Objective: Bile acids are regulators of lipid and glucose metabolism, and modulate inflammation in the liver and other tissues. Primary bile acids such as cholic acid and chenodeoxycholic acid (CDCA) are produced in the liver, and converted into secondary bile acids such as deoxycholic acid (DCA) and lithocholic acid by gut microbiota. Here we investigated the possible roles of bile acids in non-alcoholic fatty liver disease (NAFLD) pathogenesis and the impact of the gut microbiome on bile acid signalling in NAFLD. Design: Serum bile acid levels and fibroblast growth factor 19 (FGF19), liver gene expression profiles and gut microbiome compositions were determined in patients with NAFLD, high-fat diet-fed rats and their controls. Results: Serum concentrations of primary and secondary bile acids were increased in patients with NAFLD. In per cent, the farnesoid X receptor (FXR) antagonistic DCA was increased, while the agonistic CDCA was decreased in NAFLD. Increased mRNA expression for cytochrome P450 7A1, Na⁺-taurocholate cotransporting polypeptide and paraoxonase 1, no change in mRNA expression for small heterodimer partner and bile salt export pump, and reduced serum FGF19 were evidence of impaired FXR and fibroblast growth factor receptor 4 (FGFR4)-mediated signalling in NAFLD. Taurine and glycine metabolising bacteria were increased in the gut of patients with NAFLD, reflecting increased secondary bile acid production. Similar changes in liver gene expression and the gut microbiome were observed in high-fat diet-fed rats. Conclusions: The serum bile acid profile, the hepatic gene expression pattern and the gut microbiome composition consistently support an elevated bile acid production in NAFLD. The increased proportion of FXR antagonistic bile acid explains, at least in part, the suppression of hepatic FXR-mediated and FGFR4-mediated signalling. Our study suggests that future NAFLD intervention may target the components of FXR signalling, including the bile acid converting gut microbiome.

The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients’ Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user's reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.

Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from 8 distinct geographical cohorts. Integrated analysis identified 20 archaeal, 27 bacterial, 20 fungal and 21 viral species for each single-kingdom diagnostic model. However, our data revealed superior diagnostic accuracy for models constructed with multi-kingdom markers, in particular the addition of fungal species. Specifically, 16 multi-kingdom markers including 11 bacterial, 4 fungal and 1 archaeal feature, achieved good performance in diagnosing patients with CRC (area under the receiver operating characteristic curve (AUROC) = 0.83) and maintained accuracy across 3 independent cohorts. Coabundance analysis of the ecological network revealed associations between bacterial and fungal species, such as Talaromyces islandicus and Clostridium saccharobutylicum. Using metagenome shotgun sequencing data, the predictive power of the microbial functional potential was explored and elevated D-amino acid metabolism and butanoate metabolism were observed in CRC. Interestingly, the diagnostic model based on functional EggNOG genes achieved high accuracy (AUROC = 0.86). Collectively, our findings uncovered CRC-associated microbiota common across cohorts and demonstrate the applicability of multi-kingdom and functional markers as CRC diagnostic tools and, potentially, as therapeutic targets for the treatment of CRC.

A number of studies have associated obesity with altered gut microbiota, although results are discordant regarding compositional changes in the gut microbiota of obese animals. Herein we used a meta-analysis to obtain an unbiased evaluation of structural and functional changes of the gut microbiota in diet-induced obese rodents. The raw sequencing data of nine studies generated from high-fat diet (HFD)-induced obese rodent models were processed with QIIME to obtain gut microbiota compositions. Biological functions were predicted and annotated with KEGG pathways with PICRUSt. No significant difference was observed for alpha diversity and Bacteroidetes-to-Firmicutes ratio between obese and lean rodents. Bacteroidia, Clostridia, Bacilli, and Erysipelotrichi were dominant classes, but gut microbiota compositions varied among studies. Meta-analysis of the nine microbiome data sets identified 15 differential taxa and 57 differential pathways between obese and lean rodents. In obese rodents, increased abundance was observed for Dorea, Oscillospira, and Ruminococcus, known for fermenting polysaccharide into short chain fatty acids (SCFAs). Decreased Turicibacter and increased Lactococcus are consistent with elevated inflammation in the obese status. Differential functional pathways of the gut microbiome in obese rodents included enriched pyruvate metabolism, butanoate metabolism, propanoate metabolism, pentose phosphate pathway, fatty acid biosynthesis, and glycerolipid metabolism pathways. These pathways converge in the function of carbohydrate metabolism, SCFA metabolism, and biosynthesis of lipid. HFD-induced obesity results in structural and functional dysbiosis of gut microbiota. The altered gut microbiome may contribute to obesity development by promoting insulin resistance and systemic inflammation.

Associations between gut microbiota and colorectal cancer (CRC) have been widely investigated. However, the replicable markers for early-stage adenoma diagnosis across multiple populations remain elusive. Here, we perform an integrated analysis on 1056 public fecal samples, to identify adenoma-associated microbial markers for early detection of CRC. After adjusting for potential confounders, Random Forest classifiers are constructed with 11 markers to discriminate adenoma from control (area under the ROC curve (AUC) = 0.80), and 26 markers to discriminate adenoma from CRC (AUC = 0.89), respectively. Moreover, we validate the classifiers in two independent cohorts achieving AUCs of 0.78 and 0.84, respectively. Functional analysis reveals that the altered microbiome is characterized with increased ADP-l-glycero-beta-d-manno-heptose biosynthesis in adenoma and elevated menaquinone-10 biosynthesis in CRC. These findings are validated in a newly-collected cohort of 43 samples using quantitative real-time PCR. This work proves the validity of adenoma-specific markers across multi-populations, which would contribute to the early diagnosis and treatment of CRC.

The identification of synergistic chemotherapeutic agents from a large pool of candidates is highly challenging. Here, we present a Ranking-system of Anti-Cancer Synergy (RACS) that combines features of targeting networks and transcriptomic profiles, and validate it on three types of cancer. Using data on human β-cell lymphoma from the Dialogue for Reverse Engineering Assessments and Methods consortium we show a probability concordance of 0.78 compared with 0.61 obtained with the previous best algorithm. We confirm 63.6% of our breast cancer predictions through experiment and literature, including four strong synergistic pairs. Further in vivo screening in a zebrafish MCF7 xenograft model confirms one prediction with strong synergy and low toxicity. Validation using A549 lung cancer cells shows similar results. Thus, RACS can significantly improve drug synergy prediction and markedly reduce the experimental prescreening of existing drugs for repurposing to cancer treatment, although the molecular mechanism underlying particular interactions remains unknown.