Going off the rails with the biomarker paradigm

This 2018 US government rodent study used extreme dosages to achieve its directed goals of demonizing nicotine and extolling the biomarker paradigm:

“This study examined whether adolescent nicotine exposure alters adult hippocampus-dependent learning, involving persistent changes in hippocampal DNA methylation and if choline, a dietary methyl donor, would reverse and mitigate these alterations.

Mice were chronically treated with nicotine (12.6mg/kg/day) starting at post-natal day 23 (pre-adolescent), p38 (late adolescent), or p54 (adult) for 12 days followed by a 30-day period during which they consumed either standard chow or chow supplemented with choline (9g/kg).

Our gene expression analyses support this model and point to two particular genes involved in chromatin remodeling, Smarca2 and Bahcc1. Both Smarca2 and Bahcc1 showed a similar inverse correlation pattern between promoter methylation and gene expression.

Our findings support a role for epigenetic modification of hippocampal chromatin remodeling genes in long-term learning deficits induced by adolescent nicotine and their amelioration by dietary choline supplementation.”


Let’s use the average weight of a US adult male, which is published by the US Centers for Disease Control at 88.8 kg, to compare dosages:

  1. Nicotine at “12.6mg/kg/day” x 88.8 = 1119 mg, The estimated lower limit of a lethal dose of nicotine has been reported as between 500 and 1000 mg!
  2. Choline at “9g/kg” x 88.8 = 799 g. The US National Institutes of Health published the Tolerable Upper Intake Levels for Choline as 3.5 g!

A funding source of this study was National Institute on Drug Abuse (NIDA) Identification of Biomarkers for Nicotine Addiction award (T-DA-1002 MG). Is the biomarker paradigm institutionalized to the point where studies that don’t have biomarkers as goals aren’t funded?

Is the main purpose of animal studies to help humans? Or is it to promote an agenda?

https://www.sciencedirect.com/science/article/pii/S107474271830193X “Choline ameliorates adult learning deficits and reverses epigenetic modification of chromatin remodeling factors related to adolescent nicotine exposure” (not freely available)

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How do memories transfer?

This 2018 Chinese study electronically modeled the brain’s circuits to evaluate memory transfer mechanisms:

“During non-rapid-eye-movement (NREM) sleep, thalamo-cortical spindles and hippocampal sharp wave-ripples have been implicated in declarative memory consolidation. Evidence suggests that long-term memory consolidation is coordinated by the generation of:

  • Hierarchically nested hippocampal ripples (100-250 Hz),
  • Thalamo-cortical spindles (7-15 Hz), and
  • Cortical slow oscillations (<1 Hz)

enabling memory transfer from the hippocampus to the cortex.

Consolidation has also been demonstrated in other brain tasks, such as:

  • In the acquisition of motor skills, where there is a shift from activity in prefrontal cortex to premotor, posterior parietal, and cerebellar structures; and
  • In the transfer of conscious to unconscious tasks, where activity in initial unskilled tasks and activity in skilled performance are located in different regions, the so-called ‘scaffolding-storage’ framework.

By separating a neural circuit into a feedforward chain of gating populations and a second chain coupled to the gating chain (graded chain), graded information (i.e. information encoded in firing rate amplitudes) may be faithfully propagated and processed as it flows through the circuit. The neural populations in the gating chain generate pulses, which push populations in the graded chain above threshold, thus allowing information to flow in the graded chain.

In this paper, we will describe how a set of previously learned synapses may in turn be copied to another module with a pulse-gated transmission paradigm that operates internally to the circuit and is independent of the learning process.”


The study has neither been peer-reviewed, nor have the mechanisms been tested in living beings.

https://www.biorxiv.org/content/early/2018/07/27/351114 “A Mechanism for Synaptic Copy between Neural Circuits”

Hidden hypotheses of epigenetic studies

This 2018 UK review discussed three pre-existing conditions of epigenetic genome-wide association studies:

“Genome-wide technology has facilitated epigenome-wide association studies (EWAS), permitting ‘hypothesis-free’ examinations in relation to adversity and/or mental health problems. Results of EWAS are in fact conditional on several a priori hypotheses:

  1. EWAS coverage is sufficient for complex psychiatric problems;
  2. Peripheral tissue is meaningful for mental health problems; and
  3. The assumption that biology can be informative to the phenotype.

1. CpG sites were chosen as potentially biologically informative based on consultation with a consortium of DNA methylation experts. Selection was, in part, based on data from a number of phenotypes (some medical in nature such as cancer), and thus is not specifically targeted to brain-based, stress-related complex mental health phenotypes.

2. The assumption is often that distinct peripheral tissues are interchangeable and equally suited for biomarker detection, when in fact it is highly probable that peripheral tissues themselves correspond differently to environmental adversity and/or disease state.

3. Analyses result in general statements such as ‘neurodevelopment’ or the ‘immune system’ being involved in the aetiology of a given phenotype. Whether these broad categories play indeed a substantial role in the aetiology of the mental health problem is often hard to determine given the post hoc nature of the interpretation.”


The reviewers mentioned in item #2 the statistical flaw of assuming that measured entities are interchangeable with one another. They didn’t mention that this problem also affects item #1 methodologies of averaging CpG methylation measurements in fixed genomic bins or over defined genomic regions. This was discussed in:

The reviewers offered suggestions for reducing the impacts of these three hypotheses. But will doing more of the same, only better, advance science?

Is it too much to ask of researchers whose paychecks and reputations depend on a framework’s paradigm – such as the “biomarker” mentioned a dozen and a half times – to admit the uselessness of gathering data when the framework in which the data operates isn’t viable?

The truth about complex traits and GWAS added another example of how this framework and many of its paradigms haven’t produced effective explanations of “the aetiology of the mental health problem”:

“The most investigated candidate gene hypotheses of schizophrenia are not well supported by genome-wide association studies, and it is likely that this will be the case for other complex traits as well.”

Researchers need to reevaluate their framework if they want to make a difference in their fields. Recasting GWAS as EWAS won’t make it more effective.

https://www.sciencedirect.com/science/article/pii/S2352250X18300940 “Hidden hypotheses in ‘hypothesis-free’ genome-wide epigenetic associations”

Restoration of a “normal” epigenetic landscape

This 2018 Texas human review subject was prostate cancer epigenetics:

“We comprehensively review the up-to-date roles of epigenetics in the development and progression of prostate cancer. We especially focus on three epigenetic mechanisms: DNA methylation, histone modifications, and noncoding RNAs. We elaborate on current models/theories that explain the necessity of these epigenetic programs in driving the malignant phenotypes of prostate cancer cells.

It is now generally accepted that epigenetics contributes to the development of nearly every stage of PCa [prostate cancer]. Considering the highly heterogeneous nature of PCa, it is quite likely that [the] effect of a particular epigenetic pattern on growth of cancer cells varies from case to case and [is] context specific.

Restoration of a “normal” epigenetic landscape holds promise as a cure for prostate cancer.”


The review’s Epigenetic Therapy section explained much of what’s going on in the above graphic. Its Table 3 was instructive for up-to-date clinical trial information on epigenetic treatments of prostate cancer.

“Restoration of a “normal” epigenetic landscape” won’t guarantee a healthy outcome once diseases start. Prevention seems desirable, especially to avoid:

“Numerous epigenetic alterations [that] reinforce the establishment of a context-specific transcriptional profile that favors self-renewal, survival, and invasion of PCa cells.”

http://www.ajandrology.com/preprintarticle.asp?id=238758 “Epigenetic regulation of prostate cancer: the theories and the clinical implications”

A book review of “Neuroepigenetics and Mental Illness”

A 2018 online book “Neuroepigenetics and Mental Illness” was published at https://www.sciencedirect.com/bookseries/progress-in-molecular-biology-and-translational-science/vol/158/suppl/C (not freely available). Three chapters are reviewed here, with an emphasis on human studies.

“Chapter Five: Neuroepigenetics of Prenatal Psychological Stress” https://www.sciencedirect.com/science/article/pii/S1877117318300747 (not freely available)

“Chapter Eleven: Using Epigenetic Tools to Investigate Antidepressant Response” https://www.sciencedirect.com/science/article/pii/S1877117318300711 (not freely available)

“Chapter Twelve: Transgenerational Epigenetics of Traumatic Stress” https://www.sciencedirect.com/science/article/pii/S187711731830053X (not freely available)


Actually, I won’t waste my time or your time with what I planned to do. The lack of scientific integrity and ethics displayed by the book’s publisher, editor, and contributors in the below chapter spoke volumes.

How can the information in any other chapter of this book be trusted?


“Chapter Twelve: Transgenerational Epigenetics of Traumatic Stress”

This chapter continued propagating a transgenerational meme that had more to do with extending paradigms than science. The meme is that there are adequately evidenced transgenerational epigenetic inheritance human results.

As I most recently noted in Epigenetic variations in metabolism, there aren’t any published human studies that provide incontrovertible evidence from the F0 great-grandparents, F1 grandparents, F2 parents, and F3 children to confirm definitive transgenerational epigenetic inheritance causes and effects. Researchers urgently need to do this human research, and stop pretending it’s already done.

How did the book’s editor overlook what this chapter admitted?

“Literature about the inheritance of the effects of traumatic stress in humans has slowly accumulated in the past decade. However, it remains thin and studies in humans also generally lack clear “cause and effect” association, mechanistic explanations or germline assessment.”

Were the publisher and editor determined to keep the chapter heading and the reviewers determined to add another entry to their CVs in the face of this weasel-wording?

“In conclusion, although less studied from a mechanistic point of view, inter- and possibly transgenerational inheritance of the effects of traumatic stress is supported by empirical evidence in humans.”

See the comments below for an example of the poor substitutes for evidence that propagators of the transgenerational meme use to pronounce human transgenerational epigenetic inheritance a fait accompli.

Measuring epigenetic changes at a single-cell level

This 2018 Canadian cell study described the development of a single-cell protocol to:

“Profile primitive hematopoietic cells of mouse and human origin to identify epigenetically distinct subpopulations. Deep sampling of the CpG content of individual HSCs allowed for the near complete reconstitution of regulatory states from epigenetically defined subpopulations of HSCs and revealed a high level of redundancy of CpG methylation states within these phenotypically defined hematopoietic cell types.

Hematopoietic stem cells (HSCs) are functionally defined cells that display evidence of extensive self-renewal of their ability to generate mature blood cells for the lifetime of the organism and following transplantation into myelosuppressed permissive hosts. Most of the epigenetic measurements underpinning these observations represent consensus values experimentally derived from thousands of cells partially enriched in HSCs or their progeny, thus failing to discern distinct epigenetic states within HSCs.

Current analytical strategies for single-cell DNA methylation measurements average DNA methylation in fixed genomic bins or over defined genomic regions. However, inference across cells (as well as sequence context) assumes homogeneity across cells, which is at cross-purposes with the generation of single-cell molecular measurements through the potential to mask rare subpopulations.

We identified donor as a significant source of consistent epigenetic heterogeneity, which was reduced but not eliminated by correcting for personal genetic variants. This observation is consistent with previous reports that showed genetic diversity as related to but not accountable for all DNA methylation differences and suggests that in utero environmental differences may be encoded within the HSC compartment.”


The study advanced science not only by measuring single-CpG methylation within each HSC but also by producing another data point “that in utero environmental differences may be encoded within the HSC compartment.”

The paragraph with “assumes homogeneity across cells” bold text provided another example of the statistical analysis flaw that gives individually inapplicable results per Group statistics don’t necessarily describe an individual. The above graphic of human hematopoietic phenotypes demonstrated that the researchers have potentially solved this problem by measuring individual cells.

The researchers discussed another aspect of the study that’s similar to the epigenetic clock methodology:

“Phenotype-specific methylation signatures are characterized by extensive redundancy such that distinct epigenetic states can be accurately described by only a small fraction of single-CpG methylation states. In support of such a notion, the unique components of a DNA methylation “age” signature are contained in ∼353 CpGs sites, presumably representing a random sample of a total age signature that involves many more sites not detected using the reduced representation strategies from which these signatures have been derived.”

Also, in The epigenetic clock theory of aging the originator of the epigenetic clock characterized HSCs as an effective intervention against epigenetic aging:

“In vivo, haematopoietic stem cell therapy resets the epigenetic age of blood of the recipient to that of the donor.”

https://www.cell.com/stem-cell-reports/article/S2213-6711(18)30308-4/fulltext “High-Resolution Single-Cell DNA Methylation Measurements Reveal Epigenetically Distinct Hematopoietic Stem Cell Subpopulations”

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.”

https://www.sciencedirect.com/science/article/pii/S1879406818301176 “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:

https://link.springer.com/article/10.1007%2Fs10549-017-4218-4 “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.”