CKD Risk gene screening from GWAS
Based on the summary GWAS statistics provided by databases of GWAS Catalogue and UK Biobank (UKB), 928 CKD risk SNPs were included in the analysis (Supplementary Dataset 1). After filtration of the overlapped and strongly linked SNPs (r2 > 0.8 in LD analysis), 779 risk SNPs were identified to be significantly associated with CKD phenotypes (Fig. 1a). Among these SNPs, 681 genes were mapped according to these two databases.
Since different kidney complications were explored in CKD GWAS studies [19], we primary focused on the traits referring kidney function, glomerular function, and kidney disorders with diabetes. According to the records in databases, the GWAS population is further classified into renal failure (shorted as ESRD), decreased eGFR (shorted as CKD) and (micro)proteinuria. The groups of ESRD and CKD were further classified as diabetic and non-diabetic subgroups. Majority of risk genes were identified in CKD populations, and an incidence rate exceeding 50% in CKD patients without diabetes (Fig. 1b). Almost these genes, while 70 risk genes (~ 10%) were identified in two different population studies and only 1–5 risk genes were shared by two groups (Fig. 1c-d). However, the CKD without diabetes (non-specific CKD) group shared 12 risk genes with non-specific ESRD groups, and 35 risk genes with (micro)proteinuria group. The genes identified in each group were detailed in Supplementary Dataset 2.
In addition, these shared risk genes only slightly interacted within a few models (Fig. 1e), specifically in cell-cell adhesion model including Cadherins (e.g., CDH 4, 6 &10), Adhesion G protein-coupled receptor L3 (ADGRL3) and Collagens (Col4A3 & Col24A1). KEGG analysis also showed that cell adhesion (CDH4, NEGR1 & HLA-DQA1) was enriched in these shared CKD risk genes (Fig. 1f).
Thus, integrated GWAS profiled the risk genes associated with CKD. Different traits in CKD were associated with different gene sets, suggesting different pathological factors and Tissue remodelling genes, refereeing to Cell adhesion and collagens, were highlighted as common genes contributing to kidney diseases.
Functional enrichment of the CKD risk genes
Functional enrichment analysis was further conducted on CKD-associated genes identified from GWAS investigations. Across various CKD traits, analysis of enriched KEGG pathways revealed several shared pathways, highlighted by representative genes. We identified and reported the top seven pathways with the highest enrichment scores (Fig. 2). Notably, pathways such as the extracellular matrix (ECM) pathway, circadian entrainment, and energy metabolism were enriched across different sets of GWAS-derived studies.
The ECM is recognized as a key factor in the progression of fibrosis [20], a prevalent mechanism in CKD pathogenesis [21], while circadian rhythms have been shown to significantly influence cellular metabolic processes [22]. Recent studies have also elucidated the link between renal fibrosis and metabolic changes [23]. Meanwhile, kidneys play a crucial role in the body's endocrine system, secreting hormones such as renin, erythropoietin (EPO), and 1,25-dihydroxy-vitamin D3, in addition to various autocrine and paracrine factors [24], and involved genes also showed enrichment in distinct GWAS cohorts. Furthermore, genes related to the immune network were predominantly identified in GWAS groups for non-end-stage renal disease (non-ESRD), whereas calcium signalling genes were more common in non-specific CKD groups, and cell cycle genes exhibited a broad but weak association across different cohorts.
These findings highlight the potential common pathogenic mechanisms underlying various CKD conditions, despite differences in specific traits and associated risk genes derived from different GWAS studies.
CKD transcriptionally regulate genes in the kidneys.
Moreover, we reanalyzed the transcriptome data derived from the C-PROBE Investigator group [11, 14]. The bulk-RNA seq data were derived from micro-dissected human kidney biopsy samples in a group of CKD patients (n = 47), and the glomerular and tubular transcriptome were analyzed in both typical primary CKD (e.g., FSGS, MN and IgAN) and secondary CKD (e.g., LN and DN). The transcriptomes from 9 healthy living donors served as controls.
In our findings, a total of 533 genes were identified as downregulated in the glomerular region, and 269 genes were downregulated in the tubular transcriptome, with an intersection of 131 genes showing downregulation in both areas (Fig. 3a). KEGG pathway analysis revealed enrichment in both glomerular and tubular transcriptomes for pathways such as steroid hormone biosynthesis, collecting duct acid secretion, and mineral absorption.
Interestingly, certain pathways previously associated with CKD, such as the complement and coagulation pathways [25], the renin-angiotensin system[26], and AGE-RAGE signaling, were exclusively downregulated in the tubular region. The FOXO and Apelin signaling pathways, alongside AMPK signaling — all implicated in processes like cell proliferation, division, migration, apoptosis, oxidative stress resistance, and metabolism [27, 28] — were also exclusively downregulated in the tubular region. Notably, no pathways were specifically downregulated in the glomerular region alone.
Conversely, the analysis identified 339 upregulated genes in the glomerular transcriptome and 222 in the tubular transcriptome, with a subset of 86 genes upregulated in both areas (Fig. 3b). Despite the lower number of upregulated genes compared to downregulated ones in both regions, a greater variety of pathways were enriched in the upregulated transcriptomes. This suggests a predominant activation of pathologic mechanisms that may contribute to CKD progression. Most of these upregulated pathways, shared between glomerular and tubular areas, are predominantly involved in the immune response, including pathways regulated by NK cells, T cells, and B cells, as well as necroptosis and NF-kB signaling. Pathways associated with cell adhesion/migration and the extracellular matrix were also notably upregulated in both CKD-affected glomerular and tubular genes.
Moreover, similar to the downregulated tubular genes, upregulated genes in both the glomerular and tubular transcriptomes were enriched in signaling pathways regulating cell proliferation, division, apoptosis, and metabolism. The circadian entrainment pathway exhibited mixed responses, containing both upregulated and downregulated tubular genes in CKD.
Our data indicate a multifaceted regulatory landscape of gene expression in CKD, showcasing distinct pathways uniquely modulated within the glomerular and tubular areas. Several pathways are consistently highlighted across both regions, underscoring their importance in the disease process.
Risk genes and pathways transcriptionally regulated in CKD
We further concentrated on the intersecting genes identified by CKD GWAS studies and those that are transcriptionally regulated in CKD. In total, 35 shared genes were identified, with 60% of these genes showing downregulation in either the glomerular or tubular regions (Fig. 4a). Specifically, LUC7L3 and LONRF1 were downregulated in both glomerular and tubular regions, while GXYLT2 was upregulated in these areas. LUC7L3 encoding a protein that localizes with a speckled pattern in the nucleus and could be involved in the formation of spliceosome via the RE and RS domains, which were reported as a risk gene for non-specific CKD [29]. LONRF1 is associated with Diabetic Nephropathy [30], Which belongs to the LONRF family of proteins, harbors RING (Really Interesting New Gene) domain and Lon substrate binding domain and plays a key role in linking oxidative damage responses and tissue remodeling [31]. GXYLT2, a member of the human α-1,3-D-xylosyltransferases, functions to modify the first xylose to the O-Glucose residue on epidermal growth factor (EGF) repeats of Notch receptors [32].
Functionally, the relationship between these genes is not tightly knit. A PPI (Protein-Protein Interaction) network constructed from these 35 genes (Fig. 4b) revealed only one module, which is associated with collagen genes, including COL1A2, COL6A3, and COL8A1. These genes were mostly upregulated in the glomerular region. Moreover, KEGG pathway enrichment analysis identified three significant terms (p.adjust < 0.05) (Fig. 4c), namely, pathways related to ECM, cell adhesion and transcriptional regulation. This suggests that tissue remodeling is a key pathway for CKD, consistent with the fact that real fibrosis is a common pathway for the progression of kidney disease.
Moreover, we investigated how the common risk pathways, which were shared by different GWAS studied, was transcriptionally regulated in kidneys with CKD. Specifically, pathways involving ECM and immunology are upregulated in both glomerular and tubular regions. Hormone synthesis and secretion pathways are downregulated in these areas. Circadian entrainment pathways are both upregulated and downregulated exclusively in the tubular area. Other pathways, including metabolism, calcium signaling, and cell cycle, were not detected in this bulk-RNA array data.
Cell type-specific expression of regulated risk genes in the kidney
To analyze cell type-specific expression, we employed public scRNA-seq data from kidney biopsy samples [15], which included 51,849 cells with four groups: endothelial cells, epithelial cells (including podocytes), mesenchymal cells (primarily fibroblasts and myofibroblasts), and immune cells. The 35 CKD-related GWAS genes previously described were plotted across these cell types, and three genes (CPNE4, MFF-DT, and SHOC1) were not shown due to their absence in these kidney cell types (Fig. 5).
In detail, collagen genes such as COL8A1, COL6A3, and COL1A2 are prevalent in fibroblasts and myofibroblasts. They play a pivotal role in the structure and functionality of the extracellular matrix (ECM) and are key contributors to renal fibrosis. TBX3, belonging to the T-box family of transcription factors, is primarily expressed in glomerular capillaries, and is known to be crucial for maintaining the integrity and functionality of glomerular structures. NTNG1, THSD7A, and PTPN13, found abundantly in podocytes, are instrumental in podocyte adhesion, signal transduction, and kidney filtration processes. In tubular cells, PROM1, CCSER1, GRB14, PKHD1, TFCP2L1, and FGF9 are predominantly expressed. Notably, PKHD1 is associated with polycystic kidney disease, and its presence in tubular cells may be linked to tubular dilation and cyst formation. Regarding immune cells, genes SAMHD1 and CD53 are primarily expressed. CD53, a leukocyte surface antigen, plays a significant role in modulating signaling pathways in immune cells, indicating its importance in immune responses within the kidney. In addition, there were 4 genes (ANKRD30B, LINC00113, SORCS1 and L3MBTL3) were rarely expressed in kidney.