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      Systematic Discovery of Archaeal Transcription Factor Functions in Regulatory Networks through Quantitative Phenotyping Analysis

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          Abstract

          To ensure survival in the face of stress, microorganisms employ inducible damage repair pathways regulated by extensive and complex gene networks. Many archaea, microorganisms of the third domain of life, persist under extremes of temperature, salinity, and pH and under other conditions. In order to understand the cause-effect relationships between the dynamic function of the stress network and ultimate physiological consequences, this study characterized the physiological role of nearly one-third of all regulatory proteins known as transcription factors (TFs) in an archaeal organism. Using a unique quantitative phenotyping approach, we discovered functions for many novel TFs and revealed important secondary functions for known TFs. Surprisingly, many TFs are required for resisting multiple stressors, suggesting cross-regulation of stress responses. Through extensive validation experiments, we map the physiological roles of these novel TFs in stress response back to their position in the regulatory network wiring. This study advances understanding of the mechanisms underlying how microorganisms resist extreme stress. Given the generality of the methods employed, we expect that this study will enable future studies on how regulatory networks adjust cellular physiology in a diversity of organisms.

          ABSTRACT

          Gene regulatory networks (GRNs) are critical for dynamic transcriptional responses to environmental stress. However, the mechanisms by which GRN regulation adjusts physiology to enable stress survival remain unclear. Here we investigate the functions of transcription factors (TFs) within the global GRN of the stress-tolerant archaeal microorganism Halobacterium salinarum. We measured growth phenotypes of a panel of TF deletion mutants in high temporal resolution under heat shock, oxidative stress, and low-salinity conditions. To quantitate the noncanonical functional forms of the growth trajectories observed for these mutants, we developed a novel modeling framework based on Gaussian process regression and functional analysis of variance (FANOVA). We employ unique statistical tests to determine the significance of differential growth relative to the growth of the control strain. This analysis recapitulated known TF functions, revealed novel functions, and identified surprising secondary functions for characterized TFs. Strikingly, we observed that the majority of the TFs studied were required for growth under multiple stress conditions, pinpointing regulatory connections between the conditions tested. Correlations between quantitative phenotype trajectories of mutants are predictive of TF-TF connections within the GRN. These phenotypes are strongly concordant with predictions from statistical GRN models inferred from gene expression data alone. With genome-wide and targeted data sets, we provide detailed functional validation of novel TFs required for extreme oxidative stress and heat shock survival. Together, results presented in this study suggest that many TFs function under multiple conditions, thereby revealing high interconnectivity within the GRN and identifying the specific TFs required for communication between networks responding to disparate stressors.

          IMPORTANCE To ensure survival in the face of stress, microorganisms employ inducible damage repair pathways regulated by extensive and complex gene networks. Many archaea, microorganisms of the third domain of life, persist under extremes of temperature, salinity, and pH and under other conditions. In order to understand the cause-effect relationships between the dynamic function of the stress network and ultimate physiological consequences, this study characterized the physiological role of nearly one-third of all regulatory proteins known as transcription factors (TFs) in an archaeal organism. Using a unique quantitative phenotyping approach, we discovered functions for many novel TFs and revealed important secondary functions for known TFs. Surprisingly, many TFs are required for resisting multiple stressors, suggesting cross-regulation of stress responses. Through extensive validation experiments, we map the physiological roles of these novel TFs in stress response back to their position in the regulatory network wiring. This study advances understanding of the mechanisms underlying how microorganisms resist extreme stress. Given the generality of the methods employed, we expect that this study will enable future studies on how regulatory networks adjust cellular physiology in a diversity of organisms.

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          Most cited references67

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          Integration of biological networks and gene expression data using Cytoscape.

          Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
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            Pfam: clans, web tools and services

            Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tools and the first set of Pfam web services that allow programmatic access to the database and associated tools are also presented. Pfam is available on the web in the UK (), the USA (), France () and Sweden ().
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              Population genomics of domestic and wild yeasts

              Since the completion of the genome sequence of Saccharomyces cerevisiae in 19961,2, there has been an exponential increase in complete genome sequences accompanied by great advances in our understanding of genome evolution. Although little is known about the natural and life histories of yeasts in the wild, there are an increasing number of studies looking at ecological and geographic distributions3,4, population structure5-8, and sexual versus asexual reproduction9,10. Less well understood at the whole genome level are the evolutionary processes acting within populations and species leading to adaptation to different environments, phenotypic differences and reproductive isolation. Here we present one- to four-fold or more coverage of the genome sequences of over seventy isolates of the baker's yeast, S. cerevisiae, and its closest relative, S. paradoxus. We examine variation in gene content, SNPs, indels, copy numbers and transposable elements. We find that phenotypic variation broadly correlates with global genome-wide phylogenetic relationships. Interestingly, S. paradoxus populations are well delineated along geographic boundaries while the variation among worldwide S. cerevisiae isolates shows less differentiation and is comparable to a single S. paradoxus population. Rather than one or two domestication events leading to the extant baker's yeasts, the population structure of S. cerevisiae consists of a few well-defined geographically isolated lineages and many different mosaics of these lineages, supporting the idea that human influence provided the opportunity for cross-breeding and production of new combinations of pre-existing variation.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mSystems
                mSystems
                msys
                msys
                mSystems
                mSystems
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5077
                19 September 2017
                Sep-Oct 2017
                : 2
                : 5
                : e00032-17
                Affiliations
                [a ]Department of Biology, Duke University, Durham, North Carolina, USA
                [b ]Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, USA
                [c ]Department of Statistical Science, Duke University, Durham, North Carolina, USA
                [d ]Department of Computer Science, Duke University, Durham, North Carolina, USA
                University of North Carolina at Chapel Hill
                Author notes
                Address correspondence to Amy K. Schmid, amy.schmid@ 123456duke.edu .
                [*]

                Present address: Jordan G. Gulli, Department of Biology, Georgia Institute of Technology, Atlanta, Georgia, USA.

                C.L.D. and P.D.T. contributed equally to this work.

                Citation Darnell CL, Tonner PD, Gulli JG, Schmidler SC, Schmid AK. 2017. Systematic discovery of archaeal transcription factor functions in regulatory networks through quantitative phenotyping analysis. mSystems 2:e00032-17. https://doi.org/10.1128/mSystems.00032-17.

                Article
                mSystems00032-17
                10.1128/mSystems.00032-17
                5605881
                becd4697-9e45-4f77-8c4c-04c4a9ae95da
                Copyright © 2017 Darnell et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 3 May 2017
                : 3 August 2017
                Page count
                supplementary-material: 10, Figures: 6, Tables: 2, Equations: 36, References: 86, Pages: 20, Words: 12770
                Funding
                Funded by: National Science Foundation (NSF) https://doi.org/10.13039/100000001
                Award ID: MCB-1615685
                Award Recipient : Amy K. Schmid
                Funded by: National Science Foundation (NSF) https://doi.org/10.13039/100000001
                Award ID: CAREER-1651117
                Award Recipient : Amy K. Schmid
                Funded by: National Science Foundation (NSF) https://doi.org/10.13039/100000001
                Award ID: DMS-1407622
                Award Recipient : Scott Schmidler
                Funded by: National Science Foundation (NSF) https://doi.org/10.13039/100000001
                Award ID: Graduate Research Fellowship
                Award Recipient : Peter D. Tonner
                Funded by: National Science Foundation (NSF) https://doi.org/10.13039/100000001
                Award ID: MCB-1417750
                Award Recipient : Amy K. Schmid
                Categories
                Research Article
                Novel Systems Biology Techniques
                Custom metadata
                September/October 2017

                archaea,functional anova,phenomics,transcription factors

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