Small RNA sequencing reveals snoRNAs and piRNA-019825 as novel players in diabetic kidney disease.

INTRODUCTION: Micro- and macrovascular complications are common among persons with type 2 diabetes. Recently there has been growing interest to investigate the potential of circulating small non-coding RNAs (sncRNAs) as contributors to the development of diabetic complications. In this study we investigate to what extent circulating sncRNAs levels associate with prevalent diabetic kidney disease (DKD) in persons with type 2 diabetes. METHODS: Plasma sncRNAs levels were determined using small RNA-seq, allowing detection of miRNAs, snoRNAs, piRNAs, tRNA fragments, and various other sncRNA classes. We tested for differentially expressed sncRNAs in persons with type 2 diabetes, with DKD (n = 69) or without DKD (n = 405). In secondary analyses, we also tested the association with eGFR, albuminuria (UACR), and the plasma proteome. RESULTS: In total seven sncRNAs were negatively associated with prevalent DKD (all PFDR ≤ 0.05). Including one microRNA (miR-143-5p), five snoRNAs (U8, SNORD118, SNORD24, SNORD107, SNORD87) and a piRNA (piR-019825 | DQ597218). Proteomic analyses showed that the seven sncRNAs, and especially the piRNA piR-019825, were associated with plasma levels of 24 proteins of which several have known associations with kidney function including TNF sR-I (TNFRFS1A), DAN (NBL1) and cystatin C (CST3). CONCLUSION: We have identified novel small non-coding RNAs, primarily from classes other than microRNAs, that are associated with diabetic kidney disease. Our results show that the involvement of small non-coding RNAs in DKD goes beyond the already known microRNAs and also involves other classes of sncRNA, in particular snoRNAs and the piRNA piR-019825, that have never been studied before in relation to kidney function.

Clinical profiles of post-load glucose subgroups and their association with glycaemic traits over time: An IMI-DIRECT study.

AIM: To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration. METHODS: We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months. RESULTS: At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (ß = 0.36, 95% CI 0.13-0.58), Subgroup 3 (ß = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (ß = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months. CONCLUSIONS: Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.

Neo-cartilage engineered from primary chondrocytes is epigenetically similar to autologous cartilage, in contrast to using mesenchymal stem cells.

OBJECTIVES: To compare the epigenetic landscape of 3D cell models of human primary articular chondrocytes (hPACs) and human bone-marrow derived mesenchymal stem cells (hBMSCs) and their respective autologous articular cartilage. DESIGN: Using Illumina Infinium HumanMethylation450 BeadChip arrays, the DNA methylation landscape of the different cell sources and autologous cartilage was determined. Pathway enrichment was analyzed using DAVID. RESULTS: Principal Component Analysis (PCA) of methylation data revealed separate clustering of hBMSC samples. Between hBMSCs and autologous cartilage 86,881 cytosine-phosphate-guanine dinucleotides (CpGs) (20.2%), comprising 3,034 differentially methylated regions (DMRs; Δß > 0.1; with the same direction of effect), were significantly differentially methylated. In contrast, between hPACs and autologous cartilage only 5,706 CpGs (1.33%) were differentially methylated. Of interest was the finding of the transcriptionally active, hyper-methylation of a Cartilage Intermediate Layer Protein (CILP) annotated DMR (Δß = 0.16) in PAC-cartilage, corresponding to a profound decrease in CILP expression after in vitro culturing of hPACs as compared to autologous cartilage. CONCLUSIONS: In vitro engineered neo-cartilage tissue from primary chondrocytes, hPACs, exhibits a DNA methylation landscape that is almost identical (99% similarity) to autologous cartilage, in contrast to neo-cartilage engineered from bone marrow-derived mesenchymal stem cells (MSCs). Although hBMSCs are widely used for cartilage engineering purposes the effects of these vast differences on cartilage regeneration and long term consequences of implantation, are not known. The use of hBMSCs or hPACs for future cartilage tissue regeneration purposes should therefore be investigated in more depth in future endeavors to better understand the consequences of the differential methylome on neo-cartilage.