Metabolic Constraint-Based Refinement of Transcriptional Regulatory Networks

<div><p>There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach <i>Gene Expression and Metabolism Integrated for Network Inference</i> (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10⁻¹⁷²), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for <i>Saccharomyces cerevisiae</i> involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10⁻¹⁴) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to integrate and reconcile inconsistencies across different data-types. The algorithm and associated data are available at <a href="https://sourceforge.net/projects/gemini-data/" target="_blank">https://sourceforge.net/projects/gemini-data/</a></p></div

Iterative approach for network refinement and phenotype prediction.

<p>By using an iterative approach, we increased the comprehensiveness of the integrated network model by adding new interactions (Network III) and iteratively refining the model using GEMINI. This process enriched the fraction of validated interactions in the network (shown in red) and improved the predictive ability of the integrated network model.</p

Network sizes and the number of interactions retained after running GEMINI for each network type.

<p>The hyper-geometric enrichment p-value compared to the original inferred network is shown. Note that for Network III with validated interactions, a single p-value was obtained because we were unable to differentiate between direct and indirect interactions in some of the new interactions that were added. So a single p-value for validated interactions was obtained.</p

Refining regulatory interaction data in yeast using GEMINI.

<p><b>A.</b> GEMINI was evaluated for its ability to preferentially retain the gold-standard interactions (blue edges) and the indirect interactions (green edges). The hyper-geometric p-values for enrichment with various data sets are shown. <b>B.</b> Running GEMINI on the network derived using Yeastract resulted in the elimination of ∼9,000 phenotype-inconsistent interactions and produced a refined integrated network model that was more highly enriched for known interactions than the original network (p-value<10⁻¹⁷², hyper-geometric test). Most of the interactions eliminated by GEMINI were found to have little supporting experimental evidence (interactions that did have strong supporting evidence were preferentially retained). <b>C.</b> The number of true interactions (direct and indirect) recalled was significantly higher than could be recalled using mutual information (MI) or correlation (Corr)-based approaches, which rely on gene expression alone (estimated from the same gene expression dataset and for networks of the same size). We also measured the best prediction obtained by MI and correlation over all possible cut offs and this was still significantly lower than the enrichment obtained by GEMINI. The supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003370#pcbi.1003370.s001" target="_blank">figures S1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003370#pcbi.1003370.s002" target="_blank">S2</a> show the enrichment for direct interactions over the entire range of thresholds for both MI and correlation. The number of interactions recalled by random sampling from the Yeastract database (DB) is also shown, as a reference.</p

Distribution of inconsistencies across the regulatory and metabolic network.

<p>The distribution of phenotype inconsistencies was exponential across the TRN, suggesting that a few TFs led to most of the inconsistencies. In contrast, the distribution of inconsistencies across the metabolic network was linear and did not reveal any strong trend towards specific metabolic genes.</p

Enrichment across different carbon sources.

<p>Using growth viability information from different environmental conditions (rich media and minimal media with galactose, glycerol, and ethanol as the carbon source, respectively) had a similar effect on the network refinement. Generally defined minimal media were more useful than rich media, which provided the least enrichment for gold standard interactions. Importantly, there was considerable overlap in the interactions retained by running GEMINI in each condition.</p

Process of identifying phenotype-consistent interactions using GEMINI.

<p><b>A.</b> High-throughput interaction data were mapped onto a biochemically detailed metabolic network using PROM and phenotypic consequences of these interactions were predicted. The metabolic network is represented <i>in silico</i> in the form of a stoichiometric matrix, where every column corresponds to a reaction and every row corresponds to a metabolite. The regulatory interactions are represented as probabilities, which are estimated from microarray data. By using constraint-based analysis, it is possible to determine the possible configurations in the biochemical network that correspond to physiologically meaningful states; this is done by applying various physico-chemical constraints, such as reaction stoichiometry and thermodynamics. The interaction probabilities were then used to further constrain the fluxes through the metabolic network and an optimal network state that satisfied both thermodynamic and transcriptional constraints (shaded in red) was determined using PROM. <b>B.</b> Interactions that lead to inconsistencies between model predictions and experiments were identified and removed. This was achieved by comparing the flux state predicted by PROM for the TF knockout with the closest flux state that represented the measured growth phenotype; reactions regulated by the perturbed TF were then prioritized based on the magnitude of their deviation. Interactions regulating these reactions were then removed and PROM was run iteratively on each new network to predict the growth phenotype. <b>C.</b> The final network that matched the phenotype was evaluated based on its ability to retain known interactions, and predict growth phenotype outcomes in new conditions.</p

Dynamic genome-scale cell-specific metabolic models reveal novel inter-cellular and intra-cellular metabolic communications during ovarian follicle development

BACKGROUND: The maturation of the female germ cell, the oocyte, requires the synthesis and storing of all the necessary metabolites to support multiple divisions after fertilization. Oocyte maturation is only possible in the presence of surrounding, diverse, and changing layers of somatic cells. Our understanding of metabolic interactions between the oocyte and somatic cells has been limited due to dynamic nature of ovarian follicle development, thus warranting a systems approach. RESULTS: Here, we developed a genome-scale metabolic model of the mouse ovarian follicle. This model was constructed using an updated mouse general metabolic model (Mouse Recon 2) and contains several key ovarian follicle development metabolic pathways. We used this model to characterize the changes in the metabolism of each follicular cell type (i.e., oocyte, granulosa cells, including cumulus and mural cells), during ovarian follicle development in vivo. Using this model, we predicted major metabolic pathways that are differentially active across multiple follicle stages. We identified a set of possible secreted and consumed metabolites that could potentially serve as biomarkers for monitoring follicle development, as well as metabolites for addition to in vitro culture media that support the growth and maturation of primordial follicles. CONCLUSIONS: Our systems approach to model follicle metabolism can guide future experimental studies to validate the model results and improve oocyte maturation approaches and support growth of primordial follicles in vitro

Temporal dynamics of neurogenomic plasticity in response to social interactions in male threespined sticklebacks

<div><p>Animals exhibit dramatic immediate behavioral plasticity in response to social interactions, and brief social interactions can shape the future social landscape. However, the molecular mechanisms contributing to behavioral plasticity are unclear. Here, we show that the genome dynamically responds to social interactions with multiple waves of transcription associated with distinct molecular functions in the brain of male threespined sticklebacks, a species famous for its behavioral repertoire and evolution. Some biological functions (e.g., hormone activity) peaked soon after a brief territorial challenge and then declined, while others (e.g., immune response) peaked hours afterwards. We identify transcription factors that are predicted to coordinate waves of transcription associated with different components of behavioral plasticity. Next, using H3K27Ac as a marker of chromatin accessibility, we show that a brief territorial intrusion was sufficient to cause rapid and dramatic changes in the epigenome. Finally, we integrate the time course brain gene expression data with a transcriptional regulatory network, and link gene expression to changes in chromatin accessibility. This study reveals rapid and dramatic epigenomic plasticity in response to a brief, highly consequential social interaction.</p></div

Integrating TFs with DEG_x and chromatin accessibility.

<p>These TFs are in the TRN and are enriched in the DAPDEG_x with accessibility indicated. Some of the TFs (in bold) were differentially expressed and in a cluster. The general expression pattern of their cluster is indicated. A complete set of enrichments is in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006840#pgen.1006840.s012" target="_blank">S10 Table</a>.</p