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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.

Deep venous thrombosis (DVT) is a severe complication of coronavirus disease 2019 (COVID-19). The purpose of this study was to study the prevalence, risk factors, anticoagulant therapy and sex differences of DVT in patients with COVID-19. The enrolled 121 hospitalized non-ventilator patients were confirmed positive for COVID-19. All suspected patients received color Doppler ultrasound (US) to screen for DVT in both lower extremities. Multivariate logistic regression was performed to identify risk factors related to DVT in COVID-19 patients. DVT was found in 48% of the asymptomatic COVID-19 patients with an increased PADUA or Caprini index using US scanning. The multivariate logistic regression determined that age (OR, 1.05; p = .0306), C-reactive protein (CRP) (OR, 1.02; p = .0040), and baseline D-dimer (OR, 1.42; p = .0010) were risk factors among COVID-19 patients. Although the most common DVT location was infrapopliteal (classes I and II), higher mortality in DVT-COVID-19 patients was confirmed. DVT-COVID-19 patients presented significant increases in CRP, neutrophil count, and D-dimer throughout the whole inpatient period compared to non-DVT-COVID-19 patients. Although anticoagulation therapy accelerated the recovery of lymphocytopenia in DVT patients, men DVT-COVID-19 patients with anticoagulant therapy showed significant higher CRP and neutrophil count vs. lymphocyte count (N/L) ratio, but showed lower lymphocyte counts compared to women DVT-COVID-19 patients. DVT is common in COVID-19 patients with high-risk factors, especially for older age and higher CRP and baseline D-dimer populations. It is important to consider sex differences in anticoagulant therapy among DVT-COVID-19 patients.

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.

Preterm birth (PTB) is the largest contributor to infant death in sub-Saharan Africa and globally. With a global estimate of 773,600, Nigeria has the third highest rate of PTB worldwide. There have been a number of microbiome profiling studies to identify vaginal microbiomes suggestive of preterm and healthy birth outcome. However, studies on the pregnancy vaginal microbiome in Africa are sparse with none performed in Nigeria. Moreover, few studies have considered the concurrent impact of steroid hormones and the vaginal microbiome on pregnancy outcome. We assessed two key determinants of pregnancy progression to gain a deeper understanding of the interactions between vaginal microbiome composition, steroid hormone concentrations, and pregnancy outcome. Vaginal swabs and blood samples were prospectively collected from healthy midtrimester pregnant women. Vaginal microbiome compositions were assessed by analysis of the V3-V5 region of 16S rRNA genes, and potential functional metabolic traits of identified vaginal microbiomes were imputed by PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states) analysis, while plasma estradiol (E2) and progesterone (P1) levels were quantified by the competitive enzyme-linked immunosorbent assay (ELISA). PTB vaginal samples were characterized by increased microbial richness, high diversity, and depletion of lactobacilli compared to term delivery samples. Women who delivered preterm were characterized by an Atopobium vaginae-dominated vagitype. High relative abundance of Atopobium vaginae at the midtrimester was highly predictive of PTB (area under the receiving operator characteristics [AUROC] of 0.983). There was a marked overlap in the range of plasma E2 and P1 values between term and PTB groups. IMPORTANCE Giving birth too soon accounts for half of all newborn deaths worldwide. Clinical symptoms alone are not sufficient to identify women at risk of giving birth too early, as such a pragmatic approach to reducing the incidence of preterm birth entails developing early strategies for intervention before it materializes. In view of the role played by the vaginal microbiome and maternal steroid hormones in determining obstetric outcome, we assessed the vaginal microbiome composition and steroid hormone during pregnancy and examined their relationship in predicting preterm birth risk in Nigerian women. This study highlights a potential early-driver microbial marker for prediction of preterm birth risk and supports the notion that vaginal microbiome composition varies across populations. A knowledge of relevant preterm birth microbial markers specific to populations would enhance the development of personalized therapeutic interventions toward restoring a microbiome that optimizes reproductive health fitness, therefore reducing the incidence of preterm birth.

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.

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Infection with severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is responsible for the COVID-19 pandemic. Angiotensin-converting enzyme 2 ( Ace2 ) is expressed in the gastrointestinal (GI) tract and a receptor for SARS-CoV-2, making the GI tract a potential infection site. This study investigated the effects of commensal intestinal microbiota on colonic Ace2 expression using a humanized mouse model. We found that colonic Ace2 expression decreased significantly upon microbial colonization. Humanization with healthy volunteer or dysbiotic microbiota from irritable bowel syndrome (IBS) patients resulted in similar Ace2 expression. Despite the differences in microbiota, no associations between α-diversity, β-diversity or individual taxa, and Ace2 were noted post-humanization. These results highlight that commensal microbiota play a key role in regulating intestinal Ace2 expression and the need to further examine the underlying mechanisms of this regulation.