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1. Escherichia coli cells are primed for survival before lethal antibiotic stress

   https://doi.org/10.1128/spectrum.01219-23  

   Hossain, Tahmina ; Singh, Abhyudai ; Butzin, Nicholas C. ( October 2023 , Microbiology Spectrum)

   Pagliara, Stefano (Ed.)

   Non-genetic factors can cause significant fluctuations in gene expression levels. Regardless of growing in a stable environment, this fluctuation leads to cell-to-cell variability in an isogenic population. This phenotypic heterogeneity allows a tiny subset of bacterial cells in a population called persister cells to tolerate long-term lethal antibiotic effects by entering into a non-dividing, metabolically repressed state. We occasionally noticed a high variation in persister levels, and to explore this, we tested clonal populations starting from a single cell using a modified Luria-Delbrück fluctuation test. Although we kept the conditions same, the diversity in persistence level among clones was relatively consistent: varying from ~60- to 100- and ~40- to 70-fold for ampicillin and apramycin, respectively. Then, we divided and diluted each clone to observe whether the same clone had comparable persister levels for more than one generation. Replicates had similar persister levels even when clones were divided, diluted by 1:20, and allowed to grow for approximately five generations. This result explicitly shows a cellular memory passed on for generations and eventually lost when cells are diluted to 1:100 and regrown (>seven generations). Our result demonstrates (1) the existence of a small population prepared for stress (“primed cells”) resulting in higher persister numbers; (2) the primed memory state is reproducible and transient, passed down for generations but eventually lost; and (3) a heterogeneous persister population is a result of a transiently primed reversible cell state and not due to a pre-existing genetic mutation.

   Antibiotics have been highly effective in treating lethal infectious diseases for almost a century. However, the increasing threat of antibiotic resistance is again causing these diseases to become life-threatening. The longer a bacteria can survive antibiotics, the more likely it is to develop resistance. Complicating matters is that non-genetic factors can allow bacterial cells with identical DNA to gain transient resistance (also known as persistence). Here, we show that a small fraction of the bacterial population called primed cells can pass down non-genetic information (“memory”) to their offspring, enabling them to survive lethal antibiotics for a long time. However, this memory is eventually lost. These results demonstrate how bacteria can leverage differences among genetically identical cells formed through non-genetic factors to form primed cells with a selective advantage to survive antibiotics.

    

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2. The number of neutral mutants in an expanding Luria-Delbrück population is approximately Fréchet

   https://doi.org/10.12688/f1000research.127469.2  

   Frank, Steven A. ( January 2022 , F1000Research)

   Background: A growing population of cells accumulates mutations. A single mutation early in the growth process carries forward to all descendant cells, causing the final population to have a lot of mutant cells. When the first mutation happens later in growth, the final population typically has fewer mutants. The number of mutant cells in the final population follows the Luria-Delbrück distribution. The mathematical form of the distribution is known only from its probability generating function. For larger populations of cells, one typically uses computer simulations to estimate the distribution. Methods: This article searches for a simple approximation of the Luria-Delbrück distribution, with an explicit mathematical form that can be used easily in calculations. Results: The Fréchet distribution provides a good approximation for the Luria-Delbrück distribution for neutral mutations, which do not cause a growth rate change relative to the original cells. Conclusions: The Fréchet distribution apparently provides a good match through its description of extreme value problems for multiplicative processes such as exponential growth. 

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3. Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics

   https://doi.org/10.1371/journal.pbio.3001633  

   Avecilla, Grace ; Chuong, Julie N. ; Li, Fangfei ; Sherlock, Gavin ; Gresham, David ; Ram, Yoav ( May 2022 , PLOS Biology)

   de Visser, J. Arjan (Ed.)

   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 2 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 CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood–free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright–Fisher model and a chemostat model. We evaluated 2 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 the yeast Saccharomyces cerevisiae to be 10 −4.7 to 10 −4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods—barcode lineage tracking and pairwise fitness assays—which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network–based likelihood–free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution. 

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4. Considering unseen arrivals in predictions of establishment risk based on border biosecurity interceptions

   https://doi.org/10.1002/eap.2194  

   Turner, Rebecca M. ; Plank, Michael J. ; Brockerhoff, Eckehard G. ; Pawson, Stephen ; Liebhold, Andrew ; James, Alex ( September 2020 , Ecological Applications)

   Assessing species establishment risk is an important task used for informing biosecurity activities aimed at preventing biological invasions. Propagule pressure is a major contributor to the probability of invading species establishment; however, direct assessment of numbers of individuals arriving is virtually never possible. Inspections conducted at borders by biosecurity officials record counts of species (or higher‐level taxa) intercepted during inspections, which can be used as proxies for arrival rates. Such data may therefore be useful for predicting species establishments, though some species may establish despite never being intercepted. We present a stochastic process‐based model of the arrival–interception–establishment process to predict species establishment risk from interception count data. The model can be used to estimate the probability of establishment, both for species that were intercepted and species that had no interceptions during a given observation period. We fit the stochastic model to data on two insect families, Cerambycidae and Aphididae, that were intercepted and/or established in the United States or New Zealand. We also explore the effects of variation in model parameters and the inclusion of an Allee effect in the establishment probability. Although interception data sets contain much noise due to variation in inspection policy, interception effort and among‐species differences in detectability, our study shows that it is possible to use such data for predicting establishments and distinguishing differences in establishment risk profile between taxonomic groups. Our model provides a method for predicting the number of species that have breached border biosecurity, including both species detected during inspections but also “unseen arrivals” that have never been intercepted, but have not yet established a viable population. These estimates could inform prioritization of different taxonomic groups, pathways or identification effort in biosecurity programs.

    

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5. Bayesian inference of distributed time delay in transcriptional and translational regulation

   https://doi.org/10.1093/bioinformatics/btz574  

   Choi, Boseung ; Cheng, Yu-Yu ; Cinar, Selahattin ; Ott, William ; Bennett, Matthew R. ; Josić, Krešimir ; Kim, Jae Kyoung ; Valencia, ed., Alfonso ( July 2019 , Bioinformatics)

   Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks.

   We propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth–death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy.

   Accompanying code in R is available at https://github.com/cbskust/DDE_BD.

   Supplementary data are available at Bioinformatics online.

    

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