Analysis of the human kidney transcriptome and plasma proteome identifies markers of proximal tubule maladaptation to injury

Abstract

Acute kidney injury (AKI) is a major risk factor for long-term adverse outcomes, including chronic kidney disease. In mouse models of AKI, maladaptive repair of the injured proximal tubule (PT) prevents complete tissue recovery. However, evidence for PT maladaptation and its etiological relationship with complications of AKI is lacking in humans. We performed single-nucleus RNA sequencing of 120,985 nuclei in kidneys from 17 participants with AKI and seven healthy controls from the Kidney Precision Medicine Project. Maladaptive PT cells, which exhibited transcriptomic features of dedifferentiation and enrichment in pro-inflammatory and profibrotic pathways, were present in participants with AKI of diverse etiologies. To develop plasma markers of PT maladaptation, we analyzed the plasma proteome in two independent cohorts of patients undergoing cardiac surgery and a cohort of marathon runners, linked it to the transcriptomic signatures associated with maladaptive PT, and identified nine proteins whose genes were specifically up- or down-regulated by maladaptive PT. After cardiac surgery, both cohorts of patients had increased transforming growth factor-β2 (TGFB2), collagen type XXIII-α1 (COL23A1), and X-linked neuroligin 4 (NLGN4X) and had decreased plasminogen (PLG), ectonucleotide pyrophosphatase/phosphodiesterase 6 (ENPP6), and protein C (PROC). Similar changes were observed in marathon runners with exercise-associated kidney injury. Postoperative changes in these markers were associated with AKI progression in adults after cardiac surgery and post-AKI kidney atrophy in mouse models of ischemia-reperfusion injury and toxic injury. Our results demonstrate the feasibility of a multiomics approach to discovering noninvasive markers and associating PT maladaptation with adverse clinical outcomes.

Fig. 1.. SnRNA-seq analysis of 17 participants with AKI and seven healthy references from the KPMP cohort identifies PT cells at different states of health.

(A) Uniform manifold approximation and projection (UMAP) of 120,985 kidney epithelium, stroma, and immune cell nuclei. (B) Dot plot of canonical marker gene expression of major kidney cell types. (C) UMAP of PT subclusters. (D) Dot plot of marker gene expression of PT subclusters in the overall biopsy cohort. (E) Bar plot of PT subcluster composition in 17 participants with AKI and seven healthy references. (F to H) Dot plot displaying enriched Gene Ontology pathways (F), genes involved in ferroptosis pathways (G), and genes involved in necroptosis pathways (H) among PT subclusters. CD, collecting duct; CNT, connecting tubule; DC, dendritic cell; DCT, distal convoluted tubule; EC, endothelial cell; Fib, fibroblast; Glom, glomerulus; ICA, intercalated cell of collecting duct type A; ICB, intercalated cell of collecting duct type B; Mac, macrophage; MD, macula densa; Mes, mesangial cell; Mono, monocyte; PC, principal cell of collecting duct; Per, pericyte; Pod, podocyte; TAL, thick ascending limb of loop of Henle; TL: thin limb of loop of Henle.

Fig. 2.. Gene regulatory network analysis of PT subclusters in 17 participants with AKI demonstrates distinct regulatory networks in PT cells at different states of health.

(A) Heatmap depicting average regulon enrichment in each PT subcluster. (B) Average expression of selected regulons enriched in each PT subcluster. (C) Heatmap demonstrating unsupervised clustering of PT subclusters by the top 10 regulons from each subcluster. (D) Louvain clustering of the top 10% of transcription factor–target gene pairs demonstrates clusters of transcription factors (nodes depicted by colors) forming coregulatory networks.

Fig. 3.. Integration of the kidney tissue transcriptome in participants with AKI and plasma proteome in patients undergoing cardiac surgery.

(A) Workflow for identifying markers of PT maladaptation. (B) Workflow for identifying markers of PT cells in healthy states.

Fig. 4.. Kidney gene expression of markers of PT maladaptation and PT cells in healthy states in 17 participants with AKI and in mouse models of AKI.

(A) Tissue gene expression of identified markers in 17 participants with AKI from the KPMP cohort at single-nucleus resolution. (B) Wild-type mice were subjected to unilateral IRI (atrophy model) or IRI with contralateral nephrectomy (repair model) (31). Quantitative RT-PCR analysis was performed on whole-kidney RNA harvested 0 (healthy control), 1, 7, 14, and 30 days after injury; n = 9 or 10 kidneys per time point per model. (C to F) Gene expression of Tgfb2 (C), Col23a1 (D), Enpp6 (E), and Proc (F) was compared across time course and between the atrophy versus repair model, using two-way ANOVA followed by Šidák’s posttests (*P < 0.05; **P < 0.01; ****P < 0.0001 at the indicated time points). (G and H) Wild-type mice were subjected to intraperitoneal injection of aristolochic acid (5 mg/kg ) (n = 14) or vehicle (n = 4). Blood samples were collected on days 0, 3, 7, 10, 14, and 21 for blood urea nitrogen (BUN) measurements. Quantitative RT-PCR analysis was performed on whole-kidney RNA harvested on days 0 (baseline), 7 (AKI phase, n = 7), and 21 (CKD phase, n = 7) after aristolochic acid injection, and day 21 (controls, n = 4) after vehicle injection. (I to L) Gene expression of Tgfb2 (I), Col23a1 (J), Enpp6 (K), and Proc (L) was compared using ANOVA followed by the Tukey’s test for subgroup comparison (*P < 0.05, ***P < 0.001, and ****P < 0.0001 comparing gene expression at AKI and CKD time points to baseline by Student’s t tests).