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Multi-omics Insights Into Type 2 Diabetes

Type 2 diabetes (T2D), the most prevalent form of diabetes, poses substantial burdens on human health and on global healthcare worldwide. To date, full recovery of T2D has not been reported. The goal of the vast majority of common treatments is to avoid complications that could affect major organs such as heart and blood vessel disease, nerve damage, kidney damage, eye damage, skin conditions, and more. This condition affects hundreds of millions of people across the globe, which justifies an efficient utilization of existing therapies and/or the exploration and continued development of effective therapies.


To identify potential therapeutic targets that can improve the treatment of T2D, we performed a multi-omics analysis using multiple datasets of T2D from transcriptomics, proteomics, and metabolomics (Table 1) with the support of the CDIAM Multi-Omics Studio platform. In this blog, we summarized our key findings, in terms of potential T2D targets and important ligand-receptor interactions.

To read the full report, please follow the link below:


A protein was ranked as the most potential target in both bulkRNA datasets.


We performed a DEG analysis to compare the changes between pancreatic tissues of T2D patients versus those of normal patients obtained from both bulk RNA-sequencing datasets was used as the input for our custom Pathway2Target (P2T) bioinformatics pipeline. This analytical workflow within the CDIAM platform predicts and prioritizes potential intracellular gene products and therapeutics that could be targeted for T2D.


Interestingly, both bulk RNA-sequencing datasets yielded the same protein as the highest-scoring target. This protein is a typical inflammatory biomarker, whose elevation has been reported with obesity, hypertension, heavy drinking, low physical activity, and smoking. Several studies examining people with different ethnic backgrounds have reported a positive correlation between elevated level of this protein and the risk of T2D development, signifying this protein level as an inflammatory indicator in the pathogenesis of T2D. Consistently, treatment of T2D patients with inflammatory blockers induced a reduction in this protein level, ultimately improving glycemia and insulin secretion.


The combination of P2T results from scRNA-seq, proteomics, and metabolomics identified a target that could improve glucose metabolism.


P2T results from different -omics datasets were submitted to the CDIAM Target Priority Pipeline, which calculates the combined results with ranked scores. This analysis yielded a potential protein target that is involved in regulating the development, growth, and function of diverse cell types. This protein presented with a positive correlation between its elevated plasma levels and fasting insulin and glucose levels, insulin resistance, and obesity in mice and humans. Its role in the development of hyperglycemia was recently described in more detail. Interestingly, this protein was also suggested to be involved in the mechanism of action of metformin, a common antidiabetic agent.


Targets found within β-cells could be manipulated to enhance functional β-cell mass or control inflammation in T2D.


Insulin resistance, which is one of the main driving components of T2D pathogenesis, intensifies β-cell dysfunction. We consequently used the C-DIAM P2T workflow to identify possible new targets that could improve β-cell function in β-cells from T2D vs. normal that were shared between two scRNA sequencing datasets.


Interestingly, the highest-ranked target from this analysis was recognized as a critical link between a transcription factor and the pathway by which this transcription factor could regulate the maturation and enhanced proliferation of β-cells. The second-highest ranked protein has been shown to significantly contribute to islet inflammation, resulting in β-cell dysfunction and destruction. The blockade of this protein and its receptor could represent a potential promising therapeutic approach for controlling inflammation in T2D.


Most down-regulated ligand-receptor interactions in scRNA-seq datasets belong to fatty acid regulatory mechanisms, as well as β-cell growth and function.


We subsequently predicted intercellular interactions in pancreatic tissues from T2D patients and healthy contols from two scRNA datasets. This effort involved the curation, analysis, and comparison via CellPhoneDB available on CDIAM.


Both datasets revealed the majority of ligand-receptor interactions to be down-regulated, including the interaction between insulin and its receptor as expected.


We found that in the GSE154125 scRNA-seq dataset most of the interactions contain a protein that is essential for angiogenesis and maintenance of vessel integrity. This protein supports regulates fatty acid metabolism that, when dysregulated in T2D, could cause insulin resistance in which pancreatic β-cells eventually fail to produce enough compensatory insulin. Meanwhile, two of the top five down-regulated ligand-receptor interactions in the GSE153855 scRNA-seq dataset are consistent with previous findings that the downregulation of these interactions could contribute to the dysfunction of β-cell growth and function in diabetes.


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In summary, our analysis provides insights into the possible T2D drug targets that were consistent with previous T2D research findings. Feel free to download the full report to view all the details about our analytical methods, results and insights!

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