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Record W2526104012 · doi:10.1097/hjh.0000000000001092

Epigenome and exposome in prenatal programming

2016· letter· en· W2526104012 on OpenAlexaff
Pavel Hamet

Bibliographic record

VenueJournal of Hypertension · 2016
Typeletter
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsExposomeEpigenomeMedicineFetal programmingComputational biologyPregnancyGeneticsEnvironmental healthDNA methylationFetusBiology

Abstract

fetched live from OpenAlex

Personalized/precision medicine is forthcoming on the path of Medicine from art to science. Hippocrates stated that ‘it is more relevant to know which person has the disease than to know which disease the person has’. In reality, we are just beginning to have access, mostly because of applications of ‘Omics’, starting with ‘Genomics’, to tools accurate or ‘precise’ enough to accomplish such personalization. Many definitions of personalized medicine exist and most of them omit the interactions between genetic and environmental factors with one notable exception, the National Institutes of Health Cancer Institute that defines personalized medicine as ‘…a form of healthcare that considers information about a person's genes, proteins, and environment to prevent, diagnose, and treat disease’. Precision medicine can perhaps do without environment, but personalized medicine without consideration of environment, a person's exposure to it or adapting to it, cannot function as our capacity to react, adapt, and control is driven by our inherited as well as cultural capacity to cope with it. Topol [1], who coined the term ‘individualized medicine’, nicely summarized the evidence that it is there from ‘Prewomb to Tomb’, encompassing prenatal planning, conception, 9-month development in utero, monogenic diseases of childhood, genetic susceptibility to infectious diseases followed later by complex diseases with an increasing impact of environmental exposure, determinants of healthy or not ageing and finally death. Although geneticists generally accept that the environment plays a role in complex diseases and epidemiologists are now keener to accept that genetic factors play a role in common diseases, the respective roles of genetic and environmental factors and the mechanisms of their interactions are still not completely understood. We have recently reviewed and explored how concepts such as the impact of genes on early penetrance, the heterogeneity of complex traits and the effect of varying physiological conditions can affect our capacity to detect G × E interactions [2]. At the other end of the spectrum of life is yet another type of exposome, affecting sperm maturation, fecundation, and foetal development. All are affected by both maternal and paternal imprinting. One major regulator of these events is epigenetics; DNA modification without sequence change. This relatively new and fast growing field of research has been scholarly reviewed in this issue of Journal of Hypertension by Li from Hocher's group [3] who analysed specifically paternal programming that leads to cardiometabolic disorders later in life. The focus on potential mechanisms underlying the impact of early life exposure on later disease is timely as we know the phenomenon for a relatively long time from Barker's [4] hypothesis but its mechanisms remained elusive until recently. The review focuses on paternal programming in relation to obesity and diabetes. It covers epidemiological evidence presenting convincing data supporting the contribution of paternal transmission. It is recognized that epidemiological evidence in itself cannot distinguish between transmissions of ‘obesity genes’ versus environmentally modified epigenetic transmission from the father's sperm. This is a specific role of studies in experimental animal models that can provide mechanistic evidence for paternal transmissions. Several examples are discussed, most clear-cut are the experiments with transgenerational transmission of insulin levels and secretion from father with nongenetic, stretozotocin-induced diabetes. The evidence in this case is complete with demonstration of epigenetic mechanisms, increased methylation of such genes as Igf2 and H19 resulting in modified transcription leading to abnormalities in Langerhans Islets. Additional examples are consequences of exposure of paternal grandfathers from generation F0 to different diets, adverse stimuli, or diseases such as diabetes to F1 and F2 progenies. Noticeably, the transmission of some of the traits to the F2 generation was more pronounced for maternal F1 lines compared with the paternal ones, yet the evidence for the existence of paternal transmission is convincing. The most highlighting part is the section that describes the mechanisms of epigenetic control of parental programming. What may be novel to some, is the fact that while it is generally recognized that most of methylated DNA is ‘cleaned’ prior to fecundation, some of it is resistant to DNA demethylation and is transmitted to the next generations by environmentally impacted genetic functionality, without sequence change roles of perm RNA. Histone acetylation are well discussed as additional mechanisms. Somewhat less attention is paid to the importance of the ‘genetic background’ as a modulator of epigenetic regulation. Admittedly, the area did not receive all attention it deserves. Specifically in hypertension where these studies are feasible as a plethora of congenic, recombinant inbred or transgenic rodent strains exist. They were used to demonstrate the involvement of many genomic factors on the ontogenesis of hypertension. It is possible to evaluate neonates of the F2 generation that are genetically identical to their parent and grandparent counterparts as recombinant inbred strains have been developed by Dumas et al. [5] in Prague and found to be useful for such studies [6]. The impact of environmental intervention during pregnancy and lactation was determined [7]. The review in this issue of Journal of Hypertension lays the ground for further work and is useful to situate the current knowledge and understanding to what remains to be uncovered. One of the future tasks could be to identify the genomic determinants of interactions with environment particularly when influencing the development of offspring, determine their future susceptibility to disease and capacity to adapt to life. The consequence for public health to integrate environmental, epigenetic, and genetic efforts for holistic understanding is enormous. Preventive interventions require the power to predict and full mechanistic understanding of environment and genetic interactions, and their relative importance is necessary to further stratify our therapeutic targets and tools. ACKNOWLEDGEMENTS P.H. wishes to thank Professor Johanne Tremblay for comments and fruitful discussions. Conflicts of interest There are no conflicts of interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.269
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2016
Admission routes1
Has abstractyes

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