Explore the vast range of titles available.

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Despite a well-documented effect of high dietary zinc oxide on the pig intestinal microbiota composition less is it yet known about changes in microbial functional properties or the effect of organic zinc sources. Forty weaning piglets in four groups were fed diets supplemented with 40 or 110 ppm zinc as zinc oxide, 110 ppm as Zn-Lysinate, or 2500 ppm as zinc oxide. Host zinc homeostasis, intestinal zinc fractions, and ileal nutrient digestibility were determined as main nutritional and physiological factors putatively driving colon microbial ecology. Metagenomic sequencing of colon microbiota revealed only clear differences at genus level for the group receiving 2500 ppm zinc oxide. However, a clear group differentiation according to dietary zinc concentration and source was observed at species level. Functional analysis revealed significant differences in genes related to stress response, mineral, and carbohydrate metabolism. Taxonomic and functional gene differences were accompanied with clear effects in microbial metabolite concentration. Finally, a selection of certain antibiotic resistance genes by dietary zinc was observed. This study sheds further light onto the consequences of concentration and chemical form of dietary zinc on microbial ecology measures and the resistome in the porcine colon.

Yield losses caused by fungal pathogens represent a major threat to global food production. One of the most devastating fungal wheat pathogens is Zymoseptoria tritici . Despite the importance of this fungus, the underlying mechanisms of plant–pathogen interactions are poorly understood. Here we present a conceptual framework based on coinfection assays, comparative metabolomics, and microbiome profiling to study the interaction of Z. tritici in susceptible and resistant wheat. We demonstrate that Z. tritici suppresses the production of immune-related metabolites in a susceptible cultivar. Remarkably, this fungus-induced immune suppression spreads within the leaf and even to other leaves, a phenomenon that we term “systemic induced susceptibility”. Using a comparative metabolomics approach, we identify defense-related biosynthetic pathways that are suppressed and induced in susceptible and resistant cultivars, respectively. We show that these fungus-induced changes correlate with changes in the wheat leaf microbiome. Our findings suggest that immune suppression by this hemibiotrophic pathogen impacts specialized plant metabolism, alters its associated microbial communities, and renders wheat vulnerable to further infections. The fungal plant pathogen Zymoseptoria tritici is a major threat to wheat yield. Here Seybold et al. show that Z. tritici can suppress immune responses not only in infected tissue but also on other leaves, a phenomenon termed “systemic induced susceptibility” that is correlated with systemic changes in metabolite accumulation.

Background Dysbiosis, an imbalance in the bacterial composition of the human gut microbiota, is linked to inflammatory bowel disease (IBD). Advances in biological techniques have generated vast microbiota datasets, presenting both opportunities and challenges for clinical research in that field. Network theory offers powerful tools to analyze these complex datasets. Methods Utilizing genetically unrelated individuals from the Kiel IBD-KC cohort, we compared network properties of the gut microbiota between patients with inflammatory bowel disease (IBD, n  = 522) and healthy controls ( n  = 365), and between Crohn's disease (CD, n  = 230) and Ulcerative Colitis (UC, n  = 280). Correlation-based microbial networks were constructed, with genera as nodes and significant pairwise correlations as edges. We used centrality measures to identify key microbial constituents, called hubs, and suggest a network-based definition for a core microbiota. Using Graphlet theoretical approaches, we analyzed network topology and individual node roles. Results Global network properties differed between cases and controls, with controls showing a potentially more robust network structure characterized by e.g., a greater number of components and a lower edge density. Local network properties varied across all groups. For cases and both UC and CD, Faecalibacterium and Veillonella , and for unaffected controls Bacteroides , Blautia , Clostridium XIVa , and Clostridium XVIII emerged as unique hubs in the respective networks. Graphlet analysis revealed significant differences in terminal node orbits among all groups. Four genera which act as hubs in one state, were found to be terminal nodes in the opposite disease state: Bacteroides , Clostridium XIVa , Faecalibacterium , and Subdoligranulum . Comparing our network-based core microbiota definition with a conventional one showed an overlap in approximately half of the core taxa, while core taxa identified through our new definition maintained high abundance. Conclusion The network-based approach complements previous investigations of alteration of the human gut microbiota in IBD by offering a different perspective that extends beyond a focus solely on highly abundant taxa. Future studies should further investigate functional roles of hubs and terminal nodes as potential targets for interventions and preventions. Additionally, the advantages of the newly proposed network-based core microbiota definition, should be investigated more systematically.

The intestinal microbiome is implicated as an important modulating factor in multiple inflammatory 1 , 2 , neurologic 3 and neoplastic diseases 4 . Recent genome-wide association studies yielded inconsistent, underpowered and rarely replicated results such that the role of human host genetics as a contributing factor to microbiome assembly and structure remains uncertain 5 – 11 . Nevertheless, twin studies clearly suggest host genetics as a driver of microbiome composition 11 . In a genome-wide association analysis of 8,956 German individuals, we identified 38 genetic loci to be associated with single bacteria and overall microbiome composition. Further analyses confirm the identified associations of ABO histo-blood groups and FUT2 secretor status with Bacteroides and Faecalibacterium spp. Mendelian randomization analysis suggests causative and protective effects of gut microbes, with clade-specific effects on inflammatory bowel disease. This holistic investigative approach of the host, its genetics and its associated microbial communities as a ‘metaorganism’ broaden our understanding of disease etiology, and emphasize the potential for implementing microbiota in disease treatment and management. Genome-wide association analysis of 8,956 German individuals identifies 38 genetic loci associated with single bacteria and overall microbiome composition.

ObjectiveConflicting microbiota data exist for primary sclerosing cholangitis (PSC) and experimental models. Goal: define the function of complex resident microbes and their association relevant to PSC patients by studying germ-free (GF) and antibiotic-treated specific pathogen-free (SPF) multidrug-resistant 2 deficient (mdr2−/− ) mice and microbial profiles in PSC patient cohorts.DesignWe measured weights, liver enzymes, RNA expression, histological, immunohistochemical and fibrotic biochemical parameters, faecal 16S rRNA gene profiling and metabolomic endpoints in gnotobiotic and antibiotic-treated SPF mdr2−/− mice and targeted metagenomic analysis in PSC patients.ResultsGF mdr2−/− mice had 100% mortality by 8 weeks with increasing hepatic bile acid (BA) accumulation and cholestasis. Early SPF autologous stool transplantation rescued liver-related mortality. Inhibition of ileal BA transport attenuated antibiotic-accelerated liver disease and decreased total serum and hepatic BAs. Depletion of vancomycin-sensitive microbiota exaggerated hepatobiliary disease. Vancomycin selectively decreased Lachnospiraceae and short-chain fatty acids (SCFAs) but expanded Enterococcus and Enterobacteriaceae. Antibiotics increased Enterococcus faecalis and Escherichia coli liver translocation. Colonisation of GF mdr2−/− mice with translocated E. faecalis and E. coli strains accelerated hepatobiliary inflammation and mortality. Lachnospiraceae colonisation of antibiotic pretreated mdr2−/− mice reduced liver fibrosis, inflammation and translocation of pathobionts, and SCFA-producing Lachnospiraceae and purified SCFA decreased fibrosis. Faecal Lachnospiraceae negatively associated, and E. faecalis/ Enterobacteriaceae positively associated, with PSC patients’ clinical severity by Mayo risk scores.ConclusionsWe identified novel functionally protective and detrimental resident bacterial species in mdr2−/− mice and PSC patients with associated clinical risk score. These insights may guide personalised targeted therapeutic interventions in PSC patients.

Background The T-cell receptor (TCR), located on the surface of T cells, is responsible for the recognition of the antigen-major histocompatibility complex, leading to the initiation of an inflammatory response. Analysing the TCR repertoire may help to gain a better understanding of the immune system features and of the aetiology and progression of diseases, in particular those with unknown antigenic triggers. The extreme diversity of the TCR repertoire represents a major analytical challenge; this has led to the development of specialized methods which aim to characterize the TCR repertoire in-depth. Currently, next generation sequencing based technologies are most widely employed for the high-throughput analysis of the immune cell repertoire. Results Here, we report on the latest methodological advancements in the field by describing and comparing the available tools; from the choice of the starting material and library preparation method, to the sequencing technologies and data analysis. Finally, we provide a practical example and our own experience by reporting some exemplary results from a small internal benchmark study, where current approaches from the literature and the market are employed and compared. Conclusions Several valid methods for clonotype identification and TCR repertoire analysis exist, however, a gold standard method for the field has not yet been identified. Depending on the purpose of the scientific study, some approaches may be more suitable than others. Finally, due to possible method specific biases, scientists must be careful when comparing results obtained using different methods.

During cold stimulation, cholesterol is converted to bile acids in an alternative pathway. The bile acids then alter the microbiota, which in turn promotes more heat generation. Adaptive thermogenesis is an energy-demanding process that is mediated by cold-activated beige and brown adipocytes, and it entails increased uptake of carbohydrates, as well as lipoprotein-derived triglycerides and cholesterol, into these thermogenic cells. Here we report that cold exposure in mice triggers a metabolic program that orchestrates lipoprotein processing in brown adipose tissue (BAT) and hepatic conversion of cholesterol to bile acids via the alternative synthesis pathway. This process is dependent on hepatic induction of cytochrome P450, family 7, subfamily b, polypeptide 1 (CYP7B1) and results in increased plasma levels, as well as fecal excretion, of bile acids that is accompanied by distinct changes in gut microbiota and increased heat production. Genetic and pharmacological interventions that targeted the synthesis and biliary excretion of bile acids prevented the rise in fecal bile acid excretion, changed the bacterial composition of the gut and modulated thermogenic responses. These results identify bile acids as important metabolic effectors under conditions of sustained BAT activation and highlight the relevance of cholesterol metabolism by the host for diet-induced changes of the gut microbiota and energy metabolism.

BackgroundPatients with primary sclerosing cholangitis (PSC) display an altered colonic microbiome compared with healthy controls. However, little is known on the bile duct microbiome and its interplay with bile acid metabolism in PSC.MethodsPatients with PSC (n=43) and controls without sclerosing cholangitis (n=22) requiring endoscopic retrograde cholangiography were included prospectively. Leading indications in controls were sporadic choledocholithiasis and papillary adenoma. A total of 260 biospecimens were collected from the oral cavity, duodenal fluid and mucosa and ductal bile. Microbiomes of the upper alimentary tract and ductal bile were profiled by sequencing the 16S-rRNA-encoding gene (V1–V2). Bile fluid bile acid composition was measured by high-performance liquid chromatography mass spectrometry and validated in an external cohort (n=20).ResultsThe bile fluid harboured a diverse microbiome that was distinct from the oral cavity, the duodenal fluid and duodenal mucosa communities. The upper alimentary tract microbiome differed between PSC patients and controls. However, the strongest differences between PSC patients and controls were observed in the ductal bile fluid, including reduced biodiversity (Shannon entropy, p=0.0127) and increase of pathogen Enterococcus faecalis (FDR=4.18×10−5) in PSC. Enterococcus abundance in ductal bile was strongly correlated with concentration of the noxious secondary bile acid taurolithocholic acid (r=0.60, p=0.0021).ConclusionPSC is characterised by an altered microbiome of the upper alimentary tract and bile ducts. Biliary dysbiosis is linked with increased concentrations of the proinflammatory and potentially cancerogenic agent taurolithocholic acid.

Despite the increasing knowledge about factors shaping the human microbiome, the host genetic factors that modulate the skin-microbiome interactions are still largely understudied. This contrasts with recent efforts to characterize host genes that influence the gut microbiota. Here, we investigated the effect of genetics on skin microbiota across three different skin microenvironments through meta-analyses of genome-wide association studies (GWAS) of two population-based German cohorts. We identified 23 genome-wide significant loci harboring 30 candidate genes involved in innate immune signaling, environmental sensing, cell differentiation, proliferation and fibroblast activity. However, no locus passed the strict threshold for study-wide significance ( P  < 6.3 × 10 −10 for 80 features included in the analysis). Mendelian randomization (MR) analysis indicated the influence of staphylococci on eczema/dermatitis and suggested modulating effects of the microbiota on other skin diseases. Finally, transcriptional profiles of keratinocytes significantly changed after in vitro co - culturing with Staphylococcus epidermidis , chosen as a representative of skin commensals. Seven candidate genes from the GWAS were found overlapping with differential expression in the co-culturing experiments, warranting further research of the skin commensal and host genetic makeup interaction. Microbiome composition is influenced by genetics, although the specific host genetic factors responsible are not well known. Here, the authors performed a genome-wide meta-analysis to discover host genetic effects on skin microbiota and finding potential causal effects of microbiota composition on skin diseases.