The effect of Chinese herbal formulas combined with metformin on modulating the gut microbiota in the amelioration of type 2 diabetes mellitus: A systematic review and meta-analysis.

To assess and analyse the effectiveness and safety of combined Chinese herbal formula (CHF) and metformin treatment in the modulation of the gut microbiota in the amelioration of type 2 diabetes mellitus(T2DM), all publications addressing the effect of this combination treatment on the quantitative alterations in the gut microbiota and glucose parameters were collected. Rob tool in the Cochrane handbook was performed to evaluate the methodological quality of all included studies. Relevant information and statistics were abstracted and synthesized in Review Manager 5.4 to evaluate the efficacy of combination treatment. Sensitivity analyses and subgroup analyses were used to analyse the sources of heterogeneity. Publication bias analyses were performed by Stata software to assess the robustness and quality of the outcomes. As a result, a total of 12 eligible RCTs with 1307 T2DM participants from 7 electronic databases were included. Combined CHF with metformin treatment showed better efficacies than metformin monotherapy in regulating the structure of the gut microbiota, characterized by increased Bifidobacterium, Lactobacillus and Bacteroidetes and decreased Enterobacteriaceae, Enterococcus, and Saccharomyces along with better decreases in glycated haemoglobin, fasting plasma glucose, 2-hour postprandial blood glucose, fasting insulin and homeostasis model assessment of insulin resistance. Subgroup analyses further analysed the effect of metformin doses and CHF classifications on controlling hyperglycaemia and altering the gut microbiota. In conclusion, our meta-analysis suggested that combined CHF with metformin treatment is promising for the modulation of the gut microbiota along with ameliorating hyperglycemia in T2DM patients. Importantly, more well-designed RCTs are needed to validate the outcomes and verify the treatment value for clinical purposes. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021291524, identifier CRD42021291524.

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal.

Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

DrugComb: an integrative cancer drug combination data portal.

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users' own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.

Tissue-specific pathway association analysis using genome-wide association study summaries.

MOTIVATION: Pathway association analysis has made great achievements in elucidating the genetic basis of human complex diseases. However, current pathway association analysis approaches fail to consider tissue-specificity. RESULTS: We developed a tissue-specific pathway interaction enrichment analysis algorithm (TPIEA). TPIEA was applied to two large Caucasian and Chinese genome-wide association study summary datasets of bone mineral density (BMD). TPIEA identified several significant pathways for BMD [false discovery rate (FDR) < 0.05], such as KEGG FOCAL ADHESION and KEGG AXON GUIDANCE, which had been demonstrated to be involved in the development of osteoporosis. We also compared the performance of TPIEA and classical pathway enrichment analysis, and TPIEA presented improved performance in recognizing disease relevant pathways. TPIEA may help to fill the gap of classic pathway association analysis approaches by considering tissue specificity. AVAILABILITY AND IMPLEMENTATION: The online web tool of TPIEA is available at https://sourceforge.net/projects/tpieav1/files CONTACT: fzhxjtu@mail.xjtu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.