Biology subjects

Grad, Y.

Publications and source records attributed to Grad, Y..

7 recordsLinked to original sources

bioRxiv2019

Identification of hidden population structure in time-scaled phylogenies

Population structure influences genealogical patterns, however data pertaining to how populations are structured are often unavailable or not directly observable. Inference of population structure is highly important in molecular epidemiology where pathogen phylogenetics is increasingly used to infer transmission patterns and detect outbreaks. Discrepancies between observed and idealised genealogies, such as those generated by the coalescent process, can be quantified, and where significant differences occur, may reveal the action of natural selection, host population structure, or other demographic and epidemiological heterogeneities. We have developed a fast non-parametric statistical test for detection of cryptic population structure in time-scaled phylogenetic trees. The test is based on contrasting estimated phylogenies with the theoretically expected phylodynamic ordering of common ancestors in two clades within a coalescent framework. These statistical tests have also motivated the development of algorithms which can be used to quickly screen a phylogenetic tree for clades which are likely to share a distinct demographic or epidemiological history. Epidemiological applications include identification of outbreaks in vulnerable host populations or rapid expansion of genotypes with a fitness advantage. To demonstrate the utility of these methods for outbreak detection, we applied the new methods to large phylogenies reconstructed from thousands of HIV-1 partial pol sequences. This revealed the presence of clades which had grown rapidly in the recent past, and was significantly concentrated in young men, suggesting recent and rapid transmission in that group. Furthermore, to demonstrate the utility of these methods for the study of antimicrobial resistance, we applied the new methods to a large phylogeny reconstructed from whole genome Neisseria gonorrhoeae sequences. We find that population structure detected using these methods closely overlaps with the appearance and expansion of mutations conferring antimicrobial resistance.

evolutionary biology↗

bioRxiv2019

Fine-scale haplotype structure reveals strong signatures of positive selection in a recombining bacterial pathogen

Identifying the forces that create and shape ecologically meaningful variation in bacteria remains an important challenge. For recombining bacteria, the sign and strength of linkage provide a unique lens into ongoing selection. We show derived alleles less than 300bp apart in Neisseria gonorrhoeae exhibit more coupling linkage than repulsion linkage, a pattern that cannot be explained by limited recombination or neutrality as these couplings are significantly stronger for nonsynonymous alleles compared to synonymous alleles. While linkage is shaped by many evolutionary processes, extensive simulations show only two distinct forms of positive selection can drive an excess of coupling linkage between neighboring nonsynonymous alleles: directional selection on introgressed alleles or selection that maintains distinct haplotypes in the presence of recombination. Our results establish a framework for identifying patterns of selection in fine-scale haplotype structure that indicate specific ecological processes in species that recombine with distantly related lineages or possess coexisting adaptive haplotypes.

evolutionary biology↗

bioRxiv2019

RNA polymerase mutations cause cephalosporin resistance in clinical Neisseria gonorrhoeae isolates

Increasing Neisseria gonorrhoeae resistance to ceftriaxone, the last antibiotic recommended for empiric gonorrhea treatment, poses an urgent public health threat. However, the genetic basis of reduced susceptibility to ceftriaxone is not completely understood: while most ceftriaxone resistance in clinical isolates is caused by target site mutations in penA, others lack these mutations. Here, we show that penA-independent ceftriaxone resistance has evolved multiple times through distinct mutations in rpoB and rpoD. We identify five mutations in these genes that each increase resistance to ceftriaxone, including one mutation that arose independently in two lineages, and show that clinical isolates from multiple lineages are a single nucleotide change from ceftriaxone resistance. These RNA polymerase mutations result in large-scale transcriptional changes without altering susceptibility to other antibiotics, reducing growth rate, or deranging cell morphology. These results underscore the unexpected diversity of pathways to resistance and the importance of continued surveillance for novel resistance mutations.

microbiology↗

bioRxiv2018

A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation

Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.git.\n\nAuthor summaryAs colonizing bacterial populations are the source for much transmission and a reservoir for infection, they are a major focus of interest clinically and epidemiologically. Understanding the dynamics of colonization depends on being able to confidently identify acquisition and clearance events given intermittent sampling of hosts. To do so, we need a model of within-host bacterial population evolution from acquisition through the time of sampling that enables estimation of whether two samples are derived from the same population. Past efforts have frequently relied on empirical genetic distance thresholds that forgo an underlying model or employ a simple molecular clock model. Here, we present an inferential method that accounts for the timing of sample collection and population diversification, to provide a probabilistic estimate for whether two isolates represent the same colonizing strain. This method has implications for understanding the dynamics of acquisition and clearance of colonizing bacteria, and the impact on these rates by factors such as sensitivity of the sampling method, pathogen genotype, competition with other carriage bacteria, host immune response, and antibiotic exposure.

bioinformatics↗

bioRxiv2018

Estimating the proportion of bystander selection for antibiotic resistance in the US

Bystander selection -- the selective pressures exerted by antibiotics on microbial flora that are not the target pathogen of treatment -- is critical to understanding the total impact of broad-spectrum antibiotic use; however, to our knowledge, this effect has never been quantified. Using the 2010-2011 National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey (NAMCS/NHAMCS), the Human Microbiome Project, and additional carriage and etiological data from existing literature, we estimate the magnitude of bystander selection for a range of clinically relevant antibiotic-species pairs as the proportion of all exposures of an antibiotic experienced by a species for conditions in which that species was not the causative pathogen (\"proportion of bystander exposures\"). For outpatient prescribing in the United States, we find that this proportion over all included antibiotics is over 80% for 8 out of 9 organisms of interest. Low proportions of bystander exposure are often associated with infrequent bacterial carriage or a high proportion of antibiotic prescribing focused on conditions caused by the species of interest. Using the proportion of bystander exposures, we roughly estimate that S. aureus and E. coli may benefit from 90.7% and 99.7%, respectively, of the estimated reduction in antibiotic use due to pneumococcal conjugate vaccination, despite not being the pathogen targeted by the vaccine. These results underscore the importance of considering antibiotic exposures to bystanders, in addition to the targeted pathogen, in measuring the impact of antibiotic resistance interventions.\n\nSignificance StatementThe forces that contribute to changing population prevalence of antibiotic resistance are not well understood. Bystander selection -- the inadvertent pressures imposed by antibiotics on the microbial flora other than the pathogen targeted by treatment -- is hypothesized to be a major factor in the propagation of antibiotic resistance, but its extent has not been characterized. We estimate the proportion of bystander exposures across a range of antibiotics and organisms and describe factors driving variability of these proportions. Impact estimates for antibiotic resistance interventions, including vaccination, are often limited to effects on a target pathogen. However, the reduction of antibiotic treatment for illnesses caused by the target pathogen may have the broader potential to decrease bystander selection pressures for resistance on many other organisms.

epidemiology↗

bioRxiv2017

Increasing Antibiotic Susceptibility In Staphylococcus aureus In Boston, Massachusetts, 2000-2014: An Observational Study

BackgroundMethicillin resistant S. aureus (MRSA) has been declining over the past decade, but changes in S. aureus overall and the implications for trends in antibiotic resistance remain unclear.\n\nObjectiveTo determine whether the decline in rates of infection by MRSA has been accompanied by changes in rates of infection by methicillin susceptible, penicillin resistant S. aureus (MSSA) and penicillin susceptible S. aureus (PSSA). We test if these dynamics are associated with specific genetic lineages and evaluate gains and losses of resistance at the strain level.\n\nMethodsWe conducted a 15 year retrospective observational study at two tertiary care institutions in Boston, MA of 31,589 adult inpatients with S. aureus infections. Surveillance swabs and duplicate specimens were excluded. We also sequenced a sample of contemporary isolates (n = 180) obtained between January 2016 and July 2016. We determined changes in the annual rates of infection per 1,000 inpatient admissions by S. aureus subtype and in the annual mean antibiotic resistance by subtype. We performed phylogenetic analysis to generate a population structure and infer gain and loss of the genetic determinants of resistance.\n\nResultsOf the 43,954 S. aureus infections over the study period, 21,779 were MRSA, 17,565 MSSA and 4,610 PSSA. After multivariate adjustment, annual rates of infection by S. aureus declined from 2003 to 2014 by 2.9% (95% CI, 1.6%-4.3%), attributable to an annual decline in MRSA of 9.1% (95% CI, 6.3%-11.9%) and in MSSA by 2.2% (95% CI, 0.4%-4.0%). PSSA increased over this time period by 4.6% (95% CI, 3.0%-6.3%) annually. Resistance in S. aureus decreased from 2000 to 2014 by 0.86 antibiotics (95% CI, 0.81-0.91). By phylogenetic inference, 5/35 MSSA and 2/20 PSSA isolates in the common MRSA lineages ST5/USA100 and ST8/USA300 arose from the loss of genes conferring resistance.\n\nConclusions and relevanceAt two large tertiary care centers in Boston, MA, S. aureus infections have decreased in rate and have become more susceptible to antibiotics, with a rise in PSSA making penicillin an increasingly viable and important treatment option.

epidemiology↗

bioRxiv2017

Weak epistasis may drive adaptation in recombining bacteria

The impact of epistasis on the evolution of multilocus traits depends on recombination. Population genetic theory has been largely developed for eukaryotes, many of which recombine so frequently that epistasis between polymorphisms has not been considered to play a large role in adaptation and has been compared to the fleeting influence of non-heritable effects. Many bacteria also recombine, some to the degree that their populations are described as panmictic or freely recombining. However, whether this recombination is sufficient to limit the ability of selection to act on epistatic contributions to fitness is unknown. We create a sensitive method to quantify homologous recombination in five bacterial pathogens and use these parameter estimates in a multilocus model of bacterial evolution with additive and epistatic effects. We find that even for highly recombining species (e.g. Streptococcus pneumoniae or Helicobacter pylori), selection may act on the cumulative effects of weak (as well as strong) interactions between distant mutations since homologous recombination typically transfers only short segments. Furthermore, whether selection acts more efficiently on physically proximal loci depends on the average recombination tract length. Epistasis may thus play an important role in the adaptive evolution of bacteria and, unlike in eukaryotes, does not need to be strong, involve near loci, or require specific metapopulation dynamics.

evolutionary biology↗