Multi-omics integration identifies key biomarkers in retinopathy of prematurity through 16S rRNA sequencing and metabolomics
BackgroundThe gut microbiome is increasingly recognized for its role in the pathogenesis of neonatal conditions commonly associated with retinopathy of prematurity (ROP). This study aimed to identify key intestinal microbiota and metabolites in ROP and examine their relationships.MethodsFecal sample...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2025.1601292/full |
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| author | Linlin Guo Ruoming Wang Liping Han Yongcheng Fu Xiujuan Wang Lintao Nie Wenjun Fu Hongyan Ren Lijia Wu Guangshuai Li Juan Ding |
| author_facet | Linlin Guo Ruoming Wang Liping Han Yongcheng Fu Xiujuan Wang Lintao Nie Wenjun Fu Hongyan Ren Lijia Wu Guangshuai Li Juan Ding |
| author_sort | Linlin Guo |
| collection | DOAJ |
| description | BackgroundThe gut microbiome is increasingly recognized for its role in the pathogenesis of neonatal conditions commonly associated with retinopathy of prematurity (ROP). This study aimed to identify key intestinal microbiota and metabolites in ROP and examine their relationships.MethodsFecal samples were collected from infants with and without ROP at weeks 2 (T1) and 4 (T2) for 16S rRNA sequencing. At T2, additional fecal samples underwent non-targeted metabolomic analyses. A combined analysis of the 16S rRNA sequencing and metabolomics data was performed.ResultsNo significant differences in α-diversity indexes were observed between the ROP and non-ROP at T1. However, at T2, the Chao, ACE, and Shannon indices were significantly higher, whereas the Simpson index was lower in ROP compared to non-ROP. At the phylum level, the dominant phyla at T2 included Pseudomonadota, Bacillota, Actinomycetota, Bacteroidota, and Verrucomicrobiota. LEfSe analysis of T2 showed that Bifidobacterium, Rhodococcus, Staphyloococcus, Caulobacter, Sphingomonas, Aquabacterium, and Klebsiella as key genera associated with ROP. Metabolomic analysis identified 382 differentially accumulated metabolites, which were enriched in steroid hormone biosynthesis; the PPAR signaling pathway; linoleic acid metabolism; histidine metabolism; and alanine, aspartate, and glutamate metabolism. Additionally, the AUC of the combined analysis exceeded that of differential bacterial communities (0.9958) alone.ConclusionThis study revealed characteristic changes in the intestinal flora and metabolites in ROP, which provide promising targets/pathways for ROP diagnosis and therapy. |
| format | Article |
| id | doaj-art-b01e9b75ab4442e88aa3a73e9f13c33f |
| institution | OA Journals |
| issn | 1664-302X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Microbiology |
| spelling | doaj-art-b01e9b75ab4442e88aa3a73e9f13c33f2025-08-20T02:07:38ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2025-06-011610.3389/fmicb.2025.16012921601292Multi-omics integration identifies key biomarkers in retinopathy of prematurity through 16S rRNA sequencing and metabolomicsLinlin Guo0Ruoming Wang1Liping Han2Yongcheng Fu3Xiujuan Wang4Lintao Nie5Wenjun Fu6Hongyan Ren7Lijia Wu8Guangshuai Li9Juan Ding10The Second Department of Radiotherapy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Nursing, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Gynecology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Nursing, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Nursing, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Obstetrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaShanghai Mobio Biomedical Technology Co., Ltd., Shanghai, ChinaDepartment of Neonatal Intensive Care Unit, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Plastic and Reconstructive Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Nursing, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaBackgroundThe gut microbiome is increasingly recognized for its role in the pathogenesis of neonatal conditions commonly associated with retinopathy of prematurity (ROP). This study aimed to identify key intestinal microbiota and metabolites in ROP and examine their relationships.MethodsFecal samples were collected from infants with and without ROP at weeks 2 (T1) and 4 (T2) for 16S rRNA sequencing. At T2, additional fecal samples underwent non-targeted metabolomic analyses. A combined analysis of the 16S rRNA sequencing and metabolomics data was performed.ResultsNo significant differences in α-diversity indexes were observed between the ROP and non-ROP at T1. However, at T2, the Chao, ACE, and Shannon indices were significantly higher, whereas the Simpson index was lower in ROP compared to non-ROP. At the phylum level, the dominant phyla at T2 included Pseudomonadota, Bacillota, Actinomycetota, Bacteroidota, and Verrucomicrobiota. LEfSe analysis of T2 showed that Bifidobacterium, Rhodococcus, Staphyloococcus, Caulobacter, Sphingomonas, Aquabacterium, and Klebsiella as key genera associated with ROP. Metabolomic analysis identified 382 differentially accumulated metabolites, which were enriched in steroid hormone biosynthesis; the PPAR signaling pathway; linoleic acid metabolism; histidine metabolism; and alanine, aspartate, and glutamate metabolism. Additionally, the AUC of the combined analysis exceeded that of differential bacterial communities (0.9958) alone.ConclusionThis study revealed characteristic changes in the intestinal flora and metabolites in ROP, which provide promising targets/pathways for ROP diagnosis and therapy.https://www.frontiersin.org/articles/10.3389/fmicb.2025.1601292/fullretinopathy of prematuritydifferential intestinal floraldifferentially accumulated metabolitesmulti-omics integrationbiomarkers |
| spellingShingle | Linlin Guo Ruoming Wang Liping Han Yongcheng Fu Xiujuan Wang Lintao Nie Wenjun Fu Hongyan Ren Lijia Wu Guangshuai Li Juan Ding Multi-omics integration identifies key biomarkers in retinopathy of prematurity through 16S rRNA sequencing and metabolomics Frontiers in Microbiology retinopathy of prematurity differential intestinal floral differentially accumulated metabolites multi-omics integration biomarkers |
| title | Multi-omics integration identifies key biomarkers in retinopathy of prematurity through 16S rRNA sequencing and metabolomics |
| title_full | Multi-omics integration identifies key biomarkers in retinopathy of prematurity through 16S rRNA sequencing and metabolomics |
| title_fullStr | Multi-omics integration identifies key biomarkers in retinopathy of prematurity through 16S rRNA sequencing and metabolomics |
| title_full_unstemmed | Multi-omics integration identifies key biomarkers in retinopathy of prematurity through 16S rRNA sequencing and metabolomics |
| title_short | Multi-omics integration identifies key biomarkers in retinopathy of prematurity through 16S rRNA sequencing and metabolomics |
| title_sort | multi omics integration identifies key biomarkers in retinopathy of prematurity through 16s rrna sequencing and metabolomics |
| topic | retinopathy of prematurity differential intestinal floral differentially accumulated metabolites multi-omics integration biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2025.1601292/full |
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