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 ~450,000 distinct lineages in E. coli, which we use to test the effect of subinhibitory concentrations of common antibiotics on the evolutionary dynamics of lowfrequency lineages. We find that populations lose lineage diversity at distinct rates corresponding to their antibiotic regimen. We also determine that some lineages have similar fates across independent experiments. By analyzing 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 both a valuable methodology for studying bacterial evolution as well as insights into evolution under sub-inhibitory antibiotic levels.

Motivation Protein evolution is determined by forces at multiple levels of biological organization. Random mutations have an immediate effect on the biophysical properties, structure and function of proteins. These same mutations also affect the fitness of the organism. However, the evolutionary fate of mutations, whether they succeed to fixation or are purged, also depends on population size and dynamics. There is an emerging interest, both theoretically and experimentally, to integrate these two factors in protein evolution. Although there are several tools available for simulating protein evolution, most of them focus on either the biophysical or the population-level determinants, but not both. Hence, there is a need for a publicly available computational tool to explore both the effects of protein biophysics and population dynamics on protein evolution. Results To address this need, we developed SodaPop, a computational suite to simulate protein evolution in the context of the population dynamics of asexual populations. SodaPop accepts as input several fitness landscapes based on protein biochemistry or other user-defined fitness functions. The user can also provide as input experimental fitness landscapes derived from deep mutational scanning approaches or theoretical landscapes derived from physical force field estimates. Here, we demonstrate the broad utility of SodaPop with different applications describing the interplay of selection for protein properties and population dynamics. SodaPop is designed such that population geneticists can explore the influence of protein biochemistry on patterns of genetic variation, and that biochemists and biophysicists can explore the role of population size and demography on protein evolution. Availability and implementation Source code and binaries are freely available at https://github.com/louisgt/SodaPop under the GNU GPLv3 license. The software is implemented in C++ and supported on Linux, Mac OS/X and Windows. Supplementary information Supplementary data are available at Bioinformatics online.

Evolutionary dynamics in large asexual populations is strongly influenced by multiple 15 competing beneficial lineages, most of which segregate at very low frequencies. However, 16 technical barriers to tracking a large number of these rare lineages have so far prevented a 17 detailed elucidation of evolutionary dynamics in large bacterial populations. Here, we 18 overcome this hurdle by developing a chromosomal barcoding technique that allows 19 simultaneous tracking of ~450,000 distinct lineages in E. coli. We used this technique to 20 gather insights into the evolutionary dynamics of large (>10 7 cells) E. coli populations 21 propagated for ~420 generations in the presence of sub-inhibitory concentrations of 22 common antibiotics. By deep sequencing the barcodes, we reconstructed trajectories of 23 individual lineages at high frequency resolution (< 10 -5 ). Using quantitative tools from 24 ecology, we found that populations lost lineage diversity at distinct rates corresponding to 25 their antibiotic regimen. Additionally, by quantifying the reproducibility of these dynamics 26 across replicate populations, we found that some lineages had similar fates over 27 independent experiments. Combined with an analysis of individual lineage trajectories, 28 these results suggest how standing genetic variation and new mutations may contribute to 29 adaptation to sub-inhibitory antibiotic levels. Altogether, our results demonstrate the power 30 of high-resolution barcoding in studying the dynamics of bacterial evolution. 31 32 33 34 35 36 37Advances in sequencing technologies have generated tremendous breakthroughs in 1 identification of beneficial mutations arising in controlled laboratory evolution 2 experiments, as well as mutations contributing to the emergence of anti-cancer or anti-3 bacterial drug resistance in the clinic 1-4 . Yet, experimental measurements of the dynamics 4 of evolutionary processes remains a major challenge, particularly in large asexual 5 populations, where multiple low-frequency small-effect mutations are known to spread 6 simultaneously 5-8 . A quantitative description of evolutionary dynamics requires the ability 7 to follow numerous individual lineages, most of which occur at extremely low frequency 8 (10 −5 -10 −6 ), and to do so in parallel and over multiple generations. Whole genome 9 sequencing (WGS) techniques, although becoming routine and well-established, fall short 10 of fulfilling this requirement, as they are usually unable to detect mutations at frequencies 11 below ~0.1% 9,10 . Various alternative solutions have been applied over the years to 12 reconstruct population dynamics from trajectories of individual lineages at much higher 13 resolution than accessed by WSG [11][12][13] . A particularly successful method that dramatically 14 increases the frequency resolution of individual lineages is based on uniquely tagging 15 chromosomes of individual cells with a genetic "barcode" that can be easily recovered by 16 deep sequencing 14 . This approach was implemented in S. cere...

The stability and dynamics of ecological communities are dictated by interaction networks typically quantified at the level of species. But how such networks are influenced by intra-species variation (ISV) is poorly understood. Here, we use ~500,000 chromosomal barcodes to track high-resolution intra-species clonal lineages of Escherichia coli invading mice gut with the increasing complexity of gut microbiome: germ-free, antibiotic-perturbed, and innate microbiota. By co-clustering the dynamics of intra-species clonal lineages and those of gut bacteria from 16S rRNA profiling, we show the emergence of complex time-dependent interactions between E. coli clones and resident gut bacteria. With a new approach, dynamic covariance mapping (DCM), we differentiate three phases of invasion in susceptible communities: 1) initial loss of community stability as E. coli enters; 2) recolonization of some gut bacteria; and 3) recovery of stability with E. coli coexisting with resident bacteria in a quasi-steady state. Comparison of the dynamics, stability and fitness from experimental replicates and different cohorts suggest that phase 1 is driven by mutations in E. coli before colonization, while phase 3 is by de novo mutations. Our results highlight the transient nature of interaction networks in microbiomes driven by the persistent coupling of ecological and evolutionary dynamics.

9Motivation: Simulating protein evolution with realistic constraints from population genetics is 10 essential in addressing problems in molecular evolution, from understanding the forces shaping 11 the evolutionary landscape to the clinical challenges of antibiotic resistance, viral evolution and 12 cancer. 13 Results: To address this need, we present SodaPop, a new forward-time simulator of large 14 asexual populations aimed at studying their structure, dynamics and the distribution of fitness 15 effects with flexible assumptions on the fitness landscape. SodaPop integrates biochemical and 16 biophysical properties in a cell-based, object-oriented framework and provides an efficient, 17 open-source toolkit for performing large-scale simulations of protein evolution. 18 Availability and implementation: Source code and binaries are freely available at