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Background Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Numerous DAA tools have been proposed in the past decade addressing the special characteristics of microbiome data such as zero inflation and compositional effects. Disturbingly, different DAA tools could sometimes produce quite discordant results, opening to the possibility of cherry-picking the tool in favor of one’s own hypothesis. To recommend the best DAA tool or practice to the field, a comprehensive evaluation, which covers as many biologically relevant scenarios as possible, is critically needed. Results We performed by far the most comprehensive evaluation of existing DAA tools using real data-based simulations. We found that DAA methods explicitly addressing compositional effects such as ANCOM-BC, Aldex2, metagenomeSeq (fitFeatureModel), and DACOMP did have improved performance in false-positive control. But they are still not optimal: type 1 error inflation or low statistical power has been observed in many settings. The recent LDM method generally had the best power, but its false-positive control in the presence of strong compositional effects was not satisfactory. Overall, none of the evaluated methods is simultaneously robust, powerful, and flexible, which makes the selection of the best DAA tool difficult. To meet the analysis needs, we designed an optimized procedure, ZicoSeq, drawing on the strength of the existing DAA methods. We show that ZicoSeq generally controlled for false positives across settings, and the power was among the highest. Application of DAA methods to a large collection of real datasets revealed a similar pattern observed in simulation studies. Conclusions Based on the benchmarking study, we conclude that none of the existing DAA methods evaluated can be applied blindly to any real microbiome dataset. The applicability of an existing DAA method depends on specific settings, which are usually unknown a priori. To circumvent the difficulty of selecting the best DAA tool in practice, we design ZicoSeq, which addresses the major challenges in DAA and remedies the drawbacks of existing DAA methods. ZicoSeq can be applied to microbiome datasets from diverse settings and is a useful DAA tool for robust microbiome biomarker discovery.

Introduction: Helicobacter pylori infection consistently leads to chronic and low degree of inflammatory response in gastric mucosa and is closely related with gastrointestinal and extra-gastric diseases. Effects of local microbiome in the stomach have been studied in adults and children with H. pylori infection. It is, however, not known whether the intestinal microbial community differs in children with varying H. pylori infection. The aim of this study is to characterize the altered composition of microbiome induced by H. pylori infection and in gastritis.Materials and Methods: This study involved 154 individuals, including 50 children affected by H. pylori-induced gastritis, 42 children with H. pylori-negative gastritis, and 62 healthy controls. Gut microbiome composition was analyzed using 16S rRNA gene-based pyrosequencing. Fecal bacterial diversity and composition were then compared.Results: On the basis of an analysis of similarities and differences, we found that children with H. pylori-induced gastritis exhibited gut bacteria dysbiosis. The ratio of Firmicutes/Bacteroidetes (F:B) at the phylum level had dramatically decreased in H. pylori-positive gastritis group (HPG) and H. pylori-negative gastritis group (HNG), compared with the healthy control group (HCG). At the family and genus levels, relative abundance of Bacteroidaceae and Enterobacteriaceae was prevalent in HPG and HNG, whereas relative abundance of Lachnospiraceae, Bifidobacteriaceae, and Lactobacillaceae was seen in HCG. Prevalence of different taxa of gut microbiome at the class, order, family, and genus levels was also observed among the three groups.Conclusions: Gastritis can cause changes in composition of fecal microbiome, which is exacerbated by H. pylori infection. These changes in gut microbiome may be related to drug resistance and development of chronic gastrointestinal diseases.

Intestinal proteases mediate digestion and immune signaling, while increased gut proteolytic activity disrupts the intestinal barrier and generates visceral hypersensitivity, which in common in irritable bowel syndrome (IBS). However, the mechanisms controlling protease function are unclear. Here we show that members of the gut microbiota suppress intestinal proteolytic activity through production of unconjugated bilirubin. This occurs via microbial β-glucuronidase-mediated conversion of bilirubin conjugates. Metagenomic analysis of fecal samples from patients with post-infection IBS (n=52) revealed an altered gut microbiota composition, in particular a reduction in Alistipes taxa, and high gut proteolytic activity driven by specific host serine proteases compared to controls. Germ-free mice showed 10-fold higher proteolytic activity compared with conventional mice. Colonization with microbiota from high proteolytic activity IBS patients failed to suppress proteolytic activity in germ-free mice, but suppression of proteolytic activity was achieved with colonization using microbiota from healthy donors. High proteolytic activity mice had higher intestinal permeability, a higher relative abundance of Bacteroides and a reduction in Alistipes taxa compared with low proteolytic activity mice. High proteolytic activity IBS patients had lower fecal β-glucuronidase activity and end-products of bilirubin deconjugation. Mice treated with unconjugated bilirubin and β-glucuronidase overexpressing E. coli , which significantly reduced proteolytic activity, while inhibitors of microbial β-glucuronidases increased proteolytic activity. Together, these data define a disease-relevant mechanism of host-microbial interaction that maintains protease homeostasis in the gut.