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 .

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.

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.

One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1v, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the SARS-CoV-2 RBD domain. SEMA is available at https:// github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.

Accurate prediction of changes in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is dominant in many fields of machine learning. In this work, we introduce PROSTATA, a predictive model built in knowledge transfer fashion on a new curated dataset. PROSTATA demonstrates superiority over existing solutions based on neural networks. We show that the large margin of improvement is due to both the architecture of the model and the quality of the new training data set. This work opens up opportunities for developing new lightweight and accurate models for protein stability assessment. PROSTATA is available at https://github.com/AIRI-Institute/PROSTATA.