Cargando…

Using 16S rDNA and metagenomic sequencing technology to analyze the fecal microbiome of children with avoidant/restrictive food intake disorder

To investigate the gut microbiota distribution and its functions in children with avoidant/restrictive food intake disorder (ARFID). A total of 135 children were enrolled in the study, including 102 children with ARFID and 33 healthy children. Fecal samples were analyzed to explore differences in gu...

Descripción completa

Detalles Bibliográficos
Autores principales: Ye, Qina, Sun, Shaodan, Deng, Jian, Chen, Xiaogang, Zhang, Jing, Lin, Suihua, Du, Hongxuan, Gao, Jinxiong, Zou, Xiaoyin, Lin, Xiaoling, Cai, Yawen, Lu, Zhuoming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661725/
https://www.ncbi.nlm.nih.gov/pubmed/37985845
http://dx.doi.org/10.1038/s41598-023-47760-y
_version_ 1785148477932568576
author Ye, Qina
Sun, Shaodan
Deng, Jian
Chen, Xiaogang
Zhang, Jing
Lin, Suihua
Du, Hongxuan
Gao, Jinxiong
Zou, Xiaoyin
Lin, Xiaoling
Cai, Yawen
Lu, Zhuoming
author_facet Ye, Qina
Sun, Shaodan
Deng, Jian
Chen, Xiaogang
Zhang, Jing
Lin, Suihua
Du, Hongxuan
Gao, Jinxiong
Zou, Xiaoyin
Lin, Xiaoling
Cai, Yawen
Lu, Zhuoming
author_sort Ye, Qina
collection PubMed
description To investigate the gut microbiota distribution and its functions in children with avoidant/restrictive food intake disorder (ARFID). A total of 135 children were enrolled in the study, including 102 children with ARFID and 33 healthy children. Fecal samples were analyzed to explore differences in gut microbiota composition and diversity and functional differences between the ARFID and healthy control (HC) groups via 16S rDNA and metagenomic sequencing. The gut microbiota composition and diversity in children with ARFID were different from those in heathy children, but there is no difference in the composition and diversity of gut microbiota between children at the age of 3–6 and 7–12 with ARFID. At the phylum level, the most abundant microbes in the two groups identified by 16S rDNA and metagenomic sequencing were the same. At the genus level, the abundance of Bacteroides was higher in the ARFID group (P > 0.05); however, different from the result of 16SrDNA sequencing, metagenomic sequencing showed that the abundance of Bacteroides in the ARFID group was significantly higher than that in the HC group (P = 0.041). At the species level, Escherichia coli, Streptococcus thermophilus and Lachnospira eligens were the most abundant taxa in the ARFID group, and Prevotella copri, Bifidobacterium pseudocatenulatum, and Ruminococcus gnavus were the top three microbial taxa in the HC group; there were no statistically significant differences between the abundance of these microbial taxa in the two groups. LefSe analysis indicated a greater abundance of the order Enterobacterales and its corresponding family Enterobacteriaceae, the family Bacteroidaceae and corresponding genus Bacteroides, the species Bacteroides vulgatus in ARFID group, while the abundance of the phylum Actinobacteriota and its corresponding class Actinobacteria , the order Bifidobacteriales and corresponding family Bifidobacteriaceae, the genus Bifidobacterium were enriched in the HC group. There were no statistically significant differences in the Chao1, Shannon and Simpson indices between the Y1 and Y2 groups (P = 0.1, P = 0.06, P = 0.06). At the phylum level, Bacillota, Bacteroidota, Proteobacteria and Actinobacteriota were the most abundant taxa in both groups, but there were no statistically significant differences among the abundance of these bacteria (P = 0.958, P = 0.456, P = 0.473, P = 0.065). At the genus level, Faecalibacterium was more abundant in the Y2 group than in the Y1 group, and the difference was statistically significant (P = 0.037). The KEGG annotation results showed no significant difference in gut microbiota function between children with ARFID and healthy children; however, GT26 was significantly enriched in children with ARFID based on the CAZy database. The most abundant antibiotic resistance genes in the ARFID group were the vanT, tetQ, adeF, ermF genes, and the abundance of macrolide resistance genes in the ARFID group was significantly higher than that in the HC group (P = 0.041). Compared with healthy children, children with ARFID have a different distribution of the gut microbiota and functional genes. This indicates that the gut microbiome might play an important role in the pathogenesis of ARFID. Clinical trial registration: ChiCTR2300074759
format Online
Article
Text
id pubmed-10661725
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-106617252023-11-20 Using 16S rDNA and metagenomic sequencing technology to analyze the fecal microbiome of children with avoidant/restrictive food intake disorder Ye, Qina Sun, Shaodan Deng, Jian Chen, Xiaogang Zhang, Jing Lin, Suihua Du, Hongxuan Gao, Jinxiong Zou, Xiaoyin Lin, Xiaoling Cai, Yawen Lu, Zhuoming Sci Rep Article To investigate the gut microbiota distribution and its functions in children with avoidant/restrictive food intake disorder (ARFID). A total of 135 children were enrolled in the study, including 102 children with ARFID and 33 healthy children. Fecal samples were analyzed to explore differences in gut microbiota composition and diversity and functional differences between the ARFID and healthy control (HC) groups via 16S rDNA and metagenomic sequencing. The gut microbiota composition and diversity in children with ARFID were different from those in heathy children, but there is no difference in the composition and diversity of gut microbiota between children at the age of 3–6 and 7–12 with ARFID. At the phylum level, the most abundant microbes in the two groups identified by 16S rDNA and metagenomic sequencing were the same. At the genus level, the abundance of Bacteroides was higher in the ARFID group (P > 0.05); however, different from the result of 16SrDNA sequencing, metagenomic sequencing showed that the abundance of Bacteroides in the ARFID group was significantly higher than that in the HC group (P = 0.041). At the species level, Escherichia coli, Streptococcus thermophilus and Lachnospira eligens were the most abundant taxa in the ARFID group, and Prevotella copri, Bifidobacterium pseudocatenulatum, and Ruminococcus gnavus were the top three microbial taxa in the HC group; there were no statistically significant differences between the abundance of these microbial taxa in the two groups. LefSe analysis indicated a greater abundance of the order Enterobacterales and its corresponding family Enterobacteriaceae, the family Bacteroidaceae and corresponding genus Bacteroides, the species Bacteroides vulgatus in ARFID group, while the abundance of the phylum Actinobacteriota and its corresponding class Actinobacteria , the order Bifidobacteriales and corresponding family Bifidobacteriaceae, the genus Bifidobacterium were enriched in the HC group. There were no statistically significant differences in the Chao1, Shannon and Simpson indices between the Y1 and Y2 groups (P = 0.1, P = 0.06, P = 0.06). At the phylum level, Bacillota, Bacteroidota, Proteobacteria and Actinobacteriota were the most abundant taxa in both groups, but there were no statistically significant differences among the abundance of these bacteria (P = 0.958, P = 0.456, P = 0.473, P = 0.065). At the genus level, Faecalibacterium was more abundant in the Y2 group than in the Y1 group, and the difference was statistically significant (P = 0.037). The KEGG annotation results showed no significant difference in gut microbiota function between children with ARFID and healthy children; however, GT26 was significantly enriched in children with ARFID based on the CAZy database. The most abundant antibiotic resistance genes in the ARFID group were the vanT, tetQ, adeF, ermF genes, and the abundance of macrolide resistance genes in the ARFID group was significantly higher than that in the HC group (P = 0.041). Compared with healthy children, children with ARFID have a different distribution of the gut microbiota and functional genes. This indicates that the gut microbiome might play an important role in the pathogenesis of ARFID. Clinical trial registration: ChiCTR2300074759 Nature Publishing Group UK 2023-11-20 /pmc/articles/PMC10661725/ /pubmed/37985845 http://dx.doi.org/10.1038/s41598-023-47760-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ye, Qina
Sun, Shaodan
Deng, Jian
Chen, Xiaogang
Zhang, Jing
Lin, Suihua
Du, Hongxuan
Gao, Jinxiong
Zou, Xiaoyin
Lin, Xiaoling
Cai, Yawen
Lu, Zhuoming
Using 16S rDNA and metagenomic sequencing technology to analyze the fecal microbiome of children with avoidant/restrictive food intake disorder
title Using 16S rDNA and metagenomic sequencing technology to analyze the fecal microbiome of children with avoidant/restrictive food intake disorder
title_full Using 16S rDNA and metagenomic sequencing technology to analyze the fecal microbiome of children with avoidant/restrictive food intake disorder
title_fullStr Using 16S rDNA and metagenomic sequencing technology to analyze the fecal microbiome of children with avoidant/restrictive food intake disorder
title_full_unstemmed Using 16S rDNA and metagenomic sequencing technology to analyze the fecal microbiome of children with avoidant/restrictive food intake disorder
title_short Using 16S rDNA and metagenomic sequencing technology to analyze the fecal microbiome of children with avoidant/restrictive food intake disorder
title_sort using 16s rdna and metagenomic sequencing technology to analyze the fecal microbiome of children with avoidant/restrictive food intake disorder
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661725/
https://www.ncbi.nlm.nih.gov/pubmed/37985845
http://dx.doi.org/10.1038/s41598-023-47760-y
work_keys_str_mv AT yeqina using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder
AT sunshaodan using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder
AT dengjian using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder
AT chenxiaogang using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder
AT zhangjing using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder
AT linsuihua using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder
AT duhongxuan using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder
AT gaojinxiong using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder
AT zouxiaoyin using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder
AT linxiaoling using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder
AT caiyawen using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder
AT luzhuoming using16srdnaandmetagenomicsequencingtechnologytoanalyzethefecalmicrobiomeofchildrenwithavoidantrestrictivefoodintakedisorder