Identification of novel therapeutic targets for chronic kidney disease and kidney function by integrating multi-omics proteome with transcriptome

Abstract

Background: Chronic kidney disease (CKD) is a progressive disease for which there is no effective cure. We aimed to identify potential drug targets for CKD and kidney function by integrating plasma proteome and transcriptome.

Methods: We designed a comprehensive analysis pipeline involving two-sample Mendelian randomization (MR) (for proteins), summary-based MR (SMR) (for mRNA), and colocalization (for coding genes) to identify potential multi-omics biomarkers for CKD and combined the protein-protein interaction, Gene Ontology (GO), and single-cell annotation to explore the potential biological roles. The outcomes included CKD, extensive kidney function phenotypes, and different CKD clinical types (IgA nephropathy, chronic glomerulonephritis, chronic tubulointerstitial nephritis, membranous nephropathy, nephrotic syndrome, and diabetic nephropathy).

Results: Leveraging pQTLs of 3032 proteins from 3 large-scale GWASs and corresponding blood- and tissue-specific eQTLs, we identified 32 proteins associated with CKD, which were validated across diverse CKD datasets, kidney function indicators, and clinical types. Notably, 12 proteins with prior MR support, including fibroblast growth factor 5 (FGF5), isopentenyl-diphosphate delta-isomerase 2 (IDI2), inhibin beta C chain (INHBC), butyrophilin subfamily 3 member A2 (BTN3A2), BTN3A3, uromodulin (UMOD), complement component 4A (C4a), C4b, centrosomal protein of 170 kDa (CEP170), serologically defined colon cancer antigen 8 (SDCCAG8), MHC class I polypeptide-related sequence B (MICB), and liver-expressed antimicrobial peptide 2 (LEAP2), were confirmed. To our knowledge, 20 novel causal proteins have not been previously reported. Five novel proteins, namely, GCKR (OR 1.17, 95% CI 1.10-1.24), IGFBP-5 (OR 0.43, 95% CI 0.29-0.62), sRAGE (OR 1.14, 95% CI 1.07-1.22), GNPTG (OR 0.90, 95% CI 0.86-0.95), and YOD1 (OR 1.39, 95% CI 1.18-1.64,) passed the MR, SMR, and colocalization analysis. The other 15 proteins were also candidate targets (GATM, AIF1L, DQA2, PFKFB2, NFATC1, activin AC, Apo A-IV, MFAP4, DJC10, C2CD2L, TCEA2, HLA-E, PLD3, AIF1, and GMPR1). These proteins interact with each other, and their coding genes were mainly enrichment in immunity-related pathways or presented specificity across tissues, kidney-related tissue cells, and kidney single cells.

Conclusions: Our integrated analysis of plasma proteome and transcriptome data identifies 32 potential therapeutic targets for CKD, kidney function, and specific CKD clinical types, offering potential targets for the development of novel immunotherapies, combination therapies, or targeted interventions.

Keywords: Chronic kidney disease; Kidney function; Mendelian randomization; Proteome; Therapeutic targets; Transcriptome.

Fig. 1

Overview of the study design. (1) The exposure summary data include three proteome-wide pQTL and five transcriptome-wide eQTL datasets. The outcome summary data include four CKD outcomes (data CKD1–4), two kidney function phenotypes (eGFRcrea + eGFRcys), two rapid kidney function decline phenotypes (Rapid3 + CKDi25), annualized relative slope change of eGFR in four populations, and six CKD clinical types. (2) The workflow of the statistical analysis included proteome-wide MR (PWAS), transcriptome-wide MR (TWAS), sensitivity, replication, tissue-specific analysis, colocalization analysis, protein–protein interaction analysis, GO enrichment analysis, and single-cell enrichment annotation. (3) the three evidence tiers were determined according to MR, SMR, and colocalization analysis and compared with previous evidence

Fig. 2

Associations of the 32 identified proteins with CKD. A Volcano plot of individual proteins associated with the primary CKD outcome across three data sources. The red line represented the threshold of FDR correction (q < 0.05), and the red point indicated significant proteins. B Forest plot of identified proteins in A that passed the FDR corrections for the risk of CKD. The ORs and 95% CIs of the significant proteins in any 1 dataset were reported. For proteins that were available from more than 1 dataset, the ORs and 95% CIs were combined by fixed effect meta-analysis and are shown as combined effects. Hollow dots represent P > 0.05, and solid dots represent P < 0.05. Abbreviations: IGFBP-5, insulin-like growth factor-binding protein 5; C2CD2L, C2 domain-containing protein 2-like; DQA2, HLA class II histocompatibility antigen, DQ alpha 2 chain; DJC10, DnaJ homolog subfamily C member 10; SDCCAG8, serologically defined colon cancer antigen 8; PLD3, phospholipase D3; Apo A-IV, apolipoprotein A-IV; C4a, complement component 4A; MFAP4, microfibril-associated glycoprotein 4; IDI2, isopentenyl-diphosphate delta-isomerase 2; GATM, glycine amidinotransferase, mitochondrial; TCEA2, transcription elongation factor A protein 2; GNPTG, N-acetylglucosamine-1-phosphotransferase subunit gamma; FGF5, fibroblast growth factor 5; C4, complement C4; BTN3A2, butyrophilin subfamily 3 member A2; BTNA3 (equal to BTN3A3), butyrophilin subfamily 3 member A3; MICB, MHC class I polypeptide-related sequence B; GMPR1, GMP reductase 1; INHBC, inhibin beta C chain; AIF1L, allograft inflammatory factor 1-like; LEAP2, liver-expressed antimicrobial peptide 2; sRAGE, advanced glycosylation end product-specific receptor, soluble; HLA-E, HLA class I histocompatibility antigen, alpha chain E; AIF1, allograft inflammatory factor 1; GCKR, glucokinase regulatory protein; PFKFB2, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 2; UMOD, uromodulin; YOD1, ubiquitin thioesterase OTU1; activin AC, inhibin beta A chain:inhibin beta C chain heterodimer; NFATC1, nuclear factor of activated T cells, cytoplasmic 1; CEP170, centrosomal protein of 170 kDa

Fig. 3

Heatmap of identified protein-coding genes associated with CKD. Heatmap of the effect of plasma and tissue-specific protein-coding gene expression on CKD risk for the identified proteins. The color represents the β estimators of SMR analysis, where green represents a decreased CKD risk and red represents an increased CKD risk for per-SD increased gene expression. *P < 0.05; **multiple tests, P < 0.05/29 (genes). The missing values marked with “-” represent the genes without effective eQTLs in the SMR analysis or failed in the HEIDI test

Fig. 4

Balloon plot of identified proteins associated with extensive CKD-related phenotypes. The direction (increased or decreased risk) was determined by the estimators in the primary analysis for CKD (CKDGen, European). The color represents the β estimators of MR analysis, where green represents a decreased risk and red represents an increased risk for per-SD increased proteins. *The effects were adjusted and corresponded to increased risk (declined eGFR slope) and decreased risk (increased eGFR slope). EUR, European participants; Trans, trans-ancestry; DM, diabetes mellitus

Fig. 5

Associations of the 32 identified proteins with different CKD data sources, kidney function phenotypes, rapid kidney function decline phenotypes, and CKD clinical types. A Forest plot of the effect of 32 proteins on the risk of 3 additional CKD outcomes (data CKD2–4). B Forest plot of the effect of 32 proteins on 2 kidney function (eGFR) outcomes. C Forest plot of the effect of 32 proteins on the risk of 2 rapid kidney function decline outcomes. D Forest plot of the effect of these proteins on the risk of 6 CKD clinical types (only significant results with P < 0.05 were shown). Hollow dots represent P > 0.05, and solid dots represent P < 0.05

Fig. 6

Results of protein–protein interaction network (A) and Gene Ontology enrichment pathways (B). For Gene Ontology enrichment pathways, only the top 20 GO terms are shown