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Cited by 106Bioinformatics and Genomic NetworksAdipokines, Inflammation, and Metabolic DiseasesGenetic and phenotypic traits in livestock

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Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

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Kogelman, Lisette J A, Cirera, Susanna, Zhernakova, Daria V, Fredholm, Merete, Franke, Lude, Kadarmideen, Haja N·2014·BMC Medical Genomics
DOI 10.1186/1755-8794-7-57PMC PMC4183073
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Abstract

BACKGROUND: Obesity is a complex metabolic condition in strong association with various diseases, like type 2 diabetes, resulting in major public health and economic implications. Obesity is the result of environmental and genetic factors and their interactions, including genome-wide genetic interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model for human obesity, offering the possibility to study in-depth organ-level transcriptomic regulations of obesity, unfeasible in humans. Our aim was to reveal adipose tissue co-expression networks, pathways and transcriptional regulations of obesity using RNA Sequencing based systems biology approaches in a porcine model. METHODS: We selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. RESULTS: WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P < 0.001). Functional annotation identified pathways enlightening the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E-7), and immune-related complications (e.g. Natural killer cell mediated cytotoxity, P = 3.8E-5; B cell receptor signaling pathway, P = 7.2E-5). Lemon-Tree identified three potential regulator genes, using confident scores, for the WGCNA module which was associated with osteoclast differentiation: CCR1, MSR1 and SI1 (probability scores respectively 95.30, 62.28, and 34.58). Moreover, detection of differentially connected genes identified various genes previously identified to be associated with obesity in humans and rodents, e.g. CSF1R and MARC2. CONCLUSIONS: To our knowledge, this is the first study to apply systems biology approaches using porcine adipose tissue RNA-Sequencing data in a genetically characterized porcine model for obesity. We revealed complex networks, pathways, candidate and regulatory genes related to obesity, confirming the complexity of obesity and its association with immune-related disorders and osteoporosis.

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Figure 1 Gene dendrogram showing the co-expression modules defined by the WGCNA labeled by colors.
Figure 1 Gene dendrogram showing the co-expression modules defined by the WGCNA labeled by colors.CC BY · PubMed Central
Figure 2 Matrix with the Module-Trait Relationships (MTRs) and corresponding p-values between the detected modules on the y-axis and selected obesity and obesity-related traits on the x-axis. The MTRs are colored based on their correlation: red is a strong positive correlation, while blue is a strong negative correlation. Explanation of the traits: Weight, Abdominal Circumference, BMI (Body Mass Index: weight/length2), and BAI (Body Adiposity Index: abdominal circumference/length1.5) were measured at approximately 7 months of age. DXA scanning was performed at approximately 2 months of age. Fasting glucose levels were determined using an oral glucose tolerance test at approximately 7 months of age. Animals were slaughtered at approximately 100 kg (~7 months). More detailed information can be found in Kogelman et al., 2013 [3].
Figure 2 Matrix with the Module-Trait Relationships (MTRs) and corresponding p-values between the detected modules on the y-axis and selected obesity and obesity-related traits on the x-axis. The MTRs are colored based on their correlation: red is a strong positive correlation, while blue is a strong negative correlation. Explanation of the traits: Weight, Abdominal Circumference, BMI (Body Mass Index: weight/length2), and BAI (Body Adiposity Index: abdominal circumference/length1.5) were measured at approximately 7 months of age. DXA scanning was performed at approximately 2 months of age. Fasting glucose levels were determined using an oral glucose tolerance test at approximately 7 months of age. Animals were slaughtered at approximately 100 kg (~7 months). More detailed information can be found in Kogelman et al., 2013 [3].CC BY · PubMed Central
Figure 3 WGCNA module (Blue) associated with immunity. A) Association between the Module Membership and Gene-Trait correlation within the blue module. B) Module eigengene values (y-axis) across samples (x-axis), with the 12 lean animals colored green, the 12 intermediate animals colored yellow and the 12 obese animals colored red. C) Pie chart of all significant KEGG pathways in the blue module. D) The top 10 significant Gene Ontology terms in the Blue module.
Figure 3 WGCNA module (Blue) associated with immunity. A) Association between the Module Membership and Gene-Trait correlation within the blue module. B) Module eigengene values (y-axis) across samples (x-axis), with the 12 lean animals colored green, the 12 intermediate animals colored yellow and the 12 obese animals colored red. C) Pie chart of all significant KEGG pathways in the blue module. D) The top 10 significant Gene Ontology terms in the Blue module.CC BY · PubMed Central
Figure 4 Plot of the differentially co-expressed genes with on the y-axis thet-test statistic (lean vs. obese) and on the x-axis the differential connectivity. Genes are colored grey when they are differentially connected and colored red if they are also being differentially expressed. In total, 29 genes (19 of which were assigned a gene name) were colored red, which were selected for functional annotation. Discontinuous lines represent thresholds for gene selection: absolute t-test statistic > 1.96 and absolute differential connectivity > 0.6.
Figure 4 Plot of the differentially co-expressed genes with on the y-axis thet-test statistic (lean vs. obese) and on the x-axis the differential connectivity. Genes are colored grey when they are differentially connected and colored red if they are also being differentially expressed. In total, 29 genes (19 of which were assigned a gene name) were colored red, which were selected for functional annotation. Discontinuous lines represent thresholds for gene selection: absolute t-test statistic > 1.96 and absolute differential connectivity > 0.6.CC BY · PubMed Central
Figure 5 Color coded expression values of two clusters resulting from the Lemon-Tree algorithm. A) Cluster1, and B) Cluster10. The upper panel represents the high-scoring regulator genes, ordered by their probabilistic score (highest on top). The lower panel represents the genes present in the cluster. Each column represents a sample. The expression of the genes and regulator gene is color coded, with dark blue representing low expression, while bright yellow indicates highly expressed genes. The hierarchical tree on top of the figure is the tree used to assign the regulator gene. The vertical pink colored line represents the partition of samples defined by the first node of the tree.
Figure 5 Color coded expression values of two clusters resulting from the Lemon-Tree algorithm. A) Cluster1, and B) Cluster10. The upper panel represents the high-scoring regulator genes, ordered by their probabilistic score (highest on top). The lower panel represents the genes present in the cluster. Each column represents a sample. The expression of the genes and regulator gene is color coded, with dark blue representing low expression, while bright yellow indicates highly expressed genes. The hierarchical tree on top of the figure is the tree used to assign the regulator gene. The vertical pink colored line represents the partition of samples defined by the first node of the tree.CC BY · PubMed Central
Figure 6 Workflow of the RNA-Seq data analysis.
Figure 6 Workflow of the RNA-Seq data analysis.CC BY · PubMed Central

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Research works printed but not linked: 2

  1. Adams JU: Transcriptome: Connecting the Genome to Gene Function. Nat Educ. 2008, 1 (1): 195.
  2. Kadarmideen HN, Kogelman LJA: RNA-Sequencing of lean, intermediate, and obese pigs. NCBI's Gene Expression Omnibus. 2014, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61271, 1.

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Metadata

Authors
Kogelman, L. J. A., Cirera, S., Zhernakova, D. V., Fredholm, M., Franke, L., & Kadarmideen, H. N.
Affiliations
  • University of Copenhagen (DK)
  • University Medical Center Groningen (NL)
Corresponding author
Kadarmideen, Haja N
ORCID
Venue
BMC Medical Genomics
Publisher
Springer Science and Business Media LLC
Published
2014-09-30
Volume
7
Issue
1
Pages
57-57
ISSN
1755-8794 (ISSN-L)
DOI
10.1186/1755-8794-7-57
PMC
PMC4183073
OpenAlex
W2153892583
Cited by
106
References
113
Citations by year
2026: 1 · 2025: 5 · 2024: 8 · 2023: 5 · 2022: 14 · 2021: 10 · 2020: 12 · 2019: 7 · 2018: 13 · 2017: 8 · 2016: 14 · 2015: 9
FWCI
4.24
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Genomics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
Funders
  • Strategiske Forskningsråd
  • European Commission (293511)
  • Copenhagen Graduate School for Nanoscience and Nanotechnology
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adipokines, Inflammation, and Metabolic Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Keywords
obesity, adipose tissue, porcine model, co-expression network, gene regulatory networks, transcriptional regulation, osteoclastogenesis, osteoporosis, immune pathways, CCR1, MSR1, CSF1R, type 2 diabetes, B cell receptor signaling, NK cell cytotoxicity
MeSH terms
Adipose Tissue, Animals, Disease Models, Animal, Female, Humans, Male, Obesity, Swine, Transcription, Genetic, Sequence Analysis, RNA, Systems Biology, Gene Regulatory Networks

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