High-throughput sequencing allows for the comprehensive analysis of the human intestinal microbiota. However, extensive association analyses between the microbiome and lifestyle differences in the Chinese population are limited. Here, we carried out an independent cohort study—the Chinese Healthy Gut Project (n = 483)—where correlations between the gut microbiota and dietary and lifestyle variables in a healthy Chinese population are defined. We collected both questionnaire data, including basic information and lifestyle and dietary variables, and fecal stools from the enrolled volunteers. We then performed 16S rRNA sequencing on the microbial DNA isolated from the stools to assess the composition of the intestinal microbiota. We found that Prevotella and Bacteroides were the most abundant genera in the healthy Chinese gut microbiome. Additionally, 9 out of 29 clinical and questionnaire-based phenotype covariates were found to be associated with the variation in the composition of the gut microbiota. Among these lifestyle phenotypes, sleep procrastination, negative mood, and drinking habits had the largest effect size. Additionally, an appreciable effect of urbanization was observed, resulting in decreased intra-individual diversity, increased inter-individual diversity, and an increased abundance of the Bacteroides enterotype. The results of this study provide a foundation for assessing the healthy Chinese gut microbiota community structure at baseline in a healthy Chinese population. Furthermore, this study also provides insights into understanding how distinctive living habits influence the relationships between the Chinese gut microbiome and systemic health state.

The COVID-19 disease caused by the SARS-CoV-2 virus is a health crisis worldwide. While developing novel drugs and vaccines is long, repurposing existing drugs against COVID-19 can yield treatments with known preclinical, pharmacokinetic, pharmacodynamic, and toxicity profiles, which can rapidly enter clinical trials. In this study, we present a novel network-based drug repurposing platform to identify candidates for the treatment of COVID-19. At the time of the initial outbreak, knowledge about SARS-CoV-2 was lacking, but based on its similarity with other viruses, we sought to identify repurposing candidates to be tested rapidly at the clinical or preclinical levels. We first analyzed the genome sequence of SARS-CoV-2 and confirmed SARS as the closest virus by genome similarity, followed by MERS and other human coronaviruses. Using text mining and database searches, we obtained 34 COVID-19-related genes to seed the construction of a molecular network where our module detection and drug prioritization algorithms identified 24 disease-related human pathways, five modules, and 78 drugs to repurpose. Based on clinical knowledge, we re-prioritized 30 potentially repurposable drugs against COVID-19 (including pseudoephedrine, andrographolide, chloroquine, abacavir, and thalidomide). Our work shows how in silico repurposing analyses can yield testable candidates to accelerate the response to novel disease outbreaks.

Benefiting from the development of network pharmacology, Traditional Chinese Medicine (TCM) shows great potential in modern drug discovery. Recently, more and more TCM-related databases have been established for both academic and industry research, but they are still insufficient in data standardization, integrity, and precision. To better accelerate the TCM research and overcome these shortcomings, we construct a web-based TCM platform, LTM-TCM, which is currently the most comprehensive TCM database that includes the following advantages: (1) High-quality data integration from fourteen TCM authoritative databases, especially with additional manual collected 41,025 clinical treatment records and 213 ancient Chinese medical books. (2) Accurate correction of multi-source TCM interactions (between symptoms, prescriptions, herbs, ingredients and targets) through in-house Biomedical Natural Language Processing (BioNLP) approaches in more than 30 million articles. (3) Diverse cross-field pipelines (e.g., bioactive ingredients screening, targets prediction, and mechanism prediction, etc.) help integrating traditional medicine with modern science in common aspects at both the molecular and phenotypic levels. In summary, LTM-TCM contains 1928 symptoms, 48,126 prescriptions, 9122 plants, 34,967 ingredients, 13,109 targets and 1170,133 interactions among all TCM related components. LTM-TCM has both Chinese and English interfaces, and it is accessible at http://cloud.tasly.com/#/tcm/home.

Predicting candidate genes using gene expression profiles and unbiased protein-protein interactions (PPI) contributes a lot in deciphering the pathogenesis of complex diseases. Recent studies showed that there are significant disparities in network topological features between non-disease and disease genes in protein-protein interaction settings. Integrated methods could consider their characteristics comprehensively in a biological network. In this study, we introduce a novel computational method, based on combined network topological features, to construct a combined classifier and then use it to predict candidate genes for coronary artery diseases (CAD). As a result, 276 novel candidate genes were predicted and were found to share similar functions to known disease genes. The majority of the candidate genes were cross-validated by other three methods. Our method will be useful in the search for candidate genes of other diseases.

MicroRNAs (miRNAs) play important roles in several human cancers. Although miR-188 has been suggested to function as a tumor repressor in cancers, its precise role in glioma and the molecular mechanism remain unknown. In the present study, we investigated the effect of miR-188 on glioma and explored its relevant mechanisms. We found that the expression of miR-188 is dramatically downregulated in glioma tissues and cell lines. Subsequent investigation revealed that miR-188 expression was inversely correlated with β-catenin expression in glioma tissue samples. Using a luciferase reporter assay, β-catenin was determined to be a direct target of miR-188. Overexpression of miR-188 reduced β-catenin expression at both the mRNA and protein levels, and inhibition of miR-188 increased β-catenin expression. Moreover, we found that overexpression of miR-188 suppressed glioma cell proliferation and cell cycle G₁‐S transition, whereas inhibition of miR-188 promoted glioma cell proliferation. Importantly, silencing β-catenin recapitulated the cellular and molecular effects seen upon miR-188 overexpression, which included inhibiting glioma cell proliferation and G₁‐S transition. Taken together, our results demonstrated that miR-188 inhibits glioma cell proliferation by targeting β-catenin, representing an effective therapeutic strategy for glioma.

Advances in high-throughput technologies have generated diverse informative molecular markers for cancer outcome prediction. Long non-coding RNA (lncRNA) and DNA methylation as new classes of promising markers are emerging as key molecules in human cancers; however, the prognostic utility of such diverse molecular data remains to be explored. We proposed a computational pipeline (IDFO) to predict patient survival by identifying prognosis-related biomarkers using multi-type molecular data (mRNA, microRNA, DNA methylation, and lncRNA) from 3198 samples of five cancer types. We assessed the predictive performance of both single molecular data and integrated multi-type molecular data in patient survival stratification, and compared their relative importance in each type of cancer, respectively. Survival analysis using multivariate Cox regression was performed to investigate the impact of the IDFO-identified markers and traditional variables on clinical outcome. Using the IDFO approach, we obtained good predictive performance of the molecular datasets (bootstrap accuracy: 0.71–0.97) in five cancer types. Impressively, lncRNA was identified as the best prognostic predictor in the validated cohorts of four cancer types, followed by DNA methylation, mRNA, and then microRNA. We found the incorporating of multi-type molecular data showed similar predictive power to single-type molecular data, but with the exception of the lncRNA + DNA methylation combinations in two cancers. Survival analysis of proportional hazard models confirmed a high robustness for lncRNA and DNA methylation as prognosis factors independent of traditional clinical variables. Our study provides insight into systematically understanding the prognostic performance of diverse molecular data in both single and aggregate patterns, which may have specific reference to subsequent related studies.