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How difficult is inference of mammalian causal gene regulatory networks?

Djordjevic D, Yang A, Zadoorian A, Rungrugeecharoen K, Ho JW - PLoS ONE (2014)

Bottom Line: Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges.Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult.Our result supports the importance of using perturbation experimental data in causal network reconstruction.

View Article: PubMed Central - PubMed

Affiliation: Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; The University of New South Wales, Sydney, New South Wales, Australia.

ABSTRACT
Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.

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Scatter plots show the extent of tissue-specific differential expression in dental epithelium (y-axis) and dental mesenchyme (x-axis) as a result of Pax9 knockout (A), Msx1 knockout (B), Bmp4 stimulation (C) and Wnt stimulation (D).Coloured points represent probes of differentially responsive genes between the two tissues. Pearson correlation is also shown.
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pone-0111661-g005: Scatter plots show the extent of tissue-specific differential expression in dental epithelium (y-axis) and dental mesenchyme (x-axis) as a result of Pax9 knockout (A), Msx1 knockout (B), Bmp4 stimulation (C) and Wnt stimulation (D).Coloured points represent probes of differentially responsive genes between the two tissues. Pearson correlation is also shown.

Mentions: We sought to investigate the extent to which different tissues display different genetic responses to the same stimulus. Using the ToothCODE microarray profiles on genetic perturbation experiments, we found that the magnitude of tissue specific responses varies considerably between different perturbations. First we examined epithelial and mesenchymal tissue microarray profiles from Pax9 and Msx1 mice (Figure 5A,B). We identified hundreds of genes are significantly differentially expression in only one tissue type and not the other, even in the same genetic mouse model (FDR). Similarly, we observed that distinct sets of genes are differentially expressed in response to the same signalling pathway stimulation (BMP and Wnt) in dental epithelium versus dental mesenchyme (Figure 5C,D; see also Figure S6). In addition, we also observed many tissue and/or temporal specific causal gene regulation in our tooth and heart literature datasets (Table S2). These results suggest that the causal gene regulatory network structure may be specific to individual cell or tissue types. Therefore, it is important to consider cell-type specificity when constructing GRNs in multicellular organisms [30], [31].


How difficult is inference of mammalian causal gene regulatory networks?

Djordjevic D, Yang A, Zadoorian A, Rungrugeecharoen K, Ho JW - PLoS ONE (2014)

Scatter plots show the extent of tissue-specific differential expression in dental epithelium (y-axis) and dental mesenchyme (x-axis) as a result of Pax9 knockout (A), Msx1 knockout (B), Bmp4 stimulation (C) and Wnt stimulation (D).Coloured points represent probes of differentially responsive genes between the two tissues. Pearson correlation is also shown.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219746&req=5

pone-0111661-g005: Scatter plots show the extent of tissue-specific differential expression in dental epithelium (y-axis) and dental mesenchyme (x-axis) as a result of Pax9 knockout (A), Msx1 knockout (B), Bmp4 stimulation (C) and Wnt stimulation (D).Coloured points represent probes of differentially responsive genes between the two tissues. Pearson correlation is also shown.
Mentions: We sought to investigate the extent to which different tissues display different genetic responses to the same stimulus. Using the ToothCODE microarray profiles on genetic perturbation experiments, we found that the magnitude of tissue specific responses varies considerably between different perturbations. First we examined epithelial and mesenchymal tissue microarray profiles from Pax9 and Msx1 mice (Figure 5A,B). We identified hundreds of genes are significantly differentially expression in only one tissue type and not the other, even in the same genetic mouse model (FDR). Similarly, we observed that distinct sets of genes are differentially expressed in response to the same signalling pathway stimulation (BMP and Wnt) in dental epithelium versus dental mesenchyme (Figure 5C,D; see also Figure S6). In addition, we also observed many tissue and/or temporal specific causal gene regulation in our tooth and heart literature datasets (Table S2). These results suggest that the causal gene regulatory network structure may be specific to individual cell or tissue types. Therefore, it is important to consider cell-type specificity when constructing GRNs in multicellular organisms [30], [31].

Bottom Line: Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges.Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult.Our result supports the importance of using perturbation experimental data in causal network reconstruction.

View Article: PubMed Central - PubMed

Affiliation: Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia; The University of New South Wales, Sydney, New South Wales, Australia.

ABSTRACT
Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on > 2,000 pieces of experimental genetic perturbation evidence from manually reading > 150 primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.

Show MeSH
Related in: MedlinePlus