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Molecular subsets in the gene expression signatures of scleroderma skin.

Milano A, Pendergrass SA, Sargent JL, George LK, McCalmont TH, Connolly MK, Whitfield ML - PLoS ONE (2008)

Bottom Line: The disease is characterized by vascular dysfunction, tissue fibrosis, internal organ dysfunction, and immune dysfunction resulting in autoantibody production.Using this property of the gene expression, we selected a set of 'intrinsic' genes and analyzed the inherent data-driven groupings.Each group can be distinguished by unique gene expression signatures indicative of proliferating cells, immune infiltrates and a fibrotic program.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Dartmouth Medical School, Hanover, New Hampshire, United States of America.

ABSTRACT

Background: Scleroderma is a clinically heterogeneous disease with a complex phenotype. The disease is characterized by vascular dysfunction, tissue fibrosis, internal organ dysfunction, and immune dysfunction resulting in autoantibody production.

Methodology and findings: We analyzed the genome-wide patterns of gene expression with DNA microarrays in skin biopsies from distinct scleroderma subsets including 17 patients with systemic sclerosis (SSc) with diffuse scleroderma (dSSc), 7 patients with SSc with limited scleroderma (lSSc), 3 patients with morphea, and 6 healthy controls. 61 skin biopsies were analyzed in a total of 75 microarray hybridizations. Analysis by hierarchical clustering demonstrates nearly identical patterns of gene expression in 17 out of 22 of the forearm and back skin pairs of SSc patients. Using this property of the gene expression, we selected a set of 'intrinsic' genes and analyzed the inherent data-driven groupings. Distinct patterns of gene expression separate patients with dSSc from those with lSSc and both are easily distinguished from normal controls. Our data show three distinct patient groups among the patients with dSSc and two groups among patients with lSSc. Each group can be distinguished by unique gene expression signatures indicative of proliferating cells, immune infiltrates and a fibrotic program. The intrinsic groups are statistically significant (p<0.001) and each has been mapped to clinical covariates of modified Rodnan skin score, interstitial lung disease, gastrointestinal involvement, digital ulcers, Raynaud's phenomenon and disease duration. We report a 177-gene signature that is associated with severity of skin disease in dSSc.

Conclusions and significance: Genome-wide gene expression profiling of skin biopsies demonstrates that the heterogeneity in scleroderma can be measured quantitatively with DNA microarrays. The diversity in gene expression demonstrates multiple distinct gene expression programs in the skin of patients with scleroderma.

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Cluster analysis using the scleroderma intrinsic gene set.The 995 most ‘intrinsic’ genes selected from 75 microarray hybridizations analyzing 34 individuals. Two major branches of the dendrogram tree are evident which divide a subset of the dSSc samples from all other samples. Within these major groups are smaller branches with identifiable biological themes, which have been colored accordingly: blue for diffuse 1, red for diffuse 2, purple for inflammatory, orange for limited and green for normal-like. Statistically significant clusters (p<0.001) identified by SigClust are indicated by an asterisk (*) at the lowest significant branch. A. Experimental sample hierarchical clustering dendrogram. Black bars indicate forearm-back pairs which cluster together based on this analysis. B. Scaled down overview of the intrinsic gene expression signatures. C. Limited SSc gene expression cluster. D. Proliferation cluster. E. Immunoglobulin gene expression cluster. F. T-lymphocyte and IFNγ gene expression cluster. The full figure with all gene names can be viewed in Supplemental Figure S2.
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pone-0002696-g002: Cluster analysis using the scleroderma intrinsic gene set.The 995 most ‘intrinsic’ genes selected from 75 microarray hybridizations analyzing 34 individuals. Two major branches of the dendrogram tree are evident which divide a subset of the dSSc samples from all other samples. Within these major groups are smaller branches with identifiable biological themes, which have been colored accordingly: blue for diffuse 1, red for diffuse 2, purple for inflammatory, orange for limited and green for normal-like. Statistically significant clusters (p<0.001) identified by SigClust are indicated by an asterisk (*) at the lowest significant branch. A. Experimental sample hierarchical clustering dendrogram. Black bars indicate forearm-back pairs which cluster together based on this analysis. B. Scaled down overview of the intrinsic gene expression signatures. C. Limited SSc gene expression cluster. D. Proliferation cluster. E. Immunoglobulin gene expression cluster. F. T-lymphocyte and IFNγ gene expression cluster. The full figure with all gene names can be viewed in Supplemental Figure S2.

Mentions: A list of genes selected by their fold change alone is not ideal for classifying samples because they emphasize differences between samples rather than the intrinsic differences between patients [47], [56]. To select genes that captured the intrinsic differences between patients, we exploited the observation that the forearm-back pairs from each SSc patient show nearly identical patterns of gene expression to select the ‘intrinsic’ genes in SSc. We selected 995 genes with the most consistent expression between each forearm-back pair and technical replicates, but with the highest variance across all samples analyzed [47], [56] (Supplementary Data File S2). Each of the 995 intrinsic genes was centered on its median value across all experiments, and the data clustered hierarchically in both the gene and experiment dimension using average linkage hierarchical clustering. The dendrogram summarizes the relationship among the samples and shows their clear separation into distinct groups (Figure 2A). As a direct result of our gene selection, all forearm-back pairs cluster together and all technical replicate hybridizations cluster together when using the intrinsic genes. Sample identifiers have been colored according to the patient diagnosis: dSSc is red, lSSc is orange, morphea and EF are black, and normal controls are green (Figure 2A). The dendrogram has been colored to reflect the signatures of gene expression that are an inherent feature of the biopsies (Figure 2A–C).


Molecular subsets in the gene expression signatures of scleroderma skin.

Milano A, Pendergrass SA, Sargent JL, George LK, McCalmont TH, Connolly MK, Whitfield ML - PLoS ONE (2008)

Cluster analysis using the scleroderma intrinsic gene set.The 995 most ‘intrinsic’ genes selected from 75 microarray hybridizations analyzing 34 individuals. Two major branches of the dendrogram tree are evident which divide a subset of the dSSc samples from all other samples. Within these major groups are smaller branches with identifiable biological themes, which have been colored accordingly: blue for diffuse 1, red for diffuse 2, purple for inflammatory, orange for limited and green for normal-like. Statistically significant clusters (p<0.001) identified by SigClust are indicated by an asterisk (*) at the lowest significant branch. A. Experimental sample hierarchical clustering dendrogram. Black bars indicate forearm-back pairs which cluster together based on this analysis. B. Scaled down overview of the intrinsic gene expression signatures. C. Limited SSc gene expression cluster. D. Proliferation cluster. E. Immunoglobulin gene expression cluster. F. T-lymphocyte and IFNγ gene expression cluster. The full figure with all gene names can be viewed in Supplemental Figure S2.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2481301&req=5

pone-0002696-g002: Cluster analysis using the scleroderma intrinsic gene set.The 995 most ‘intrinsic’ genes selected from 75 microarray hybridizations analyzing 34 individuals. Two major branches of the dendrogram tree are evident which divide a subset of the dSSc samples from all other samples. Within these major groups are smaller branches with identifiable biological themes, which have been colored accordingly: blue for diffuse 1, red for diffuse 2, purple for inflammatory, orange for limited and green for normal-like. Statistically significant clusters (p<0.001) identified by SigClust are indicated by an asterisk (*) at the lowest significant branch. A. Experimental sample hierarchical clustering dendrogram. Black bars indicate forearm-back pairs which cluster together based on this analysis. B. Scaled down overview of the intrinsic gene expression signatures. C. Limited SSc gene expression cluster. D. Proliferation cluster. E. Immunoglobulin gene expression cluster. F. T-lymphocyte and IFNγ gene expression cluster. The full figure with all gene names can be viewed in Supplemental Figure S2.
Mentions: A list of genes selected by their fold change alone is not ideal for classifying samples because they emphasize differences between samples rather than the intrinsic differences between patients [47], [56]. To select genes that captured the intrinsic differences between patients, we exploited the observation that the forearm-back pairs from each SSc patient show nearly identical patterns of gene expression to select the ‘intrinsic’ genes in SSc. We selected 995 genes with the most consistent expression between each forearm-back pair and technical replicates, but with the highest variance across all samples analyzed [47], [56] (Supplementary Data File S2). Each of the 995 intrinsic genes was centered on its median value across all experiments, and the data clustered hierarchically in both the gene and experiment dimension using average linkage hierarchical clustering. The dendrogram summarizes the relationship among the samples and shows their clear separation into distinct groups (Figure 2A). As a direct result of our gene selection, all forearm-back pairs cluster together and all technical replicate hybridizations cluster together when using the intrinsic genes. Sample identifiers have been colored according to the patient diagnosis: dSSc is red, lSSc is orange, morphea and EF are black, and normal controls are green (Figure 2A). The dendrogram has been colored to reflect the signatures of gene expression that are an inherent feature of the biopsies (Figure 2A–C).

Bottom Line: The disease is characterized by vascular dysfunction, tissue fibrosis, internal organ dysfunction, and immune dysfunction resulting in autoantibody production.Using this property of the gene expression, we selected a set of 'intrinsic' genes and analyzed the inherent data-driven groupings.Each group can be distinguished by unique gene expression signatures indicative of proliferating cells, immune infiltrates and a fibrotic program.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Dartmouth Medical School, Hanover, New Hampshire, United States of America.

ABSTRACT

Background: Scleroderma is a clinically heterogeneous disease with a complex phenotype. The disease is characterized by vascular dysfunction, tissue fibrosis, internal organ dysfunction, and immune dysfunction resulting in autoantibody production.

Methodology and findings: We analyzed the genome-wide patterns of gene expression with DNA microarrays in skin biopsies from distinct scleroderma subsets including 17 patients with systemic sclerosis (SSc) with diffuse scleroderma (dSSc), 7 patients with SSc with limited scleroderma (lSSc), 3 patients with morphea, and 6 healthy controls. 61 skin biopsies were analyzed in a total of 75 microarray hybridizations. Analysis by hierarchical clustering demonstrates nearly identical patterns of gene expression in 17 out of 22 of the forearm and back skin pairs of SSc patients. Using this property of the gene expression, we selected a set of 'intrinsic' genes and analyzed the inherent data-driven groupings. Distinct patterns of gene expression separate patients with dSSc from those with lSSc and both are easily distinguished from normal controls. Our data show three distinct patient groups among the patients with dSSc and two groups among patients with lSSc. Each group can be distinguished by unique gene expression signatures indicative of proliferating cells, immune infiltrates and a fibrotic program. The intrinsic groups are statistically significant (p<0.001) and each has been mapped to clinical covariates of modified Rodnan skin score, interstitial lung disease, gastrointestinal involvement, digital ulcers, Raynaud's phenomenon and disease duration. We report a 177-gene signature that is associated with severity of skin disease in dSSc.

Conclusions and significance: Genome-wide gene expression profiling of skin biopsies demonstrates that the heterogeneity in scleroderma can be measured quantitatively with DNA microarrays. The diversity in gene expression demonstrates multiple distinct gene expression programs in the skin of patients with scleroderma.

Show MeSH
Related in: MedlinePlus