Protection against Glucolipotoxicity by High Density Lipoprotein in Human PANC-1 Hybrid 1.1B4 Pancreatic Beta Cells: The Role of microRNA

Modulation of the Cellular microRNA Landscape: Contribution to the Protective Effects of High-Density Lipoproteins (HDL)

High-density lipoproteins (HDL) play an established role in protecting against cellular dysfunction in a variety of different disease contexts; however, harnessing this therapeutic potential has proved challenging due to the heterogeneous and relative instability of this lipoprotein and its variable cargo molecules. The purpose of this study is to examine the contribution of microRNA (miRNA; miR) sequences, either delivered directly or modulated endogenously, to these protective functions. This narrative review introduces the complex cargo carried by HDL, the protective functions associated with this lipoprotein, and the factors governing biogenesis, export and the uptake of microRNA. The possible mechanisms by which HDL can modulate the cellular miRNA landscape are considered, and the impact of key sequences modified by HDL is explored in diseases such as inflammation and immunity, wound healing, angiogenesis, dyslipidaemia, atherosclerosis and coronary heart disease, potentially offering new routes for therapeutic intervention.

Homo Sapiens (Hsa)-microRNA (miR)-6727-5p Contributes to the Impact of High-Density Lipoproteins on Fibroblast Wound Healing In Vitro

Chronic, non-healing wounds are a significant cause of global morbidity and mortality, and strategies to improve delayed wound closure represent an unmet clinical need. High-density lipoproteins (HDL) can enhance wound healing, but exploitation of this finding is challenging due to the complexity and instability of these heterogeneous lipoproteins. The responsiveness of primary human neonatal keratinocytes, and neonatal and human dermal fibroblasts (HDF) to HDL was confirmed by cholesterol efflux, but promotion of ‘scrape’ wound healing occurred only in primary human neonatal (HDFn) and adult fibroblasts (HDFa). Treatment of human fibroblasts with HDL induced multiple changes in the expression of small non-coding microRNA sequences, determined by microchip array, including hsa-miR-6727-5p. Intriguingly, levels of hsa-miR-6727-5p increased in HDFn, but decreased in HDFa, after exposure to HDL. Delivery of a hsa-miR-6727-5p mimic elicited repression of different target genes in HDFn (ZNF584) and HDFa (EDEM3, KRAS), and promoted wound closure in HDFn. By contrast, a hsa-miR-6727-5p inhibitor promoted wound closure in HDFa. We conclude that HDL treatment exerts distinct effects on the expression of hsa-miR-6727-5p in neonatal and adult fibroblasts, and that this is a sequence which plays differential roles in wound healing in these cell types, but cannot replicate the myriad effects of HDL.

Clustering patterns of metabolic syndrome: A cross-sectional study in children and adolescents in Kyiv

OBJECTIVE

The aim: to identify subgroups by cluster analysis according parameters: original homeostatic model of insulin resistance (HOMA-1 IR), updated computer model of insulin resistance (HOMA-2 IR), β-cell function (%B) and insulin sensitivity (%S) for the prognosis of different variants of metabolic syndrome in children for more individualized treatment selection.

PATIENTS AND METHODS

The observational cross-sectional study on 75 children aged from 10 to 17 with metabolic syndrome according to the International Diabetes Federation criteria was conducted at the Cardiology Department of Children's Clinical Hospital No.6 in Kyiv. HOMA-1 IR was calculated as follows: fasting insulin (µIU/ml) × fasting glucose (mmol/L)/22.5. HOMA-2 IR with %B and %S were calculated according to the computer model in [http://www.dtu.ox.ac.uk]. All biochemical analysis were carried out using Cobas 6000 analyzer and Roche Diagnostics (Switzerland). The statistical analysis was performed using STATISTICA 7.0 and Easy R. The hierarchical method Ward was used for cluster analysis according the parameters: HOMA-1 IR, HOMA-2 IR, %B and %S.

RESULTS

Four clusters were identified from the dendrogram, which could predict four variants in the course of metabolic syndrome such that children in cluster 1 would have the worst values of the studied parameters and those in cluster 4 - the best. It was found that HOMA-1 IR was much higher in cluster 1 (6.32 ± 0.66) than in cluster 4 (2.19 ± 0.13). HOMA-2 IR was also much higher in cluster 1 (3.80 ± 0.34) than in cluster 4 (1.31 ± 0.06). By the analysis of variance using Scheffe's multiple comparison method, a statistically significant difference was obtained between the laboratory parameters among the subgroups: HOMA-1 IR (p < 0,001), glucose (p < 0.001), insulin (p < 0,001), HOMA-2 IR (p < 0.001), %B (p < 0.001), %S (p < 0.001), TG ( p = 0.005) and VLDL-C (p = 0.002).

CONCLUSIONS

A cluster analysis revealed that the first two subgroups of children had the worst insulin resistance and lipid profile parameters. It was found positive correlation between HOMA-1 IR, HOMA-2 IR, %B and %S with lipid metabolism parameters TG and VLDL-C and negative correlation between %B and HDL-C in children with metabolic syndrome (MetS).The risk of getting a high TG result in the blood analysis in children with MetS was significantly dependent with the HOMA-2 IR >2.26.

MicroRNA Sequences Modulated by Beta Cell Lipid Metabolism: Implications for Type 2 Diabetes Mellitus

Alterations in lipid metabolism within beta cells and islets contributes to dysfunction and apoptosis of beta cells, leading to loss of insulin secretion and the onset of type 2 diabetes. Over the last decade, there has been an explosion of interest in understanding the landscape of gene expression which influences beta cell function, including the importance of small non-coding microRNA sequences in this context. This review sought to identify the microRNA sequences regulated by metabolic challenges in beta cells and islets, their targets, highlight their function and assess their possible relevance as biomarkers of disease progression in diabetic individuals. Predictive analysis was used to explore networks of genes targeted by these microRNA sequences, which may offer new therapeutic strategies to protect beta cell function and delay the onset of type 2 diabetes.