Month: November 2017

An update on brain zapping

gettingwell4 2-3 stars Required further work, Cerebrum, Limbic system, Thalamus ,

This 2017 general-audience article entitled Ultrasound for the brain provided a hyped update on brain zapping:

“Ultrasound could potentially treat other movement disorders, as well as depression, anxiety and a host of intract­able neuropsychiatric disorders..

This could be a breakthrough..

Researchers hope one day to help people with neuropsychiatric conditions by repairing or resetting the relevant neural pathways..

The potential advantages, especially for deep brain areas, are huge..”

Though not the main thrust of the article, another potential use of ultrasound would be to activate drugs delivered to a specific area, as this image portrays:

Vanderbilt University was again at the forefront of brain zapping, as noted in What’s an appropriate control group for a schizophrenia study? for example. I hope the disclosures for subjects participating in Vanderbilt’s brain-zapping studies made it clear that:

“At high intensities, such as those used to relieve essential tremor, ultrasound’s effects are largely thermal: the tissue heats up and cells die.”

Comments are disabled because this post has somehow become a target for spammers. Readers can click the above control group link to comment.

Epigenetic mechanisms regulate bone growth

gettingwell4 2-3 stars Required further work, Epigenetics ,

This 2017 Baltimore/China rodent study found:

“MSPC [Mesenchymal stem/progenitor cell] senescence is epigenetically controlled by the polycomb histone methyltransferase enhancer of zeste homolog 2 (Ezh2) and its trimethylation of histone H3 on Lysine 27 (H3K27me3) mark. Ezh2 maintains the repression of key cell senescence inducer genes through H3K27me3.

Our work establishes the role of Ezh2-H3K27me3 as a key epigenetic regulator that controls the onset and progression of MSPC senescence during the transition of fast- to slow-growing phase of long bones.

The self-renewal and proliferative capacity of cells in primary spongiosa of fast-growing bones are maintained by a high level of Ezh2-H3K27me3, whereas loss of Ezh2-H3K27me3 during late puberty leads to cell senescence.”

One of the experiments led to this note in the Discussion section:

“An epidemiologic study demonstrated that 60% of the risk of osteoporosis can be explained by the bone mineral acquired by early adulthood.

Our finding that deletion of Ezh2 in nestin+ cells during early puberty increases the risk of osteoporosis in later adulthood suggests that premature cellular senescence in the primary spongiosa region during the prepubertal or early pubertal phase may also be a major cause of osteoporosis/bone loss in later life.”

The study was short of explanations in several areas. For example, causes for the effect of “loss of Ezh2-H3K27me3 during late puberty” weren’t specified.

In another example, this statement referenced nestin-positive cells:

“Because these cells are likely no longer required in this particular region during adulthood, they stop proliferating and undergo senescence during late puberty.”

but what caused the cells to be “no longer required” wasn’t specified.

The “programmed” and “fate” words were used in the Abstract:

“Our data reveals a programmed cell fate change in postnatal skeleton..”

but not explained until the Discussion section:

“The senescence process is program[m]ed by a conserved mechanism because it restricts in a particular region of long bone and follows a specific time course.”

https://www.nature.com/articles/s41467-017-01509-0 “Programmed cell senescence in skeleton during late puberty”

Beliefs about genetic and environmental influences in twin studies

gettingwell4 < 0 Detracted from science, Beliefs, Cerebrum, Epigenetics ,

This 2017 Penn State simulation found:

“By taking advantage of the natural variation in genetic relatedness among identical (monozygotic: MZ) and fraternal (dizygotic: DZ) twins, twin studies are able to estimate genetic and environmental contributions to complex human behaviors.

In the standard biometric model when MZ or DZ twin similarity differs from 1.00 or 0.50, respectively, the variance that should be attributed to genetic influences is instead attributed to nonshared environmental influences, thus deflating the estimates of genetic influences and inflating the estimates of nonshared environmental influences.

Although estimates of genetic and nonshared environmental influences from the standard biometric model were found to deviate from “true” values, the bias was usually smaller than 10% points indicating that the interpretations of findings from previous twin studies are mostly correct.”

The study model’s input was five phenotypes that varied the degrees of:

  1. Genetic and epigenetic heritability;
  2. Shared environmental factors; and
  3. Nonshared environmental factors.

Item 1 above was different than the standard model’s treatment of heritable factors, which considers only additive genetic influences.

The authors cited studies for moderate and significant shared environmental influences in child and adolescent psychopathology and parenting to support the model’s finding that overall, item 2 above wasn’t underestimated.

I wasn’t satisfied with the simulation’s description of item 1 above. With

  1. Environmental influences accounted for elsewhere, and
  2. No references to transgenerational epigenetic inheritance,
  3. Randomness seemed to be the only remaining explanation for an epigenetic heritability factor.

Inserting the model’s non-environmental randomness explanation for epigenetic heritability into the abstract’s statement above exposed the non sequitur:

In the standard biometric model when MZ or DZ twin similarity differs from 1.00 or 0.50, respectively, the variance that should be attributed to genetic [and non-environmental stochastic heritability] influences is instead attributed to nonshared environmental influences, thus deflating the estimates of genetic [and non-environmental stochastic heritability] influences and inflating the estimates of nonshared environmental influences.

Why did the researchers design their model with an adjustment for non-environmental epigenetic heritability? Maybe it had something to do with:

“Estimates of genetic and nonshared environmental influences from the standard biometric model were found to deviate from “true” values.”

In any event, I didn’t see that this simulation was much more than an attempt to reaffirm a belief that:

“The interpretations of findings from previous twin studies are mostly correct.”

Empirical rather than simulated findings in human twin study research are more compelling, such as The primary causes of individual differences in DNA methylation are environmental factors with its finding:

“Differential methylation is primarily non-genetic in origin, with non-shared environment accounting for most of the variance. These non-genetic effects are mainly tissue-specific.

The full scope of environmental variation remains underappreciated.”

https://link.springer.com/article/10.1007/s10519-017-9875-x “The Impact of Variation in Twin Relatedness on Estimates of Heritability and Environmental Influences” (not freely available)

Does a societal mandate cause DNA methylation?

gettingwell4 2-3 stars Required further work, Epigenetics

This 2017 worldwide meta-analysis of humans of recent European ancestry found:

“Here we provide evidence on the associations between epigenetic modifications-in our case, CpG methylation and educational attainment (EA), a biologically distal environmental factor that is arguably among the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study [EWAS] meta-analysis of EA based on data from 27 cohort studies with a total of 10,767 individuals.”

These researchers found no association between the societal mandate of educational attainment and the most widely studied category of epigenetic marks.

Society mandates year after year of school attendance. This mandate continues on to require a four-year degree just to get an entry-level job in many lines of work.

The researchers stated:

“Our EWAS associations are small in magnitude relative to EWAS associations reported for more biologically proximal environmental factors.”

educational attainment

Panels a and b display the same results but with a different scaling of the y axis in order for smaller effect sizes to be visible.

Smoking, alcohol consumption, and maternal smoking were measured to have detrimental effects. BMI was fun with numbers.

Would a study categorize it as detrimental when an individual breaks from expectations about what they should do, and terminates their educational attainment? One individual I know didn’t go to graduate school after Princeton University although they were capable of quality graduate and doctorate work. It would be detrimental to their life if they stopped a software development career that pays a million dollars a year to go back to school.

Would a study evaluate it as beneficial when an individual lengthens their educational attainment past society’s thirteen-year educational requirement? Would these extra four years still be considered beneficial when – after foregoing four more years of full-time income, and accumulating tens of thousands of dollars of nondischargeable debt – they achieve an expected outcome of an entry-level job, and then can’t unassistedly provide for their basic needs?

Are further epigenetic studies of educational attainment as an environmental factor really worthwhile?

Why not use research funds and efforts on more promising topics like human transgenerational epigenetic inheritance? Suitable subjects may already be selected for this research, as several of the “27 cohort studies” that provided data for this meta-analysis included at least three human generations.

http://www.nature.com/mp/journal/vaop/ncurrent/full/mp2017210a.html “An epigenome-wide association study meta-analysis of educational attainment”

These authors preregistered their analysis plan. This practice discourages fishing expeditions that researchers are so often tempted to go on when study data provides evidence for the null hypothesis, as this meta-analysis did.

I was puzzled that they described part of the preregistered analysis plan to be:

“Hypothesis-free as it is performed genome-wide without an expected direction of effect for individual CpG loci.”

Their abstract, though, declared:

“If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.”

Was this meta-analysis “hypothesis-free” or did it have “the hypothesis that such factors have large effects on the epigenome”?

Here’s 48 minutes of Brian Nosek, a co-founder of the Open Science Framework (where this meta-analysis was preregistered):

http://rationallyspeakingpodcast.org/172-why-science-needs-openness-brian-nosek/