Biology subjects

Kennedy, D. A.

Publications and source records attributed to Kennedy, D. A..

9 recordsLinked to original sources

bioRxiv2025

High-Fidelity Long-Read Sequencing of an Avian Herpesvirus Reveals Extensive Intrapopulation Diversity in Tandem Repeat Regions

Comparative genomic studies of Mareks disease virus (MDV) have suggested that attenuated and virulent strains share >98% sequence identity. However, these estimates fail to account for variation in regions of the MDV genome harboring tandem repeats. To resolve these loci and enable assessments of intrapopulation diversity, we used a PacBio Sequel II platform to sequence MDV strains CVI988/Rispens (attenuated), HPRS-B14 (virulent), Md5 (very virulent) and 675A (very virulent plus). This approach enabled us to identify patterns of variation in tandem repeat regions that are consistent with known phenotypic differences across these strains. We also found CVI988/Rispens variants showing a 4.3-kb deletion in the Unique Short (US) region, resulting in the loss of five genes. These findings support a potential link between MDV tandem repeats and phenotypic traits like virulence and attenuation, and demonstrate that DNA viruses can harbor high levels of intrapopulation diversity in tandem repeat regions.

microbiology↗

bioRxiv2024

Spillover rate is not predictive of host jump risk

Host jumps--defined as the process by which a pathogen establishes sustained transmission in novel hosts--are threats to human and animal welfare, but prediction of successful host jumps remains elusive. A spillover event must precede a host jump, and so spillover rate is thought to be related to risk. However, non-endemic pathogens that spill over frequently have demonstrated a poor ability to host jump from any given spillover. So which is riskier, pathogens that spill over rarely or commonly? Applying a Bayesian framework to a general model of host jump risk, we show that 1) the riskiest pathogens can be those that spill over at low, intermediate, or high rates, and 2) as the rate of spillover gets large, the information gained from past spillovers is exactly counterbalanced by the increased number of future spillovers. Taken together, this means that spillover rate has little to no value for explaining host jump risk. Rather, we show that novel pathogens (i.e. pathogens with a relatively short history of spilling over in their current form) are substantially more likely to result in host jumps than pathogens that have had long-associated opportunities for spillover into the novel host. Therefore novelty, but not spillover rate, is an important predictor of host jump risk. Significance StatementSpillovers of infectious diseases pose significant threats to human and animal health in part due to pandemic risk and the potential for successful host jumps. Predicting successful host jumps remains elusive, though it is often assumed that increased spillover rates are associated with increased host jump risk. We developed a quantitative framework that uses past spillover outcomes to predict future host jump risk. Our model demonstrates that spillover rate is not predictive of host jump risk. However, pathogens that recently started having opportunities for spillover are substantially riskier than those that have a longer history of past spillover. We therefore show that pathogen novelty is a much stronger driver of host jump risk than spillover rate. This ideological shift may significantly enhance our ability to identify contexts where host jumps are most likely, yielding substantial economic and public health benefits globally.

systems biology↗

bioRxiv2023

Detecting and quantifying heterogeneity in susceptibility using contact tracing data

The presence of heterogeneity in susceptibility, differences between hosts in their likelihood of becoming infected, can fundamentally alter disease dynamics and public health responses, for example, by changing the final epidemic size, the duration of an epidemic, and even the vaccination threshold required to achieve herd immunity. Yet, heterogeneity in susceptibility is notoriously difficult to detect and measure, especially early in an epidemic. Here we develop a method that can be used to detect and estimate heterogeneity in susceptibility given contact by using contact tracing data, which is typically collected early in the course of an outbreak. This approach provides the capability, given sufficient data, to estimate and account for the effects of this heterogeneity before they become apparent during an epidemic. It additionally provides the capability to analyze the wealth of contact tracing data available for previous epidemics and estimate heterogeneity in susceptibility for disease systems in which it has never been estimated previously. The premise of our approach is that highly susceptible individuals become infected more often than less susceptible individuals, and so individuals not infected after appearing in contact networks should be less susceptible than average. This change in susceptibility can be detected and quantified when individuals show up in a second contact network after not being infected in the first. To develop our method, we simulated contact tracing data from artificial populations with known levels of heterogeneity in susceptibility according to underlying discrete or continuous distributions of susceptibilities. We analyzed this data to determine the parameter space under which we are able to detect heterogeneity and the accuracy with which we are able to estimate it. We found that our power to detect heterogeneity increases with larger sample sizes, greater heterogeneity, and intermediate fractions of contacts becoming infected in the discrete case or greater fractions of contacts becoming infected in the continuous case. We also found that we are able to reliably estimate heterogeneity and disease dynamics. Ultimately, this means that contact tracing data alone is sufficient to detect and quantify heterogeneity in susceptibility.

ecology↗

bioRxiv2023

Deep Whole-Genome Resequencing of Marek's Disease Virus Strains Reveals Intrastrain Variation

Current strategies to understand the molecular basis of Mareks disease virus (MDV) virulence primarily consist of cataloguing divergent nucleotides between strains with different phenotypes. However, each MDV strain is typically represented by a single consensus genome despite the confirmed existence of mixed viral populations. To assess the reliability of single-consensus interstrain genomic comparisons, we obtained two additional consensus genomes of vaccine strain CVI988 (Rispens) and two additional consensus genomes of the very virulent strain Md5 by sequencing viral stocks and cultured field isolates. In conjunction with the published genomes of CVI988 and Md5, this allowed us to perform 3-way comparisons between consensus genomes of the same strain. We found that consensus genomes of CVI988 can vary in as many as 236 positions involving 13 open reading frames (ORFs). In contrast, we found that Md5 genomes varied only in 11 positions involving a single ORF. Phylogenomic analyses showed all three Md5 consensus genomes clustered closely together, while also showing that CVI988GenBank.BAC diverged from CVI988Pirbright.lab and CVI988USDA.PA.field. Comparison of CVI988 consensus genomes revealed 19 SNPs in the unique regions of CVI988GenBank.BAC that were not present in either CVI988Pirbright.lab or CVI988USDA.PA.field. Finally, we evaluated the genomic heterogeneity of CVI988 and Md5 populations by identifying positions with >2% read support for alternative alleles in two ultra-deeply sequenced samples. We were able to confirm that both populations of CVI988 and Md5 were mixed, exhibiting a total of 29 and 27 high-confidence minor variant positions, respectively. We did not find any evidence of minor variants in the positions corresponding to the 19 SNPs in the unique regions of CVI988GenBank.BAC. Taken together, our findings confirm that consensus genomes of the same strain of MDV can vary and suggest that multiple consensus genomes per strain are needed in order to maximize the accuracy of interstrain genomic comparisons.

microbiology↗

bioRxiv2022

Y-box binding protein 1 interacts with dengue virus nucleocapsid and mediates viral assembly

Infection with dengue virus (DENV) induces vast rearrangements of the endoplasmic reticulum, which allows the compartmentalization of viral RNA replication and particle assembly. Both processes occur in concert with viral and cellular proteins. Prior studies from our group suggest that the host RNA-binding protein (RBP) Y-box binding protein 1 (YBX1) is required for a late step in the DENV replication cycle. Here we report that YBX1 interacts with the viral nucleocapsid, distributes to DENV assembly sites and is required for efficient assembly of intracellular infectious virions and their secretion. Genetic ablation of YBX1 decreased the spatial proximity between capsid and envelope, increased the susceptibility of envelope to proteinase-K mediated degradation, resulted in the formation of rough empty-looking particles, and decreased the secretion of viral particles. We propose a model wherein YBX1 enables the interaction between the viral nucleocapsid with the structural protein E, which is required for proper assembly of intracellular virus particles and their secretion. ImportanceThe global incidence of dengue virus (DENV) infections has steadily increased over the past decades representing an enormous challenge for public health. During infection, DENV viral RNA interacts with numerous host RNA binding proteins (RBPs) that aid viral replication and thus constitute potential molecular targets to curb infection. We recently reported that Y-box-binding protein 1 (YBX1) interacts with DENV RNA and is required at a late step of the replication cycle. Here we describe the molecular mechanism by which YBX1 mediates DENV infection. We show that YBX1 interacts with the viral nucleocapsid, distributes to DENV assembly sites and is required for efficient assembly of intracellular infectious virions. These results provide important insights into DENV assembly, revealing novel functions of host RBPs during viral infection and opening new avenues for antiviral intervention.

molecular biology↗

bioRxiv2021

Exceptions to the rule: Why does resistance evolution not undermine antibiotic therapy in all bacterial infections?

Antibiotic resistance poses one of the greatest public health challenges of the 21st century. Yet not all pathogens are equally affected by resistance evolution. Why? Here we examine what underlies variation in antibiotic resistance across human bacterial pathogens and the drugs used to treat them. We document the observed prevalence of antibiotic resistance for pathogen x drug combinations across 57 different human bacterial pathogens and 53 antibiotics from 15 drug classes used to treat them. Using AIC-based model selection we analyze 14 different traits of bacteria and antibiotics that are believed to be important in resistance evolution. Using these data, we identify the traits that best explain observed variation in resistance evolution. Our results show that nosocomial pathogens and indirectly transmitted pathogens are significantly associated with increased prevalence of resistance whereas zoonotic pathogens, specifically those with wild animal reservoirs, are associated with reduced prevalence of resistance. We found partial support for associations between drug resistance and gram classification, human microbiome reservoirs, horizontal gene transfer, and documented human-to human transfer. Global drug use, time since drug discovery, mechanism of drug action, and environmental reservoirs did not emerge as statistically robust predictors of drug resistance in our analyses. To the best of our knowledge this work is the first systematic analysis of resistance across such a wide range of human bacterial pathogens, encompassing the vast majority of common bacterial pathogens. Insights from our study may help guide public health policies and future studies on resistance control.

evolutionary biology↗

bioRxiv2021

Developing an empirical model for spillover and emergence: Orsay virus host range in Caenorhabditis

A lack of tractable experimental systems in which to test hypotheses about the ecological and evolutionary drivers of disease spillover and emergence has limited our understanding of these processes. Here we introduce a promising system: Caenorhabditis hosts and Orsay virus, a positive-sense single-stranded RNA virus that naturally infects C. elegans. We assayed the susceptibility of species across the Caenorhabditis tree and found 21 of 84 wild strains belonging to 14 of 44 species to be susceptible to Orsay virus. Confirming patterns documented in other systems, we detected effects of host phylogeny on susceptibility. We then tested whether susceptible strains were capable of transmitting Orsay virus by transplanting exposed hosts and determining whether they transmitted infection to conspecifics during serial passage. We found no evidence of transmission in 10 strains (virus undetectable after passaging), evidence of low-level transmission in 5 strains (virus lost between passage 1 and 5), and evidence of sustained transmission in 6 strains (including all 3 experimental C. elegans strains). Transmission was associated with host phylogeny and with viral amplification in exposed populations. Variation in Orsay virus susceptibility and transmission among Caenorhabditis species suggests that the system could be powerful for studying spillover and emergence.

ecology↗

bioRxiv2021

Detection, not mortality, constrains the evolution of virulence

Why would a pathogen evolve to kill its hosts when killing a host ends a pathogens own opportunity for transmission? A vast body of scientific literature has attempted to answer this question using "trade-off theory," which posits that host mortality persists due to its cost being balanced by benefits of other traits that correlate with host mortality. The most commonly invoked trade-off is the mortality-transmission trade-off, where increasingly harmful pathogens are assumed to transmit at higher rates from hosts while the hosts are alive, but the pathogens truncate their infectious period by killing their hosts. Here I show that costs of mortality are too small to plausibly constrain the evolution of disease severity except in systems where survival is rare. I alternatively propose that disease severity can be much more readily constrained by a cost of behavioral change due to the detection of infection, whereby increasingly harmful pathogens have increasing likelihood of detection and behavioral change following detection, thereby limiting opportunities for transmission. Using a mathematical model, I show the conditions under which detection can limit disease severity. Ultimately, this argument may explain why empirical support for trade-off theory has been limited and mixed.

evolutionary biology↗

bioRxiv2017

Modeling Marek’s disease virus transmission: a framework for evaluating the impact of farming practices and evolution on disease

Mareks disease virus (MDV) is a pathogen of chickens whose control has twice been un-dermined by pathogen evolution. Disease ecology is believed to be the main driver of this evolution, yet mathematical models of MDV disease ecology have never been confronted with data to test their reliability. Here, we develop a suite of MDV models that differ in the ecological mechanisms they include. We fit these models with maximum likelihood in pomp to data on MDV concentration in dust collected from two commercial broiler farms. Using AIC to compare the models, we find that virus dynamics are influenced by between-flock variation in host susceptibility to virus, shedding rate from infected birds, and cleanout efficiency. We also find evidence that virus is reintroduced to farms approximately once per month, but we do not find evidence that virus sanitization rates vary between flocks. Of the models that survive model selection, we find agreement between parameter estimates and previous experimental data, as well as agreement between field data and the predictions of these models, suggesting robustness of model predictions. Using the set of surviving models, we explore how changes to farming practices are predicted to influence MDV costs, should pathogen evolution undermine existing control measures. By quantitatively capturing the mechanisms of disease ecology, we have laid the groundwork to explore the future trajectory of virus evolution.

ecology↗