Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

Background

Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL), but also for discriminating general from cell type-specific effects.

Results

Here, we present a two-step computational framework MAGAR (https://bioconductor.org/packages/MAGAR), which fully supports the identification of methQTLs from matched genotyping and DNA methylation data, and additionally allows for illuminating cell type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T cells, B cells) from healthy individuals and demonstrate the discrimination of common from cell type-specific methQTLs. We experimentally validate both types of methQTLs in an independent data set comprising additional cell types and tissues. Finally, we validate selected methQTLs located in the PON1, ZNF155, and NRG2 genes by ultra-deep local sequencing. In line with previous reports, we find cell type-specific methQTLs to be preferentially located in enhancer elements.

Conclusions

Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13072-021-00415-6.

Physical Activity Is Associated With Sleep Quality: Results of the ESSE-RF Epidemiological Study

Background and hypothesis

Physical activity (PA) is an important behavioral factor associated with the quality of life and healthy longevity. We hypothesize that extremely low and extremely high levels of daily PA (including occupational PA) may have a negative impact on sleep quality and psychological well-being.

Objective

The aim of the study is to investigate the association between the level and type of PA and sleep problems in adult population.

Materials and methods

The sample of the study consisted of the participants from the population-based cohort of The Epidemiology of Cardiovascular Risk Factors and Diseases in Regions of the Russian Federation Study (ESSE-RF). The data of three regions (Saint Petersburg, Samara, Orenburg), varying in geographic, climatic, socioeconomic characteristics, was included into analysis. The total sample consisted of 4,800 participants (1,600 from each region; 1,926 males, 2,874 females), aged 25–64. The level of PA was evaluated using three parameters: the type of PA at work, the frequency of an intensive/high PA including sport (times a week), the mean duration of leisure-time walking (minutes a day). The measures of sleep quality were sleep duration and the frequency of difficulty falling asleep, difficulty maintaining sleep, daytime sleepiness, and sleep medication use. PA and sleep characteristics were assessed by interview carried by the trained medical staff.

Results

When controlling for gender, age and socioeconomic status (SES) extremely high occupational PA was a significant risk factor for difficulty falling asleep three or more times a week [OR(CI95%) = 1.9(1.2–3.0), p = 0.003] while working in a sitting position or having moderate physical load at work were not associated with sleep characteristics. Having a high physical load six or more times a week was a risk factor for difficulty falling asleep controlling for gender, age and SES [OR(CI95%) = 1.9(1.4–3.4), p = 0.001]. The association between leisure-time walking and sleep characteristics was insignificant. Walking less than an hour a day was associated with increased depression scores (46.5 vs. 41.9%, p = 0.006).

Conclusion

High physical load at work and excessively frequent intensive PA are associated with difficulties initiating sleep and may represent a risk factor for insomnia.

Metabolic Phenotypes as Potential Biomarkers for Linking Gut Microbiome With Inflammatory Bowel Diseases

The gut microbiome is of utmost importance to human health. While a healthy microbiome can be represented by a variety of structures, its functional capacity appears to be more important. Gene content of the community can be assessed by “shotgun” metagenomics, but this approach is still too expensive. High-throughput amplicon-based surveys are a method of choice for large-scale surveys of links between microbiome, diseases, and diet, but the algorithms for predicting functional composition need to be improved to achieve good precision. Here we show how feature engineering based on microbial phenotypes, an advanced method for functional prediction from 16S rRNA sequencing data, improves identification of alterations of the gut microbiome linked to the disease. We processed a large collection of published gut microbial datasets of inflammatory bowel disease (IBD) patients to derive their community phenotype indices (CPI)—high-precision semiquantitative profiles aggregating metabolic potential of the community members based on genome-wide metabolic reconstructions. The list of selected metabolic functions included metabolism of short-chain fatty acids, vitamins, and carbohydrates. The machine-learning approach based on microbial phenotypes allows us to distinguish the microbiome profiles of healthy controls from patients with Crohn's disease and from ones with ulcerative colitis. The classifiers were comparable in quality to conventional taxonomy-based classifiers but provided new findings giving insights into possible mechanisms of pathogenesis. Feature-wise partial dependence plot (PDP) analysis of contribution to the classification result revealed a diversity of patterns. These observations suggest a constructive basis for defining functional homeostasis of the healthy human gut microbiome. The developed features are promising interpretable candidate biomarkers for assessing microbiome contribution to disease risk for the purposes of personalized medicine and clinical trials.

Knomics-Biota - a system for exploratory analysis of human gut microbiota data

Background

Metagenomic surveys of human microbiota are becoming increasingly widespread in academic research as well as in food and pharmaceutical industries and clinical context. Intuitive tools for investigating experimental data are of high interest to researchers.

Results

Knomics-Biota is a web-based resource for exploratory analysis of human gut metagenomes. Users can generate and share analytical reports corresponding to common experimental schemes (like case-control study or paired comparison). Interactive visualizations and statistical analysis are provided in association with the external factors and in the context of thousands of publicly available datasets arranged into thematic collections. The web-service is available at https://biota.knomics.ru.

Conclusions

Knomics-Biota web service is a comprehensive tool for interactive metagenomic data analysis.

Electronic supplementary material

The online version of this article (10.1186/s13040-018-0187-3) contains supplementary material, which is available to authorized users.

Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations

BACKGROUND

Intestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes.

METHODOLOGY/PRINCIPAL FINDINGS

In this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery.

CONCLUSION/SIGNIFICANCE

The model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.