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      Convergent application of traditional Chinese medicine and gut microbiota in ameliorate of cirrhosis: a data mining and Mendelian randomization study

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          Abstract

          Objective

          Traditional Chinese medicine (TCM) has been used for the treatment of chronic liver diseases for a long time, with proven safety and efficacy in clinical settings. Previous studies suggest that the therapeutic mechanism of TCM for hepatitis B cirrhosis may involve the gut microbiota. Nevertheless, the causal relationship between the gut microbiota, which is closely linked to TCM, and cirrhosis remains unknown. This study aims to utilize two-sample Mendelian randomization (MR) to investigate the potential causal relationship between gut microbes and cirrhosis, as well as to elucidate the synergistic mechanisms between botanical drugs and microbiota in treating cirrhosis.

          Methods

          Eight databases were systematically searched through May 2022 to identify clinical studies on TCM for hepatitis B cirrhosis. We analyzed the frequency, properties, flavors, and meridians of Chinese medicinals based on TCM theories and utilized the Apriori algorithm to identify the core botanical drugs for cirrhosis treatment. Cross-database comparison elucidated gut microbes sharing therapeutic targets with these core botanical drugs. MR analysis assessed consistency between gut microbiota causally implicated in cirrhosis and microbiota sharing therapeutic targets with key botanicals.

          Results

          Our findings revealed differences between the Chinese medicinals used for compensated and decompensated cirrhosis, with distinct frequency, dosage, properties, flavors, and meridian based on TCM theory. Angelicae Sinensis Radix, Salviae Miltiorrhizae Radix Et Rhizoma, Poria, Paeoniae Radix Alba, Astragali Radix, Atrctylodis Macrocephalae Rhizoma were the main botanicals. Botanical drugs and gut microbiota target MAPK1, VEGFA, STAT3, AKT1, RELA, JUN, and ESR1 in the treatment of hepatitis B cirrhosis, and their combined use has shown promise for cirrhosis treatment. MR analysis demonstrated a positive correlation between increased ClostridialesvadinBB60 and Ruminococcustorques abundance and heightened cirrhosis risk. In contrast, Eubacteriumruminantium, Lachnospiraceae, Eubacteriumnodatum, RuminococcaceaeNK4A214, Veillonella, and RuminococcaceaeUCG002 associated with reduced cirrhosis risk. Notably, Lachnospiraceae shares key therapeutic targets with core botanicals, which can treat cirrhosis at a causal level.

          Conclusion

          We identified 6 core botanical drugs for managing compensated and decompensated hepatitis B cirrhosis, despite slight prescription differences. The core botanical drugs affected cirrhosis through multiple targets and pathways. The shared biological effects between botanicals and protective gut microbiota offer a potential explanation for the therapeutic benefits of these key herbal components in treating cirrhosis. Elucidating these mechanisms provides crucial insights to inform new drug development and optimize clinical therapy for hepatitis B cirrhosis.

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          Most cited references50

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          Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

          Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
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            DrugBank 5.0: a major update to the DrugBank database for 2018

            Abstract DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year’s update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.
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              Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

              Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene–gene interactions, linkage disequilibrium, and ‘weak instruments’ on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized data give similar estimates and precision to the two-stage least squares method for individual-level data, even when there are gene–gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P-value in a linear regression of the risk factor for each variant is less than , then weak instrument bias will be small. We use these methods to estimate the causal association of low-density lipoprotein cholesterol (LDL-C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL-C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual-level data, although the necessary assumptions cannot be so fully assessed.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/1368335Role:
                URI : https://loop.frontiersin.org/people/1570311Role: Role:
                URI : https://loop.frontiersin.org/people/2539022Role: Role:
                URI : https://loop.frontiersin.org/people/2357491Role: Role:
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                URI : https://loop.frontiersin.org/people/2358364Role:
                URI : https://loop.frontiersin.org/people/2540190Role: Role:
                URI : https://loop.frontiersin.org/people/2358802Role:
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                Journal
                Front Cell Infect Microbiol
                Front Cell Infect Microbiol
                Front. Cell. Infect. Microbiol.
                Frontiers in Cellular and Infection Microbiology
                Frontiers Media S.A.
                2235-2988
                06 November 2023
                2023
                : 13
                : 1273031
                Affiliations
                [1] 1 The First College of Clinical Medicine, Henan University of Chinese Medicine , Zhengzhou, China
                [2] 2 Department of Orthopaedics, Jiangsu Province Hospital of Chinese Medicine , Nanjing, China
                [3] 3 Department of Hepatobiliary Surgery, Xianyang Central Hospital Affiliated to Shaanxi University of Chinese Medicine , Xianyang, China
                [4] 4 Department of Oncology, Affiliated Hospital of Shaanxi University of Chinese Medicine , Xianyang, China
                Author notes

                Edited by: Hui Ao, Chengdu University of Traditional Chinese Medicine, China

                Reviewed by: Qing Ji, Shanghai University of Traditional Chinese Medicine, China; Yotsawat Pomyen, Chulabhorn Research Institute, Thailand; Faming Zhang, Nanjing Medical University, China

                *Correspondence: Lin Yang, doctor0523@ 123456163.com ; Haijuan Xiao, xhjdoc@ 123456163.com

                †These authors have contributed equally to this work

                Article
                10.3389/fcimb.2023.1273031
                10657829
                38029250
                be6a71b2-3b54-4506-89d7-9eea7d820099
                Copyright © 2023 Zhou, Wei, Yu, Yang, Liu, Jia, Wang, Sun, Yang and Xiao

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 August 2023
                : 18 October 2023
                Page count
                Figures: 10, Tables: 2, Equations: 0, References: 50, Pages: 16, Words: 7470
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study has been supported by the National Natural Science Foundation of China (No.82074255), the Subject Innovation Team of Shaanxi University of Chinese Medicine (No.2019-YL06), Special Project for Double First Class Scientific Research in Traditional Chinese Medicine in Henan Province (HSRP-DFCTCM-2023-8-16) and Graduate Student Research and Innovation Enhancement Program Project (2022KYCX010).
                Categories
                Cellular and Infection Microbiology
                Original Research
                Custom metadata
                Intestinal Microbiome

                Infectious disease & Microbiology
                hepatitis b cirrhosis,traditional chinese medicine,data mining,network analysis,gut microbiota,mendelian randomization

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