Gene Expression Data Explorer
Info Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples. You can learn more about ARCHS4 and its pipeline here.
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GROUP CONDITION SAMPLES
Islet
GSM4636088 GSM4636090 GSM4636092
GSM4636152 GSM4636154 GSM4636156 GSM4636158
GSM4636096 GSM4636098 GSM4636100 GSM4636102
GSM4636120 GSM4636122 GSM4636124 GSM4636126
GSM4636072 GSM4636074 GSM4636076 GSM4636078
GSM4636128 GSM4636130 GSM4636132 GSM4636134
GSM4636080 GSM4636080 GSM4636084 GSM4636086
GSM4636112 GSM4636114 GSM4636116 GSM4636118
GSM4636136 GSM4636138 GSM4636140 GSM4636142
GSM4636160 GSM4636162 GSM4636164 GSM4636166
GSM4636144 GSM4636146 GSM4636148 GSM4636150
GSM4636104 GSM4636106 GSM4636108 GSM4636110
Liver
GSM4636161
GSM4636073
iver
GSM4636135
GSM4636091
GSM4636165
liver
GSM4636089 GSM4636093 GSM4636095
GSM4636153 GSM4636155 GSM4636157 GSM4636159
GSM4636097 GSM4636099 GSM4636101 GSM4636103
GSM4636121 GSM4636123 GSM4636125 GSM4636127
GSM4636075 GSM4636077 GSM4636079
GSM4636129 GSM4636131 GSM4636133
GSM4636080 GSM4636080 GSM4636087
GSM4636113 GSM4636115 GSM4636117 GSM4636119
GSM4636137 GSM4636139 GSM4636141 GSM4636143
GSM4636163 GSM4636167
GSM4636145 GSM4636147 GSM4636149 GSM4636151
GSM4636105 GSM4636107 GSM4636109 GSM4636111
slet
GSM4636094
Description

Submission Date: Jun 25, 2020

Summary: Purpose: Hyperinsulinemia and insulin resistance are co-existing characteristics of type 2 diabetes, whereas the forerunner initiating the deleterious cycle remains elusive. The temporal transcriptomic landscape of islets (responsible for hyperinsulinemia) and liver (involved in insulin resistance) could provide new insights.

Methods: C57BL/6N mice were fed a 60% high-fat diet (HFD) or control diet (CD) for 24 weeks. RNA-sequencing of islet and liver samples were respectively performed in quadruplicates at six consecutive time points of diet treatments (week 4, 8, 12, 16, 20 and 24), using BGISEQ-500 sequencing platform by the Beijing Genomics Institute (Shenzhen, China).The sequencing raw reads were filtered for low-quality, adaptor-polluted, high content of unknown base reads by SOAPnuke (v1.5.2). We used Trinity (v2.0.6) to perform de novo assembly, and Tgicl (v2.0.6) on cluster transcripts to remove redundancy and get unigenes. The high-quality clean reads were then mapped to the mouse reference genome (GRCm38) via HISAT2 (v2.0.4) and full-length transcriptome database via Bowtie2 (v2.2.5). The gene expression levels were then quantified by RSEM (v1.1.12) and were normalized by the method of fragments per kilobase of exon model per million reads mapped (FPKM). To interpret the functional significance of differentially expressed genes (DEGs), pathway analyses was conducted to determine enriched canonical pathways.

Results: Combined analyses of all 96 samples yielded the identification of 21990 annotated genes. Differentially expressed genes (DEGs) between the two groups (HFD vs. CD) at each time point were identified using the criteria of fold change ≥2 and adjusted P-value ≤0.05. In total, 3844 DEGs were found in islets, of which 33 were shared among all six time points. With regard to liver, 4101 DEGs were discovered throughout 24 weeks of feeding, of which 39 were overlapped. Our islet and liver RNA-sequencing datasets outlined the impact of HFD on dynamics of molecular network at different stages. Correlation analyses of islet and liver modules with metabolic phenotypes illustrated that these two tissues jointly program β-cell adaption to irreversible impairment. Top scored networks combining islet and liver transcriptomes showed potential interactions of genes implicated in cell cycle during week 4, organismal development around week 12, and immune cell trafficking at week 24.

Conclusions: Our data provide a comprehensive landscape of crosstalk between islets and liver in diet-induced diabetes, linking to the development of islet dysfunction and insulin resistance.

GEO Accession ID: GSE153222

PMID: 33817571

Description

Submission Date: Jun 25, 2020

Summary: Purpose: Hyperinsulinemia and insulin resistance are co-existing characteristics of type 2 diabetes, whereas the forerunner initiating the deleterious cycle remains elusive. The temporal transcriptomic landscape of islets (responsible for hyperinsulinemia) and liver (involved in insulin resistance) could provide new insights.

Methods: C57BL/6N mice were fed a 60% high-fat diet (HFD) or control diet (CD) for 24 weeks. RNA-sequencing of islet and liver samples were respectively performed in quadruplicates at six consecutive time points of diet treatments (week 4, 8, 12, 16, 20 and 24), using BGISEQ-500 sequencing platform by the Beijing Genomics Institute (Shenzhen, China).The sequencing raw reads were filtered for low-quality, adaptor-polluted, high content of unknown base reads by SOAPnuke (v1.5.2). We used Trinity (v2.0.6) to perform de novo assembly, and Tgicl (v2.0.6) on cluster transcripts to remove redundancy and get unigenes. The high-quality clean reads were then mapped to the mouse reference genome (GRCm38) via HISAT2 (v2.0.4) and full-length transcriptome database via Bowtie2 (v2.2.5). The gene expression levels were then quantified by RSEM (v1.1.12) and were normalized by the method of fragments per kilobase of exon model per million reads mapped (FPKM). To interpret the functional significance of differentially expressed genes (DEGs), pathway analyses was conducted to determine enriched canonical pathways.

Results: Combined analyses of all 96 samples yielded the identification of 21990 annotated genes. Differentially expressed genes (DEGs) between the two groups (HFD vs. CD) at each time point were identified using the criteria of fold change ≥2 and adjusted P-value ≤0.05. In total, 3844 DEGs were found in islets, of which 33 were shared among all six time points. With regard to liver, 4101 DEGs were discovered throughout 24 weeks of feeding, of which 39 were overlapped. Our islet and liver RNA-sequencing datasets outlined the impact of HFD on dynamics of molecular network at different stages. Correlation analyses of islet and liver modules with metabolic phenotypes illustrated that these two tissues jointly program β-cell adaption to irreversible impairment. Top scored networks combining islet and liver transcriptomes showed potential interactions of genes implicated in cell cycle during week 4, organismal development around week 12, and immune cell trafficking at week 24.

Conclusions: Our data provide a comprehensive landscape of crosstalk between islets and liver in diet-induced diabetes, linking to the development of islet dysfunction and insulin resistance.

GEO Accession ID: GSE153222

PMID: 33817571

Visualize Samples

Info Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.

Precomputed Differential Gene Expression

Info Differential expression signatures are automatically computed using the limma R package. More options for differential expression are available to compute below.

Signatures:

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Control Condition

Perturbation Condition

Only conditions with at least 1 replicate are available to select

Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
Select differential expression analysis method:
Bulk RNA-seq Appyter

This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.