Predicting Spatial and Temporal Gene Expression Using an Integrative Model of Transcription Factor Occupancy and Chromatin State

Abstract

Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a gene's expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal predictions and 50% for spatial. While this is, to our knowledge, the first genome-wide approach to predict tissue-specific gene expression in metazoan development, our results suggest that integrative models of this type will become more prevalent in the future.

Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal

“…Some of these models achieve very high prediction accuracy, although they tend to focus on promoter proximal CREs, or at least give preference to these elements, and do not give full weight to more distal elements, as we have tried to do here. Other studies have set out to identify the genes expressed in different cell types or tissues based on CREs (Ouyang et al 2009;Cheng et al 2012;Natarajan et al 2012;Wilczynski et al 2012). Our approach is conceptually similar to these latter methods; with the key difference that we explain more subtle changes, namely, the differentiation or quiescence process in one cell type and its descendants, instead of gross differences between very diverse cell states.…”

Genetic Networks Governing Heart Development

“…The network is in effect hardwired through the global arrangement of TF binding sites within cis-acting elements controlling gene expression. However, TFs do not operate on the genome in isolation and simple occupancy of their binding sites cannot always explain the complex patterns of gene expression occurring in development (Spitz and Furlong 2012;Wilczynski et al 2012). As alluded to above, other levels of regulation are important-these include combinatorial coding, noncoding RNAs (ncRNAs), long-and short-range epigenetic control through modifications to chromatin and DNA, DNA looping, and the establishment of insulators, boundary elements, and specialized nuclear compartments (Spitz and Furlong 2012).…”

Deep conservation ofcis-regulatory elements in metazoans

“…Although functional CRE testing is currently a limiting factor (particularly given that putative orthologous CREs should be ideally tested in their multiple endogenous contexts, a very uncommon practice to date [30,36], and for which reliable techniques are not yet established in many systems), it is likely that new methods combining NGS with computational techniques will not only predict CRE activity, but also accurately infer the expression patterns that they drive; the first efforts in this direction are promising [123]. Finding the set of ancestral CREs and their functions opens up the extremely exciting possibility of unravelling GRNs shared across phyla.…”