PT  - JOURNAL ARTICLE
AU  - Ferenc Tajti
AU  - Asier Antoranz
AU  - Mahmoud M. Ibrahim
AU  - Hyojin Kim
AU  - Francesco Ceccarelli
AU  - Christoph Kuppe
AU  - Leonidas G. Alexopoulos
AU  - Rafael Kramann
AU  - Julio Saez-Rodriguez
TI  - A functional landscape of chronic kidney disease entities from public transcriptomic data
AID  - 10.1101/265447
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 265447
4099  - http://biorxiv.org/content/early/2018/02/14/265447.short
4100  - http://biorxiv.org/content/early/2018/02/14/265447.full
AB  - To develop efficient therapies and identify novel early biomarkers for chronic kidney disease (CKD) an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in CKD origin are reflected in gene regulatory mechanisms. To this end, we collected and integrated publicly available human-kidney glomerular microarray gene expression data for nine kidney disease entities that account for a majority of CKD worldwide [Focal segmental glomerulosclerosis (FSGS), Minimal Change Disease (MCD), FSGS-MCD, IgA nephropathy (IgAN), Lupus nephritis (LN), Membranous glomerulonephropathy (MGN), Diabetic nephropathy (DN), Hypertensive nephropathy (HN) and Rapidly progressive glomerulonephritis (RPGN)]. We included data from five distinct studies and compared glomerular gene expression profiles to that of non-tumor part 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. This allowed us to perform a global transcriptome-based delineation of different kidney disease entities, obtaining a landscape of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and tumor nephrectomy. Furthermore, we derived functional insights by inferring signaling pathway and transcription factor activity from the collected gene expression data, and identified potential drug candidates based on expression signature matching. 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.