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Cited by 9Gene Regulatory Network AnalysisBacterial Genetics and BiotechnologyRNA and protein synthesis mechanisms

Cross-evaluation of E. coli’s operon structures via a whole-cell model suggests alternative cellular benefits for low- versus high-expressing operons

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Sun, Gwanggyu, DeFelice, Mialy M., Gillies, Taryn E., Ahn-Horst, Travis A., Andrews, Cecelia J., Krummenacker, Markus, Karp, Peter D., Morrison, Jerry H. +1 more·2024·Cell Systems
DOI 10.1016/j.cels.2024.02.002PMC PMC10957310
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Abstract

Many bacteria use operons to coregulate genes, but it remains unclear how operons benefit bacteria. We integrated E. coli's 788 polycistronic operons and 1,231 transcription units into an existing whole-cell model and found inconsistencies between the proposed operon structures and the RNA-seq read counts that the model was parameterized from. We resolved these inconsistencies through iterative, model-guided corrections to both datasets, including the correction of RNA-seq counts of short genes that were misreported as zero by existing alignment algorithms. The resulting model suggested two main modes by which operons benefit bacteria. For 86% of low-expression operons, adding operons increased the co-expression probabilities of their constituent proteins, whereas for 92% of high-expression operons, adding operons resulted in more stable expression ratios between the proteins. These simulations underscored the need for further experimental work on how operons reduce noise and synchronize both the expression timing and the quantity of constituent genes. A record of this paper's transparent peer review process is included in the supplemental information.

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  • Well-stirred simulations can take as little as a few hours per cell cycle and can simulate multiple cells in parallel.

    “Well-stirred simulations can take as little as a few hours per cell cycle and can simulate multiple cells in parallel.1”

    From Bringing the genetically minimal cell to life on a computer in 4D (2026) · cites this paper as [12]

    The “Whole-cell simulation descriptions” passage (page unavailable) reports simulations of multiple cells initialized with different random seeds, supporting the multiple-cell aspect. It does not say they ran in parallel or give a computational runtime of a few hours per cell cycle. The 180-minute limit in “Comparison of doubling times” is a simulated-time limit, not a runtime measurement; a search for “well-stirred” also found no matching description. (LLM, conf=0.83)

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References 77 · Cited by 9

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  1. [1]1. Jacob, F. ∙ Monod, J. Genetic regulatory mechanisms in the synthesis of proteins J. Mol. Biol. 1961; 3:318-356 Crossref Scopus (3157) PubMed Google Scholar
  2. [2]2. Lawrence, J.G. ∙ Roth, J.R. Selfish Operons: Horizontal Transfer May Drive the Evolution of Gene Clusters Genetics. 1996; 143:1843-1860 Crossref PubMed Google Scholar
  3. [3]3. Lawrence, J.G. GENE ORGANIZATION: Selection, Selfishness, and Serendipity Annu. Rev. Microbiol. 2003; 57:419-440 Crossref Scopus (67) PubMed Google Scholar
  4. [4]4. Price, M.N. ∙ Huang, K.H. ∙ Arkin, A.P. ... Operon formation is driven by co-regulation and not by horizontal gene transfer Genome Res. 2005; 15:809-819 Crossref Scopus (121) PubMed Google Scholar
  5. [5]5. Shieh, Y.-W. ∙ Minguez, P. ∙ Bork, P. ... Operon structure and cotranslational subunit association direct protein assembly in bacteria Science. 2015; 350:678-680 Crossref Scopus (127) PubMed Google Scholar
  6. [6]6. Wells, J.N. ∙ Bergendahl, L.T. ∙ Marsh, J.A. Operon Gene Order Is Optimized for Ordered Protein Complex Assembly Cell Rep. 2016; 14:679-685 Full Text Full Text (PDF) Scopus (71) PubMed Google Scholar
  7. [7]7. Swain, P.S. Efficient Attenuation of Stochasticity in Gene Expression Through Post-transcriptional Control J. Mol. Biol. 2004; 344:965-976 Crossref Scopus (172) PubMed Google Scholar
  8. [8]8. Cerretti, D.P. ∙ Dean, D. ∙ Davis, G.R. ... The spc rtbosomal protein operon of Eschenchia coli: sequence and cotranscriptlon of the rlbosomal protein genes and a protein export gene Nucleic Acids Res. 1983; 11:2599-2616 Crossref Scopus (187) PubMed Google Scholar
  9. [9]9. Cho, B.-K. ∙ Zengler, K. ∙ Qiu, Y. ... The transcription unit architecture of the Escherichia coli genome Nat. Biotechnol. 2009; 27:1043-1049 Crossref Scopus (209) PubMed Google Scholar
  10. [11]11. Mao, X. ∙ Ma, Q. ∙ Liu, B. ... Revisiting operons: an analysis of the landscape of transcriptional units in E. coli BMC Bioinformatics. 2015; 16, 356 Crossref Scopus (20) PubMed Google Scholar
  11. [13]13. Crick, F.H.C. Project K: "The Complete Solution of E. Coli" Perspect. Biol. Med. 1973; 17:67-70 Crossref Google Scholar
  12. [16]16. Sanghvi, J.C. ∙ Regot, S. ∙ Carrasco, S. ... Accelerated discovery via a whole-cell model Nat. Methods. 2013; 10:1192-1195 Crossref Scopus (53) PubMed Google Scholar
  13. [17]17. Ahn-Horst, T.A. ∙ Mille, L.S. ∙ Sun, G. ... An expanded whole-cell model of E. coli links cellular physiology with mechanisms of growth rate control npj Syst. Biol. Appl. 2022; 8, 30 Crossref Scopus (13) PubMed Google Scholar
  14. [18]18. Iber, D. A quantitative study of the benefits of co-regulation using the spoIIA operon as an example Mol. Syst. Biol. 2006; 2:43 Crossref Scopus (16) PubMed Google Scholar
  15. [20]20. Ray, J.C.J. ∙ Igoshin, O.A. Interplay of Gene Expression Noise and Ultrasensitive Dynamics Affects Bacterial Operon Organization PLoS Comp. Biol. 2012; 8, e1002672 Crossref Scopus (17) Google Scholar
  16. [21]21. Nagalakshmi, U. ∙ Wang, Z. ∙ Waern, K. ... The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing Science. 2008; 320:1344-1349 Crossref Scopus (1901) PubMed Google Scholar
  17. [22]22. Taggart, J.C. ∙ Lalanne, J.-B. ∙ Li, G.-W. Quantitative Control for Stoichiometric Protein Synthesis Annu. Rev. Microbiol. 2021; 75:243-267 Crossref Scopus (10) PubMed Google Scholar
  18. [23]23. Bernstein, J.A. ∙ Khodursky, A.B. ∙ Lin, P.H. ... Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays Proc. Natl. Acad. Sci. USA. 2002; 99:9697-9702 Crossref Scopus (629) PubMed Google Scholar
  19. [24]24. Grünberger, F. ∙ Knüppel, R. ∙ Jüttner, M. ... Exploring prokaryotic transcription, operon structures, rRNA maturation and modifications using Nanopore-based native RNA sequencing Preprint at bioRxiv. 2020; Crossref Scopus (0) Google Scholar
  20. [25]25. Stephenson, W. ∙ Razaghi, R. ∙ Busan, S. ... Direct detection of RNA modifications and structure using single-molecule nanopore sequencing Cell Genomics. 2022; 2, 100097 Full Text Full Text (PDF) Scopus (43) PubMed Google Scholar
  21. [27]27. Keseler, I.M. ∙ Gama-Castro, S. ∙ Mackie, A. ... The EcoCyc Database in 2021 Front. Microbiol. 2021; 12, 711077 Crossref Scopus (97) Google Scholar
  22. [28]28. Chen, H. ∙ Shiroguchi, K. ∙ Ge, H. ... Genome-wide study of mRNA degradation and transcript elongation in Escherichia coli Mol. Syst. Biol. 2015; 11, 781 Google Scholar
  23. [30]30. Bremer, H. ∙ Dennis, P.P. Modulation of chemical composition and other parameters of the cell by growth rate. Escherichia coli and Salmonella Cell. Mol. Biol. 1996; 2:1553-1569 Google Scholar
  24. [31]31. Cecchini, G. ∙ Schröder, I. ∙ Gunsalus, R.P. ... Succinate dehydrogenase and fumarate reductase from Escherichia coli Biochim. Biophys. Acta-Bioenerg. 2002; 1553:140-157 Crossref Scopus (213) PubMed Google Scholar
  25. [32]32. Lalanne, J.-B. ∙ Taggart, J.C. ∙ Guo, M.S. ... Evolutionary Convergence of Pathway-Specific Enzyme Expression Stoichiometry Cell. 2018; 173:749-761.e738 Full Text Full Text (PDF) Scopus (104) PubMed Google Scholar
  26. [33]33. Dar, D. ∙ Sorek, R. Extensive reshaping of bacterial operons by programmed mRNA decay PLoS Genet. 2018; 14, e1007354 Crossref Scopus (46) PubMed Google Scholar
  27. [35]35. Post, L.E. ∙ Strycharz, G.D. ∙ Nomura, M. ... Nucleotide sequence of the ribosomal protein gene cluster adjacent to the gene for RNA polymerase subunit beta in Escherichia coli Proc. Natl. Acad. Sci. USA. 1979; 76:1697-1701 Crossref Scopus (265) PubMed Google Scholar
  28. [36]36. Hui, I. ∙ Maltman, K. ∙ Little, R. ... Insertions of transposon Tn5 into ribosomal protein PNA polymerase operons J. Bacteriol. 1982; 152:1022-1032 Crossref PubMed Google Scholar
  29. [37]37. Ralling, G. ∙ Linn, T. Relative activities of the transcriptional regulatory sites in the rplKAJLrpoBC gene cluster of Escherichia coli J. Bacteriol. 1984; 158:279-285 Crossref PubMed Google Scholar
  30. [38]38. Steward, K.L. ∙ Linn, T. In vivo analysis of overlapping transcription units in the rplKAJLrpoBC ribosomal protein—RNA polymerase gene cluster of Escherichia coli J. Mol. Biol. 1991; 218:23-31 Crossref Scopus (13) PubMed Google Scholar
  31. [39]39. Barry, G. ∙ Squires, C.L. ∙ Squires, C. Control features within the rplJL-rpoBC transcription unit of Escherichia coli Proc. Natl. Acad. Sci. USA. 1979; 76:4922-4926 Crossref Scopus (65) PubMed Google Scholar
  32. [40]40. Li, B. ∙ Dewey, C.N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome BMC Bioinformatics. 2011; 12, 323 Crossref Scopus (13397) Google Scholar
  33. [41]41. Sarantopoulou, D. ∙ Brooks, T.G. ∙ Nayak, S. ... Comparative evaluation of full-length isoform quantification from RNA-Seq BMC Bioinformatics. 2021; 22:266 Crossref Scopus (12) PubMed Google Scholar
  34. [43]43. Patro, R. ∙ Duggal, G. ∙ Love, M.I. ... Salmon provides fast and bias-aware quantification of transcript expression Nat. Methods. 2017; 14:417-419 Crossref Scopus (5267) PubMed Google Scholar
  35. [44]44. Grauel, A. ∙ Kägi, J. ∙ Rasmussen, T. ... Structure of Escherichia coli cytochrome bd-II type oxidase with bound aurachin D Nat. Commun. 2021; 12, 6498 Crossref Scopus (22) PubMed Google Scholar
  36. [45]45. Yanofsky, C. Attenuation in the control of expression of bacterial operons Nature. 1981; 289:751-758 Crossref Scopus (481) PubMed Google Scholar
  37. [46]46. Turnbough, C.L. Regulation of Bacterial Gene Expression by Transcription Attenuation Microbiol. Mol. Biol. Rev. 2019; 83, e00019 Crossref Scopus (40) PubMed Google Scholar
  38. [47]47. Karp, P.D. ∙ Paley, S. ∙ Krieger, C.J. ... An Evidence Ontology for Use in Pathway/Genome Databases Pacific Symposium on Biocomputing. 2004; 190-201 PubMed Google Scholar
  39. [48]48. Romero, P.R. ∙ Karp, P.D. Using functional and organizational information to improve genome-wide computational prediction of transcription units on pathway-genome databases Bioinformatics. 2004; 20:709-717 Crossref Scopus (66) PubMed Google Scholar
  40. [49]49. Westover, B.P. ∙ Buhler, J.D. ∙ Sonnenburg, J.L. ... Operon prediction without a training set Bioinformatics. 2005; 21:880-888 Crossref Scopus (75) PubMed Google Scholar
  41. [50]50. Pearce, S.R. ∙ Mimmack, M.L. ∙ Gallagher, M.P. ... Membrane topology of the integral membrane components, OppB and OppC, of the oligopeptide permease of Salmonella typhimurium Mol. Microbiol. 1992; 6:47-57 Crossref Scopus (55) PubMed Google Scholar
  42. [51]51. Hussain, H. ∙ Grove, J. ∙ Griffiths, L. ... A seven-gene operon essential for formate-dependent nitrite reduction to ammonia by enteric bacteria Mol. Microbiol. 1994; 12:153-163 Crossref Scopus (150) PubMed Google Scholar
  43. [52]52. Swain, P.S. ∙ Elowitz, M.B. ∙ Siggia, E.D. Intrinsic and extrinsic contributions to stochasticity in gene expression Proc. Natl. Acad. Sci. USA. 2002; 99:12795-12800 Crossref Scopus (1268) PubMed Google Scholar
  44. [53]53. Leimkühler, S. The biosynthesis of the molybdenum cofactors in Escherichia coli Environ. Microbiol. 2020; 22:2007-2026 Crossref Scopus (27) PubMed Google Scholar
  45. [54]54. Li, G.-W. ∙ Burkhardt, D. ∙ Gross, C. ... Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources Cell. 2014; 157:624-635 Full Text Full Text (PDF) Scopus (896) PubMed Google Scholar
  46. [55]55. Goldberg, A.L. Protein degradation and protection against misfolded or damaged proteins Nature. 2003; 426:895-899 Crossref Scopus (1737) PubMed Google Scholar
  47. [56]56. Papp, B. ∙ Pál, C. ∙ Hurst, L.D. Dosage sensitivity and the evolution of gene families in yeast Nature. 2003; 424:194-197 Crossref Scopus (660) PubMed Google Scholar
  48. [57]57. Oromendia, A.B. ∙ Dodgson, S.E. ∙ Amon, A. Aneuploidy causes proteotoxic stress in yeast Genes Dev. 2012; 26:2696-2708 Crossref Scopus (216) PubMed Google Scholar
  49. [59]59. Dandekar, T. ∙ Snel, B. ∙ Huynen, M. ... Conservation of gene order: a fingerprint of proteins that physically interact Trends Biochem. Sci. 1998; 23:324-328 Full Text Full Text (PDF) Scopus (840) PubMed Google Scholar
  50. [61]61. Garcia-Bernardo, J. ∙ Dunlop, M.J. Tunable Stochastic Pulsing in the Escherichia coli Multiple Antibiotic Resistance Network from Interlinked Positive and Negative Feedback Loops PLoS Comp. Biol. 2013; 9, e1003229 Crossref Scopus (31) PubMed Google Scholar
  51. [62]62. Grimbergen, A.J. ∙ Siebring, J. ∙ Solopova, A. ... Microbial bet-hedging: the power of being different Curr. Opin. Microbiol. 2015; 25:67-72 Crossref Scopus (97) PubMed Google Scholar
  52. [63]63. Skalnik, C.J. ∙ Cheah, S.Y. ∙ Yang, M.Y. ... Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses PLOS Comp. Biol. 2023; 19, e1011232 Crossref Scopus (3) PubMed Google Scholar
  53. [64]64. Hardo, G. ∙ Bakshi, S. Challenges of analysing stochastic gene expression in bacteria using single-cell time-lapse experiments Essays Biochem. 2021; 65:67-79 Crossref Scopus (10) PubMed Google Scholar
  54. [65]65. Blattner, iii, F.R. ∙ Plunkett, G. ∙ Bloch, C.A. ... The Complete Genome Sequence of Escherichia coli K-12 Science. 1997; 277:1453-1462 Crossref Scopus (6199) PubMed Google Scholar
  55. [66]66. Hayashi, K. ∙ Morooka, N. ∙ Yamamoto, Y. ... Highly accurate genome sequences of Escherichia coli K-12 strains MG1655 and W3110 Mol. Syst. Biol. 2006; 2, 2006.0007 Crossref Scopus (382) PubMed Google Scholar
  56. [67]67. Kiss, A. ∙ Sain, B. ∙ Venetianer, P. The number of rRNA genes in Escherichia coli FEBS Lett. 1977; 79:77-79 Crossref Scopus (99) PubMed Google Scholar
  57. [68]68. Anders, S. ∙ Reyes, A. ∙ Huber, W. Detecting differential usage of exons from RNA-seq data Genome Res. 2012; 22:2008-2017 Crossref Scopus (1018) PubMed Google Scholar
  58. [69]69. Trapnell, C. ∙ Williams, B.A. ∙ Pertea, G. ... Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation Nat. Biotechnol. 2010; 28:511-515 Crossref Scopus (11657) PubMed Google Scholar
  59. [70]70. Burkhardt, D.H. ∙ Rouskin, S. ∙ Zhang, Y. ... Operon mRNAs are organized into ORF-centric structures that predict translation efficiency eLife. 2017; 6, e22037 Crossref Scopus (102) PubMed Google Scholar
  60. [71]71. McCormick, D.M. ∙ Lalanne, J.-B. ∙ Lan, T.C.T. ... Differential translation of mRNA isoforms transcribed with distinct sigma factors RNA. 2021; 27:791-804 Crossref Scopus (3) PubMed Google Scholar
  61. [72]72. Schümperli, D. ∙ McKenney, K. ∙ Sobieski, D.A. ... Translational coupling at an intercistronic boundary of the Escherichia coli galactose operon Cell. 1982; 30:865-871

Authors
Sun, G., DeFelice, M. M., Gillies, T. E., Ahn-Horst, T. A., Andrews, C. J., Krummenacker, M., Karp, P. D., Morrison, J. H., & Covert, M. W.
Affiliations
  • Stanford University (US)
  • SRI International (US)
Corresponding author
Covert, Markus W.
ORCID
Venue
Cell Systems
Publisher
Elsevier BV
Published
2024-03
Volume
15
Issue
3
Pages
227-245.e7
ISSN
2405-4712 (ISSN-L)
DOI
10.1016/j.cels.2024.02.002
PMC
PMC10957310
OpenAlex
W4392203312
Cited by
9
References
77
Citations by year
2026: 4 · 2025: 5
FWCI
1.16
Type
journal article
Language
en
Open access
Licence
Locations
  • Cell Systems (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
Funders
  • Kwanjeong Educational Foundation
  • The Paul G Allen Frontiers Group
  • Alfred P Sloan Foundation
  • National Institutes of Health
  • National Institute of General Medical Sciences (GM137528, R01GM140008)
  • National Library of Medicine (R01LM013229)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Keywords
operon, Escherichia coli, whole-cell model, gene co-expression, transcriptional regulation, RNA-seq read counts, stochastic gene expression, polycistronic operon
MeSH terms
Bacteria, Escherichia coli, Operon

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