Large-Scale Genetic Perturbations Reveal Regulatory Networks and an Abundance of Gene-Specific Repressors

Kemmeren, Patrick; Sameith, Katrin; Pasch, Loes A.L. van de; Benschop, Joris J.; Lenstra, Tineke L.; Margaritis, Thanasis; O’Duibhir, Eoghan; Apweiler, Eva; Wageningen, Sake van; Ko, Cheuk W.; Heesch, Sebastiaan van; Kashani, Mehdi Mehdi M; Ampatziadis-Michailidis, Giannis; Brok, Mariël; Brabers, Nathalie; Miles, Anthony J.; Bouwmeester, Diane; Hooff, Sander R. van; Bakel, Harm van; Sluiters, Erik; Bakker, Linda V.; Snel, Berend; Lijnzaad, Philip; Leenen, Dik van; Koerkamp, Marian J. A. Groot; Holstege, Frank C.P.

doi:10.1016/j.cell.2014.02.054

Cited by 272 publications

(499 citation statements)

References 120 publications

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“…Currently, the TFs that regulate a process of interest are typically discovered by large-scale mutant screens, and TF-target relations are mapped in big-science projects that do not focus on TFs with specific biological functions (Harbison et al 2004;Hu et al 2007;Kemmeren et al 2014). Our approach brings TF discovery and mapping together through focused, iterative network construction and analysis.…”

Section: Discussionmentioning

confidence: 99%

Model-driven mapping of transcriptional networks reveals the circuitry and dynamics of virulence regulation

et al. 2015

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Key steps in understanding a biological process include identifying genes that are involved and determining how they are regulated. We developed a novel method for identifying transcription factors (TFs) involved in a specific process and used it to map regulation of the key virulence factor of a deadly fungus-its capsule. The map, built from expression profiles of 41 TF mutants, includes 20 TFs not previously known to regulate virulence attributes. It also reveals a hierarchy comprising executive, midlevel, and "foreman" TFs. When grouped by temporal expression pattern, these TFs explain much of the transcriptional dynamics of capsule induction. Phenotypic analysis of TF deletion mutants revealed complex relationships among virulence factors and virulence in mice. These resources and analyses provide the first integrated, systems-level view of capsule regulation and biosynthesis. Our methods dramatically improve the efficiency with which transcriptional networks can be analyzed, making genomic approaches accessible to laboratories focused on specific physiological processes.[Supplemental material is available for this article.]In this paper we present an efficient means of comprehensively mapping the network of transcription factors (TFs) that regulate a particular physiological process. Our approach cycles through deletion of TFs, expression profiling of TF mutants, model construction, and model-directed selection of TFs for the next round of deletion. This predictive genetics approach identifies TFs that affect the process of interest, providing a valuable complement to undirected mutagenesis and screening. Simultaneously, it builds a network model that explains how the TFs affect the process, yielding novel insights into the biological system under study.Mapping the network that regulates a specific process requires knowing which TFs affect that process. One way to identify such TFs is to screen comprehensive mutant libraries, but generating such libraries is not always feasible. Furthermore, genome-scale screening assays must be fast and scalable; such assays may not exist for the process of interest or may be less sensitive than other, more laborious assays. An alternative approach is to map the targets of all TFs encoded in a genome by using methods such as chromatin-immunoprecipitation (ChIP) or large-scale TF deletion and expression analysis. However, undirected, genome-wide approaches are costly and inefficient for probing a specific biological process in detail. We report a model-guided approach that addresses all of these problems by focusing experimental effort on the TFs most likely to be involved in the process of interest. Furthermore, our approach generates a network that provides mechanistic explanations for the phenotypes of TF deletion mutants.Our approach alternates network building by using an algorithm we call NetProphet with identifying relevant TFs by using an algorithm we call PhenoProphet. NetProphet is a validated method for mapping direct, functional regulation that significantly out...

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Section: Discussionmentioning

confidence: 99%

Model-driven mapping of transcriptional networks reveals the circuitry and dynamics of virulence regulation

et al. 2015

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“…With limited knowledge of CRCs in most cell types, attempts to map the control of gene expression programs have thus far been dominated by efforts to integrate global information regarding gene-gene, protein-protein, gene-protein, and regulatory element interactions nested in these networks (Lefebvre et al 2010;Gerstein et al 2012;Neph et al 2012;Yosef et al 2013;Kemmeren et al 2014;Rolland et al 2014). These global studies have provided foundational resources and important insights into basic principles governing transcriptional regulatory networks.…”

Section: [Supplemental Materials Is Available For This Article]mentioning

confidence: 99%

Models of human core transcriptional regulatory circuitries

et al. 2016

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A small set of core transcription factors (TFs) dominates control of the gene expression program in embryonic stem cells and other well-studied cellular models. These core TFs collectively regulate their own gene expression, thus forming an interconnected auto-regulatory loop that can be considered the core transcriptional regulatory circuitry (CRC) for that cell type. There is limited knowledge of core TFs, and thus models of core regulatory circuitry, for most cell types. We recently discovered that genes encoding known core TFs forming CRCs are driven by super-enhancers, which provides an opportunity to systematically predict CRCs in poorly studied cell types through super-enhancer mapping. Here, we use super-enhancer maps to generate CRC models for 75 human cell and tissue types. These core circuitry models should prove valuable for further investigating cell-type–specific transcriptional regulation in healthy and diseased cells.

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“…We consider large-scale gene deletion experiments in yeast (Saccharomyces cerevisiae) (40). Genome-wide mRNA expression levels are measured for 6,170 genes: 160 observational data points from wild-type individuals and 1,479 interventional data arising from single gene deletions (1,479 perturbation/deletion experiments where a single gene has been deleted from a strain).…”

Section: Validation: Gene Perturbation Experimentsmentioning

confidence: 99%

Methods for causal inference from gene perturbation experiments and validation

Meinshausen

Hauser

Mooij

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for largescale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae. The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques.interventional-observational data | invariant causal prediction | genome database validation | graphical models I n this article, we discuss statistical methods for causal inference from perturbation experiments. As this is a rather general topic, we focus on the following problem: based on data from observational and perturbation settings, we want to predict the effect and outcome of an unseen and new intervention or perturbation. Taking applications in genomics as an example, a typical task is as follows: based on observational data from wild-type organisms and interventional data from gene knockout or knockdown experiments, we want to predict the effect of a new gene knockout or knockdown on a phenotype of interest. For example, the organism is the model plant Arabidopsis thaliana, the gene knockouts correspond to mutant plants, and the phenotype of interest is the time it takes until the plant is flowering (1).From a methodological viewpoint, the prediction of unseen future interventions belongs to the area of causal inference where one aims to quantify presence and strength of causal effects among various variables. Loosely speaking, a causal effect is the effect of an external intervention (or say the response to a "What if I do?" question). The corresponding theory, e.g., using Pearl's do-operator (2), provides a link between causal effects and perturbations or randomized experiments. We mostly assume here that all of the variables in the causal model (for inferring causal effects) are observed: the case with hidden variables is mentioned only briefly in a later section, although it is an important theme in causal inference (due to the problem of hidden confounding variables) (cf. refs. 2 and 3).A popular and powerful route for causal modeling is given by structural equation models (SEMs) (2, 4). We consider a set of random variables X 1 , . . . , X p , X p+1 , and we often denote by Y = X 1 , emphasizing that Y is our response variable of interest (e.g., a phenotype of interest). The main building blocks of a SEM...

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Large-Scale Genetic Perturbations Reveal Regulatory Networks and an Abundance of Gene-Specific Repressors

Cited by 272 publications

References 120 publications

Model-driven mapping of transcriptional networks reveals the circuitry and dynamics of virulence regulation

Model-driven mapping of transcriptional networks reveals the circuitry and dynamics of virulence regulation

Models of human core transcriptional regulatory circuitries

Methods for causal inference from gene perturbation experiments and validation

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