Frontiers in microbiology · 2025
Multi-omics integration identifies key biomarkers in retinopathy of prematurity through 16S rRNA sequencing and metabolomics.
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Guo Linlin, Wang Ruoming, Han Liping, Fu Yongcheng, Wang Xiujuan, Nie Lintao, Fu Wenjun, Ren Hongyan, Wu Lijia, Li Guangshuai, Ding Juan
Аннотация
BACKGROUND: The 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.
METHODS: Fecal 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.
RESULTS: No 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.
CONCLUSION: This study revealed characteristic changes in the intestinal flora and metabolites in ROP, which provide promising targets/pathways for ROP diagnosis and therapy.
METHODS: Fecal 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.
RESULTS: No 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.
CONCLUSION: This study revealed characteristic changes in the intestinal flora and metabolites in ROP, which provide promising targets/pathways for ROP diagnosis and therapy.