Single-Cell RNA Sequencing of Kidney-Gut Axis during Microbiome Manipulation of Severe AKI to CKD Progression

Background: The gut microbiome modifies experimental acute kidney injury (AKI) outcomes. The antibiotic drug amoxicillin, accelerates kidney repair and decreases fibrosis after severe AKI, likely through CD8 T cells (Kidney International, 2023). The goal of this study was to uncover specific genes and pathways that mediate T cell response to amoxicillin and regulate recovery and repair from severe AKI. Methods: 8-10 week old male C57BL6 mice underwent unilateral ischemia reperfusion injury (UIRI) to kidney (n=10/group). Mice were given vehicle (1% glucose) or amoxicillin (50 mg/ml) in drinking water starting soon after UIRI and kidney and colon tissues collected after 15 days. Non surgery mice treated with vehicle and amoxicillin were used as controls. CD3+ cells were flow sorted for 10X Chromium 5-prime single cell RNA sequencing (scRNA-seq). Cells were annotated using machine learning classification and manual annotation using established cell markers. Differential gene expression and Gene Set Enrichment Analysis (GSEA) was performed to identify specific genes and pathways that differed between corresponding cell types across conditions. Results: After quality control, we recovered 22,260 cells from the kidney and 14,669 cells from the colon. Nine cell types were annotated, including Th1, Th2, Th17, regulatory T cells, effector and memory CD8+ cells, and naive CD4+, CD8+, double negative and double positive T cells. Amoxicillin treatment after UIRI increased the proportion of CD8+ T cells and reduced that of CD4+ T cells in kidney, while the colon decreased the fraction of CD8+ T cells. GSEA revealed aldosterone-regulated sodium reabsorption (downregulated Prkcb, Insr, Nedd4l, etc) and JAK-STAT signaling pathway in kidney (downregulated Jak1, Ccnd3, and Socs1, etc), as well as estrogen signaling pathway (downregulation was repaired after Amoxicillin, marked by Jun, Fos, Hsp90aa1, etc) and renin secretion (downregulated Gnai2, Adrb1, Gnas, etc) in the colon. Conclusion: These scRNA-seq results show shifts in T cell populations in gut-kidney axis during severe AKI and corroborated our prior flow cytometry observations. Our scRNA-seq data and analyses promise to yield novel mechanisms, markers, therapeutic options for severe AKI and gut microbiome-kidney cross talk. Funding: NIDDK Support

Authors

Institutions

Publication Details

Journal
Journal of the American Society of Nephrology
Published
2024-10-01
DOI
https://doi.org/10.1681/asn.2024xfwq6vgc
Primary Topic
Renal and Vascular Pathologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Single-Cell RNA Sequencing of Kidney-Gut Axis during Microbiome Manipulation of Severe AKI to CKD Progression

Patrick Cahan, Hamid Rabb, Jiahui Yao, Sanjeev Noel et al.
Journal of the American Society of Nephrology
Renal and Vascular Pathologies
article

Single-Cell RNA Sequencing of Kidney-Gut Axis during Microbiome Manipulation of Severe AKI to CKD Progression

Patrick Cahan, Hamid Rabb, Jiahui Yao, Sanjeev Noel, Shishir Kumar Patel
article en

Abstract

Background: The gut microbiome modifies experimental acute kidney injury (AKI) outcomes. The antibiotic drug amoxicillin, accelerates kidney repair and decreases fibrosis after severe AKI, likely through CD8 T cells (Kidney International, 2023). The goal of this study was to uncover specific genes and pathways that mediate T cell response to amoxicillin and regulate recovery and repair from severe AKI. Methods: 8-10 week old male C57BL6 mice underwent unilateral ischemia reperfusion injury (UIRI) to kidney (n=10/group). Mice were given vehicle (1% glucose) or amoxicillin (50 mg/ml) in drinking water starting soon after UIRI and kidney and colon tissues collected after 15 days. Non surgery mice treated with vehicle and amoxicillin were used as controls. CD3+ cells were flow sorted for 10X Chromium 5-prime single cell RNA sequencing (scRNA-seq). Cells were annotated using machine learning classification and manual annotation using established cell markers. Differential gene expression and Gene Set Enrichment Analysis (GSEA) was performed to identify specific genes and pathways that differed between corresponding cell types across conditions. Results: After quality control, we recovered 22,260 cells from the kidney and 14,669 cells from the colon. Nine cell types were annotated, including Th1, Th2, Th17, regulatory T cells, effector and memory CD8+ cells, and naive CD4+, CD8+, double negative and double positive T cells. Amoxicillin treatment after UIRI increased the proportion of CD8+ T cells and reduced that of CD4+ T cells in kidney, while the colon decreased the fraction of CD8+ T cells. GSEA revealed aldosterone-regulated sodium reabsorption (downregulated Prkcb, Insr, Nedd4l, etc) and JAK-STAT signaling pathway in kidney (downregulated Jak1, Ccnd3, and Socs1, etc), as well as estrogen signaling pathway (downregulation was repaired after Amoxicillin, marked by Jun, Fos, Hsp90aa1, etc) and renin secretion (downregulated Gnai2, Adrb1, Gnas, etc) in the colon. Conclusion: These scRNA-seq results show shifts in T cell populations in gut-kidney axis during severe AKI and corroborated our prior flow cytometry observations. Our scRNA-seq data and analyses promise to yield novel mechanisms, markers, therapeutic options for severe AKI and gut microbiome-kidney cross talk. Funding: NIDDK Support

Journal of the American Society of NephrologyVol. 35(10S)
Johns Hopkins University (US)
Zero hunger
Openalex Percentile: Top 65%
Renal and Vascular Pathologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.