The landscape of transcriptional and 1translational changes over 22 years of bacterial adaptation
Abstract
Organisms can adapt to an environment by taking multiple mutational paths. This redundancy at the genetic level, where many mutations have similar phenotypic and fitness effects, can make untangling the molecular mechanisms of complex adaptations difficult. Here we use the E. coli long-term evolution experiment (LTEE) as a model to address this challenge. To understand how different genomic changes could lead to parallel fitness gains, we characterize the landscape of transcriptional and translational changes across 12 replicate populations evolving in parallel for 50,000 generations. By quantifying absolute changes in mRNA abundances, we show that not only do all evolved lines have more mRNAs but that this increase in mRNA abundance scales with cell size. We also find that despite few shared mutations at the genetic level, clones from replicate populations in the LTEE are remarkably similar in their gene expression patterns at both the transcriptional and translational levels. Furthermore, we show that the majority of the expression changes are due to changes at the transcriptional level with very few translational changes. Finally, we show how mutations in transcriptional regulators lead to consistent and parallel changes in the expression levels of downstream genes. These results deepen our understanding of the molecular mechanisms underlying complex adaptations and provide insights into the repeatability of evolution.
Data availability
Sequencing data have been deposited in GEO under accession code GSE164308.All data generated or analyzed during this study are included in the manuscript and supporting file; Source Data files have been provided for all figures.Code for all data processing and subsequent analysis can be found in a series of R markdown documents uploaded to GitHub https://github.com/shahlab/LTEE_gene_expression_2
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Landscape of transcriptional and translational changes over 22 years of bacterial adaptationNCBI Gene Expression Omnibus, GSE164308.
Article and author information
Author details
Funding
National Institute of General Medical Sciences (ESI-MIRA R35 GM124976)
- Premal Shah
National Science Foundation (DBI 1936046)
- Premal Shah
Rutgers, The State University of New Jersey (Start-up funds)
- Srujana Samhita Yadavalli
- Premal Shah
National Institutes of Health (IRACDA NJ/NY for Science Partnerships in Research and Education Postdoctoral program NIH PAR-19-366)
- Alexander L Cope
National Institute of Diabetes and Digestive and Kidney Diseases (Subcontract from R01 DK056645)
- Premal Shah
National Institute of Diabetes and Digestive and Kidney Diseases (Subcontract from R01 DK109714)
- Premal Shah
National Institute of Diabetes and Digestive and Kidney Diseases (Subcontract from R01 DK124369)
- Premal Shah
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Favate et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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