Reversing epigenetic changes with CRISPR/Cas9

This 2018 Chinese review highlighted areas in which CRISPR/Cas9 technology has, is, and could be applied to rewrite epigenetic changes:

“CRISPR/Cas9-mediated epigenome editing holds a great promise for epigenetic studies and therapeutics.

It could be used to selectively modify epigenetic marks at a given locus to explore mechanisms of how targeted epigenetic alterations would affect transcription regulation and cause subsequent phenotype changes. For example, inducing histone methylation or acetylation at the Fosb locus in the mice brain reward region, nucleus accumbens, could affect relevant transcription network and thus control behavioral responses evoked by drug and stress.

Epigenome editing has the potential for epigenetic treatment, especially for the disorders with abnormal gene imprinting or epigenetic marks. Targeted epigenetic silencing or reactivation of the mutant allele could be a potential therapeutic approach for diseases such as Rett syndrome and Huntington’s disease.

Noncoding RNA plays important roles in gene imprinting and chromatin remodeling. CRISPR/Cas9 has been shown to be potential for manipulating noncoding RNA expression, including microRNA, long noncoding RNA, and miRNA families and clusters.

In vivo overexpression of the Yamanaka factors have proven to be able to fully or partially help somatic cells to regain pluripotency in situ. These rejuvenated cells would subsequently differentiate again to replace the lost cell types.”

The last paragraph was described in The epigenetic clock theory of aging as a promising technique:

“To date, the most effective in vitro intervention against epigenetic ageing is achieved through expression of Yamanaka factors, which convert somatic cells into pluripotent stem cells, thereby completely resetting the epigenetic clock.”

The reviewers cited three references for in vivo studies of this technique. Overall, I didn’t see that any of the review’s references were in vivo human studies. “Novel Epigenetic Techniques Provided by the CRISPR/Cas9 System”


The epigenetic clock now includes skin

The originator of the 2013 epigenetic clock improved its coverage with this 2018 UCLA human study:

“We present a new DNA methylation-based biomarker (based on 391 CpGs) that was developed to accurately measure the age of human fibroblasts, keratinocytes, buccal cells, endothelial cells, skin and blood samples. We also observe strong age correlations in sorted neurons, glia, brain, liver, and bone samples.

The skin & blood clock outperforms widely used existing biomarkers when it comes to accurately measuring the age of an individual based on DNA extracted from skin, dermis, epidermis, blood, saliva, buccal swabs, and endothelial cells. Thus, the biomarker can also be used for forensic and biomedical applications involving human specimens.

The biomarker applies to the entire age span starting from newborns, e.g. DNAm of cord blood samples correlates with gestational week.

Furthermore, the skin & blood clock confirms the effect of lifestyle and demographic variables on epigenetic aging. Essentially it highlights a significant trend of accelerated epigenetic aging with sub-clinical indicators of poor health.

Conversely, reduced aging rate is correlated with known health-improving features such as physical exercise, fish consumption, high carotenoid levels. As with the other age predictors, the skin & blood clock is also able to predict time to death.

Collectively, these features show that while the skin & blood clock is clearly superior in its performance on skin cells, it crucially retained all the other features that are common to other existing age estimators.” “Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies”

An introduction to the study highlighted several items:

“Although the skin-blood clock was derived from significantly less samples (~900) than Horvath’s clock (~8000 samples), it was found to more accurately predict chronological age, not only across fibroblasts and skin, but also across blood, buccal and saliva tissue. A potential factor driving this improved accuracy in blood could be related to the approximate 18-fold increase in genomic coverage afforded by using Illumina 450k/850k beadarrays.

It serves as a roadmap for future clock studies, pointing towards the importance of constructing tissue or cell-type specific epigenetic clocks, to more accurately measure biological aging in the given tissue/cell-type, and therefore with the potential to be more informative of disease-risk or the success of disease interventions in the tissue or cell-type of interest.” “Epigenetic clocks galore: a new improved clock predicts age-acceleration in Hutchinson Gilford Progeria Syndrome patients”

Hijacking the epigenetic clock paradigm

This 2018 German human study’s last sentence was:

“Additionally we found an association between DNAm [DNA methylation] age acceleration and rLTL [relative leukocyte telomere length], suggesting that this epigenetic clock, at least partially and possibly better than other epigenetic clocks, reflects biological age.”

Statements in the study that contradicted, qualified, and limited the concluding sentence included:

“The epigenetic clock seems to be mostly independent from the mitotic clock as measured by the rLTL.

It could be possible that associations are confounded due to short age ranges or non-continuous age distribution, as displayed in the BASE-II cohort (no participants between the age of 38 and 59 years). [see the below graphic]

The BASE-II is a convenience sample and participants have been shown to be positively selected with respect to education, health and cognition.

Samples in which DNAm age and chronological age differed more than three standard deviations from the mean were excluded (N=19).

While the original publication employed eight CpG sites for DNAm age estimation, we found that one of these sites did not significantly improve chronological age prediction in BASE-II. Thus, we reduced the number of sites considered to seven in the present study and adapted the algorithm to calculate DNAm age.

  • Horvath described a subset of 353 methylation sites predicting an individual’s chronological age with high accuracy..
  • Even though the available methods using more CpG sites to estimate DNAm age predict chronological age with higher accuracy..
  • It is not clear how much of the deviation between chronological age and DNAm age reflects measurement error/low number of methylation sites and which proportion can be attributed to biological age.

Due to the statistical method employed, we encountered a systematic deviation of DNAm age in our dataset.”

Findings that aren’t warranted by the data is an all-too-common problem with published research. This study illustrated how researcher hypothesis-seeking behavior – that disregarded what they knew or should have known – can combine with a statistics package to produce almost any finding.

It reminded me of A skin study that could have benefited from preregistration that made a similar methodological blunder:

The barbell shape of the subjects’ age distribution wouldn’t make sense if the researchers knew they were going to later use the epigenetic clock method.

The researchers did so, although the method’s instructive study noted:

“The standard deviation of age has a strong relationship with age correlation”

and provided further details in “The age correlation in a data set is determined by the standard deviation of age” section.

Didn’t the researchers, their organizations, and their sponsors realize that this study’s problematic design and performance could misdirect readers away from the valid epigenetic clock evidence they referenced? What purposes did it serve for them to publish this study? “Epigenetic clock and relative telomere length represent largely different aspects of aging in the Berlin Aging Study II (BASE-II)” (not freely available)

Epigenetic effects of breast cancer treatments

This 2018 UC San Diego review subject was the interplay between breast cancer treatments and their effects on aging:

“Although current breast cancer treatments are largely successful in producing cancer remission and extending lifespan, there is concern that these treatments may have long lasting detrimental effects on cancer survivors, in part, through their impact on non-tumor cells. It is unclear whether breast cancer and/or its treatments are associated with an accelerated aging phenotype.

In this review, we have highlighted five of nine previously described cellular hallmarks of aging that have been described in the context of cytotoxic breast cancer treatments:

  1. Telomere attrition;
  2. Mitochondrial dysfunction;
  3. Genomic instability;
  4. Epigenetic alterations; and
  5. Cellular senescence.”

The review was full of caveats weakening the above graphic’s associations. To their credit, these reviewers at least presented some of the contrary evidence, and didn’t continue on with a directed narrative as many other reviewers are prone to do:

  1. “Telomere attrition – Blood TL [telomere length] was not associated with chemotherapy in three out of four studies;
  2. Mitochondrial dysfunction – How cancer therapies affect cellular energetics as they relate to rate of aging is unclear;
  3. Genomic instability – Potentially contributing to accelerated aging;
  4. Epigenetic alterations – Although some of the key regulators of these processes have begun to be identified, including DNA and histone methylases and demethylases, histone acetylases and de-acetylases and chromatin remodelers, how they regulate the changes in aging through alteration of global transcriptional programs, remains to be elucidated; and
  5. Cellular senescence – Dysregulated pathways can be targeted by cytotoxic chemotherapies, resulting in preferential cell death of tumor cells, but how these treatments also affect normal cells with intact pathways is unclear.” “Breast cancer treatment and its effects on aging” (not freely available)

The originator of the epigenetic clock methodology was a coauthor of the review. Only one of his works was cited in the Epigenetic alterations subsection: “DNA methylation age is elevated in breast tissue of healthy women”

This freely-available 2017 study quoted below highlighted that epigenetic clock measurements as originally designed were tissue-specific:

“To our knowledge, this is the first study to demonstrate that breast tissue epigenetic age exceeds that of blood tissue in healthy female donors. In addition to validating our earlier finding of age elevation in breast tissue, we further demonstrate that the magnitude of the difference between epigenetic age of breast and blood is highest in the youngest women in our study (age 20–30 years) and gradually diminishes with advancing age. As women approach the age of the menopausal transition, we found that the epigenetic of age of blood approaches that of the breast.”

Additional caution was justified in both interpreting age measurements and extending them into “cellular hallmarks” when the tissue contained varying cell types:

“Our studies were performed on whole breast tissue. Diverse types of cells make up whole breast tissue, with the majority of cells being adipocytes. Other types of cells include epithelial cells, cuboidal cells, myoepithelial cells, fibroblasts, inflammatory cells, vascular endothelial cells, preadipocytes, and adipose tissue macrophages.

This raises the possibility that the magnitude of the effects we observe, of breast tissue DNAm age being greater than other tissues, might be an underestimation, since it is possible that not all of the cells of the heterogenous sample have experienced this effect. Since it is difficult to extract DNA from adipose tissue, we suspect that the majority of DNA extracted from our whole breast tissues was from epithelial and myoepithelial cells.”

Starving awakens ancient parasite DNA within us

This 2018 Italian human cell study conducted a series of experiments on the effects of nutrient deprivation:

“Reduced food intake, and in particular protein or amino acid (AA) restriction, extends lifespan and healthspan.

We have previously shown that, in mammalian cells, deprivation of essential AAs (methionine/cysteine or tyrosine) leads to the transcriptional reactivation of integrated silenced transgenes by a process involving epigenetic chromatic remodeling and histone acetylation.

Here we show that the deprivation of methionine/cysteine also leads to the transcriptional upregulation of endogenous retroviruses [ERVs], suggesting that essential AA starvation affects the expression not only of exogenous non-native DNA sequences, but also of endogenous anciently-integrated and silenced parasitic elements of the genome.

ERVs, comprising 8% of the human genome, represent the remnants of past infections of germ cells by exogenous retroviruses, and are mostly unable to retrotranspose in the human genome. However, they can reactivate during physiological development, or in pathological conditions like cancer, and regulate the expression of nearby genes by their LTR elements, leading to general transcriptional reprogramming.

Dissection of the underlying mechanism ruled out a role for the main AA-deficiency sensor GCN2 and pointed to the ribosome as the possible master controller.”

The study found that reality is sometimes stranger than what fiction writers dream up. 🙂

The authors cited a 2016 Danish review I hadn’t previously curated: “The role of diet and exercise in the transgenerational epigenetic landscape of T2DM” (not freely available)

Contrary to what’s implied by its title, though, and as I noted in How to hijack science: Ignore its intent and focus on the 0.0001%, those reviewers didn’t cite any human studies that adequately demonstrated transgenerational epigenetic inheritance causes and effects. They admitted:

“Direct evidence that epigenetic factors drive the inheritance of T2DM [type 2 diabetes mellitus] in humans is lacking.”

then went on as is if such proof was a foregone conclusion. “Amino acid deprivation triggers a novel GCN2-independent response leading to the transcriptional reactivation of non-native DNA sequences”

A mid-year selection of epigenetic topics

Here are the most popular of the 65 posts I’ve made so far in 2018, starting from the earliest:

The pain societies instill into children

DNA methylation and childhood adversity

Epigenetic mechanisms of muscle memory

Sex-specific impacts of childhood trauma

Sleep and adult brain neurogenesis

This dietary supplement is better for depression symptoms than placebo

The epigenetic clock theory of aging

A flying human tethered to a monkey

Immune memory in the brain

The lack of oxygen’s epigenetic effects on a fetus

An evolutionary view of stress and cancer

This 2018 Michigan review subject was cancer evolution:

“Based on the fact that cancer typically represents a complex adaptive system, where there is no linear relationship between lower-level agents (such as each individual gene mutation) and emergent properties (such as cancer phenotypes), we call for a new strategy based on the evolutionary mechanism of aneuploidy [abnormal number of chromosomes] in cancer, rather than continuous analysis of various individual molecular mechanisms.

Cancer evolution can be understood by the dynamic interaction among four key components:

  1. Internal and external stress;
  2. Elevated genetic and non-genetic variations (either necessary for cellular adaptation or resulting from cellular damages under stress);
  3. Genome-based macro-cellular evolution (genome replacement, emergent as new systems); and
  4. Multiple levels of system constraint which prevent/slow down cancer evolution (from tissue/organ organization to the immune system interaction).

Since the sources of stress are unlimited and unavoidable (as they are required by all living systems), there are large numbers of gene mutations / epigenetic events / chromosomal aberrations, such as aneuploidy, that can be linked to stress-mediated genomic variants. Furthermore, as environmental constraints are constantly changing, even identical instances of aneuploidy will have completely different outcomes in the context of cancer evolution, as the results of each independent run of evolution will most likely differ.

Most current research efforts are focusing on molecular profiles based on an average population, and outliers are eliminated or ignored, either by the methods used or statistical tools. The traditional view of biological research is to identify patterns from “noise,” without the realization that the so-called “noise” in fact is heterogeneity, which represents a key feature of cancer evolution by functioning as the evolutionary potential.

Understanding the molecular mechanism (both cause and effect) of aneuploidy is far from enough. A better strategy is to monitor the evolutionary process by measuring evolutionary potential. For example, the overall degree of CIN [chromosome instability] is more predictive than individual gene mutation profile.”

Although I read many abstracts of cancer research papers every week, I usually don’t curate them. I curated this paper because the reviewers emphasized several themes of this blog, including:

  • Further examples of how stress may shape one’s life.
  • How researchers miss information when they ignore or process away variation:

    Studies have demonstrated the importance of outliers in cancer evolution, as cancer is an evolutionary game of outliers. While this phenomenon can provide a potential advantage for cellular adaptation, it can also, paradoxically, generate non-specific system stress, which can further produce more genetic and non-genetic variants which favor the disease condition.”

Epigenetics researchers may benefit from evolutionary viewpoints that incorporate the interactions of stress and “genetic and non-genetic variants.”

Since epigenetic changes require inheritance in order to persist, it would be a step forward to see researchers start “measuring evolutionary potential” of these inheritance processes. “Understanding aneuploidy in cancer through the lens of system inheritance, fuzzy inheritance and emergence of new genome systems”