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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

Heat map showing clustering of transcripts that differentiate the sub-clusters of craniosynostosis samples. The plot is based on the standardized least square mean estimates for 880 genes that are significant at 5% FDR for at least one of the pairwise contrasts of the three cluster types. Red bars represent up-regulated transcripts and blue down-regulated transcripts. Genes up regulated in A (green), down regulated in A (blue), up regulated in B (purple), down regulated in B (orange), up regulated in C (gray), or down regulated in C (brown) are highlighted.
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Figure 4: Heat map showing clustering of transcripts that differentiate the sub-clusters of craniosynostosis samples. The plot is based on the standardized least square mean estimates for 880 genes that are significant at 5% FDR for at least one of the pairwise contrasts of the three cluster types. Red bars represent up-regulated transcripts and blue down-regulated transcripts. Genes up regulated in A (green), down regulated in A (blue), up regulated in B (purple), down regulated in B (orange), up regulated in C (gray), or down regulated in C (brown) are highlighted.

Mentions: Among the craniosynostosis samples, 880 genes were identified by ANOVA as differentially expressed among the three groups of craniosynostosis at a FDR of 10%. Figure 4 shows a heat map of all of these genes, and clearly indicates sets of genes that are representative for all six patterns of differential expression: genes up-regulated in A (green), down-regulated in A (blue), up-regulated in B (purple), down-regulated in B (pink), up-regulated in C (gray), or down-regulated in C (brown). Group C has the fewest genes with biased expression. To quantify these patterns, we also performed pair-wise contrasts of the three groups and selected genes that were significant (p<0.001, NLP >3) for each of the six pairs of conditions (A>B and A>C; A<B and A<C; B>A and B>C; B<A and B<C; C>A and C>B; C<A and C<B). The 1,769 genes satisfying those contrasts are listed in Additional file 2: Suppl. Table 1.


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)

Heat map showing clustering of transcripts that differentiate the sub-clusters of craniosynostosis samples. The plot is based on the standardized least square mean estimates for 880 genes that are significant at 5% FDR for at least one of the pairwise contrasts of the three cluster types. Red bars represent up-regulated transcripts and blue down-regulated transcripts. Genes up regulated in A (green), down regulated in A (blue), up regulated in B (purple), down regulated in B (orange), up regulated in C (gray), or down regulated in C (brown) are highlighted.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4150121&req=5

Figure 4: Heat map showing clustering of transcripts that differentiate the sub-clusters of craniosynostosis samples. The plot is based on the standardized least square mean estimates for 880 genes that are significant at 5% FDR for at least one of the pairwise contrasts of the three cluster types. Red bars represent up-regulated transcripts and blue down-regulated transcripts. Genes up regulated in A (green), down regulated in A (blue), up regulated in B (purple), down regulated in B (orange), up regulated in C (gray), or down regulated in C (brown) are highlighted.
Mentions: Among the craniosynostosis samples, 880 genes were identified by ANOVA as differentially expressed among the three groups of craniosynostosis at a FDR of 10%. Figure 4 shows a heat map of all of these genes, and clearly indicates sets of genes that are representative for all six patterns of differential expression: genes up-regulated in A (green), down-regulated in A (blue), up-regulated in B (purple), down-regulated in B (pink), up-regulated in C (gray), or down-regulated in C (brown). Group C has the fewest genes with biased expression. To quantify these patterns, we also performed pair-wise contrasts of the three groups and selected genes that were significant (p<0.001, NLP >3) for each of the six pairs of conditions (A>B and A>C; A<B and A<C; B>A and B>C; B<A and B<C; C>A and C>B; C<A and C<B). The 1,769 genes satisfying those contrasts are listed in Additional file 2: Suppl. Table 1.

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