Biology subjects

Dwyer, G.

Publications and source records attributed to Dwyer, G..

11 recordsLinked to original sources

bioRxiv2024

GABA, Glutamate dynamics and BOLD observed during cognitive processing in psychosis patients with hallucinatory traits

The perception of a voice in the absence of an external auditory source - an auditory verbal hallucination - is a characteristic symptom of schizophrenia. To better understand this phenomenon requires integration of findings across behavioural, functional, and neurochemical levels. We address this with a locally adapted MEGA-PRESS sequence incorporating interleaved unsuppressed water acquisitions, allowing concurrent assessment of behaviour, blood-oxygenation-level-dependent (BOLD) functional changes, Glutamate+Glutamine (Glx), and GABA, synchronised with a cognitive (flanker) task. We acquired data from the anterior cingulate cortex (ACC) of 51 patients with psychosis (predominantly schizophrenia spectrum disorder) and hallucinations, matched to healthy controls. Consistent with the notion of an excitatory/inhibitory imbalance, we hypothesized differential effects for Glx and GABA between groups, and aberrant dynamics in response to task. Results showed impaired task performance, lower baseline Glx and positive association between Glx and BOLD in patients, contrasting a negative correlation in healthy controls. Task-related increases in Glx were observed in both groups, with no significant difference between groups. No significant effects were observed for GABA. These findings suggest that a putative excitatory/inhibitory imbalance affecting inhibitory control in the ACC is primarily observed as tonic, baseline glutamate differences, rather than GABAergic effects or aberrant dynamics in relation to a task. HighlightsO_LIIn-vivo, GABA-edited functional 1H-MRS data were collected from 51 patients with hallucinations and a similar number of matched healthy controls C_LIO_LIReduced Glutamate+Glutamine (Glx) levels were observed in the patient group. C_LIO_LIBOLD association to baseline Glutamate+Glutamine (Glx) differed between patients and controls C_LIO_LIRobust task-related increases in measured Glx were observed in the Anterior Cingulate Cortex (ACC) C_LIO_LITask-related changes in measured Glx did not differ between patients and controls C_LI

neuroscience↗

bioRxiv2024

Effects of Host-Tree Foliage on Polymorphism in an Insect Pathogen

The theory of host-pathogen interactions has successfully shown that persistent pathogen virulence may be explained through tradeoffs between different pathogen fitness components, but classical theory cannot explain pathogen coexistence. More recent theory invokes both tradeoffs and environmental heterogeneity, but resembles classical theory in focusing on a limited range of possible tradeoffs, and therefore has seen few applications. To better understand the usefulness of tradeoff theory for explaining pathogen coexistence in nature, we measured components of pathogen fitness in two distantly related morphotypes of a baculovirus that infects larvae of the Douglas-fir tussock moth (Orgyia pseudotsugata). We show that the two morphotypes vary in multiple components of fitness, including the probability of infection given exposure to the pathogen, the incubation time of the pathogen, variability in the incubation time of the pathogen, and the detectability of the pathogen. Moreover, because the baculovirus is transmitted when host larvae accidentally consume infectious virus particles while feeding on foliage of the insects host trees, the strength and direction of the differences in fitness components of the two morphotypes depends on the host-tree species on which host larvae consume the virus. Through simulations of a model parameterized using our experimental data, we demonstrate how several varying fitness components can work in concert to promote strain coexistence, particularly highlighting the role of variability in incubation time. Our results suggest that the two morphotypes may coexist because of variation in forest tree-species composition, providing important empirical evidence that tradeoffs and environmental heterogeneity can together modulate pathogen competition.

ecology↗

bioRxiv2023

Climate-Change Driven Decline of an Insect Pathogen Increases the Risk of Defoliation by a Forest Pest Insect

The effects of climate change on forest-defoliating insects are poorly understood, a problem that is especially urgent in the case of the spongy moth (formerly "the gypsy moth", Lymantria dispar). For decades following its introduction in 1869, the spongy moth severely defoliated North American forests, but the introduction of the pathogen Entomophaga maimaiga in 1989 drastically lowered defoliation levels. E. maimaiga, however, needs cool, moist conditions, whereas climate change is bringing hot, dry conditions to the range of the spongy moth. Here we use an empirically validated mathematical model to project that climate change will sharply reduce E. maimaiga infection rates, greatly increasing spongy moth defoliation. Recent data show that defoliation has strongly rebounded, supporting our projections. Our work shows that the effects of climate change on insect pathogens can have dire consequences for forests, and demonstrates the importance of understanding how climate change can alter species interactions.

ecology↗

bioRxiv2023

GABA, glutamatergic dynamics and BOLD contrast concurrently assessed using functional MR spectroscopy during a cognitive task

A recurring issue in functional neuroimaging is how to link task-driven hemodynamic BOLD-fMRI responses to underlying neurochemistry at the synaptic level. Glutamate and GABA, the major excitatory and inhibitory neurotransmitters respectively, are typically measured with MR spectroscopy (MRS) sequences in a resting state, in the absence of a task. We propose to resolve this disconnect by applying a novel method to concurrently acquire BOLD, Glx and GABA measurements from a single voxel, using a locally adapted MEGA-PRESS sequence implementation which incorporates unsuppressed water reference signals at a regular interval, allowing continuous assessment of BOLD-related linewidth variation for fMRS applications. Healthy subjects (N = 81) performed a cognitive task (Eriksen Flanker) which was presented visually in a task-OFF, task-ON block design, with individual event stimulus timing varied with respect to the MRS readout. BOLD data acquired with the adapted MEGA-PRESS sequence were correlated with data acquired using a standard fMRI EPI sequence as a means of validating the concurrent approach: a significant (although moderate) correlation was observed, specific to the fMRS-targeted region of interest. We additionally present a novel linear model for extracting modelled spectra associated with discrete functional stimuli, building on well-established processing and quantification tools. Behavioural outcomes from the Flanker task, and activation patterns from the BOLD-fMRI sequence, were as expected from the literature. fMRS-assessed BOLD response correlated strongly with slowing of response time in the incongruent Flanker condition. Moreover, there was a significant increase in measured Glx levels (~8.8%), between task-OFF and task-ON periods. These findings verify the efficacy of our functional task and analysis pipelines for the simultaneous assessment of BOLD and metabolite fluctuations in a single voxel. As well as providing a robust basis for further work using these techniques, we also identify a number of clear directions for further refinement in future studies. HighlightsO_LIConcurrent measurement of temporally resolved metabolite estimates and local BOLD-related signal changes is demonstrated C_LIO_LIIn-vivo, GABA-edited functional 1H-MRS data were collected from 81 healthy subjects whilst they performed a cognitive task C_LIO_LIRobust task-related increases in measured Glutamate+Glutamine were observed in the Anterior Cingulate Cortex C_LIO_LIModerate correlation was observed between BOLD contrast strength as assessed by fMRS and fMRI techniques, specific to the prescribed region C_LIO_LIA novel technique for extraction of block- or event-related sub-spectra is demonstrated C_LI Graphical AbstractA novel sequence adaption for concurrent measurement of GABA-edited spectroscopy and functional BOLD changes is demonstrated on N=81 healthy subjects. Significant changes in measured Glutamate+Glutamine concentration are found, along with the expected behavioural and BOLD effects in response to a Flanker task. O_FIG O_LINKSMALLFIG WIDTH=170 HEIGHT=200 SRC="FIGDIR/small/539017v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@e06295org.highwire.dtl.DTLVardef@1683a30org.highwire.dtl.DTLVardef@1c45f7aorg.highwire.dtl.DTLVardef@1587cf0_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2019

Use of a mechanistic growth model in fish habitat restoration: juvenile Chinook salmon and steelhead as a test case

Individual growth data are useful in assessing relative habitat quality, but this approach is less common when evaluating the efficacy of habitat restoration. Furthermore, available models describing growth are infrequently combined with computational approaches capable of handling large data sets. We apply a mechanistic model to evaluate whether selection of restored habitat can affect individual growth. We used mark-recapture to collect size and growth data on sub-yearling Chinook salmon and steelhead in restored and unrestored habitat in five sampling years (2009, 2010, 2012, 2013, 2016). Modeling strategies differed for the two species: For Chinook, we compared growth patterns of individuals recaptured in restored habitat over 15-60 d with those not recaptured regardless of initial habitat at marking. For steelhead, we had enough recaptured fish in each habitat type to use the model to directly compare habitats. The model generated spatially explicit growth parameters describing size of fish over the growing season in restored vs. unrestored habitat. Model parameters showed benefits of restoration for both species, but that varied by year and time of season, consistent with known patterns of habitat partitioning among them. The model was also supported by direct measurement of growth rates in steelhead and by known patterns of spatio-temporal partitioning of habitat between these two species. Model parameters described not only the rate of growth, but the timing of size increases, and is spatially explicit, accounting for habitat differences, making it widely applicable across taxa. The model usually supported data on density differences among habitat types in Chinook, but only in a couple of cases in steelhead. Modeling growth can thus prevent overconfidence in distributional data, which are commonly used as the metric of restoration success.

ecology↗

bioRxiv2018

Untangling the dynamics of persistence and colonization in microbial communities

A central goal of community ecology is to infer biotic interactions from observed distributions of co-occurring species. Evidence for biotic interactions, however, can be obscured by shared environmental requirements, posing a challenge for statistical inference. Here we introduce a dynamic statistical model that quantifies the effects of spatial and temporal covariance in longitudinal co-occurrence data. We separate the fixed pairwise effects of species occurrences on persistence and colonization rates, a potential signal of direct interactions, from latent pairwise correlations in occurrence, a potential signal of shared environmental responses. We apply our modeling approach to a pressing epidemiological question by examining how human papillomavirus (HPV) types coexist. Our results suggest that while HPV types respond similarly to common host traits, direct interactions are sparse and weak, so that HPV type diversity depends largely on shared environmental drivers. Our modeling approach is widely applicable to microbial communities and provides valuable insights that should lead to more directed hypothesis testing and mechanistic modeling.

ecology↗

bioRxiv2018

Using biological control data to understand host-pathogen dynamics

A key assumption of models of infectious disease is that population-scale spread is driven by transmission between host individuals at small scales. This assumption, however, is rarely tested, likely because observing disease transmission between host individuals is non-trivial in many infectious diseases. Quantifying the transmission of insect baculoviruses at a small scale is in contrast straightforward. We fit a disease model to data from baculovirus epizootics (= epidemics in animals) at the scale of whole forests, while using prior parameter distributions constructed from branch-scale experiments. Our experimentally-constrained model fits the large-scale data very well, supporting the role of small-scale transmission mechanisms in baculovirus epizootics. We further compared our experimentally-based model to an unconstrained model that ignores our experimental data, serving as a proxy for models that include large-scale mechanisms. This analysis supports our hypothesis that small-scale mechanisms are important, especially individual variability in host susceptibility to the virus. Comparison of transmission rates in the two models, however, suggests that large-scale mechanisms increase transmission compared to our experimental estimates. Our study shows that small-scale and large-scale mechanisms drive forest-wide epizootics of baculoviruses, and that synthesizing mathematical models with data collected across scales is key to understanding the spread of infectious disease.

ecology↗

bioRxiv2017

Effects of multiple sources of genetic drift on pathogen variation within hosts

Changes in pathogen genetic variation within hosts alter the severity and spread of infectious diseases, with important implications for clinical disease and public health. Genetic drift may play a strong role in shaping pathogen variation, but analyses of drift in pathogens have oversimplified pathogen population dynamics, either by considering dynamics only at a single scale (within hosts, between hosts), or by making drastic simplifying assumptions (host immune systems can be ignored, transmission bottlenecks are complete). Moreover, previous studies used genetic data to infer the strength of genetic drift, whereas we test whether the genetic drift imposed by pathogen population processes can be used to explain genetic data. We first constructed and parameterized a mathematical model of gypsy moth baculovirus dynamics that allows genetic drift to act within and between hosts. We then quantified the genome-wide diversity of baculovirus populations within each of 143 field-collected gypsy moth larvae using Illumina sequencing. Finally, we determined whether the genetic drift imposed by host-pathogen population dynamics in our model explains the levels of pathogen diversity in our data. We found that when the model allows drift to act at multiple scales, including within hosts, between hosts, and between years, it can accurately reproduce the data, but when the effects of drift are simplified by neglecting transmission bottlenecks and stochastic variation in virus replication within hosts, the model fails. A de novo mutation model and a purifying selection model similarly fail to explain the data. Our results show that genetic drift can play a strong role in determining pathogen variation, and that mathematical models that account for pathogen population growth at multiple scales of biological organization can be used to explain this variation.

evolutionary biology↗

bioRxiv2017

Recurring infection with ecologically distinct human papillomavirus (HPV) types explains high prevalence and diversity

The high prevalence of human papillomavirus (HPV), the most common sexually transmitted infection, arises from the coexistence of over 200 genetically distinct types. Accurately predicting the impact of vaccines that target multiple types requires understanding the factors that determine HPV diversity. The diversity of many pathogens is driven by type-specific or \"homologous\" immunity, which promotes the spread of variants to which hosts have little immunity. To test for homologous immunity and to identify mechanisms determining HPV transmission, we fitted nonlinear mechanistic models to longitudinal data on genital infections in unvaccinated men. Our results provide no evidence for homologous immunity, instead showing that infection with one HPV type strongly increases the risk of infection with that type for years afterwards. For HPV16, the type responsible for most HPV-related cancers, an initial infection increases the one-year probability of reinfection by 20-fold, and the probability of reinfection remains 14-fold higher two years later. This increased risk occurs in both sexually active and celibate men, suggesting that it arises from auto-inoculation, episodic reactivation of latent virus, or both. Overall our results show that high HPV prevalence and diversity can be explained by a combination of a lack of homologous immunity, frequent reinfections, weak competition between types, and variation in type fitness between host subpopulations. Due to the high risk of reinfection, vaccinating boys that have not yet been exposed may be crucial to reduce prevalence, but our results suggest that there may also be large benefits from vaccinating previously infected individuals.

epidemiology↗

bioRxiv2016

Eco-evolutionary theory and insect outbreaks

Eco-evolutionary theory argues that population cycles in consumer-resource interactions are partly driven by natural selection, such that changes in densities and changes in trait values are mutually reinforcing. Evidence that this theory explains cycles in nature, however, is almost nonexistent. Experimental tests of model predictions are almost always impossible because of the long time scales over which cycles occur, but for most organisms, even tests of model assumptions are logistically impractical. For insect baculoviruses in contrast, tests of model assumptions are straightforward, and baculoviruses often drive outbreaks of forest-defoliating insects, as in the gypsy moth that we study here. We therefore used field experiments with the gypsy moth baculovirus to test two key assumptions of eco-evolutionary models of host-pathogen population cycles, that reduced host infection risk is heritable and costly. Our experiments confirm the two assumptions, and inserting parameters estimated from our data into the models gives cycles closely resembling gypsy moth outbreak cycles in North America, whereas standard models predict unrealistic stable equilibria. Our work shows that eco-evolutionary models are useful for explaining outbreaks of forest insect defoliators, while widespread observations of intense selection imposed by natural enemies on defoliators, and frequent laboratory observations of heritable and costly resistance in defoliators, suggest that eco-evolutionary dynamics may play a general role in defoliator outbreaks.

ecology↗

bioRxiv2016

Disentangling the Effects of Weather and Densityon the Dynamics of a Fungal Pathogen

Nonlinear fitting algorithms have illuminated the role of weather in human diseases, by allowing for robust tests of mechanistic transmission models, but a lack of data has prevented applications to animal diseases. This is important because classical models that neglect weather predict that there will be a host density threshold, below which epidemic intensity will be slight, but models that include weather predict that this threshold will often be obliterated by weather variability. To test the applicability of thresholds to animal diseases, we estimated infection rates of the fungal pathogen Entomophaga maimaiga in the gypsy moth, by collecting larvae during epidemics at a range of host densities and weather conditions, and we estimated the pathogen's force of infection, by exposing experimental larvae to the pathogen for 24 h periods in the field. By fitting a range of models to our data, we show that epidemics of this pathogen are best explained by a model that allows for positive effects of both host density and cool, moist weather on transmission, such that weather-only and density-dependence-only models provide vastly poorer explanations for the data. Despite the effects of weather, the combined model shows that the effects of density in E. maimaiga are strong enough to ensure that the density threshold will have important effects on the probability of epidemics. Our work shows that weather and density-dependent transmission can interact in non-intuitive ways, and provides an illustration of the usefulness of nonlinear fitting for understanding animal diseases.

ecology↗