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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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Correlation between gene expression and clinical covariates.A. Shown is the color-coded heatmap of the 75 arrays and 995 intrinsic genes. The graph on the right of the heat map shows disease duration for each sample. Disease duration was set to zero for normal controls and morphea samples. B. Pearson correlations were calculated between skin score and the expression values for each gene in the list. The moving average of the Pearson correlation (10-gene window) was plotted. Regions of high negative and high positive correlations to the three different clinical parameters are indicated (regions I–III shaded grey). C. Moving average of the Pearson correlation coefficients (10-gene window) between the self-reported Raynaud's severity score and the expression of each gene, D. Moving average of the Pearson Correlations (10-gene window) between extent of skin involvement and a diagnosis vector (see Methods) for dSSc(red), lSSc (orange) and healthy controls (green). E. Box plot of disease duration for dSSc patients. The patients included in the diffuse-proliferation group had disease duration of 8.4±6.4 years. The dSSc patients that fell into the inflammatory or normal-like groups have disease duration of 3.2±3.9 yrs (p<0.12, t-test). F. Genes that ideally discriminate ‘Diffuse 1’ and ‘Diffuse 2’ groups were selected using Significance Analysis of Microarrays (SAM). 329 genes were selected with an FDR<1%. Pearson correlation coefficients were calculated between each clinical parameter and the expression for each gene and plotted as a 10-gene moving window.
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pone-0002696-g005: Correlation between gene expression and clinical covariates.A. Shown is the color-coded heatmap of the 75 arrays and 995 intrinsic genes. The graph on the right of the heat map shows disease duration for each sample. Disease duration was set to zero for normal controls and morphea samples. B. Pearson correlations were calculated between skin score and the expression values for each gene in the list. The moving average of the Pearson correlation (10-gene window) was plotted. Regions of high negative and high positive correlations to the three different clinical parameters are indicated (regions I–III shaded grey). C. Moving average of the Pearson correlation coefficients (10-gene window) between the self-reported Raynaud's severity score and the expression of each gene, D. Moving average of the Pearson Correlations (10-gene window) between extent of skin involvement and a diagnosis vector (see Methods) for dSSc(red), lSSc (orange) and healthy controls (green). E. Box plot of disease duration for dSSc patients. The patients included in the diffuse-proliferation group had disease duration of 8.4±6.4 years. The dSSc patients that fell into the inflammatory or normal-like groups have disease duration of 3.2±3.9 yrs (p<0.12, t-test). F. Genes that ideally discriminate ‘Diffuse 1’ and ‘Diffuse 2’ groups were selected using Significance Analysis of Microarrays (SAM). 329 genes were selected with an FDR<1%. Pearson correlation coefficients were calculated between each clinical parameter and the expression for each gene and plotted as a 10-gene moving window.

Mentions: To map the intrinsic groups to specific clinical covariates, Pearson correlations were calculated between the gene expression of each of the 995 intrinsic genes and different clinical covariates. Shown are the results for three different covariates: the modified Rodnan skin score (MRSS; 0–51 scale), a self-reported Raynaud's severity score (0–10 scale), and the extent of skin involvement (dSSc, lSSc and unaffected). Each group was analyzed for correlation to each of the clinical parameters listed in Table 1; only the significant associations are shown. Figure 5A shows the gene expression patterns of the 995 intrinsic genes with each row representing a microarray and each column representing a gene. Pearson correlation coefficients were calculated between each of the clinical parameters and the expression of each gene. The moving average (10-gene window) of the resultant correlation coefficients is plotted for MRSS (Figure 5B), Raynaud's severity (Figure 5C) and degree of skin involvement (Figure 5D). Areas of high positive correlation between a clinical parameter and the expression of a group of genes indicate that increased expression of those genes is associated with an increase in that clinical covariate; a negative correlation indicates a relationship between a decrease in expression of the genes and an increase in a clinical covariate.


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)

Correlation between gene expression and clinical covariates.A. Shown is the color-coded heatmap of the 75 arrays and 995 intrinsic genes. The graph on the right of the heat map shows disease duration for each sample. Disease duration was set to zero for normal controls and morphea samples. B. Pearson correlations were calculated between skin score and the expression values for each gene in the list. The moving average of the Pearson correlation (10-gene window) was plotted. Regions of high negative and high positive correlations to the three different clinical parameters are indicated (regions I–III shaded grey). C. Moving average of the Pearson correlation coefficients (10-gene window) between the self-reported Raynaud's severity score and the expression of each gene, D. Moving average of the Pearson Correlations (10-gene window) between extent of skin involvement and a diagnosis vector (see Methods) for dSSc(red), lSSc (orange) and healthy controls (green). E. Box plot of disease duration for dSSc patients. The patients included in the diffuse-proliferation group had disease duration of 8.4±6.4 years. The dSSc patients that fell into the inflammatory or normal-like groups have disease duration of 3.2±3.9 yrs (p<0.12, t-test). F. Genes that ideally discriminate ‘Diffuse 1’ and ‘Diffuse 2’ groups were selected using Significance Analysis of Microarrays (SAM). 329 genes were selected with an FDR<1%. Pearson correlation coefficients were calculated between each clinical parameter and the expression for each gene and plotted as a 10-gene moving window.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2481301&req=5

pone-0002696-g005: Correlation between gene expression and clinical covariates.A. Shown is the color-coded heatmap of the 75 arrays and 995 intrinsic genes. The graph on the right of the heat map shows disease duration for each sample. Disease duration was set to zero for normal controls and morphea samples. B. Pearson correlations were calculated between skin score and the expression values for each gene in the list. The moving average of the Pearson correlation (10-gene window) was plotted. Regions of high negative and high positive correlations to the three different clinical parameters are indicated (regions I–III shaded grey). C. Moving average of the Pearson correlation coefficients (10-gene window) between the self-reported Raynaud's severity score and the expression of each gene, D. Moving average of the Pearson Correlations (10-gene window) between extent of skin involvement and a diagnosis vector (see Methods) for dSSc(red), lSSc (orange) and healthy controls (green). E. Box plot of disease duration for dSSc patients. The patients included in the diffuse-proliferation group had disease duration of 8.4±6.4 years. The dSSc patients that fell into the inflammatory or normal-like groups have disease duration of 3.2±3.9 yrs (p<0.12, t-test). F. Genes that ideally discriminate ‘Diffuse 1’ and ‘Diffuse 2’ groups were selected using Significance Analysis of Microarrays (SAM). 329 genes were selected with an FDR<1%. Pearson correlation coefficients were calculated between each clinical parameter and the expression for each gene and plotted as a 10-gene moving window.
Mentions: To map the intrinsic groups to specific clinical covariates, Pearson correlations were calculated between the gene expression of each of the 995 intrinsic genes and different clinical covariates. Shown are the results for three different covariates: the modified Rodnan skin score (MRSS; 0–51 scale), a self-reported Raynaud's severity score (0–10 scale), and the extent of skin involvement (dSSc, lSSc and unaffected). Each group was analyzed for correlation to each of the clinical parameters listed in Table 1; only the significant associations are shown. Figure 5A shows the gene expression patterns of the 995 intrinsic genes with each row representing a microarray and each column representing a gene. Pearson correlation coefficients were calculated between each of the clinical parameters and the expression of each gene. The moving average (10-gene window) of the resultant correlation coefficients is plotted for MRSS (Figure 5B), Raynaud's severity (Figure 5C) and degree of skin involvement (Figure 5D). Areas of high positive correlation between a clinical parameter and the expression of a group of genes indicate that increased expression of those genes is associated with an increase in that clinical covariate; a negative correlation indicates a relationship between a decrease in expression of the genes and an increase in a clinical covariate.

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