Laboratory Evolution, Transcriptomics, and Modeling Reveal Mechanisms of Paraquat Tolerance

Rychel, Kevin; Tan, Jiaying; Patel, Arjun; Lamoureux, Cameron; Hefner, Ying; Szubin, Richard; Johnsen, Josefin; Mohamed, Elsayed Tharwat Tolba; Phaneuf, Patrick V.; Anand, Amitesh; Olson, Connor A.; Park, Joon Ho; Sastry, Anand V.; Yang, Laurence T.; Feist, Adam M.; Palsson, Bernhard

doi:10.2139/ssrn.4382766

Cited by 2 publications

(10 citation statements)

References 78 publications

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“…Thus, the evolved cells downregulate general stress responses (RpoS) to improve growth, but upregulate specific responses to heat (RpoH). This represents an effective strategy for stress tolerization ALE; indeed, it mirrors the response of oxidative stress evolved strains, which maintain activity of the specific oxidative stress response, SoxS, while also downregulating RpoS (14). A major fraction (18%) of the variance in the transcriptome of the thermotolerant strains is explained by motility iModulons, which respond to two transcription factors, FlhDC and FliA.…”

Section: Stress Sigma Factors Shift Transcriptomic Allocation From Ge...mentioning

confidence: 96%

“…To explore the systems-level changes to energy metabolism that arise from these genomic and transcriptomic changes, we measured the glucose uptake rate and biomass yield of the wild type and evolved strains at three temperatures and plotted them on a rate-yield plane (Figure 5D). Regions of the rate-yield plane are associated with distinct states of energy metabolism called aero-types, as has been characterized in prior studies (13,14,60). Samples with high biomass yields are in the highest aero-type, corresponding to efficient aerobic growth.…”

Section: Redox Metabolism Shifts Toward Less Aerobic Metabolismmentioning

confidence: 97%

“…Prior iModulon studies have explored a "fear/greed tradeoff", where typical strains exhibit a negative correlation between the RpoS and Translation iModulon activities. Faster growing cells activate the Translation and downregulate the RpoS iModulons (6,11,14,15,28,29). As the prior generation of 42°C evolved strains mutated to tolerate high temperatures, they experienced less stress and therefore downregulated RpoS (22) (Figure 3A, "42c Other").…”

Section: Stress Sigma Factors Shift Transcriptomic Allocation From Ge...mentioning

confidence: 99%

“…The mutation appears to have mostly deactivated RpoS, allowing sigma factors that do not suppress growth to outcompete it and providing a growth rate benefit during ALE. Deactivating RpoS is generally a good strategy for ALE (6,11,14,(28)(29)(30)(31), but the temperature adapted strains take this to an extreme via this mutation.…”

Section: Stress Sigma Factors Shift Transcriptomic Allocation From Ge...mentioning

confidence: 99%

“…iModulon structures have been established for several organisms and are available to browse, search, or download from iModulonDB.org (9). iModulons have proven useful for analyzing non-hypermutator ALE strains in several cases (10)(11)(12)(13)(14)(15), making them a promising option for characterizing hypermutator strains. We recently compiled over 1,000 E. coli K-12 RNA sequencing (RNAseq) expression profiles into a dataset called PRECISE-1K and characterized its iModulons (8), which are available on iModulonDB.org.…”

Section: Introductionmentioning

confidence: 99%

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Laboratory evolution reveals transcriptional mechanisms underlying thermal adaptation ofEscherichia coli

Rychel,

Chen,

Catoiu

et al. 2024

Preprint

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Adaptive laboratory evolution (ALE) is able to generate microbial strains which exhibit extreme phenotypes, revealing fundamental biological adaptation mechanisms. Here, we use ALE to evolveEscherichia colistrains that grow at temperatures as high as 45.3°C, a temperature lethal to wild type cells. The strains adopted a hypermutator phenotype and employed multiple systems-level adaptations that made global analysis of the DNA mutations difficult. Given the challenge at the genomic level, we were motivated to uncover high temperature tolerance adaptation mechanisms at the transcriptomic level. We employed independently modulated gene set (iModulon) analysis to reveal five transcriptional mechanisms underlying growth at high temperatures. These mechanisms were connected to acquired mutations, changes in transcriptome composition, sensory inputs, phenotypes, and protein structures. They are: (i) downregulation of general stress responses while upregulating the specific heat stress responses; (ii) upregulation of flagellar basal bodies without upregulating motility, and upregulation fimbriae; (iii) shift toward anaerobic metabolism, (iv) shift in regulation of iron uptake away from siderophore production, and (v) upregulation ofyjfIJKL, a novel heat tolerance operon which we characterized using AlphaFold. iModulons associated with these five mechanisms explain nearly half of all variance in the gene expression in the adapted strains. These thermotolerance strategies reveal that optimal coordination of known stress responses and metabolism can be achieved with a small number of regulatory mutations, and may suggest a new role for large protein export systems. ALE with transcriptomic characterization is a productive approach for elucidating and interpreting adaptation to otherwise lethal stresses.

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Section: Stress Sigma Factors Shift Transcriptomic Allocation From Ge...mentioning

confidence: 96%

Section: Redox Metabolism Shifts Toward Less Aerobic Metabolismmentioning

confidence: 97%

Section: Stress Sigma Factors Shift Transcriptomic Allocation From Ge...mentioning

confidence: 99%

Section: Stress Sigma Factors Shift Transcriptomic Allocation From Ge...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Laboratory evolution reveals transcriptional mechanisms underlying thermal adaptation ofEscherichia coli

Rychel,

Chen,

Catoiu

et al. 2024

Preprint

Self Cite

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Trade-off Between Resistance and Persistence in High Cell DensityEscherichia ColiCultures

Beulig,

Bafna-Rührer,

Jensen

et al. 2024

Preprint

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Microbes experience high cell density in many environments that come with diverse resource limitations and stresses. However, high density physiology remains poorly understood. We utilized well-controlled culturing systems to grow wild-type and metabolically engineered Escherichia coli strains into high cell densities (50-80 g Cdry cell weight L-1) and explore the associated transcriptional dynamics. Knowledge-enriched machine-learning-based analytics reveal distinct stress-related gene expression patterns that are consistent with a fundamental trade-off between resistance and persistence. We suggest that this trade-off explains observed growth arrests in high-density cultures and that it results from the disruption of cellular homeostasis, due to reallocation of limited cellular resources from resistance functions towards maintenance requirements of engineered production pathways. This study deepens our understanding of high-density physiology and demonstrates its importance to fundamental biomanufacturing challenges.

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Laboratory Evolution, Transcriptomics, and Modeling Reveal Mechanisms of Paraquat Tolerance

Cited by 2 publications

References 78 publications

Laboratory evolution reveals transcriptional mechanisms underlying thermal adaptation ofEscherichia coli

Laboratory evolution reveals transcriptional mechanisms underlying thermal adaptation ofEscherichia coli

Trade-off Between Resistance and Persistence in High Cell DensityEscherichia ColiCultures

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