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Characterization of distinct classes of differential gene expression in osteoblast cultures from non-syndromic craniosynostosis bone.

Rojas-Peña ML, Olivares-Navarrete R, Hyzy S, Arafat D, Schwartz Z, Boyan BD, Williams J, Gibson G - J Genomics (2014)

Bottom Line: Similar constellations of sub-types were also observed upon re-analysis of a similar dataset of 199 calvarial osteoblast cultures.Annotation of gene function of differentially expressed transcripts strongly implicates physiological differences with respect to cell cycle and cell death, stromal cell differentiation, extracellular matrix (ECM) components, and ribosomal activity.Based on these results, we propose non-syndromic craniosynostosis cases can be classified by differences in their gene expression patterns and that these may provide targets for future clinical intervention.

View Article: PubMed Central - PubMed

Affiliation: 1. Center for Integrative Genomics, School of Biology, Georgia Institute of Technology, Atlanta, GA, USA.

ABSTRACT
Craniosynostosis, the premature fusion of one or more skull sutures, occurs in approximately 1 in 2500 infants, with the majority of cases non-syndromic and of unknown etiology. Two common reasons proposed for premature suture fusion are abnormal compression forces on the skull and rare genetic abnormalities. Our goal was to evaluate whether different sub-classes of disease can be identified based on total gene expression profiles. RNA-Seq data were obtained from 31 human osteoblast cultures derived from bone biopsy samples collected between 2009 and 2011, representing 23 craniosynostosis fusions and 8 normal cranial bones or long bones. No differentiation between regions of the skull was detected, but variance component analysis of gene expression patterns nevertheless supports transcriptome-based classification of craniosynostosis. Cluster analysis showed 4 distinct groups of samples; 1 predominantly normal and 3 craniosynostosis subtypes. Similar constellations of sub-types were also observed upon re-analysis of a similar dataset of 199 calvarial osteoblast cultures. Annotation of gene function of differentially expressed transcripts strongly implicates physiological differences with respect to cell cycle and cell death, stromal cell differentiation, extracellular matrix (ECM) components, and ribosomal activity. Based on these results, we propose non-syndromic craniosynostosis cases can be classified by differences in their gene expression patterns and that these may provide targets for future clinical intervention.

No MeSH data available.


Related in: MedlinePlus

Comparison of RNA-Seq and Stamper datasets. (A) Heat map of standardized least square means of abundance levels of 428 transcripts significantly differentially expressed between the 4 clusters of Stamper samples (NLP>10), as well as the 3 clusters of RNA-Seq samples (NLP > 5), in the Stamper dataset. (B) The same 428 genes in the RNA-Seq dataset. The narrow band of color above the three cluster profiles correspond to the sets of genes indicated in (A), while the bars below the plot indicate genes that are up- or down-regulated in the indicated clusters. (C) Matrix of correspondence of sets of genes in (A) and (B) ordered by high or low expression in clusters A, B, and C in the RNA-Seq dataset. The bar to the right shows the proportion of genes in each Stamper set, color-coded as in (A). The eight largest sets of co-varying genes in both datasets are labeled, and listed in Additional file 4: Suppl. Table 3.
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Figure 6: Comparison of RNA-Seq and Stamper datasets. (A) Heat map of standardized least square means of abundance levels of 428 transcripts significantly differentially expressed between the 4 clusters of Stamper samples (NLP>10), as well as the 3 clusters of RNA-Seq samples (NLP > 5), in the Stamper dataset. (B) The same 428 genes in the RNA-Seq dataset. The narrow band of color above the three cluster profiles correspond to the sets of genes indicated in (A), while the bars below the plot indicate genes that are up- or down-regulated in the indicated clusters. (C) Matrix of correspondence of sets of genes in (A) and (B) ordered by high or low expression in clusters A, B, and C in the RNA-Seq dataset. The bar to the right shows the proportion of genes in each Stamper set, color-coded as in (A). The eight largest sets of co-varying genes in both datasets are labeled, and listed in Additional file 4: Suppl. Table 3.

Mentions: To compare our dataset with the larger Stamper et al. 17 dataset, we extracted 1,728 transcripts that were specific for our RNA-Seq clusters RC-A through RC-C at NLP>5, and asked whether they tend to be in the same sub-types in the Stamper dataset of 2,883 probes at NLP > 10 that are characteristic of the four sub-types SC1 through SC4. Figure 6 panels A and B show the clustering of the 428 genes in common, where each is partitioned into 6 sub-sets of co-regulated transcripts. For the RNA-Seq data, these clusters correspond to up- or down-regulation of genes in each of the three clusters; for the Stamper data, they also correspond to cluster-specific expression. Panel C presents the frequency of genes in each of the 36 possible 6 × 6 matrix of sets and shows that there is highly significant overlap (p<10-60 likelihood ratio test of clustering of categories). Of the 428 genes in common, 280 (65%) are present in the eight groups highlighted in the panel. There was very strong overlap between the red, green, and blue Stamper sets on the left half of Panel A with generally low expression in SC4 and high expression in SC2, and the left half of the RNA-Seq data in Panel A with high expression in clusters A and C but low expression in cluster B. Conversely, the brown, yellow, and purple Stamper clusters dominate the low in A and C, high in B RNA-Seq clusters. Superimposed in this are differences in the relative abundance of the subsets of gene expression. We conclude that the six groups of genes represent highly co-regulated patterns of variation in gene expression in the osteoblasts that are consistently found in the two independent datasets ascertained with two different transcript-profiling strategies.


Characterization of distinct classes of differential gene expression in osteoblast cultures from non-syndromic craniosynostosis bone.

Rojas-Peña ML, Olivares-Navarrete R, Hyzy S, Arafat D, Schwartz Z, Boyan BD, Williams J, Gibson G - J Genomics (2014)

Comparison of RNA-Seq and Stamper datasets. (A) Heat map of standardized least square means of abundance levels of 428 transcripts significantly differentially expressed between the 4 clusters of Stamper samples (NLP>10), as well as the 3 clusters of RNA-Seq samples (NLP > 5), in the Stamper dataset. (B) The same 428 genes in the RNA-Seq dataset. The narrow band of color above the three cluster profiles correspond to the sets of genes indicated in (A), while the bars below the plot indicate genes that are up- or down-regulated in the indicated clusters. (C) Matrix of correspondence of sets of genes in (A) and (B) ordered by high or low expression in clusters A, B, and C in the RNA-Seq dataset. The bar to the right shows the proportion of genes in each Stamper set, color-coded as in (A). The eight largest sets of co-varying genes in both datasets are labeled, and listed in Additional file 4: Suppl. Table 3.
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Related In: Results  -  Collection

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Figure 6: Comparison of RNA-Seq and Stamper datasets. (A) Heat map of standardized least square means of abundance levels of 428 transcripts significantly differentially expressed between the 4 clusters of Stamper samples (NLP>10), as well as the 3 clusters of RNA-Seq samples (NLP > 5), in the Stamper dataset. (B) The same 428 genes in the RNA-Seq dataset. The narrow band of color above the three cluster profiles correspond to the sets of genes indicated in (A), while the bars below the plot indicate genes that are up- or down-regulated in the indicated clusters. (C) Matrix of correspondence of sets of genes in (A) and (B) ordered by high or low expression in clusters A, B, and C in the RNA-Seq dataset. The bar to the right shows the proportion of genes in each Stamper set, color-coded as in (A). The eight largest sets of co-varying genes in both datasets are labeled, and listed in Additional file 4: Suppl. Table 3.
Mentions: To compare our dataset with the larger Stamper et al. 17 dataset, we extracted 1,728 transcripts that were specific for our RNA-Seq clusters RC-A through RC-C at NLP>5, and asked whether they tend to be in the same sub-types in the Stamper dataset of 2,883 probes at NLP > 10 that are characteristic of the four sub-types SC1 through SC4. Figure 6 panels A and B show the clustering of the 428 genes in common, where each is partitioned into 6 sub-sets of co-regulated transcripts. For the RNA-Seq data, these clusters correspond to up- or down-regulation of genes in each of the three clusters; for the Stamper data, they also correspond to cluster-specific expression. Panel C presents the frequency of genes in each of the 36 possible 6 × 6 matrix of sets and shows that there is highly significant overlap (p<10-60 likelihood ratio test of clustering of categories). Of the 428 genes in common, 280 (65%) are present in the eight groups highlighted in the panel. There was very strong overlap between the red, green, and blue Stamper sets on the left half of Panel A with generally low expression in SC4 and high expression in SC2, and the left half of the RNA-Seq data in Panel A with high expression in clusters A and C but low expression in cluster B. Conversely, the brown, yellow, and purple Stamper clusters dominate the low in A and C, high in B RNA-Seq clusters. Superimposed in this are differences in the relative abundance of the subsets of gene expression. We conclude that the six groups of genes represent highly co-regulated patterns of variation in gene expression in the osteoblasts that are consistently found in the two independent datasets ascertained with two different transcript-profiling strategies.

Bottom Line: Similar constellations of sub-types were also observed upon re-analysis of a similar dataset of 199 calvarial osteoblast cultures.Annotation of gene function of differentially expressed transcripts strongly implicates physiological differences with respect to cell cycle and cell death, stromal cell differentiation, extracellular matrix (ECM) components, and ribosomal activity.Based on these results, we propose non-syndromic craniosynostosis cases can be classified by differences in their gene expression patterns and that these may provide targets for future clinical intervention.

View Article: PubMed Central - PubMed

Affiliation: 1. Center for Integrative Genomics, School of Biology, Georgia Institute of Technology, Atlanta, GA, USA.

ABSTRACT
Craniosynostosis, the premature fusion of one or more skull sutures, occurs in approximately 1 in 2500 infants, with the majority of cases non-syndromic and of unknown etiology. Two common reasons proposed for premature suture fusion are abnormal compression forces on the skull and rare genetic abnormalities. Our goal was to evaluate whether different sub-classes of disease can be identified based on total gene expression profiles. RNA-Seq data were obtained from 31 human osteoblast cultures derived from bone biopsy samples collected between 2009 and 2011, representing 23 craniosynostosis fusions and 8 normal cranial bones or long bones. No differentiation between regions of the skull was detected, but variance component analysis of gene expression patterns nevertheless supports transcriptome-based classification of craniosynostosis. Cluster analysis showed 4 distinct groups of samples; 1 predominantly normal and 3 craniosynostosis subtypes. Similar constellations of sub-types were also observed upon re-analysis of a similar dataset of 199 calvarial osteoblast cultures. Annotation of gene function of differentially expressed transcripts strongly implicates physiological differences with respect to cell cycle and cell death, stromal cell differentiation, extracellular matrix (ECM) components, and ribosomal activity. Based on these results, we propose non-syndromic craniosynostosis cases can be classified by differences in their gene expression patterns and that these may provide targets for future clinical intervention.

No MeSH data available.


Related in: MedlinePlus