Loading…
Investigating the influence of the gut microbiome on cholelithiasis: unveiling insights through sequencing and predictive modeling
Cholelithiasis is one of the most common disorders of hepatobiliary system. Gut bacteria may be involved in the process of gallstone formation and are, therefore considered as potential targets for cholelithiasis prediction. To reveal the correlation between cholelithiasis and gut bacteria. Stool sa...
Saved in:
| Published in: | Journal of applied microbiology 2024-05, Vol.135 (5) |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c335t-e894c62f1ff3282c1632c9b00d7440aa5dd3386115598ae3d2a87638f02156b43 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c335t-e894c62f1ff3282c1632c9b00d7440aa5dd3386115598ae3d2a87638f02156b43 |
| container_end_page | |
| container_issue | 5 |
| container_start_page | |
| container_title | Journal of applied microbiology |
| container_volume | 135 |
| creator | Boyang, Hu Yanjun, Yao Jing, Zhuang Chenxin, Yan Ying, Mei Shuwen, Han Qiang, Yan |
| description | Cholelithiasis is one of the most common disorders of hepatobiliary system. Gut bacteria may be involved in the process of gallstone formation and are, therefore considered as potential targets for cholelithiasis prediction.
To reveal the correlation between cholelithiasis and gut bacteria.
Stool samples were collected from 100 cholelithiasis and 250 healthy individuals from Huzhou Central Hospital; The 16S rRNA of gut bacteria in the stool samples was sequenced using the third-generation Pacbio sequencing platform; Mothur v.1.21.1 was used to analyze the diversity of gut bacteria; Wilcoxon rank-sum test and linear discriminant analysis of effect sizes (LEfSe) were used to analyze differences in gut bacteria between patients suffering from cholelithiasis and healthy individuals; Chord diagram and Plot-related heat maps were used to analyze the correlation between cholelithiasis and gut bacteria; six machine algorithms were used to construct models to predict cholelithiasis.
There were differences in the abundance of gut bacteria between cholelithiasis and healthy individuals, but there were no differences in their community diversity. Increased abundance of Costridia, Escherichia flexneri, and Klebsiella pneumonae were found in cholelithiasis, while Bacteroidia, Phocaeicola, and Phocaeicola vulgatus were more abundant in healthy individuals. The top four bacteria that were most closely associated with cholelithiasis were Escherichia flexneri, Escherichia dysenteriae, Streptococcus salivarius, and Phocaeicola vulgatus. The cholelithiasis model based on CatBoost algorithm had the best prediction effect (sensitivity: 90.48%, specificity: 88.32%, and AUC: 0.962).
The identification of characteristic gut bacteria may provide new predictive targets for gallstone screening. As being screened by the predictive model, people at high risk of cholelithiasis can determine the need for further testing, thus enabling early warning of cholelithiasis. |
| doi_str_mv | 10.1093/jambio/lxae096 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_3038443496</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3038443496</sourcerecordid><originalsourceid>FETCH-LOGICAL-c335t-e894c62f1ff3282c1632c9b00d7440aa5dd3386115598ae3d2a87638f02156b43</originalsourceid><addsrcrecordid>eNpNkL1PwzAQxS0EoqWwMiKPLGntOHYTNlTxUakSC8yR45wTV0lcYqeClb8chxbEdKfze7_zPYSuKZlTkrHFVraFsYvmQwLJxAmaUiZ4FItlfPqvn6AL57aEUEa4OEcTlgqaZDyboq91twfnTSW96Srsa8Cm080AnQJs9c-gGjxujept2NSGaYdVbRtojK-NdMbd4SFATDMCTOdMVXsXjL0dqho7eB9h45vsSrzroTTKmz3g1pYwei7RmZaNg6tjnaG3x4fX1XO0eXlar-43kWKM-wjSLFEi1lRrFqexooLFKisIKZdJQqTkZcnGuyjnWSqBlbFMl4KlmsSUiyJhM3R74O56G_7kfN4ap6BpZAd2cDkjLE0SlmQiSOcHaTjauR50vutNK_vPnJJ8zD0_5J4fcw-GmyN7KFoo_-S_QbNveTeDnA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3038443496</pqid></control><display><type>article</type><title>Investigating the influence of the gut microbiome on cholelithiasis: unveiling insights through sequencing and predictive modeling</title><source>OUP_牛津大学出版社现刊</source><source>Alma/SFX Local Collection</source><creator>Boyang, Hu ; Yanjun, Yao ; Jing, Zhuang ; Chenxin, Yan ; Ying, Mei ; Shuwen, Han ; Qiang, Yan</creator><creatorcontrib>Boyang, Hu ; Yanjun, Yao ; Jing, Zhuang ; Chenxin, Yan ; Ying, Mei ; Shuwen, Han ; Qiang, Yan</creatorcontrib><description>Cholelithiasis is one of the most common disorders of hepatobiliary system. Gut bacteria may be involved in the process of gallstone formation and are, therefore considered as potential targets for cholelithiasis prediction.
To reveal the correlation between cholelithiasis and gut bacteria.
Stool samples were collected from 100 cholelithiasis and 250 healthy individuals from Huzhou Central Hospital; The 16S rRNA of gut bacteria in the stool samples was sequenced using the third-generation Pacbio sequencing platform; Mothur v.1.21.1 was used to analyze the diversity of gut bacteria; Wilcoxon rank-sum test and linear discriminant analysis of effect sizes (LEfSe) were used to analyze differences in gut bacteria between patients suffering from cholelithiasis and healthy individuals; Chord diagram and Plot-related heat maps were used to analyze the correlation between cholelithiasis and gut bacteria; six machine algorithms were used to construct models to predict cholelithiasis.
There were differences in the abundance of gut bacteria between cholelithiasis and healthy individuals, but there were no differences in their community diversity. Increased abundance of Costridia, Escherichia flexneri, and Klebsiella pneumonae were found in cholelithiasis, while Bacteroidia, Phocaeicola, and Phocaeicola vulgatus were more abundant in healthy individuals. The top four bacteria that were most closely associated with cholelithiasis were Escherichia flexneri, Escherichia dysenteriae, Streptococcus salivarius, and Phocaeicola vulgatus. The cholelithiasis model based on CatBoost algorithm had the best prediction effect (sensitivity: 90.48%, specificity: 88.32%, and AUC: 0.962).
The identification of characteristic gut bacteria may provide new predictive targets for gallstone screening. As being screened by the predictive model, people at high risk of cholelithiasis can determine the need for further testing, thus enabling early warning of cholelithiasis.</description><identifier>ISSN: 1365-2672</identifier><identifier>EISSN: 1365-2672</identifier><identifier>DOI: 10.1093/jambio/lxae096</identifier><identifier>PMID: 38614959</identifier><language>eng</language><publisher>England</publisher><subject>Adult ; Aged ; Bacteria - classification ; Bacteria - genetics ; Bacteria - isolation & purification ; Cholelithiasis - microbiology ; Feces - microbiology ; Female ; Gastrointestinal Microbiome ; Humans ; Male ; Middle Aged ; RNA, Ribosomal, 16S - genetics</subject><ispartof>Journal of applied microbiology, 2024-05, Vol.135 (5)</ispartof><rights>The Author(s) 2024. Published by Oxford University Press on behalf of Applied Microbiology International.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c335t-e894c62f1ff3282c1632c9b00d7440aa5dd3386115598ae3d2a87638f02156b43</citedby><cites>FETCH-LOGICAL-c335t-e894c62f1ff3282c1632c9b00d7440aa5dd3386115598ae3d2a87638f02156b43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,783,787,27936,27937</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38614959$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Boyang, Hu</creatorcontrib><creatorcontrib>Yanjun, Yao</creatorcontrib><creatorcontrib>Jing, Zhuang</creatorcontrib><creatorcontrib>Chenxin, Yan</creatorcontrib><creatorcontrib>Ying, Mei</creatorcontrib><creatorcontrib>Shuwen, Han</creatorcontrib><creatorcontrib>Qiang, Yan</creatorcontrib><title>Investigating the influence of the gut microbiome on cholelithiasis: unveiling insights through sequencing and predictive modeling</title><title>Journal of applied microbiology</title><addtitle>J Appl Microbiol</addtitle><description>Cholelithiasis is one of the most common disorders of hepatobiliary system. Gut bacteria may be involved in the process of gallstone formation and are, therefore considered as potential targets for cholelithiasis prediction.
To reveal the correlation between cholelithiasis and gut bacteria.
Stool samples were collected from 100 cholelithiasis and 250 healthy individuals from Huzhou Central Hospital; The 16S rRNA of gut bacteria in the stool samples was sequenced using the third-generation Pacbio sequencing platform; Mothur v.1.21.1 was used to analyze the diversity of gut bacteria; Wilcoxon rank-sum test and linear discriminant analysis of effect sizes (LEfSe) were used to analyze differences in gut bacteria between patients suffering from cholelithiasis and healthy individuals; Chord diagram and Plot-related heat maps were used to analyze the correlation between cholelithiasis and gut bacteria; six machine algorithms were used to construct models to predict cholelithiasis.
There were differences in the abundance of gut bacteria between cholelithiasis and healthy individuals, but there were no differences in their community diversity. Increased abundance of Costridia, Escherichia flexneri, and Klebsiella pneumonae were found in cholelithiasis, while Bacteroidia, Phocaeicola, and Phocaeicola vulgatus were more abundant in healthy individuals. The top four bacteria that were most closely associated with cholelithiasis were Escherichia flexneri, Escherichia dysenteriae, Streptococcus salivarius, and Phocaeicola vulgatus. The cholelithiasis model based on CatBoost algorithm had the best prediction effect (sensitivity: 90.48%, specificity: 88.32%, and AUC: 0.962).
The identification of characteristic gut bacteria may provide new predictive targets for gallstone screening. As being screened by the predictive model, people at high risk of cholelithiasis can determine the need for further testing, thus enabling early warning of cholelithiasis.</description><subject>Adult</subject><subject>Aged</subject><subject>Bacteria - classification</subject><subject>Bacteria - genetics</subject><subject>Bacteria - isolation & purification</subject><subject>Cholelithiasis - microbiology</subject><subject>Feces - microbiology</subject><subject>Female</subject><subject>Gastrointestinal Microbiome</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>RNA, Ribosomal, 16S - genetics</subject><issn>1365-2672</issn><issn>1365-2672</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNpNkL1PwzAQxS0EoqWwMiKPLGntOHYTNlTxUakSC8yR45wTV0lcYqeClb8chxbEdKfze7_zPYSuKZlTkrHFVraFsYvmQwLJxAmaUiZ4FItlfPqvn6AL57aEUEa4OEcTlgqaZDyboq91twfnTSW96Srsa8Cm080AnQJs9c-gGjxujept2NSGaYdVbRtojK-NdMbd4SFATDMCTOdMVXsXjL0dqho7eB9h45vsSrzroTTKmz3g1pYwei7RmZaNg6tjnaG3x4fX1XO0eXlar-43kWKM-wjSLFEi1lRrFqexooLFKisIKZdJQqTkZcnGuyjnWSqBlbFMl4KlmsSUiyJhM3R74O56G_7kfN4ap6BpZAd2cDkjLE0SlmQiSOcHaTjauR50vutNK_vPnJJ8zD0_5J4fcw-GmyN7KFoo_-S_QbNveTeDnA</recordid><startdate>20240501</startdate><enddate>20240501</enddate><creator>Boyang, Hu</creator><creator>Yanjun, Yao</creator><creator>Jing, Zhuang</creator><creator>Chenxin, Yan</creator><creator>Ying, Mei</creator><creator>Shuwen, Han</creator><creator>Qiang, Yan</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20240501</creationdate><title>Investigating the influence of the gut microbiome on cholelithiasis: unveiling insights through sequencing and predictive modeling</title><author>Boyang, Hu ; Yanjun, Yao ; Jing, Zhuang ; Chenxin, Yan ; Ying, Mei ; Shuwen, Han ; Qiang, Yan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c335t-e894c62f1ff3282c1632c9b00d7440aa5dd3386115598ae3d2a87638f02156b43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Bacteria - classification</topic><topic>Bacteria - genetics</topic><topic>Bacteria - isolation & purification</topic><topic>Cholelithiasis - microbiology</topic><topic>Feces - microbiology</topic><topic>Female</topic><topic>Gastrointestinal Microbiome</topic><topic>Humans</topic><topic>Male</topic><topic>Middle Aged</topic><topic>RNA, Ribosomal, 16S - genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Boyang, Hu</creatorcontrib><creatorcontrib>Yanjun, Yao</creatorcontrib><creatorcontrib>Jing, Zhuang</creatorcontrib><creatorcontrib>Chenxin, Yan</creatorcontrib><creatorcontrib>Ying, Mei</creatorcontrib><creatorcontrib>Shuwen, Han</creatorcontrib><creatorcontrib>Qiang, Yan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of applied microbiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Boyang, Hu</au><au>Yanjun, Yao</au><au>Jing, Zhuang</au><au>Chenxin, Yan</au><au>Ying, Mei</au><au>Shuwen, Han</au><au>Qiang, Yan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Investigating the influence of the gut microbiome on cholelithiasis: unveiling insights through sequencing and predictive modeling</atitle><jtitle>Journal of applied microbiology</jtitle><addtitle>J Appl Microbiol</addtitle><date>2024-05-01</date><risdate>2024</risdate><volume>135</volume><issue>5</issue><issn>1365-2672</issn><eissn>1365-2672</eissn><abstract>Cholelithiasis is one of the most common disorders of hepatobiliary system. Gut bacteria may be involved in the process of gallstone formation and are, therefore considered as potential targets for cholelithiasis prediction.
To reveal the correlation between cholelithiasis and gut bacteria.
Stool samples were collected from 100 cholelithiasis and 250 healthy individuals from Huzhou Central Hospital; The 16S rRNA of gut bacteria in the stool samples was sequenced using the third-generation Pacbio sequencing platform; Mothur v.1.21.1 was used to analyze the diversity of gut bacteria; Wilcoxon rank-sum test and linear discriminant analysis of effect sizes (LEfSe) were used to analyze differences in gut bacteria between patients suffering from cholelithiasis and healthy individuals; Chord diagram and Plot-related heat maps were used to analyze the correlation between cholelithiasis and gut bacteria; six machine algorithms were used to construct models to predict cholelithiasis.
There were differences in the abundance of gut bacteria between cholelithiasis and healthy individuals, but there were no differences in their community diversity. Increased abundance of Costridia, Escherichia flexneri, and Klebsiella pneumonae were found in cholelithiasis, while Bacteroidia, Phocaeicola, and Phocaeicola vulgatus were more abundant in healthy individuals. The top four bacteria that were most closely associated with cholelithiasis were Escherichia flexneri, Escherichia dysenteriae, Streptococcus salivarius, and Phocaeicola vulgatus. The cholelithiasis model based on CatBoost algorithm had the best prediction effect (sensitivity: 90.48%, specificity: 88.32%, and AUC: 0.962).
The identification of characteristic gut bacteria may provide new predictive targets for gallstone screening. As being screened by the predictive model, people at high risk of cholelithiasis can determine the need for further testing, thus enabling early warning of cholelithiasis.</abstract><cop>England</cop><pmid>38614959</pmid><doi>10.1093/jambio/lxae096</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1365-2672 |
| ispartof | Journal of applied microbiology, 2024-05, Vol.135 (5) |
| issn | 1365-2672 1365-2672 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_3038443496 |
| source | OUP_牛津大学出版社现刊; Alma/SFX Local Collection |
| subjects | Adult Aged Bacteria - classification Bacteria - genetics Bacteria - isolation & purification Cholelithiasis - microbiology Feces - microbiology Female Gastrointestinal Microbiome Humans Male Middle Aged RNA, Ribosomal, 16S - genetics |
| title | Investigating the influence of the gut microbiome on cholelithiasis: unveiling insights through sequencing and predictive modeling |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-11-18T01%3A30%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Investigating%20the%20influence%20of%20the%20gut%20microbiome%20on%20cholelithiasis:%20unveiling%20insights%20through%20sequencing%20and%20predictive%20modeling&rft.jtitle=Journal%20of%20applied%20microbiology&rft.au=Boyang,%20Hu&rft.date=2024-05-01&rft.volume=135&rft.issue=5&rft.issn=1365-2672&rft.eissn=1365-2672&rft_id=info:doi/10.1093/jambio/lxae096&rft_dat=%3Cproquest_cross%3E3038443496%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c335t-e894c62f1ff3282c1632c9b00d7440aa5dd3386115598ae3d2a87638f02156b43%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3038443496&rft_id=info:pmid/38614959&rfr_iscdi=true |