Evidence map›Paper›PMID 31417297›Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2019

Identification of dynamic molecular networks in peripheral blood mononuclear cells in type 1 diabetes mellitus.

Lu Li, Zongfu Pan, Xi Yang

Open access · goldAbstract read
In one paragraph

Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Lu Li *Department of Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China.
Zongfu Pan *Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, People's Republic of China.
Xi YangDepartment of Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China.
Zhejiang University · CNZhejiang Cancer Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundType 1 diabetes mellitus (T1DM) is an autoimmune disease caused by the immune destruction of islet β cells. Gene expression in peripheral blood mononuclear cells (PBMCs) could offer new disease and treatment markers in T1DM. The objective of this study was to explore the coexpression and dynamic molecular networks in PBMCs of T1DM patients.

methodsDataset GSE9006 contains PBMC samples of healthy volunteers, newly diagnosed T1DM patients, T1DM patients after insulin treatment, and newly diagnosed type 2 diabetes mellitus (T2DM) patients. Weighted correlation network analysis (WGCNA) was used to generate coexpression networks in T1DM and T2DM. Functional pathways in highly correlated modules of T1DM were enriched by gene set enrichment analysis (GSEA). We next filtered the differentially expressed genes (DEGs) and revealed their dynamic expression profiles in T1DM with or without insulin treatment. Furthermore, dynamic clusters and dynamic protein-protein interaction networks were identified. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was developed in dynamic clusters.

resultsWGCNA disclosed 12 distinct gene modules, and distinguished between correlated networks in T1DM and T2DM. Two modules were closely associated with T1DM. GSEA showed that the immune response and response to cytokines were enriched in the T1DM highly correlated module. Next, we screened 44 DEGs in newly diagnosed T1DM compared with healthy donors, and 71 DEGs in 1-month and 97 DEGs in 4-month insulin treatment groups compared with newly diagnosed T1DM. Dynamic expression profiles of DEGs indicated the potential targets for T1DM treatment. Moreover, four molecular dynamic clusters were analyzed in newly diagnosed and insulin-treated T1DM. Functional annotation showed that these clusters were mainly enriched in the IL-17 signaling pathway, nuclear factor-ϰB signaling pathway, and tumor necrosis factor signaling pathway.

conclusionThe results indicate potential drug targets or clinical efficacy markers, as well as demonstrating the underlying molecular mechanisms of T1DM treatment.

Indexed as

coexpression networksdynamic molecular networksperipheral blood mononuclear cellstype 1 diabetes mellitus

Identifiers

PMID31417297
PMCPMC6601337
OpenAlexW2949616327

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.