Background: Growing evidence has shown that alterations in gut microbiota composition are associated with multiple autoimmune diseases (ADs). However, it is unclear whether these associations reflect a causal relationship.Objective: To reveal the causal association between gut microbiota and AD, we conducted a two-sample Mendelian randomization (MR) analysis.Materials and Methods: We assessed genome-wide association study (GWAS) summary statistics for gut microbiota and six common ADs, namely, systemic lupus erythematosus, rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis, type 1 diabetes (T1D), and celiac disease (CeD), from published GWASs. Two-sample MR analyses were first performed to identify causal bacterial taxa for ADs in discovery samples. Significant bacterial taxa were further replicated in independent replication outcome samples. A series of sensitivity analyses was performed to validate the robustness of the results. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation.Results: Combining the results from the discovery and replication stages, we identified one causal bacterial genus, Bifidobacterium. A higher relative abundance of the Bifidobacterium genus was associated with a higher risk of T1D [odds ratio (OR): 1.605; 95% CI, 1.339–1.922; P_FDR = 4.19 × 10⁻⁷] and CeD (OR: 1.401; 95% CI, 1.139–1.722; P_FDR = 2.03 × 10⁻³), respectively. Further sensitivity analyses validated the robustness of the above associations. The results of reverse MR analysis showed no evidence of reverse causality from T1D and CeD to the Bifidobacterium genus.Conclusion: This study implied a causal relationship between the Bifidobacterium genus and T1D and CeD, thus providing novel insights into the gut microbiota-mediated development mechanism of ADs.

Appendicular lean mass (ALM) is a heritable trait associated with loss of lean muscle mass and strength, or sarcopenia, but its genetic determinants are largely unknown. Here we conducted a genome-wide association study (GWAS) with 450,243 UK Biobank participants to uncover its genetic architecture. A total of 1059 conditionally independent variants from 799 loci were identified at the genome-wide significance level (p < 5 × 10⁻⁹), all of which were also significant at p < 5 × 10^–5 in both sexes. These variants explained ~15.5% of the phenotypic variance, accounting for more than one quarter of the total ~50% GWAS-attributable heritability. There was no difference in genetic effect between sexes or among different age strata. Heritability was enriched in certain functional categories, such as conserved and coding regions, and in tissues related to the musculoskeletal system. Polygenic risk score prediction well distinguished participants with high and low ALM. The findings are important not only for lean mass but also for other complex diseases, such as type 2 diabetes, as ALM is shown to be a protective factor for type 2 diabetes.

Evidence supports the observational associations of gut microbiota with a variety of psychiatric disorders, but the causal nature of such associations remains obscure. Aiming to comprehensively investigate their causal relationship and to identify specific causal microbe taxa for psychiatric diseases, we conducted a two-sample Mendelian randomization (MR) analysis of gut microbiome with 15 psychiatric diseases. Specifically, the microbiome genome-wide association study (GWAS) in 18,473 individuals from the MiBioGen study was used as exposure sample, and the GWAS for 15 psychiatric diseases was used as outcome samples. One-hundred ninety bacterial taxa from six levels were available for analysis. At a multiple-testing corrected significance level (phylum P < 5.56 × 10^–3, class P < 3.33 × 10^–3, order P < 2.63 × 10^–3, family P < 1.67 × 10^–3, genus P < 4.90 × 10^–4, and species P < 3.33 × 10^–3), the following eight causal associations from seven bacterial features (one phylum + three classes + one order + one family + one species) were identified: family Prevotellaceae with autism spectrum disorder (P = 5.31 × 10^–4), class Betaproteobacteria with bipolar disorder (P = 1.53 × 10^–3), class Actinobacteria with schizophrenia (P = 1.33 × 10^–3), class Bacteroidia and order Bacteroidales with Tourette syndrome (P = 2.51 × 10^–3 and 2.51 × 10^–3), phylum Actinobacteria and class Actinobacteria with extroversion (P = 8.22 × 10^–4 and 1.09 × 10^–3), and species Clostridium innocuum with neuroticism (P = 8.92 × 10^–4). Sensitivity analysis showed no evidence of reverse causality, pleiotropy, and heterogeneity. Our findings offered novel insights into the gut microbiota–mediated development mechanism of psychiatric disorders.

Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data. In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models. We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM ), providing a useful tool for analyzing microbiome data.

Objective: The purpose of the present study was to evaluate the effectiveness of probiotics on type II diabetes mellitus (T2DM).Methods: We performed a comprehensive search on PubMed, Web of Science, China National Knowledge Infrastructure, Chinese Scientific Journal Databases, Wan Fang database and China biology medicine disc for relevant studies published before June 2019. Glycated hemoglobin A1c (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR) and fasting blood glucose (FBG) were used as indicators for T2DM. Inverse-variance weighted mean difference (WMD) with 95% confidence interval (CI) was calculated for the mean HbA1c, FBG and HOMA-IR changes from baseline.Results: 15 randomized controlled trials (RCT) with a total of 902 participants were included into the meta-analysis. Considering the clinical heterogeneity caused by variation of dosage and duration of probiotic treatment, random-effects model was used to estimate the pooled WMD. Significantly greater reduction in HbA1c% (WMD = − 0.24, 95% CI [− 0.44, − 0.04],p = 0.02), FBG (WMD = − 0.44 mmol/L, 95% CI [− 0.74, − 0.15],p = 0.003) and HOMA-IR (WMD = − 1.07, 95% CI [− 1.58, − 0.56],p < 0.00001) were observed in probiotics treated group. Further sensitivity analysis verified the reliability and stability of our results.Conclusion: The results of our meta-analysis indicated that probiotics treatment may reduce HbA1c, FBG and insulin resistance level in T2DM patients. More clinical data and research into the mechanism of probiotics are needed to clarify the role of probiotics in T2DM.

Motivation: Several new de novo assembly tools have been developed recently to assemble short sequencing reads generated by next-generation sequencing platforms. However, the performance of these tools under various conditions has not been fully investigated, and sufficient information is not currently available for informed decisions to be made regarding the tool that would be most likely to produce the best performance under a specific set of conditions. Results: We studied and compared the performance of commonly used de novo assembly tools specifically designed for next-generation sequencing data, including SSAKE, VCAKE, Euler-sr, Edena, Velvet, ABySS and SOAPdenovo. Tools were compared using several performance criteria, including N50 length, sequence coverage and assembly accuracy. Various properties of read data, including single-end/paired-end, sequence GC content, depth of coverage and base calling error rates, were investigated for their effects on the performance of different assembly tools. We also compared the computation time and memory usage of these seven tools. Based on the results of our comparison, the relative performance of individual tools are summarized and tentative guidelines for optimal selection of different assembly tools, under different conditions, are provided. Contact:hdeng2@tulane.edu Supplementary information: Supplementary data are available at Bioinformatics online.