Bacteriophage lambda overcomes a perturbation in its host‐viral genetic network through mutualism and evolution of life history traits

Gupta, Animesh, University of California, San Diego

Soto, Anechelle N., University of California, San Diego

Medina, Sarah J., University of California, San Diego

Petrie, Katherine L., University of California, San Diego

Meyer, Justin R., University of California, San Diego, https://orcid.org/0000-0001-5566-8452

anigupta12@gmail.com, jrmeyer@ucsd.edu

Published Jan 08, 2020 on Dryad. https://doi.org/10.6075/J05T3HVT

Cite this dataset

Gupta, Animesh et al. (2020). Bacteriophage lambda overcomes a perturbation in its host‐viral genetic network through mutualism and evolution of life history traits [Dataset]. Dryad. https://doi.org/10.6075/J05T3HVT

Abstract

An important driver of viral evolution is natural selection to optimize the use of their hosts’ genetic network. To learn how viruses respond to this pressure, we disrupted the genetic network of Escherichia coli to inhibit replication of its virus, bacteriophage lambda, and then observed how λ evolved to compensate. We deleted E. coli's dnaJ gene, which lambda uses to initiate DNA replication. Lambda partially restored its ability to reproduce with just two adaptive mutations associated with genes J and S. The location of the mutations was unexpected because they were not in genes that directly interact with DnaJ, rather they affected seemingly unrelated life history traits. A nonsynonymous J mutation increased λ’s adsorption rate and an S regulatory mutation delayed lysis timing. λ also recovered some of its reproductive potential through intracellular mutualism. This study offers two important lessons: first, viruses can rapidly adapt to disruptive changes in their host's genetic network. Second, organisms can employ mechanisms thought to operate at the population scale, such as evolution of life history traits and social interactions, in order to overcome hurdles at the molecular level. As life science research progresses and new fields become increasingly specialized, these results remind us of the importance of multiscale and interdisciplinary approaches to understanding adaptation.

Usage notes

1. ‘Figure data_main text_DRYAD.xlsx’ contains raw data for all the figures in the main text of the paper. Different sheets in the excel file correspond to different figure numbers in the paper. Likewise, ‘Figure data_supp info_DRYAD.xlsx’ contains data for figures in Supplementary Information.

2. ‘final_cooperationModel.m’ is the MATLAB script file which was used to generate density dependent dynamics in Figure 4c.

3. The paired-end raw sequencing data for whole genome sequencing of DNAJ1, DNAJ2 and 6 WT clones (listed in Supplementary Table 2) are in the compressed file ‘Raw whole genome sequencing data for mutualism paper.zip’.