Biology subjects

Wells, K.

Publications and source records attributed to Wells, K..

11 recordsLinked to original sources

bioRxiv2026

DDX3 Regulates the Innate Immune Response to Bone Sarcomas

Osteosarcoma (OS) and Ewing sarcoma (EWS) are the most common malignant bone tumors in children and adolescents, with survival rates around 25% in metastatic disease and few advances in treatment in decades. High DDX3 expression has been reported across various sarcoma subtypes. Depending on the context, DDX3 appears to have opposing roles in regulating the tumor immune microenvironment. Within macrophages, DDX3 promotes inflammatory cytokine expression and supports immune cell function. In contrast, in tumor cells DDX3 suppresses a pro-inflammatory state by unwinding dsRNAs, preventing a Type I interferon response. We show that inhibiting DDX3 with RK-33 leads to dsRNA accumulation, inducing a Type I interferon response and broader inflammatory gene expression changes across multiple sarcoma models, shifting macrophage polarization toward a pro-inflammatory M1-like phenotype. To evaluate whether this innate immune microenvironmental remodeling could translate into clinical benefit, we assessed the therapeutic efficacy of RK-33 alone or in combination with mifamurtide, an immunostimulant, in immune competent mouse models of osteosarcoma, with metastatic burden as the primary outcome. We found that in the absence of MYC over-expression, the combination treatment significantly reduced metastatic spread. These findings support targeting DDX3 as a novel innate immune based therapeutic strategy and highlight that the tumors molecular landscape critically influences therapeutic responsiveness.

cancer biology↗

bioRxiv2026

Multidimensional isotopic niches inform coexistence mechanisms in an Alpine ungulate community

O_LIUnderstanding functional community structure and the niche-based mechanisms that enable coexistence among sympatric species is essential for explaining how biodiversity is maintained in natural systems, and for anticipating how ecological communities will respond to ongoing environmental change. Stable isotope analysis provides a process-oriented perspective on resource use by integrating information across time and space, thereby allowing reconstruction of realised isotopic niches that reflect multiple dimensions of ecological differentiation. C_LIO_LIWe applied this framework to a community of ungulates in the Central-Eastern Italian Alps, including red deer (Cervus elaphus), roe deer (Capreolus capreolus), and Alpine chamois (Rupicapra rupicapra). Using stable isotope ratios in summer-grown hair segments ({delta}13C, {delta}15N, {delta}34S, {delta}18O, {delta}2H), we quantified species-specific n-dimensional niche hypervolumes within a Bayesian framework and estimated niche regions, overlap probabilities, univariate differentiation and multivariate structure. C_LIO_LIDespite broad dietary overlap typically observed among these ungulates, we found clear isotopic niche segregation, with mean pairwise overlap consistently remaining below 40%. Three dimensions emerged as primary drivers of differentiation: water sourcing ({delta}18O), diet quality ({delta}15N), and habitat openness ({delta}13C). Specifically, chamois appeared to derive more water from plants in their diet rather than from drinking, and to consume a higher-quality diet compared to Cervids. Red deer relied more heavily on forested habitats for resource use compared to roe deer and chamois, and additional isotopic differences between red deer and roe deer may stem from fine-scale abiotic conditions like microclimate and topography. We found no isotopic evidence for differential niche breadth among the three ungulate species. C_LIO_LITogether, these patterns highlight functional differentiation across multiple ecological axes, offering mechanistic insight into how these ungulates segregate realised niche space despite substantial potential for resource overlap. This multi-element isotope perspective underscores the value of integrative, process-based approaches for understanding current coexistence as well as improving predictions of how mammal communities may reorganise under accelerating environmental change. C_LI

ecology↗

bioRxiv2025

Optimising biodiversity offsetting to account for habitat succession and species colonisation dynamics

Biodiversity offsetting schemes aim to balance habitat loss from development through conservation and restoration, yet their long-term ecological outcomes under repeated impact-offset cycles remain uncertain. Using a conceptual modelling framework based on forest succession, we show that offsetting success hinges on two critical factors: the scale of habitat compensation and the duration of conservation commitments. We show that effective offsetting requires not only restoring areas larger than those impacted, but also sustaining conservation over timeframes that align with the ecological timescales of habitat development. Rapid habitat conversion consistently undermines offset success, particularly since the persistence and recovery of habitat-dependent, colonising species are highly sensitive to how quickly habitats are lost, how long restoration sites are protected, and how much time they need to reach sufficient habitat quality. Moreover, we show that short-term improvements do not necessarily meet long-term biodiversity targets. Given that habitat succession unfolds over decades or more, offset policies must move beyond short-term metrics. We emphasise that offsetting schemes must be scrutinized to ensure sufficient spatial compensation and time for habitat succession. Offsetting schemes should also account for different ecological indicators to avoid the ecological fallacy of short-term restoration success resulting in long-term biodiversity and ecosystem integrity loss.

systems biology↗

bioRxiv2024

The influence of environment on mosquito feeding patterns: a meta-analysis of "universal" DNA diet studies in a global context

Mosquitoes have innate preferences for their blood-meal hosts, but these can be modified by the environment, with implications for disease spread under climate and land use change. To predict the spread of vector-borne pathogens more accurately we need to better understand blood-meal plasticity under changing environmental conditions. We compiled blood-meal studies for six prominent disease vectoring mosquitoes from around the globe (Aedes aegypti, Ae. albopictus, Anopheles funestus, An. gambiae, Cx. pipiens, and Cx. quinquefasciatus). We targeted studies that used universal molecular methods (typically PCR/metabarcoding) to identify hosts from a wide range of candidates (as opposed to studies using methods that presuppose host identity - such as precipitin, ELISA). We found that blood-meals from the >15,600 analysed were mostly from the expected host-groups for each mosquito species, but we frequently encountered atypical hosts (e.g. mammalophilic species feeding on birds/reptiles). The universal methods used by the studies in our metanaalysis identified high host richness, and we found [≥]174 hosts for Culex and [≤]65 species for Aedes mosquitoes at a considerably increased discovery rate of novel hosts per sampling effort. We used a hierarchical Dirichlet regression model to analyse global variation in feeding patterns in relation to environmental datasets (land use, precipitation, mean annual temperature, latitude, human and livestock density). Land use, mean annual temperature and poultry density had noticeable effects on blood-meal selection of Ae. aegypti, Cx. pipiens and Cx. quinquefasciatus. Although host density was a factor in blood-meal selection - host choice is not a simple function of host availability, as has previously been observed, but contingent on other drivers. While our compiled dataset afforded us these insights, improving resolution and consistency of data gathering and reporting would improve the precision of how blood-meal studies can inform us of present and potential risks of pathogen transmission events.

ecology↗

bioRxiv2024

Gene editing of pigs to control influenza A virus infections

Proteolytic activation of the hemagglutinin (HA) glycoprotein by host cellular proteases is pivotal for influenza A virus (IAV) infectivity. Highly pathogenic avian influenza viruses possess the multibasic cleavage site of the HA which is cleaved by ubiquitous proteases, such as furin; in contrast, the monobasic HA motif is recognized and activated by trypsin-like proteases, such as the transmembrane serine protease 2 (TMPRSS2). Here, we aimed to determine the effects of TMPRSS2 on the replication of pandemic H1N1 and H3N2 subtype IAVs in the natural host, the pig. The use of the CRISPR/Cas 9 system led to the establishment of homozygous gene edited (GE) TMPRSS2 knockout (KO) pigs. Delayed IAV replication was demonstrated in primary respiratory cells of KO pigs in vitro. IAV infection in vivo resulted in significant reduction of virus shedding in the upper respiratory tract, and lower virus titers and pathological lesions in the lower respiratory tract of TMPRSS2 KO pigs as compared to WT pigs. Our findings could support the commercial use of GE pigs to minimize (i) the economic losses caused by IAV infection in pigs, and (ii) the emergence of novel IAVs with pandemic potential through genetic reassortment in the "mixing vessel", the pig.

microbiology↗

bioRxiv2023

Protected areas enhance avian food webs

Restoring and conserving habitat and the species they shelter has become a primary focus to mitigate the current extinction crisis. Setting aside land designated as protected areas (PAs) is an efficient way of achieving these aims. This strategy has been proven to enhance different aspects of species richness and abundance across ecosystems1-4. However, to truly understand the effects of global environmental change on biodiversity, and the efficiency of our mitigation measures, we must account for one of its fundamental dimensions: species interactions. Here we show that PAs enhance avian food webs across Europe by protecting key network and species traits. Using 376,556 observational records of 509 bird species from citizen science databases distributed across 45 networks of PAs, we found beneficial effects of protection on 10 out of 13 food web properties on an average of 25.9% of sites. PAs enhance food webs by harbouring large top predators, in turn increasing the length of biomass flow paths from basal to top species. Furthermore, we link these beneficial effects to environmental drivers and PA designations. PA benefits were augmented by specific protection goals such as European Directives for conservation. This study provides evidence for the effectiveness of PAs as a strategy to preserve fundamental aspects of biodiversity beyond species richness. We anticipate our study to be a starting point for the development of comprehensive frameworks to assess the critical role of PAs in safeguarding biodiversity worldwide. Improving the mapping of species occurrences and ecological interactions across the globe will is fundamental to develop optimal strategies for establishing networks of PAs aimed at protecting all aspects of ecosystem diversity.

ecology↗

bioRxiv2022

Dynamic Generalised Additive Models (DGAM) for forecasting discrete ecological time series

O_LIGeneralised Additive Models (GAMs) are increasingly popular for describing smooth nonlinear relationships between predictors and response variables. GAMs are particularly relevant in ecology for representing hierarchical functions for discrete responses that encompass complex features including zero-inflation, bounding and uneven sampling. However, GAMs are less useful for producing forecasts as their smooth functions provide unstable predictions outside the range of training data. C_LIO_LIWe introduce Dynamic Generalised Additive Models (DGAMs), where the GAM linear predictor is jointly estimated with unobserved dynamic components to model time series that evolve as a function of nonlinear predictor associations and latent temporal processes. These models are particularly useful for multivariate series, as they can estimate hierarchical smooths while learning complex temporal associations via dimension-reduced latent factor processes. We implement our models in the mvgam R package, which uses the mgcv and rjags packages to construct smoothing splines and estimate unobserved parameters in a probabilistic framework. C_LIO_LIUsing simulations, we illustrate how our models outperform competing formulations in realistic ecological forecasting tasks while identifying important smooth predictor functions. We use a real-world case study to highlight some of mvgams key features, which include functions for: calculating correlations among series latent trends, performing model selection using rolling window forecasts, online data augmentation via a recursive particle filter, and visualising probabilistic uncertainties for smooths and predictions. C_LIO_LIDynamic GAMs (DGAM) offer a solution to the challenge of forecasting discrete time series while estimating ecologically relevant nonlinear predictor associations. Our Bayesian latent factor approach will be particularly useful for exploring competing dynamic ecological models that encompass hierarchical smoothing structures while providing forecasts with robust uncertainties, tasks that are becoming increasingly important in applied ecology. C_LI

ecology↗

bioRxiv2021

Disruption of metapopulation structure effectively reduces Tasmanian devil facial tumour disease spread at the expense of abundance and genetic diversity

Metapopulation structure (i.e. the spatial arrangement of local populations and corridors between them) plays a fundamental role in the persistence of wildlife populations, but can also drive the spread of infectious diseases. While the disruption of metapopulation connectivity can reduce disease spread, it can also impair host resilience by disrupting gene flow and colonisation dynamics. Thus, a pressing challenge for many wildlife populations is to elucidate whether the benefits of disease management methods that reduce metapopulation connectivity outweigh the associated risks. Directly transmissible cancers are clonal malignant cell lines capable to spread through host populations without immune recognition, when susceptible and infected hosts become in close contact. Using an individual-based metapopulation model we investigate the effects of the interplay between host dispersal, disease transmission rate and inter-individual contact distance for transmission (determining within-population mixing) on the spread and persistence of a transmissible cancer, Tasmanian devil facial tumour disease (DFTD), from local to regional scales. Further, we explore population isolation scenarios to devise management strategies to mitigate disease spread. Disease spread, and the ensuing population declines, are synergistically determined by individuals dispersal, disease transmission rate and within-population mixing. Low to intermediate transmission rates can be magnified by high dispersal and inter-individual transmission distance. Once disease transmission rate is high, dispersal and inter-individual contact distance do not impact the outcome of the disease transmission dynamics. Isolation of local populations effectively reduced metapopulation-level disease prevalence but caused severe declines in metapopulation size and genetic diversity. The relative position of managed (i.e. isolated) populations within the metapopulation had a significant effect on disease prevalence, highlighting the importance of considering metapopulation structure when implementing metapopulation-scale disease control measures. Our findings suggests that population isolation is not an ideal management method for preventing disease spread in species inhabiting already fragmented landscapes, where genetic diversity and extinction risk are already a concern, such as the Tasmanian devil.

ecology↗

bioRxiv2018

Distinct spread of DNA and RNA viruses among mammals amid prominent role of domestic species

Emerging infectious diseases arising from pathogen spillover from mammals to humans comprise a substantial health threat. Tracing virus origin and predicting the most likely host species for future spillover events are major objectives in One Health disciplines. However, the species that share pathogens most widely with other mammals, and the role of different wildlife groups in sharing viruses with humans remain poorly identified. To address this challenge, we applied network analysis and Bayesian hierarchical models to a global database of mammal-virus associations. We show that domesticated mammals and some primates hold the most central positions in networks of known mammal-virus associations. We revealed strong evidence that DNA viruses were phylogenetically more host specific than RNA viruses, while the frequencies of sharing viruses among hosts and the proportion of zoonotic viruses in hosts were larger for RNA than DNA viruses. Among entire host-virus networks, Carnivora and Chiroptera hold central positions for mainly sharing RNA viruses with other host species, while network centrality of Primates scored relatively high for sharing DNA viruses. Ungulates hold central positions for sharing both RNA and DNA viruses. Acknowledging the role of domestic species in addition to host and virus traits in patterns of virus sharing is necessary to improve our understanding of virus spread and spillover in times of global change.

ecology↗

bioRxiv2018

Optimizing non-invasive sampling of an infectious bat virus

Notable outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to the ecological origins of bat-borne zoonoses, resulting in an increase in ecological and epidemiological studies on bat populations in Africa, Asia, and Australia. The aim of many of these studies is to identify new viral agents with field sampling methods that collect pooled urine samples from large plastic sheets placed under a bat roost. The efficiency of under-roost sampling also makes it an attractive method for gathering roost-level prevalence data. However, the method allows multiple individuals to contribute to a pooled sample, potentially introducing positive bias. To assess the ability of under-roost sampling to accurately estimate viral prevalence, we constructed a probabilistic model to explore the relationship between four sampling designs (quadrant, uniform, stratified, and random) and estimation bias. We modeled bat density and movement with a Poisson cluster process and spatial kernels, and simulated the four underroost sheet sampling designs by manipulating a spatial grid of hexagonal tiles. We performed global sensitivity analyses to identify major sources of estimation bias and provide recommendations for field studies that wish to estimate roost-level prevalence. We found that the quadrant-based design had a positive bias 5-7 times higher than other designs due to spatial auto-correlation among sampling sheets and clustering of bats in the roost. The sampling technique is therefore highly sensitive to viral presence; but lacks specificity, providing poor information regarding dynamics in viral prevalence. Given population sizes of 5000-14000, our simulation results indicate that using a stratified random design to collect 30-40 urine samples from 80-100 sheets, each with an area of 0.75-1m2, would provide sufficient estimation of true prevalence with minimum sampling bias and false negatives. However, acknowledging the general problem of data aggregation, we emphasize that robust inference of true prevalence from field data require information of underpinning roost sizes. Our findings refine our understanding of the underroost sampling technique with the aim of increasing its specificity, and suggest that the method be further developed as an efficient non-invasive sampling technique that provides roost-level estimates of viral prevalence within a bat population.

ecology↗

bioRxiv2018

Individual and temporal variation in pathogen load predicts long-term impacts of an emerging infectious disease

Emerging infectious diseases increasingly threaten wildlife populations. Most studies focus on managing short-term epidemic properties, such as controlling early outbreaks. Predicting long-term endemic characteristics with limited retrospective data is more challenging. We used individual-based modelling informed by individual variation in pathogen load and transmissibility to predict long-term impacts of a lethal, transmissible cancer on Tasmanian devil (Sarcophilus harrisii) populations. For this, we employed Approximate Bayesian Computation to identify model scenarios that best matched known epidemiological and demographic system properties derived from ten years of data after disease emergence, enabling us to forecast future system dynamics. We show that the dramatic devil population declines observed thus far are likely attributable to transient dynamics. Only 21% of matching scenarios led to devil extinction within 100 years following devil facial tumour disease (DFTD) introduction, whereas DFTD faded out in 57% of simulations. In the remaining 22% of simulations, disease and host coexisted for at least 100 years, usually with long-period oscillations. Our findings show that pathogen extirpation or host-pathogen coexistence are much more likely than the DFTD-induced devil extinction, with crucial management ramifications. Accounting for individual-level disease progression and the long-term outcome of devil-DFTD interactions at the population-level, our findings suggest that immediate management interventions are unlikely to be necessary to ensure the persistence of Tasmanian devil populations. This is because strong population declines of devils after disease emergence do not necessarily translate into long-term population declines at equilibria. Our modelling approach is widely applicable to other host-pathogen systems to predict disease impact beyond transient dynamics.

epidemiology↗