Epigenetics: The Next Frontier for Cancer Research

(This article was originally published in the March 2015 issue of Frontline Genomics magazine–A downloadable .pdf of that article is also available here)


by Shea Robison (@EpigeneticsGuy)

(See also Mosaic Epigenetic Dysregulation of Ectodermal Cells in Autism Spectrum DisorderAutism, Ethics and Epigenetics, and Epigenetics Minority Report Part I: Epigenetics, blame, precrime and politics)

Epigenetics deals with the biochemical processes ‘above’ or ‘before’ the genes which regulate the expression of genes in the genome, usually in response to influences in the immediate environment. What is particularly intriguing about epigenetics is how much it blurs the traditional boundaries we have erected between our genes and our environments. The novel complications introduced by epigenetics suggest—if not require—commensurately novel ways of conceptualizing our relationships with ourselves and with our environments that transcend this conventional dichotomization.

The unique nexus of genetics and the environment presented by epigenetics is of considerable practical relevance for diseases such as cancer which have as yet defied understanding via existing approaches that dichotomize genetics versus the environment (or vice versa). In this context, one major purpose of this article is to provide a brief survey of how the conventional emphasis on genetics in cancer research is being extended and empowered by epigenetics to perhaps finally realize the much-anticipated promise of cancer genomics[1].

Cancer, genes and “bad luck”

The unique perspective from epigenetics in cancer as compared to the more conventional dichotomy of genes versus the environment are particularly noticeable in the discussions of a recent paper by Cristian Tomasetti and Bert Vogelstein[2] on the causes of cancer which has already generated a significant amount of controversy[3].

In this paper the main question the authors attempt to answer is why there is such a disparity in the incidence of cancer between different kinds of tissues—e.g., as the authors note, “the lifetime risk of being diagnosed with cancer is 6.9% for lung, 1.08% for thyroid, 0.6% for brain and the rest of the nervous system, 0.003% for pelvic bone and 0.00072% for laryngeal cartilage,” just as “cancer risk in tissues within the alimentary tract can differ by as much as a factor of 24 [esophagus (0.51%), large intestine (4.82%), small intestine (0.20%), and stomach (0.86%)].” These disparities in risk of cancer between tissue types has been recognized for more than a century, but have not yet been reducible to either hereditary or environmental factors, which until now have been the only ways to parse the causes of cancer.

Building on Vogelstein’s previous pioneering work in somatic mutation, or mutational changes in cells’ DNA that are not passed along via the germ line but which occur during a person’s life[4], Tomasetti and Vogelstein hypothesized that the relative incidences of cancer in different kinds of tissues could be caused by random mistakes when DNA is copied during cell division. In other words, the more times cells in a particular tissue type divide, the more opportunities for such copying errors to occur, the greater the risk of cancer.

However, to test this idea Tomasetti and Vogelstein needed a way to assess the rates of cell division of different kinds of tissues. Because only stem cells (versus differentiated cells) live long enough to initiate a tumor, Tomasetti and Vogelstein plotted the rates of stem cell divisions of the 31 tissue types for which the rates of stem cell divisions are known against the lifetime risk for cancer for each type of tissue on a log-log axis, predicting that “there should be a strong, quantitative correlation between the lifetime number of divisions among a particular class of cells within each organ (stem cells) and the lifetime risk of cancer arising in that organ”[5].

Data from Tomasetti and Vogelstein (2015), chart from Jennifer Couzin-Frankel at http://news.sciencemag.org/biology/2015/01/simple-math-explains-why-you-may-or-may-not-get-cancer without permission
Data from Tomasetti and Vogelstein (2015), chart from Jennifer Couzin-Frankel at http://news.sciencemag.org/biology/2015/01/simple-math-explains-why-you-may-or-may-not-get-cancer without permission

As shown in the figure above, there is a clearly noticeable relationship between these two very different measures. Tomasetti and Vogelstein report a strong positive correlation (0.80) between the lifetime risk of cancer and the number of stem cell divisions for a particular tissue type. From this correlation, the authors thereby conclude that around two-thirds of the variation in cancer risk between tissue types can be explained by the total number of stem cell divisions unique to that tissue.

To distinguish this stochastic cell division from external environmental and heredity causes, Tomasetti and Vogelstein construct an “extra risk score” (ERS) as a function of lifetime risk and the total number of cell divisions (log10 values). Utilizing machine learning methods and unsupervised classification, the 31 cancers clustered into two groups, high ERS (9) and low ERS (22): the higher the ERS (basically, the higher the risk of cancer relative to the number of stem cell divisions), the more likely are external environment factors to play a role. The authors found that the high ERS cancers were those with known links to specific environmental or hereditary risk factors, with the low ERS cancers being more likely to be caused by these stochastic errors during DNA replication.

These findings are particularly noteworthy for a couple of reasons. First, because before now the term “environmental” in cancer epidemiology has been used to denote anything not hereditary, such that these kinds of developmental processes had been “grouped with external environmental influences in an uninformative way.” Now these stochastic errors in DNA replication can be distinguished from external environmental factors. Second, because these non-hereditary genetic causes were found to contribute more to cancer risk than either hereditary or external environmental factors. This is important because, as reiterated by Tomasetti in a follow-up interview with Science, “if you go to the American Cancer Society website and you check what are the causes of cancer, you will find a list of either inherited or environmental things. We are saying two-thirds is neither of them”[6].

So what?

What are the implications of this identification of a third way by Tomasetti and Vogelstein, and how is it related to epigenetics?

To the first point, as explained by one of the reviewers of the Tomasetti and Vogelstein paper[7], it is—or should be—common knowledge that even though the somatic mutations identified by Tomasetti and Vogelstein are legitimately genetic phenomena, they “are not in the germ line…are not transmitted from parents to offspring…don’t generate family risk correlations [and therefore] can’t be found by GWAS or other studies based on sequencing inherited genomes.” This reviewer also describes how it is—or should be— common knowledge that “environmental or life-history risk factors, like diet or reproductive history and so on,” can affect the risk of mutations identified by Tomasetti and Vogelstein, but that because this exposure “has to affect a cell in a given tissue and in a particular relevant gene being used by that tissue,” the net effect of these mutagens, and hence their predictability, is usually very small. In the end, for this reviewer the Tomasetti and Vogelstein paper uses new data but doesn’t show much that wasn’t already understood; perhaps the most salient point of this paper is how it demonstrates that “the love affair with inherited genotypes, enabled, encouraged, and funded by a variety of enthusiasms, opportunities, and vested interests, has distracted attention from working from what we knew.”

However, this point about the effects of age on the rate of somatic mutation is what opens the door for an epigenetic explanation of Tomasetti and Vogelstein’s results. Although Tomasetti and Vogelstein do not explicitly identify the epigenetic components of their findings as such, the copying errors which are such an important component of their model likely have epigenetic causes. This oversight is more than a little curious as Vogelstein has been a central figure in cancer epigenetics from its very beginning.[8]

To explain how this might work, the reviewer from before goes on to identify a very clear environmental factor related to cancer risk not addressed by Tomasetti and Vogelstein in their model: “If mutations arising by chance during cell division ultimately lead to transforming genotypes in some cells, the longer one lives the more likely such changes are likely to arise in at least one such cell in the person. This is generally why most cancer rates rise with age in ways correlated with rates of cell division…That is environmental causation, even if indirect!”[9] This oversight about the causal influence of age, “though it won’t change the empirical fact that neither inherited genotypes nor most environmental exposures do not have highly predictive effects,”[10] suggests that Tomasetti and Vogelstein missed something important.

There are a number of recent papers published on the connections between DNA methylation and aging which have relevance for this proposed connection between somatic mutations and cancer. In particular, a 2013 paper by Steve Horvath describes his discovery of a highly accurate epigenetic clock based on DNA methylation age as a measure of the cumulative effect of an epigenetic maintenance system which predicts not the age of the cells but of the person the cells inhabit[11]. The median error of this clock is 3.6 years, which means it can predict the age of half the donors to within 43 months for a broad selection of tissues. Horvath also analyzed 6,000 cancer samples of 20 cancer types, all of which showed significant age acceleration, except for “a significant negative relationship between age acceleration and the number of somatic mutations.” Subsequent studies have also found an advanced methylation aging rate in tumor tissue, and that DNA methylation-derived measures of accelerated ageing predict mortality independently of health status, lifestyle factors, and known genetic factors[12].

That epigenetics could be playing such a significant role in this longstanding puzzle about the disparity between the cancer risks of different tissue types, is intriguing. These results are preliminary at best, but quite suggestive of the profound role of epigenetics in cancer. Tomasetti and Vogelstein provided one important piece by identifying the role of stem cell divisions in risk of cancer. The next step is suggested by the connection between DNA methylation, somatic mutation, aging and cancer. The next step remains to be seen, but with the recent release of the first full mappings of the human epigenome, new developments are likely to come even more frequently.

[1] Lima, S. C., Hernandez-Vargasl, H., & Hercegl, Z. (2010). Epigenetic signatures in cancer: Implications for the control of cancer. Current Opinion in Molecular Therapeutics, 12(3), 316-324; Verma, M. (Ed.). (2015). Cancer Epigenetics: Risk Assessment, Diagnosis, Treatment, and Prognosis. Humana Press; Ling, H., Vincent, K., Pichler, M., Fodde, R., Berindan-Neagoe, I., Slack, F. J., & Calin, G. A. (2015). Junk DNA and the long non-coding RNA twist in cancer genetics. Oncogene 34(8). Vad-Nielsen, J., & Nielsen, A. L. (2015). Beyond the Histone Tale: HP1α Deregulation in Breast Cancer Epigenetics. Cancer biology & therapy, (Just accepted for publication).

[2] Tomasetti, C. and B. Vogelstein (2015). Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science 347(217), 78-81.

[3] Divide and Cancer? Stem Cell Divisions Impact Tissue’s Cancer Risk. (2015, January 11). Retrieved February 26, 2015, from http://epigenie.com/stem-cell-divisions-explain-variation-in-cancer-risk-among-tissues/?hvid=56AhK5; Couzin-Frankel, J. (2015, January 1). The simple math that explains why you may (or may not) get cancer. Retrieved February 26, 2015, from http://news.sciencemag.org/biology/2015/01/simple-math-explains-why-you-may-or-may-not-get-cancer; Knoepfler, P. (2015, January 2). Review of Vogelstein “Bad Luck” Cancer & Stem Cell Paper in Science. Retrieved February 26, 2015, from http://www.ipscell.com/2015/01/review-of-vogelstein-bad-luck-cancer-stem-cell-paper-in-science/; Meyer, A. (2015, January 2). The Bad Luck of Improper Data Interpretation · Ameyer.me. Retrieved February 26, 2015, from http://ameyer.me/science/2015/01/02/vogel.html; O’Hara, B., & GrrrlScientist. (2015, January 2). Bad luck, bad journalism and cancer rates. Retrieved February 26, 2015, from http://www.theguardian.com/science/grrlscientist/2015/jan/02/bad-luck-bad-journalism-and-cancer-rates; Weiss, K. (2015, January 5). Is cancer just bad luck? Part I. Known risk factors are poor predictors. Retrieved February 20, 2015, from http://ecodevoevo.blogspot.com/2015/01/is-cancer-just-bad-luck-part-i-known.html; Weiss, K. (2015, January 6). Is cancer just bad luck? Part II. It’s a genetic, but usually unpredictable, disease. Retrieved February 20, 2015, from http://ecodevoevo.blogspot.com/2015/01/is-cancer-just-bad-luck-part-ii-its.html; Couzin-Frankel, J. (2015, January 13). Bad luck and cancer: A science reporter’s reflections on a controversial story. Retrieved February 26, 2015, from http://news.sciencemag.org/biology/2015/01/bad-luck-and-cancer-science-reporter-s-reflections-controversial-story.

[4] Powell, S. M., Zilz, N., Beazer-Barclay, Y., Bryan, T. M., Hamilton, S. R., Thibodeau, S. N., Vogelstein, B., and Kinzler, K. W. (1992). APC mutations occur early during colorectal tumorigenesis. Nature 359, 235-237; Su, L. K., Vogelstein, B., and Kinzler, K. W. (1993). Association of the APC tumor suppressor protein with catenins. Science 262, 1734-1737.

[5] Tomasetti, C. and B. Vogelstein (2015). Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science 347(217), 78-81.

[6] Couzin-Frankel, J. (2015, January 13). Bad luck and cancer: A science reporter’s reflections on a controversial story. Retrieved February 23, 2015, from http://news.sciencemag.org/biology/2015/01/bad-luck-and-cancer-science-reporter-s-reflections-controversial-story

[7] Weiss, K. (2015, January 5). Is cancer just bad luck? Part II. It’s a genetic, but usually unpredictable, disease. Retrieved February 20, 2015, from http://ecodevoevo.blogspot.com/2015/01/is-cancer-just-bad-luck-part-ii-its.html.

[8] Feinberg, A. P., & Vogelstein, B. (1983). Hypomethylation distinguishes genes of some human cancers from their normal counterparts. Nature, 301(5895), 89-92.

[9] Weiss, K. (2015, January 5). Is cancer just bad luck? Part II. It’s a genetic, but usually unpredictable, disease. Retrieved February 20, 2015, from http://ecodevoevo.blogspot.com/2015/01/is-cancer-just-bad-luck-part-ii-its.html.

[10] Weiss, K. (2015, January 5). Is cancer just bad luck? Part II. It’s a genetic, but usually unpredictable, disease. Retrieved February 20, 2015, from http://ecodevoevo.blogspot.com/2015/01/is-cancer-just-bad-luck-part-ii-its.html.

[11] Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome biology, 14(10), R115.

[12] Marioni, R. E., Shah, S., McRae, A. F., Chen, B. H., Colicino, E., Harris, S. E., … & Deary, I. J. (2015). DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol, 16(1), 25.

“Understanding the Impact of Epigenetics” podcast


by Shea Robison (@EpigeneticsGuy)

Understanding the Impact of Epigenetics podcast

Below are links to posts and papers I mention in this podcast about epigenetics and health that I participated in as a panel member hosted by the health and fitness website BreakingMuscle.com:

When it Comes to Epigenetics, How Much Fun is Too Much? Comment and Reply

Epigenetics By Any Other Name? What Epigenetics Should and Should Not Be

Epigenetics and Drug Discovery: The Missing Link?

Gene Sequence but not Structure? The Costs of Excluding Epigenetics from Genomics

Ben Laufer Comments on “Gene Sequence but not Structure”

Epigenetics Minority Report Part I: Epigenetics, blame, precrime and politics

All of these posts have many links to other posts on this blog and to external materials about epigenetics so click on these links for supplemental information. You can also navigate through the posts about the different topics I discuss on this blog using the pages in the header above or the Categories list located on the righthand margin of this blog.

Below is a link to a PowerPoint presentation in which I discuss the Agouti mice experiments and the longitudinal studies in humans that I mention in the podcast:

Agout Mice 2

Epigenetics PowerPoint

The link below is to the paper on the emerging narratives of epigenetics in regards to obesity that I reference in the podcast, which I presented at the 2014 annual conference of the Association of Politics and the Life Sciences:

The Emerging Obesity Policy Narratives of Epigenetics

I have also summarized a number of research papers on epigenetics. You can find these research summaries here:

Reasearch Summaries

Additional Information:

For a visual of how epigenetics work, you can watch this video from the University of Utah Genetic Science Learning Center.

Also, in this video:

a world class epigeneticist explains some of the mechanisms of epigenetics, as well as discusses some of the intriguing possibilities (video from the RWJF).

You’ve come this far, so I am curious about what brought you here. Read the posts that interest you, leave a comment or question, and let’s see what we can do.

Feel free to contact me at epigenetics.guy@gmail.com with any questions or comments.

Follow my epigenetics and policy themed Twitter feed @EpigeneticsGuy

Mosaic Epigenetic Dysregulation of Ectodermal Cells in Autism Spectrum Disorder


by Shea Robison (@EpigeneticsGuy)

Mosaic Epigenetic Dysregulation of Ectodermal Cells in Autism Spectrum Disorder

Authors: Esther R. Berko, Masako Suzuki, Faygel Beren, et al.

Journal: PLosGenetics

Publication Date: May 29, 2014

This week’s paper deals with trying to trace the biological causes of Autism Spectrum Disorder (ASD). The authors begin by noting that one of the causes associated with ASD is the age of the mother, although the reasons for this increased risk are unknown. What is known is that the eggs of older women are more prone to chromosomal abnormalities, and so this has been suggested as a likely reason for this association between parental age and ASD. However, as the authors write, “age is also associated with a loss of control of epigenetic regulatory patterns that govern gene expression,” which suggests epigenetic dysregulation as a second potential mechanism. Thus, for this paper the authors tested both possibilities.

This effort to distinguish between genetic or epigenetic causes of ASD is the first reason for the selection of this paper as the paper of the week; the second reason is the extensive descriptions the authors give about the methods they use to test between these two mechanisms. For anyone interested in epigenetics, this discussion of the cutting edge of the technical side of epigenetics research can only be helpful.

Genetic mutations have long been proposed as the predominant cause for ASD, but the explanation of epigenetic mechanisms has recently gained credence as a cause for ASD. The authors cite three recent studies in particular which support epigenetic dysregulation as a potential mechanism in the incidence of ASD. A 2012 paper reported the discovery of distinctive chromatin features in the brains of subjects with ASD.[1] Authors of a paper published in 2013 tested blood leukocytes and found differences in DNA methylation between a monozygotic twin affected with ASD and their unaffected twin.[2] A second paper published in 2013 found differences in DNA methylation from subjects with ASD and subjects without ASD.[3]


To test whether ASD is the result of these genetic or epigenetic causes, the authors tested “homogeneous ectodermal cell types” from 47 individuals with ASD compared with 48 typically developing (TD) controls born to mothers of ≥35 years. Genome-wide tests were then performed on these cells to look for unusual chromosome numbers to test the hypothesis of genetic mutations; epigenome-wide analyses (EWASs) were used to test DNA methylation patterns in regards to the hypothesis of epigenetic dysregulation. This study, the authors note, “represents the largest epigenome-wide analysis to date testing a single cell type in ASD.”

The choice of this specific cell type served a couple of different purposes. First, the type of cell was selected because comes from the same developmental origin as brain cells. Second, the selection of this specific cell type minimizes the problem of “mixed cellularity” which has been already identified as a problem with EWASs.

The authors also addressed other such problems with EWASs, the biggest issue being “that the generally small changes in DNA methylation found may not be substantially in excess of the noise introduced by technical or biological effects influencing DNA methylation that have no relationship to the phenotype being tested.” To achieve what the authors call “the currently necessary level of stringency for EWAS studies,” the authors incorporated parallel SNP genotyping and “Surrogate Variable Analysis” (SVA) to account for these different possible sources of variability, as well as stringent pre-processing of the microarray data that was gathered and iterative use of this preprocessing data as means to focus in on only those regions of the DNA sequence that are being differentially methylated (i.e., to avoid ‘false positives’). Through these different efforts, the authors reduced the impact of these methodological issues due to “cell type and subpopulation heterogeneity, chromosomal aneuploidy, copy number variability, genetic polymorphism, age, sex and technical influences.” The authors go into considerable detail as to how these different approaches resolve these issues, so anyone interested should the relevant sections of the original paper.


Using these rigorous and intensive methods to test the competing hypotheses of mutations in DNA versus epigenetic dysregulation as increasing the risk for ASD, the authors discover 15 differentially methylated regions (DMRs) at 14 genes which distinguished the ASD and TD samples. From subsequent analysis of these DMRs, the authors conclude that “DNA methylation patterns are dysregulated in ectodermal cells” in individuals with ASD, but did not find evidence of chromosomal abnormalities in those same DMRs. In their own words, the authors conclude that “of the two mechanisms we originally proposed for AMA causing ASD, covert aneuploidy occurring at detectable levels (≥20%) is not as likely to be involved as epigenetic dysregulation.”

Interestingly, though, the genes in these DMRs are those already associated with ASD, which means that instead of genetic mutations this analysis reveals “a perturbation by epigenomic dysregulation of the same networks compromised by DNA mutational mechanisms.” In other words, the genes previously associated with ASD are still implicated in ASD, but through epigenetic dysregulation rather than through mutations in DNA sequences. However, the exact pathways of this epigenetic dysregulation are still unclear. Given the results of their study, the authors suggest aging parental gametes, environmental influences during embryogenesis, or mutations of the chromatin regulatory genes implicated in ASD as the most likely possible environmental factors in this epigenetic dysregulation.


Some additional points of interest about this study are worth mention:

First, mosaicism in this context usually refers to differences in chromosomal makeup between cell populations within the same individual (the “covert aneuploidy” just mentioned). As this was not found to be significant, the “mosaicism” in the title of this paper refers not to chromosomal differences between cell types but rather “the presence of a mosaic subpopulation of epigenetically-dysregulated, ectodermally-derived cells in subjects with ASD.”

Second, the authors note prior epigenetic studies of ASD had used mixed cell types, which may have limited the ability to detect the effects found by the authors and their use of homogeneous ectodermal cell types.

Third, the authors note that their study implicated the same gene in epigenetic dysregulation (OR2L13) as found in two previous studies associated with altered DNA methylation in individuals with ASD. This replication suggests this gene is “especially labile in ASD in terms of DNA methylation and expression.”

Fourth, the authors observe that while the epigenetic changes they observed from a cohort of subjects born to mothers with AMA may be the result of the aging of the mother’s egg, the sperm of the fathers—who are likely as old as the mothers—may also be experiencing epigenetic changes of their own which are contributing to the epigenetic dysregulation observed in their study; as this was not controlled for in their study, subsequent efforts should include such controls.

Finally, given the ages of the parents and the increased probability of mutational events, the authors allow that the observed epigenetic dysregulation may actually be a secondary effect of the mutations in the genes involved, and not actually the cause but rather a symptom of ASD. What is most interesting to me about this suggestion is the authors’ recommendation that “combined genetic and epigenetic analyses of the same subjects will be needed to test these possibilities.” I have written about the substantial benefits of this combination of epigenetics and genomics before in terms of the need to identify the impacts of both gene sequence and three-dimensional structure on gene expression in general, and as it pertains to expanding the scope of disease phenotypes which are amenable to drug discovery in particular, and this paper provides yet another concrete example of the need to combine both epigenetics and genomics.

[1] Shulha HP, Cheung I, Whittle C, Wang J, Virgil D, et al. (2012) Epigenetic signatures of autism: trimethylated H3K4 landscapes in prefrontal neurons. Arch Gen Psychiatry 69: 314-324.

[2] Wong CC, Meaburn EL, Ronald A, Price TS, Jeffries AR, et al. (2013) Methylomic analysis of monozygotic twins discordant for autism spectrum disorder and related behavioural traits. Mol Psychiatry.

[3] Ladd-Acosta C, Hansen KD, Briem E, Fallin MD, Kaufmann WE, et al. (2013) Common DNA methylation alterations in multiple brain regions in autism. Mol Psychiatry.

Early Life Nutrition, Epigenetics and Programming of Later Life Disease


by Shea Robison (@EpigeneticsGuy)

Early Life Nutrition, Epigenetics and Programming of Later Life Disease

Author: Mark H. Vickers

Journal: Nutrient

Publication Date: June 2, 2014

Affiliation: Liggins Institute and Gravida, National Centre for Growth and Development, University of Auckland, 85 Park Road, Grafton, Auckland 1142, New Zealand

Policy Implications: This paper is a review of the human and animal studies of epigenetic changes in early development – many of which can be passed on to multiple generations – which manifest as metabolic conditions later in life. Some of the conditions in the studies which are cited which contribute to increased incidence of metabolic disorders in later life are maternal nutrition and paternal obesity at the time of conception. Therefore, the most obvious policy implications from the research cited in this review are for obesity policy: That circumstances such as maternal nutrition and paternal weight – which are ostensibly under the control of the parents – are found to be such significant factors in the subsequent metabolic diseases of their offspring provides support for obesity prevention policies which focus on the behavior and choices of the parents. However, this focus on the choices and behaviors of parents as a matter of policy raises all sorts of personal liberty and individual freedom issues which would also need to be addressed, especially in  political cultures like that of the United States which emphasize individualism and personal responsibility.

Summary: This paper is a review of the contemporary work being done on epigenetic changes in early development which manifest as metabolic conditions later in life. As such, it is a good source for recent research on this topic.

As the author writes, there is a substantial body of work in epigenetics on the effects on fetal and post-natal development of methylation or demethylation resulting from maternal nutrition, levels of maternal care, and other environmental conditions. This paper focuses on the phenotypic effects of epigenetic modifications during these developmental stages which manifest as metabolic conditions such as obesity and metabolic diseases such as Type 2 diabetes much later in the life cycle. The author presents evidence from human epidemiology and animal models, and discusses transgenerational epigenetic programming in particular as an example of the long-term effects of epigenetic modifications in early development.

In regards to human epidemiology, the author acknowledges that the evidence linking these epigenetic changes to metabolic diseases later in life is limited for humans, but cites evidence for the inheritance of tissue-specific DNA methylation patterns. The author also refers to studies which have found epigenetic differences between twins related to life history, and to studies on the Dutch Hunger Winter (1944-1945) cohort in which significant epigenetic changes in later life have been correlated with different early developmental stages during the famine. The author also mentions that while macronutrients have been implicated in such changes, maternal micronutrient levels (such as vitamin B12) are of particular interest, as is parental obesity at the time of conception.

In reference to the animal studies, the author cites the substantial evidence that has been gathered for the manifestation of these early epigenetic changes in later life. As cited by the author, researchers have extensively studied the effects of maternal undernutrition, restricted intrauterine growth, and paternal obesity in animals. The author also summarizes different intervention strategies studied by epigeneticists “to ameliorate or reverse the effects” of this early developmental programming. These include neonatal leptin treatments, remethylation via dietary intake, and exercise, which have all been shown to change DNA methylation in ways which reduce or prevent subsequent manifestation of these metabolism-related disorders.

Evidence of the transgenerational inheritance of acquired characteristics is perhaps the most interesting and the most controversial results of research in epigenetics. As the author notes, there is substantial evidence for both the germline and somatic inheritance of non-genetic traits, and that the transgenerational inheritance of these non-genetic traits has the potential to “result in a population-wide manifestation of a phenotype over several generations,” and that “such transmission can exacerbate the rapid onset of phenotypes such as obesity and diabetes currently observed in human populations.”  The author reviews a number of studies which show the non-genetic transmission of traits to the F1 generation, and some which show such inheritance to the F2 generation and even the F3 generation. However, the author also cites a meta-analysis of nine transgenerational studies which were carried through to F3, and that five of these studies failed to show any effect. Again, though, paternal nutrition and paternal obesity are both shown to initiate transgenerationally-inherited epigenetic changes.

Research Paper of the Week: Pesticide Methoxychlor Promotes the Epigenetic Transgenerational Inheritance of Adult-Onset Disease through the Female Germline


by Shea Robison (@EpigeneticsGuy)

Pesticide Methoxychlor Promotes the Epigenetic Transgenerational Inheritance of Adult-Onset Disease through the Female Germline

Policy implications: As this research involves the epigenetic effects of exposure to a very common pesticide and insecticide, there are all sorts of implications for agricultural policies and FDA food safety regulations. These results also address the transgenerational causes of obesity and adult-onset diseases such as kidney disease, so there are significant implications for obesity and health care policies as well. However, given the protocols of the experiments in this study the results are valid primarily as demonstrations of the transgenerational epigenetic effects of methoxychlor, and are only suggestive of the risks of exposure. Still, the fact that methoxychlor is shown to produce these significant and transgenerational epigenetic effects is suggestive of their importance for consideration in the policy domains mentioned.

Summary: As the title clearly states, this paper investigates the potential of methoxychlor to promote the epigenetic transgenerational inheritance of adult-onset disease in both males and females.

Methoxychlor is an insecticide and pesticide that replaced DDT and has been approved for use on crops and livestock since 1946. The effects of methoxychlor have been extensively studied in animals, demonstrating significant estrogenic and reproductive toxicity in both males and females. Methoxychlor has also been found to be carcinogenic. In humans, the toxicity profile of methoxychlor shows “death, systemic (aplastic anemia), cardiovascular (low blood pressure), and neurological (blurred vision, dizziness and paresthesia) effects, and cancer (leukemia).”

In this study, gestating female rats in the F0 generation were given a transient exposure to methoxychlor via intraperitoneal injections during fetal gonadal sex determination. The dose was in the high range of environmental exposure, but no direct toxic effects were anticipated or observed. Diseases of the testis, prostate, kidney, ovary and uterus, as well as tumor development, abnormal puberty onset, obesity and sperm epimutations were evaluated in F1, F3 and F4 generations for both control and methoxychlor lineages. However, because of the dose and the injection method, a realistic risk assessment of environmental exposure to methoxychlor could not be assessed; rather, this was a study of the effects of exposure to methoxychlor on the epigenetic transgenerational inheritance of specific disease phenotypes.

Significant differences from the control lineages in female pubertal abnormalities were observed for the F1 methoxychlor lineages, but not the F3 generations. Significant differences were found for ovary diseases for the F3 methoxychlor, but not the F1. Significant differences in male and female obesity and incidence of multiple disease were also found for F3 methoxychlor generations. For F4 generations, significant differences from the control were found for male and female kidney disease, total disease incidence, and male obesity.

As the authors conclude, “previous studies with vinclozolin or high fat diet showed transmission of increased incidence of disease through the male germline.” What this study adds is that it shows “a transmission of increased incidence of kidney disease in females and males, and obesity in males through the female germline after toxicant exposure to pregnant F0 generation females,” and that “the transmission of transgenerational female obesity may involve a combination of male and female germline transmission.”

Authors: Mohan Manikkam, M. Muksitul Haque, Carlos Guerrero-Bosagna, Eric E. Nilsson, Michael K. Skinner from the Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, Washington, United States of America

Journal: PLoS One

Publication date: July 24, 2014