Limits...
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

Reanalysis of the Stamper microarray dataset. (A) Clustering of craniosynostosis samples by overall similarity (as in Figure 2) shows 4 clusters of samples. Colors to the left indicate the technical clusters observed in the raw data Additional file 1: Suppl. Figure 2), and indicate that this effect has been effectively removed by the SNM normalization procedure. (B) Two-way hierarchical clustering of 2883 probes significantly different in any pairwise contrast at NLP10 tends to recapitulate the four clusters. Each row is a sample, and column a transcript, and most of the rows are clustered with respect to the assignments in (A). The plot also clearly indicates genes that are specifically up regulated or down regulated in each cluster type. (C) Heat map of standardized least square means of genes within the four cluster types.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4150121&req=5

Figure 5: Reanalysis of the Stamper microarray dataset. (A) Clustering of craniosynostosis samples by overall similarity (as in Figure 2) shows 4 clusters of samples. Colors to the left indicate the technical clusters observed in the raw data Additional file 1: Suppl. Figure 2), and indicate that this effect has been effectively removed by the SNM normalization procedure. (B) Two-way hierarchical clustering of 2883 probes significantly different in any pairwise contrast at NLP10 tends to recapitulate the four clusters. Each row is a sample, and column a transcript, and most of the rows are clustered with respect to the assignments in (A). The plot also clearly indicates genes that are specifically up regulated or down regulated in each cluster type. (C) Heat map of standardized least square means of genes within the four cluster types.

Mentions: After statistically removing what appears to be a technical batch effect from their data (see methods, Additional file 1: Suppl. Fig. 1), we ran a similar analysis pipeline as for our data and observed four sub-types of craniosynostosis profile (Figure 5A). The first 5 principal components explain 47.4% of the overall variation, 52% of which is explained by the four cluster types whereas only 1.0% is due to suture location. There was not a significant correlation between suture location and cluster type, but we did observe a small correlation between technical batch effect and suture location, suggesting that the differences between sagittal and metopic/coronal samples may be attributed at least in part to this artifact. Thousands of genes differentiate each of the four biological sub-types at the 5% FDR level, reflecting both the power of the comparison with an average of 50 samples per sub-type and the fact that the sub-type differences are a much greater source of variance than suture location. Analysis of only the 1141 transcripts significantly different between clusters at p<10-20 recapitulates the overall cluster identities (Figure 5B). Figure 5C shows standardized average gene expression among the Stamper sub-types.


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)

Reanalysis of the Stamper microarray dataset. (A) Clustering of craniosynostosis samples by overall similarity (as in Figure 2) shows 4 clusters of samples. Colors to the left indicate the technical clusters observed in the raw data Additional file 1: Suppl. Figure 2), and indicate that this effect has been effectively removed by the SNM normalization procedure. (B) Two-way hierarchical clustering of 2883 probes significantly different in any pairwise contrast at NLP10 tends to recapitulate the four clusters. Each row is a sample, and column a transcript, and most of the rows are clustered with respect to the assignments in (A). The plot also clearly indicates genes that are specifically up regulated or down regulated in each cluster type. (C) Heat map of standardized least square means of genes within the four cluster types.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4150121&req=5

Figure 5: Reanalysis of the Stamper microarray dataset. (A) Clustering of craniosynostosis samples by overall similarity (as in Figure 2) shows 4 clusters of samples. Colors to the left indicate the technical clusters observed in the raw data Additional file 1: Suppl. Figure 2), and indicate that this effect has been effectively removed by the SNM normalization procedure. (B) Two-way hierarchical clustering of 2883 probes significantly different in any pairwise contrast at NLP10 tends to recapitulate the four clusters. Each row is a sample, and column a transcript, and most of the rows are clustered with respect to the assignments in (A). The plot also clearly indicates genes that are specifically up regulated or down regulated in each cluster type. (C) Heat map of standardized least square means of genes within the four cluster types.
Mentions: After statistically removing what appears to be a technical batch effect from their data (see methods, Additional file 1: Suppl. Fig. 1), we ran a similar analysis pipeline as for our data and observed four sub-types of craniosynostosis profile (Figure 5A). The first 5 principal components explain 47.4% of the overall variation, 52% of which is explained by the four cluster types whereas only 1.0% is due to suture location. There was not a significant correlation between suture location and cluster type, but we did observe a small correlation between technical batch effect and suture location, suggesting that the differences between sagittal and metopic/coronal samples may be attributed at least in part to this artifact. Thousands of genes differentiate each of the four biological sub-types at the 5% FDR level, reflecting both the power of the comparison with an average of 50 samples per sub-type and the fact that the sub-type differences are a much greater source of variance than suture location. Analysis of only the 1141 transcripts significantly different between clusters at p<10-20 recapitulates the overall cluster identities (Figure 5B). Figure 5C shows standardized average gene expression among the Stamper sub-types.

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