Protein N-glycans in Healthy and Sclerotic Glomeruli in Diabetic Kidney Disease.

BACKGROUND: Diabetes is expected to directly impact renal glycosylation, yet to date, there has not been a comprehensive evaluation of alterations in N-glycan composition in the glomeruli of patients with diabetic kidney disease (DKD). METHODS: We used untargeted mass spectrometry imaging to identify N-glycan structures in healthy and sclerotic glomeruli in FFPE sections from needle biopsies of five patients with DKD and three healthy kidney samples. Regional proteomics was performed on glomeruli from additional biopsies from the same patients to compare the abundances of enzymes involved in glycosylation. Secondary analysis of single nuclei transcriptomics (snRNAseq) data was used to inform on transcript levels of glycosylation machinery in different cell types and states. RESULTS: We detected 120 N-glycans, and among them identified twelve of these protein post-translated modifications that were significantly increased in glomeruli. All glomeruli-specific N-glycans contained an N-acetyllactosamine (LacNAc) epitope. Five N-glycan structures were highly discriminant between sclerotic and healthy glomeruli. Sclerotic glomeruli had an additional set of glycans lacking fucose linked to their core, and they did not show tetra-antennary structures that are common in healthy glomeruli. Orthogonal omics analyses revealed lower protein abundance and lower gene expression involved in synthesizing fucosylated and branched N-glycans in sclerotic podocytes. In snRNAseq and regional proteomics analyses, we observed that genes and/or proteins involved in sialylation and LacNAc synthesis were also downregulated in DKD glomeruli, but this alteration remained undetectable by our spatial N-glycomics assay. CONCLUSIONS: Integrative spatial glycomics, proteomics, and transcriptomics revealed protein N-glycosylation characteristic of sclerotic glomeruli in DKD.

Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.