The hallmarks of a tradeoff in transcriptomes that balances stress and growth functions

Fit phenotypes are achieved through optimal transcriptomic allocation. Here, we performed a high-resolution, multi-scale study of the transcriptomic tradeoff between two key fitness phenotypes, stress response (fear) and growth (greed), in Escherichia coli. We introduced twelve RNA polymerase (RNAP) mutations commonly acquired during adaptive laboratory evolution (ALE) and found that single mutations resulted in large shifts in the fear vs. greed tradeoff, likely through destabilizing the rpoB-rpoC interface. RpoS and GAD regulons drive the fear response while ribosomal proteins and the ppGpp regulon underlie greed. Growth rate selection pressure during ALE results in endpoint strains that often have RNAP mutations, with synergistic mutations reflective of particular conditions. A phylogenetic analysis found the tradeoff in numerous bacteria species. The results suggest that the fear vs. greed tradeoff represents a general principle of transcriptome allocation in bacteria where small genetic changes can result in large phenotypic adaptations to growth conditions.

[1]  Anand V. Sastry,et al.  Proteome allocation is linked to transcriptional regulation through a modularized transcriptome , 2023, bioRxiv.

[2]  David Banks,et al.  Adversarial Risk Analysis , 2015, IWSPA@CODASPY.

[3]  Adam M. Feist,et al.  Lab evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance , 2022, bioRxiv.

[4]  Daniel C. Zielinski,et al.  A multi-scale transcriptional regulatory network knowledge base for Escherichia coli , 2021, bioRxiv.

[5]  Adam M. Feist,et al.  Experimental Evolution Reveals Unifying Systems-Level Adaptations but Diversity in Driving Genotypes , 2022, mSystems.

[6]  L. Loiseau,et al.  Methionine oxidation under anaerobic conditions in Escherichia coli , 2022, Molecular microbiology.

[7]  Adam M. Feist,et al.  Membrane transporter identification and modulation via adaptive laboratory evolution. , 2022, Metabolic engineering.

[8]  Víctor H. Tierrafría,et al.  RegulonDB 11.0: Comprehensive high-throughput datasets on transcriptional regulation in Escherichia coli K-12 , 2022, Microbial genomics.

[9]  Jonathan M. Monk,et al.  Laboratory-acquired mutations fall outside the wild-type alleleome of Escherichia coli , 2022, bioRxiv.

[10]  J. Utrilla,et al.  Regulatory perturbations of ribosome allocation in bacteria reshape the growth proteome with a trade-off in adaptation capacity , 2022, iScience.

[11]  Anand V. Sastry,et al.  RiboRid: A low cost, advanced, and ultra-efficient method to remove ribosomal RNA for bacterial transcriptomics , 2021, PLoS genetics.

[12]  Anand V. Sastry,et al.  Machine Learning of Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators , 2021, bioRxiv.

[13]  Anand V. Sastry,et al.  PRECISE 2. , 2023 .

[14]  David S. Goodsell,et al.  RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences , 2020, Nucleic Acids Res..

[15]  A. C. Dantas Machado,et al.  The RNA Polymerase α Subunit Recognizes the DNA Shape of the Upstream Promoter Element. , 2020, Biochemistry.

[16]  R. Corrigan,et al.  The stringent response and physiological roles of (pp)pGpp in bacteria , 2020, Nature Reviews Microbiology.

[17]  H. Schellhorn Function, Evolution, and Composition of the RpoS Regulon in Escherichia coli , 2020, Frontiers in Microbiology.

[18]  Bernhard O Palsson,et al.  iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning , 2020, bioRxiv.

[19]  Anand V. Sastry,et al.  Independent component analysis of E. coli's transcriptome reveals the cellular processes that respond to heterologous gene expression. , 2020, Metabolic engineering.

[20]  Troy E. Sandberg,et al.  Synthetic Cross-Phyla Gene Replacement and Evolutionary Assimilation of Major Enzymes , 2020, Nature Ecology & Evolution.

[21]  L. Jarboe,et al.  Reverse engineering of fatty acid-tolerant Escherichia coli identifies design strategies for robust microbial cell factories. , 2020, Metabolic engineering.

[22]  Adam M. Feist,et al.  Laboratory evolution of multiple E. coli strains reveals unifying principles of adaptation but diversity in driving genotypes , 2020, bioRxiv.

[23]  Adam M. Feist,et al.  Decomposition of transcriptional responses provides insights into differential antibiotic susceptibility , 2020, bioRxiv.

[24]  José Utrilla,et al.  Trade-offs between gene expression, growth and phenotypic diversity in microbial populations , 2020, Current opinion in biotechnology.

[25]  Albert Y. Chen,et al.  E. coli TraR allosterically regulates transcription initiation by altering RNA polymerase conformation , 2019, eLife.

[26]  Adam M. Feist,et al.  Adaptive evolution reveals a tradeoff between growth rate and oxidative stress during naphthoquinone-based aerobic respiration , 2019, Proceedings of the National Academy of Sciences.

[27]  Richard Szubin,et al.  OxyR Is a Convergent Target for Mutations Acquired during Adaptation to Oxidative Stress-Prone Metabolic States , 2019, Molecular biology and evolution.

[28]  S. Gottesman Trouble is coming: Signaling pathways that regulate general stress responses in bacteria , 2019, The Journal of Biological Chemistry.

[29]  Zachary A. King,et al.  The Escherichia coli transcriptome mostly consists of independently regulated modules , 2019, Nature Communications.

[30]  Adam M. Feist,et al.  Pseudogene repair driven by selection pressure applied in experimental evolution , 2019, Nature Microbiology.

[31]  S. Kelly,et al.  OrthoFinder: phylogenetic orthology inference for comparative genomics , 2019, Genome Biology.

[32]  Adam M. Feist,et al.  Reframing gene essentiality in terms of adaptive flexibility , 2018, BMC Systems Biology.

[33]  James T. Yurkovich,et al.  Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655 , 2018, bioRxiv.

[34]  B. Palsson,et al.  ChIP-exo interrogation of Crp, DNA, and RNAP holoenzyme interactions , 2018, PloS one.

[35]  Adam M. Feist,et al.  ALEdb 1.0: a database of mutations from adaptive laboratory evolution experimentation , 2018, bioRxiv.

[36]  Adam M. Feist,et al.  Enzyme promiscuity shapes evolutionary innovation and optimization , 2018, bioRxiv.

[37]  A. Motter,et al.  Experimental evolution of diverse Escherichia coli metabolic mutants identifies genetic loci for convergent adaptation of growth rate , 2018, PLoS genetics.

[38]  Edward J. O'Brien,et al.  Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation , 2017, Proceedings of the National Academy of Sciences.

[39]  B. Palsson,et al.  Revealing genome-scale transcriptional regulatory landscape of OmpR highlights its expanded regulatory roles under osmotic stress in Escherichia coli K-12 MG1655 , 2017, Scientific Reports.

[40]  Adam M. Feist,et al.  Laboratory Evolution to Alternating Substrate Environments Yields Distinct Phenotypic and Genetic Adaptive Strategies , 2017, Applied and Environmental Microbiology.

[41]  Alejandra Rodríguez-Verdugo,et al.  Adaptive Mutations in RNA Polymerase and the Transcriptional Terminator Rho Have Similar Effects on Escherichia coli Gene Expression , 2016, bioRxiv.

[42]  Jing Li,et al.  Development of a fast and easy method for Escherichia coli genome editing with CRISPR/Cas9 , 2016, Microbial Cell Factories.

[43]  Gabriela I. Guzmán,et al.  Systems assessment of transcriptional regulation on central carbon metabolism by Cra and CRP , 2016, bioRxiv.

[44]  Albert Y. Chen,et al.  ppGpp Binding to a Site at the RNAP-DksA Interface Accounts for Its Dramatic Effects on Transcription Initiation during the Stringent Response. , 2016, Molecular cell.

[45]  Ke Chen,et al.  Global Rebalancing of Cellular Resources by Pleiotropic Point Mutations Illustrates a Multi-scale Mechanism of Adaptive Evolution. , 2016, Cell systems.

[46]  O. Tenaillon,et al.  First-Step Mutations during Adaptation Restore the Expression of Hundreds of Genes , 2015, Molecular biology and evolution.

[47]  C. Gross,et al.  DksA regulates RNA polymerase in Escherichia coli through a network of interactions in the secondary channel that includes Sequence Insertion 1 , 2015, Proceedings of the National Academy of Sciences.

[48]  Donghyuk Kim,et al.  Genome-wide Reconstruction of OxyR and SoxRS Transcriptional Regulatory Networks under Oxidative Stress in Escherichia coli K-12 MG1655. , 2015, Cell reports.

[49]  Edward J. O'Brien,et al.  Decoding genome-wide GadEWX-transcriptional regulatory networks reveals multifaceted cellular responses to acid stress in Escherichia coli , 2015, Nature Communications.

[50]  K. Murakami Structural Biology of Bacterial RNA Polymerase , 2015, Biomolecules.

[51]  Edward J. O'Brien,et al.  Use of Adaptive Laboratory Evolution To Discover Key Mutations Enabling Rapid Growth of Escherichia coli K-12 MG1655 on Glucose Minimal Medium , 2014, Applied and Environmental Microbiology.

[52]  Edward J. O'Brien,et al.  Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli , 2014, Nature Communications.

[53]  Adam M. Feist,et al.  Evolution of Escherichia coli to 42 °C and Subsequent Genetic Engineering Reveals Adaptive Mechanisms and Novel Mutations , 2014, Molecular biology and evolution.

[54]  A. F. Bennett,et al.  The Molecular Diversity of Adaptive Convergence , 2012, Science.

[55]  V. Fromion,et al.  Bacterial growth rate reflects a bottleneck in resource allocation. , 2011, Biochimica et biophysica acta.

[56]  Simone C. Wiesler,et al.  Activity Map of the Escherichia coli RNA Polymerase Bridge Helix* , 2011, The Journal of Biological Chemistry.

[57]  Byung-Kwan Cho,et al.  RNA polymerase mutants found through adaptive evolution reprogram Escherichia coli for optimal growth in minimal media , 2010, Proceedings of the National Academy of Sciences.

[58]  Sergey Lyskov,et al.  PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta , 2010, Bioinform..

[59]  Craig D. Kaplan,et al.  Structural Basis of Transcription: Role of the Trigger Loop in Substrate Specificity and Catalysis , 2006, Cell.

[60]  C. Francke,et al.  How Phosphotransferase System-Related Protein Phosphorylation Regulates Carbohydrate Metabolism in Bacteria , 2006, Microbiology and Molecular Biology Reviews.

[61]  Paul Slovic,et al.  Affect, risk, and decision making. , 2005, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[62]  Thomas Müller-Bohn,et al.  Cost-Benefit Analysis , 2015 .

[63]  L. Isaksson,et al.  The Downstream DNA Jaw of Bacterial RNA Polymerase Facilitates Both Transcriptional Initiation and Pausing* , 2002, The Journal of Biological Chemistry.

[64]  S. Busby,et al.  Transcription activation by the Escherichia coli cyclic AMP receptor protein: determinants within activating region 3. , 2000, Journal of molecular biology.

[65]  R. Ebright,et al.  Identification of the activating region of catabolite gene activator protein (CAP): isolation and characterization of mutants of CAP specifically defective in transcription activation. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[66]  Y. Nakamura,et al.  Effects of rifampicin resistant rpoB mutations on antitermination and interaction with nusA in Escherichia coli. , 1988, Journal of molecular biology.