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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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Molecular network of inflammatory and fibroproliferative consensus genes.The consensus genes for the inflammatory and fibroproliferative subsets are connected in the IMP functional network. Inflammatory genes are colored purple, while fibroproliferative genes are colored red. Genes with polymorphisms are colored in green and MRSS biomarker genes are colored yellow. One MRSS biomarker gene (IFI44) was also an inflammatory consensus gene (pink), while three polymorphic genes were inflammatory consensus genes (turquoise). Note the five distinct subnetworks corresponding to type I interferons, M2 macrophages, ECM proteins and TGFβ signaling, adaptive immunity, and cell proliferation. The interferon, M2 macrophage, and adaptive immunity subnetworks are composed almost exclusively of inflammatory genes, while the ECM subnetwork shares genes from both intrinsic subsets. Furthermore, the polymorphic genes interact primarily with inflammatory subset genes indicating that the genetic risk in SSc is related to immune abnormalities.
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pcbi-1004005-g004: Molecular network of inflammatory and fibroproliferative consensus genes.The consensus genes for the inflammatory and fibroproliferative subsets are connected in the IMP functional network. Inflammatory genes are colored purple, while fibroproliferative genes are colored red. Genes with polymorphisms are colored in green and MRSS biomarker genes are colored yellow. One MRSS biomarker gene (IFI44) was also an inflammatory consensus gene (pink), while three polymorphic genes were inflammatory consensus genes (turquoise). Note the five distinct subnetworks corresponding to type I interferons, M2 macrophages, ECM proteins and TGFβ signaling, adaptive immunity, and cell proliferation. The interferon, M2 macrophage, and adaptive immunity subnetworks are composed almost exclusively of inflammatory genes, while the ECM subnetwork shares genes from both intrinsic subsets. Furthermore, the polymorphic genes interact primarily with inflammatory subset genes indicating that the genetic risk in SSc is related to immune abnormalities.

Mentions: The biology of CC3 and CC9 show the processes common to the intrinsic subsets that have been observed across multiple gene expression datasets: inflammation, cell interactions with ECM, and cell proliferation (Table 3). To determine if there was a more interconnected relationship between these conserved processes (such as genes related to specific cell types) than could be gained from an ontological annotation analysis like g:Profiler, we used CC3 and CC9 as a query gene set for the IMP gene-gene interaction Bayesian network (IMP) (Fig. 4) [20]. IMP is a gene-gene interaction network developed using a large compendium of high-throughput biological data including all publicly available microarray data that predicts the probability that pairs of genes have a co-expression interaction. A list of genes is imported into IMP, and a list of high-probability interactions between the genes on the imported list and (up to 50 additional genes in) the rest of the genome is generated. IMP is completely agnostic to SSc-specific biology and reports predicted interactions that are based on the preponderance of evidence across all publicly available gene expression data. As our query, we pooled the two consensus clusters CC3 and CC9 to discover possible molecular links between the inflammatory and fibroproliferative intrinsic subsets. We added polymorphic genes from genome-wide association studies (GWAS), as well as genes from candidate gene studies that have been replicated in at least one follow up study (see Materials and Methods; S6 Data file). In addition, we added four genes that are putative predictors of Modified Rodnan Skin Score (MRSS), a widely used clinical measure of skin fibrosis [5].


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)

Molecular network of inflammatory and fibroproliferative consensus genes.The consensus genes for the inflammatory and fibroproliferative subsets are connected in the IMP functional network. Inflammatory genes are colored purple, while fibroproliferative genes are colored red. Genes with polymorphisms are colored in green and MRSS biomarker genes are colored yellow. One MRSS biomarker gene (IFI44) was also an inflammatory consensus gene (pink), while three polymorphic genes were inflammatory consensus genes (turquoise). Note the five distinct subnetworks corresponding to type I interferons, M2 macrophages, ECM proteins and TGFβ signaling, adaptive immunity, and cell proliferation. The interferon, M2 macrophage, and adaptive immunity subnetworks are composed almost exclusively of inflammatory genes, while the ECM subnetwork shares genes from both intrinsic subsets. Furthermore, the polymorphic genes interact primarily with inflammatory subset genes indicating that the genetic risk in SSc is related to immune abnormalities.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1004005-g004: Molecular network of inflammatory and fibroproliferative consensus genes.The consensus genes for the inflammatory and fibroproliferative subsets are connected in the IMP functional network. Inflammatory genes are colored purple, while fibroproliferative genes are colored red. Genes with polymorphisms are colored in green and MRSS biomarker genes are colored yellow. One MRSS biomarker gene (IFI44) was also an inflammatory consensus gene (pink), while three polymorphic genes were inflammatory consensus genes (turquoise). Note the five distinct subnetworks corresponding to type I interferons, M2 macrophages, ECM proteins and TGFβ signaling, adaptive immunity, and cell proliferation. The interferon, M2 macrophage, and adaptive immunity subnetworks are composed almost exclusively of inflammatory genes, while the ECM subnetwork shares genes from both intrinsic subsets. Furthermore, the polymorphic genes interact primarily with inflammatory subset genes indicating that the genetic risk in SSc is related to immune abnormalities.
Mentions: The biology of CC3 and CC9 show the processes common to the intrinsic subsets that have been observed across multiple gene expression datasets: inflammation, cell interactions with ECM, and cell proliferation (Table 3). To determine if there was a more interconnected relationship between these conserved processes (such as genes related to specific cell types) than could be gained from an ontological annotation analysis like g:Profiler, we used CC3 and CC9 as a query gene set for the IMP gene-gene interaction Bayesian network (IMP) (Fig. 4) [20]. IMP is a gene-gene interaction network developed using a large compendium of high-throughput biological data including all publicly available microarray data that predicts the probability that pairs of genes have a co-expression interaction. A list of genes is imported into IMP, and a list of high-probability interactions between the genes on the imported list and (up to 50 additional genes in) the rest of the genome is generated. IMP is completely agnostic to SSc-specific biology and reports predicted interactions that are based on the preponderance of evidence across all publicly available gene expression data. As our query, we pooled the two consensus clusters CC3 and CC9 to discover possible molecular links between the inflammatory and fibroproliferative intrinsic subsets. We added polymorphic genes from genome-wide association studies (GWAS), as well as genes from candidate gene studies that have been replicated in at least one follow up study (see Materials and Methods; S6 Data file). In addition, we added four genes that are putative predictors of Modified Rodnan Skin Score (MRSS), a widely used clinical measure of skin fibrosis [5].

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