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      Suboptimal resource allocation in changing environments constrains response and growth in bacteria

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          Abstract

          To respond to fluctuating conditions, microbes typically need to synthesize novel proteins. As this synthesis relies on sufficient biosynthetic precursors, microbes must devise effective response strategies to manage depleting precursors. To better understand these strategies, we investigate the active response of Escherichia coli to changes in nutrient conditions, connecting transient gene expression to growth phenotypes. By synthetically modifying gene expression during changing conditions, we show how the competition by genes for the limited protein synthesis capacity constrains cellular response. Despite this constraint cells substantially express genes that are not required, trapping them in states where precursor levels are low and the genes needed to replenish the precursors are outcompeted. Contrary to common modeling assumptions, our findings highlight that cells do not optimize growth under changing environments but rather exhibit hardwired response strategies that may have evolved to promote fitness in their native environment. The constraint and the suboptimality of the cellular response uncovered provide a conceptual framework relevant for many research applications, from the prediction of evolution to the improvement of gene circuits in biotechnology.

          Abstract

          Analyses of how allocation of cellular resources to different genes shapes Escherichia coli's response to changing nutrient conditions show that growth transitions are determined by the competition between genes that are directly required in the encountered conditions and those that are not.

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          Most cited references64

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            HTSeq—a Python framework to work with high-throughput sequencing data

            Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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              The COG database: a tool for genome-scale analysis of protein functions and evolution.

              Rational classification of proteins encoded in sequenced genomes is critical for making the genome sequences maximally useful for functional and evolutionary studies. The database of Clusters of Orthologous Groups of proteins (COGs) is an attempt on a phylogenetic classification of the proteins encoded in 21 complete genomes of bacteria, archaea and eukaryotes (http://www. ncbi.nlm. nih.gov/COG). The COGs were constructed by applying the criterion of consistency of genome-specific best hits to the results of an exhaustive comparison of all protein sequences from these genomes. The database comprises 2091 COGs that include 56-83% of the gene products from each of the complete bacterial and archaeal genomes and approximately 35% of those from the yeast Saccharomyces cerevisiae genome. The COG database is accompanied by the COGNITOR program that is used to fit new proteins into the COGs and can be applied to functional and phylogenetic annotation of newly sequenced genomes.
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                Author and article information

                Contributors
                rbalakrishnan@ucsd.edu
                jbcremer@stanford.edu
                Journal
                Mol Syst Biol
                Mol Syst Biol
                10.1002/(ISSN)1744-4292
                MSB
                msb
                Molecular Systems Biology
                John Wiley and Sons Inc. (Hoboken )
                1744-4292
                20 December 2021
                December 2021
                : 17
                : 12 ( doiID: 10.1002/msb.v17.12 )
                : e10597
                Affiliations
                [ 1 ] Department of Physics University of California at San Diego La Jolla CA USA
                [ 2 ] Department of Biology Stanford University Stanford CA USA
                [ 3 ] Division of Biological Sciences University of California at San Diego La Jolla CA USA
                Author notes
                [*] [* ] Corresponding author. Tel: +1 858 534 5817; E‐mail: rbalakrishnan@ 123456ucsd.edu

                Corresponding author. Tel: +1 650 724 7178; E‐mail: jbcremer@ 123456stanford.edu

                Author information
                https://orcid.org/0000-0002-7547-8565
                https://orcid.org/0000-0003-1837-6842
                https://orcid.org/0000-0003-2328-5152
                Article
                MSB202110597
                10.15252/msb.202110597
                8687047
                34928547
                8cbdec1e-1b27-4a63-ae45-4e8e031cacfb
                © 2021 The Authors Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 29 November 2021
                : 25 July 2021
                : 01 December 2021
                Page count
                Figures: 13, Tables: 0, Pages: 21, Words: 12570
                Funding
                Funded by: NIH
                Award ID: 5R01GM109069
                Categories
                Article
                Articles
                Custom metadata
                2.0
                December 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.7.0 mode:remove_FC converted:20.12.2021

                Quantitative & Systems biology
                cellular response,diauxie,environmental changes,growth optimality,resource allocation,metabolism,microbiology, virology & host pathogen interaction

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