Analysis of mRNA-miRNA-lncRNA differential expression in prediabetes/type 2 diabetes mellitus patients as potential players in insulin resistance.

Introduction: Type 2 diabetes mellitus (T2DM) is a major global health concern. It usually develops gradually and is frequently preceded by undetectable pre-diabetes mellitus (pre-DM) stage. The purpose of this study was to identify a novel set of seven candidate genes associated with the pathogenesis of insulin resistance (IR) and pre-DM, followed by their experimental validation in patients' serum samples. Methods: We used the bioinformatics tools and through a two-step process, we first identified and verified two mRNA candidate genes linked to insulin resistance molecular pathogenesis. Second, we identified a non-coding RNAs related to the selected mRNAs and implicated in the insulin resistance molecular pathways followed by pilot study for the RNA panel differential expression in 66 patients with T2DM, 49 individuals with prediabetes and 45 matched controls using real time PCR. Results: The levels of expression of TMEM173 and CHUK mRNAs, hsa-miR (-611, -5192, and -1976) miRNAs gradually increased from the healthy control group to the prediabetic group, reaching their maximum levels in the T2DM group (p <10-3), whereas the levels of expression of RP4-605O3.4 and AC074117.2 lncRNAs declined gradually from the healthy control group to the prediabetic group, reaching their lowest levels in the T2DM group (p <10-3). TMEM173, CHUK mRNAs, hsa_miR (-611 & -1976) and RP4-605O3.4 lncRNA were useful in distinguishing insulin resistant from insulin sensitive groups. miR_611 together with RP4-605O3.4 exhibited significant difference in good versus poor glycemic control groups. Discussion: The presented study provides an insight about this RNA based STING/NOD/IR associated panel that could be used for PreDM-T2DM diagnosis and also as a therapeutic target based on the differences of its expression level in the pre-DM and T2DM stages.

Identification of a Multi-Messenger RNA Signature as Type 2 Diabetes Mellitus Candidate Genes Involved in Crosstalk between Inflammation and Insulin Resistance.

Type 2 Diabetes Mellitus (T2DM) is a metabolic disease associated with inflammation widening the scope of immune-metabolism, linking the inflammation to insulin resistance and beta cell dysfunction. New potential and prognostic biomarkers are urgently required to identify individuals at high risk of ß-cell dysfunction and pre-DM. The DNA-sensing stimulator of interferon genes (STING) is an important component of innate immune signaling that governs inflammation-mediated T2DM. NOD-like receptor (NLR) reduces STING-dependent innate immune activation in response to cyclic di-GMP and DNA viruses by impeding STING-TBK1 interaction. We proposed exploring novel blood-based mRNA signatures that are selective for components related to inflammatory, immune, and metabolic stress which may reveal the landscape of T2DM progression for diagnosing or treating patients in the pre-DM state. In this study, we used microarray data set to identify a group of differentially expressed mRNAs related to the cGAS/STING, NODlike receptor pathways (NLR) and T2DM. Then, we comparatively analyzed six mRNAs expression levels in healthy individuals, prediabetes (pre-DM) and T2DM patients by real-time PCR. The expressions of ZBP1, DDX58, NFKB1 and CHUK were significantly higher in the pre-DM group compared to either healthy control or T2DM patients. The expression of ZBP1 and NFKB1 mRNA could discriminate between good versus poor glycemic control groups. HSPA1B mRNA showed a significant difference in its expression regarding the insulin resistance. Linear regression analysis revealed that LDLc, HSPA1B and NFKB1 were significant variables for the prediction of pre-DM from the healthy control. Our study shed light on a new finding that addresses the role of ZBP1 and HSPA1B in the early prediction and progression of T2DM.

In Silico Identification and Clinical Validation of a Novel Long Non-Coding RNA/mRNA/miRNA Molecular Network for Potential Biomarkers for Discriminating SARS CoV-2 Infection Severity.

(1) Background: The coronavirus (COVID-19) pandemic is still a major global health problem, despite the development of several vaccines and diagnostic assays. Moreover, the broad symptoms, from none to severe pneumonia, and the various responses to vaccines and the assays, make infection control challenging. Therefore, there is an urgent need to develop non-invasive biomarkers to quickly determine the infection severity. Circulating RNAs have been proven to be potential biomarkers for a variety of diseases, including infectious ones. This study aimed to develop a genetic network related to cytokines, with clinical validation for early infection severity prediction. (2) Methods: Extensive analyses of in silico data have established a novel IL11RA molecular network (IL11RNA mRNA, LncRNAs RP11-773H22.4 and hsa-miR-4257). We used different databases to confirm its validity. The differential expression within the retrieved network was clinically validated using quantitative RT-PCR, along with routine assessment diagnostic markers (CRP, LDH, D-dimmer, procalcitonin, Ferritin), in100 infected subjects (mild and severe cases) and 100 healthy volunteers. (3) Results: IL11RNA mRNA and LncRNA RP11-773H22.4, and the IL11RA protein, were significantly upregulated, and there was concomitant downregulation of hsa-miR-4257, in infected patients, compared to the healthy controls, in concordance with the infection severity. (4) Conclusion: The in-silico data and clinical validation led to the identification of a potential RNA/protein signature network for novel predictive biomarkers, which is in agreement with ferritin and procalcitonin for determination of COVID-19 severity.