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. 2009 Jun;5(6):e1000407.
doi: 10.1371/journal.pcbi.1000407. Epub 2009 Jun 12.

Directed mammalian gene regulatory networks using expression and comparative genomic hybridization microarray data from radiation hybrids

Sangtae Ahn  1 , Richard T Wang, Christopher C Park, Andy Lin, Richard M Leahy, Kenneth Lange, Desmond J Smith
Affiliations

Directed mammalian gene regulatory networks using expression and comparative genomic hybridization microarray data from radiation hybrids

Sangtae Ahn et al. PLoS Comput Biol. 2009 Jun.

Abstract

Meiotic mapping of quantitative trait loci regulating expression (eQTLs) has allowed the construction of gene networks. However, the limited mapping resolution of these studies has meant that genotype data are largely ignored, leading to undirected networks that fail to capture regulatory hierarchies. Here we use high resolution mapping of copy number eQTLs (ceQTLs) in a mouse-hamster radiation hybrid (RH) panel to construct directed genetic networks in the mammalian cell. The RH network covering 20,145 mouse genes had significant overlap with, and similar topological structures to, existing biological networks. Upregulated edges in the RH network had significantly more overlap than downregulated. This suggests repressive relationships between genes are missed by existing approaches, perhaps because the corresponding proteins are not present in the cell at the same time and therefore unlikely to interact. Gene essentiality was positively correlated with connectivity and betweenness centrality in the RH network, strengthening the centrality-lethality principle in mammals. Consistent with their regulatory role, transcription factors had significantly more outgoing edges (regulating) than incoming (regulated) in the RH network, a feature hidden by conventional undirected networks. Directed RH genetic networks thus showed concordance with pre-existing networks while also yielding information inaccessible to current undirected approaches.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Overlap significance between right-tailed thresholded RH networks and existing datasets.
(A) HPRD protein-protein interaction network. (B) KEGG pathway network. (C) SymAtlas coexpression network. (D) GO annotations. (E) GO molecular function annotation. (F) GO cellular component annotation. (G) GO biological annotation. (H) Averaged formula image values over results from A to G. One-sided Fisher's exact and chi-square tests used to assess overlap significance.
Figure 2
Figure 2. Comparison of RH networks and existing datasets.
(A) Overlap between 10 randomly permuted RH networks and HPRD network. The RH networks were constructed from right-tailed thresholding and one-sided Fisher's exact and chi-square tests used to assess significance. (B) Averaged formula image values for overlap between randomly permuted RH networks and different existing datasets (HPRD, KEGG, SymAtlas coexpression, GO, GO-molecular function, GO-cellular component and GO-biological process annotation networks). (C) Overlap between RH networks constructed from a subset of randomly selected RH clones and HPRD network. Mean of overlap significance (solid line) over 50 random subsets shown with standard errors calculated by bootstrapping (dash-dot line). (D) Same as (C) except averaged formula image values over different existing datasets. (E) Comparing different thresholding approaches. Maximum formula image over varying correlation coefficient thresholds shown. (F) Comparing betweenness centralities of RH and HPRD networks. P-values of Spearman correlation coefficients (one-sided, positive direction) between the betweenness centralities of RH and HPRD networks shown.
Figure 3
Figure 3. Overlap significance between right-tailed thresholded RH networks and existing datasets, calculated using hidden random directed network models.
Same as Figure 1 except that a hidden random directed network was used to model existing undirected networks.
Figure 4
Figure 4. Essentiality, connectivity and centrality in RH networks.
(A) P-values for one-sided Wilcoxon rank-sum test assessing whether essential genes have significantly more edges than non-essential. (B) Fraction of essential genes and degree of weighted RH network. (C) P-values for one-sided Wilcoxon rank-sum test assessing whether essential genes have significantly larger betweenness centralities than non-essential. (D) Fraction of essential genes and betweenness centrality of RH network constructed with correlation coefficient threshold of 0.1 by right-tailed thresholding.
Figure 5
Figure 5. Transcription factors and edge directionality.
(A) P-values for one-sided paired signed rank test assessing whether transcription factors have significantly more outgoing than incoming edges. (B) Overlap between transcription factors and genes having ≥1 outgoing or incoming edge. P-values from one-sided Fisher's exact and chi-square tests.
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