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 pieces of experimental genetic perturbation evidence from manually reading 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.
Highlights
Developmental biology, especially in the context of mammalian development, is the study of growth, differentiation, patterning and regeneration of cells, tissues and organs [1]
Neither do we observe a difference in Pearson correlation or mutual information values (Figure S3A, S4). This agrees with previous findings based on the S. cerevisiae gene regulatory networks (GRNs) in the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge [13]
This study aims to evaluate the practical utility of genome-wide expression profiles to infer causal gene regulatory networks in mammalian organ development
Summary
Developmental biology, especially in the context of mammalian development, is the study of growth, differentiation, patterning and regeneration of cells, tissues and organs [1]. It is increasingly clear that there is a need to fully unravel cell type-specific gene regulatory networks (GRNs) in order to understand the complex mechanisms underlying many developmental processes [2,3,4]. To achieve better understanding of complex genetic causes in organ development and diseases, we need to take a systems approach that interrogates causal genetic regulatory relationships (e.g., conditional knockout of Pax reduces the expression of Msx1 [5]). In this study we mainly focus on the inference of causal GRNs in which each node represents a gene, and each edge represents a causal regulatory relationship between two genes. The identification of causal gene regulatory relationships has a long history in the study of mammalian organ development, despite being primarily driven by hypothesis-based candidate gene investigations. A classic example of this is Eric Davidson’s work on constructing and analysing GRNs in sea urchin and other animals [3,4]
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