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, 5 (2), 211-224
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A Functional Landscape of CKD Entities From Public Transcriptomic Data

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A Functional Landscape of CKD Entities From Public Transcriptomic Data

Ferenc Tajti et al. Kidney Int Rep.

Abstract

Introduction: To develop effective therapies and identify novel early biomarkers for chronic kidney disease, an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in chronic kidney disease (CKD) origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for 9 kidney disease entities that account for most of CKD worldwide. Our primary goal was to demonstrate the possibilities and potential on data analysis and integration to the nephrology community.

Methods: We integrated data from 5 publicly available studies and compared glomerular gene expression profiles of disease with that of controls from nontumor parts of kidney cancer nephrectomy tissues. A major challenge was the integration of the data from different sources, platforms, and conditions that we mitigated with a bespoke stringent procedure.

Results: We performed a global transcriptome-based delineation of different kidney disease entities, obtaining a transcriptomic diffusion map of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and nephrectomy tissue. We derived functional insights by inferring the activity of signaling pathways and transcription factors from the collected gene expression data and identified potential drug candidates based on expression signature matching. We validated representative findings by immunostaining in human kidney biopsies indicating, for example, that the transcription factor FOXM1 is significantly and specifically expressed in parietal epithelial cells in rapidly progressive glomerulonephritis (RPGN) whereas not expressed in control kidney tissue. Furthermore, we found drug candidates by matching the signature on expression of drugs to that of the CKD entities, in particular, the Food and Drug Administration-approved drug nilotinib.

Conclusion: These results provide a foundation to comprehend the specific molecular mechanisms underlying different kidney disease entities that can pave the way to identify biomarkers and potential therapeutic targets. To facilitate further use, we provide our results as a free interactive Web application: https://saezlab.shinyapps.io/ckd_landscape/. However, because of the limitations of the data and the difficulties in its integration, any specific result should be considered with caution. Indeed, we consider this study rather an illustration of the value of functional genomics and integration of existing data.

Keywords: CKD; drug repositioning; signaling pathway; transcription factor.

Figures

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Figure 1
Figure 1
Visual abstract and descriptive analysis. (a) Flow of analysis followed in this study. (b) Heatmap of the distribution of samples across studies and microarray platforms. (c) Hierarchical clustering of the arrays based on gene expression Spearman’s correlation coefficients. DN, diabetic nephropathy; FSGS, focal segmental glomerulosclerosis; GEO, Gene Expression Omnibus; HLD, healthy living donor; HN, hypertensive nephropathy; IgAN, IgA nephropathy; LN, lupus nephritis; MCD, minimal change disease; MGN, membranous glomerulonephritis; RPGN, rapidly progressive glomerulonephritis; TN, tumor nephrectomy.
Figure 2
Figure 2
Transcription-based map of chronic kidney disease (CKD) entities. (a) Radial heatmap of consistently differentially expressed genes across 6 or more disease entities (upregulation or downregulation). (b) Diffusion map of CKD entities reveals the underpinning geometric structure of the glomerular CKD transcriptomics data. DN, diabetic nephropathy; FSGS, focal segmental glomerulosclerosis; HLD, healthy living donor; HN, hypertensive nephropathy; IgAN, IgA nephropathy; LN, lupus nephritis; MCD, minimal change disease; MGN, membranous glomerulonephritis; RPGN, rapidly progressive glomerulonephritis; TN, tumor nephrectomy.
Figure 3
Figure 3
Transcription factor activity in glomerular chronic kidney disease (CKD) entities. Heatmap depicting transcription factor activity (color) for each CKD entity and tumor nephrectomy (TN) in glomerular tissue. Negative numbers (blue) signify decreased transcription factor activity, positive numbers (pink) indicate increased transcription factor activity. The significance according to the corresponding q-value of each transcription factor in each disease entity is represented by asterisk(s). The numbers to the right of factor names are Spearman’s rank-based correlation coefficients of factor activity and factor expression across different CKD entities. DN, diabetic nephropathy; FSGS, focal segmental glomerulosclerosis; HLD, healthy living donor; HN, hypertensive nephropathy; IgAN, IgA nephropathy; LN, lupus nephritis; MCD, minimal change disease; MGN, membranous glomerulonephritis; RPGN, rapidly progressive glomerulonephritis.
Figure 4
Figure 4
Validation of upstream stimulatory factor 2 (USF-2) and FOXM1 in human kidney biopsies. (a–c) Histological validation of USF-2 expression in human biopsies from patients with minimal change disease (n = 5) and controls (n = 6). (a) Immunohistochemical staining of USF-2 showed expression in nuclei of many cell types of the kidney including tubular cells (strongest in collecting duct, arrow with tails). In the glomeruli, USF-2 expression could be detected in podocytes (arrows). (b) USF-2 staining in biopsies from patients with minimal change disease demonstrated a similar staining pattern compared with controls including expression in podocytes (arrows). (c–c′′) Colocalized Ddach1 (podocyte marker in red) and USF-2 (in green). Arrows mark Dach1-USF-2 double-positive podocytes. (d–f) Histological validation of FoxM1 expression in human biopsies from patients with rapidly progressive glomerulonephritis (RPGN) (n = 5) and controls (n = 6). FoxM1 expression was detected most abundantly in glomeruli with crescentic CD44+ lesions (arrows in d–d′′). Rarely expression could be noted in the tubular compartment (arrows with tails). (e–e′) Serial sections revealed that FoxM1 expression was mainly detected in CD44+ cells in the glomerular crescentic lesions (arrows in e and e′). (f) Quantification of number of glomerular FoxM1+ cells control versus RPGN (P = 0.0043). **P < 0.01 by unpaired Mann-Whitney t test (c and f and bar plot data represent mean ± SD. Bars = 100 μm. DAPI, 4′,6-diamidino-2-phenylindole; n.s., not significant; PAS, periodic acid–Schiff.
Figure 5
Figure 5
Pathway activity alterations in chronic kidney disease (CKD) entities. (a) Heatmap depicting pathway activity (color) for each CKD entity relative to tumor nephrectomy in glomerular tissue, according to PROGENy. The magnitude and direction (positive or negative) of PROGENy scores indicate the degree of pathway deregulation in a given CKD entity with regard to the reference condition, tumor nephrectomy. Permutation q-values are used to indicate statistical significance of each pathway in each disease entity, indicated by asterisks. (b) Radial heatmap of consensually enriched pathways across 3 or more disease entities (upregulated, downregulated, or nondirectional regulation) according to Piano using MSigDB-C2-CP gene sets. DN, diabetic nephropathy; FSGS, focal segmental glomerulosclerosis; HN, hypertensive nephropathy; IgAN, IgA nephropathy; LN, lupus nephritis; MCD, minimal change disease; MGN, membranous glomerulonephritis; RPGN, rapidly progressive glomerulonephritis.
Figure 6
Figure 6
Top 20 drug candidates from drug repositioning. (a) Distribution of 20 small molecules reversely correlated with at least 3 chronic kidney disease entities. (b) Table of 4 small molecules of the 20 of (a) supported by manual curation. Table shows drugs (first row), protein coding genes targeted by these 4 drugs (second row), and pathways (MSigDB) related to the biological functions these drugs affect (third row).

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References

    1. Hill N.R., Fatoba S.T., Oke J.L. Global prevalence of chronic kidney disease - a systematic review and meta-analysis. PLoS One. 2016;11 - PMC - PubMed
    1. Hamer R.A., El Nahas A.M. The burden of chronic kidney disease. BMJ. 2006;332:563–564. - PMC - PubMed
    1. Beckerman P., Qiu C., Park J. Human kidney tubule-specific gene expression based dissection of chronic kidney disease traits. EBioMedicine. 2017;24:267–276. - PMC - PubMed
    1. Nair V., Komorowsky C.V., Weil E.J. A molecular morphometric approach to diabetic kidney disease can link structure to function and outcome. Kidney Int. 2018;93:439–449. - PMC - PubMed
    1. Schena F.P., Nistor I., Curci C. Transcriptomics in kidney biopsy is an untapped resource for precision therapy in nephrology: a systematic review. Nephrol Dial Transplant. 2017;32:1776. - PubMed

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