Frequency-dependent selection in vaccine-associated pneumococcal population dynamics

Many bacterial species are composed of multiple lineages distinguished by extensive variation in gene content. These often cocirculate in the same habitat, but the evolutionary and ecological processes that shape these complex populations are poorly understood. Addressing these questions is particularly important for Streptococcus pneumoniae, a nasopharyngeal commensal and respiratory pathogen, because the changes in population structure associated with the recent introduction of partial-coverage vaccines have substantially reduced pneumococcal disease. Here we show that pneumococcal lineages from multiple populations each have a distinct combination of intermediate-frequency genes. Functional analysis suggested that these loci may be subject to negative frequency-dependent selection (NFDS) through interactions with other bacteria, hosts or mobile elements. Correspondingly, these genes had similar frequencies in four populations with dissimilar lineage compositions. These frequencies were maintained following substantial alterations in lineage prevalences once vaccination programmes began. Fitting a multilocus NFDS model of post-vaccine population dynamics to three genomic datasets using Approximate Bayesian Computation generated reproducible estimates of the influence of NFDS on pneumococcal evolution, the strength of which varied between loci. Simulations replicated the stable frequency of lineages unperturbed by vaccination, patterns of serotype switching and clonal replacement. This framework highlights how bacterial ecology affects the impact of clinical interventions.Accessory loci are shown to have similar frequencies in diverse Streptococcus pneumoniae populations, suggesting negative frequency-dependent selection drives post-vaccination population restructuring

Estimating kinetic constants in the Michaelis–Menten model from one enzymatic assay using Approximate Bayesian Computation

The Michaelis-Menten equation is one of the most extensively used models in biochemistry for studying enzyme kinetics. However, this model requires at least a couple (e.g., eight or more) of measurements at different substrate concentrations to determine kinetic parameters. Here, we report the discovery of a novel tool for calculating kinetic constants in the Michaelis-Menten equation from only a single enzymatic assay. As a consequence, our method leads to reduced costs and time, primarily by lowering the amount of enzymes, since their isolation, storage and usage can be challenging when conducting research

Bayesian inference of spreading processes on networks

Infectious diseases are studied to understand their spreading mechanisms, to evaluate control strategies and to predict the risk and course of future outbreaks. Because people only interact with few other individuals, and the structure of these interactions influence spreading processes, the pairwise relationships between individuals can be usefully represented by a network. Although the underlying transmission processes are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumours in a social network. We study simulated simple and complex epidemics on synthetic networks and on two empirical networks, a social/contact network in an Indian village and an online social network. Our goal is to learn simultaneously the spreading process parameters and the first infected node, given a fixed network structure and the observed state of nodes at several time points. Our inference scheme is based on approximate Bayesian computation, a likelihood-free inference technique. Our method is agnostic about the network topology and the spreading process. It generally performs well and, somewhat counter-intuitively, the inference problem appears to be easier on more heterogeneous network topologies, which enhances its future applicability to real-world settings where few networks have homogeneous topologies

Data from: Coalescent models characterize sources and demographic history of recent round goby colonization of Great Lakes and inland waters

The establishment and spread of aquatic invasive species is ecologically and economically harmful and a source of conservation concern internationally. Processes of species invasion have traditionally been inferred from observational data of species presence/absence and relative abundance. However, genetic-based approaches can provide valuable sources of inference. Restriction-site associated DNA sequencing was used to identify and genotype single nucleotide polymorphism (SNP) loci for Round Gobies (Neogobius melanostomus) (N=440) from 18 sampling locations in the Great Lakes and in three Michigan, USA drainages (Flint, Au Sable, and Cheboygan River basins). Sampled rivers differed in size, accessibility, and physical characteristics including man-made dispersal barriers. Population levels of genetic diversity and inter-population variance in SNP allele frequency were used in coalescence-based Approximate Bayesian Computation (ABC) to statistically compare models representing competing hypotheses regarding source population, post-colonization dispersal, and demographic history in the Great Lakes and inland waters. Results indicate different patterns of colonization across the three drainages. In the Flint River, models indicate a strong population bottleneck (< 3% of contemporary effective population size) and a single founding event from Saginaw Bay led to the colonization of inland river segments. In the Au Sable River, analyses could not distinguish potential source populations, but supported models indicated multiple introductions from one source population. In the Cheboygan River, supported models indicated that colonization likely proceeded from east (Lake Huron source) to west among inland locales sampled in the system. Despite the recent occupancy of Great Lakes and inland habitats, large numbers of loci analyzed in an ABC framework enable statistically supported identification of source populations and reconstruction of the direction of inland spread and demographic history following establishment. Information from analyses can direct management actions to limit the spread of invasive species from identified sources and most probable vectors into additional inland aquatic habitats