Personalized nutrition is of increasing interest to individuals actively monitoring their health. The relations between the duration of diet intervention and the effects on gut microbiota have yet to be elucidated. Here we examined the associations of short-term dietary changes, long-term dietary habits and lifestyle with gut microbiota. Stool samples from 248 citizen-science volunteers were collected before and after a self-reported 2-week personalized diet intervention, then analyzed using 16S rRNA sequencing. Considerable correlations between long-term dietary habits and gut community structure were detected. A higher intake of vegetables and fruits was associated with increased levels of butyrate-producing Clostridiales and higher community richness. A paired comparison of the metagenomes before and after the 2-week intervention showed that even a brief, uncontrolled intervention produced profound changes in community structure: resulting in decreased levels of Bacteroidaceae, Porphyromonadaceae and Rikenellaceae families and decreased alpha-diversity coupled with an increase of Methanobrevibacter, Bifidobacterium, Clostridium and butyrate-producing Lachnospiraceae- as well as the prevalence of a permatype (a bootstrapping-based variation of enterotype) associated with a higher diversity of diet. The response of microbiota to the intervention was dependent on the initial microbiota state. These findings pave the way for the development of an individualized diet.

Varicose veins of lower extremities (VVs) are a common multifactorial vascular disease. Genetic factors underlying VVs development remain largely unknown. Here we report the first large-scale study of VVs performed on a freely available genetic data of 408,455 European-ancestry individuals. We identified the 12 reliably associated loci that explain 13% of the SNP-based heritability, and prioritized the most likely causal genes CASZ1 , PIEZO1 , PPP3R1 , EBF1 , STIM2 , HFE , GATA2 , NFATC2 , and SOX9 . VVs-associated variants within these loci exhibited pleiotropic effects on several phenotypes including blood pressure/hypertension and blood cell traits. Gene set enrichment analysis revealed gene categories related to abnormal vasculogenesis. Genetic correlation analysis confirmed known epidemiological associations between VVs and deep venous thrombosis, weight, rough labor, and standing job, and found a genetic overlap with multiple traits that have not been previously suspected to share common genetic background with VVs. These traits included educational attainment, fluid intelligence and prospective memory scores, walking pace (negative correlation with VVs), smoking, height, number of operations, pain, and gonarthrosis (positive correlation with VVs). Finally, Mendelian randomization analysis provided evidence for causal effects of plasma levels of MICB and CD209 proteins, and anthropometric traits such as waist and hip circumference, height, weight, and both fat and fat-free mass. Our results provide novel insight into both VVs genetics and etiology. The revealed genes and proteins can be considered as good candidates for follow-up functional studies and might be of interest as potential drug targets.

BackgroundIntestinal 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 FindingsIn 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/SignificanceThe 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.

Genome-wide association studies have led to a significant progress in identification of genomic loci affecting coronary artery disease (CAD) risk. However, revealing the causal genes responsible for the observed associations is challenging. In the present study, we aimed to prioritize CAD-relevant genes based on cumulative evidence from the published studies and our own study of colocalization between eQTLs and loci associated with CAD using SMR/HEIDI approach. Prior knowledge of candidate genes was extracted from both experimental and in silico studies, employing different prioritization algorithms. Our review systematized information for a total of 51 CAD-associated loci. We pinpointed 37 genes in 36 loci. For 27 genes we infer they are causal for CAD, and for 10 further genes we judge them most likely causal. Colocalization analysis showed that for 18 out of these loci, association with CAD can be explained by changes in gene expression in one or more CAD-relevant tissues. Furthermore, for 8 out of 36 loci, existing evidence suggested additional CAD-associated genes. For the remaining 15 loci, we concluded that evidence for gene prioritization remains inconsistent, insufficient, or absent. Our results provide deeper insights into the genetic etiology of CAD and demonstrate knowledge gaps where further research is warranted. Coronary artery disease (CAD) is the most prevalent cardiovascular disease, the major cause of mortality and morbidity in both developed and developing countries 1. This pathology is the manifestation of atherosclerosis in the coronary arteries. CAD can lead to a variety of complications, including chest pain, myocardial infarction (MI), arrhythmias and heart failure 2. The etiology of CAD is multifactorial and involves a genetic predisposition as well as dietary and other lifestyle risk factors 3. The genetic component to CAD has long been recognized. The Framingham Study demonstrated that positive family history is a strong risk factor for incident CAD 4-6. According to Swedish and Danish twin studies, the narrow-sense heritability of fatal CAD is about 40-60% 7,8. Today, it is widely accepted that much of the genetic component arises from the effect of many common alleles associated with modest increases in CAD risk 3,9. Genome-wide association studies demonstrated that the common variation accounts for 40-50% of heritability of MI/CAD 10,11. Genetic studies of CAD started from family-based linkage studies discovering monogenic drivers of CAD and small candidate-gene studies which often provided controversial results. Development of high-throughput genotyping technologies and new statistical methods opened the era of genome-wide association studies (GWAS) 12,13. MI was among the very first traits studied with use of genome-wide association strategy already in 2002 14 .

Alternative splicing (AS) can significantly impact the transcriptome and proteome of a eukaryotic cell. Here, using transcriptome and proteome profiling data, we analyzed AS in two life forms of the model moss Physcomitrella patens, namely protonemata and gametophores, as well as in protoplasts. We identified 12 043 genes subject to alternative splicing and analyzed the extent to which AS contributes to proteome diversity. We could distinguish a few examples that unambiguously indicated the presence of two or more splice isoforms from the same locus at the proteomic level. Our results indicate that alternative isoforms have a small effect on proteome diversity. We also revealed that mRNAs and pre-mRNAs have thousands of complementary binding sites for long non-coding RNAs (lncRNAs) that may lead to potential interactions in transcriptome. This finding points to an additional level of gene expression and AS regulation by non-coding transcripts in Physcomitrella patens. Among the differentially expressed and spliced genes we found serine/arginine-rich (SR) genes, which are known to regulate AS in cells. We found that treatment with abscisic (ABA) and methyl jasmonic acids (MeJA) led to an isoform-specific response and suggested that ABA in gametophores and MeJA in protoplasts regulate AS and the transcription of SR genes.

BackgroundMetagenomic 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.ResultsKnomics-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.ConclusionsKnomics-Biota web service is a comprehensive tool for interactive metagenomic data analysis.Electronic supplementary materialThe online version of this article (10.1186/s13040-018-0187-3) contains supplementary material, which is available to authorized users.

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.

27Varicose veins of lower extremities (VVs) are a common multifactorial vascular disease. 28Genetic factors underlying VVs development remain largely unknown. Here we report the first 29 large-scale study of VVs performed on a freely available genetic data of 408,455 European-30 ancestry individuals. We identified 7 reliably associated loci that explain 10% of the SNP-based 31 heritability, and prioritized the most likely causal genes CASZ1, PPP3R1, EBF1, STIM2, and 32 HFE. Genetic correlation analysis confirmed known epidemiological associations and found 33 genetic overlap with various traits including fluid intelligence score, educational attainment, 34 smoking, and pain. Finally, we observed causal effects of height, weight, both fat and fat-free 35 mass, and plasma levels of MICB and CD209 proteins. 36All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/368365 doi: bioRxiv preprint first posted online Jul. 13, 2018; 3 Varicose veins (VVs) are one of the clinical manifestations of chronic venous disease 37 posing both a cosmetic and medical problem. VVs can be found in different parts of the body, 38 but most commonly occur in the lower extremities. Prevalence estimates of this condition vary 39 across ethnic groups ranging from 2-4% in the Northern group of the Cook Islands to 50-60% in 40 some countries of the Western world 1 The cumulative evidence from epidemiological, family, and genetic association studies 52 strongly indicates that there is a hereditary component in VVs etiology [6][7][8] . However, despite 53 progress in this field [9][10][11][12] , current knowledge of the genetic basis of this pathology is far from 54 being complete. Elucidating genes involved in susceptibility to VVs would help to identify key 55 molecular players in the disease initiation, provide deeper insights into its pathogenesis, and 56 eventually contribute to development of improved targeted therapy aimed at VVs treating and 57 preventing. 58Large-scale biobanks linked to electronic health records open up unparalleled opportunities 59 to investigate the genetics of complex traits. Today, UK Biobank is the largest repository that 60 contains information on genotypes and phenotypes for half a million participating individuals 13 . 61This resource is open to all bona fide researchers, and access to data is provided upon approval 62All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/368365 doi: bioRxiv preprint first posted online Jul. 13, 2018; 4 of their application and payment of necessary costs. However, the need to incur high costs 63 related to data access and computation can be an insurmountable obstacle for those...