Dataset Information

Dynamic changes in immune gene co-expression networks predict development of type 1 diabetes.

ABSTRACT: Significant progress has been made in elucidating genetic risk factors influencing Type 1 diabetes (T1D); however, features other than genetic variants that initiate and/or accelerate islet autoimmunity that lead to the development of clinical T1D remain largely unknown. We hypothesized that genetic and environmental risk factors can both contribute to T1D through dynamic alterations of molecular interactions in physiologic networks. To test this hypothesis, we utilized longitudinal blood transcriptomic profiles in The Environmental Determinants of Diabetes in the Young (TEDDY) study to generate gene co-expression networks. In network modules that contain immune response genes associated with T1D, we observed highly dynamic differences in module connectivity in the 600 days (~ 2 years) preceding clinical diagnosis of T1D. Our results suggest that gene co-expression is highly plastic and that connectivity differences in T1D-associated immune system genes influence the timing and development of clinical disease.

SUBMITTER: Brænne I

PROVIDER: S-EPMC8609030 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dynamic changes in immune gene co-expression networks predict development of type 1 diabetes.

Brænne Ingrid I Onengut-Gumuscu Suna S Chen Ruoxi R Manichaikul Ani W AW Rich Stephen S SS Chen Wei-Min WM Farber Charles R CR

Scientific reports 20211122 1

Significant progress has been made in elucidating genetic risk factors influencing Type 1 diabetes (T1D); however, features other than genetic variants that initiate and/or accelerate islet autoimmunity that lead to the development of clinical T1D remain largely unknown. We hypothesized that genetic and environmental risk factors can both contribute to T1D through dynamic alterations of molecular interactions in physiologic networks. To test this hypothesis, we utilized longitudinal blood transc ...[more]

PMID: 34811390

Similar Datasets

Project description:BackgroundType 2 Diabetes Mellitus (T2DM) is an independent risk factor of hepatocellular carcinoma (HCC). However, the related genes and modules to hepatocarcinogenesis and progression in T2DM remain unclear.MethodsThe microarray data from Gene Expression Omnibus (GEO) were analyzed to screen differentially expressed genes (DEGs) of T2DM and HCC dataset. Then, weighted gene co-expression network analysis (WGCNA) was performed on these DEGs to detect the modules and genes, respectively. Common genes in modules with clinical interests of T2DM and HCC were obtained and annotated via GOSemSim package and Metascape. Genes related to late-stage HCC and high glycated haemoglobin (HbA1c) were also identified. These genes were validated by UALCAN analysis and univariate cox regression based on The Cancer Genome Atlas (TCGA). Finally, another two independent datasets were applied to confirm the results of our study.ResultsA total of 1288 and 1559 DEGs of T2DM and HCC were screened, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment revealed several shared pathways in two diseases, such as pathways in cancer and metabolism. A total of 37 common genes correlated with T2DM and HCC were then identified with WGCNA. Furthermore, 12 genes from modules associated with late-stage HCC and high HbA1c were regarded as hub genes. Among these genes, 8 genes associated with tumor invasion and metastasis were validated by UALCAN analysis. Moreover, downregulations of ACAT1, SLC2A2, PCK1 and ABAT were significantly associated with poorer prognosis in HCC patients with elevated HbA1c. Additionally, the expressions of PCK1 and ABAT were raised in HepG2 cells pre-treated with metformin and phenformin.ConclusionsThe present study confirmed several metabolic genes related to hyperglycemia and malignant tumor, which may provide not only new insights into the pathogenesis of hepatocarcinogenesis and progression in T2DM, but also novel therapeutic targets for T2DM patients with HCC in the future.

Project description:Parkinson's disease (PD) is a common neurodegenerative disease in middle-aged and elderly people, and there is less research on the relationship between immunity and PD. In this study, the protein-protein interaction networks (PPI) data, 2747 human immune-related genes (HIRGs), 2078 PD-related genes (PDRGs), and PD-related datasets (GSE49036 and GSE20292) were downloaded from the Human Protein Reference Database (HPRD), Amigo 2, DisGeNET, and Gene Expression Omnibus (GEO) databases, respectively. An immune- or PD-directed neighbor co-expressed network construction (IOPDNC) was drawn based on the GSE49036 dataset and HPRD database. Furthermore, a PD-directed neighbor co-expressed network was constructed. Modular clustering analysis was performed on the genes of the gene interaction network obtained in the first step to obtain the central core genes using the GraphWeb online website. The modules with the top 5 functional scores and the number of core genes greater than six were selected as PD-related gene modules. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of different module genes were performed. The single sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to calculate the immune cell infiltration of the PD and the normal samples. The quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) was performed to investigate the expression of module genes. An IOPDNC and PD-directed neighbor co-expressed network (PDNC network) were constructed. Furthermore, a total of 5 immune-PD modules were identified which could distinguish between PD and normal samples, and these module genes were strongly related to PD in protein interaction level or gene expression level. In addition, functional analysis indicated that module genes were involved in various neurodegenerative diseases, such as Alzheimer disease, Huntington disease, Parkinson disease, and Long-term depression. In addition, the genes of the 6 modules were significantly associated with these 4 differential immune cells (aDC cells, eosinophils, neutrophils, and Th2 cells). Finally, the result of qRT-PCR manifested that the expression of 6 module genes was significantly higher in normal samples than in PD samples. In our study, the immune-related genes were found to be strongly related to PD and might play key roles in PD.

Dataset Information

Dynamic changes in immune gene co-expression networks predict development of type 1 diabetes.

Publications

Dynamic changes in immune gene co-expression networks predict development of type 1 diabetes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets