Comparative analysis of mRNA expression profiles in Type 1 and Type 2 diabetes mellitus

Comparative Screening of the Liver Gene Expression Profiles from Type 1 and Type 2 Diabetes Rat Models

Experimental animal models of diabetes can be useful for identifying novel targets related to disease, for understanding its physiopathology, and for evaluating emerging antidiabetic treatments. This study aimed to characterize two rat diabetes models: HFD + STZ, a high-fat diet (60% fat) combined with streptozotocin administration (STZ, 35 mg/kg BW), and a model with a single STZ dose (65 mg/kg BW) in comparison with healthy rats. HFD + STZ- induced animals demonstrated a stable hyperglycemia range (350-450 mg/dL), whereas in the STZ-induced rats, we found glucose concentration values with a greater dispersion, ranging from 270 to 510 mg/dL. Moreover, in the HFD + STZ group, the AUC value of the insulin tolerance test (ITT) was found to be remarkably augmented by 6.2-fold higher than in healthy animals (33,687.0 ± 1705.7 mg/dL/min vs. 5469.0 ± 267.6, respectively), indicating insulin resistance (IR). In contrast, a more moderate AUC value was observed in the STZ group (19,059.0 ± 3037.4 mg/dL/min) resulting in a value 2.5-fold higher than the average exhibited by the control group. After microarray experiments on liver tissue from all animals, we analyzed genes exhibiting a fold change value in gene expression <-2 or >2 (p-value <0.05). We found 27,686 differentially expressed genes (DEG), identified the top 10 DEGs and detected 849 coding genes that exhibited opposite expression patterns between both diabetes models (491 upregulated genes in the STZ model and 358 upregulated genes in HFD + STZ animals). Finally, we performed an enrichment analysis of the 849 selected genes. Whereas in the STZ model we found cellular pathways related to lipid biosynthesis and metabolism, in the HFD + STZ model we identified pathways related to immunometabolism. Some phenotypic differences observed in the models could be explained by transcriptomic results; however, further studies are needed to corroborate these findings. Our data confirm that the STZ and the HFD + STZ models are reliable experimental models for human T1D and T2D, respectively. These results also provide insight into alterations in the expression of specific liver genes and could be utilized in future studies focusing on diabetes complications associated with impaired liver function.

Overview of Transcriptomic Research on Type 2 Diabetes: Challenges and Perspectives

Type 2 diabetes (T2D) is a common chronic disease whose etiology is known to have a strong genetic component. Standard genetic approaches, although allowing for the detection of a number of gene variants associated with the disease as well as differentially expressed genes, cannot fully explain the hereditary factor in T2D. The explosive growth in the genomic sequencing technologies over the last decades provided an exceptional impetus for transcriptomic studies and new approaches to gene expression measurement, such as RNA-sequencing (RNA-seq) and single-cell technologies. The transcriptomic analysis has the potential to find new biomarkers to identify risk groups for developing T2D and its microvascular and macrovascular complications, which will significantly affect the strategies for early diagnosis, treatment, and preventing the development of complications. In this article, we focused on transcriptomic studies conducted using expression arrays, RNA-seq, and single-cell sequencing to highlight recent findings related to T2D and challenges associated with transcriptome experiments.

Long non-coding RNA Dlx6os1 serves as a potential treatment target for diabetic nephropathy via regulation of apoptosis and inflammation

The present study investigated the effect of long non-coding RNA (lncRNA) Dlx6os1 silencing on cell proliferation, apoptosis and fibrosis, and further explored its influence on the mRNA expression profile in mouse mesangial cells (MMCs) of a diabetic nephropathy (DN) cellular model. A DN cellular model was constructed in SV40 MES13 MMCs under high glucose conditions (30 mmol/l glucose culture). lncRNA Dlx6os1 short hairpin (sh)RNA plasmids and negative control (NC) shRNA plasmids were transfected into the MMCs of the DN cellular model as the sh-lncRNA group and sh-NC group respectively. The mRNA expression profile was determined in the sh-lncRNA and sh-NC groups. Compared with the sh-NC group, the cell proliferation, mRNA and protein expression levels of proliferative markers (cyclin D1 and proliferating cell nuclear antigen) as well as fibrosis markers (fibronectin and collagen I) were suppressed, whereas cell apoptosis was promoted in the sh-lncRNA group. The mRNA expression profile identified 423 upregulated mRNAs and 438 downregulated mRNAs in the sh-lncRNA group compared with the sh-NC group. Additionally, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that the differentially expressed mRNAs were enriched in apoptosis and inflammation-related pathways. Further gene-set enrichment analysis of apoptosis and inflammation revealed that lncRNA Dlx6os1 inhibition promoted apoptosis and suppressed inflammation in MMCs of the DN cellular model. In conclusion, lncRNA Dlx6os1 may serve as a potential treatment target for DN via regulation of multiple apoptosis- and inflammation-related pathways.

In silico transcriptional analysis of mRNA and miRNA reveals unique biosignatures that characterizes different types of diabetes

Diabetes (DM) has a significant impact on public health. We performed an in silico study of paired datasets of messenger RNA (mRNA) micro-RNA (miRNA) transcripts to delineate potential biosignatures that could distinguish prediabetes (pre-DM), type-1DM (T1DM) and type-2DM (T2DM). Two publicly available datasets containing expression values of mRNA and miRNA obtained from individuals diagnosed with pre-DM, T1DM or T2DM, and normoglycemic controls (NC), were analyzed using systems biology approaches to define combined signatures to distinguish different clinical groups. The mRNA profile of both pre-DM and T2DM was hallmarked by several differentially expressed genes (DEGs) compared to NC. Nevertheless, T1DM was characterized by an overall low number of DEGs. The miRNA signature profiles were composed of a substantially lower number of differentially expressed targets. Gene enrichment analysis revealed several inflammatory pathways in T2DM and fewer in pre-DM, but with shared findings such as Tuberculosis. The integration of mRNA and miRNA datasets improved the identification and discriminated the group composed by pre-DM and T2DM patients from that constituted by normoglycemic and T1DM individuals. The integrated transcriptomic analysis of mRNA and miRNA expression revealed a unique biosignature able to characterize different types of DM.