Kidney diseases : insights from omics approaches

Chronic kidney diseases (CKDs) affects about 11-15% of adults worldwide. When it progresses to the end-stage renal disease (ESRD), there is no effective medication for cure, the only treatment being chronic dialysis or kidney transplantation. The 5-year survival rate for patients in dialysis is less than 40%, and generates a huge economic burden to the healthcare system. A major proble is that we still have very limited knowledge on the pathogenesis and pathomechanism of CKD. In this thesis, we studied CKDs by utilizing the large-scale omics approaches.

Paper I describes a study on the potential genetic causes of diabetic nephropathy (DN). DN is the major cause of ESRD among all CKDs worldwide. Here we studied a Finnish sibling cohort, in which sibling pairs are both affected by type 1 diabetes (T1D), but they are discordant for development of DN. Studying the genetics of DN is challenging as one is searching for genes and genomic variants that only cause disease if the patient has diabetes and hyperglycemia. The study was carried out by sequencing the whole genome of the discordant sibling pairs, and performing multiple bioinformatic analyses on the data. We studied protein altering variants and enrichment of variants in regions associated with presence or absence of DN. We replicated our findings in a larger T1D cohort of unrelated Finns with T1D, referred to as the FinnDiane cohort. We identified several top candidate genes some of which were studied in a zebrafish model. Some of the top candidate genes and genomic variants, showing highest association with the presence or absence of DN were characterized. One of them was protein kinase C epsilon that has been found to be associated with development of DN.

Paper II reports on a meta-analysis of the expression profiles of glomerular diseases. We summarized all microarray and proteomics data sets on glomerular diseases, including studies on patient biopsy and animal models. We developed a pipeline for meta-analysis on microarray data, and compared two DN human patient studies together with DN animal model studies. We have not found any consensus pathways that are significant across all glomerular diseases or disease models. Paper III uses state-of-the-art single cell RNA sequencing technology (scRNAseq) to elucidate the expression profiles of kidney organoids. The organoids were derived from induced pluripotent human stem cells and were engineered with CRISP(e)R technology to induce fluorescent reporters facilitating the monitoring of different stages of organoid development. We observed cell clusters expressing mature podocyte and tubular markers. We also compared the transcriptomic profile of these two clusters with previous reported healthy human glomerular and tubular biopsies, and observed a similarity of organoid to adult kidney.

List of scientific papers

I. Jing Guo, Owen J.L. Rackham, Bing He, Anne-May Österholm, Erkka Valo, Valma Harjutsalo, Carol Forsblom, Iiro Toppila, Maikki Parkkonen, Qibin Li, Wenjuan Zhu, Nathan Harmston, Sonia Chothani, Miina K. Öhman, Eudora Eng, Yang Sun, Niina Sandholm, Enrico Petretto, Per-Henrik Groop, Karl Tryggvason. Whole genome sequencing in Finnish type 1 diabetic siblings discordant for kidney disease reveals DNA variants associated with diabetic nephropathy. [Submitted]

II. Sam Tryggvason, Jing Guo, Masatoshi Nukui, Jenny Norlin, Börje Haraldsson, Hans Jörnvall, Karl Tryggvason, Liqun He. A meta-analysis of expression signatures in glomerular disease. Kidney International. 2013, Volume 84, Issue 3, Pages 591–599.
https://doi.org/10.1038/ki.2013.169

III. Cecilia Boreström, Anna Jonebring, Jing Guo, Henrik Palmgren, Linda Cederblad, Anna Forslöw, Anna Svensson, Magnus Söderberg, Anna Reznichenko, Jenny Nyström, Jaakko Patrakka, Ryan Hicks, Marcello Maresca, Barbara Valastro, Anna Collén. A CRISP(e)R view on kidney organoids allows generation of an induced pluripotent stem cell–derived kidney model for drug discovery. Kidney International. 2018. [Accepted]
https://doi.org/10.1016/j.kint.2018.05.003