Only Hyperuricemia with Crystalluria, but not Asymptomatic Hyperuricemia, Drives Progression of Chronic Kidney Disease.

BACKGROUND: The roles of asymptomatic hyperuricemia or uric acid (UA) crystals in CKD progression are unknown. Hypotheses to explain links between UA deposition and progression of CKD include that (1) asymptomatic hyperuricemia does not promote CKD progression unless UA crystallizes in the kidney; (2) UA crystal granulomas may form due to pre-existing CKD; and (3) proinflammatory granuloma-related M1-like macrophages may drive UA crystal-induced CKD progression. METHODS: MALDI-FTICR mass spectrometry, immunohistochemistry, 3D confocal microscopy, and flow cytometry were used to characterize a novel mouse model of hyperuricemia and chronic UA crystal nephropathy with granulomatous nephritis. Interventional studies probed the role of crystal-induced inflammation and macrophages in the pathology of progressive CKD. RESULTS: Asymptomatic hyperuricemia alone did not cause CKD or drive the progression of aristolochic acid I-induced CKD. Only hyperuricemia with UA crystalluria due to urinary acidification caused tubular obstruction, inflammation, and interstitial fibrosis. UA crystal granulomas surrounded by proinflammatory M1-like macrophages developed late in this process of chronic UA crystal nephropathy and contributed to the progression of pre-existing CKD. Suppressing M1-like macrophages with adenosine attenuated granulomatous nephritis and the progressive decline in GFR. In contrast, inhibiting the JAK/STAT inflammatory pathway with tofacitinib was not renoprotective. CONCLUSIONS: Asymptomatic hyperuricemia does not affect CKD progression unless UA crystallizes in the kidney. UA crystal granulomas develop late in chronic UA crystal nephropathy and contribute to CKD progression because UA crystals trigger M1-like macrophage-related interstitial inflammation and fibrosis. Targeting proinflammatory macrophages, but not JAK/STAT signaling, can attenuate granulomatous interstitial nephritis.

A Functional Landscape of CKD Entities From Public Transcriptomic Data.

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.