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Gut microbiome and metabolite signatures for predicting acute kidney transplant rejection: a prospective study

Title
Gut microbiome and metabolite signatures for predicting acute kidney transplant rejection: a prospective study
Authors
Cho, Hyunjeong; Nam, Hoonsik; Kim, Hyo-Eun; Kim, Ji Eun; Park, Ji In; Park, Jina; Kim, Yong Chul; Lee, Jung Pyo; Kim, Dong Ki; Joo, Kwon Wook; Kim, Yon Su; Kim, Bong-Soo; Park, Sunghyouk; Lee, Hajeong
Ewha Authors
김봉수
SCOPUS Author ID
김봉수scopus
Issue Date
2025
Journal Title
SCIENTIFIC REPORTS
ISSN
2045-2322JCR Link
Citation
SCIENTIFIC REPORTS vol. 15, no. 1
Keywords
Acute rejection; Kidney transplantation; Gut microbiome
Publisher
NATURE PORTFOLIO
Indexed
SCIE; SCOPUS WOS
Document Type
Article
Abstract
Acute rejection (AR) remains a significant challenge in kidney transplantation (KT) despite advances in immunosuppressive treatment. Recognizing the critical influence of the gut microbiome on modulating host immunity, we investigated the association between gut dysbiosis and AR in KT recipients. A total of 97 patients with KT were prospectively enrolled from two centers, and their samples were collected at multiple time points, such as pre-transplant (n = 97), three months (n = 66), and twelve months (n = 37) post-transplant. Microbial profiling was performed using 16S rRNA sequencing and fecal metabolomics was done via nuclear magnetic resonance spectroscopy. Thirty-three patients developed AR after KT, exhibiting reduced bacterial richness and diversity compared with KT recipients without AR. In addition, these patients had increased Escherichia-Shigella and decreased Phascolarctobacterium abundance. Pathway analysis identified 47 enriched pathways in AR patients, notably those involved in lipopolysaccharide biosynthesis and short-chain fatty acid metabolism. Consistent results were obtained from stool metabolomics, showing reduced propionate and lactate concentrations compared with patients without AR. Finally, combining pre-KT bacterial and fecal metabolite features with clinical parameters significantly improved AR prediction accuracy. Our results suggest that integrating clinical, microbial, and metabolomic data may provide a more holistic patient care regimen across both pre- and post-transplant phases.
DOI
10.1038/s41598-025-28725-9
Appears in Collections:
신산업융합대학 > 식품영양학과 > Journal papers
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