In several earlier blog entries I have advocated the concept that social evolution is leading now to rapid biological evolution in humans, the entries including Social evolution and biological evolution – another dialog with Marios Kyriazis, and Social ethics of longevity. How could this happen given that evolution has taken millions of years? I have suggested that the evolution involved is epigenetic evolution which moves far faster than Darwinian genetic evolution. This blog entry is about a new theory that explains how this evolution is taking place: stochastic epigenetic evolution.
Stochastic epigenetic evolution is a new and different theory offering explanations for both aging and the current rapid pace of human evolution. It is based on the notion that components of the epigenome are not stable but are in constant flux due to random events. This flux may be responsible for variable disease susceptibilities, ability of the organism to evolve very rapidly to accommodate to new conditions, and perhaps even aging itself.
It is not that there is already a scarcity of theories of aging. I have laid out 14 major theories of aging and 6 additional candidate theories in my treatise ANTI-AGING FIREWALLS – THE SCIENCE AND TECHNOLOGY OF LONGEVITY. The new theory of stochastic epigenetic evolution is interesting because it is different than the others, and because it explains some things otherwise not well explained. I present an overview of the theory here and discuss some of its ramifications.
Background on evolution
Classic “evolution (also known as biological, genetic or organic evolution) is the change in the inherited traits of a population of organisms through successive generations. This change results from interactions between processes that introduce variation into a population, and other processes that remove it. As a result, variants with particular traits become more, or less, common. A trait is a particular characteristic—anatomical, biochemical or behavioural—that is the result of gene–environment interaction(ref).”
According to the classical view of evolution “The main source of variation is mutation, which introduces genetic changes. These changes are heritable (can be passed on through reproduction), and may give rise to alternative traits in organisms. Another source of variation is genetic recombination, which shuffles the genes into new combinations which can result in organisms exhibiting different traits. Under certain circumstances, variation can also be increased by the transfer of genes between species, and by the extremely rare, but significant, wholesale incorporation of genomes through endosymbiosis (ref).”
So, in the classical view evolution is based on mutations in the genome and the process is likely to be extremely slow taking many generations to take hold. “Two main processes cause variants to become more common or rarer in a population. One is natural selection, through which traits that aid survival and reproduction become more common, while traits that hinder survival and reproduction become rarer. Natural selection occurs because only a small proportion of individuals in each generation will survive and reproduce, since resources are limited and organisms produce many more offspring than their environment can support(ref).” I comment that this classical description obviously does not match what is happening in many modern countries like Japan or Italy where too-few children are being born to maintain the population size.
Going on, “Over many generations, heritable variation in traits is filtered by natural selection and the beneficial changes are successively retained through differential survival and reproduction. This iterative process adjusts traits so they become better suited to an organism’s environment: these adjustments are called adaptations. — However, not all change is adaptive. Another cause of evolution is genetic drift, which leads to random changes in how common traits are in a population(ref).”
This classical view of evolution is far too slow to explain many examples of observed evolution. Why for example are Americans now significantly taller and longer-lived than they were a couple of generations back? And consider for examples the case of lizards transported from one Caribbean island to another. “An experiment with lizards in the Caribbean has demonstrated that evolution moves in predictable ways and can occur so rapidly that changes emerge in as little as a decade. — The experiment involved the introduction of one species of lizard to fourteen small, lizard-free Caribbean island near the Exumas in the Bahamas. The lizards were left for fourteen years. The original intent of the experiment was to study extinction. The experiment, started by Thomas Schoener of the University of California at Davis, would have provided scientists with important information as they observed the extinction of the introduced lizards. Unfortunately, the lizards adapted to their new environments, and the focus of the experiment changed to study this rapid evolution.” The lizards evolved different lengths of legs to be optimal for the vegetation of the islands concerned. And they did this fast enough to survive in their new island homes. “The rate of evolutionary change is measured in units called darwins. Darwins provide a measure of the proportional change in a given organ over time. Changes typically seen over millions of years in the fossil record usually amount to 1 darwin or less. The transplanted lizards evolved at rates of up to 2000 darwins(ref).”
Another example of observed rapid evolution is in finches “Over a ten year period, three natural selection events occurred, suggesting that evolutionary change might be more rapid than ever before suspected(ref).” There are multiple other examples of rapid evolution. “Acting as super-predators, humans are forcing changes to body size and reproductive abilities in some species 300 percent faster than would occur naturally, a new study finds. — In a review of 34 studies that tracked 29 species across 40 different geographic systems, harvested and hunted populations are on average 20 percent smaller in body size than previous generations, and the age at which they first reproduce is on average 25 percent earlier(ref).” The ability of many diseases to evolve rapidly also challenges the classical evolution model. “ — the classic model also has significant limitations in explaining common human disease; common variants can explain only a small fraction of a given disease phenotype, even the most well understood, such as adult-onset diabetes and height(ref).”
These evolutionary changes and many others are happening far too rapidly to be explained by changes in genes which remain stable for large numbers of generations. We have largely the same genes our ancestors did millions of years ago. Instead, the rapid evolutionary changes must represent inheritable changes in the respective epigenomes, not in the underlying DNA sequences.
Stochastic epigenetic evolution
Back in 2007, the publication Combinatorial epigenetics, “junk DNA”, and the evolution of complex organisms suggested a possible strong role for epigenetic shifts in determining the evolution of complex organisms. The authors suggest that epigenetic shifts facilitate classical mutations in the evolutionary process. “It is proposed that, in eukaryotes, changes in epigenetic trends and epigenetically transforming encounters between alternative chromatin structures could arise frequently enough so as to render probable particular conjunctions of changed transcription rates.– The chances for two or more particular epigenetically determined regulatory trends to occur together in a cell are increased thanks to the proposed low specificity requirements for most of the pertinent sequence changes in intergenic and intronic DNA or in the distribution of middle repetitive sequences that have teleregulatory impact. Inheritable epigenetic changes (“epimutations”) with effects at a distance would then perdure over the number of generations required for “assimilation” of the several regulatory novelties through the occurrence and selection, gene by gene, of specific classical mutations. These mutations would have effects similar to the epigenetic effects, yet would provide stability and penetrance. The described epigenetic/genetic partnership may well at times have opened the way toward certain complex new functions. Thus, the presence of “junk DNA”, through co-determining the (higher or lower) order and the variants of chromatin structure with regulatory effects at a distance, might make an important contribution to the evolution of complex organisms.” Several of the later papers listed below see evolution as also taking place purely at the epigenetic level.
The case for the theory of stochastic epigenetic evolution is laid out in the 2009 publication Epigenetic gambling and epigenetic drift as an antagonistic pleiotropic mechanism of aging. “I suggest that random changes in cellular gene expression (cellular epigenetic gambling or bet hedging) evolved as an adaptive mechanism to ensure survival of members of a group in the face of unpredictable environmental challenges. Once activated, it could lead to progressive epigenetic variegation (epigenetic drift) amongst all members of the group. Thus, while particular patterns of gene expression would be adaptive for a subset of reproductive individuals within a population early in life, once initiated, I predict that continued epigenetic drift will result in variable onsets and patterns of pathophysiology–perhaps yet another example of antagonistic pleiotropic gene action in the genesis of senescent phenotypes. The weakness of this hypothesis is that we do not currently have a plausible molecular mechanism for the putative genetic ‘randomizer’ of epigenetic expression, particularly one whose ‘setting’ may be responsive to the ecology in which a given species evolves. I offer experimental approaches, however, to search for the elusive epigenetic gambler(s).”
Proposed mechanisms for stochastic epigenetic evolution
The 2009 publication Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease discusses the stochastic epigenetic evolution theory in detail and proposes molecular mechanisms for the “genetic ‘randomizer’ of epigenetic expression” discussed in the previously-cited paper. The discussions in this paper are technical and rather tough for a layman to follow but I quote selectively. “It has occurred to us that increased variability with a given genotype might itself increase fitness. This could arise by genetic variants that do not change the mean phenotype but do change the variability of phenotype. A natural mechanism to use to consider such a model is epigenetic plasticity during development, for example, varying DNA methylation patterns. This idea differs from Lamarckian inheritance, in that in our model the genetic change is inherited, and this change leads to increased epigenetic variation. It also differs from the likely role of epigenetics in modifying mutation rate, –. As a proof of principle, we revisited previously generated data sets (14) of genome-scale analysis of DNA methylation in human and mouse tissues and explored them in two new ways. First, we investigated whether there were regions of variable methylation across individuals for a given tissue type. Then we explored whether tissue-specific differentially methylated regions (T-DMRs) differed across species and whether the underlying DNA sequence could account for these differences. — To assess the degree of intrinsic variability in DNA methylation of a given tissue, we set out to identify the location of the most highly variable regions of DNA methylation in mouse liver from four individuals. We chose this specific tissue because it is relatively homogeneous. We examined newborns in whom polyploidy is minimal, although copy number would not be expected to affect DNA methylation, because our method controls for copy number (15). Environmental effects were minimized by examining inbred mice (indeed, littermates from the same cage). Surprisingly, many loci throughout the genome showed striking variations in DNA methylation, which we term variably methylated regions (VMRs). Surprisingly, these VMRs were significantly enriched in the vicinity of genes with Gene Ontogeny (GO) functional categories for development and morphogenesis (Table 1) when using either all genes for comparison or all regions present on the CHARM array, indicating that enrichment is not explained solely by high CpG content, because the array itself is designed to assay high-CpG regions. Examples of developmental genes with VMRs—Bmp7, involved in early embryogenic programming and bone induction, Pou3f2, involved in neurogenesis and stem cell reprogramming, and Ntrk3, involved in body position sensing—are shown in Fig. 1. — Next, we were interested in whether changes in differential methylation across species (mouse and human) could be traced back to an underlying genetic basis. To address this question, we focused on T-DMRs, given the wealth of data gathered in previous studies and their relevance to human diseases, such as cancer. Previously we reported that DMRs that distinguish colorectal cancer from normal colonic mucosa (C-DMRs) are enriched for T-DMRs, and this finding was validated in a large independent set of samples. In many cases, the loss of differential methylation in one species was related to an underlying loss of CpGs at the corresponding CpG island or nearby CpG island shore (14). A typical example of an evolutionary change in differential methylation involved LHX1, a transcriptional regulator essential for vertebrate head organization and mesoderm organization, (shown in Fig. 5). Note the T-DMR in human that is not in mouse on the left of the TSS. The human has gained CpGs at a CpG island shore (with the island shown in orange tick marks in the bottom panel). In contrast, both species have a moderate CpG count to the right of the TSS, and both have DMRs in this region. This is an example of how a genetic variation (i.e., gain of CpGs) allows for development-relevant tissue-specific differences in a highly conserved gene. Thus, differential methylation that itself differs across species may be due to underlying sequence variation at the site of these DMRs. Additional examples of this are available at rafalab.jhsph.edu/evometh.pdf. – Discussion Here we have proposed a model in which increased variability with a given genotype might increase fitness not by changing mean phenotype, but rather by changing the variability of phenotype with a given genotype. We also have provided a possible mechanism by which such enhanced variability could be genetically inherited and lead to increased stochastic epigenetic variation during development. Note that the genomic loci for such variation would be well defined in our model; we have provided examples of these loci. Although these loci do not represent the primary engine of development, they do provide plasticity in the developmental program by virtue of the stochastic variation that they impart through the genes in their proximity. — Our model differs from that of a transgenerational epigenetic effect on phenotypic variation and disease risk (16), in that in our model, the genetic variant is inherited and contributes to enhanced phenotypic variation, which can be mediated epigenetically in each generation. It also differs from a hypermutable genetic-switching model, in which the genotype itself changes from generation to generation, increasing phenotypic plasticity (17). — Our model provides a mechanism for developmental plasticity and evolutionary adaptation to a fluctuating environment. Although the model is general and does not necessitate epigenetic variation, we have demonstrated the existence of VMRs that affect phenotype (i.e., gene expression) in isogenic mice raised in an identical environment, and have shown that similar VMRs exist in humans as well. We also have reported a potential genetic mechanism for differences in tissue-specific methylation across species—namely, the gain or loss of a CpG island or the associated shore. The localization near a specific gene would provide specificity of the effect of variation, but the mechanism for variation could entail the relationship to tissue-specific promoters, transcription factor binding sites, population variation in CpG density in these regions, or a combination of such factors. Distinguishing among these possibilities will require further experimentation.”
Another paper that suggests a mechanism for stochastic epigenetic evolution is the 2010 publication Epigenetics in the Extreme: Prions and the Inheritance of Environmentally Acquired Traits. “Prions are an unusual form of epigenetics: Their stable inheritance and complex phenotypes come about through protein folding rather than nucleic acid-associated changes. With intimate ties to protein homeostasis and a remarkable sensitivity to stress, prions are a robust mechanism that links environmental extremes with the acquisition and inheritance of new traits.” A 2009 paper Protein folding sculpting evolutionary change forwards the same theme. “Because changes in protein homeostasis occur with environmental stress, prions can be cured or induced by stress, creating heritable new phenotypes that depend on the genetic variation present in the organism. Both prions and Hsp90 provide plausible mechanisms for allowing genetic diversity and fluctuating environments to fuel the pace of evolutionary change. The multiple mechanisms by which protein folding can influence the evolution of new traits provide both a new paradigm for understanding rapid, stepwise evolution and a framework for targeted therapeutic interventions.”
Implications of stochastic epigenetic evolution
Rapidity of evolution
As discussed above, epigenomic evolution can happen much faster than could happen due to changes only in the genome. This is consistent with observations in both humans and other species.
Epigenetic regulation and variability in aging
There are a number of studies relating epigenetic changes to aging. The honeybee is a well-studied example of an organism where epigenetic mechanisms appear to be the main determinants of aging. Environmental conditions and specialization of functions can have major impacts on lifespans.
Examples are given in the 2004 publication Epigenetic Regulation of Aging in Honeybee Workers. “Aging and longevity are complex life history traits that are influenced by both genes and environment and exhibit significant phenotypic plasticity in a broad range of organisms. A striking example of this plasticity is seen in social insects, such as ants and bees, where different castes can have very different life spans. In particular, the honeybee worker offers an intriguing example of environmental control on aging rate, because workers are conditionally sterile and display very different aging patterns depending on which temporal caste they belong to (hive bee, forager, or a long-lived caste capable of surviving for several months on honey alone). The ubiquitous yolk protein vitellogenin appears to play a key role in the regulatory circuitry that controls this variation.”
As stated in the publication Handbook of models for human aging for the honeybee , “Epigenetic regulation is responsible for the differentiation of females into workers and queens – two cases with strongly diverging lifespan potential – and a plastic pattern of worker longevity that appears to be determined by the social colony setting rather than chronological age.”
Epigenetics and senescence
The 2010 publication The curious case of aging plasticity in honey bees reports “Curiously, aging progresses slowly in workers that engage in nursing and even slower when bees postpone nursing during unfavorable periods. We, therefore, seek to understand how senescence can emerge as a function of social task performance.”
As a matter fact, in the honeybee worker the regulation of aging appears to be mainly epigenetic and have or little to do with functional senescence. The 2007 paper Aging without functional senescence in honey bee workers reports “The limited existing data support a direct connection between old age, increased mortality rate and decreased behavioral or physiological performance in organisms ranging from flies to humans . A recent study , however, suggests that the linkage may be less universal than previously postulated. To investigate this linkage directly in the non-traditional aging model Apis mellifera , old honey bee workers were studied with respect to survival and performance. A test battery of behavioral assays showed a significant increase in experimental mortality rate with chronological age, but no evidence for an age-dependent performance decline in locomotion, learning or responsiveness to light or sucrose. The explanation for this decoupling of intrinsic mortality and functional decline may lie in the social evolution of honey bees .”
It appears that in some cases epigenetic regulation can reverse cellular senescence. The 2005 paper Social reversal of immunosenescence in honey bee workers relates “A striking example of immunosenescence is seen in the honey bee (Apis mellifera) worker caste. The bees’ age-associated transition from hive duties to more risky foraging activities is linked to a dramatic decline in immunity. Explicitly, it has been shown that an increase in the juvenile hormone (JH) level, which accompanies onset of foraging behavior, induces extensive hemocyte death through nuclear pycnosis. Here, we demonstrate that foragers that are forced to revert to hive-tasks show reversal of immunosenescence, i.e. a recovery of immunity with age. This recovery, which is triggered by a social manipulation, is accompanied by a drop in the endogenous JH titer and an increase in the hemolymph vitellogenin level. Vitellogenin is a zinc binding glycolipoprotein that has been implicated in the regulation of honey bee immune integrity. We also establish that worker immunosenescence is mediated by apoptosis, corroborating that reversal of immunosenescence emerges through proliferation of new cells. The results presented here, consequently, reveal a unique flexibility in honey bee immunity–a regulatory plasticity that may be of general biological interest.” Loss or gain of longevity or immunity with change of circumstances requires a fast-acting epigenetic mechanism, and stochastic epigenetic evolution is a candidate for that mechanism.
There is a body of interesting literature relevant to DNA methylation changes in humans as a function of age, changes that are quite possibly due to stochastic epigenetic evolution. I cannot review these here but mention specifically the 2010 publication Widespread and tissue specific age-related DNA methylation changes in mice. “Our findings demonstrate a surprisingly high rate of hyper- and hypomethylation as a function of age in normal mouse small intestine tissues and a strong tissue-specificity to the process. We conclude that epigenetic deregulation is a common feature of aging in mammals.” Of course this is the premise of the 13th theory of aging outlined in my treatise Programmed Epigenomic Changes. If the stochastic epigenomic evolution theory is correct, then the “programming” would consist of epigenetic drift due to accumulation of multiple random changes in the epigenome.
Rapid changes in disease susceptibility
It has also been suggested that stochastic epigenomic evolution may be responsible for the development of disease susceptibilities. The concern is that it can drive rapid “epigenomic drift” as mentioned above. The 2008 paper Age-Specific Epigenetic Drift in Late-Onset Alzheimer’s Disease relates “Despite an enormous research effort, most cases of late-onset Alzheimer’s disease (LOAD) still remain unexplained and the current biomedical science is still a long way from the ultimate goal of revealing clear risk factors that can help in the diagnosis, prevention and treatment of the disease. Current theories about the development of LOAD hinge on the premise that Alzheimer’s arises mainly from heritable causes. Yet, the complex, non-Mendelian disease etiology suggests that an epigenetic component could be involved. Using MALDI-TOF mass spectrometry in post-mortem brain samples and lymphocytes, we have performed an analysis of DNA methylation across 12 potential Alzheimer’s susceptibility loci. In the LOAD brain samples we identified a notably age-specific epigenetic drift, supporting a potential role of epigenetic effects in the development of the disease. Additionally, we found that some genes that participate in amyloid-Î² processing (PSEN1, APOE) and methylation homeostasis (MTHFR, DNMT1) show a significant interindividual epigenetic variability, which may contribute to LOAD predisposition. The APOE gene was found to be of bimodal structure, with a hypomethylated CpG-poor promoter and a fully methylated 3â€²-CpG-island, that contains the sequences for the Îµ4-haplotype, which is the only undisputed genetic risk factor for LOAD. Aberrant epigenetic control in this CpG-island may contribute to LOAD pathology. We propose that epigenetic drift is likely to be a substantial mechanism predisposing individuals to LOAD and contributing to the course of disease.”
The 2010 papers Epigenetic Epidemiology of Common Complex Disease: Prospects for Prediction, Prevention, and Treatment, Epigenetics in molecular epidemiology of cancer a new scope and the 2009 paper Prospects for epigenetic epidemiology are among many other recent publications relating epigenetic changes to disease processes.
As is the case for many of my blog entries, I have been able here to cite only a few of the very many relevant literature citations. I believe they have been sufficient, however, to illustrate the importance of epigenomic changes in aging and disease processes and to render plausible the new stochastic epigenomic theory of evolution. I expect that I will be writing more blog entries related to epigenomics as the amount of research in this area continues to explode.