Network-Guided Discovery of Extensive Epistasis between ... Network-Guided Discovery of Extensive Epistasis

  • View
    0

  • Download
    0

Embed Size (px)

Text of Network-Guided Discovery of Extensive Epistasis between ... Network-Guided Discovery of Extensive...

  • Network-Guided Discovery of Extensive Epistasis between Transcription Factors Involved in Aliphatic Glucosinolate Biosynthesis

    Baohua Li,a Michelle Tang,a,b Ayla Nelson,a Hart Caligagan,a Xue Zhou,a Caitlin Clark-Wiest,a Richard Ngo,a

    Siobhan M. Brady,b and Daniel J. Kliebensteina,c,1

    a Department of Plant Sciences, University of California, Davis, Davis, California 95616 bDepartment of Plant Biology and Genome Center, University of California, Davis, Davis, California 95616 cDynaMo Center of Excellence, University of Copenhagen, DK-1871 Frederiksberg C, Denmark

    ORCID IDs: 0000-0001-7235-0470 (B.L.); 0000-0001-9424-8055 (S.M.B.); 0000-0001-5759-3175 (D.J.K.)

    Plants use diverse mechanisms influenced by vast regulatory networks of indefinite scale to adapt to their environment. These regulatory networks have an unknown potential for epistasis between genes within and across networks. To test for epistasis within an adaptive trait genetic network, we generated and tested 47 Arabidopsis thaliana double mutant combinations for 20 transcription factors, which all influence the accumulation of aliphatic glucosinolates, the defense metabolites that control fitness. The epistatic combinations were used to test if there is more or less epistasis depending on gene membership within the same or different phenotypic subnetworks. Extensive epistasis was observed between the transcription factors, regardless of subnetwork membership. Metabolite accumulation displayed antagonistic epistasis, suggesting the presence of a buffering mechanism. Epistasis affecting enzymatic estimated activity was highly conditional on the tissue and environment and shifted between both antagonistic and synergistic forms. Transcriptional analysis showed that epistasis shifts depend on how the trait is measured. Because the 47 combinations described here represent a small sampling of the potential epistatic combinations in this genetic network, there is potential for significantly more epistasis. Additionally, the main effect of the individual gene was not predictive of the epistatic effects, suggesting that there is a need for further studies.

    INTRODUCTION

    To adapt and maximize fitness, plants perceive and respond to a myriad of signals that in combination provide an image of the environment. These signals can arise from the biotic environment, includingbacteria, fungi, insects, andother plants, plus stimuli from the abiotic environment, including light, temperature, water, and nutrient availability (Goldwasser et al., 2002; Shinozaki et al., 2003; Jones and Dangl, 2006; Howe and Jander, 2008; Vidal and Gutiérrez, 2008; Harmer, 2009; Chory, 2010;Mengiste, 2012; Xuan et al., 2017). Critically, each specific signal is typically perceived by a separate mechanism that stimulates a downstream regulatory network involving at least tens of genes (Li et al., 2006; Hickman et al., 2017). The current models often suggest that these genetic regulatorynetworks coalescearoundmaster regulators that are the central controllers for specific pathways and/or phenotypes (Gu et al., 2004; Kazan and Manners, 2013). Often these master reg- ulators are transcription factors (TFs) that are both necessary and sufficient for the changes in expression of genes or pathways that modulate the growth, defense, and metabolic phenotype of the plant to adapt to that specific environment. We call this the master regulator hypothesis. This concept is predominant within

    developmental regulatory networks that often exhibit switch-like behavior, shifting from one state to another. It is not clear how this concept may translate to metabolic pathways that may instead display a rheostatbehavior,where there isa continuousadjustment in response to external and internal stimuli. However, in spite of the advanced knowledge about specific regulatory networks in plants, the exact size and interconnected structure of these genetic net- works is a key unanswered question in systems biology (Phillips, 2008). The size of networks is of critical importance for adaptive traits because as genetic networks increase in size and inter- connectivity, the concept of a single master regulator at the be- ginning point of a specific regulatory network is less essential. Additionally, as gene membership increases, there is a concurrent increase inthepotential forepistasisbetweenthesegenes (Mackay, 2014; Gaudinier et al., 2015). In this context, we are defining epistasis as any nonadditive interaction between genotypes at two or more loci influencing a trait. Thus, there is a need to understand how large regulatory networks may be influenced by epistasis, especially for adaptive metabolic traits. One set of adaptive traits that could be used to study these

    questions of network scale and epistasis are plant secondary metabolites (Wink, 1988; Burow et al., 2010; Kroymann, 2011). Recent work has shown that plant secondary metabolites have strong epistatic interactions that can influence fitness in the field (Brachi et al., 2015; Kerwin et al., 2015, 2017). Additionally, mechanistic and quantitative genetic studies are showing that plant defense metabolites have vast genetic regulatory networks (Chan et al., 2010, 2011; Harper et al., 2012; Riedelsheimer et al., 2012; Wurschum et al., 2013; Wen et al., 2016). These studies

    1 Address correspondence to kliebenstein@ucdavis.edu. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantcell.org) is: Daniel J. Kliebenstein (kliebenstein@ucdavis.edu). www.plantcell.org/cgi/doi/10.1105/tpc.17.00805

    The Plant Cell, Vol. 30: 178–195, January 2018, www.plantcell.org ã 2018 ASPB.

    http://orcid.org/0000-0001-7235-0470 http://orcid.org/0000-0001-7235-0470 http://orcid.org/0000-0001-7235-0470 http://orcid.org/0000-0001-9424-8055 http://orcid.org/0000-0001-9424-8055 http://orcid.org/0000-0001-9424-8055 http://orcid.org/0000-0001-5759-3175 http://orcid.org/0000-0001-5759-3175 http://orcid.org/0000-0001-5759-3175 http://orcid.org/0000-0001-5759-3175 http://orcid.org/0000-0001-5759-3175 http://orcid.org/0000-0001-5759-3175 http://orcid.org/0000-0001-7235-0470 http://orcid.org/0000-0001-9424-8055 http://orcid.org/0000-0001-5759-3175 http://crossmark.crossref.org/dialog/?doi=10.1105/tpc.17.00805&domain=pdf&date_stamp=2018-02-01 mailto:kliebenstein@ucdavis.edu http://www.plantcell.org mailto:kliebenstein@ucdavis.edu http://www.plantcell.org/cgi/doi/10.1105/tpc.17.00805 http://www.plantcell.org

  • provide an alternative hypothesis where regulation occurs via a promoter integration model. In this model, the pathway is controlled by suites of TFs that interact with distinct subsets of promoters within a metabolic pathway. This promoter integration model leads to a greatly extended gene network influencing ametabolicpathwayandallows forpotentially increasedprecision in the regulation of metabolic pathways. Furthermore, this raises the potential for there to be different types of epistasis across a pathway, depending upon the promoter/gene that influences that part of the pathway. For example, if two TFs bind different promoters within a pathway without interacting molecularly, they have the potential to show nonadditive epistasis at themetabolite level, as they are influencing multiple enzymatic reactions within the pathway. In the absence of metabolite-triggered transcrip- tional feedback, this metabolic epistasis might not be mirrored at the transcript level, which may display an additive model. Thus, metabolic pathways where it is possible to measure different outputs fromasinglepathwaycanenable thedissectionofgenetic networks andepistatic interactions andhow theycompare at both the metabolic and transcriptional levels.

    In thisstudy,weusedthealiphaticglucosinolate (GLS)pathwayto test the extent of epistasis within an adaptive regulatory network. GLSs are becoming amodel system for the study of plant adaption to ever-changing environments (Hopkins et al., 2009; Kliebenstein, 2009;Kroymann, 2011). AliphaticGLSsarederived frommethionine,

    and genetic variation influencing aliphatic GLS composition is a key mechanismusedbyplants toadapt totheirecologicalniches (Lankau and Kliebenstein, 2009; Burow et al., 2010; Züst et al., 2012). Fur- thermore, the almost complete elucidationof themethionine-derived aliphatic biosynthesis pathway in the model plant Arabidopsis thaliana has provided a unique system to test systems biology concepts (Sønderby et al., 2010a). Combining the full catalog of biosyntheticgeneswith large-scalesystemsbiologyapproacheshas allowedarapidcharacterizationof theregulatorynetworkscontrolling this pathway to address plants’ defense and survival challenges in connected regulatory networks. Previous studies identified and confirmed the critical importance of transcriptional regulation of the GLS pathway, including the cloning of TF genes MYB28, MYB29, and MYB76, which regulate the accumulation of aliphatic GLS (Gigolashvili etal.,2007,2008;Hiraietal.,2007;Sønderbyetal.,2007; Malitskyetal., 2008;Sønderbyetal., 2010c).More recently,TFs in the jasmonatesignalingpathway,MYC2,MYC3,andMYC4,wereshown to be important regulators of both aliphatic and indolic GLS (Dombrecht et al., 2007; Fernández-Calvo et al., 2011; Schweizer et al., 2013). These key MYB and MYC regulators of GLS pathways are positive regulators and belong to evolutionarily conserved sub- sets of their corresponding families (Stracke et al., 2001; Fernández- Calvo et al., 2011). Intriguingly, while mutants of these proposed master regulators abolish the accumulation of the GLS metabolites, theyonlyabolish theexpressionofa fewkeygenes in thebiosynthetic

    Figure 1. Genetic Networks under Investigation.

    The20TFsunder