Transcriptomic Analysis from Normal Glucose Tolerance to T2D of Obese Individuals Using Bioinformatic Tools

Abstract

Understanding the role of white adipose tissue (WAT) in the occurrence and progression of metabolic syndrome is of considerable interest; among the metabolic syndromes are obesity and type 2 diabetes (T2D). Insulin resistance is a key factor in the development of T2D. When the target cells become resistant to insulin, the pancreas responds by producing more insulin to try to lower blood glucose. Over time, this can lead to a state of hyperinsulinemia (high levels of insulin in the blood), which can further exacerbate insulin resistance and contribute to the development of T2D. In order to understand the difference between healthy and unhealthy obese individuals, we have used published transcriptomic profiling to compare differences between the WAT obtained from obese diabetics and subjects who are obese with normal glucose tolerance and insulin resistance. The identification of aberrantly expressed messenger RNA (mRNA) and the resulting molecular interactions and signaling networks is essential for a better understanding of the progression from normal glucose-tolerant obese individuals to obese diabetics. Computational analyses using Ingenuity Pathway Analysis (IPA) identified multiple activated signaling networks in obesity progression from insulin-resistant and normal glucose-tolerant (IR-NGT) individuals to those with T2D. The pathways affected are: Tumor Necrosis Factor (TNF), Extracellular signal-Regulated protein Kinase 1/2 ERK1/2, Interleukin 1 A (IL1A), Protein kinase C (Pkcs), Convertase C5, Vascular endothelial growth factor (Vegf), REL-associated protein (RELA), Interleukin1/1 B (IL1/1B), Triggering receptor expressed on myeloid cells (TREM1) and Nuclear factor KB1 (NFKB1) networks, while functional annotation highlighted Liver X Receptor (LXR) activation, phagosome formation, tumor microenvironment pathway, LPS/IL-1 mediated inhibition of RXR function, TREM1 signaling and IL-6 signaling. Together, by conducting a thorough bioinformatics study of protein-coding RNAs, prospective targets could be exploited to clarify the molecular pathways underlying the development of obesity-related type 2 diabetes.

Keywords: T2D; insulin resistance; obesity; transcriptomics.

Figure 1

Cluster analysis of most significant DEGs. The abscissa represents different samples n = 25; the vertical axis represents clusters of DEGs. Red color represents upregulation; blue color represents downregulation. Expression data are represented as normalized values. Legend shows the clustering of sample categories purple: lean/control, green: obese T2D, yellow: Obese IR-NGT.

Figure 2

Venn diagram of differential expression analysis DEGs of a comparison between both obese IR-NGT and obese T2D groups. Yellow color represents obese T2D vs. lean, and the blue color represents Obese IR-NGT vs. lean.

Figure 3

Functional category annotation analysis of obese individuals in IR-NGT group, when compared to lean group, shows inhibition of cellular movement, immune cell trafficking and cell-to-cell signaling and interaction. Blue color indicates inhibition; red color indicates activation.

Figure 4

Functional category annotation analysis of obese patients with T2D shows a significant enrichment of hematological system development and function, cellular movement, immune cell trafficking and cell-to-cell signaling and interaction, inflammatory response and cellular functions and maintenance. Blue color indicates inhibition; red color indicates activation.

Figure 5

Upstream regulator analysis of differentially expressed genes in obese patients with IR-NGT and obese patients with T2D. Y-axis indicates the upstream regulator, and the x-axis represents the activation Z score. Red bars represent obese individuals with T2D, and blue bars represent obese individuals with IR-NGT.

Figure 6

Regulator affects network analysis based on IPA highlighting a role for activated TNF-affected signaling networks comparison. (A) This figure is representation of TNF downregulated network according to IPA prediction in obese individuals with IR-NGT. (B) TNF is enriched as an upstream regulator network in obese individuals with T2D compared to lean individuals based on IPA analysis.

Figure 7

Network of TNF in obese subjects with T2D, its targeted genes and corresponding T2D. For example, activation of TNF leads to upregulation (indicated by orange line) of DEG (shown by orange color). Upregulation of DEGs further affects insulin secretion signaling pathway (CP). For other indicators, please refer to the prediction legend.

Figure 8

Top dysregulated canonical signaling pathways. The log (p-value), z-score and ratio of top significantly activated canonical signaling pathways are indicated in red color. A scale from light green to dark red indicates the level of inhibition/activation in/of the canonical signaling pathways, respectively.

Figure 9

Dataset baseline information.

Figure 10

Bioinformatics analysis workflow from the retrieval of the public data of GEO to the analysis of dysregulated/affected pathways.