Identification of Pre-Diabetic Biomarkers in the Progression of Diabetes Mellitus

A reduced proteomic signature in critically ill Covid-19 patients determined with plasma antibody micro-array and machine learning

BACKGROUND

COVID-19 is a complex, multi-system disease with varying severity and symptoms. Identifying changes in critically ill COVID-19 patients' proteomes enables a better understanding of markers associated with susceptibility, symptoms, and treatment. We performed plasma antibody microarray and machine learning analyses to identify novel proteins of COVID-19.

METHODS

A case-control study comparing the concentration of 2000 plasma proteins in age- and sex-matched COVID-19 inpatients, non-COVID-19 sepsis controls, and healthy control subjects. Machine learning was used to identify a unique proteome signature in COVID-19 patients. Protein expression was correlated with clinically relevant variables and analyzed for temporal changes over hospitalization days 1, 3, 7, and 10. Expert-curated protein expression information was analyzed with Natural language processing (NLP) to determine organ- and cell-specific expression.

RESULTS

Machine learning identified a 28-protein model that accurately differentiated COVID-19 patients from ICU non-COVID-19 patients (accuracy = 0.89, AUC = 1.00, F1 = 0.89) and healthy controls (accuracy = 0.89, AUC = 1.00, F1 = 0.88). An optimal nine-protein model (PF4V1, NUCB1, CrkL, SerpinD1, Fen1, GATA-4, ProSAAS, PARK7, and NET1) maintained high classification ability. Specific proteins correlated with hemoglobin, coagulation factors, hypertension, and high-flow nasal cannula intervention (P < 0.01). Time-course analysis of the 28 leading proteins demonstrated no significant temporal changes within the COVID-19 cohort. NLP analysis identified multi-system expression of the key proteins, with the digestive and nervous systems being the leading systems.

CONCLUSIONS

The plasma proteome of critically ill COVID-19 patients was distinguishable from that of non-COVID-19 sepsis controls and healthy control subjects. The leading 28 proteins and their subset of 9 proteins yielded accurate classification models and are expressed in multiple organ systems. The identified COVID-19 proteomic signature helps elucidate COVID-19 pathophysiology and may guide future COVID-19 treatment development.

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.

Anti-Obesity and Anti-Hyperglycemic Effects of Meretrix lusoria Protamex Hydrolysate in ob/ob Mice

Meretrix lusoria (M. lusoria) is an economically important shellfish which is widely distributed in South Eastern Asia that contains bioactive peptides, proteins, and enzymes. In the present study, the extracted meat content of M. lusoria was enzymatic hydrolyzed using four different commercial proteases (neutrase, protamex, alcalase, and flavourzyme). Among the enzymatic hydrolysates, M. lusoria protamex hydrolysate (MLPH) fraction with MW ≤ 1 kDa exhibited the highest free radical scavenging ability. The MLPH fraction was further purified and an amino acid sequence (KDLEL, 617.35 Da) was identified by LC-MS/MS analysis. The purpose of this study was to investigate the anti-obesity and anti-hyperglycemic effects of MLPH containing antioxidant peptides using ob/ob mice. Treatment with MLPH for 6 weeks reduced body and organ weight and ameliorated the effects of hepatic steatosis and epididymal fat, including a constructive effect on hepatic and serum marker parameters. Moreover, hepatic antioxidant enzyme activities were upregulated and impaired glucose tolerance was improved in obese control mice. In addition, MLPH treatment markedly suppressed mRNA expression related to lipogenesis and hyperglycemia through activation of AMPK phosphorylation. These findings suggest that MLPH has anti-obesity and anti-hyperglycemic potential and could be effectively applied as a functional food ingredient or pharmaceutical.