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Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms.

Mahoney JM, Taroni J, Martyanov V, Wood TA, Greene CS, Pioli PA, Hinchcliff ME, Whitfield ML - PLoS Comput. Biol. (2015)

Bottom Line: Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets.We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms.The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hannover, New Hampshire, United States of America.

ABSTRACT
Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6-12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk.

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Schematic of the analysis pipeline for integrative analysis of multiple SSc skin datasets.(A) Each microarray dataset (Milano et al., Pendergrass et al., and Hinchcliff et al.) was independently clustered by WGCNA into gene coexpression modules (colored circles). Each module is a set of genes that was highly correlated within a dataset. (B) Modules were compared across datasets using a novel procedure (MICC) to determine which were approximately conserved across all three datasets. The network in (B) is called the information graph and encodes the nontrivial overlaps of modules across datasets. Triangles in this network correspond to approximately conserved modules across all three datasets. Communities in this network (dotted ovals) represent collections of modules that are conserved together and thus have similar biological function. Note that communities in the network can overlap (e.g. module P1 in the schematic belongs to two communities). (C) Genes derived from the module communities are called consensus genes and were used for downstream bioinformatics analyses including gene ontology enrichment analysis using the g:Profiler tool, testing for intrinsic subset-specificity, and functional interaction network analysis using the IMP functional network. Each of these downstream analyses is independent and complementary.
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pcbi-1004005-g001: Schematic of the analysis pipeline for integrative analysis of multiple SSc skin datasets.(A) Each microarray dataset (Milano et al., Pendergrass et al., and Hinchcliff et al.) was independently clustered by WGCNA into gene coexpression modules (colored circles). Each module is a set of genes that was highly correlated within a dataset. (B) Modules were compared across datasets using a novel procedure (MICC) to determine which were approximately conserved across all three datasets. The network in (B) is called the information graph and encodes the nontrivial overlaps of modules across datasets. Triangles in this network correspond to approximately conserved modules across all three datasets. Communities in this network (dotted ovals) represent collections of modules that are conserved together and thus have similar biological function. Note that communities in the network can overlap (e.g. module P1 in the schematic belongs to two communities). (C) Genes derived from the module communities are called consensus genes and were used for downstream bioinformatics analyses including gene ontology enrichment analysis using the g:Profiler tool, testing for intrinsic subset-specificity, and functional interaction network analysis using the IMP functional network. Each of these downstream analyses is independent and complementary.

Mentions: We analyzed a compendium of three whole transcriptome datasets from SSc skin biopsies (Milano et al. [1], Pendergrass et al. [11], and an expanded version of Hinchcliff et al. [4]; see Materials and Methods). These datasets consist of 70 patients with dSSc, 10 patients with limited SSc (lSSc), 4 morphea samples, and 26 healthy controls (Table 1). Our aim was a comprehensive picture of the gene expression abnormalities in SSc skin and we integrated several publicly available tools with a novel consensus clustering procedure. As demonstrated in Fig. 1, our analysis began with gene coexpression clustering (Fig. 1A), followed by a novel post-processing step called Mutual Information Consensus Clustering (MICC) that identified conserved gene expression modules across the three cohorts (Fig. 1B). The outputs from MICC were consensus clusters, i.e. modules that were conserved across datasets, which were the objects of further study, including ontology annotation and functional interaction analysis (Fig. 1C).


Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms.

Mahoney JM, Taroni J, Martyanov V, Wood TA, Greene CS, Pioli PA, Hinchcliff ME, Whitfield ML - PLoS Comput. Biol. (2015)

Schematic of the analysis pipeline for integrative analysis of multiple SSc skin datasets.(A) Each microarray dataset (Milano et al., Pendergrass et al., and Hinchcliff et al.) was independently clustered by WGCNA into gene coexpression modules (colored circles). Each module is a set of genes that was highly correlated within a dataset. (B) Modules were compared across datasets using a novel procedure (MICC) to determine which were approximately conserved across all three datasets. The network in (B) is called the information graph and encodes the nontrivial overlaps of modules across datasets. Triangles in this network correspond to approximately conserved modules across all three datasets. Communities in this network (dotted ovals) represent collections of modules that are conserved together and thus have similar biological function. Note that communities in the network can overlap (e.g. module P1 in the schematic belongs to two communities). (C) Genes derived from the module communities are called consensus genes and were used for downstream bioinformatics analyses including gene ontology enrichment analysis using the g:Profiler tool, testing for intrinsic subset-specificity, and functional interaction network analysis using the IMP functional network. Each of these downstream analyses is independent and complementary.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4288710&req=5

pcbi-1004005-g001: Schematic of the analysis pipeline for integrative analysis of multiple SSc skin datasets.(A) Each microarray dataset (Milano et al., Pendergrass et al., and Hinchcliff et al.) was independently clustered by WGCNA into gene coexpression modules (colored circles). Each module is a set of genes that was highly correlated within a dataset. (B) Modules were compared across datasets using a novel procedure (MICC) to determine which were approximately conserved across all three datasets. The network in (B) is called the information graph and encodes the nontrivial overlaps of modules across datasets. Triangles in this network correspond to approximately conserved modules across all three datasets. Communities in this network (dotted ovals) represent collections of modules that are conserved together and thus have similar biological function. Note that communities in the network can overlap (e.g. module P1 in the schematic belongs to two communities). (C) Genes derived from the module communities are called consensus genes and were used for downstream bioinformatics analyses including gene ontology enrichment analysis using the g:Profiler tool, testing for intrinsic subset-specificity, and functional interaction network analysis using the IMP functional network. Each of these downstream analyses is independent and complementary.
Mentions: We analyzed a compendium of three whole transcriptome datasets from SSc skin biopsies (Milano et al. [1], Pendergrass et al. [11], and an expanded version of Hinchcliff et al. [4]; see Materials and Methods). These datasets consist of 70 patients with dSSc, 10 patients with limited SSc (lSSc), 4 morphea samples, and 26 healthy controls (Table 1). Our aim was a comprehensive picture of the gene expression abnormalities in SSc skin and we integrated several publicly available tools with a novel consensus clustering procedure. As demonstrated in Fig. 1, our analysis began with gene coexpression clustering (Fig. 1A), followed by a novel post-processing step called Mutual Information Consensus Clustering (MICC) that identified conserved gene expression modules across the three cohorts (Fig. 1B). The outputs from MICC were consensus clusters, i.e. modules that were conserved across datasets, which were the objects of further study, including ontology annotation and functional interaction analysis (Fig. 1C).

Bottom Line: Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets.We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms.The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hannover, New Hampshire, United States of America.

ABSTRACT
Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6-12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk.

Show MeSH
Related in: MedlinePlus