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Biology subjects

Leclerc, M.

Publications and source records attributed to Leclerc, M..

12 recordsLinked to original sources

Plant architecture as a tool to mitigate late blight: significant but variable contributions of erect, aerated potato canopies over six years of field trials

To reduce reliance on pesticides in potato production, alternative partial control methods are needed, yet their efficacy and reliability are seldom quantified over multiple years. Canopy architecture is a potential lever against late blight, caused by Phytophthora infestans, a pathogen whose infection and development are highly driven by humidity and temperature. However, its practical value under field conditions in pure crop stands remains poorly documented. We compared two commercial potato cultivars with contrasting canopy architectures but similar leaf tissue susceptibility to P. infestans, in replicated field trials conducted over six years (2011 2016) under irrigation, and over three of these years also without irrigation. Disease and canopy development (height, closure, leaf area, stem number) were monitored throughout the growing seasons and analysed using non linear growth models. Monalisa, an erect and ramose cultivar, consistently slowed epidemic progress relative to Bintje, a semi-erect and leafy cultivar. This effect was significant in every non irrigated trial but in only half of the irrigated ones. No single canopy trait explained this suppression across all years and conditions. However, canopy closure at the time of inoculation was the most consistent correlate, supporting the hypothesis of a microclimate mediated effect. Disease reduction seldom translated into a yield benefit, except under irrigation, where Monalisa occasionally out yielded Bintje. Our results confirm that canopy architecture can complement other levers within integrated late blight management. Nevertheless, its significant year to year variability and dependence on environmental conditions must be accounted for if it is to be deployed as a reliable control method.

plant biology↗

Environmental drivers of heritable trait variation and lag of adaptation to climate in Hordeum murinum

1. Most plant species are genetically differentiated among populations, often reflected by phenotypic trait variation that corresponds to local adaptation. Yet the strength of local adaptation and heritable contribution to phenotypic traits vary across traits, species, and environments. Additionally, climate change is rapidly altering environmental conditions, and the climate may shift faster than populations can adapt or track the change via dispersal, resulting in adaptive lags. However, it remains unclear how widespread such adaptive lags are across plant species. 2. We focused on Hordeum murinum, an annual ruderal grass widespread in Europe. We combined continental-scale in situ measurements of 2070 plants across 207 populations with common garden experiments across two contrasting climates and two soil types to disentangle heritable variation from phenotypic plasticity and assess potential adaptive lags under climate change. 3. We found that heritable variation was pronounced in developmental traits, particularly flowering time and plant height, while seed weight, reproductive investment and SLA showed intermediate heritable contribution, and flag leaf area and total biomass were primarily plastic. Heritable trait variation was strongly associated with temperature at the populations origin, and trait clines were consistent with in situ patterns, suggesting that temperature is the main driver of genetic differentiation in H. murinum. However, we detected that fitness peaked in populations originating from warmer climates, indicating that evolutionary responses may not keep pace with rapid environmental shifts. 4. Synthesis: Our results highlight that H. murinum harbors substantial heritable variation, shaped primarily by temperature. However, the pace of evolutionary change may be insufficient to track ongoing climate change, leaving populations potentially vulnerable to future environmental conditions.

ecology↗

Text guidance is powerful but prompt-sensitive for weakly-supervised leaf symptom segmentation

Accurate segmentation of plant disease symptoms is essential for crop monitoring and phenotyping, yet it typically requires costly pixel-level annotations. Weakly supervised semantic segmentation (WSSS) alleviates this burden using image-level labels, but its performance depends on the quality of spatial priors such as class activation maps (CAMs). We investigate whether text-guided segmentation with the Segment Anything Model 3 (SAM3) can serve as an alternative weak supervision signal. Three pseudo-mask generation strategies are compared: (i) CAMs refined with SAM or SAM3, (ii) zero-shot text-guided SAM3, and (iii) a hybrid approach combining weak spatial cues with text prompts. The resulting pseudo-masks are used to train a DeepLabV3 model. Text guidance alone matches or outperforms conventional WSSS, achieving up to 0.46 IoU without spatial supervision and 0.61 IoU on a public dataset, although performance is sensitive to text prompt formulation. The hybrid strategy improves robustness, reaching 0.50 IoU on the primary dataset and 0.58 IoU on the additional dataset while reducing prompt sensitivity. Overall, text guidance is a promising alternative to conventional weak supervision, while hybrid approaches provide a more robust solution for plant disease segmentation.

plant biology↗

NeighborFinder: an R package inferring local microbial network around a species of interest

1MotivationUnderstanding interactions from microbiome data is a central aspect in microbial ecology, as it provides insights into ecosystem stability, disease mechanisms, and can be used to design synthetic communities. Current network inference tools reconstruct global networks from co-abundance data, which means they capture the overall correlation structure for the entire set of taxa considered. These approaches are computationally intensive and suboptimal when the focus is on the local neighborhood of specific taxa of interest. ResultsWe introduce NeighborFinder, a local network inference method that enables the targeted discovery of direct neighbors around a species of interest. Using cross-validated multiple linear regression with[l] 1 penalty and microbiome-specific filters, our approach infers interpretable species-centered interactions, with F1 score [≥] 0.95 on simulated cohorts ranging from 250 to 1000 samples. This method is well-suited for large metagenomic datasets and is particularly valuable for exploratory studies where the targeted hypotheses outweigh the need for global community structure. The approach complements existing methods by being a biologically intuitive and computationally efficient. Availability and ImplementationThe R package is freely available on GitHub: https://github.com/metagenopolis/NeighborFinder. The data and source code used to calculate performances and produce the use case example in this paper can be found respectively at: https://doi.org/10.57745/UPITJ0 and https://doi.org/10.57745/HJLWW4. Supplementary informationSupplementary data are available

systems biology↗

Spatio-temporal modeling of host-pathogen interactions using level set method

Phenotyping host-pathogen interactions is crucial for understanding infectious diseases in plants. Traditionally, this process has relied on visual assessments or manual measurements, which can be subjective and labor-intensive. Recent advances in image processing and mathematical modeling enable precise and high-throughput phenotyping. In this study, we propose an innovative approach in plant pathology by combining image processing techniques with the level set method. This integrated approach leverages the strengths of both methodologies to provide accurate, robust, and detailed analysis of leaf and lesion evolution. By employing this combination, we achieve precise delineation of lesion boundaries and track their progression over time, offering clear visual feedback. This enhances the ability of the method to monitor plant health status comprehensively. The results, which track the growth of Peyronellaea pinodes on the stipules of two pea cultivars and the associated leaf deformation, provide an accurate visual representation of disease progression. This model represents a significant advancement in plant disease phenotyping, offering precise and detailed insights that can enhance our understanding of host-pathogen interactions.

plant biology↗

Energetical equivalence between air resistance and gradients in running

For a given running speed and for any wind speed, we calculate the slope for which the effort to overcome in the absence of wind is energetically equivalent to running with the original wind speed on a flat track. The influence of headwind and tailwind is thus made numerically comparable to the influence of a positive or negative slope. The same applies to the lack of air resistance on a treadmill and its compensation by adjusting the incline. Moreover, for turning point routes physiological corrections are considered and the impact of speed adjustments is analyzed.

physiology↗

Higher Angiotensin I Converting Enzyme 2 (ACE2) levels in the brain of individuals with Alzheimer's disease.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major cause of death in the elderly. Cognitive decline due to Alzheimers disease (AD) is frequent in the geriatric population disproportionately affected by the COVID-19 pandemic. Interestingly, central nervous system (CNS) manifestations have been reported in SARS-CoV-2-infected patients. In this study, we investigated the levels of Angiotensin I Converting Enzyme 2 (ACE2), the main entry receptor of SARS-COV-2 in cells, in postmortem parietal cortex samples from two independent AD cohorts, totalling 142 persons. Higher concentrations of ACE2 protein and mRNA were found in individuals with a neuropathological diagnosis of AD compared to age-matched healthy control subjects. Brain levels of soluble ACE2 were inversely associated with cognitive scores (p = 0.02), markers of pericytes (PDGFR{beta}, p=0.02 and ANPEP, p = 0.007) and caveolin1 (p = 0.03), but positively correlated with soluble amyloid-{beta} peptides (A{beta}) concentrations (p = 0.01) and insoluble phospho- tau (S396/404, p = 0.002). No significant differences in ACE2 were observed in the 3xTgAD mouse model of tau and A{beta} neuropathology. Results from immunofluorescence and Western blots showed that ACE2 protein is mainly localized in neurons in the human brain but predominantly in microvessels in the mouse brain. The present data show that an AD diagnosis is associated with higher levels of soluble ACE2 in the human brain, which might contribute to a higher risk of CNS SARS-CoV-2 infection.

neuroscience↗

Imaging with spatio-temporal modelling to characterize the dynamics of plant-pathogen lesions

Within-host spread of pathogens is an important process for the study of plant-pathogen interactions. However, the development of plant-pathogen lesions remains practically difficult to characterize beyond the common traits such as lesion area. Here, we address this question by combining image-based phenotyping with mathematical modelling. We consider the spread of Peyronellaea pinodes on pea stipules that were monitored daily with visible imaging. We assume that pathogen propagation on host-tissues can be described by the Fisher-KPP model where lesion spread depends on both a logistic growth and an homogeneous diffusion. Model parameters are estimated using a variational data assimilation approach on sets of registered images. This modelling framework is used to compare the spread of an aggressive isolate on two pea cultivars with contrasted levels of partial resistance. We show that the expected slower spread on the most resistant cultivar is actually due to a significantly lower diffusion coefficient. This study shows that combining imaging with spatial mechanistic models can offer a mean to disentangle some processes involved in host-pathogen interactions and further development may allow a better identification of quantitative traits thereafter used in genetics and ecological studies.

ecology↗

Cerebrovascular insulin receptors are defective in Alzheimer's disease

Central response to insulin is suspected to be defective in Alzheimers disease (AD), but its localization in the brain remains unknown. While most insulin is secreted in the bloodstream by the pancreas, how it interacts with the blood-brain barrier (BBB) to alter brain function remains poorly defined. Here, we show that human and murine cerebral insulin receptors (INSR), particularly the long isoform INSR-B, are concentrated in microvessels rather than in the parenchyma. Vascular concentrations of INSR-B were lower in the parietal cortex of subjects diagnosed with AD, positively correlating with cognitive scores, leading to a shift toward a higher INSR-A/B ratio, consistent with cerebrovascular insulin resistance in the AD brain. Vascular INSR was inversely correlated with {beta}-amyloid (A{beta}) plaques and {beta}-site APP cleaving enzyme 1 (BACE1), but positively correlated with insulin-degrading enzyme (IDE), neprilysin and ABCB1. Using brain cerebral intracarotid perfusion, we found that the transport rate of insulin across the BBB remained very low (<0.03 {micro}l.g-1.s-1) and was not inhibited by an INSR antagonist. However, intracarotid perfusion of insulin induced the phosphorylation of INSR{beta} which was restricted to microvessels. Such an activation of vascular INSR was blunted in 3xTg-AD mice, suggesting that AD neuropathology induces insulin resistance at the level of the BBB. Overall, the present data in postmortem AD brains and an animal model of AD indicate that defects in the INSR localized at the BBB strongly contribute to brain insulin resistance in AD, in association with A{beta} pathology. HighlightsO_LICirculating insulin activates brain insulin receptors in microvessels. C_LIO_LIBBB INSR contribute to cerebral insulin resistance in AD. C_LIO_LICognitive impairment in AD is associated with a loss of cerebrovascular INSR-B. C_LIO_LILoss of isoform INSR-B is associated with increased BACE1 activity. C_LI SummaryLeclerc et al. show that circulating insulin activates cerebral insulin receptor localized on the blood-brain-barrier level (BBB), not in the parenchyma. Experiments with human brain samples and animal models provide evidence that INSR at the BBB are impaired in Alzheimers disease, thereby contributing to brain insulin resistance.

neuroscience↗

Optimal spatial monitoring of populations described by reaction-diffusion models

Using spatialised population measurements and related geographic habitat data, it is quite feasible nowadays to derive parsimonious spatially explicit population models and to carry on their parameter estimation. To achieve such goal, reaction-diffusion models are fairly common in conservation biology and agricultural plant health where they are used, for example, for landscape planning or epidemiological surveillance. Unfortunately, if the mathematical methods and computational power are readily available, biological measurements are not. Despite the high throughput of some habitat related remote sensors, the experimental cost of biological measurements are, in our view, one of the worst bottleneck against a widespread usage of reaction-diffusion models. Hence, in this paper, we will recall some classical methods for optimal experimental design that we deem useful to spatial ecologist. Using two case studies, one in landscape ecology and one in conservation biology, we will show how to construct a priori experimental design minimizing variance of parameter estimates, enabling optimal experimental setup with pre and post hoc filtering for accommodating additional constraints.

ecology↗

Besides stem canker severity, oilseed rape host genotype matters for the production of Leptosphaeria maculans fruiting bodies

For fungal cyclic epidemics on annual crops, the pathogen carry-over is an important step in designing disease control strategies. However, it remains particularly difficult to estimate and predict. Plant resistance affects the pathogen development within the epidemics but we lack data on the inter-annual transmission of inoculum. We addressed this question by considering Leptosphaeria maculans on 15 oilseed rape genotypes in field during 4 growing seasons. Stem canker severity of host genotypes was visually scored at harvest while the number of fruiting bodies produced on incubated stubble was quantified using an automated image analysis framework. Our results confirm that higher severity at harvest leads to higher fruiting body production and is significantly affected by host genotype and Nitrogen supply. Most interestingly, we show that the production of fruiting bodies is significantly and substantially affected by host genotype, independently of severity at harvest. Tracking individual stems through incubation, we confirm for the first time that the oilseed rape genotype has a direct effect, not only through disease severity. While the genericity of this finding should be investigated on other fungi, this major effect of genotype on inoculum carry-over should be taken into account in models of varietal deployment strategies.

microbiology↗

Automated image processing to support the analysis of between-year transmission of Leptosphaeria maculans in field conditions

Understanding the transmission of inoculum between periods where the host plants are present is central for predicting the development of plant diseases and optimising mitigation strategies. However, the production at the end of the growing period, the survival during the intercrop period, and the emergence or emission of inoculum after sowing or planting can be highly variable, difficult to assess and generally inferred indirectly from symptoms data. As a result, there is a lack of large data sets which is a major brake for the study of these epidemiological processes. Here we focus on Leptosphaeria maculans that causes the black leg of oilseed rape. After having infected leaves, at early stages of the plant, and migrating into the stem, it causes a basal stem canker before harvest. It then survives on stubble left in the field from which ascospores are emitted at the beginning of the next growing period. In this study we first developed an image processing framework to estimate the density of fruiting bodies produced on stubble. Then, we used this framework to analyse automatically a large number of stems collected in oilseed rape fields among a cultivated area. Having performed a quality assessment of the processing chain we used the output data to investigate how the potential level of inoculum may change with the source field, the considered year and the stem canker severity at harvest. Besides the insights gain into the blackleg of oilseed rape, this work shows how image-based phenotyping may support epidemiological studies by increasing substantially the precision of high throughput disease data.

epidemiology↗