Biology subjects

Chuong, J.

Publications and source records attributed to Chuong, J..

4 recordsLinked to original sources

bioRxiv2025

Engineered Acinetobacter baylyi ADP1-ISx cells are sensitive DNA biosensors for antibiotic resistance genes and a fungal pathogen of bats

Naturally competent bacteria can be engineered into platforms for detecting environmental DNA. This capability could be used to monitor the spread of pathogens, invasive species, and resistance genes, among other applications. Here, we create Acinetobacter baylyi ADP1-ISx biosensors that detect specific target DNA sequences through natural transformation. We tested strains with DNA sensors that consisted of either a mutated antibiotic resistance gene (TEM-1 bla or nptII) or a counterselectable gene flanked by sequences from the fungus Pseudogymnoascus destructans, which causes white-nose syndrome in bats. Upon uptake of homologous DNA, recombination restored antibiotic resistance gene function or removed the counterselectable gene, enabling selection of cells that sensed the target DNA. The antibiotic resistance gene and P. destructans biosensors could detect as few as 3,000 or 5,000,000 molecules of their DNA targets, respectively, and their sensitivity was not affected by excess off-target DNA. These results demonstrate how A. baylyi can be reprogrammed into a modular platform for monitoring environmental DNA.

synthetic biology↗

bioRxiv2024

DNA replication errors are a major source of adaptive gene amplification

Copy number variants (CNVs)--gains and losses of genomic sequences--are an important source of genetic variation underlying rapid adaptation and genome evolution. However, despite their central role in evolution little is known about the factors that contribute to the structure, size, formation rate, and fitness effects of adaptive CNVs. Local genomic sequences are likely to be an important determinant of these properties. Whereas it is known that point mutation rates vary with genomic location and local DNA sequence features, the role of genome architecture in the formation, selection, and the resulting evolutionary dynamics of CNVs is poorly understood. Previously, we have found that the GAP1 gene in Saccharomyces cerevisiae undergoes frequent and repeated amplification and selection under long-term experimental evolution in glutamine-limiting conditions. The GAP1 gene has a unique genomic architecture consisting of two flanking long terminal repeats (LTRs) and a proximate origin of DNA replication (autonomously replicating sequence, ARS), which are likely to promote rapid GAP1 CNV formation. To test the role of these genomic elements on CNV-mediated adaptive evolution, we performed experimental evolution in glutamine-limited chemostats using engineered strains lacking either the adjacent LTRs, ARS, or all elements. Using a CNV reporter system and neural network simulation-based inference (nnSBI) we quantified the formation rate and fitness effect of CNVs for each strain. We find that although GAP1 CNVs repeatedly form and sweep to high frequency in strains with modified genome architecture, removal of local DNA elements significantly impacts the rate and fitness effect of CNVs and the rate of adaptation. We performed genome sequence analysis to define the molecular mechanisms of CNV formation for 177 CNV lineages. We find that across all four strain backgrounds, between 26% and 80% of all GAP1 CNVs are mediated by Origin Dependent Inverted Repeat Amplification (ODIRA) which results from template switching between the leading and lagging strand during DNA synthesis. In the absence of the local ARS, a distal ARS can mediate CNV formation via ODIRA. In the absence of local LTRs, homologous recombination mechanisms still mediate gene amplification following de novo insertion of retrotransposon elements at the locus. Our study demonstrates the remarkable plasticity of the genome and reveals that template switching during DNA replication is a frequent source of adaptive CNVs.

evolutionary biology↗

bioRxiv2021

Simulation-based inference of evolutionary parameters from adaptation dynamics using neural networks

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these two parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use the observed CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based Bayesian likelihood-free inference approaches. We tested the suitability of two evolutionary models: a standard Wright-Fisher model and a chemostat growth model. We evaluated two likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models we show that NPE has several advantages over ABC-SMC and that a Wright-Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in yeast as 10-4.7 -10-4 per cell division, and a selection coefficient of 0.04 - 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our estimates using barcode lineage tracking and pairwise fitness assays. Our results are consistent with a high beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining their outsized importance in rapid adaptive evolution. More generally, our study demonstrates the utility of novel simulation-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data.

evolutionary biology↗

bioRxiv2017

The polygenic basis of an ancient divergence in yeast thermotolerance

Some of the most unique and compelling survival strategies in the natural world are fixed in isolated species. To date, molecular insight into these ancient adaptations has been limited, as classic experimental genetics has focused on interfertile individuals in populations. Here we use a new mapping approach, which screens mutants in a sterile interspecific hybrid, to identify eight housekeeping genes that underlie the growth advantage of Saccharomyces cerevisiae over its distant relative S. paradoxus at high temperature. Pro-thermotolerance alleles at these mapped loci were required for the adaptive trait in S. cerevisiae and sufficient for its partial reconstruction in S. paradoxus. The emerging picture is one in which S. cerevisiae improved the heat resistance of multiple components of the fundamental growth machinery in response to selective pressure. This study lays the groundwork for the mapping of genotype to phenotype in clades of sister species across Eukarya.

evolutionary biology↗