MetaQuad: shared informative variants discovery in metagenomic samples.

Motivation: Strain-level analysis of metagenomic data has garnered significant interest in recent years. Microbial single nucleotide polymorphisms (SNPs) are genomic variants that can reflect strain-level differences within a microbial species. The diversity and emergence of SNPs in microbial genomes may reveal evolutionary history and environmental adaptation in microbial populations. However, efficient discovery of shared polymorphic variants in a large collection metagenomic samples remains a computational challenge. Results: MetaQuad utilizes a density-based clustering technique to effectively distinguish between shared variants and non-polymorphic sites using shotgun metagenomic data. Empirical comparisons with other state-of-the-art methods show that MetaQuad significantly reduces the number of false positive SNPs without greatly affecting the true positive rate. We used MetaQuad to identify antibiotic-associated variants in patients who underwent Helicobacter pylori eradication therapy. MetaQuad detected 7591 variants across 529 antibiotic resistance genes. The nucleotide diversity of some genes is increased 6 weeks after antibiotic treatment, potentially indicating the role of these genes in specific antibiotic treatments. Availability and implementation: MetaQuad is an open-source Python package available via https://github.com/holab-hku/MetaQuad.

Sex Differences in Association Between Gut Microbiome and Essential Hypertension Based on Ambulatory Blood Pressure Monitoring.

BACKGROUND: Sex differences in the pathogenesis of hypertension exist. While gut microbiota (GM) has been associated with hypertension, it is unclear whether there are sex-linked differences in the association between GM and hypertension. METHODS: We conducted a cross-sectional study to investigate the sex differences in associations between GM characterized by shotgun sequencing, GM-derived short-chain fatty acids, and 24-hour ambulatory blood pressure in 241 Hong Kong Chinese (113 men and 128 women; mean age, 54±6 years). RESULTS: The hypertensive group was associated with GM alterations; however, significant differences in ß-diversity and GM composition in hypertensive versus normotensive groups were only observed in women and not in men under various statistical models adjusting for the following covariates: age, sex, body mass index, sodium intake estimated by spot urine analysis, blood glucose, triglycerides, low- and high-density lipoprotein cholesterol, smoking, menopause, and fatty liver status. Specifically, Ruminococcus gnavus, Clostridium bolteae, and Bacteroides ovatus were significantly more abundant in the hypertensive women, whereas Dorea formicigenerans was more abundant in the normotensive women. No bacterial species were found to be significantly associated with hypertension in men. Furthermore, total plasma short-chain fatty acids and propionic acid were independent predictors of systolic and diastolic blood pressure in women but not men. CONCLUSIONS: GM dysregulation was strongly associated with 24-hour ambulatory blood pressure in women but not men, which may be mediated through propionic acid. Our work suggests that sex differences may be an important consideration while assessing the role of GM in the development and treatment of hypertension.

Deficiency of exchange protein directly activated by cAMP (EPAC)-1 in mice augments glucose intolerance, inflammation, and gut dysbiosis associated with Western diet.

BACKGROUND: Gut microbiota (GM) dysregulation, known as dysbiosis, has been proposed as a crucial driver of obesity associated with "Western" diet (WD) consumption. Gut dysbiosis is associated with increased gut permeability, inflammation, and insulin resistance. However, host metabolic pathways implicated in the pathophysiology of gut dysbiosis are still elusive. Exchange protein directly activated by cAMP (Epac) plays a critical role in cell-cell junction formation and insulin secretion. Here, we used homozygous Epac1-knockout (Epac1-/-), Epac2-knockout (Epac2-/-), and wild-type (WT) mice to investigate the role of Epac proteins in mediating gut dysbiosis, gut permeability, and inflammation after WD feeding. RESULTS: The 16S rRNA gene sequencing of fecal DNA showed that the baseline GM of Epac2-/-, but not Epac1-/-, mice was represented by a significantly higher Firmicutes to Bacteroidetes ratio and significant alterations in several taxa compared to WT mice, suggesting that Epac2-/- mice had gut dysbiosis under physiological conditions. However, an 8-week WD led to a similar gut microbiome imbalance in mice regardless of genotype. While Epac1 deficiency modestly exacerbated the WD-induced GM dysbiosis, the WD-fed Epac2-/- mice had a more significant increase in gut permeability than corresponding WT mice. After WD feeding, Epac1-/-, but not Epac2-/-, mice had significantly higher mRNA levels of tumor necrosis factor-alpha (TNF-α) and F4/80 in the epididymal white adipose tissue (EWAT), increased circulating lipocalin-2 protein and more severe glucose intolerance, suggesting greater inflammation and insulin resistance in WD-fed Epac1-/- mice than corresponding WT mice. Consistently, Epac1 protein expression was significantly reduced in the EWAT of WD-fed WT and Epac2-/- mice. CONCLUSION: Despite significantly dysregulated baseline GM and a more pronounced increase in gut permeability upon WD feeding, WD-fed Epac2-/- mice did not exhibit more severe inflammation and glucose intolerance than corresponding WT mice. These findings suggest that the role of gut dysbiosis in mediating WD-associated obesity may be context-dependent. On the contrary, we demonstrate that deficiency of host signaling protein, Epac1, drives inflammation and glucose intolerance which are the hallmarks of WD-induced obesity. Video abstract.

Challenges and emerging systems biology approaches to discover how the human gut microbiome impact host physiology.

Research in the human gut microbiome has bloomed with advances in next generation sequencing (NGS) and other high-throughput molecular profiling technologies. This has enabled the generation of multi-omics datasets which holds promises for big data-enabled knowledge acquisition in the form of understanding the normal physiological and pathological involvement of gut microbiomes. Ample evidence suggests that distinct microbial compositions in the human gut are associated with different diseases. However, the biological mechanisms underlying these associations are often unclear. There is a need to move beyond statistical associations to discover how changes in the gut microbiota mechanistically affect host physiology and disease development. This review summarises state-of-the-art big data and systems biology approaches for mechanism discovery.

Discovery of perturbation gene targets via free text metadata mining in Gene Expression Omnibus.

There exists over 2.5 million publicly available gene expression samples across 101,000 data series in NCBI's Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEO's free text metadata to annotate the experimental type and sample type, this database remains difficult to harness computationally without significant manual intervention. In this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control samples and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network. GEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available at https://github.com/VCCRI/GEOracle.