inflammation that promotes septic shock, suggesting that LL-37 serves as a watchdog to control inflammatory processes. “This . . . indicates that you can get the good aspects of innate immunity without the bad,” says Hancock.
Scientists at Inimex are designing new drug compounds based on LL-37. The new strategy will encourage the body’s innate immune system to attack foreign invaders, rather than bombard bacteria with antibiotics—an approach that increasingly leads to antibiotic-resistant strains. “It’s a new perspective that’s desperately needed to counteract antibiotic-resistant bacteria,” says Hancock.
Everybody’s got one. The ability of innate immunity to block pathogens at the mucous membranes appears highly conserved across species.
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Environ Health Perspect. 2004 Nov; 112(16):A932
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0093315625739EnvironewsForumInnovative Technologies: Cellular Jigsaw Puzzles Dahl Richard 11 2004 112 16 A933 A933 Publication 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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Although scientists have great understanding of individual molecules, the limitations of modern technology have restricted the study of molecular groupings, or “machines,” within cells. Now, however, scientists with the Structural and Computational Biology Programme at the European Molecular Biology Laboratory (EMBL) in Heidelberg, Germany, have developed a method to predict and gain understanding of how molecules assemble into machines—an advancement with significant potential application to toxicogenomics.
Group leader Rob Russell says advances in the study of functional genomics in recent years have provided the basis for knowing what components make up these molecular machines, even though little is known about what the structures look like. This new knowledge about the makeup of molecular complexes, combined with electron microscopy technology and computer methods developed by Russell and computational biologist Patrick Aloy, provided the framework for the project.
Russell and Aloy studied yeast proteins, identifying the components of hundreds of molecular machines in these cells. Using the “tandem affinity purification” method developed at EMBL Heidelberg, they attached molecular tags to selected proteins and “went fishing” for other proteins in the yeast that would interact with the bait. These interactive complexes form the basis of protein networks.
They liken the process, from that point on, to that of assembling a jigsaw puzzle, where the pieces are individual components of particular machines in yeast cells. They first divided the components into groups containing structural similarities, then proceeded to look for recurring patterns of molecular interactions. For example, if two similar molecules in one machine were also found in another, they were considered likely to fit together in the same way.
The scientists looked for those kinds of relationships and built upward, using knowledge about how various protein molecules fit together in one machine to predict the structure of other machines. In some cases, they were able to draw three-dimensional images of machines on their computer screens. Aloy cautions, however, that these images are predictions—not depictions—of structure.
The potential application of the research at EMBL Heidelberg, which was conducted in conjunction with the private German biopharmaceutical company Cellzome, may be broad. “If you know something about structure, you know a lot about how something works,” Russell says. “If you’re confident that the structure is right, you could conceivably design chemicals to target particular types of machines.”
Andrej Sali, a professor at the University of California, San Francisco, departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry, says that scientists working on structural genomics have become keenly interested in how protein assemblies function. “The general point is that structures of assemblies are informative about what the function of the assemblies is and how that function is performed mechanistically—how one might want to control that function, or modify it, and perhaps eventually how one could design new functions,” Sali says. “So, for these purposes, knowledge of structure is very helpful.”
Sali says Russell and Aloy’s report of their research, which appeared in the 26 March 2004 issue of Science, has been widely read because it presents a new way to envision molecular structures as systems that appear in three dimensions, and not just as individual proteins.
Russell believes that knowledge of molecular machines is useful in toxicogenomics because so much in this science relies on being able to understand the relationship between often highly disparate processes. “For instance,” he says, “how does liver hyperplasia arise when one is taking a drug acting on a particular kinase? This essentially boils down to understanding the relationship between pathways in the cell, and certainly a structural perspective on this can be a great boon.”
Russell says he and his colleagues have only begun to scratch the surface with their work on the functions of molecular machines. EMBL Heidelberg, which is funded by public research monies from 17 member states, recently received a grant from the Sixth Framework Programme of the European Community (which funds research, development, and demonstration activities) to carry the work to the next level. Russell says his laboratory will be working with approximately 20 other groups in Europe to embark on a variety of further experiments using tools including electron microscopy and X-ray crystallography. “The hope,” he says, “is to do this in more detail than what we were able to do in the original paper.”
Although there is obviously much work left to do to realize the potential of this new method, the possibilities appear wide open. “It’s an exciting area,” Russell says. “Our ultimate goal is to have a kind of dynamically updated view of the cell. . . . Ultimately, we want a complete picture of the cell.”
Pieces of the puzzle. Researchers at EMBL Heidelberg have devised a way to apply functional genomics to derive the structure of a molecular machine within the yeast cell. Such models may also suggest a potential mode of interaction between polymerase and transcription initiation factors.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00934EnvironewsForumTXGnet: Y.F. Leung’s Functional Genomics Dooley Erin E. 11 2004 112 16 A934 A934 Publication 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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With its sequencing completed in 2003, scientists set their sights on determining the basic structure and inner workings of the human genome. This movement has spawned numerous new scientific specialties that have been supported by the growth of data-generating technologies. One of these interdisciplinary fields, functional genomics, is devoted to linking gene expression to function (or dysfunction) in cells, organs, and tissue. On his website titled Y.F. Leung’s Functional Genomics, located at http://genomicshome.com/, Harvard researcher Yuk Fai Leung sketches out the current state of this new field of study.
The homepage of the site is divided into three central sections. The main section, titled All About Functional Genomics, is an assemblage of links to relevant outside resources such as “omics” glossaries and the Department of Energy Genomes to Life program. Also in this section are pages of resources for related fields including bioinformatics and proteomics. Leung has also brought together resources on the use of chaos and nonlinear dynamics in genomics, and on the ethical, legal, and social issues surrounding genomics. A group of links to institutes and core facilities conducting functional genomics work is also provided.
Microarray technology has been crucial to the development of functional genomics. The Microarrays subsection gives an overview of what exactly these tools are, as described through videos, technology reviews, even cartoons, and provides descriptions of all of the various equipment and technology required to perform this sort of analysis. The Language & Standard subpage lists links to resources on communicating results with others within the discipline. Listings of relevant courses, video seminars, conferences, and workshops are also available.
The Bioinformatics subsection of the website contains links to more than 50 databases. This subsection, like the Microarrays subsection, also has pages devoted to the language and algorithms used in bioinformatics as well as data standardization. The Ontology page has links to resources on the efforts to develop a standard, universal vocabulary that can be used across the “omics” fields for all organisms.
Other subsections are devoted to proteomics and to genome mapping, complex disease mapping, and linkage analysis. They are populated much as the other two subsections, with pages of glossaries, educational opportunities, calendars, and the like.
This website also has a novel Feature Sections element that bench scientists will find useful. Leung has put together a microarray software comparison featuring 13 primary types of software, including programs for data preprocessing, analysis, and annotation. The page for each software type has a definition of the software’s use, suggested readings, and lists of the products available in each category. Also on offer is a compendium of peer-reviewed journal articles related to functional genomics, including a list of biotech business articles. Leung also provides a reading list of books on functional genomics topics and an in-depth functional genomics glossary.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0093615598607EnvironewsNCT UpdateMass Spectrometry Group Has Mass Appeal Eubanks Mary 11 2004 112 16 A936 A937 Publication 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 field of proteomics seeks to define, on a global scale, the levels, activities, regulation, and interaction of proteins in a biological sample. Proteomics is analogous to transcriptomics—the global analysis of mRNA transcripts that arise from the expression of genes in the genome—although the former is considerably more complicated. Whereas the human genome comprises approximately 30,000 genes, there are likely over 100,000 unique proteins in the human proteome due to the multiple ways each gene can be transcribed and translated into proteins by cellular machinery.
Because proteins are fundamental components of all living cells, including enzymes, hormones, and antibodies, they are constantly in flux as the body takes in food, metabolizes it, and stores or burns energy. Furthermore, different proteins are produced and expressed at different developmental stages of an organism’s life cycle, from the moment of conception throughout the aging process. Consequently, proteomics is a dynamic, challenging research area. One of the essential tools being used to meet this challenge is mass spectrometry (MS), which is the focal point of the National Center for Toxicogenomics (NCT) Mass Spectrometry Group.
There are a large number of proteins in a system at any one time, and they are always changing as an organism eats and metabolizes food, exercises, and sleeps. The proteome—all the proteins expressed in a living system—is therefore unique to the cell or tissue under study. The more narrowly one can define where the proteins are localized within the system (such as in a specific tissue or location in a cell), the easier it is to characterize the specific proteins and quantify their varying levels of expression.
Proteomics research at the NCT focuses largely on changes in an organism’s proteome in response to an event such as exposure to an environmental toxicant, in order to advance understanding of how people might respond to chemical exposures in their environment. The Mass Spectrometry Group, led by NIEHS principal investigator Kenneth B. Tomer, employs high-throughput techniques, including MS, to examine hundreds or thousands of protein changes in a large number of samples. Because proteomics techniques produce large amounts of data, sophisticated analysis tools are used to decipher the results and identify key changes in select protein biomarkers that convey valuable information about exposure to harmful chemicals. The MS facility performs analyses for the intramural NIEHS research community, and Tomer also conducts his own collaborative research projects as part of the NCT Proteomics Group.
Critical Mass
MS enables identification of the composition of a compound based on its mass-to-charge ratio. A sample is ionized, and the charged molecular particles are propelled through an electromagnetic or electric field for separation by differences in mass (molecular weight). A detector records the abundance and mass information for each charged mass and produces a pattern called a mass spectrum. The composition of the sample can be determined based on the mass and relative abundances of ions.
The proteins coming from a specific tissue, cell, or cellular component are separated before they are identified using MS. Separation can be done simply at the whole-protein level using two-dimensional gel electrophoresis; this technique separates molecules by molecular weight and isoelectric point (the pH at which a molecule carries no net electrical charge). The proteins are visualized with various dyes as bands or spots on the gel. The proteins can be more easily identified after the band or spot is cut out, at which point they are digested with enzymes (typically the pancreatic enzyme trypsin) into smaller peptides.
The resulting peptides are identified by “mass fingerprinting,” in which masses of the individual peptides from a gel band are compared with peptide sequences included in the National Center for Biotechnology Information protein database (http://ncbi.nlm.nih.gov/). There are publicly available search engines that can match the mass fingerprint against this database for over 1,000 different types of organisms. The search produces a list of possible proteins that might match the mass fingerprint and gives a probability score.
A high probability score indicates a good match between the unknown peptide sequence and a sequence in the database. The lower the score, the less likely it is that the unknown protein matches peptides in the database. For example, if 10 peptides can be identified in a sample and all 10 have a high probability for match accuracy with a particular protein in the database, then there is high confidence that the identification is accurate. However, if the match probabilities are low, the identification cannot be made with any degree of certainty.
A researcher can then go a step further toward identifying the unknown peptides by using an instrument known as a tandem MS to obtain sequence information on their constituent amino acids. This information complements mass assignment and improves identification reliability. This procedure involves digestion of an entire sample with trypsin, followed by separation of the peptides with a high-performance liquid chromatograph and then identification with sequential MS steps. At the second step of MS, the peptide ion is fragmented into smaller amino acids to obtain the mass and sequence information. When sequence information for amino acids correlates well with mass assignment, there is a stronger case for the identity of the protein. Because the result is a probability, additional separation and identification techniques such as antibody arrays and/or Western blotting are employed to provide backup data to corroborate important identifications.
Cracking a Tough Nut
A particularly challenging area of study within proteomics is post-translational modification of proteins. Post-translational modification is a process by which proteins undergo specific structural changes at certain sites that impart special functions to the protein. The cellular processes involved in post-translational modifications are highly dynamic and very localized, such that modifications can be added or removed very rapidly to a small portion of each protein population as the cell requires. The transient, localized, and proportional nature of post-translational processes makes it difficult to detect this special subset of molecules. For example, says Tomer, phosphorylation is a frequent post-translational modification involved in cell signaling, but even during active signaling, phosphorylation may occur at only one spot on a protein and upon less than 5% of that particular protein population for less than one minute. Yet this type of tiny, rapid modification is vital to cellular function—and crucial to our understanding of toxicity.
Tomer and NCT Proteomics Group toxicologist Alex Merrick addressed the phosphorylation question directly in a model system designed to increase their chances of finding phosphorylations on p53, a key protein controlling cell growth and death. In research published 3 April 2001 in Biochemistry, they separated p53 from the proteome of an expression line of Sf9 insect cells in which some cells had been exposed to okadaic acid, a natural phosphatase inhibitor produced by marine algae. Phosphatase inhibition by okadaic acid results in an overall increase in protein phosphorylation and an imbalance in cell signaling that leads to toxicity.
Using MS, Tomer, Merrick, and colleagues identified a number of amino acid sites on the p53 protein that were phosphorylated, and they further discovered that okadaic acid completely phosphorylated one particular site on p53 (serine 315). They speculate that phosphorylation at that site may have particular biological significance for p53, and that studying these processes could improve understanding of the health consequences resulting from phosphorylation of proteins through exposure to environmental contaminants.
The Power of Comparison
Merrick points to protein profiling as an important use of proteomics in toxicology (toxicoproteomics). Protein profiling, he says, allows a “differential quantification of proteins in one sample that are compared with another sample to see the differences in protein expression.” Comparison of protein differences after exposure to toxicants is one way to find out which proteins respond to the chemicals. Sometimes, these changed proteins lead to unexpected discoveries during the course of toxicoproteomics studies.
For example, it is well recognized that dioxin has major effects on the immune system, but how dioxin mediates these effects is still a mystery as the exact biochemical targets have not been identified. A collaborative study between Tomer, Merrick, and Nigel Walker of the National Toxicology Program, published 15 October 2002 in Archives of Biochemistry and Biophysics, led to the discovery of immune-responsive proteins in the endoplasmic reticulum, a region of the cell where they had not been observed before. This is an example of how, in addition to identifying proteins involved in toxicity, proteomics research can be extended into discovery of specific areas within the cell where a toxic action occurs.
The NCT Mass Spectrometry and Proteomics groups, in concert with the NIEHS Micro-array Center, are currently investigating the toxicity of acetaminophen. Although acetaminophen is normally a safe and effective analgesic when used in accordance with the manufacturer’s instructions, cases of overdose or patient susceptibility do occur, and are cause for emergency room visits and concern to public health. Certain populations such as small children, older adults with compromised liver function, and substance abusers are more susceptible to overdose complications. By using discovery genomics and proteomics technologies to profile gene and protein expression in experimental animals, NCT scientists hope to use MS and other technologies to identify biomarkers in the liver and blood that will be informative about the degree of toxicity and prognosis for survival and recovery.
Advances in MS techniques for pro-teome analysis have made this tool an excellent choice for the identification and quantification of proteins and post-translational modification of proteins, with a high level of specificity and sensitivity. Through collaborative research on a variety of intramural projects, NCT researchers are employing MS to shed light on how protein expression and protein–protein interactions are affected by exposure to different environmental toxicants, and are making progress toward development of protein biomarkers that can be used for diagnosing exposure.
Keeping an eye on ions. After proteins are separated, they are digested into peptides and deposited on a plate (left), which is then inserted into the mass spectrometer (above). Analysis provides information on the ionized sample’s molecular weight and chemical structure.
Peak power. Spectra like this protein signature generated during acetaminophen studies at the NCT/NIEHS offer clues about the potential toxicity of drugs.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0093815598608EnvironewsFocusSystems Biology: The Big Picture Spivey Angela 11 2004 112 16 A938 A943 Publication 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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Genomics, proteomics, and metabolomics have all vastly advanced our understanding of human biology and disease. But the functioning of even a simple system such as a single yeast cell or bacterium is much more complicated than the sum of its genes or proteins or metabolites; it’s the activity of all those components and their relationships to one another that add up to a living organism. Recognizing that complexity, the emerging field of systems biology attempts to harness the power of mathematics, engineering, and computer science to analyze and integrate data from all the “omics” and ultimately create working models of entire biological systems.
“Traditionally, scientists—toxicologists included—have relied on a reductionist approach to biology,” says William Suk, director of the NIEHS Center for Risk and Integrated Sciences. Even now, many studies examine complex systems by looking at cellular components in isolation. For instance, a common experiment involves using DNA microarrays to observe the effect of a chemical exposure on thousands of genes at once. This technique can quickly tell a scientist which genes may be vulnerable to that exposure. But a systems biology approach would attempt to model not only the chemical’s effect on gene expression but also how that expression will affect protein function, and in turn how the exposure will affect cell signaling. “There’s nothing wrong with what we’ve been doing,” Suk says. “But systems biology is going to take it to another level.”
Building a New Science
From one perspective, systems biology is nothing new. At the turn of the twentieth century, physiologists such as Walter B. Cannon were developing the concept of homeostasis—the self-regulatory mechanisms, hunger and thirst for example, that a living organism uses to keep its internal systems in balance despite an ever-changing external environment. The term “systems biology” was first used in the 1960s, when theoretical biologists began creating computer-run mathematical models of biological systems.
We’re exposed to lots of chemicals but at very low concentrations over time. We need tools to help us understand how complex exposures perturb complex systems.
— William Suk
But the field took a leap forward beginning in the 1990s, when the high-throughput tools developed for the sequencing of the human genome brought experimental scientists up to the speed of theoretical biologists. The widespread use of the Internet has also made possible for the first time the international collaborations and sharing of huge amounts of data that systems biology requires. “The way that computer science has responded to genomics is one of the great stories of the sociology of twentieth-century science,” says Charles DeLisi, senior associate provost for bioscience and chair of the Bioinformatics Program at Boston University. Computer scientists have taken a great interest in biology and have stepped up to collaborate with biologists to develop the tools needed to sequence genomes and analyze the resulting data.
Leroy Hood, a biochemist who is president and cofounder of the nonprofit Institute for Systems Biology, agrees. “What uniquely defines the systems biology that I’m thinking about has really come from the genome project and its delineation of a complete parts list of all the genes,” he says. “If you know all the genes, you have the ability to do DNA arrays, follow the behavior of all the messenger RNAs, and even the proteins, in principle.”
Measuring gene expression is one important component of systems biology, and methods for doing so are fairly well developed for the needs of this field. However, proteomics—the science of analyzing all the proteins present in a system at any one time—still has some maturing to do before scientists can integrate its data payload into a true systems approach. Proteomics has been hailed as having even more potential than genomics, because whereas DNA is a set of static instructions for an organism, proteins—the machines that actually carry out the work—are a more fluid medium and may reflect the effects of chemical exposure more accurately. But to integrate protein expression into systems biology, scientists need to better understand its relationship to gene expression. DeLisi says scientists still don’t understand why in many cases there is a tight correlation between gene and protein expression, while in others (as with transcription factors) the correlation is very loose. “That [understanding] will develop over the next five to ten years,” he predicts.
Other researchers have pointed to the need for more quantitative techniques to not only detect the presence of proteins but also determine their size, purity, and concentration in a system. Hood agrees that to achieve truly global analyses of complex biological systems, proteomics technology needs more development. “The big problem that proteomics has is that proteins that are expressed at very low levels are generally invisible to the analytic techniques we have,” he says. Hood suggests the answer to that problem lies in highly miniaturized sensors and detectors developed through nanotechnology and microfluidics.
Another “omics” that promises to fill in gaps in systems biology models is metabolomics—the evaluation of tissues and biofluids such as urine, blood plasma, and saliva for metabolite changes that may result from environmental exposures or from disease. Because metabolites (which include carbohydrates, amino acids, and lipids) are the actual by-products of processing food into energy, this “omics” has the potential to paint a picture of what has actually happened in the cell.
Work in metabolomics seeks to go beyond sampling single metabolites to developing profiles of four or five related metabolites. But to create meaningful metabolite profiles, scientists need better tools for measuring tiny amounts of metabolites and for determining which are most important in the activity of the cell. Existing analytical tools such as nuclear magnetic resonance spectrometry and mass spectrometry go only so far. Indeed, the Metabolomics Technology Development initiative of the NIH Roadmap for Medical Research encourages projects that develop new methods for measuring metabolites that are present only in low concentrations or at specific subcellular locations.
The Measure Is in the Models
For those working on a systems biology approach, the goal of developing these various “omics” technologies is to combine their data into interactive models. Hood says, “The ultimate object of systems biology is to understand how the elements and their interactions together give rise to the emergent properties of the system.”
To do that, scientists begin by modeling individual components such as protein networks and signal transduction pathways. Initially, says Hood, the models are descriptive. They may involve perhaps a relatively simple equation showing relationships between a few proteins in a cell. As more information comes to light, the models become more graphical. A graphical model may visualize a cell as a very complicated flow chart, as a series of pinwheels, or as a spiderweb. Relationships between elements are depicted through color or distance.
Next, researchers can experiment with an actual system such as yeast to see what will happen at the organism level when one component of the system is perturbed. “You can do genetic perturbations where you knock out genes, for example, or environmental perturbations where you give or take away certain kind of sugars,” Hood says. “And then you observe how all the other elements behave in response to those perturbations.” Those experiments will usually yield data that aren’t explained by the model. “So you formulate hypotheses to explain the discrepancies, and you go back and do more of these global and integrated experiments,” Hood says.
It’s a long way from modeling yeast to modeling a human being. But Hood believes that the knowledge gained from a model of a simple system can be scaled up. Such comparative genomics—the ability to learn about complex systems by modeling simpler systems that have similar genetics—is one of the most powerful tools in systems biology, Hood says. “The basic idea is that once evolution latches on to a good idea, it generally uses it over and over again,” he says. “But when you do these comparisons you have to be very aware that although the very basic strategy may be similar, there might be many elaborations.” So scientists must look for “control elements,” small elements such as binding sites for transcription factors that are conserved in both organisms.
The next step in modeling is to describe relationships mathematically. The Institute for Systems Biology has created a mathematical model of the relatively simple metabolic process by which yeast cells get energy by breaking down the sugar galactose. After defining all the genes in the yeast genome and the particular genes, proteins, and other small molecules that are known to play a role in the galactose metabolism pathway, the researchers created a model. Then they grew, in the presence of galactose and without, normal yeast as well as strains that had particular genes deleted. They analyzed the reactions using microarrays, quantitative proteomics, and databases of known physical interactions. “We can write down a series of differential equations, and we can choose parameters to put into those differential equations and can predict a lot of the behavior of the system,” Hood says.
Some scientists believe that current bioinformatics capabilities can handle many biological modeling tasks if a complex system is portrayed as a series of smaller models that overlap one another. But the experimental design must integrate all the “omics” from the beginning. “The phrase ‘data integration’ has been closely associated with buying big mainframes and having engineers design big databases,” says Eric Neumann, global head of knowledge management at Aventis Pharmaceuticals. All of this computational power is aimed at trying to combine information from experiments that don’t have an integrated design—for instance, trying to relate gene expression data from one study to protein data collected in a separate study.
Neumann says this integration can be done better for one-tenth of the cost by adopting good experimental design and focusing more on the downstream. Neumann’s ideal experiment involves collecting gene microarray data, protein levels, metabolite levels, clinical phenotypes, and serum biomarkers in one experiment. “If one compares across experiments, then unless everything is kept constant, the data may not be statistically equivalent,” he says. “Some high-level comparisons may be made if one accepts the independent interpretations from both studies, but these will often be more qualitative mergings than quantitative in nature.”
The integrated experiment design that Neumann favors would allow statistically valid merging, he says. That merging can be accomplished with several simple databases that all point to the same experimental design. The end result is a relational database that “looks like a big spiderweb,” Neumann says.
The ultimate object of systems biology is to understand how the elements and their interactions together give rise to the emergent properties of the system.
— Leroy Hood
John Doyle, a professor of control and dynamical systems at the California Institute of Technology, has likened the complexity of a biological system to that of a Boeing 777 jet. Each system needs only a small portion of its control systems for basic functioning (for instance, Escherichia coli can survive in the laboratory even when 90% of its genes are knocked out). The jet includes more than 100,000 components such as computers and sensors, most of which are not needed under ideal conditions, but which enable the plane to stabilize if conditions suddenly change. Likewise, biological organisms include complex control systems that kick in only during potential threats—such as variations in temperature or nutrients—to keep the organism stable.
In general, this complexity makes the organism robust. But some scientists hypothesize that such complexity can leave a system vulnerable to unplanned disruptions such as genetic mutation. The mutation may be tiny, but because the gene is involved in such a complex, multilayered control network, the tiny mutation can trigger a “cascading failure”—a kind of domino effect that leads to a major threat such as cancer or autoimmune disease. Mathematicians and engineers are at work on algorithms and other tools to better model this robust yet fragile nature and other aspects of complex biological systems.
High Hopes for Tiny Tools
Some researchers say that developments in nanotechnology and microfluidics may revolutionize systems biology. Nanotechnology involves manipulating molecules smaller than 100 nanometers—the scale of viruses. Microfluidics, which is commonly used in ink-jet printing, uses pumps and valves to transport nanoliter volumes of fluids through microchannnels in a tiny glass or plastic chip. Hood says that nanotechnology and microfluidics will eventually enable scientists to make many different measurements in parallel and in small amounts of material. “In principle, you can make these measurements down to the single-molecule level,” he says.
In theory, researchers could create nanobiosensors no bigger than 100 nanometers that could be surgically implanted into the body or injected into the bloodstream to measure biomarkers of environmental exposure or diagnose problems in cell function. Suk says nanobiosensors could potentially measure processes as sensitive as the flux of calcium ions inside a cell.
David Walt, a professor of chemistry at Tufts University, is using fiber optic technology to develop tiny sensors that could be used to screen toxicants. Optical fibers are extremely fine strands of glass that can transmit light to and from a sample. Right now the sensors directly measure chemical changes in arrays of yeast or E. coli cells in response to toxicants such as mercury or the chemotherapeutic agent mitomycin C. The cells are fluorescently labeled, and the researchers monitor chemical changes by correlating those changes to changes in fluorescent intensity.
“The goal is to be able to measure solutions and determine their toxic potential,” Walt says. A similar method could eventually be used to quickly screen potential toxicants, replacing some animal testing. First, though, studies would need to be run to ensure that the cell-based arrays consistently yield the same results as animal testing, Walt says.
Suk is excited about the idea that nanobiosensors could enter cells and make direct measurements of their inner workings. “That will allow us to have a better understanding—maybe even a complete understanding for certain types of tissues—of how cells and systems communicate with each other,” he says. “If you can understand how a cell works, you can then scale up to tissues, then organs.” He predicts that nanobiosensors will make it much easier to measure human exposures in as soon as five years.
A Surprising Challenge
Once all the measurements are made, what must happen to make the megamodels of systems biology a reality? Surprisingly perhaps, some of the players in the field say that the biggest challenge for systems biology isn’t technical—rather, it’s a matter of community.
One problem is the lack of a common language for systems biology. As with other multidisciplinary collaborations, all the players will need to develop a language they can share. That became apparent at a December 2003 retreat on systems biology that Suk organized for the NIEHS Division of Extramural Research and Training, where speakers included an engineer, a biochemist, a computer scientist, and a physician—each of whom approaches the field from his or her own perspective.
Most of us in this area now have educated ourselves. People end up learning what they need to learn to solve the problems they’re interested in solving.
— Charles DeLisi
Neumann agrees the biggest challenges for systems biology are human ones—language and data sharing. “There’s a side to it that involves data analysis by people who feel comfortable looking at data, writing programs, some numerics,” he says. But that analysis needs interpretation from scientists who know about disease and about environmental exposures.
Neumann believes that getting the relevant data to those who can interpret it is the biggest bottleneck for the field. “There are more papers than ever before in science, and most of us can’t read them all,” he says. Simple text queries such as those used to search literature databases capture words or phrases out of context. But it’s possible now, Neumann says, to populate scientific papers with embedded, machine-readable phrases that convey the relevance of the work. Such databases would use ontologies—formal, machine-readable definitions of terms and the relationships between those terms. Programs that know something about logic and relationships can help weed out irrelevant information, Neumann says, and help reveal connections between concepts.
In the 2004 Proceedings of the Pacific Symposium on Biocomputing, Daniel McShan of the University of Colorado and colleagues present an example of how such data mining can be used to predict the behavior of a biopathway. The team developed an algorithm to extract, or infer, biotransformation rules from the Kyoto Encyclopedia of Genes and Genomes (KEGG), a web-accessible database of pathways, genes, and gene expressions. Using KEGG, the team inferred 110 biotransformation rules about what happens when certain compounds interact. They used these rules as well as mathematical algorithms to predict how detoxification pathways would metabolize ethyl and furfuryl alcohol. The model’s prediction correlated with known patterns of alcohol metabolism.
Automated data mining tools are well on their way to development now. “Vendors are working on various applications that would support the enhanced linking of documents anywhere, and eighty to ninety percent of that [technology] already exists,” Neumann says. [For an example of one such tool, see “Literature Searchlight,” EHP 112:A872 (2004).]
The real test is how willing scientists are to use these tools. “We already take enough time footnoting our papers. Imagine if those footnotes were machine-readable,” Neumann says. Authors would use software that works like a spell-checker to screen their papers and choose the formal concepts that are most relevant. “So all of a sudden the whole search and review of literature changes overnight,” he says. “Right now we all are doing text mining and extraction by ourselves. But in the future it will be done by authors as they submit their papers.”
Neumann believes that PubMed Central and other efforts to make all federally funded research freely available will provide an opening for data mining to become commonplace. “To survive, the scientific publishers are going to have to ask, ‘What’s our added value?’ If added value can be putting [text] in a smart ontology—bingo. I think we will see quick embracement of this so that [scientific publishers] find a whole new market strategy,” he says.
DeLisi adds that more emphasis on computational and mathematical training for scientists will help make systems biology more mainstream. “Most of us in this area now have educated ourselves,” he says. “People end up learning what they need to learn to solve the problems they’re interested in solving.”
Several training programs in bioinformatics and systems biology exist, such as the one that DeLisi is involved in at Boston University and those at various University of California campuses. Other programs are under development. “I’m very optimistic,” DeLisi says. “Over the next ten to fifteen years I expect to see a very large shift toward a much more mathematical biology, and certainly toward a highly computationally intensive biology. I have no doubt that in the next ten to twenty years, biology will be the most computational of all the sciences.”
Optimistic Predictions
Suk’s biggest hope for systems biology is that it will create more realistic models of complex environmental exposures. “We’re exposed to lots of chemicals but at very low concentrations over time,” he says. “We need tools to help us understand how complex exposures perturb complex systems.”
Such tools to help elucidate system interrelationships are forthcoming. For example, in the August 2004 toxicogenomics issue of EHP, Hiroyoshi Toyoshiba and colleagues at the NIEHS Laboratory of Computational Biology and Risk Analysis reported the creation of a statistical software program that quantitatively sorts gene expression data to identify which genes interact in a network. The team has used the program to determine the effect of the carcinogen tetrachlorodibenzo-p-dioxin on 11 genes in lung epithelial cells and the genes’ subsequent effect on the retinoic acid signal transduction pathway.
This program looks further than comparing a simple pair of genes. Instead it shows the relationships between a whole set of genes in a network. The study authors have said that when the tool is expanded to include other data such as protein levels, it will help researchers understand biopathways in cells, tissues, and eventually entire organisms.
David Schwartz, a professor of medicine and genetics at Duke University, acknowledges the benefit of the systems biology approach but tempers his enthusiasm with a focus on the here and now. “Systems biology may help us understand biological processes, but we have to put them into a context of human disease,” he says. Schwartz’s lab investigates how gene expression profiles can be used as preclinical markers to help understand the biology and genetics of complex environmentally related diseases such as asthma. “We can also use global ‘omic’ approaches to identify biologic pathways that are specifically affected by disease-based environmental toxicants,” he says.
But Suk is optimistic that systems biology will deliver on its promise for environmental health in the near future. “We’re only limited by two things—by our ability to [grasp] it and by money,” he says.
A working model of an entire biological system would possess enormous power for learning how environmental exposures result in disease. And some scientists say that many elements of such a model are within science’s grasp.
Systems biology may help us understand biological processes, but we have to put them into a context of human disease.
— David Schwartz
But fully embracing the systems approach will also require scientists to embrace change. They must create a new language for the field. They must design experiments always with the whole system in mind. They must even learn a new way of footnoting their papers. How will all this change happen? Like the intricate webs of systems biology models themselves, the answer is sure to be complex.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a00948AnnouncementsFellowships, Grants, & AwardsFellowships, Grants, & Awards 11 2004 112 16 A948 A951 Publication 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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Tools for Genetic and Genomic Studies in Emerging Model Organisms
This Program Announcement (PA) is to encourage investigator-initiated applications for research designed to generate genetic tools and genomic resources that will enable researchers to exploit the full potential of novel or developing model systems for comparative and functional genomic studies. The typical model organism to be considered should have a publicly available draft of the genomic DNA sequence with a minimum of 5X coverage. In addition, it should have at least one of the following characteristics: shows promise as, or is, a model for basic biological or behavioral mechanisms; occupies an important evolutionary niche that may yield novel insights in comparative studies; or has potential as, or is, a model for developmental or disease processes. Applicants are expected to ensure that reagents, technologies, and resources developed under this initiative are made widely available to the research community.
This PA is not intended to encourage genome sequencing projects or studies of model organisms for which there are well-established databases and other genome-related resources, e.g, mouse, Drosophila, C. elegans, and S. cerevisiae. In general, resources to study organisms that fit primarily within the mission of another National Institutes of Health (NIH) Institute or Center, such as pathogenic microorganisms, are not encouraged through this PA.
The advent of the genomic era has been a boon for the investigation of a growing number of model organisms. Completion of DNA sequencing of each genome presents opportunities for novel insights into genomic function, the regulation of gene expression, and evolutionary processes. Yet, the large scale of many sequencing projects and the sheer volume of sequence data create a considerable challenge for the individual investigator as well as consortia of researchers to obtain the resources and tools required to make maximal use of genomic information for comparative or functional studies.
The major goal of this PA is to support research that will enhance the usefulness of DNA sequence information for newly emerging or developing model organisms for which there are limited genomic resources. Objectives to be addressed in applications submitted in response to this PA include, but are not limited to, the following: 1) improvements in tools for mining of data for genomes having unique composition or structure; 2) improved database management and integration with other databases (requests for the maintenance of databases alone are not encouraged); 3) generation of comprehensive cDNA libraries; 4) development of microarray reagents and/or services; 5) improved methods for linking expression arrays with standard phenotypes or with specific biological or behavioral outcomes; 6) development of novel approaches for mutagenesis and for rapid identification and characterization of point mutations; 7) development of novel transposable element-based techniques for the generation of knockouts or other mutations; 8)improvements in gene transfer technology and in vectors for genomic manipulation; and 9) generation of sets of gene knockouts or knock-downs.
This PA will use the NIH research resource grant (R24) mechanism. Responsibility for the planning, direction, and execution of the proposed project will be solely that of the applicant. The total project period for an application submitted in response to this PA may not exceed four years. A maximum of $250,000 direct costs (exclusive of subcontractual indirect costs) per year will be provided.
This PA uses just-in-time concepts. It uses the non-modular budgeting format. Follow the instructions for non-modular budget research grant applications. This program does not require cost sharing as defined in the current NIH Grants Policy Statement at http://grants.nih.gov/grants/policy/nihgps_2003/NIHGPS_Part2.htm.
The NIH is interested in ensuring that the research resources (constructs, reagents, cell lines, software tools, expression data, methods, etc.) developed through this PA become readily available to the research community for further research, development, and application, in the expectation that this will lead to products and knowledge of benefit to the public. At the same time, NIH recognizes the rights of grantees to elect and retain title to subject inventions developed under federal funding under the provision of the Bayh-Dole Act.
This PA has two special requirements regarding research resources produced in proposed projects: 1) Applicants are required to include in their applications a specific plan by which they will share research resources with the wider scientific community. A reasonable time frame for periodic deposition of mutants, reagents, and data should be specified in the application. 2) Applicants are required to include a plan addressing if, or how, they will exercise their intellectual property rights while making available to the broader scientific community patentable research resources. The plan should address the following questions: Will material transfers be made with no more restrictive terms than in the Simple Letter Material Transfer Agreement or the Uniform Biological Material Transfer Agreement? Will there be reach-through requirements on materials transferred? Should any intellectual property arise that requires a patent, will the technology remain widely available to the research community?
Both the sharing and intellectual property plans should, at a minimum, address these elements in a clear and concise manner. Applicants are encouraged to inform and/or confer with their institutional offices of technology transfer to develop plans for addressing these requirements.
Applicants are reminded that the grantee institution is required to disclose each subject invention to NIH within two months after the inventor discloses it in writing to grantee institutional personnel responsible for patent matters. The awarding Institute reserves the right to monitor awardee activity in this area to ascertain if patents or patent applications are adversely affecting the goals of this PA.
Applications must be prepared using the PHS 398 research grant application instructions and forms (rev. 5/2001). Applications must have a Dun and Bradstreet (D&B) Data Universal Numbering System (DUNS) number as the Universal Identifier when applying for federal grants or cooperative agreements. The DUNS number can be obtained by calling 866-705-5711 or through the web site at http://www.dunandbradstreet.com/. The DUNS number should be entered on line 11 of the face page of the PHS 398 form. The PHS 398 is available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance contact GrantsInfo, 301-435-0714, e-mail: [email protected]. The title and number of this PA must be typed on line two of the face page of the application form and the YES box must be checked.
In the background section, the applicant should include a description of existing publicly available resources for the model organism being studied. The applicant should define how the proposal will enhance available resources and provide evidence of research community consultation and consensus regarding the potential value of the resource. In a brief section following the research plan, the applicant must describe plans to share research resources and to exercise intellectual property rights
Applications submitted in response to this PA will be accepted at the standard application deadlines, which are available at http://grants.nih.gov/grants/dates.htm. Application deadlines are also indicated in the PHS 398 application kit.
Submit a signed, typewritten original of the application, including the checklist, and five signed photocopies in one package to: Center for Scientific Review (CSR), NIH, 6701 Rockledge Drive, Room 1040, MSC 7710, Bethesda, MD 20892-7710 USA; Bethesda, MD 20817 (for express/courier service).
Applications must be mailed on or before the receipt dates described at http://grants.nih.gov/grants/funding/submissionschedule.htm. The CSR will not accept any application in response to this PA that is essentially the same as one currently pending initial review unless the applicant withdraws the pending application. The CSR will not accept any application that is essentially the same as one already reviewed. This does not preclude the submission of a substantial revision of an unfunded version of an application already reviewed, but such application must include an introduction addressing the previous critique.
Contact: Anthony Carter, Division of Genetics and Developmental Biology, National Institute of General Medical Sciences (NIGMS), Building 45, Room 2AS-25R, MSC 6200, Bethesda, MD 20892-6200 USA, 301-594-0943, fax: 301-480-2228, e-mail: [email protected].
Reference: PA No. PA-04-141
NLM Research Grants in Biomedical Informatics and Bioinformatics (R01)
The purpose of this PA is to reissue and update the National Library of Medicine’s (NLM) research grant program for biomedical informatics and bioinformatics. NLM’s research funding centers on understanding data, information and knowledge, in particular their nature, forms and uses in the domains of health care and basic biological sciences.
NLM defines biomedical informatics as the intersection of basic informational and computing sciences with an application domain in biomedicine, as discussed in the work of the American College of Medical Informatics referenced below. The term biomedical informatics encompasses the closely-aligned field of bioinformatics, which can be defined as the intersection of basic informational and computer sciences with an application domain in biological/biochemical sciences. NLM’s research focuses on management and efficient utilization of data, information, and knowledge in health care and basic biomedical sciences.
In clinical medicine, health services administration, education, and basic biomedical sciences, computers and networks are fundamental tools of discovery, learning, decision making and management. NLM’s biomedical informatics research grants support the study of how information is best captured, represented, stored, retrieved, manipulated, managed and disseminated for use in these kinds of activities.
The following general themes demonstrate the range and scope of NLM’s research interests in biomedical informatics and bioinformatics: 1) information and knowledge processing, including natural language processing, information extraction, integration of data from heterogeneous sources or domains, event detection, feature recognition; 2) tools for analyzing and/or storing very large datasets, including genomic and proteomic data, data supporting clinical trials, and other data used in clinical or health services research; 3) knowledge representation, including vocabularies, ontologies, simulations and virtual reality; 4) linkage of clinical and genomic information to benefit health care; 5) innovative uses of information technology in health care delivery, including decision support, error reduction, outcomes analysis, and information at the point of care; 6) efficient management and utilization of information and data, including knowledge acquisition and management, process modeling, data mining, acquisition and dissemination, novel visual presentations, and stewardship of large-scale data repositories and archives; 7) human-machine interaction, including interface design, use and understanding of health related-information, intelligent agents, information needs and uses. 8) high-performance computing and communications relating to biomedical applications, including efficient machine-machine interfaces, transmission and storage, and real-time decision support; 9) innovative uses of information technology to enhance learning, retention and understanding of health-related information.
Informatics research is interdisciplinary and employs a range of research methodologies. NLM expects that investigators will employ sound techniques that lead to the collection and analysis of empirical evidence. These techniques may include quantitative and/or qualitative approaches, including laboratory and field studies, surveys and needs analyses, ’in silico’ experiments, modeling and simulation studies.
NLM is a participant in the National Institutes of Health (NIH) Roadmap initiatives, many of which include biomedical computing and interdisciplinary research as essential elements, in the programs of the NIH BISTI initiative, and other NIH informatics initiatives.
While biomedical informatics research projects funded by this program often require software development and tool-building, a well-defined research problem and rigorous research design are essential elements of NLM’s R01 grants. Investigators interested in demonstration projects, proofs of concept or other feasibility testing should consider NLM’s Exploratory/Developmental grant program (http://grants.nih.gov/grants/guide/pa-files/PA-03-107.html) rather than this biomedical informatics research grant program. NLM’s Small Project grants (http://grants.nih.gov/grants/guide/pa-files/PA-03-108.html) are most appropriate for investigators who are just beginning their research in an area and/or need preliminary data to inform a more substantial research project.
Research in biomedical informatics or bioinformatics often employs a specific scientific discipline or medical subspecialty as the subject field or domain in which the research is undertaken, or in which tools and ideas are applied. However, grant applications whose primary focus is on a disease or biological question, rather than the informatics or computational issues that pertain to them, are more appropriate for other Institutes at NIH.
This PA will use the NIH R01 award mechanism. As an applicant, you will be solely responsible for planning, directing, and executing the proposed project.
This PA uses just-in-time concepts. It also uses the modular budgeting as well as the non-modular budgeting 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. Otherwise follow the instructions for non-modular budget research grant applications. This program does not require cost sharing as defined in the current NIH Grants Policy Statement at http://grants.nih.gov/grants/policy/nihgps_2003/NIHGPS_Part2.htm.
An applicant can request funding for up to five years of support. The average duration of recent NLM informatics research awards is about three years.
Applications must be prepared using the PHS 398 research grant application instructions and forms (rev. 5/2001). Applications must have a Dun and Bradstreet (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.dunandbradstreet.com/. The D&B number should be entered on line 11 of the face page of the PHS 398 form. The PHS 398 is available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance contact GrantsInfo, 301-435-0714, e-mail: GrantsInfo@ nih.gov. The title and number of this PA must be typed on line two of the face page of the application form and the YES box must be checked.
Applications submitted in response to this PA will be accepted at the standard application deadlines, which are available at http://grants.nih.gov/grants/dates.htm. Application deadlines are also indicated in the PHS 398 application kit.
Submit a signed, typewritten original of the application, including the checklist, and five signed photocopies in one package to: Center for Scientific Review, NIH, 6701 Rockledge Drive, Room 1040, MSC 7710, Bethesda, MD 20892-7710 USA; Bethesda, MD 20817 (for express/courier service).
Applications must be mailed on or before the receipt dates described at http://grants.nih.gov/grants/funding/submissionschedule.htm. The CSR will not accept any application in response to this PA that is essentially the same as one currently pending initial review unless the applicant withdraws the pending application. The CSR will not accept any application that is essentially the same as one already reviewed. This does not preclude the submission of a substantial revision of an unfunded version of an application already reviewed, but such application must include an introduction addressing the previous critique.
Contact: Valerie Florance, Deputy Director, Extramural Programs Division, National Library of Medicine, Rockledge 1, Suite 301, 6705 Rockledge Drive, Bethesda, MD 20892-0001 USA, 301-594-4882, fax: 301-402-2952, e-mail: floranv@ mail.nih.gov; Hua-Chuan Sim, Scientific Review Administrator, Extramural Programs Division, National Library of Medicine, Rockledge 1, Suite 301, 6705 Rockledge Drive , Bethesda, MD 20892-0001 USA, 301-496-4253, fax: 301-402-2952, e-mail: [email protected]
Reference: PA No. 04-141
Quick-Trials for Novel Cancer Therapies: Exploratory Grants
Continuing progress in basic cancer research and drug development has led to discoveries of new agents or approaches for molecular targeting in novel cancer therapies. These new agents or approaches suppress tumor growth through multiple mechanisms such as cell cycle control, activation of tumor suppressor genes, essential signal pathway blockage, tumor vaccines, tumor microenvironment modification, etc. Rapid translation of these exciting discoveries into clinical practice requires timely support to accommodate the special needs of novel cancer therapy development. This Program Announcement (PA) is intended to provide investigators with rapid access to support for pilot, Phase I, and Phase II cancer clinical trials as well as support for patient monitoring and laboratory studies linked to a cancer clinical trial. Phase III trials are not excluded but such trials generally require greater resources and duration than available from an R21 award. Applications that do not propose a cancer clinical trial or patient monitoring or laboratory studies linked to a cancer clinical trial may be returned to applicants without being reviewed. The focus of this Quick-Trial PA is on translational research in new agent development to ensure the timely exploitation of new cancer therapeutic approaches including the development of new cancer prevention agents. This PA is aimed at providing a new approach in the grant application process by offering a rapid turnaround from application submission to funding. Features of this initiative include a modular grant application and award process, inclusion of the clinical protocol within the grant application, and an accelerated peer review with the goal of issuing new awards within six months of application receipt. Inclusion of the complete clinical protocol within the PHS 398 grant application is intended to simplify the application process by eliminating the need to duplicate protocol details in the Research Plan section and to insure proper peer review of the application. In addition, Quick-Trial applications do not require extensive preliminary data in the grant application and support exploratory translational and clinical research studies involving cancer prevention, chemotherapy and rapid development and application of novel clinical cancer therapies including image guided therapeutic procedures. Investigators may apply for a maximum of two years of funding support using the exploratory or developmental (R21) grant mechanism for up to $250,000 direct costs per year.
Advances in the understanding of molecular cancer genetics, basic cancer biology, and the development of powerful technologies such as microarray, proteomics and bioinformatics have led to the identification of many new molecular targets and pathways in cancer cells. These discoveries have created new frontiers for novel molecular cancer prevention and treatment leading to the development of molecular medicine in cancer therapy. In addition, these novel targets and pathways have presented excellent translational research opportunities for revolutionizing cancer drug development and bringing more effective molecular cancer therapies and cancer prevention strategies into clinical practice.
Novel approaches or agents for inhibiting tumor growth either directly or by impacting the tumor microenvironment are ready to be tested in the clinic with new tools and laboratory analyses that allow investigators to ascertain how specific targets are affected by therapy. These agents include new classes of cytotoxic agents, agents or approaches that act via immune-stimulatory effects, agents that stimulate apoptosis, inhibit angiogenesis and metastasis or alter tumor cell signaling pathways, and agents targeted specifically to novel cancer cell targets. New clinical therapeutic trials may employ drugs/agents, biologics, radiation, heat, or surgery used as single agents/modalities or in combination for the treatment of early and advanced disease. In addition, clinical trials of therapies for cancer treatment, including but not limited to herbal therapies, dietary supplements, bioactive food components, or unconventional pharmacological and biological interventions (e.g. antineoplastons, Coley's toxin, enzyme therapies, etc.) will be considered. Another relevant area of investigation is the use of anatomical and molecular image guidance for targeted treatment with ablative techniques or delivery of chemotherapeutic agents. At present, there are few funding mechanisms targeted to stimulate the translation of promising and potentially relevant advances in new prevention or therapeutic agent development from the laboratory into the clinical setting. Quite frequently the initial stages of clinical investigation are the most difficult to accomplish. They are resource intensive, and, to be done well, they require laboratory, pharmacology, and other resource support, as well as substantial personnel effort, none of which is supported by traditional health benefit programs. Nonetheless, these early studies tend to fare poorly in competition for conventional grant support precisely because they are preliminary and cannot serve as the definitive tests of new approaches. Even when funding is received, the review and award cycle may introduce a year or more of delay. Except where there is an industrial sponsor with a particular commitment to development of an agent, it may take a long time for a promising approach to get through the initial phase of demonstrating feasibility and interest, or it may never be tested in more than one or two diseases.
This PA will continue to support scientific, technological, clinical and logistical needs in novel cancer therapy development. In addition, this PA will complement the Rapid Access to Intervention Development (RAID) program http://dtp.nci.nih.gov/docs/raid/raid_index.html by providing an initiative with accelerated peer review and funding to support the clinical and laboratory costs of early clinical testing to ensure the timely development of new therapeutic approaches.
This PA will use the NIH exploratory/development (R21) award mechanism. As an applicant, you will be solely responsible for planning, directing, and executing the proposed project. The applicant may request a project period of up to 2 years with direct costs limited to $250,000 per year.
This PA uses just-in-time concepts. It also uses the modular budgeting format (see http://grants.nih.gov/grants/funding/modular/modular.htm). This program does not require cost sharing as defined in the current NIH Grants Policy Statement at http://grants.nih.gov/grants/policy/nihgps_2003/NIHGPS_Part2.htm.
Applications must be prepared using the PHS 398 research grant application instructions and forms (rev. 5/2001). Applications must have a Dun and Bradstreet (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.dunandbradstreet.com/. The D&B number should be entered on line 11 of the face page of the PHS 398 form. The PHS 398 document is available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance, contact GrantsInfo, 301-435-0714, e-mail: [email protected]. The title and number of this PA must be typed on line 2 of the face page of the application form and the YES box must be checked.
Because Quick-Trial applications will propose cancer clinical trials or patient monitoring or laboratory studies linked to a cancer clinical trial, applicants are reminded to properly complete item e (Humans Subjects Research) of the Research Plan. This is described in PHS 398. As applicable, the Human Subjects Research portion of the Research Plan includes, but is not limited to, a Data and Safety Monitoring Plan, Women, Minority, and Children Inclusion sections, and a Targeted/Planned Enrollment Table.
Submit a signed, typewritten original of the application, including the checklist, and five signed photocopies in one package to: Center for Scientific Review, National Institutes of Health, 6701 Rockledge Drive, Room 1040, MSC 7710, Bethesda, MD 20892-7710 USA; Bethesda, MD 20817 (for express/courier service).
The CSR will not accept any application in response to this PA that is essentially the same as one currently pending initial review unless the applicant withdraws the pending application. The CSR will not accept any application that is essentially the same as one already reviewed. This does not preclude the submission of a substantial revision of an unfunded version of an application already reviewed, but such application must include an introduction addressing the previous critique.
Contact: Roy Wu, Clinical Grants and Contracts Branch, Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute (NCI), 6130 Executive Boulevard, EPN Room 7009, Bethesda, MD 20892-7432 USA; Rockville, MD 20852 (for express/courier service), 301-496-8866, fax: 301-480-4663, e-mail: [email protected]
Reference: PA No. PAR-04-155
Genetics and Pathobiology of Vascular Cognitive Impairment
The purpose of this Program Announcement with set-aside funds (PAS) is to invite applications to study the biological basis of vascular cognitive impairment (VCI). VCI causes a burden of illness similar to that caused by Alzheimer’s disease (AD), but has been far less well-studied. Recently, however, some important strides have been made in understanding the etiology of VCI. These include the discovery of a monogenic form of vascular dementia, CADASIL, and identification of the causative gene as Notch 3. In addition, MRI and other pathological data have provided a clearer delineation of the various clinical subtypes of VCI, and awareness of the synergistic interaction between vascular and classical Alzheimer’s pathologies in producing cognitive impairment. The goal of this PAS is to build on these first critical achievements to obtain a better understanding of the cellular and molecular mechanisms causing vascular, neural, and glial dysfunction in human VCI and animal models of VCI.
The number of people affected by dementia in the US is expected to increase three-fold in the next 50 years, to a total of over 13 million. The best-known form of dementia is AD, whose definitive diagnostic sign is the presence of plaques and tangles in brain neurons upon autopsy. However, a large proportion of dementia cases in the aged population are not due to AD, but rather to cerebrovascular disease. Dementia due to cerebrovascular disease is referred to as “vascular dementia”, and can occur in the absence of Alzheimer’s pathology. In addition to this so-called “pure” vascular dementia, there are also cases of “mixed” dementia in which cerebrovascular and Alzheimer’s pathologies coexist. Recent studies suggest that pure vascular dementia and mixed dementia together comprise the majority of dementia cases in some populations.
Vascular dementia can arise from any of several cerebrovascular disease conditions, but its two major causes are focal ischemic infarcts (i.e., strokes) and subcortical ischemic vascular disease. Focal ischemic infarcts result from occlusion of large vessels, in either cortical or subcortical locations, and are accompanied by acute clinical signs of neurological impairment. (Dementia arising from large infarcts is also sometimes referred to as “multi-infarct dementia”.) Subcortical ischemic vascular disease, on the other hand, results from occlusion of small vessels, and creates widespread small lesions (lacunae) and/or areas of demyelination. The areas affected are generally subcortical, including the basal ganglia, cerebral white matter and brainstem. This form of vascular disease generally does not produce sudden, acute symptoms, but rather causes longer-term, insidious changes in neurological function. In a significant portion of cases, this disease can even remain clinically silent for the life of the individual. Subcortical small vessel disease can be diagnosed by imaging even in cases where it is clinically silent.
In recent years, the term “vascular dementia” has been replaced by the term “vascular cognitive impairment (VCI)”. This change reflects the realization that cerebrovascular disease can cause significant cognitive and functional decline in the absence of dementia as defined by standard criteria. In addition, there is increasing evidence that VCI differs from AD in terms of precise range of cognitive defects associated with each disease. AD is characterized primarily by episodic memory loss due to loss of cholinergic basal forebrain neurons and their projections to the hippocampus. In contrast, VCI in its purest forms seems to be characterized more by loss of executive function and attentional mechanisms associated with prefrontal circuitry. However, the spectrums of defects seen in VCI and AD overlap substantially. This fact, together with the frequent coexistence of vascular and Alzheimer’s pathologies within individual patients, renders it difficult to provide definitive diagnoses based strictly on cognitive tests.
Despite the enormous prevalence of VCI, the biological basis of this disease has been much less well studied than that of AD. This lack has been due in part to the clinical heterogeneity of the disease, and also to poor understanding of its pathology at the cellular level. Recently however, research in VCI has taken some critical first steps forward. A genetic form of vascular dementia, CADASIL, has been discovered, and the mutant gene identified as Notch 3. Previous research in animal models had shown Notch 3 to be important in early neural and vascular development. The finding that mutation of Notch 3 leads to stroke and dementia (both seen in CADASIL) suggests that the gene also plays an important role in the function or maintenance of vascular and/or neural cells in the adult. Consistent with this possibility, a transgenic mouse carrying the mutant form of Notch 3 has now been generated which shows degeneration of smooth muscle cells similar to that seen in human patients. These findings provide an important foothold for understanding the cell biology as well as the genetics of VCI. Moreover, the known interaction of Notch with the presenilin proteins suggests a juncture in the disease pathways underlying VCI and AD, which also could be further explored in mouse models.
Another major area ripe for exploration concerns the genes and other risk factors that link vascular pathology to neural pathology or that render individuals susceptible to neuronal damage and cognitive impairment in response to cerebrovascular disease. Some progress has been made in recent years in defining genes that predispose individuals to stroke and cerebrovascular disease per se, but no studies have yet examined genes that control the ability of neural tissue to recover from ischemic injury. Identifying such genes would provide clear paths both to understanding the cell biology of VCI, and also to the design of protective agents and therapeutics.
Research areas appropriate for this PAS would include, but are not imited by the following examples: 1) genetics of VCI, in both animal models and humans, in particular, identification of genes that render individuals susceptible to cognitive impairment secondary to cerebrovascular disease; 2) analysis of cellular and molecular changes occurring in vascular, neuronal, and glial cells during the development of VCI in human patients, and correlation of these with MRI signs and changes in cognitive function; 3) studies of cellular and molecular pathological processes occurring in vascular, neuronal, and glial cells in animal models of VCI, such as mouse lines carrying mutant forms of Notch 3 or the stroke-prone spontaneously hypertensive rat; 4) studies of Notch 3 function in the maintenance and repair of vascular, neuronal, and glial cells in normal adult animals; studies of the cellular and molecular bases of the pathogenic actions of mutant Notch 3; 5) studies of the cellular and molecular bases of the interaction between the VCI and AD pathways (for example, studies of vascular function and pathology in animal models of AD); 6) development and characterization of new animal models for the study of VCI, and of the interaction between VCI and AD pathogenic mechanisms; 7) analysis of cognitive function in animal models of VCI, and correlation of changes in cognitive function with cellular and molecular pathologies; 8) studies on the cellular and molecular effects of hypertension, diabetes, hyperlipidemia, coagulant and anticoagulant proteins, inflammatory cytokines, and complement proteins on the vessel wall in appropriate animal models for VCI.
This PAS will use the NIH R01 and R21 award mechanism(s). As an applicant, you will be solely responsible for planning, directing, and executing the proposed project. The proposed project period during which the research will be conducted should adequately reflect the time required to accomplish the stated goals The R21 mechanism (see http://grants.nih.gov/grants/guide/pa-files/PA-03-107.html) is intended to encourage exploratory and developmental research projects by providing support for the early and conceptual stages of these projects. These one-time awards support innovative, high impact research projects that assess the feasibility of a novel area of investigation or a new experimental system, include the unique and innovative use of an existing methodology to explore a new scientific area, involve considerable risk but may lead to a breakthrough in a particular area, or develop new technology or methodology that could have major impact in a specific research area. Applications for R21 awards should describe projects distinct from those supported through the traditional R01 mechanism. For example, long-term projects, or projects designed to increase knowledge in a well-established area will not be considered for R21 awards. Applications submitted under this mechanism should be exploratory and novel. These studies should break new ground or extend previous discoveries toward new directions or applications.
R21 applications may request a project period of up to two years with a combined budget for direct costs of up to $275,000 for the two year period. For example, you may request $100,000 in the first year and $175,000 in the second year. The request should be tailored to the needs of your project. Normally, no more than $200,000 may be requested in any single year. For further information on the R21 mechanism, including Institute-specific information, see http://grants.nih.gov/grants/guide/pa-files/PA-03-107.html.
This PAS uses just-in-time concepts. It also uses the modular budgeting as well as the non-modular budgeting 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. Otherwise follow the instructions for non-modular budget research grant applications.
The National Institute of Neurological Disorders and Stroke (NINDS) has set aside a total of $2,250,000, in addition to funds available for applications sent in response to this PA that score within the NINDS payline (see NINDS Funding Strategy http://www.ninds.nih.gov/funding/ninds_funding_strategy.htm), depending on the overall scientific merit of the applications and the availability of funds throughout the duration of this solicitation (three years). The National Institute on Aging (NIA) has set aside a total of $300,000, and the National Heart, Lung and Blood Institute (NHLBI) has set aside a total of $350,000.
PHS policy requires that investigators make unique research resources available for research purposes to qualified individuals within the scientific community when they have been published (see the NIH Grants Policy Statement at http://grants.nih.gov/grants/guide/notice-files/not96-184.html). In addition, NIH recently released a statement on the sharing of research data that applies to all investigator-initiated applications with direct costs greater than $500,000 in any single year (http://grants.nih.gov/grants/guide/notice-files/NOT-OD-03-032.html).
All applicants who respond to this PAS must propose plans for sharing data and biomaterials generated through the grant. Applicants should explain how funds for the storage and distribution of data and biomaterials will be obtained, and may request such funds in the budget of the application. It is expected that the data to be shared will be clinical, diagnostic, and pedigree structure information, and information about the genetic backgrounds and phenotypes of mutant or transgenic animal strains. Biomaterials to be shared will include patient DNAs and cell lines, and mutant or transgenic animal strains. When possible, data and biomaterials should be placed in databases or repositories that will permit their efficient distribution to investigators throughout the scientific community. An example of such a facility is the NINDS Human Genetics Resource Center (http://locus.umdnj.edu/ninds).
The Initial Review Group will evaluate the proposed sharing plan and comment on its adequacy in an administrative note in the summary statement. Reviewers will not factor the proposed data-sharing plan into the determination of scientific merit or priority score. The adequacy of the plan will be considered by NIH staff in determining whether the grant shall be awarded. The sharing plan as approved, after negotiation with the applicant when necessary, will be a condition of the award.
Our understanding of pathogenic mechanisms in VCI would benefit tremendously from the use of standardized criteria for diagnosing this condition, including standardized methods for measuring cognitive function. NINDS plans to encourage and coordinate the use of a minimal diagnostic dataset in studies funded by this PAS. Until such a dataset is defined, applicants to this PAS should provide detailed descriptions of the patient data to be collected, including methods for independently assessing the presence and type of cerebrovascular disease, and levels of cognitive function. Rationale for choice of specific cognitive test(s) should be included. In addition, plans should be included for entry of disease and cognitive phenotypic data into a computerized database that may be easily shared with other researchers.
Applicants to be funded under this PAS will be expected to travel to NIH once a year to share progress with NIH program staff, other investigators funded under this PAS, and additional advisers as deemed necessary by NIH program staff. Applicants should include funds to support one trip per year to Bethesda, MD (for the principal investigator and co-principal investigators only).
Applications must be prepared using the PHS 398 research grant application instructions and forms (rev. 5/2001). Applications must have a Dun and Bradstreet (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.dunandbradstreet.com/. The D&B number should be entered on line 11 of the face page of the PHS 398 form. The PHS 398 is available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance contact GrantsInfo, 301-435-0714, e-mail: [email protected]. The title and number of this PA must be typed on line 2 of the face page of the application form and the YES box must be checked.
Submit a signed, typewritten original of the application, including the checklist, and five signed photocopies in one package to: Center for Scientific Review, National Institutes of Health (NIH), 6701 Rockledge Drive, Room 1040, MSC 7710, Bethesda, MD 20892-7710 USA; Bethesda, MD 20817 (for express/courier service).
Applications must be mailed on or before the receipt dates described at http://grants.nih.gov/grants/funding/submissionschedule.htm. The CSR will not accept any application in response to this PAS that is essentially the same as one currently pending initial review unless the applicant withdraws the pending application. The CSR will not accept any application that is essentially the same as one already reviewed. This does not preclude the submission of a substantial revision of an unfunded version of an application already reviewed, but such application must include an introduction addressing the previous critique.
Contact: Gabrielle G. Leblanc, Program Director, Neurogenetics, National Institute of Neurological Disorders and Stroke (NINDS), Neuroscience Center, Suite 2136, MSC 9537, Bethesda, MD 20892-0001 USA, 301-496-5745, fax: 301-402-1501; e-mail: [email protected]; Creighton H. Phelps, Director, Alzheimer's Disease Centers Program, Neuroscience and Neuropsychology of Aging, National Institute on Aging (NIA), National Institutes of Health, 7201 Wisconsin Ave., Suite 350, Bethesda, MD, 20892-0001 USA; 301-496-9350, fax: 301-496-1494; e-mail: [email protected]; Stephen S. Goldman, Vascular Biology Research Program, Division of Heart and Vascular Diseases, National Heart, Lung, and Blood Institute (NHLBI), 6701 Rockledge Drive, Suite 10192, MSC 7956, Bethesda, MD 20892-0001 USA, 301-435-0560, fax: 301-480-2858, e-mail: [email protected].
Reference: PA No. PAS-04-149
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0944aEnvironewsScience SelectionsTemplate for Toxicants: Gene Expression Varies by Cell Type Barrett Julia R. 11 2004 112 16 A944 A944 Publication 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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Gene expression profiling shows that cells generally respond to toxicant stress by repressing genes that guide cell growth and inducing those that govern DNA repair and other protective functions. However, the specific genes repressed or induced vary, depending on the cell type and—according to research presented in this issue—the toxicant to which the cells are exposed [EHP 112:1607–1613]. Melissa Troester of the University of North Carolina at Chapel Hill and colleagues note that this study demonstrates the utility of microarrays in predictive toxicology.
The current study builds upon previous research showing that separate breast cancer cell lines have distinctive responses to two different chemotherapeutic agents, doxorubicin (DOX) and 5-fluorouracil (5FU). Because DOX and 5FU have different mechanisms of action, the researchers hypothesized that cells treated with one compound would express a different transcription profile compared with cells treated with the other. In establishing support for this hypothesis, the researchers were also able to demonstrate that a profile of expressed genes could serve as a template to predict the mechanism of action for a third cancer drug, etoposide (ETOP).
The researchers cultured four breast cell lines for their experiments—two each of basal-like and luminal epithelium—and determined comparable toxic concentrations for DOX, 5FU, and ETOP at 36 hours’ exposure. Next, cell cultures were treated at these concentrations for 12, 24, or 36 hours in order to identify genes that were consistently expressed over time. At the end of the treatment periods, mRNA was extracted from the cells, pooled according to treatment and cell line, and used to create labeled complementary DNA samples. These samples were hybridized to microarrays representing 22,000 genes.
Microarray analysis identified which genes had been up- or down-regulated and revealed unique patterns of gene expression in response to DOX and 5FU in each cell type as well as each cell line. In general, luminal epithelial cells responded by regulating a large number of genes—974 in one line, 883 in the other. Basal-like epithelial cells regulated fewer genes (76 and 193) and also exhibited significant differences in gene expression over time. The cells exhibited a distinctly different profile at the 12-hour time point as compared with the 24- and 36-hour points. The difference was great enough that the DOX-treated samples clustered with 5FU-treated samples at 12 hours but not at 24 or 36 hours. This temporal shift blurred the lines between profiles and affected the accuracy of predictions.
Further investigation pinpointed 100 genes that could be used to differentiate between DOX- and 5FU-treated samples. This list of genes provided the basis for the final evaluation—testing whether the mechanism of action for ETOP could be accurately classified based upon the genes expressed following exposure. Because ETOP acts by a mechanism similar to that of DOX, it was expected that the gene set expressed by ETOP-treated cells would more closely resemble that of DOX-treated cells as compared to 5FU-treated cells.
Indeed, the mechanism of action for ETOP was predicted with 100% accuracy. When the researchers included cell type in the predictive model, the accuracy dropped to 75%, due in part to the temporal variability in gene expression in the basal-like cell lines.
With regard to the identity of regulated genes, published reports corroborate this toxicant-specific expression. For example, DOX has previously been shown to impair cellular respiration; the current research reveals that DOX alters mitochondrial gene expression, which provides a plausible explanation for the documented impairment. The findings also show several unanticipated changes in gene expression. For example, 5FU treatment induced the genes ID1 and ID3, an effect that has not been previously noted. Knowledge of Id proteins is incomplete, and the researchers suggest that their pathways warrant attention as potential targets for therapeutic treatments.
Many toxicogenomics studies are providing expression data for toxicants that have known mechanisms of action, with the eventual goal of inferring mechanisms of action for novel compounds. Based on the success of their own mechanistic analysis, Troester and colleagues contend that this is feasible.
Profiles in chemistry. New research examining chemotherapeutic agents applied to breast cancer cells shows how known gene expression profiles may be used to predict the mechanism of action of other drugs.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0944bEnvironewsScience SelectionsHow E2 Induces Uterine Effects: Transcription Coordinates Cascade Barrett Julia R. 11 2004 112 16 A944 A945 Publication 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 rodent uterotrophic assay, a standard method for assessing a compound’s estrogenicity, offers a model for phenotypic anchoring, or linking changes in gene expression to specific pathologic changes. Typically, when an immature rodent uterus is exposed to the endogenous estrogen 17β-estradiol (E2), it undergoes cell proliferation and differentiation that can be measured through weighing and histological analysis. The uterine changes triggered by estrogens are directed by numerous genes, but little has been known about the molecular events involved and how they relate to observable physical change. A wealth of detail is now provided through research by Jonathan Moggs of Syngenta’s Central Toxicology Laboratory in the United Kingdom and colleagues [EHP 112:1589–1606]. According to the team’s findings, E2 induces a highly coordinated transcriptional program that orchestrates a cascade of cellular events related to uterine growth.
The scientists’ findings are based on a standard rodent uterotrophic assay. Female mice were given a single E2 or control injection at approximately 3 weeks of age and then euthanized at specified time intervals (1, 2, 4, 8, 24, 48, or 72 hours). After the animals’ uteri were weighed, samples were taken for histological analysis, and remaining tissue was subjected to RNA extraction for microarray analysis.
The researchers confirmed the physical events of this typical assay. Uterine weights began to change rapidly after the E2 injections. A significant increase was seen by 4 hours, with maximum weight gain reached at 24–72 hours. Cellular changes were also rapid. By 4 hours after injection, the stromal endometrium had thickened due to water uptake; cell growth and proliferation were apparent between 8 and 24 hours.
Total RNA was isolated from the pooled uteri for each treatment group, and labeled complementary RNAs were constructed and hybridized to microarrays to yield 42 data sets. Analysis of gene expression led to the identification of 3,538 E2-responsive genes. Further analysis allowed the grouping of these genes into coregulated clusters and the identification of the predominant gene functions associated with each cluster. Finally, by comparing gene expression and changes in uterine weight and histology with regard to time, the scientists were able to anchor changes in gene expression to changes in uterine characteristics.
These new microarray data reveal that the interaction of an exogenous estrogen with estrogen receptors initiates a highly coordinated molecular cascade that drives uterine growth and cell differentiation. The molecular program begins with the induction of genes that regulate transcription and signal transduction. It continues with the regulation of genes involved in protein biosynthesis, cell proliferation, and epithelial cell differentiation. Other gene functions are interwoven into the program, including the direction of fluid uptake and coordination of cell division.
With regard to time, changes in gene expression and uterine characteristics fell into four distinct phases. In the first phase, covering the first 4 hours after injection, E2 rapidly induced transcriptional regulators and signaling components for a multitude of pathways, including those responsible for regulating fluid influx. The second phase, 4–8 hours after injection, was characterized by induction of genes needed for mRNA and protein synthesis, but no changes in physical uterine characteristics. During the third phase, occurring 8–24 hours after injection, uterine weight doubled, and cells entered the replication cycle, while genes controlling chromosome regulation and cell cycle were under active regulation. Finally, in the fourth phase, 24–72 hours following E2 exposure, the genes being induced were those involved in uterine cell differentiation and defense responses.
The researchers write that their findings provide a basis for understanding the mechanisms by which other estrogenic compounds, including environmental chemicals, induce their effects. Also, the large number of E2-responsive genes that they identified provides an array of potential marker genes that could be useful in short-term estrogenicity assays. Finally, the scientists note that their work provides a paradigm for understanding the mechanisms of action for estrogen as well as other nuclear receptors.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0962aAnnouncementsBook ReviewToxicogenomics: Principles and Applications Omenn Gilbert S. Gilbert S. Omenn is professor of internal medicine, human genetics, and public health at the University of Michigan. His research interests include cancer proteomics, chemoprevention of cancers, public health genetics, science-based risk analysis, and health policy. He leads the Plasma Proteome Project for the international Human Proteome Organization and is president-elect of the American Association for the Advancement of Science.11 2004 112 16 A962 A962 Publication 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 Hisham K. Hamadeh and Cynthia A. Afshari
Hoboken, NJ:Wiley-Liss, 2004. 361 pp. ISBN: 0-471-43417-5. $69.95 cloth
This timely book presents basic toxicology for molecular biologists and detailed approaches and methods of gene expression, protein, and metabolic global analyses for toxicologists and others learning to use such methods. There are high expectations that “molecular signatures” will become useful as biomarkers of exposure, early effect, and differential susceptibility and reveal targets for new drugs. I particularly appreciated the effort to link genotypes to phenotypes in the form of “target organ toxicity patterns”—in liver, kidney, lung, nervous system, skin, reproductive system—-from Chapter 1 onward. The detailed pathology and physiology along the periportal–centrilobular gradient in liver, combined with references to mRNA analyses of 15 hepatotoxins, and along the segments of the renal tubule exposed to cisplatin or ochratoxins, illustrate (e.g., Chapter 8) the opportunity to link traditional toxicology and pathology with the molecular analyses. Conversely, global molecular analyses have begun to reveal many previously unsuspected or unknown targets for desired and adverse effects of drugs and other chemicals.
The aim of the new approaches is to transform toxicology from descriptive to predictive, including prediction of in vivo effects from in vitro models and other species. Technical features are presented in considerable detail. Innovative manufacture, miniaturization, scanning, and statistical analyses of DNA microarrays have yielded sequence and gene expression information with remarkable throughput. Many technical advances are still needed, such as better evidence that fluorophores match up well in two-color experiments. Statistical analyses of a methapyrilene study vividly demonstrate sources of “false discoveries” (Chapter 6). Differential cell loss in heterogeneous tissues will change mRNA ratios, requiring estimates by pathologists of changes in tissue composition. Expressed sequence tags unannotated for function may be discriminants for disease associations (Chapter 9). In vitro response patterns may not match in vivo patterns (Chapter 10). When pathways and regulation are highly conserved, yeast cells or other model organisms may reveal a lot about the multiple targets and interactions of a drug or its intended protein target.
“Toxicogenomics” is defined explicitly to embrace proteins and metabolites as the key effector classes in functional genomics. There are two chapters on proteomics and one on metabolomics and metabo-nomics. Multiple, rapidly evolving fractionation, chemical tagging, mass spectrometry, microarray format affinity methods, and database search algorithms are highlighted; key challenges are much higher throughput, validation of protein identifications, and quantitation. Plasma and tissue lysate proteomes are very complex mixtures, reflecting the huge range of concentrations and many isoforms of large numbers of proteins, and the dynamic nature of the structure–function relationships. International cooperative strategies are recommended (Chapter 12). Proteins are targets of many environmental agents, with potential effects on all functions and pathways in the cell, including the posttranslational modification of proteins themselves. Peptide adducts with electrophiles have been characterized and mapped on target proteins with tandem mass spectrometry scans and SALSA software (Chapter 13); finding low abundance targets and focusing instrument time on peptides of highest interest are big challenges. Finally, metabolic profiles of toxicologic exposures have yielded many potential markers of early effects.
The editors, formerly at the National Institute of Environmental Health Sciences (NIEHS) Center for Toxicogenomics and now at Amgen Inc., and their colleagues from the NIEHS, academe, and many companies have highlighted emerging techniques, practical laboratory planning, and explicit biostatistical and bioinformatic interpretations. The book is well referenced, with particular attention to web-based resources. The emphasis on methods rather than signatures reveals the still-early nature of this promising field.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institue of Environmental Health Sciences ehp0112-a0962bAnnouncementsNew BooksNew Books 11 2004 112 16 A962 A962 Publication 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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Algorithms in Bioinformatics
Inge Jonassen, Junhyong Kim, eds.
New York:Springer-Verlag, 2004. 476 pp.
ISBN: 3-540-23018-1, $84
Antisense Therapeutics, 2nd ed.
M. Ian Phillips
Totowa, NJ:Humana Press, 2004. 344 pp.
ISBN: 1-58829-205-3, $125
Assembling the Tree of Life
Joel Cracraft, Michael J. Donoghue
New York:Oxford University Press, 2004. 592 pp.
ISBN: 0-19-517234-5, $59.95
Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, 3rd ed.
Andreas D. Baxevanis, B.F. Francis Ouellette, Mark Boguski, Gerard Bouffard, Stephen Bryant, Barbara Butler, Sam Cartinhour, Colombe Chappey, Mark Hershkovitz, Christopher Hogue, Jonathan Kans, David Landsman, Detlef Leipe, James Ostell, Greg Schuler
Hoboken, NJ:John Wiley & Sons, 2004. 512 pp.
ISBN: 0-471-47878-4, $79.95
Cancer Gene Therapy
David T. Curiel, Joanne T. Douglas
Totowa, NJ:Humana Press, 2004. 504 pp.
ISBN: 1-58829-213-4, $165
Epigenetics Protocols
Trygve O. Tollefsbol
Totowa, NJ:Humana Press, 2004. 320 pp.
ISBN: 1-58829-336-x, $99.50
Gene Genealogies, Variation and Evolution: A Primer in Coalescent Theory
Jotun Hein, Mikkel Schierup, Carsten Wiuf
New York:Oxford University Press, 2004. 350 pp.
ISBN: 0-19-852995-3, $124.50
Genome Transcriptome and Proteome Analysis
Alain Bernot
Hoboken, NJ:John Wiley & Sons, 2004. 240 pp.
ISBN: 0-470-84954-1, $115
Guide to Mutation Detection
Graham R. Taylor, Ian N. Day
Hoboken, NJ:John Wiley & Sons, 2004. 352 pp.
ISBN: 0-471-23444-3, $89.95
Mobile Genetic Elements: Protocols and Genomic Applications
Wolfgang J. Miller, Pierre Capy
Totowa, NJ:Humana Press, 2004. 304 pp.
ISBN: 1-58829-007-7, $89.50
Modular Protein Domains
Giovanni Cesareni, Marlo Gimona, Martus Sudol, Michael Yaffe, eds.
Totowa, NJ:Humana Press, 2004. 524 pp.
ISBN: 3-527-30813-X, $185
Oligonucleotide Synthesis
Piet Herdewijn
Totowa, NJ:Humana Press, 2004. 456 pp.
ISBN: 1-58829-233-9, $125
Protein Synthesis and Ribosome Structure: Translating the Genome
Knud H. Nierhaus, Daniel N. Wilson
Hoboken, NJ:John Wiley & Sons, 2004. 592 pp.
ISBN: 3-527-30638-2, $180
Statistical Methods in Molecular Evolution
Rasmus Nielsen, ed.
New York:Springer-Verlag, 2004. 520 pp.
ISBN: 0-387-22333-9, $89.95
Stroke Genomics: Methods and Reviews
Simon J. Read, David Virley
Totowa, NJ:Humana Press, 2004. 352 pp.
ISBN: 1-58829-333-5, $125
Thompson & Thompson Genetics in Medicine, Revised
Robert Nussbaum, Roderick McInnes, Huntington Willard
New York:Springer-Verlag, 2004. 540 pp.
ISBN: 0-7216-0244-4, $49.95
The Proteus Effect: Stem Cells and Their Promise
Ann B. Parson
Washington, DC:National Academies Press, 2004. 256 pp.
ISBN: 0-309-08988-3, $24.95
Transcription Factors
Manfred Gossen, Jörg Kaufmann, Steven J. Triezenberg, eds.
New York:Springer-Verlag, 2004. 581 pp.
ISBN: 3-540-21095-4, $399
Understanding DNA: The Molecule and How it Works
Chris Calladine, Horace Drew, Ben Luisi, Andrew Travers
New York:Springer-Verlag, 2004. 352 pp.
ISBN: 0-12-155089-3, $45
Welcome to the Genome: A User’s Guide to the Genetic Past, Present, and Future
Rob DeSalle
Hoboken, NJ:John Wiley & Sons, 2004. 240 pp.
ISBN: 0-471-45331-5, $29.95
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BMC Complement Altern MedBMC Complementary and Alternative Medicine1472-6882BioMed Central London 1472-6882-5-181616806410.1186/1472-6882-5-18Research ArticleEvaluation of antimotility effect of Lantana camara L. var. acuelata constituents on neostigmine induced gastrointestinal transit in mice Sagar Lenika [email protected] Rajesh [email protected] Sudarshan [email protected] Department of Biochemistry, Panjab University, Chandigarh – 160 014 India2005 17 9 2005 5 18 18 30 3 2005 17 9 2005 Copyright © 2005 Sagar et al; licensee BioMed Central Ltd.2005Sagar 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
Lantana camara L. (Verbenaceae), a widely growing shrub which is toxic to some animal species, has been used in the traditional medicine for treating many ailments. The purpose of the present study was to evaluate the antimotility effects of Lantana camara leaf constituents in mice intestine.
Methods
Evaluation of antimotility activity was done in intestine of mice treated with Lantana camara leaf powder, Lantana camara methanolic extract (LCME), lantadene A, neostigmine and neostigmine + LCME. Neostigmine was used as a promotility agent. Intestinal motility was assessed by charcoal meal test and gastrointestinal transit rate was expressed as the percentage of the distance traversed by the charcoal divided by the total length of the small intestine. The antidiarrheal effect of LCME was studied against castor oil induced diarrhea model in mice.
Results
The intestinal transit with LCME at a dose of 500 mg/kg was 26.46% whereas the higher dose (1 g/kg) completely inhibited the transit of charcoal in normal mice. The % intestinal transit in the neostigmine pretreated groups was 24 and 11 at the same doses respectively. When the plant extracts at 125 and 250 mg/kg doses were administered intraperitonealy, there was significant reduction in fecal output compared with castor oil treated mice. At higher doses (500 and 1000 mg/kg), the fecal output was almost completely stopped.
Conclusion
The remarkable antimotility effect of Lantana camara methanolic extract against neostigmine as promotility agent points towards an anticholinergic effect due to Lantana camara constituents and attests to its possible utility in secretory and functional diarrheas and other gastrointestinal disorders. This effect was further confirmed by significant inhibition of castor oil induced diarrhea in mice by various doses of LCME.
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Background
Diarrhea is one of the most prevalent human disorders and understandably its remedy occupies a special place in the annals of medicine [1]. Neurohormonal mechanisms, pathogens, malnutrition, chronic diseases and drugs can alter gastrointestinal physiology resulting in changes in either secretion or absorption of fluid by the intestinal epithelium. Altered motility contributes in a general way to this process, as the extent of absorption, by and large, parallels transit time. Prokinetic agents, organophosphate pesticides, nerve gases, surgery, irritation bowel syndrome, collagen vascular disease and diabetes are some of the pathophysiological conditions that may alter intestinal motility and transit time. Antimotility compounds such as diphenoxylate, loperamide, opium alkaloids, anticholinergics etc. have been tried against diarrheal disorders but often with side effects after prolonged use [2].
Acetylcholine, the vagal neurotransmitter, enhances and atropine, a known anticholinergic agent decreases intestinal motility and secretion. Although various derivatives and congeners of atropine (such as propantheline, isopropamide and glycopyrrolate) have been advocated in patients in peptic ulcer or with non-specific diarrhea, the prolonged use of such agents is limited by other manifestations of parasympathetic inhibition such as dry mouth and urinary retention [3]. There is, thus, a need for identifying new compounds and evaluating their antimotility activity and developing these as selective inhibitors that decrease gastric secretion and intestinal motility at doses that have minimal anti-cholinergic effects at other sites and are completely free from other adverse effects [4].
Lantana camara L. (Verbenaceae) is one of the most prevalent and noxious weeds causing hepatotoxicity in grazing animals [5,6]. Lantana poisoning causes obstructive jaundice and within a few hours of browsing upon its foliage, animals go off-feed and become severely constipated within 48 h [7]. On the contrary, Lantana plant has been reported to possess a number of medicinal properties [8,9]. Some metabolites isolated from their leaves possess antitumor activity [10], antithrombin activity [11], anti-inflammatory, antinociceptive and antipyretic activity [12].
Present investigations were planned to study the effect of Lantana camara leaf powder, Lantana camara methanolic extract (LCME) and lantadene A administration on mice intestinal motility using neostigmine as a promotility agent. Antidiarrheal effect of LCME was studied using castor oil induced diarrhea in mice.
Results
The percent intestinal transit was increased significantly with neostigmine, but was decreased significantly by all concentrations of LCME and lantadene A. The intraperitoneal administration of the LCME (125, 250 & 500 mg/kg) alone decreased the percent intestinal transit significantly. However, with a LCME dose of 1000 mg/kg, intestinal transit was nearly abolished. Leaf powder (1% of feed) feeding for 10 days reduced the % intestinal transit by 34.78. Inhibition in % intestinal transit by 85 and 170 mg/kg lantadene A was 39.47 and 27.34 respectively. The prokinetic effect of neostigmine was opposed by both the doses (1.0 g/kg and 500 mg/kg) of LCME and % intestinal transit was reduced to 11 and 24 respectively (Table 1).
Table 1 Effect of Lantana camara leaf powder, LCME, lantadene A and neostigmine on small intestinal transit in mice.
Treatment Total length of intestine (cm) Distance traveled by charcoal (cm) % Intestinal Transit
CMC Control 45.66 ± 6.24 21.66 ± 0.57 47.25 ± 6.10
Leaf Powder (1% for 10 days) 46.50 ± 6.19 15.50 ± 7.70 34.78 ± 3.52**
LCME (1 g/kg) 50.00 ± 0.90 0.50 ± 0.01 1.00 ± 0.01***
LCME (500 mg/kg) 53.66 ± 6.02 11.00 ± 6.02 26.46 ± 6.82***
LCME (250 mg/kg) 48.30 ± 1.36 15.30 ± 0.51 31.74 ± 1.49***
LCME (125 mg/kg) 49.00 ± 0.89 19.00 ± 3.58 38.67 ± 6.60***
Lantadene A (85 mg/kg) 44.00 ± 7.92 18.01 ± 7.37 39.47 ± 10.05**
Lantadene A (170 mg/kg) 47.00 ± 4.73 13.00 ± 3.03 27.34 ± 4.58**
Neostigmine (1 μg/kg) 49.00 ± 4.69 34.33 ± 9.15 69.30 ± 12.47***
Neostigmine (1 μg/kg)+LCME (1 g/kg) 48.00 ± 3.00 5.33 ± 0.76 11.10 ± 1.14***
Neostigmine (1 μg/kg)+LCME (500 mg/kg) 50.50 ± 0.70 12.00 ± 1.40 24.00 ± 2.19***
Statistical analysis: Values are mean ± S.D. of 6 observations in each group. Values having P < 0.01 were considered significant. **P < 0.01, ***P < 0.001 as compared to control.
Diarrhea was apparent in all the animals of control group 45 minutes after the administration of castor oil and fecal count was taken for 4 h. A marked reduction in the number of defecations over 4 h was observed with the i.p. administration of all doses of LCME. The animals of group 4 and 5 appeared to be completely constipated, where as those of group 2 and 3 showed a significant reduction in defecations as compared to control group (Table 2).
Table 2 Effect of Lantana camara methanolic extract (LCME) on castor oil-induced diarrhea in mice.
Treatments Mean defecation in 4 h
Castor oil + saline (2 ml/kg) 24 ± 3.10
Castor oil + LCME (125 mg/Kg) 9 ± 1.18***
Castor oil + LCME (250 mg/kg) 9 ± 2.06***
Castor oil + LCME (500 mg/kg) 1 ± 0.05***
Castor oil + LCME (1000 mg/kg) Completely constipated
Statistical analysis: Values are mean ± S.D. of 4 observations in each group. Values having P < 0.01 were considered significant. ** P < 0.01, *** P < 0.001 as compared to control.
Discussion
The pathophysiological mechanisms underlying the loss of intestinal fluid in diarrhea have been the subject of much debate for decades [17]. Diarrhea may be caused by an increase in osmotic load within the intestine, excessive secretion of electrolytes and water into the intestinal lumen, exudation of protein and fluid from the mucosa, infection and inflammation; and altered intestinal motility, resulting in rapid transit [18]. In most instances, multiple processes are simultaneously affected involving several factors, a particular factor becoming a dominant player in a given environment, however, motility and/or secretory disturbances usually remain a common denominator in most cases [2]. The mucosal lining of the gastrointestinal tract is provided with an extensive nerve supply from the enteric nervous system [19]. Neurotransmitters such as acetylcholine and noradrenaline and neurotransmitter candidates such as ATP, CGRP, CCK-8, ENK, GAL, GABA, serotonin, NO, somatostatin, SP, VIP etc have been implicated to different extents in normal and pathophysiological situations. Based on the knowledge gained about the divergent factors controlling the processes of secretion of electrolytes and motility, many interventional strategies have been adopted by researchers and numerous antidiarrheal compounds have been developed but not many compounds are without side effects and therefore there has always been a need for finding new ones.
Acetylcholine is the endogenous neurotransmitter at cholinergic synapses in the central and peripheral nervous system. The stimulation of vagal input to the gastrointestinal tract increases tone, amplitude of contraction and secretory activity of the stomach and intestine. Since such responses are inconsistently seen with administered acetylcholine, possibly because of poor perfusion and rapid hydrolysis by plasma butryl cholinesterase, use of neostigmine was made in the present investigation. Neostigmine is an inhibitor of acetylcholinesterase and increases the amount of acetylcholine at the synapse [3] and thus exerts a pro-kinetic effect. The results show that the Lantana camara leaf powder and LCME significantly reduced the % intestinal transit in a dose dependent manner. Lantadene A also produced a statistically significant reduction in % intestinal transit.
The induction of diarrhea with castor oil results from the action of ricinoleic acid formed from hydrolysis of its triglyceride in the oil [20,21]. The released ricinoleic acid produces changes in the transport of water and electrolytes resulting in a hypersecretory response and speeds intestinal transit [3]. The involvement of nitric oxide from neurons in the diarrhea induced by the castor oil has also been proposed [22]. Castor oil increases the induction of prostaglandins [23], causes changes in the permeability and mucosal injuries and stimulates PAF [24] biosynthesis which may result in inflammation of intestinal mucosa. The preventive administration of LCME was associated with significant protection against diarrhea induced by castor oil in mice. Lantana camara might possess some compounds with antisecretory properties which may account for its efficacy against diarrhea induced by castor oil in mice.
Lantana camara has been reported to be toxic to grazing animals such as cow, buffaloes, sheep and goats [7,25] and laboratory animals such as guinea pigs [8] and female rats [26]. In spite of its widespread toxicity in the Lantana affected animals, various parts of this plant have been used in the traditional medicines for treating cuts, ulcers, swelling, eczema, inflammation, fever etc [8]. Gastrointestinal stasis, ruminal stasis, constipation, discolorization of conjunctiva, photosensitization, decreased bile flow and urinary retention in the Lantana poisoned animals has been noticed [27-29]. These symptoms resembled those due to atropine toxicity i.e., anticholinergic excess [30,31].
Anti-dysenteric and anti-diarrheal properties of medicinal plants have been suggested to be due to tannins, alkaloids, saponins, flavonoids, sterols and triterpenes and reducing sugars [32]. The sesquiterpene lactones have been reported to have the ability to relax smooth muscles and thereby relieve gastrointestinal disorders [33]. The phytochemical analysis of the Lantana camara leaf extract has earlier been shown to contain flavonoids [34], terpenes [35] and their derivatives and pentacyclic triterpenoids [36]. These constituents may mediate the anti-diarrheal action of the Lantana camara extract. A verbascoside [37] isolated from Lantana camara has been shown to be an inhibitor of protein kinase C. The role of this enzyme has been demonstrated in signal transduction, inflammation and smooth muscle contraction [38] and an inhibition of its activity by a constituent of Lantana camara shall result in decrease in motility. Although the anti-diarrheal properties of the reported active terpenoids are well established, aspects of their mechanism of action remain poorly understood. Terpenes, flavonoids and terpenoid derivatives may act by inhibiting release of autocoids and prostaglandins [39,4] thereby inhibit the motility and secretion induced by neostigmine. Intestinal motility alterations in Lantana camara foliage poisoned sheep has been described by Pass et al. [40] but no mechanism has been suggested.
Conclusion
The remarkable antimotility effect of Lantana camara methanolic extract against neostigmine as promotility agent points towards an anticholinergic effects due to Lantana camara constituents and attest to its wide range of utility in secretory and functional diarrheas and other gastrointestinal disorders in the folklore. This effect was further confirmed by significant inhibition of castor oil induced diarrhea in mice by various doses of LCME. Whatever may be the mechanism of action, LCME may be useful in a wide range of diarrheal states due to disorders of intestinal transit and secretion. Further studies with purified constituents are needed to completely understand the mechanism of anti-diarrheal action of Lantana camara.
Methods
Plant material and preparation of extract (LCME)
Fresh leaves of Lantana camara (red variety) were collected from Palampur (Himachal Pradesh, India). The air-dried, pulverized leaves (100 g) were then exhaustively extracted with methanol (800 ml). The extract was treated with 20 g of activated charcoal and evaporated under reduced pressure. The semi-solid residue (10% yield, w/w) obtained was blackish brown in color, henceforth called Lantana camara methanolic extract (LCME). Different doses of LCME (125–1000 mg/kg) and lantadene A (85 and 170 mg/kg, i.p.) were prepared in 0.25% carboxy methyl cellulose (CMC) just before use. The injection volume for each treatment varied from 0.2–0.3 ml depending upon the weight of the animal. The control group animals were given equivalent volumes of 0.25% CMC.
Preparation of lantadene A
Lantadene A was prepared by method of Barton et al. [13] and its purity (94%) was determined by the method of Sharma et al. [14].
Animals and treatments
Male mice (laca strain) weighing 20–25 g were obtained from central animal house of Panjab University, Chandigarh and were housed in polypropylene cages under hygienic conditions for one week for acclimatization. The animal ethics committee of Panjab University had approved the study protocol of this project. The animals were given the following treatments:
Control
This group received standard pellet diet (Ashirwad Industries, Chandigarh, India) and was given 0.25% CMC, 30 minutes before charcoal meal test.
Lantana camara leaf powder treated
The animals from this group received leaf powder orally (1% of feed) for 10 days prior to the assessment of intestinal motility.
LCME treated
The animals from this group received a single dose of LCME (125, 250, 500 and 1000 mg/kg, i.p.), 30 min before charcoal administration.
Lantadene A treated
A single dose of lantadene A (85 and 170 mg/kg, i.p.) was injected.
Neostigmine treated
Neostigmine obtained from Tablets (India) Limited, Chennai, India was administered subcutaneously (1 μg/kg) in normal saline.
Neostigmine + LCME treated
This group consisted of two subgroups:
Neostigmine (1 μg/kg, s.c.) and LCME (1 g/kg, i.p.) was administered to one subgroup.
The second subgroup received neostigmine (1 μg/kg, s.c.) and LCME (500 mg/kg, i.p.)
The animals were fasted for 24 hours prior to the experiment but permitted water ad libitum. On the day of the experiment, treated groups received LCME, lantadene A, neostigmine, neostigmine + LCME.
After 30 minutes of having given the doses as described above, intestinal motility was assessed by orally administrating semisolid test charcoal meal (0.3 ml per mouse) consisting of 10% charcoal and 5% gum acacia. The animals were sacrificed 30 minutes later. The abdomen was opened and the entire small intestine starting from the pyloric end was removed and placed on the blotting paper. The distance traveled by charcoal was measured and expressed as percent intestinal transit [15].
Castor oil induced diarrhea
Mice were divided into five groups of four animals each, diarrhea was induced by administering 1 ml of castor oil (Qualikems Fine Chemicals Pvt. Ltd. New Delhi, India) orally to mice. Group 1 served as control (2 ml/kg, i.p. saline), groups 2, 3, 4 and 5 received LCME (125, 250, 500 and 1000 mg/kg, i.p.) 1 h before castor oil administration. The number of both wet and dry diarrheal droppings was counted every hour for a period of 4 h and was compared with that of the positive control animals [16].
Statistical analysis
The results are represented as mean ± S.D. Dunnett's test was used for the evaluation of data and P < 0.01 accepted as significant.
Abbreviations
LCME: Lantana camara methanolic extract GIT: Gastro-intestinal tract, VIP: vasoactive intestinal peptide, GABA: gamma aminobutyric acid, CCK8: cholecystokinin octapeptide, CGRP: calcitonin gene-related peptide, GAL: galanin, SP: substance P, ENK: Enkephalin.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
LS and RS were responsible for practically carrying out the experiments
SO – supervised the design and co-ordination of the study
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors are grateful to University Grants Commision, New Delhi, India for providing funds for research and fellowship to LS
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BMC BiochemBMC Biochemistry1471-2091BioMed Central London 1471-2091-6-161615329210.1186/1471-2091-6-16Research ArticleAnalysis of Escherichia coli nicotinate mononucleotide adenylyltransferase mutants in vivo and in vitro Stancek Martin [email protected] Robert [email protected]én-Aulin Monica [email protected] Department of Genetics, Microbiology and Toxicology, Stockholm University, S-106 91 Stockholm, Sweden2 In vitro Sweden AB, Box 21160, S-100 31 Stockholm, Sweden3 Department of Medical Biochemistry and Biophysics, Karolinska Institutet, S-171 77 Stockholm, Sweden2005 9 9 2005 6 16 16 7 6 2005 9 9 2005 Copyright © 2005 Stancek et al; licensee BioMed Central Ltd.2005Stancek 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
Adenylation of nicotinate mononucleotide to nicotinate adenine dinucleotide is the penultimate step in NAD+ synthesis. In Escherichia coli, the enzyme nicotinate mononucleotide adenylyltransferase is encoded by the nadD gene. We have earlier made an initial characterization in vivo of two mutant enzymes, NadD72 and NadD74. Strains with either mutation have decreased intracellular levels of NAD+, especially for one of the alleles, nadD72.
Results
In this study these two mutant proteins have been further characterized together with ten new mutant variants. Of the, in total, twelve mutations four are in a conserved motif in the C-terminus and eight are in the active site. We have tested the activity of the enzymes in vitro and their effect on the growth phenotype in vivo. There is a very good correlation between the two data sets.
Conclusion
The mutations in the C-terminus did not reveal any function for the conserved motif. On the other hand, our data has lead us to assign amino acid residues His-19, Arg-46 and Asp-109 to the active site. We have also shown that the nadD gene is essential for growth in E. coli.
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Background
Biosynthesis of nicotinamide adenine dinucleotides plays a central role in the metabolism of all organisms [1,2]. Their primary function is to serve as either donors or acceptors in biochemical oxidation-reduction reactions. The nucleotides can also be used as substrates in non-redox reactions e.g. ADP ribosylation [3], biosynthesis of cyclic ADP-ribose [4], and as a dehydrating agent for DNA ligase [5].
There are several metabolic pathways for biosynthesis of nicotinamide adenine dinucleotide (NAD+) in bacteria (Figure 1). The de novo pathway consists of five steps; it starts with the oxidation of aspartate to iminosuccinic acid, which in turn reacts with dihydroxyacetone phosphate to give quinolinic acid (QA). QA is phosphoribosylated and decarboxylated resulting in nicotinic acid mononucleotide (NAMN). NAMN is adenylated to nicotinic acid adenine dinucleotide (NAAD), which in turn is amidated to complete NAD+ biosynthesis. The genes coding for the different enzymes in Escherichia coli (E. coli) have been identified [6,7].
Figure 1 Biosynthesis of NAD in bacteria. Abbreviations: Na, nicotinic acid; Nm, nicotinamide; NMN, nicotinamide mononucleotide; NAMN, nicotinic acid mononucleotide; NmR, nicotinamide ribonucleoside; NAAD, nicotinic acid adenine dinucleotide; NAD, nicotinamide adenine dinucleotide; NADP, nicotinamide adenine dinucleotide phosphate. Enzymes: nadA codes for quinolinate synthase; nadB L-aspartate oxidase; nadC quinolinic acid phosphoribosyltransferase; nadD nicotinic acid mononucleotide adenylyltransferase; nadE NAD synthetase; nadK (nadF) NAD kinase; pncA nicotinamide deamidase; pncB nicotinic acid phosphoribosyltransferase; pncC NMN deamidase; nadV* Nm phosphoribosyltransferase (in Haemophilus ducreyi); ligase+? DNA ligase, only partially responsible for the activity; nadR bifunctional activity; a)* N-ribosylnicotinamide kinase, b)* NMN adenylyltransferase (in H. influenzae); 1) NMN glycohydrolase (gene not yet identified). Some of these steps may occur in the periplasmic space or at the inner membrane. Asterisks indicate activities not identified in vivo in E. coli.
Besides de novo synthesis of NAD+ there are several salvage pathways where exogenous precursors are converted to NAMN, which then can be converted to NAD+. This means that all the steps leading to NAMN are nonessential. On the contrary, in the last two reactions, NAMN to NAAD and NAAD to NAD+, metabolites cannot be supplied from outside the cell. Thus, the enzymes nicotinic acid mononucleotide adenylyltransferase (NAMNAT) and NAD synthetase are essential for growth of E. coli (coded for by the genes nadD and nadE, respectively). However, if the nadD gene is essential for growth has been questioned. In a survey with transposon insertions into metabolic genes, insertions into the nadD gene were found, at the same time the authors show that the entire gene cannot be deleted [8]. It should be pointed out that the insertion site in the nadD gene was not shown.
The E. coli nadD gene has been identified [9] and the crystal structure of the enzyme has been solved [10]. The enzyme has a molecular weight of 24.5 kD and unlike the counterparts in Archaea and Eukarya it is suggested to function as a monomer. The E. coli enzyme shows strong substrate preference for NAMN rather than for nicotinamide mononucleotide (NMN), this is in contrast to the human and archaeal enzymes [9]. NAMNAT is a member of the nucleotidyltransferase super family [11], that includes ATP surfurylase, cytidyltransferase, pantothenate synthetase, and class I tRNA synthetases. The group is characterized by a modified dinucleotide-binding fold (Rossmann-fold) and by the presence of a conserved ATP binding motif, T/HXGH [11]. The importance of the latter has been shown [12-14].
We have earlier shown that two mutations in the nadD gene (nadD72 and nadD74) lead to decreased levels of NAD+ in the cell [15]. This, in turn, leads to temperature sensitive growth on synthetic minimal medium for both mutants. A strain with the nadD72 mutation is most severely affected and has additional phenotypic changes like; complete inability to grow on minimal medium and temperature sensitive growth on rich medium. Thus, a link between NAD+ synthesis and ability to grow on different substrates at different temperatures was found.
In this paper we have shown that the nadD gene is essential for growth and we have extended the analysis of the enzyme by creating ten additional mutants. Seven of them have mutated residues close to or in the active site, as is nadD74, and three were created to study the role of the C-terminus, which is affected by nadD72. All twelve mutant proteins were analyzed in vitro for activity and in vivo for their effect on the growth phenotype. The two data sets correlate very well. We have not been able to find a function for the C-terminus, while amino acid residues His-19, Arg-46 and Asp-109 can be assigned to the active site in accordance with the structure.
Results and discussion
In this work we have studied the function of the E. coli NAMNAT enzyme. Understanding the enzyme is necessary not only for its central role in metabolism but also for its possible role as a target for the development of new antibiotics. In an earlier study we initiated functional studies of two E. coli NAMNAT mutants [15]. One of the mutants, nadD72, is a frameshift mutation that changes the ten last amino acid residues of the protein and adds seventeen amino acids to the C-terminus [15]. When the amino acid sequences of bacterial NAMNATs are analyzed, two conserved residues, Tyr-205 and Ile-206 were found in the C-terminus. They are both located in the F helix of the E. coli enzyme, using the nomenclature given by Zhang et al. [10]. These two residues are changed in the NadD72 mutant. This lead us to ask whether it is the elongated C-terminus and/or the changed amino acids that cause the phenotype associated with the mutation. The other mutation, nadD74, changes Asp-13 to Val. The residue is next to the ATP-binding motif and is highly conserved [13].
To extend the study of NAMNAT we made three additional mutant alleles in the C-terminus and seven around the active site. For the in vivo studies we made a strain with a chromosomal deletion of the nadD gene, which makes it possible to study cloned, mutant enzymes in vivo. The gene on the chromosome was replaced by a cassette encoding chloramphenicol acetyltransferase [16] and the strain was called MS10. The gene nadD has been shown essential in Salmonella typhimurium [17]. However, some conflicting data on this matter was recently published [8]. The authors have isolated strains with transposon insertions affecting the nadD gene and the strains are viable on LB. The exact location of the insertion was not shown, moreover, attempts to delete the nadD gene from the chromosome failed. Our results are clear; we could only delete the nadD gene when the wild type nadD gene was present on a plasmid in the cell, thus, confirming the essentiality of the gene.
To study the mutant enzymes in vitro the different alleles were cloned in a vector under the control of the arabinose promoter. We first tried to fuse a His6-tag to the N-terminus of the proteins, but the expression level of the proteins was very low and some mutant proteins were undetectable when analyzed by Western blot (not shown). We changed to an IgG binding ZZ'-domain as a tag and the expression level increased considerably. A thrombin recognition sequence was engineered in the linker between the ZZ'-tag and the enzyme. However, removal of the ZZ'-tag from the purified proteins was not possible. Therefore, all investigated mutants and the wild type enzyme were assayed with the N-terminal ZZ-tag.
All enzymes were expressed as soluble proteins in strain TOP10. The final yield was typically 5–20 mg protein/l culture, similar to an earlier report [9]. The purity was about 90–95% as estimated by SDS-PAGE after Coomasie staining. The purified NadD72short protein gave two bands on the gel. One band had the correct enzyme size, the other, smaller, is probably a degradation product.
Investigation of the C-terminus
Based on the nadD72 mutation, we designed the mutant NadD72short with the same change of the ten C-terminal amino acid residues as nadD72, while the length of the protein is the same as in the wild type enzyme. We also constructed two mutants where either Tyr-205 or Ile-206 is changed to alanine.
We have earlier shown that the intracellular level of NAD+ correlates to growth ability on different media and at different temperatures [15]. Thus, we decided to investigate the effect on growth by the different mutant enzymes. Plasmids with either of the four mutant alleles, pZZNadD72, pZZNadD72short, pZZNadDY205A, pZZNadDI206A, or the wild type gene (pZZNadD) were transformed into strain MS10 with plasmid pKanNadD selecting for ApR. Transformants were restreaked on LB with ampicillin and arabinose to induce expression of the respective nadD alleles. The transformants were tested for loss of KmR to ensure plasmid exchange. Thereafter, MS10 with each respective plasmid were streaked on LB plates with or without arabinose and incubated at 30°C. The diameter of the colonies was measured. The results are shown in Table 4. Addition of 0.1 mM arabinose resulted in growth of all strains. In the absence of arabinose leakage expression of the wild type enzyme is enough to support normal growth while neither of the two mutant proteins NadD72 or NadD72short are active enough to do so. This indicates that it is the change in the last 10 amino acids and not the elongated C-terminus that impairs the enzyme. The two mutants with either of the two conserved residues changed, supported growth like the wild type enzyme. This shows that to affect enzyme activity more than one amino acid has to be changed in the C-terminus.
The same test was performed on minimal medium and it was found that NadD72 and NadD72short could not support growth in the presence of 0.1 mM arabinose. However, at 0.2 mM arabinose enough enzyme was produced to allow growth (not shown).
A test was also performed on strain MS10 containing plasmids carrying the nadD alleles without a ZZ'-tag. We could not detect any difference in growth behavior whether the nadD alleles were tagged or not (not shown). This makes us confident to use the ZZ'-tagged enzymes in vitro.
The enzyme activity of the mutants changed in the C-terminus were measured in vitro. The result can be seen in Figure 2. The activity of the wild type enzyme was set to 1. The enzymes NadD72 and NadD72short have almost no activity in good agreement with the in vivo phenotype. The other two mutants, NadDY205A and NadDI206A are less efficient than the wild type enzyme but not enough to show as a change in the growth phenotype.
Figure 2 Relative enzyme activity of different NAMNAT mutants based on in vitro measurements. The results are based on at least three independent measurements with a standard error of the mean of less than 5%.
The results obtained both in vivo and in vitro for NadD72short could indicate that it is not the extension of NadD72 that causes the deficiency in the protein but rather the change in the C-terminus. However, this conclusion is complicated by the finding during purification of NadD72short that two bands were visible on the protein gel, indicating instability of the protein. Thus, the results obtained for NadD72short are inconclusive.
Another possibility should also be considered. Since the nadD72 allele causes temperature sensitivity it is possible that the enzyme activity is lowered at 37°C the temperature at which the in vitro experiments were performed. To test this, we grew all mutants at 30°C, 37°C, and 42°C, respectively. We found that the Ts phenotype not only disappears in the presence of arabinose but that the cells grow better at the higher temperatures than at 30°C. Therefore, we do not think that the changes in enzyme activity are caused by changes in reaction temperature optimum.
To understand the role of the elongated C-terminus of the NadD72 mutant, we consider the role of the corresponding region of the human NMNAT. Human NMNAT has a 24 amino acid residues longer C-terminus than E. coli NAMNAT. It has been suggested that the C-terminus in human NMNAT plays a role in substrate recognition [18,19]. The NadD72 enzyme has 17 extra amino acid residues and it is possible that the extension interferes with substrate binding, which would lead to low enzyme activity. All we can say with certainty is that the C-terminus is important for stability of the protein and that the two conserved amino acid residues do not have a great influence on activity.
Table 4 MS10 + different plasmids 24 hours 48 hours
pZZNadD 1.1† 2.5
pZZNadD72 n.g.* n.g.
pZZNadD74 n.g. n.g.
pZZNadDY205A 1 2.2
pZZNadDI206A 1 2.3
pZZNadDT11A 0.2–0.5 1–1.5
pZZNadD74 0.6 1.2
pZZNadDH19A 0.2 0.6
pZZNadDN40A 1 2.1
pZZNadDH45A 0.8 1.4
pZZNadDR46A 0.2 1
pZZNadDD109A 0.1 0.3
pZZNadDS110A 1 2.2
†Colony size (diameter in mm); grown on LB without arabinose at 30°C; *n.g – no growth
Investigation of the active site
The nadD74 mutation leads to an amino acid change in position 13 (Asp to Val). The mutated residue is two amino acids away from the ATP-binding motif, T/HXGH (position 16 to 19). Crystal structure information was used to decide which amino acid positions to mutagenize to learn more about the active site. Residues within 6 Å distance from the oxygens of the two adjacent phosphate-groups of the bound NAAD molecule are shown in Figure 3. Based on their close contact and H-bonding abilities Thr-11, His-19, Asn-40, His-45, Arg-46, Asp-109 and Ser-110 were selected for mutagenesis. All these residues were changed to alanine by site-directed mutagenesis. The recombinant proteins were cloned and analyzed, as were the C-terminal mutants.
Figure 3 The active site of E. coli nicotinic acid mononucleotide adenylyltransferase with bound NAAD (yellow carbons). Amino acid residues (grey carbons) within 6 Å to NAAD are shown (tested in this study). Suggested H-bonds are marked by dotted lines [27]
First, growth on LB plates with and without arabinose was tested. The results are shown in Table 4. All strains grew in the presence of arabinose as expected. In the absence of arabinose MS10/pZZNadDN40A and MS10/pZZNadDS110A behaved basically as MS10 with the wild type enzyme while the other mutations affected growth on LB to a varying degree. Second, the enzymatic activity for the active site mutants was determined in vitro. The results are summarized in Figure 2. As with the C-terminal mutants, the correlation between the two experiments is very good.
Asp-109 and Ser-110 is located in the region connecting strand d and helix D of NAMNAT [10]. This region is one of three regions observed to undergo large conformational changes upon substrate binding. Interestingly, mutations of these two amino acids affect enzymatic activity very differently. On the one hand, binding of the substrate brings Ser-110 closer to the substrate. It is possible that the side chain of Ser-110 is H-bonded to the 2'-OH of AMP-ribose. However, mutation of Ser-110 to alanine resulted in an enzyme with 80% activity as compared to that of the wild type NadD. Our results indicate that the interaction between Ser-110 and the ribose is dispensable for substrate coordination. On the other hand, the change of the highly conserved Asp-109 had a severe effect on activity. Asp-109 has been proposed to form an H-bond to the 2'-OH group of AMP [10]. It was also suggested by the same authors that the carboxylate oxygen of the residue might be involved in the coordination of a Mg2+ ion shown to be important for the enzyme function [10,12]. The location of Asp-109 is ideal to position an Mg2+ ion which could act as a Lewis acid stabilizing the transition state in the transesterification reaction [10]. However, the functional importance of this conserved residue has not been investigated earlier. The serious decrease in enzymatic activity (0.038%) that we observe supports the above mechanism and serves as an experimental evidence for the involvement of Asp-109 in the catalysis.
The Thr-11 main chain nitrogen is H-bonded to the AMP phosphate in the crystal structure of the NadD-NAAD complex. This H-bond is expected to be independent of the side-chain character of the amino acid. However, the NadDT11A mutant leads to decreased enzyme activity (2%, Figure 2). The explanation might be that the hydroxyl group of Thr-11 forms an H-bridge to Asn-40, which has been shown to interact with 2'-hydroxyl group of NAMN-ribose [10]. Such an interaction could contribute to substrate binding and coordination. The change of Asn-40 to alanine leads to a decreased enzyme activity (23%, Figure 2). We conclude that the disruption of this H-bonding network might lead to inefficient substrate coordination. Therefore, the effect on enzyme activity by the T11A and/or N40A mutation is possibly indirect.
The mutation D13V (NadD74) was found to lead to decreased activity of the enzyme. A similar mutation has been described in CTP:glycerol-3-phosphate cytidyltranferase that is a member of the same enzyme super family [20]. The residue Asp-11 (which corresponds to Asp-13 in NAMNAT) was changed to alanine and enzyme activity was severely reduced. Both valine and alanine are hydrophobic amino acids and it is possible that the change disturbs ATP binding; resulting in lower enzyme activity. It is not clear whether the role of Asp in this position is catalytical or structural.
The role of the second histidine (His-19) in the conserved T/HXGH motif has been tested in several studies and been shown to play a role in ATP-binding and stabilization, but the role for the two histidine residues can vary between enzymes [14,21]. Our results confirm the previous observations. The mutation H19A leads to a decrease in enzyme activity to 0.62% of the wild type activity.
Amino acid residues His-45 and Arg-46 are part of a flexible loop in the NadD enzyme, which moves upon substrate binding. The two residues are conserved within NMNAT from Bacteria and Eukarya but not in the known archaeal enzymes. His-45 is involved in a hydrophobic stacking interaction with the pyridine ring in NAMN and is also likely to form an H-bond with the NAMN phosphate group. Histidines are often involved in acid base catalysis, and prone to activate nucleophiles by abstracting a proton. The H45A change led to an enzyme with 4% residual activity, while the R46A mutant was the most affected of all tested active site mutants. If the role of Arg-46 in the substrate binding loop was simply to protect the bound substrates, higher rates of ATP hydrolysis is expected in the case of the R46A mutant. Since the R46A mutant did not produce AMP as by-product in the in vitro experiment, the role of this arginine side chain must be more than simply protecting the bound substrates from water molecules. The guanidinium group of Arg-46 lies in an ideal position to serve as a positively charged moiety that stabilizes the juxtaposed and negatively charged phosphates of the substrate molecules, as well as of the product NAAD. The archaeal orthologues lack the precise sequence homology in the sequence aligned to the H45-R46, but they have a conserved arginine (Arg-8 in Methanococcus jannaschii) that occupies the same position with its side chain in the ATP-enzyme complex as that of Arg-46 in the E. coli NadD enzyme [10,12]. On the other hand another hypothesis should be considered as well. Mutational studies on NMNAT from Methanobacterium thermoautotrophicum indicate that the archaeal enzyme is involved in the reaction merely by placing the substrates in an ideal position [14]. Combining the facts above, we conclude that Arg-46 in E. coli NadD plays an important role in stabilizing the two adjacent, negatively charged, phosphate moieties during catalysis. A similar role might be attributed to Arg-8 in the archaeal counterparts but this has to be tested.
Conclusion
We have investigated mutants in both the C-terminus and the active site of E. coli NAMNAT in two assay systems; effect on growth on plates and in vitro activity of the enzyme. Correlation between the two data sets is very good and shows that there is a distinct threshold where there is enough activity to support growth.
The data obtained are not enough to assess the function of the C-terminus; more work is needed. As for to the active site we have found that amino acid residues His-19, His-45, Arg-46 and Asp-109 are likely needed for catalysis, while Asp-13 probably affects substrate binding indirectly. We have also shown the essentiality of the nadD gene in E. coli.
Methods
Bacterial strains and media
Bacterial strains and plasmids used in this work are listed in Tables 1 and 2, respectively. Luria-Bertani (LB) medium and M9 minimal medium were prepared according to Miller [22]. The concentrations of antibiotics were, 15 μg/ml chloramphenicol, 200 μg/ml ampicillin (Ap) and 50 μg/ml kanamycin (Km).
Table 1 Strains used in this study
Strain Genotype Source
MRA530 rph nadA::Tn10 gal490(?) λ cI857 Δ(cro-biol)(?) MRA strain collection
RI8 ara Δ(gpt-lac)5 nadD72 zbe280::Tn10 [15]
RI10 ara Δ(gpt-lac)5 zbe280::Tn10 [15]
RI12 ara Δ(gpt-lac)5 nadD74 zbe280::Tn10 [15]
MS10 as RI10 but nadD::Cm This study
TOP10 F- mcrA Δ(mrr-hsdRMS-mcrBC) φ80lacZΔM15 ΔlacX74 deoR recA1 araD139 Δ(ara-leu)7697 galU galK rpsL endA1 nupG Invitrogen, Carslbad, CA
DH5α F-,φ80dlacZΔM15, Δ(lacZYA-argF)U169, deoR, recA1, endA, hsdR17(rk-, rk+), supE44, gyrA96, relA1 [25]
Table 2 Plasmids used in this study.
Plasmid name description
pEZZ18 Pharmacia Biotech/GE Healthcare
pUC4K Pharmacia Biotech/GE Healthcare
pBADmyc-hisA Invitrogen (ApR)
pBAD-Kan As pBADmyc-hisA but KmR
pKanNadD wild type nadD in pBAD-Kan
pNadD wild type nadD
pNadD72 nadD72(D13V)
pNadD74 nadD74
pNadD72short nadD72 without the extension
pNadDT11A nadD with mutation T11A
pNadDH19A nadD with mutation H19A
pNadDN40A nadD with mutation N40A
pNadDH45A nadD with mutation H45A
pNadDR46A nadD with mutation R46A
pNadDD109A nadD with mutation D109A
pNadDS110A nadD with mutation S110A
pNadDY205A nadD with mutation Y205A
pNadDI206A nadD with mutation I206A
pZZNadD pNadD + ZZ'-tag N-terminal fusion
pZZNadD72 pNadD72 + ZZ'-tag N-terminal fusion
pZZNadD74 pNadD74 + ZZ'-tag N-terminal fusion
pZZNadD72short pNadD72short + ZZ'-tag N-terminal fusion
pZZNadDT11A pNadDT11A + ZZ'-tag N-terminal fusion
pZZNadDH19A pNadDH19A + ZZ'-tag N-terminal fusion
pZZNadDN40A pNadDN40A + ZZ'-tag N-terminal fusion
pZZNadDH45A pNadDH45A + ZZ'-tag N-terminal fusion
pZZNadDR46A pNadDR46A + ZZ'-tag N-terminal fusion
pZZNadDD109A pNadDD109A + ZZ'-tag N-terminal fusion
pZZNadDS110A pNadDS110A + ZZ'-tag N-terminal fusion
pZZNadDY205A pNadDY205A+ ZZ'-tag N-terminal fusion
pZZNadDI206A pNadDI206A + ZZ'-tag N-terminal fusion
All plasmids below line three were made in this study. All plasmids below line five are derivatives of pBADMyc-hisA.
Standard recombinant DNA techniques were used for cloning of DNA [23]. E. coli strain DH5α was used as a recipient for cloned DNA. Restriction and modification enzymes were purchased from New England Biolabs, Amersham Pharmacia Biotech or Life Technologies. DNA fragments were separated by agarose gel electrophoresis, excised and purified using the Qiaex II Gel Extraction Kit (Qiagen). Oligonucleotides were purchased from MWG Biotech. Plasmid DNA was purified with QiaPrep Kit (Qiagen). MWG Biotech did DNA sequencing. ATP and NAMN were purchased from Sigma.
Construction of a nadD strain and a plasmid-exchange system
Primers used in this study are listed in Table 3. Before deleting the nadD gene from the chromosome we had to clone the wild type gene. The nadD wild type gene was amplified from RI10 with primers PARA1 and PARA2 and cloned into plasmid pBAD-Kan. The plasmid was named pKanNadD. The plasmid pBAD-Kan is identical to pBADmyc-hisA but with a kanamycin resistance gene from pUC4K inserted into the ampicillin resistance gene. Thus, pBAD-Kan confers kanamycin resistance and not ampicillin resistance.
Table 3 Oligonucleotides used in this study.
Primer sequence
PNadko1b 5'- ATAAACCCCTGGCGGACGTATTTATCGACGGTTGATCATATGAATATCCTCCTTAG-3'
PNadko2b 5'- TGGTCGCCGAGATGTTAAACCACGGCGTTTCAGCCAGTGTAGGCTGGAGCTGCTTC-3'
PARA1 5'-AACCATGGAATCTTTACAGGCTCTGTTTGGC-3'
PARA2 5'-TTAAGGTACCGTAACGACAGGTATCAGCGAT-3'
PD1 5'-TTAAGGTACCTCACCATGACGAATTAACCAC-3'
PD72short 5'-TTAAGGTACCTCAAGCGATACAAGCCTTGTT-3'
T11A* 5'-ACAGGCTCTGTTTGGCGGCGCCTTTGATCCGGT-3'
H19A* 5'-GATCCGGTGCACTATGGTGCTCTAAAACCCGTGGAA-3'
N40A* 5'-CGGGTCACAATCATCCCTGCTAATGTTCCTCCGCAT-3'
H45A* 5'-CTAATAATGTTCCTCCGGCTCGTCCCCAAGCCGGAAGC-3'
R46A* 5'-CTAATAATGTTCCTCCGCATGCTCCCCAGCCGGAAGC-3'
D109A* 5'-TTTATTATTGGTCAGGCTTCACTGCTGACCTTTCCG-3'
S110A* 5'-ATTATTGGTCAGGATGCACTGCTGACCTTTCCGACC-3'
Y205A* 5'-GGAACCGGTACTGACTGCCATTAACCAACAAGGCTTG-3'
I206A* 5'-GGAACCGGTACTGACTTACGCTAACCAACAAGGCTTG-3'
* oligonucleotides used for site directed mutagenesis. The mutated codons are shown in bold, the changed nucleotides are underlined.
To delete the nadD gene from the chromosome we used linear DNA transformation and the λ red recombination system [16]. Primers used for amplification of the chloramphenicol acetyltransferase gene with homologies to the ends of nadD were PNadko1b and PNadko2b. The amplified linear DNA was electroporated into strain MRA530/pKanNadD and chloramphenicol resistant (CmR) colonies were selected. Recombinants were checked for proper exchange by PCR amplification and sequence verification. A P1 phage lysate made on one such recombinant was transduced to RI10/pKanNadD selecting for CmR. Transductants were verified by PCR, one clone was kept and named MS10. The plasmid could now be exchanged for a pBADmyc-hisA (ApR) plasmid carrying different nadD alleles. The exchange relies on the incompatibility of pKanNadD and pBADmyc-hisA.
Construction of expression vectors with different nadD alleles
The nadD72 allele from RI8 was amplified with primers PARA1 and PD1, the nadD74 allele from RI12 with primers PARA1 and PARA2 and the nadD72short allele from RI8 with primers PARA1 and PD72short. The nadD72short allele has the same change at the C-terminus as the nadD72 allele but with the same length as the wild type allele. Site-directed mutagenesis was performed on the cloned wild type allele using QuikChange Site-Directed Mutagenesis Kit (Stratagene, La Jolla, CA). Oligonucleotides used are listed in Table 3.
All allele variants were cloned in the expression vector pBADmyc-hisA under the control of an arabinose-promoter using the NcoI and KpnI sites. The resulting plasmids are listed in Table 2. Thereafter all alleles were fused at their 5'-end to a fragment containing an IgG binding ZZ'-domain from the pEZZ18 vector. This set of plasmids has the same origin of replication as the pKanNadD but they confer ampicillin resistance (Table 2). This makes it possible to exchange plasmid pKanNadD in the MS10 strain for plasmids carrying mutant nadD alleles.
Protein expression and purification
E. coli TOP10 cells containing plasmids with respective nadD allele (pZZNadDXX) were grown in LB medium at 30°C. When the cultures reached OD550 0.5, arabinose was added to a final concentration of 0.4 mM. After ~4 h growth, the cultures were quickly chilled on ice and harvested by centrifugation. Pellets were stored at -20°C. The frozen pellets were thawed, resuspended in 10 × TST (0.5 M Tris pH 7.4, 1.5 M NaCl, 5% w/v Tween 20), lysozyme (1 mg/ml), DNase I (20 μg/ml) and RNase A (20 μg/ml). After incubation at 37°C and several freezing (in liquid nitrogen) and thawing cycles, samples were sonicated and the lysate was cleared by centrifugation. Proteins were purified as described [24]. The solvent was changed to reaction buffer [9] on a NAP5 desalting column (Amersham Biosciences, Uppsala, Sweden). Purification efficiency was monitored by SDS-polyacrylamide gel elecrophoresis (PAGE) and stained by Coomasie blue. The protein concentration was determined by UV spectrophotometry at 280 nm [26].
Assay of enzymatic activity
In vitro enzymatic activity of NAMNAT was determined as described [9]. The substrate concentrations were 2 mM ATP and 1 mM NAMN and the reaction was carried out at 37°C. The enzyme concentration was 1 μg/100 μl. Reactions were terminated by immersing the tubes in boiling water for 5 minutes. They were thereafter cooled on ice and filtered through Nanosep 10 K microcentrifugal devices to take away the enzyme (Pall, Ann Arbor, MI). 20 μl aliquots were analyzed by high-pressure liquid chromatography on a Gilson LC system by using Supelcosil LC-18-T 15 cm by 4.6 mm column (Supelco, Bellefonte, PA).
The product formation was monitored at 254 nm as a function of time and the initial reaction rate was calculated from the slope of the curve. The rate for the wild type enzyme that was set to 1, rates for the mutant enzymes were correlated to this.
Authors' contributions
RS expressed and prepared all the mutant enzymes, discussed the work and helped to draft the manuscript. MS did all the rest of the lab work, designed the experiments and drafted the manuscript. MRA conceived of the study, participated in its design and coordination, and drafted the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We thank Professor Håkan Steiner for helpful discussions and technical assistance. This work was supported by a grant from the Swedish Research Council (VR) to Leif A. Isaksson.
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BMC BiochemBMC Biochemistry1471-2091BioMed Central London 1471-2091-6-181618802510.1186/1471-2091-6-18Methodology ArticleMethodological modifications on quantification of phosphatidylethanol in blood from humans abusing alcohol, using high-performance liquid chromatography and evaporative light scattering detection Aradottir Steina [email protected] Bo L [email protected] Department of Laboratory Medicine, Division of Clinical Chemistry and Pharmacology, Lund University, Lund University Hospital, S-221 85 Lund, Sweden2 AstraZeneca, R&D, Lund, Sweden2005 27 9 2005 6 18 18 13 6 2005 27 9 2005 Copyright © 2005 Aradottir and Olsson; licensee BioMed Central Ltd.2005Aradottir and Olsson; 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
Phosphatidylethanol (PEth) is an abnormal phospholipid formed slowly in cell membranes by a transphosphatidylation reaction from phosphatidylcholine in the presence of ethanol and catalyzed by the enzyme phospholipase D. PEth in blood is a promising new marker of ethanol abuse depending on the high specificity and sensitivity of this marker. None of the biological markers used in clinical routine at the present time are sensitive and specific enough for the diagnosis of alcohol abuse.
The method for PEth analysis includes lipid extraction of whole blood, a one-hour HPLC separation of lipids and ELSD (evaporative light scattering) detection of PEth.
Results
Methodological improvements are presented which comprise a simpler extraction procedure, the use of phosphatidylbutanol as internal standard and a new algorithm for evaluation of unknown samples. It is further demonstrated that equal test results are obtained with blood collected in standard test tubes with EDTA as with the previously used heparinized test tubes. The PEth content in blood samples is stable for three weeks in the refrigerator.
Conclusion
Methodological changes make the method more suitable for routine laboratory use, lower the limit of quantification (LOQ) and improve precision.
==== Body
Background
The enzyme phospholipase D (PLD) normally catalyses the formation of phosphatidic acid from phosphatidylcholin using water as a substrate. In the presence of primary alcohols the reaction is diverted to transphosphatidylation and phosphatidyl alcohol is formed instead. As a consequence, human ethanol consumption leads to formation of phosphatidylethanol (PEth) in the tissues. In 1997 Hansson et al. published an article on measurements of PEth in whole blood extracts from alcoholics [1]. The separation method used was thin layer chromatography (TLC) and subsequently PEth was quantified by image analyses. It was demonstrated that PEth was measurable in blood from alcoholics several days, up to weeks, after the last alcohol intake. PEth was suggested to be a potential marker of alcohol abuse. The TLC separation followed by densitometric scanning was time consuming and had a relatively high coefficient of variation. To overcome these issues, a HPLC method optimised for separating PEth from other lipids in whole blood extracts was developed. PEth has physiochemical properties that differ considerably from other phospholipids, which make it possible to separate PEth as a single peak at safe distances from other peaks. An evaporative light scattering (ELSD) detector was used for detecting the lipids [2,3]. The ELSD detector quantifies any solute less volatile than the solvents. Functional groups, fatty acid chain-length or saturation has little or no effect on the detector response. However, a non-linear detector response following the quantitative principles of ELSD complicates the quantification. The method was optimized for measuring PEth in extracts from whole blood but the same performance parameters are also valid for quantification of PEth in extracts from organs [4-6].
In this paper, five methodological improvements of importance for usefulness and practicability are presented. First, the extraction procedure is simplified, second, it is shown that PEth can be analyzed in blood with EDTA as anticoagulant, third, the limit of quantification (LOQ) is lowered, fourth, an internal standard (IS) for the method is presented, and fifth, an improved method for calculation of PEth over a broad range is introduced.
Results and discussion
Extraction
Blood samples have in previous publications been extracted according to Radin [7] with 33 volumes of propan-2-ol:hexane, 3:2 v/v. In practice, this means that 300 μl blood can be extracted using 10 ml tubes. The use of 10 ml tubes is a limiting factor imposed by the standards in routine clinical laboratories. It was speculated that adding the blood first to propan-2-ol and thereafter adding the hexane, maintaining the same proportion of solvents, would make the extraction procedure more practical without negatively affecting the recovery of PEth. This variation of Radin's extraction method (Type II) was investigated and compared to the ordinary extraction according to Radin (Type I). Thus, in Type I extractions, blood was added to the premixed solvents, and in Type II extractions, blood was added to propan-2-ol during mixing before hexane was added. The same proportion (33 volumes) of solvent was used in both extraction methods. It was found that the stepwise addition of ingredients resulted in 25% higher recovery of PEth (p < 0.001, using 2-way analyses of variance (ANOVA), four patient samples were extracted in quintuple of each extraction type).
Beside of giving a higher recovery of Peth, the stepwise addition is easier to perform because less solvent is present in the tube when the blood is added. The reason for the higher recovery might be that cell membranes are disrupted and the lipid-protein linkages are broken by the propan-2-ol, which makes the phospholipids from the cell membrane more available than when hexane is present from the start. Recovery studies of extraction procedures for PEth have not been performed because of problems in incorporating defined amounts of PEth in the same way in the membranes as PEth occurs in vivo.
Internal standard technique
Earlier attempts to find an internal standard (IS) for the PEth analysis method were not fruitful [8]. Internal standard methodology was judged to be an important improvement in quantification methodology because of its potential to correct for experimental variation (e.g., sample evaporation, injection volume and detector response). It was speculated that either phospatidylmethanol (PMet) or phosphatidylbutanol (PBut) might be suitable as internal standard because of their similarity to PEth and for not being endogenous lipids. The test run of PMet and PBut together with PEth standard (Fig 1) showed an Rf value for PBut at safe distances from both PEth and other peaks while PMet eluted at almost the same Rf value as carbamazepine, which is a drug that has anticonvulsant properties and is given to many alcoholic patients at hospitalisation. The dose response of PBut was very similar to that of PEth and by inspecting earlier runs of blood extracts from patients it was asserted that no endogenous compounds eluted at the same Rf value as PBut. It was decided to use PBut as internal standard for the PEth determinations. PBut did not interfere with endogenous lipids in blood.
Figure 1 HPLC chromatogram. HPLC chromatogram of a 100 μl blood sample from abstainer. To the blood sample 2 nmol of each phosphatidylbutanol (PBut), phosphatidylethanol (PEth) and phosphatidylmethanol (PMet) was added. An expanded view is given in the insert.
Figure 2 Comparison of PEth content in heparin and EDTA blood during refrigerated storage. Blood samples from 5 sober alcoholic patients was analysed for PEth amount on day 1 and after being stored in refrigerator for one, two and three weeks. No significant differences in PEth content could be found between heparin and EDTA blood at any time point (p = 0.33, 0.31, 0.77, 0.27, respectively, 2-way ANOVA). No significant changes in PEth content could be found during the three weeks of storage (p = 0.46, and 0.10 for heparin and EDTA respectively, 2-way ANOVA).
The extraction method was accommodated to internal standard use by adding 2.0 nmol of PBut to the propan-2-ol in the test-tube for unknown samples before blood and subsequently hexane were added. Because the detector response is non-linear, it is not optimal to use the simple IS evaluation technique where the same amount of IS is added to the unknown samples and the calibration samples, and where the calibration curve is constructed from the peak area ratios. A more accurate approach is to evaluate the apparent amount of both IS and analyte from separate standard curves and then apply the IS correction. Therefore, typical standard curves were prepared by adding both PEth and PBut in different amounts (e.g., 0.2, 0.5, 1.0, 2.0 and 4.0 nmol) to the propan-2-ol before blood and subsequently hexane were added. Aliquots of blood from abstainer were kept in a -20°C freezer and used for preparing standard curves; one aliquot was thawed for each preparation.
Limit of quantification (LOQ)
The original extraction method [7] was developed to obtain good recovery of all lipids in blood. Since PEth is the only lipid of interest in the present method, it was investigated if smaller relative volumes for extraction would be adequate, because that would potentially improve the limit of quantification for PEth within the constraint of using 10 ml tubes. The extraction efficiency using 300, 600 and 900 μl blood and 10 ml extraction solution (corresponding to 33, 16.7 or 11 volumes of extraction solution) was investigated. Whole blood (300, 600 or 900 μl) was extracted by first adding blood to 4 ml of propan-2-ol containing IS, and then 6 ml of hexane, during agitation. After mixing twice, the samples were centrifuged at 1500 g for 10 min and the supernatants were transferred to new tubes. The PEth amount recovered per volume of blood was found to be independent of the relative volumes of extraction. The limit of quantification (LOQ) for the method has previously been determined to 0.2 nmol of PEth [9]. By increasing the amount of blood that can be extracted for a sample by 3 times, the LOQ, expressed in terms of whole blood concentration, was therefore lowered from 0.67 to 0.22 μmol/l, without making the extraction procedure more complicated. The modified extraction procedure was validated by extracting 26 patient samples using 300, 600 and 900 μl whole blood and 10 ml solvent. The result of a 2-way ANOVA of log-transformed values of PEth blood concentration showed no significant difference (p = 0.24) between the three treatment groups. The PEth blood concentration for these patients varied between 0.5 and 12 μmol/l.
Comparison of PEth values in blood with different anticoagulants added
Blood from five alcoholic patients, being treated for alcohol abstinence, was drawn into both heparin and EDTA tubes. All patients had reached zero ethanol in expired air. The PEth amount in the samples was analyzed on day one and thereafter the samples were kept refrigerated and analyzed 1, 2 and 3 weeks after sampling. The PEth amount in the samples was not significantly different between heparin and EDTA blood at any time point (p = 0.33, 0.31, 0.77, 0.27 initially and after 1, 2, and 3 weeks of storage, respectively, using 2-way ANOVA). Furthermore, the PEth amount in the samples did not change during three weeks of refrigerated storage (Fig. 2, p = 0.46 and 0.10 for heparin and EDTA, respectively, 2-way ANOVA).
Figure 3 Refrigerated storage of blood with ethanol present. Blood samples from nine ethanol intoxicated patients analysed for PEth amount on day 1 and after being stored in refrigerator for one, two and three weeks. No significant changes in PEth content could be found during the three weeks of storage (p = 0.37, 2-way ANOVA).
The ability to use EDTA blood tubes for sampling makes the analyses more user-friendly since whole blood for haematological test is routinely collected in EDTA tubes that are not centrifuged, while most routine tests run on blood sampled in heparinized tubes use the plasma or serum after centrifugation, which causes a risk for wrong handling of the sample. In order to study possible PEth formation in vitro in refrigerated blood samples containing ethanol, blood from nine ethanol-intoxicated patients was drawn into EDTA tubes. Samples were also taken for blood ethanol analyses. The mean blood ethanol concentration was 43 mmol/L (range 23–77 mmol/L). The PEth amount in the samples was analyzed on day one and thereafter the samples were kept refrigerated and analyzed 1, 2 and 3 weeks after sampling. No significant effect of storage time on PEth amount was found (Fig. 3, p = 0.37, 2-way ANOVA).
Figure 4 Dose response curves. Dose response curves of the ELSD detector to phosphatidylethanol. The solid line represents the fitted mixed linear-exponential function y = a + bx - a exp(-dx). An expanded view of the low-amount area is given in the inset.
This stability of PEth in refrigerated samples with ethanol present has earlier only been investigated for a time period of three days [4]. Freezing blood at -20°C with ethanol present has been shown to highly elevate PEth levels whereas such samples can be frozen in liquid nitrogen and stored at -80°C without in vitro formation of PEth [4]. The latter method is, however, not practical in a routine setting. It is therefore important that the present investigation has demonstrated that samples can be stored refrigerated for an extended period of time with no in vitro formation of PEth, which in a routine laboratory is an advantage.
Calibration and sample evaluation
According to the quantitative principles of ELSD, the detector response is non-linear. Curve fitting was therefore used to estimate the calibration function for evaluation of unknown samples. For the present application the detector response is exponentially shaped at lower amounts of PEth and gradually becomes linear at higher amounts. Exploiting this observation, a mixed linear-exponential function, y = a + bx + c exp(-dx), was tried and found to accurately trace the calibration data over the complete range of detector response. Here, y is the peak area and x the amount of PEth. The function can easily be reduced to a 3-parameter function by forcing it through the origin (by constraining c to equal -a). After substitution and rearrangement, the 3-parameter function becomes y = a(1-exp(-dx)) + bx. The reduction to a 3-parameter model that is bound to the origin is appropriate considering that the peak area is zero in the absence of Peth. In the present application further constraints are that the exponential component diminishes with growing x, that y increases monotonically with growing x, and that x and y are not negative. Therefore, the following numerical constraints apply in the present application: a ≤ 0, b ≥ 0, and d ≤ -b/a. A standard Levenberg-Marquardt algorithm [10] was employed for the non-linear fitting procedure, which for numerical stability, was performed on scaled data. Initial estimates of a and b were obtained by ordinary linear least squares regression of y on a + bx, while d was initially set to -b/a (largest ratio that maintain monotonicity). Then, in order to provide further robustness, the initial estimates were perturbed, one at a time, by a factor of two (up and down) and the best fit from the seven starting points was used as the final fit. An error model with weights taken as 1/y was used because variability is roughly proportional to the response.
To estimate the amount of PEth in an unknown sample (x) from the observed peak area (y), the inverse of the function was calculated numerically by an iterative range-halving method to a suitable precision defined as the absolute difference between observed y and y calculated from the function. This approach, of course, hinges on the constraint that the function is monotonously increasing with increasing x. Typically, the root-mean-square deviation between the fitted calibration function and the calibration data was about 3% over the range 0.25–25 nmol PEth (Fig. 4). The range of the curve fitting procedure used in earlier work [5,6] was limited to one order of magnitude.
Evaluation of unknown samples with internal standard
With each batch of samples, a standard curve, evaluated using the mixed linear-exponential calibration function, was run. For every unknown sample the apparent amount of PBut was read from the standard curve for PBut and the inverse of this value was multiplied with the known amount of PBut added to the sample to give a sample correction factor. The apparent amount of PEth, obtained from the standard curve for PEth was then multiplied with this factor to obtain the corrected amount of PEth. The precision of the method was investigated by running duplicate samples at 4 different levels of PEth (3, 7, 10 and 15 μmol/l) together with a standard curve on 12 separate days. These samples had been frozen in aliquots of 250 μl and for each run, one aliquot was thawed and two extractions (100 μl each) were performed as described. Two different persons did the sample preparations, and the sample order was randomised during preparation and in the HPLC run. The within-day and between-day coefficients of variation (CV) were <6% and <12%, respectively.
The precision obtained using the IS methodology and the mixed linear-exponential calibration function (LinExp) (A) compared with LinExp without IS (B) and with the previous method (C) was validated as follows. Samples from 26 patients (with PEth concentration range between 0.6–11 μmol/L) were extracted in duplicates of each 300, 600, and 900 μl whole blood and were evaluated according to the three evaluation models (A, B, and C). Precision was calculated as the CV% for the 6 samples from each patient (Fig. 5). The precision obtained using the IS methodology and the LinExp (A) is several-fold better than obtained with LinExp without IS (B) or with the previous method (C). This clearly demonstrates the improved ruggedness provided by the IS methodology due to its potential to correct for experimental variation (e.g., sample evaporation, injection volume and detector response).
Figure 5 The precision of the method. Box plot of CV% of 26 patient samples. For each sample, extraction was done in duplicates of 300, 600, and 900 μl (sextet from each patient) analysed and evaluated in three different ways, A is evaluated with the new algorithm and internal standard (IS), B is evaluated with the new algorithm and C is evaluated with the old algorithm. The Box-plot identifies the median, the middle 50% of the data, the range, and the outliers (o).
Conclusion
Both heparinized and EDTA tubes are suitable as blood sampling tubes. Blood samples with and without ethanol present are stable in refrigerator for three weeks.
The extraction studies showed that more blood could be extracted with the same amount of extraction solution without sacrificing recovery, leading to a threefold improvement in the limit of quantification. The lowered LOQ makes it possible to detect intake of lower amount of ethanol than previously. The development of the internal standard methodology and the mixed linear-exponential calibration function considerably improved robustness, precision and measurable concentration range, all of which are important to facilitate routine application. The analytical method for PEth has been made more robust for use in the routine analytical laboratory.
Methods
Human sample collection
Peripheral blood samples were collected from patients abusing alcohol and healthy subjects into 4-ml standard blood sample tubes containing either sodium-heparin or EDTA as anticoagulant. The ethics committee of the Medical Faculty at Lund University, Sweden gave approval for this study (LU694-03).
Chemicals
The solvents used for extraction and HPLC analysis (hexane, 1-propanol, propan-2-ol and triethylamine) were obtained from Merck (Darmstadt, Germany); all were of HPLC grade, except triethylamine which was of biochemistry grade. Acetic acid was obtained from BDH Laboratory Supplies (Pool, England) and was of HPLC grade. Deionized, sterile filtered water was obtained from a Millipore Milli-Q Plus water purification system and was checked regularly for conductivity. Ethanol was obtained from Kemetyl AB (Haninge, Sweden). Phosphatidylmethanol, phosphatidylethanol and phosphatidylbutanol were from Avanti Polar Lipids (Alabaster, USA). Sodium-heparin and EDTA tubes for blood withdrawal were of the brand Vacutainer® (Becton Dickinson Vacutainer Systems Europe (UK)).
HPLC analyses
The HPLC analysis was carried out with a Waters HPLC system Alliance 2695 with a thermostatted auto injector and an injection loop of 100 μl. A 250 × 4 mm, Licrosphere 100 DIOL, 5-μm particle size column (Merck, Germany) was used with a tertiary gradient of hexane (A), 1-propanol:water (17:3 v/v) (B) and 1-propanol:acetic acid:triethylamine (316:16:1 v/v) (C). A flow-rate of 1 ml/min and column temperature of 55°C was used, the gradient was run as described in Table 1. An evaporative light scattering detector Alltech 500 ELSD was used with nebulizer gas flow at 2.0 L/min and evaporator at 80°C. A computer-aided calculation of the peak area was performed with Waters MillenniumTM chromatographic software program.
Table 1 Gradient profile. Gradient profile used for the separation of PEth with the HPLC method.
Mobile phase
Time (min) %A %B %C
0–3 85 1 14
3–8 85-63 1–23 14
8–12 63-36 23–50 14
12–13 36-0 50–86 14
13–23 0 86 14
23–28 0–85 86-1 14
28–58 85 1 14
A Hexane
B Propan-1-ol:water; 17:3 v/v
C Propan-1-ol:acetic acid:triethylamin; 316:16:1 v/v
Each sample has a total HPLC analysis time of 1 hour yielding a sample throughput of 24 per day using an auto injector. Attempts to increase the sample throughput, which would be of practical benefit in the routine setting has yet not been successful.
List of abbreviations
ANOVA Analyses of Variance
EDTA Ethylene Diamine Tetraacetic Acid
ELSD Evaporative Light Scattering Detector
IS Internal Standard
LinExp Linear-Exponential calibration function
LOQ Limit of Quantification
PBut Phosphatidylbutanol
PEth Phosphatidylethanol
PMet Phosphatidylmethanol
TLC Thin Layer Chromatography
Authors' contributions
SA contributed the subject area expertise, planned the study design, carried out all the experimental work and drafted the manuscript. BO contributed the conception of the calibration function and internal standard methodology and helped with the statistical evaluations and the drafting of the manuscript. This work is not associated with the professional affiliation of BO.
Acknowledgements
This study was supported by the Swedish Medical Research Council (05249), the Swedish Alcohol Research Fund, the Royal Physiographic society in Lund, and the Medical Faculty of Lund University. The authors thank Dr Christer Alling, Department of Laboratory Medicine, Division of Clinical Chemistry and Pharmacology, Lund University, for contributing with expert knowledge.
==== Refs
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Gunnarsson T Karlsson A Hansson P Johnson G Alling C Odham G Determination of phosphatidylethanol in blood from alcoholic males using high-performance liquid chromatography and evaporative light scattering or electrospray mass spectrometric detection J Chromatogr B Biomed Sci Appl 1998 705 243 249 9521560 10.1016/S0378-4347(97)00541-0
Varga A Hansson P Johnson G Alling C Normalization rate and cellular localization of phosphatidylethanol in whole blood from chronic alcoholics Clin Chim Acta 2000 299 141 150 10900300 10.1016/S0009-8981(00)00291-6
Aradottir S Seidl S Wurst FM Jonsson BA Alling C Phosphatidylethanol in human organs and blood: A study on autopsy material and influences by storage conditions Alcohol Clin Exp Res 2004 28 1718 1723 15547459
Aradottir S Moller K Alling C Phosphatidylethanol formation and degradation in human and rat blood Alcohol Alcohol 2004 39 8 13 14691067
Aradottir S Lundqvist C Alling C Phosphatidylethanol in rat organs after ethanol exposure Alcohol Clin Exp Res 2002 26 514 518 11981128 10.1097/00000374-200204000-00012
Radin NS Extraction of tissue lipids with a solvent of low toxicity Methods Enzymol 1981 72 5 7 7311848
Varga A Phosphatidylethanol in blood as a marker of alcohol abuse 2001 Lund: Studentlitteratur
Varga A Hansson P Lundqvist C Alling C Phosphatidylethanol in blood as a marker of ethanol consumption in healthy volunteers: comparison with other markers Alcohol Clin Exp Res 1998 22 1832 1837 9835304
Press WH Flannery BP Teukolsky SA Wetterling WT Numerical recipes in C: The art of scientific computing 1992 2 Cambridge University Press
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PMC1249557
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CC BY
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2021-01-04 16:26:24
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no
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BMC Biochem. 2005 Sep 27; 6:18
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utf-8
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BMC Biochem
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10.1186/1471-2091-6-18
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oa_comm
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