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. 2025 Apr 10;23(1):420.
doi: 10.1186/s12967-025-06090-5.

Characterization of gut microbiota and metabolites in renal transplant recipients during COVID-19 and prediction of one-year allograft function

Zijie Wang #  1 , Xiang Gao #  1 , Hongsheng Ji #  2   3 , Ming Shao  3 , Bin Ni  1 , Shuang Fei  4 , Li Sun  4 , Hao Chen  4 , Ruoyun Tan  5 , Mulong Du  6   7   8 , Min Gu  9   10
Affiliations

Characterization of gut microbiota and metabolites in renal transplant recipients during COVID-19 and prediction of one-year allograft function

Zijie Wang et al. J Transl Med. .

Abstract

Background: The gut-lung-kidney axis is pivotal in immune-related kidney diseases, with gut dysbiosis potentially exacerbating the severity of Coronavirus disease 2019 (COVID-19) in recipients of kidney transplant. This study aimed to characterize the gut microbiome and metabolome in renal transplant recipients with COVID-19 pneumonia over a one-year follow-up period.

Methods: A total of 30 renal transplant recipients were enrolled, comprising 17 with COVID-19 pneumonia, six with mild COVID-19, and seven without COVID-19. Fecal samples were collected at the onset of infection for gut microbiome and metabolome analysis. Generalized Estimating Equations (GEE) model and Latent Class Growth Mixed Model (LCGMM) were employed to dissect the relationships among clinical characteristics, laboratory tests, and gut microbiota and metabolites.

Results: Four microbial phyla (Deferribacteres, TM7, Fusobacteria, and Gemmatimonadetes) and 13 genera were significantly enriched across three recipients groups, correlating with baseline inflammatory response and allograft function. Additionally, 52 differentially expressed metabolites were identified, with seven significantly correlating with eight altered microbiota genera. LCGMM revealed two distinct classes of recipients, with those suffering from COVID-19 pneumonia exhibiting significantly elevated serum creatinine (Scr) trajectories over the one-year period. GEE further identified 12 genera and 181 metabolites closely associated with these trajectories; a multivariable model incorporating gut metabolites of 1-Caffeoylquinic Acid and PMK was found to effectively predict one-year allograft function.

Conclusions: Our study indicates a possible interaction between the composition of the gut microbiota and metabolites community and COVID-19 in renal transplant recipients, particularly in relation to disease severity and the prediction of one-year allograft function.

Keywords: Allograft function; COVID-19; Gut metabolome; Gut microbiome; Kidney transplantation.

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Conflict of interest statement

Declarations. Conflict of interest: The authors of this manuscript have no conflicts of interest to disclose. Ethics approval and consent to participate: The study was conducted following the declaration of Helsinki. Ethical approval for this study was granted by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2023-SRFA-007). Patient consent: Written informed consent from all transplant recipients was obtained. The study was strictly limited to the living-related transplantations of kidney donors to lineal or collateral relatives not beyond the third degree of kinship, or transplantations of kidney donors from cadaveric allograft donors after cardiac death.

Figures

Fig. 1
Fig. 1
Study design and allograft function alteration in renal transplant recipients with COVID-19 pneumonia during one-year follow-up. (A) Timeline of one-year follow-up in 30 recipients. 0 represents the day recipients underwent SARS-CoV-2 infection; positive and negative number represent the day after or before SARS-CoV-2 infection; (B) Flow diagram of this study; (C) Fluctuation of serum creatinine (Scr) in recipients across three groups during one-year follow-up; (D-F) Time-to-event analysis of renal transplant recipients in three groups based on the principle of Scr declined more than 10% (D), more than 15% (E), and more than 20% (F), respectively
Fig. 2
Fig. 2
Alternations in gut microbiota composition from renal transplant recipients among three groups. (A) Average relative abundance of microbial phyla detected in fecal samples from three groups (COVID-19 pneumonia, COVID-19 and Control group); (B) Characteristic microbiota phyla identified by linear discriminant analysis (LDA) analysis; (C) Average relative abundance of microbiota genera detected in fecal samples from three groups; (D) Characteristic microbiota genera identified by LDA analysis; (E) Diversity comparison of gut microbiota in three groups by Alpha diversity analysis; (F) Beta diversity analysis of microbial community composition in samples from three groups using Partial Least Squares Discriminant Analysis (PLS-DA); (G) Association of gut microbiota composition alteration and blood inflammatory markers by Canonical Correspondence Analysis (CCA) analysis; (H) Association of gut microbiota composition alteration and renal allograft function biomarkers by CCA analysis. *P < 0.05; **P < 0.01; ***P < 0.001
Fig. 3
Fig. 3
Alternations in gut metabolites from renal transplant recipients among three groups. (A) Top 20 altered gut metabolites among three groups presented by biological function; (B) Multivariate statistical analysis of PLS-DA model was applied to examine the difference of gut metabolites among three groups; (C) 52 differential metabolites of 1348 total gut metabolites across three group; (D) metabolic enrichment analysis based on KEGG Compound Reaction Network; (E) Spearman’s correlation analysis of gut metabolites and microbiota
Fig. 4
Fig. 4
Altered gut microbiota and metabolites in one-year allograft function in renal transplant recipients with COVID-19. (A). Scr fluctuation of each recipient from three groups during one-year follow-up; (B-C). A two-class model (B) with a quadric slope for Scr alternation (C) by LCGMM analysis; (D). Presentation of recipients from three groups and their corresponding class; (E). Top 20 metabolic pathways selected by KEGG metabolic enrichment analysis; (F). Scatter plot of model fitting values and true values by multivariate linear regression modeling
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