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A compendium of canine normal tissue gene expression.

Briggs J, Paoloni M, Chen QR, Wen X, Khan J, Khanna C - PLoS ONE (2011)

Bottom Line: Public access, using infrastructure identical to that currently in use for human normal tissues, has been established and allows for additional comparisons across species.These data advance our understanding of the canine genome through a comprehensive analysis of gene expression in a diverse set of tissues, contributing to improved functional annotation that has been lacking.Importantly, it will be used to inform future studies of disease in the dog as a model for human translational research and provides a novel resource to the community at large.

View Article: PubMed Central - PubMed

Affiliation: Tumor and Metastasis Biology Section, Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT

Background: Our understanding of disease is increasingly informed by changes in gene expression between normal and abnormal tissues. The release of the canine genome sequence in 2005 provided an opportunity to better understand human health and disease using the dog as clinically relevant model. Accordingly, we now present the first genome-wide, canine normal tissue gene expression compendium with corresponding human cross-species analysis.

Methodology/principal findings: The Affymetrix platform was utilized to catalogue gene expression signatures of 10 normal canine tissues including: liver, kidney, heart, lung, cerebrum, lymph node, spleen, jejunum, pancreas and skeletal muscle. The quality of the database was assessed in several ways. Organ defining gene sets were identified for each tissue and functional enrichment analysis revealed themes consistent with known physio-anatomic functions for each organ. In addition, a comparison of orthologous gene expression between matched canine and human normal tissues uncovered remarkable similarity. To demonstrate the utility of this dataset, novel canine gene annotations were established based on comparative analysis of dog and human tissue selective gene expression and manual curation of canine probeset mapping. Public access, using infrastructure identical to that currently in use for human normal tissues, has been established and allows for additional comparisons across species.

Conclusions/significance: These data advance our understanding of the canine genome through a comprehensive analysis of gene expression in a diverse set of tissues, contributing to improved functional annotation that has been lacking. Importantly, it will be used to inform future studies of disease in the dog as a model for human translational research and provides a novel resource to the community at large.

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Hierarchical clustering defines relationships between canine and                            human normal tissues.Orthologous probesets from canine and human Affymetrix gene expression                            platforms were mapped using NetAffx™ “best sequence”                            matches. In cases where there were multiple probesets representing the                            same gene symbol, the one with highest expression was used. This                            resulted in a total of 2,598 expression measures for comparison between                            species. No prior information about differential expression was used.                            The only filtering done was to exclude probesets in each species that                            were not expressed in at least one tissue. A. Hierarchical                            clustering of canine and human matched tissues based on 2,598 sequence                            matched orthologous probesets. Sample distances were calculated using                            Pearson correlation metrics and clusters joined using Ward linkage.                            Bootstrap re-sampling was conducted (10,000 iterations) in order to                            determine cluster stability. Confidence measures for multi-level                            bootstrap analysis are based on approximately unbiased p-values (AU),                            and simple bootstrap analysis probabilities (BP) for each node of the                            dendrogram, which are labeled numerically. B. Hierarchical                            clustering of samples and genes was conducted using 294 probesets                            differentially expressed in at least one tissue based on multi-factor                            ANOVA (species and tissue). Euclidean distance measure and complete                            linkage was used for clustering. Within the heatmap, red denotes greater                            relative expression whereas green denotes lower.
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pone-0017107-g002: Hierarchical clustering defines relationships between canine and human normal tissues.Orthologous probesets from canine and human Affymetrix gene expression platforms were mapped using NetAffx™ “best sequence” matches. In cases where there were multiple probesets representing the same gene symbol, the one with highest expression was used. This resulted in a total of 2,598 expression measures for comparison between species. No prior information about differential expression was used. The only filtering done was to exclude probesets in each species that were not expressed in at least one tissue. A. Hierarchical clustering of canine and human matched tissues based on 2,598 sequence matched orthologous probesets. Sample distances were calculated using Pearson correlation metrics and clusters joined using Ward linkage. Bootstrap re-sampling was conducted (10,000 iterations) in order to determine cluster stability. Confidence measures for multi-level bootstrap analysis are based on approximately unbiased p-values (AU), and simple bootstrap analysis probabilities (BP) for each node of the dendrogram, which are labeled numerically. B. Hierarchical clustering of samples and genes was conducted using 294 probesets differentially expressed in at least one tissue based on multi-factor ANOVA (species and tissue). Euclidean distance measure and complete linkage was used for clustering. Within the heatmap, red denotes greater relative expression whereas green denotes lower.

Mentions: As shown in Fig. 2A, hierarchical clustering with bootstrap resampling revealed that samples mainly grouped together based on tissue type rather than by species. In addition, sample grouping was consistent with overlapping anatomical functions and/or cellular composition. For example, lymph node, spleen and lung grouped together in a clade separate from all other tissues (branch point 13). These tissues grouped similarly based on shared expression of genes involved in immune response/functions. Canine lung and spleen were the only two examples of ambiguous cluster assignment at the final branch point. Brain, skeletal muscle and heart also form a distinct clade (branch point 17) while kidney, liver, pancreas and jejunum group together in a final cluster (branch point 16). These results are consistent with our previous hierarchical cluster analysis using all canine tissue replicates and more than 10,000 probesets (Fig. 1B). In addition, these results suggest that orthologous canine and human genes share similar tissue enriched and/or tissue selective expression patterns. Next, a multi-factor ANOVA was conducted in order to determine genes differentially expressed based on tissue. After correcting for multiple testing (FDR = 0.001), this resulted in the identification of 294 transcripts which were then analyzed by hierarchical clustering to find tissue enriched and tissue specific orthologous gene clusters. As shown in Fig. 2B, the overall structure of sample clustering remained the same with the notable exception being brain, which now separated into a distinct branch due to the high number of very tissue selective transcripts compared to all other tissues. Taken together, this comparative analysis provides further validation of the quality and consistency of the canine expression dataset and suggests the opportunity to add value to the data set from cross-species analysis.


A compendium of canine normal tissue gene expression.

Briggs J, Paoloni M, Chen QR, Wen X, Khan J, Khanna C - PLoS ONE (2011)

Hierarchical clustering defines relationships between canine and                            human normal tissues.Orthologous probesets from canine and human Affymetrix gene expression                            platforms were mapped using NetAffx™ “best sequence”                            matches. In cases where there were multiple probesets representing the                            same gene symbol, the one with highest expression was used. This                            resulted in a total of 2,598 expression measures for comparison between                            species. No prior information about differential expression was used.                            The only filtering done was to exclude probesets in each species that                            were not expressed in at least one tissue. A. Hierarchical                            clustering of canine and human matched tissues based on 2,598 sequence                            matched orthologous probesets. Sample distances were calculated using                            Pearson correlation metrics and clusters joined using Ward linkage.                            Bootstrap re-sampling was conducted (10,000 iterations) in order to                            determine cluster stability. Confidence measures for multi-level                            bootstrap analysis are based on approximately unbiased p-values (AU),                            and simple bootstrap analysis probabilities (BP) for each node of the                            dendrogram, which are labeled numerically. B. Hierarchical                            clustering of samples and genes was conducted using 294 probesets                            differentially expressed in at least one tissue based on multi-factor                            ANOVA (species and tissue). Euclidean distance measure and complete                            linkage was used for clustering. Within the heatmap, red denotes greater                            relative expression whereas green denotes lower.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3104984&req=5

pone-0017107-g002: Hierarchical clustering defines relationships between canine and human normal tissues.Orthologous probesets from canine and human Affymetrix gene expression platforms were mapped using NetAffx™ “best sequence” matches. In cases where there were multiple probesets representing the same gene symbol, the one with highest expression was used. This resulted in a total of 2,598 expression measures for comparison between species. No prior information about differential expression was used. The only filtering done was to exclude probesets in each species that were not expressed in at least one tissue. A. Hierarchical clustering of canine and human matched tissues based on 2,598 sequence matched orthologous probesets. Sample distances were calculated using Pearson correlation metrics and clusters joined using Ward linkage. Bootstrap re-sampling was conducted (10,000 iterations) in order to determine cluster stability. Confidence measures for multi-level bootstrap analysis are based on approximately unbiased p-values (AU), and simple bootstrap analysis probabilities (BP) for each node of the dendrogram, which are labeled numerically. B. Hierarchical clustering of samples and genes was conducted using 294 probesets differentially expressed in at least one tissue based on multi-factor ANOVA (species and tissue). Euclidean distance measure and complete linkage was used for clustering. Within the heatmap, red denotes greater relative expression whereas green denotes lower.
Mentions: As shown in Fig. 2A, hierarchical clustering with bootstrap resampling revealed that samples mainly grouped together based on tissue type rather than by species. In addition, sample grouping was consistent with overlapping anatomical functions and/or cellular composition. For example, lymph node, spleen and lung grouped together in a clade separate from all other tissues (branch point 13). These tissues grouped similarly based on shared expression of genes involved in immune response/functions. Canine lung and spleen were the only two examples of ambiguous cluster assignment at the final branch point. Brain, skeletal muscle and heart also form a distinct clade (branch point 17) while kidney, liver, pancreas and jejunum group together in a final cluster (branch point 16). These results are consistent with our previous hierarchical cluster analysis using all canine tissue replicates and more than 10,000 probesets (Fig. 1B). In addition, these results suggest that orthologous canine and human genes share similar tissue enriched and/or tissue selective expression patterns. Next, a multi-factor ANOVA was conducted in order to determine genes differentially expressed based on tissue. After correcting for multiple testing (FDR = 0.001), this resulted in the identification of 294 transcripts which were then analyzed by hierarchical clustering to find tissue enriched and tissue specific orthologous gene clusters. As shown in Fig. 2B, the overall structure of sample clustering remained the same with the notable exception being brain, which now separated into a distinct branch due to the high number of very tissue selective transcripts compared to all other tissues. Taken together, this comparative analysis provides further validation of the quality and consistency of the canine expression dataset and suggests the opportunity to add value to the data set from cross-species analysis.

Bottom Line: Public access, using infrastructure identical to that currently in use for human normal tissues, has been established and allows for additional comparisons across species.These data advance our understanding of the canine genome through a comprehensive analysis of gene expression in a diverse set of tissues, contributing to improved functional annotation that has been lacking.Importantly, it will be used to inform future studies of disease in the dog as a model for human translational research and provides a novel resource to the community at large.

View Article: PubMed Central - PubMed

Affiliation: Tumor and Metastasis Biology Section, Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT

Background: Our understanding of disease is increasingly informed by changes in gene expression between normal and abnormal tissues. The release of the canine genome sequence in 2005 provided an opportunity to better understand human health and disease using the dog as clinically relevant model. Accordingly, we now present the first genome-wide, canine normal tissue gene expression compendium with corresponding human cross-species analysis.

Methodology/principal findings: The Affymetrix platform was utilized to catalogue gene expression signatures of 10 normal canine tissues including: liver, kidney, heart, lung, cerebrum, lymph node, spleen, jejunum, pancreas and skeletal muscle. The quality of the database was assessed in several ways. Organ defining gene sets were identified for each tissue and functional enrichment analysis revealed themes consistent with known physio-anatomic functions for each organ. In addition, a comparison of orthologous gene expression between matched canine and human normal tissues uncovered remarkable similarity. To demonstrate the utility of this dataset, novel canine gene annotations were established based on comparative analysis of dog and human tissue selective gene expression and manual curation of canine probeset mapping. Public access, using infrastructure identical to that currently in use for human normal tissues, has been established and allows for additional comparisons across species.

Conclusions/significance: These data advance our understanding of the canine genome through a comprehensive analysis of gene expression in a diverse set of tissues, contributing to improved functional annotation that has been lacking. Importantly, it will be used to inform future studies of disease in the dog as a model for human translational research and provides a novel resource to the community at large.

Show MeSH