Biology subjects

McGee, R. S.

Publications and source records attributed to McGee, R. S..

3 recordsLinked to original sources

bioRxiv2023

Improving the accuracy of bulk fitness assays by correcting barcode processing biases

Measuring the fitnesses of genetic variants is a fundamental objective in evolutionary biology. A standard approach for measuring microbial fitnesses in bulk involves labeling a library of genetic variants with unique sequence barcodes, competing the labeled strains in batch culture, and using deep sequencing to track changes in the barcode abundances over time. However, idiosyncratic properties of barcodes (e.g., GC content) can induce non-uniform amplification or uneven sequencing coverage that cause some barcodes to be over-or under-represented in samples. This systematic bias can result in erroneous read count trajectories and misestimates of fitness. Here we develop a computational method for inferring the effects of processing bias by leveraging the structure of systematic deviations in the data. We illustrate this approach by applying it to fitness assay data collected for a large library of yeast variants, and show that this method estimates and corrects for bias more accurately than standard proxies, such as GC-based corrections. Our method mitigates bias and improves fitness estimates in high-throughput assays with-out introducing additional complexity to the experimental protocols, with potential value in a range of experimental evolution and mutation screening contexts.

evolutionary biology↗

bioRxiv2022

Evolutionary crowdsourcing: alignment of fitness landscapes allows cross-species adaptation of a horizontally transferred gene

Genes that undergo horizontal gene transfer (HGT) evolve in different genomic backgrounds as they move between hosts, in contrast to genes that evolve under strict vertical inheritance. Despite the ubiquity of HGT in microbial communities, the effects of host-switching on gene evolution have been understudied. Here, we present a novel framework to examine the consequences of host-switching on gene evolution by probing the existence and form of host-dependent mutational effects. We started exploring the effects of HGT on gene evolution by focusing on an antibiotic resistance gene (encoding a beta-lactamase) commonly found on conjugative plasmids in Enterobacteriaceae pathogens. By reconstructing the resistance landscape for a small set of mutationally connected alleles in three enteric species (Escherichia coli, Salmonella enterica, and Klebsiella pneumoniae), we uncovered that the landscape topographies were largely aligned with very low levels of host-dependent mutational effects. By simulating gene evolution with and without HGT using the species-specific empirical landscapes, we found that evolutionary outcomes were similar despite HGT. These findings suggest that the adaptive evolution of a mobile gene in one species can translate to adaptation in another species. In such a case, vehicles of cross-species HGT such as plasmids enable a distributed form of genetic evolution across a bacterial community, where species can crowdsource adaptation from other community members. The role of evolutionary crowdsourcing on the evolution of bacteria merits further investigation.

evolutionary biology↗

bioRxiv2022

The cost of information acquisition by natural selection

Natural selection enriches genotypes that are well-adapted to their environment. Over successive generations, these changes to the frequencies of types accumulate information about the selective conditions. Thus, we can think of selection as an algorithm by which populations acquire information about their environment. Kimura (1961) pointed out that every bit of information that the population gains this way comes with a minimum cost in terms of unrealized fitness (substitution load). Due to the gradual nature of selection and ongoing mismatch of types with the environment, a population that is still gaining information about the environment has lower mean fitness than a counter-factual population that already has this information. This has been an influential insight, but here we find that experimental evolution of Escherichia coli with mutations in a RNA polymerase gene (rpoB) violates Kimuras basic theory. To overcome the restrictive assumptions of Kimuras substitution load and develop a more robust measure for the cost of selection, we turn to ideas from computational learning theory. We reframe the learning problem faced by an evolving population as a population versus environment (PvE) game, which can be applied to settings beyond Kimuras theory - such as stochastic environments, frequency-dependent selection, and arbitrary environmental change. We show that the learning theoretic concept of regret measures relative lineage fitness and rigorously captures the efficiency of selection as a learning process. This lets us establish general bounds on the cost of information acquisition by natural selection. We empirically validate these bounds in our experimental system, showing that computational learning theory can account for the observations that violate Kimuras theory. Finally, we note that natural selection is a highly effective learning process in that selection is an asymptotically optimal algorithm for the problem faced by evolving populations, and no other algorithm can consistently outperform selection in general. Our results highlight the centrality of information to natural selection and the value of computational learning theory as a perspective on evolutionary biology.

evolutionary biology↗