TY - JOUR
T1 - Chromosomal barcoding of E. coli populations reveals lineage diversity dynamics at high resolution
AU - Jasinska, Weronika
AU - Manhart, Michael
AU - Lerner, Jesse
AU - Gauthier, Louis
AU - Serohijos, Adrian W.R.
AU - Bershtein, Shimon
N1 - Funding Information:
We thank L. Zhao, J. Rojas Echenique, S. Levy and A. Pascual Garcia for advice on analysing the data, and D. Tawfik and A. Aharoni for comments and help with preparation of the manuscript. This work was supported by an F32 fellowship from the US National Institutes of Health (GM116217) and an Ambizione grant from the Swiss National Science Foundation (PZ00P3_180147) to M.M.; a grant from the Canadian Natural Sciences and Engineering Research Council (NSERC RN000524) to A.W.R.S.; and a personal Israel Science Foundation grant 1630/15 to S.B. We also acknowledge support from the FAS Division of Science, Research Computing Group at Harvard University for the computations performed on the Odyssey cluster.
Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - Evolutionary dynamics in large asexual populations is strongly influenced by multiple competing beneficial lineages, most of which segregate at very low frequencies. However, technical barriers to tracking a large number of these rare lineages in bacterial populations have so far prevented a detailed elucidation of evolutionary dynamics. Here, we overcome this hurdle by developing a chromosomal-barcoding technique that allows simultaneous tracking of approximately 450,000 distinct lineages in Escherichia coli, which we use to test the effect of sub-inhibitory concentrations of common antibiotics on the evolutionary dynamics of low-frequency lineages. We find that populations lose lineage diversity at distinct rates that correspond to their antibiotic regimen. We also determine that some lineages have similar fates across independent experiments. By analysing the trajectory dynamics, we attribute the reproducible fates of these lineages to the presence of pre-existing beneficial mutations, and we demonstrate how the relative contribution of pre-existing and de novo mutations varies across drug regimens. Finally, we reproduce the observed lineage dynamics by simulations. Altogether, our results provide a valuable methodology for studying bacterial evolution as well as insights into evolution under sub-inhibitory antibiotic levels.
AB - Evolutionary dynamics in large asexual populations is strongly influenced by multiple competing beneficial lineages, most of which segregate at very low frequencies. However, technical barriers to tracking a large number of these rare lineages in bacterial populations have so far prevented a detailed elucidation of evolutionary dynamics. Here, we overcome this hurdle by developing a chromosomal-barcoding technique that allows simultaneous tracking of approximately 450,000 distinct lineages in Escherichia coli, which we use to test the effect of sub-inhibitory concentrations of common antibiotics on the evolutionary dynamics of low-frequency lineages. We find that populations lose lineage diversity at distinct rates that correspond to their antibiotic regimen. We also determine that some lineages have similar fates across independent experiments. By analysing the trajectory dynamics, we attribute the reproducible fates of these lineages to the presence of pre-existing beneficial mutations, and we demonstrate how the relative contribution of pre-existing and de novo mutations varies across drug regimens. Finally, we reproduce the observed lineage dynamics by simulations. Altogether, our results provide a valuable methodology for studying bacterial evolution as well as insights into evolution under sub-inhibitory antibiotic levels.
UR - http://www.scopus.com/inward/record.url?scp=85079821539&partnerID=8YFLogxK
U2 - 10.1038/s41559-020-1103-z
DO - 10.1038/s41559-020-1103-z
M3 - Article
C2 - 32094541
AN - SCOPUS:85079821539
SN - 2397-334X
VL - 4
SP - 437
EP - 452
JO - Nature Ecology and Evolution
JF - Nature Ecology and Evolution
IS - 3
ER -