Kidney Week

Abstract: TH-PO068

Gut Microbiome Metagenomic Sequencing Reveals Distinct Profiles in Participants with AKI and CKD from the KPMP

Session Information

• AKI: Clinical, Outcomes, and Trials - Epidemiology and Pathophysiology
  October 24, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Acute Kidney Injury

• 102 AKI: Clinical, Outcomes, and Trials

Authors

• Noel, Sanjeev, Johns Hopkins University, Baltimore, Maryland, United States

Sanjeev Noel, PhD

• White, James Robert, Resphera Biosciences LLC, Balitmore, Maryland, United States

James Robert White, PhD

• Patel, Shishir Kumar, Johns Hopkins University, Baltimore, Maryland, United States

Shishir Kumar Patel, PhD

• Menez, Steven, Johns Hopkins University, Baltimore, Maryland, United States

Steven Menez, MD

• Raj, Dominic S., The George Washington University, Washington, District of Columbia, United States

Dominic S. Raj, MD, MBBS, FASN

• Parikh, Chirag R., Johns Hopkins University, Baltimore, Maryland, United States

Chirag R. Parikh, MD, PhD, FASN

• Rabb, Hamid, Johns Hopkins University, Baltimore, Maryland, United States

Hamid Rabb, MD, FASN

Group or Team Name

• Kidney Precision Medicine Project (KPMP).

Background

Previous experimental studies using 16S RNA sequencing demonstrated distinct gut microbial changes following acute kidney injury (AKI) in mice and dysbiosis in chronic kidney disease (CKD) patients. Metagenomic whole genome sequencing (mWGS) has several advantages over 16S, including the ability to identify bacteria, fungi and viruses plus provide functional information. We applied mWGS to KPMP stool samples from AKI and CKD participants for species level identification and functional pathway analysis.

Methods

mWGS on AKI (n=7) and CKD (n=70) stool was performed to achieve >25 million reads/sample. Metagenomic data of healthy controls (n=94) from 4 published studies was downloaded from the NCBI Sequence Read Archive. Kraken2 and Metaphlan3 were used for taxonomic assignment and HUMAnN3 for functional annotation. Unsupervised clustering was performed using pheatmap, and comparisons between groups were computed with the non-parametric Mann-Whitney U test.

Results

Kraken2 analysis showed a decline in the percent mean abundance of Ruminococcus bicirculans in AKI (1.82) compared to CKD (6.47; p=0.07) and healthy individuals (2.42; p=0.01). Furthermore, genus Chryseobacterium declined in AKI (0.05) compared to CKD (0.07; p=0.05) and healthy individuals (0.20). Conversely, Gordonibacter pamelaeae increased in AKI (0.07) compared to healthy individuals (0.03) but was less abundant compared to CKD (0.30; p=0.05). Metaphlan3 identified 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. Gemmiger formicilis was significantly reduced in AKI (0.01) compared to CKD (0.51; p=0.06) and healthy (0.26; p=0.001) individuals. HUMAnN3 analysis showed significant correlation between amino acid metabolism and Clostridium asparagiforme in AKI compare to CKD and healthy individuals.

Conclusion

These preliminary results demonstrate distinct microbiota profile during an episode of AKI compared to CKD and healthy individuals. Future studies with larger sample sizes are needed to confirm these findings and highlight the pathophysiologic significance of these changes.

Funding

• NIDDK Support