Biology subjects

Parnell, S.

Publications and source records attributed to Parnell, S..

6 recordsLinked to original sources

bioRxiv2026

Apelin inhibits cyst growth and improves kidney function in mice with polycystic kidney disease

BackgroundAutosomal dominant polycystic kidney disease (ADPKD) is a common inherited disorder marked by numerous renal cysts that impair kidney function, with about half of affected individuals progressing to kidney failure by midlife. Patients exhibit reduced circulating apelin, a ligand of the apelin receptor, known to regulate cardiovascular function including hypertension. We tested whether diminished apelin signaling contributes to cystogenesis and if exogenous apelin receptor activation can improve disease outcomes. MethodsPlasma samples from age- and sex-matched healthy controls and ADPKD participants were analyzed for circulating apelin peptides. To assess direct cystic effects, primary ADPKD renal epithelial cells were grown as 3D collagen-embedded cysts and treated with apelin agonists. Male and female Pkd1RC/RC; Pkd2+/- (PKD) mice were treated for 27 days with apelin agonists, vehicle, or the standard of care drug, Mozavaptan. Kidney and heart weight ratios, BUN, renal cAMP, and kidney transcriptional profiles were evaluated. ResultsCirculating apelin peptides were significantly reduced in ADPKD patients despite normal kidney function (eGFR, BUN, and creatinine). In vitro, both apelin and the small molecule apelin receptor agonist Azelaprag inhibited cyst growth. Apelin and Mozavaptan reduced kidney weight, cystic index, blood urea nitrogen and renal cAMP in PKD mice, whereas Azelaprag did not. Apelin downregulated expression of genes associated with cyst progression, including Lcn2 (Ngal), Postn, and Havcr1 (Kim-1). Mozavaptan, but not apelin, induced diuresis and reduced urinary concentration. ConclusionApelin receptor activation by exogenous apelin inhibited cAMP synthesis and cyst growth and improved kidney function in an orthologous mouse model of ADPKD. We propose that the apelin receptor may be a potential therapeutic target in ADPKD.

pharmacology and toxicology↗

bioRxiv2025

Unlocking plant health survey data: an approach to quantify the sensitivity and specificity of visual inspections

Invasive plant pests and pathogens cause significant environmental and economic damage. Visual inspection remains a central tenet of plant health surveys, but its sensitivity (probability of correctly identifying the presence of a pest) and specificity (probability of correctly identifying the absence of a pest) is usually ignored. These parameters facilitate calculation of surveillance metrics which are critical for effective contingency planning and outbreak management. To address this, twenty-three citizen scientist surveyors assessed up to 175 oak trees for three symptoms of acute oak decline. The same trees were also assessed by an expert who has monitored these trees annually for over a decade. The sensitivity and specificity of surveyors was calculated using the expert data as the gold-standard. The utility of a workflow utilising Bayesian modelling was then examined using simulated data to estimate these parameters in the absence of a rarely available gold-standard dataset. There was large variation in sensitivity and specificity between surveyors and symptoms, although the sensitivity was positively related to the number of symptoms on a tree. By leveraging surveyor observations of two symptoms from a minimum of 80 trees on two sites, with knowledge of whether a site has higher ([~]0.6) or lower ([~]0.3) true disease prevalence we show that sensitivity and specificity can be estimated without gold-standard data. We highlight that sensitivity and specificity will depend on the symptoms of a pest or disease, the individual surveyor, and the survey protocol. This has consequences for how surveys are designed to detect and monitor outbreaks, as well as the interpretation of survey data that is used to inform outbreak management. Author summaryThe increasing occurrence of emerging plant pests and diseases is affecting both agricultural and natural ecosystems. Effective management and control of such pests and diseases is much easier when they are detected early. Currently, visual surveys underpin plant health surveillance, but basic metrics of the reliability of visual detection such as the sensitivity (probability of correctly identifying a positive) and specificity (probability of correctly identifying a negative) are not routinely quantified. In this study, we first quantify the sensitivity and specificity of 23 trained citizen scientist surveyors at detecting three symptoms of acute oak decline, by comparing their symptom classifications against a dataset from an expert who has conducted long-term monitoring of these trees. We demonstrate how individuals vary greatly in their ability to detect symptoms, and how different symptoms are associated with different detection error. Secondly, based on this dataset we outline a workflow developed for scenarios realistic in plant health which utilises Bayesian modelling to estimate these parameters in the absence of a rarely available gold-standard expert dataset. In summary, our results highlight variation in the reliability of visual detection, and we provide a workflow to calculate this and facilitate optimisation of risk-based surveillance strategies in plant health.

ecology↗

bioRxiv2025

Modelling the effectiveness of Integrated Pest Management strategies for the control of Septoria tritici

Reducing reliance on pesticides is an important global challenge. With increasing constraints on their use, in recent years there has been a declining trend in pesticide use for arable crops in the UK. But with increasing disease pressures and global demand for food, there is a greater need for effective measures of pest and disease control. These circumstances highlight the need for widespread adoption of sustainable alternative control measures. Integrated Pest Management (IPM) is one such solution, comprising a set of management strategies which focus on the long-term prevention, detection and control of pests, weeds and diseases. While many of these methods are acknowledged to offer effective control, their implementation has thus far been limited in practice. As a case study we considered Septoria tritici blotch (STB) (Zymoseptoria tritici), an economically important disease of wheat. We used epidemiological modelling techniques to investigate the potential of different IPM control strategies (crop residue burial, delayed sowing, variety mixtures and biocontrols). Combining existing data with a deterministic, compartmental infectious disease model of STB transmission, we simulated the implementation of an IPM regime into the STB disease system. We investigated the outcomes on disease prevalence and crop yield when comparing conventional and IPM control regimes. In a single field, for the individual implementation of IPM measures we found the starkest difference in potential yield outcomes between delayed sowing and biocontrols (greatest yields), and crop residue burial and variety mixtures (lowest yields). We also found that the collective use of IPM measures has the potential to offer individual growers comparable control to a standard fungicide regime. For a multi-field setting, representing a community of crop growers, a high proportion of growers using IPM can reduce the level of external infection incurred by the growers who maintain a fungicide regime. Author SummaryWith the UK Government seeking to reduce the environmental risk posed by pesticides, the agricultural industry is under increasing pressure to explore alternative methods of disease control. One such alternative method is Integrated Pest Management (IPM), which consists of a variety of management strategies for long-term prevention, detection and control of crop diseases. In our study, we simulate the potential outcomes of using IPM for the control of Septoria tritici blotch, a common disease of wheat. Our results suggest that a regime of IPM control methods may offer growers comparable yields and disease control to conventional fungicide treatments. Furthermore, in a wider system of crop growers, a higher proportion using IPM can reduce the level of infection incurred by all growers in the system, including those who do not use IPM. These findings can offer insight to crop growers who are considering the use of IPM, and to policy-makers who are interested in encouraging its uptake, by validating and quantifying its effectiveness relative to current standard practices.

ecology↗

bioRxiv2024

Developing epidemiological preparedness for a plant disease invasion: modelling citrus huanglongbingin the European Union

Huanglongbing (HLB; citrus greening) is the most damaging disease of citrus worldwide. While citrus production in the USA and Brazil have been affected for decades, HLB has not been detected in the European Union (EU). However, psyllid vectors have already invaded and spread in Portugal and Spain, and in 2023 the psyllid species known to vector HLB in the Americas was first reported within the EU. We develop a landscape-scale, epidemiological model, accounting for heterogeneous citrus cultivation and vector dispersal, as well as climate and disease management. We use our model to predict HLB dynamics following introduction into high-density citrus areas in Spain, assessing detection and control strategies. Even with significant visual surveillance, we predict any epidemic will be widespread on first detection, with eradication unlikely. Introducing increased inspection and roguing following first detection, particularly if coupled with intensive insecticide use, could potentially sustain citrus production for some time. However, this may require chemical application rates that are not permissible in the EU. Disease management strategies targeting asymptomatic infection will likely lead to more successful outcomes. Our work highlights modelling as a key component of developing epidemiological preparedness for a pathogen invasion that is, at least somewhat, predictable in advance.

ecology↗

bioRxiv2022

The value of volunteer surveillance for the early detection of biological invaders.

Early detection of invaders requires finding small numbers of individuals across large landscapes. It has been argued that the only feasible way to achieve the sampling effort needed for early detection of an invader is to involve volunteer groups (citizen scientists, passive surveyors, etc.). A key concern is that volunteers may have a considerable false-positive and false-negative rate. The question then becomes whether verification of a report from a volunteer is worth the effort. This question is the topic of this paper. We show that the maximum plausible incidence when the expert samples on its own, [Formula], and the maximum plausible incidence when the expert only verifies cases reported by the volunteer surveyor to be infected, [Formula], are related as O_FD O_INLINEFIG[Formula 1]C_INLINEFIGM_FD(1)C_FD Where {theta}fp and {theta}fn are the false positive and false negative rate of the volunteer surveyor, respectively. We also show that the optimal monitoring programme consists of verifying only the cases reported by the volunteer surveyor if O_FD O_INLINEFIG[Formula 2]C_INLINEFIGM_FD(2)C_FD Where TN is the cost of a sample taken by the expert and TX is the cost of an expert verifying a case reported by a volunteer surveyor.

ecology↗

bioRxiv2022

Out of sight: Surveillance strategies for emerging vectored plant pathogens.

Emerging pests and pathogens of plants are a major threat to natural and managed ecosystems worldwide. Whilst it is well accepted that surveillance activities are key to both the early detection of new incursions and the ability to identify pest-free areas, the performance of these activities must be evaluated to ensure they are fit for purpose. This requires consideration of the number of potential hosts inspected or tested as well as the epidemiology of the pathogen and the detection method used. In the case of plant pathogens, one particular concern is whether the visual inspection of plant hosts for signs of disease is able to detect the presence of these pathogens at low prevalences, given that it takes time for these symptoms to develop. One such pathogen is the ST53 strain of the vector-borne bacterial pathogen Xylella fastidiosa in olive hosts, which was first identified in southern Italy in 2013. Additionally, X. fastidiosa ST53 in olive has a rapid rate of spread, which could also have important implications for surveillance. In the current study, we evaluate how well visual surveillance would be expected to perform for this pathogen and investigate whether molecular testing of either tree hosts or insect vectors offer feasible alternatives. Our results identify the main constraints to each of these strategies and can be used to inform and improve both current and future surveillance activities.

plant biology↗