Integrated epigenome, whole genome sequence and metabolome analyses identify novel multi-omics pathways in type 2 diabetes: a Middle Eastern study

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Epigenetic Profiling of Type 2 Diabetes Mellitus: An Epigenome-Wide Association Study of DNA Methylation in the Korean Genome and Epidemiology Study

Diabetes is characterized by persistently high blood glucose levels and severe complications and affects millions of people worldwide. In this study, we explored the epigenetic landscape of diabetes using data from the Korean Genome and Epidemiology Study (KoGES), specifically the Ansung-Ansan (AS-AS) cohort. Using epigenome-wide association studies, we investigated DNA methylation patterns in patients with type 2 diabetes mellitus (T2DM) and those with normal glucose regulation. Differential methylation analysis revealed 106 differentially methylated probes (DMPs), with the 10 top DMPs prominently associated with TXNIP, PDK4, NBPF20, ARRDC4, UFM1, PFKFB2, C7orf50, and ABCG1, indicating significant changes in methylation. Correlation analysis highlighted the association between the leading DMPs (e.g., cg19693031 and cg26974062 for TXNIP and cg26823705 for NBPF20) and key glycemic markers (fasting plasma glucose and hemoglobin A1c), confirming their relevance in T2DM. Moreover, we identified 62 significantly differentially methylated regions (DMRs) spanning 61 genes. A DMR associated with PDE1C showed hypermethylation, whereas DMRs associated with DIP2C, FLJ90757, PRSS50, and TDRD9 showed hypomethylation. PDE1C and TDRD9 showed a strong positive correlation between the CpG sites included in each DMR, which have previously been implicated in T2DM-related processes. This study contributes to the understanding of epigenetic modifications in T2DM. These valuable insights can be utilized in identifying potential biomarkers and therapeutic targets for effective management and prevention of diabetes.

Effect of dapagliflozin on proteomics and metabolomics of serum from patients with type 2 diabetes

BACKGROUND

Sodium-glucose co-transporter 2 (SGLT2) inhibitors reduced the risk of cardiovascular and renal outcomes in patients with type 2 diabetes (T2D), but the underlying mechanism has not been well elucidated. The circulating levels of proteins and metabolites reflect the overall state of the human body. This study aimed to evaluate the effect of dapagliflozin on the proteome and metabolome in patients with newly diagnosed T2D.

METHODS

A total of 57 newly diagnosed T2D patients were enrolled, and received 12 weeks of dapagliflozin treatment (10 mg/d, AstraZeneca). Serum proteome and metabolome were investigated at the baseline and after dapagliflozin treatment.

RESULTS

Dapagliflozin significantly decreased HbA1c, BMI, and HOMA-IR in T2D patients (all p < 0.01). Multivariate models indicated clear separations of proteomics and metabolomics data between the baseline and after dapagliflozin treatment. A total of 38 differentially abundant proteins including 23 increased and 15 decreased proteins, and 35 differentially abundant metabolites including 17 increased and 18 decreased metabolites, were identified. In addition to influencing glucose metabolism (glycolysis/gluconeogenesis and pentose phosphate pathway), dapagliflozin significantly increased sex hormone-binding globulin, transferrin receptor protein 1, disintegrin, and metalloprotease-like decysin-1 and apolipoprotein A-IV levels, and decreased complement C3, fibronectin, afamin, attractin, xanthine, and uric acid levels.

CONCLUSIONS

The circulating proteome and metabolome in newly diagnosed T2D patients were significantly changed after dapagliflozin treatment. These changes in proteins and metabolites might be associated with the beneficial effect of dapagliflozin on cardiovascular and renal outcomes.