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Lawrence, K. N.

Publications and source records attributed to Lawrence, K. N..

2 recordsLinked to original sources

Contingencies in biofilm adaptation of Mycobacterium tuberculosis

Biofilms are structured microbial communities that offer protection from a range of environmental stressors. Studies of experimental evolution within biofilms have yielded important insights into mechanisms of biofilm formation, as well as fundamental principles governing bacterial adaptation within these structured communities. Building on research using tractable species, our lab created a model for studying biofilm adaptation in Mycobacterium tuberculosis (M. tb), a fastidious, slow growing and lethal pathogen of humans. Here, we evolved eighteen M. tb populations arising from six parental genetic backgrounds under biofilm selection to investigate diversity in mechanisms of M. tb biofilm adaptation. We found that fine-scale differences among strains at the initiation of the experiment influenced the degree of replicability in their evolution. Adaptive paths were highly parallel for some strains, whereas others evolved distinct mutations across iterations of the experiment. Our data suggest that differences in replicability arise from mutational biases and variable fitness impacts of mutations across genetic backgrounds. Comparison of our results with genomic data from M. tb populations within hosts with tuberculosis (TB) revealed that several mutations associated with biofilm selection are also among the most common to emerge during natural infection. These biofilm-associated variants are not maintained in natural M. tb populations, suggesting that biofilm selection in our model mimics selection pressures that are transiently encountered by M. tb during specific phases of infection. Overall, these results support development of biofilm directed therapies for TB and demonstrate the importance of subtle genetic variation in shaping M. tb responses to changing selection pressures. SignificanceMycobacterium tuberculosis (M. tb), the causative agent of tuberculosis (TB), a difficult-to-treat, persistent infection with high mortality. One cause of this persistence is the ability of M. tb to form biofilms, aggregated structures capable of resisting antibiotics and host defenses. Here, we used an evolutionary model to investigate mechanisms of M. tb biofilm formation. We found adaptation to biofilm growth to be affected by mutational biases and interactions among mutations. Our simple in vitro model appears to mimic aspects of natural infection, as identical mutations emerge in the laboratory under biofilm selection and within hosts with TB. These results expand our knowledge of M. tb biofilm development and inform our understanding of how this bacterium responds to novel selection pressures.

evolutionary biology↗

The Precision and Power of Population Branch Statistics in Identifying the Genomic Signatures of Local Adaptation

Population branch statistics, which estimate the branch lengths of focal populations with respect to two outgroups, have been used as an alternative to FST-based genome-wide scans for identifying loci associated with local selective sweeps. In addition to the original population branch statistic (PBS), there are subsequently proposed branch rescalings: normalized population branch statistic (PBSn1), which adjusts focal branch length with respect to outgroup branch lengths at the same locus, and population branch excess (PBE), which also incorporates median branch lengths at other loci. PBSn1 and PBE have been proposed to be less sensitive to allele frequency divergence generated by background selection or geographically ubiquitous positive selection rather than local selective sweeps. However, the accuracy and statistical power of branch statistics have not been systematically assessed. To do so, we simulate genomes in representative large and small populations with varying proportions of sites evolving under genetic drift or background selection (approximated using variable Ne), local selective sweeps, and geographically parallel selective sweeps. We then assess the probability that local selective sweep loci are correctly identified as outliers by FST and by each of the branch statistics. We find that branch statistics consistently outperform FST at identifying local sweeps. When background selection and/or parallel sweeps are introduced, PBSn1 and especially PBE correctly identify local sweeps among their top outliers at a higher frequency than PBS. These results validate the greater specificity of rescaled branch statistics such as PBE to detect population-specific positive selection, supporting their use in genomic studies focused on local adaptation. Significance StatementPopulation branch statistics are widely used in genome-wide scans to identify loci associated with local adaptation. This study finds that branch statistics are more accurate than FST at identifying local selective sweeps under a wide range of demographic parameters and models of evolution. It also demonstrates that certain branch statistics have improved ability to distinguish local adaptation from other models of natural selection.

evolutionary biology↗