Metagenomic Profiling of Gut Microbiota in Kidney Precision Medicine Project Participants With CKD and AKI

Authors

Sanjeev Noel, Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Shishir Kumar Patel, Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
James White, Resphera Biosciences, LLC, Baltimore, Maryland, USA.
Deepak Verma, Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA.
Steven Menez, Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Dominic Raj, Division of Renal Disease and Hypertension, George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
Chirag Parikh, Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Hamid Rabb, Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.

Document Type

Journal Article

Publication Date

10-1-2025

Journal

Comprehensive Physiology

Volume

15

Issue

5

DOI

10.1002/cph4.70058

Abstract

BACKGROUND: The gut microbiome plays an important role in human health and disease. Kidney Precision Medicine Project (KPMP) is a well-phenotyped, kidney biopsy-proven cohort of AKI and CKD patients. Comprehensive profiling of gut microbiota can uncover novel mechanistic, diagnostic, and therapeutic strategies for CKD and AKI patients. METHODS: We performed metagenomic whole genome sequencing (mWGS; > 25 million reads) on KPMP stool samples. mWGS data of healthy controls from 4 published studies was used. Kraken2 and MetaPhlAn3 were used for taxonomic assignment, and HUMAnN3 for functional annotation. RESULTS: Kraken2 analysis showed significantly higher abundance of Ruminococcus bicirculans in CKD (6.47) compared to AKI (1.82) and healthy individuals (2.42; p = 0.01). Furthermore, the abundance of Gordonibacter pamelaeae increased in CKD (0.30) compared to AKI (0.07; p = 0.05) and healthy individuals (0.03). The percent mean abundance of genus Chryseobacterium was slightly higher in CKD (0.07) compared to AKI (0.05; p = 0.05) but reduced compared to healthy individuals (0.20; p < 0.001). MetaPhlAn3 identified alterations in Gordonibacter, Bacteroides, and Faecalibacterium with a significant increase in Clostridium asparagiforme in AKI (11.68) compared to CKD (0.03; p = 0.06) and healthy (0.01; p = 0.001) individuals. Roseburia hominis, Roseburia intestinalis, Dorea longicatena, and Gemmiger formicilis were significantly reduced in AKI compared to CKD and healthy individuals. LDA/HUMAnN3 analysis showed a significant correlation between several metabolites and bacterial species in this KPMP population. CONCLUSION: Kidney biopsy-proven CKD and AKI patients show a distinct gut microbiota profile compared to healthy individuals. This high-quality dataset is a valuable resource for developing microbiome-based diagnostics and therapies for CKD and AKI.

APA Citation

Noel, Sanjeev; Patel, Shishir Kumar; White, James; Verma, Deepak; Menez, Steven; Raj, Dominic; Parikh, Chirag; and Rabb, Hamid, "Metagenomic Profiling of Gut Microbiota in Kidney Precision Medicine Project Participants With CKD and AKI" (2025). GW Authored Works. Paper 8327.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/8327

Department

Medicine