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
-
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.
Reviewing Editor
- Detlef Weigel, Max Planck Institute for Biology Tübingen, Germany
Version history
- Preprint posted: January 13, 2021 (view preprint)
- Received: July 19, 2022
- Accepted: October 7, 2022
- Accepted Manuscript published: October 10, 2022 (version 1)
- Version of Record published: November 9, 2022 (version 2)
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.
Metrics
-
- 1,409
- views
-
- 223
- downloads
-
- 17
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Evolutionary Biology
A comprehensive census of McrBC systems, among the most common forms of prokaryotic Type IV restriction systems, followed by phylogenetic analysis, reveals their enormous abundance in diverse prokaryotes and a plethora of genomic associations. We focus on a previously uncharacterized branch, which we denote coiled-coil nuclease tandems (CoCoNuTs) for their salient features: the presence of extensive coiled-coil structures and tandem nucleases. The CoCoNuTs alone show extraordinary variety, with three distinct types and multiple subtypes. All CoCoNuTs contain domains predicted to interact with translation system components, such as OB-folds resembling the SmpB protein that binds bacterial transfer-messenger RNA (tmRNA), YTH-like domains that might recognize methylated tmRNA, tRNA, or rRNA, and RNA-binding Hsp70 chaperone homologs, along with RNases, such as HEPN domains, all suggesting that the CoCoNuTs target RNA. Many CoCoNuTs might additionally target DNA, via McrC nuclease homologs. Additional restriction systems, such as Type I RM, BREX, and Druantia Type III, are frequently encoded in the same predicted superoperons. In many of these superoperons, CoCoNuTs are likely regulated by cyclic nucleotides, possibly, RNA fragments with cyclic termini, that bind associated CARF (CRISPR-Associated Rossmann Fold) domains. We hypothesize that the CoCoNuTs, together with the ancillary restriction factors, employ an echeloned defense strategy analogous to that of Type III CRISPR-Cas systems, in which an immune response eliminating virus DNA and/or RNA is launched first, but then, if it fails, an abortive infection response leading to PCD/dormancy via host RNA cleavage takes over.
-
- Evolutionary Biology
- Neuroscience
Neuropeptides are ancient signaling molecules in animals but only few peptide receptors are known outside bilaterians. Cnidarians possess a large number of G protein-coupled receptors (GPCRs) – the most common receptors of bilaterian neuropeptides – but most of these remain orphan with no known ligands. We searched for neuropeptides in the sea anemone Nematostella vectensis and created a library of 64 peptides derived from 33 precursors. In a large-scale pharmacological screen with these peptides and 161 N. vectensis GPCRs, we identified 31 receptors specifically activated by 1 to 3 of 14 peptides. Mapping GPCR and neuropeptide expression to single-cell sequencing data revealed how cnidarian tissues are extensively connected by multilayer peptidergic networks. Phylogenetic analysis identified no direct orthology to bilaterian peptidergic systems and supports the independent expansion of neuropeptide signaling in cnidarians from a few ancestral peptide-receptor pairs.