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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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Gene expression modules associated with the intrinsic subsets of SSc.We identified 54 major sets of genes (modules) using WGCNA that define the spectrum of gene expression in SSc skin using Milano as a test case. The top 6 most significant modules are shown and each shows a statistically significant association with the intrinsic subsets (including the limited subset). Module assignment for each gene is unique. The genes that compose the subset-specific modules represent more than 40% of the protein-coding genes in the human genome. Therefore, the intrinsic subsets seem to be determined by a large fraction of the encoded genes. The module eigengene of each module is shown in a stem-plot below each heatmap with intrinsic subsets indicated by color above the heatmap. Proliferative, red; inflammatory, purple; limited, yellow; normal-like, green. (A) Inflammatory modules (p<10−9 and p<10−7; Kruskal-Wallis non-parametric ANOVA corrected for multiple testing), (B) Limited Module (p<0.006), (C) Fibroproliferative modules (p<10−7; p<10−8), (D) Fibroproliferative and Limited expression module (p<10−9). Enriched molecular processes are indicated for each subset to the right of each heat map.
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pcbi-1004005-g002: Gene expression modules associated with the intrinsic subsets of SSc.We identified 54 major sets of genes (modules) using WGCNA that define the spectrum of gene expression in SSc skin using Milano as a test case. The top 6 most significant modules are shown and each shows a statistically significant association with the intrinsic subsets (including the limited subset). Module assignment for each gene is unique. The genes that compose the subset-specific modules represent more than 40% of the protein-coding genes in the human genome. Therefore, the intrinsic subsets seem to be determined by a large fraction of the encoded genes. The module eigengene of each module is shown in a stem-plot below each heatmap with intrinsic subsets indicated by color above the heatmap. Proliferative, red; inflammatory, purple; limited, yellow; normal-like, green. (A) Inflammatory modules (p<10−9 and p<10−7; Kruskal-Wallis non-parametric ANOVA corrected for multiple testing), (B) Limited Module (p<0.006), (C) Fibroproliferative modules (p<10−7; p<10−8), (D) Fibroproliferative and Limited expression module (p<10−9). Enriched molecular processes are indicated for each subset to the right of each heat map.

Mentions: To understand the molecular processes at work in SSc skin biopsies, we constructed data-driven partitions of the expressed genes across multiple SSc skin gene expression datasets using weighted gene co-expression network analysis (WGCNA) [10] (Figs. 1A, 2). Each co-expression cluster, or module, in the partition corresponds to a collection of correlated molecular processes present in the SSc tissue at the time of biopsy. To compare these modules across SSc datasets, we used mutual information to detect when a module from one dataset is present in another dataset. The partitions of the genome-wide expression data vary from one dataset to the next due to clinical heterogeneity and treatment effects, as well as technical variation in RNA processing protocols. All samples were analyzed on Agilent DNA microarrays with the same DNA probes in the same laboratory, providing consistency of the gene expression data and genes analyzed.


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)

Gene expression modules associated with the intrinsic subsets of SSc.We identified 54 major sets of genes (modules) using WGCNA that define the spectrum of gene expression in SSc skin using Milano as a test case. The top 6 most significant modules are shown and each shows a statistically significant association with the intrinsic subsets (including the limited subset). Module assignment for each gene is unique. The genes that compose the subset-specific modules represent more than 40% of the protein-coding genes in the human genome. Therefore, the intrinsic subsets seem to be determined by a large fraction of the encoded genes. The module eigengene of each module is shown in a stem-plot below each heatmap with intrinsic subsets indicated by color above the heatmap. Proliferative, red; inflammatory, purple; limited, yellow; normal-like, green. (A) Inflammatory modules (p<10−9 and p<10−7; Kruskal-Wallis non-parametric ANOVA corrected for multiple testing), (B) Limited Module (p<0.006), (C) Fibroproliferative modules (p<10−7; p<10−8), (D) Fibroproliferative and Limited expression module (p<10−9). Enriched molecular processes are indicated for each subset to the right of each heat map.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1004005-g002: Gene expression modules associated with the intrinsic subsets of SSc.We identified 54 major sets of genes (modules) using WGCNA that define the spectrum of gene expression in SSc skin using Milano as a test case. The top 6 most significant modules are shown and each shows a statistically significant association with the intrinsic subsets (including the limited subset). Module assignment for each gene is unique. The genes that compose the subset-specific modules represent more than 40% of the protein-coding genes in the human genome. Therefore, the intrinsic subsets seem to be determined by a large fraction of the encoded genes. The module eigengene of each module is shown in a stem-plot below each heatmap with intrinsic subsets indicated by color above the heatmap. Proliferative, red; inflammatory, purple; limited, yellow; normal-like, green. (A) Inflammatory modules (p<10−9 and p<10−7; Kruskal-Wallis non-parametric ANOVA corrected for multiple testing), (B) Limited Module (p<0.006), (C) Fibroproliferative modules (p<10−7; p<10−8), (D) Fibroproliferative and Limited expression module (p<10−9). Enriched molecular processes are indicated for each subset to the right of each heat map.
Mentions: To understand the molecular processes at work in SSc skin biopsies, we constructed data-driven partitions of the expressed genes across multiple SSc skin gene expression datasets using weighted gene co-expression network analysis (WGCNA) [10] (Figs. 1A, 2). Each co-expression cluster, or module, in the partition corresponds to a collection of correlated molecular processes present in the SSc tissue at the time of biopsy. To compare these modules across SSc datasets, we used mutual information to detect when a module from one dataset is present in another dataset. The partitions of the genome-wide expression data vary from one dataset to the next due to clinical heterogeneity and treatment effects, as well as technical variation in RNA processing protocols. All samples were analyzed on Agilent DNA microarrays with the same DNA probes in the same laboratory, providing consistency of the gene expression data and genes analyzed.

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