An evolutionary view of transgenerational epigenetic inheritance

This 2020 Swiss/German review mainly cited weed, worm, and yeast studies:

“RNA interference-related mechanisms can mediate the deposition and transgenerational inheritance of specific chromatin modifications in a truly epigenetic fashion.

Epigenetics was initially defined as any heritable change in gene expression patterns without changes in the DNA sequence. Now, epigenetic phenomena are often characterized as ‘gene expression changes that are mutation independent and heritable in the absence of the triggering event’, a definition we will follow in this review. We note that this definition can be expanded to include protein only-based inheritance mechanisms that do not necessarily cause changes in gene expression.

Gene silencing can persist over multiple generations in the germline of C. elegans. Gene repression is typically maintained without the initial trigger for three to seven generations and occasionally for tens of generations. In contrast, silencing of somatically expressed genes mostly affects only the subsequent generation through nonepigenetic parental effects.

In the presence of an ‘enabling’ mutation, primary siRNAs [small interfering RNAs] can trigger an RNAe [RNA-induced epigenetic silencing] response. Secondary siRNA amplification is required for transgenerational inheritance.

The fitness of a population in a dynamic environment strongly depends on the ability of individuals to adapt to the new condition as well as to remember, inherit, and forget such adaptation:

  • (A) A well-adapted population (grey) is at its maximal density (dotted line) in a given niche until an environmental change (1st stress) creates a bottleneck. Only few individuals can adapt through mutations and repopulate the niche. After the environment changes back to the initial blue state, only individuals that acquire rare counteracting mutations survive, often leading to extinction of the population.
  • (B) Individuals of a population in the red state can gain beneficial epimutations through siRNAs and repopulate the niche. When exposed again to the blue state, the epimutations can be quickly reversed and the population rapidly reaches maximal density. After recurrence of the red state, organisms establish de novo epimutations with the same low frequency as when they first encountered this state.
  • (C) In contrast, organisms that can maintain the memory of a beneficial silencing event can quickly re-establish beneficial epimutations and grow to full density. Such memory can be maintained by phenotypically neutral epimutations, marked by the continuously high production of siRNAs without substantial reductions in the expression of a gene. A population that can adapt through phenotypically plastic epimutations is predicted to have a maximal fitness advantage in a dynamic environment.”

The Concluding Remarks section included:

“RNA-mediated epigenetic responses could contribute to adaptation.

Even though RNAe may yield significant adaptive advantages, a high induction frequency could cause silencing of multiple essential genes and therefore be detrimental. Hence, it is plausible that mechanisms would have coevolved that counteract silencing.

Similarly, if constituting a bet-hedging strategy to cope with ever-changing environments, permanent fixation of an acquired silencing response would not constitute a selective advantage and mechanisms that modify and limit the duration of RNAe would be predicted.” “Small RNAs in the Transgenerational Inheritance of Epigenetic Information”

The review’s arguments were based on evolutionary selective advantages and less-complex organisms. It predicted that there would be an endpoint generation as in the (A) case of the above graphic.

Were the mechanisms in the (B) case necessarily transgenerational throughout? The review further explained:

“Epimutations tend to occur in hot spots (e.g., in stress-related or nutritional pathway genes) and can potentially silence several homologous genes simultaneously. Incomplete penetrance of a beneficial epimutation by stochastic loss of siRNAs [59] can result in loss of adaptation in a given environment (red state), but can be beneficial if the previous blue state is re-established. However, when the environment changes back to the red state, epimutations must initiate de novo, at the same low frequency as when the population first encountered this state.”

The study cited at 59 found:

“A feedback between siRNAs and RNAi genes determines heritable silencing duration”

but not “Incomplete penetrance of a beneficial epimutation by stochastic loss of siRNAs.” Hmm.

In any event, the review stated:

“Evidence for naturally occurring RNAe-related phenomena in other animals is scarce and we should be cautious about inferring RNAe as a widely conserved phenomenon.”

It’s encouraging to read studies that find benefits to epigenetic transgenerational inheritance, albeit in organisms that are less complex than rodents and humans.


The epigenetics of perinatal stress

This 2019 McGill review discussed long-lasting effects of perinatal stress:

“Epigenetic processes are involved in embedding the impact of early-life experience in the genome and mediating between social environments and later behavioral phenotypes. Since these phenotypes are apparent a long time after early experience, changes in gene expression programming must be stable.

Although loss of methylation in a promoter is necessary for expression, it is not sufficient. Demethylation removes a barrier for expression, but expression might be realized at the right time or context when needed factors or signals are present.

DNA methylation anticipates future transcriptional response to triggers. Comparing steady-state expression with DNA methylation does not capture the full meaning and scope of regulatory roles of differential methylation.

A model for epigenetic programming by early life stress:

  1. Perinatal stress perceived by the brain triggers release of glucocorticoids (GC) from the adrenal in the mother prenatally or the newborn postnatally.
  2. GC activate nuclear glucocorticoid receptors across the body, which epigenetically program (demethylate) genes that are targets of GR in brain and white blood cells (WBC).
  3. Demethylation events are insufficient for activation of these genes. A brain specific factor (TF) is required for expression and will activate low expression of the gene in the brain but not in blood.
  4. During adulthood a stressful event transiently triggers a very high level of expression of the GR regulated gene specifically in the brain.

Horizontal arrow, transcription; circles, CpG sites; CH3 in circles, methylated sites; empty circles, unmethylated CpG sites; horizon[t]al curved lines, mRNA.”

Review points discussed:

  • “Epigenetic marks are laid down and maintained by enzymes that either add or remove epigenetic modifications and are therefore potentially reversible in contrast to genetic changes.
  • Response to early life stress and maternal behavior is also not limited to the brain and involves at least the immune system as well.
  • The placenta is also impacted by maternal social experience and early life stress.
  • Most studies are limited to peripheral tissues such as saliva and white blood cells, and relevance to brain physiology and pathology is uncertain.
  • Low absolute differences in methylation seen in most human behavioral EWAS raise questions about their biological significance.

  • Although post-mortem studies examine epigenetic programming in physiologically relevant tissues, they represent only a final and single stage that does not capture dynamic evolution of environments and epigenetic programming in living humans.” “The epigenetics of perinatal stress”

Other reviewers try to ignore times when we were all fetuses and newborns. For example, in the same journal issue was a Boston review of PTSD that didn’t mention anything about earliest times of human lives! Those reviewers speculated around this obvious gap on their way to being paid by NIH.

Why would researchers ignore perinatal stress events that prime humans for later-life PTSD? Stress generally has a greater impact on fetuses and newborns than on infants, and a greater impact on infants than on adults.

Masters of manipulating their host

This 2020 French review subject was parasitical influences on host epigenetic processes:

“Parasites have become masters of manipulating their host cells, exploiting signaling, and metabolic pathways to hijack host gene expression to their own advantage. These intracellular parasites have developed a wide range of strategies that affect transcriptional machineries and epigenetic events in the host cell nucleus.

Parasite effectors regulate host transcription. Secretion of numerous parasite effector proteins are key processes during parasite infection. Parasite effectors deregulate host expression profile which lead to host cell transformation, or escape from the host immune system to allow parasite persistence and survival.”

The first two of the six strategies discussed are shown above:

  1. “Induction of a host epigenetic enzyme. Parasite infection leads to upregulation of SMYD3, a methyltransferase that activates genes involved in host transformation, through H3K4 trimethylation.
  2. Secreting effector proteins that drive epigenetic repression of host genes. TEEGR activates a host chromatin modifier able to repress transcription of immune system genes through H3K27 trimethylation.” “The clever strategies used by intracellular parasites to hijack host gene expression” (not freely available)

I used a “parasites” paradigm while living in the Washington DC area for three decades to help understand what goes on there. Moved away several years ago, but haven’t changed my thinking that all six of this paper’s parasite strategies had analogous human actions.

Other curated papers that explored the review’s topic include:

Do epigenetic clocks measure causes or effects?

Starting the sixth year of this blog with a 2020 article authored by the founder of the PhenoAge epigenetic clock methodology:

“The Ge[r]oscience paradigm suggests that targeting the aging process could delay or prevent the risk of multiple major age-related diseases. We need clinically valid measures of the underlying biological process and/or classification criteria for what it means to be biologically, rather than chronologically, “aged”.

The majority of aging biomarkers, including the first-generation epigenetic clocks, are developed using cross-sectional data, in which the researchers take a variable that proxies aging (e.g. chronological age) and apply supervised machine learning, or deep learning, approaches to predict that variable using tens to hundreds of thousands of input variables. The problem with this approach is that it doesn’t account for mortality selection. This biases the algorithm to select markers that are not causal, but instead correlative with aging.

When considering individuals of the same chronological age, do those with higher epigenetic age look phenotypically older on average (e.g. have higher mortality rates, greater disease burden, and worse physical and cognitive functioning)? FEV1 [forced expiratory volume in one second] declined at a faster rate for individuals with higher baseline GrimAge and/or PhenoAge. A similar finding was observed for the decline in grip strength as a function of GrimAge; however, the rate of change for any of the epigenetic clocks was not associated with rate of change in any performance measure.

Loci that show consistent trends with chronological age, even at higher ages, are likely not causal. By using a cross-sectional study design for biomarker development there was a propensity away from selecting causal loci, to the point where fewer causal loci were selected than if loci had been chosen at random.

The power of these measures as diagnostic and prognostic may stem from the use of longitudinal data in training them. Rather than continuing to train chronological age predictors using diverse data, it may be more advantageous to retrain some of the existing measures by predicting longitudinal outcomes.” “Assessment of Epigenetic Clocks as Biomarkers of Aging in Basic and Population Research” (not freely available)

A cited 2019 study modeled corrections to “account for mortality selection.” It modified datasets “by incorporating correlates of mortality identified from longitudinal studies, allowing cross-sectional studies to effectively identify the causal factors of aging.” “Biomarkers for Aging Identified in Cross-sectional Studies Tend to Be Non-causative” (not freely available)

The article didn’t present a complete case to determine whether an individual’s epigenetic clock measurements over time may show causes of biological aging.

Other viewpoints include:

1. A blood plasma aging clock presented evidence with its 46-protein conserved aging signature that some causes of biological aging are under genetic control. If the principle of this finding applies to CpG DNA methylation, the statement:

Loci that show consistent trends with chronological age, even at higher ages, are likely not causal.

may not hold. Such epigenetic changes could be among both the causes of senescence and the effects of evolution’s selection mechanisms.

2. An epigenetic clock review by committee, particularly in:

  • Challenge 3 “Integration of epigenetics into large and diverse longitudinal population studies”;
  • Challenge 5 “Single-cell analysis of aging changes and disease”; and
  • Table 1 “Major biological and analytic issues with epigenetic DNA methylation clocks” with single-cell analysis as the solution to five Significant issues.