High type II error and interpretation inconsistencies when attempting to refute transgenerational epigenetic inheritance

Present and future challenges for the investigation of transgenerational epigenetic inheritance

Epigenetic pathways are essential in different biological processes and in phenotype-environment interactions in response to different stressors and they can induce phenotypic plasticity. They encompass several processes that are mitotically and, in some cases, meiotically heritable, so they can be transferred to subsequent generations via the germline. Transgenerational Epigenetic Inheritance (TEI) describes the phenomenon that phenotypic traits, such as changes in fertility, metabolic function, or behavior, induced by environmental factors (e.g., parental care, pathogens, pollutants, climate change), can be transferred to offspring generations via epigenetic mechanisms. Investigations on TEI contribute to deciphering the role of epigenetic mechanisms in adaptation, adversity, and evolution. However, molecular mechanisms underlying the transmission of epigenetic changes between generations, and the downstream chain of events leading to persistent phenotypic changes, remain unclear. Therefore, inter-, (transmission of information between parental and offspring generation via direct exposure) and transgenerational (transmission of information through several generations with disappearance of the triggering factor) consequences of epigenetic modifications remain major issues in the field of modern biology. In this article, we review and describe the major gaps and issues still encountered in the TEI field: the general challenges faced in epigenetic research; deciphering the key epigenetic mechanisms in inheritance processes; identifying the relevant drivers for TEI and implement a collaborative and multi-disciplinary approach to study TEI. Finally, we provide suggestions on how to overcome these challenges and ultimately be able to identify the specific contribution of epigenetics in transgenerational inheritance and use the correct tools for environmental science investigation and biomarkers identification.

Exercise and epigenetic inheritance of disease risk

Epigenetics is the study of gene expression changes that occur in the absence of altered genotype. Current evidence indicates a role for environmentally induced alterations to epigenetic modifications leading to health and disease changes across multiple generations. This phenomenon is called intergenerational or transgenerational epigenetic inheritance of health or disease. Environmental insults, in the form of toxins, plastics and particular dietary interventions, perturb the epigenetic landscape and influence the health of F1 through to F4 generations in rodents. There is, however, the possibility that healthy lifestyles and environmental factors, such as exercise training, could lead to favourable, heritable epigenetic modifications that augment transcriptional programmes protective of disease, including metabolic dysfunction, heart disease and cancer. The health benefits conferred by regular physical exercise training are unquestionable, yet many of the molecular changes may have heritable health implications for future generations. Similar to other environmental factors, exercise modulates the epigenome of somatic cells and researchers are beginning to study exercise epigenetics in germ cells. The germ cell epigenetic modifications affected by exercise offer a molecular mechanism for the inheritance of health and disease risk. The aims of this review are to: (i) provide an update on the expanding field of exercise epigenetics; (ii) offer an overview of data on intergenerational/transgenerational epigenetic inheritance of disease by environmental insults; (iii) to discuss the potential of exercise-induced intergenerational inheritance of health and disease risk; and finally, outline potential mechanisms and avenues for future work on epigenetic inheritance through exercise.

NEArender: an R package for functional interpretation of 'omics' data via network enrichment analysis

Background

The statistical evaluation of pathway enrichment, i.e. of gene profiles' confluence to the pathway level, allows exploring molecular landscapes using functionally annotated gene sets. However, pathway scores can also be used as predictive features in machine learning. That requires, firstly, increasing statistical power and biological relevance via a network enrichment analysis (NEA) and, secondly, a fast and convenient procedure for rendering the original data into a space of pathway scores. However, previous implementations of NEA involved multiple runs of network randomization and were therefore slow.

Results

Here, we present a new R package NEArender which can transform raw 'omics' features of experimental or clinical samples into matrices describing the same samples with many fewer NEA-based pathway scores. This is done via a parametric estimation of the null binomial distribution and is thus much faster and less biased than randomization procedures. Further, we compare estimates from these two alternative procedures and demonstrate that the summarization of individual genes to pathways increases the statistical power compared to both the default differential expression analysis on individual genes and the state-of-the-art gene set enrichment analysis. The package also contains functions for preparing input, modeling null distributions, and evaluating alternative versions of the global network.

Conclusions

Beyond the state-of-the-art exploration of molecular data through pathway enrichment, score matrices produced by NEArender can be used in larger bioinformatics pipelines as input for phenotype modeling, predicting disease outcomes etc. This approach is often more sensitive and robust than using the original data. The package NEArender is complementary to the online NEA tool EviNet (https://www.evinet.org) and, unlike of the latter, enables high performance of computations off-line.

The R package NEArender version 1.4 is available at CRAN repository

https://cran.r-project.org/web/packages/NEArender/

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1534-y) contains supplementary material, which is available to authorized users.