Biology subjects

Pichon, B.

Publications and source records attributed to Pichon, B..

9 recordsLinked to original sources

bioRxiv2025

Doallochthonous flows explain deviations from the Redfield ratio in lakes?

Lakes or streams show strong deviations of their stoichiometry compared to the Redfield ratio measured in oceans. Allochthonous inflows of resources might contribute to those deviations directly, by changing composition of detritus in ecosystems, but also indirectly, by shaping local community dynamics and stoichiometric constrains within aquatic ecosystems. Here, we developed a stoichiometric model to understand those direct and indirect mechanisms through which allochthonous inflows affect seston stoichiometry. Our results emphasize that increasing allochthonous inflows promotes heterotrophic functioning, and relax decomposer carbon limitation. This release of stoichiometric constrain of decomposers (i) destabilizes the aquatic ecosystem by promoting competition between phytoplankton and decomposers, (ii) decreases the ability of the lake to regulate allochthonous flows, and (iii) push seston stoichiometry away from the Redfield ratio. Our study emphasize how the quantity and the stoichiometry of inflows shape community dynamics and the elemental constraints within the ecosystem, and open perspectives for stoichiometry at the landscape extent.

ecology↗

bioRxiv2024

Estimating distance to tipping point from dryland ecosystem images

Resource-limited ecosystems, such as drylands, can exhibit self-organized spatial patterns. Theory suggests that these patterns can reflect increasing degradation levels as ecosystems approach possible tipping points to degradation. However, we still lack ways of estimating a distance to degradation points that is comparable across sites. Here, we present an approach to do just that from images of ecosystem landscapes. After validating the approach on simulated landscapes, we applied it to a global dryland dataset, estimated the distance of each of the sites to their degradation point and investigated the drivers of that distance. Crossing this distance with aridity projections makes it possible to pinpoint the most fragile sites among those studied. Our approach paves the way for a risk assessment method for spatially-organized ecosystems.

ecology↗

bioRxiv2024

Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology

ObjectiveThe transcriptomic classification of intrahepatic cholangiocarcinomas (iCCA) has been recently refined from two to five classes, associated with pathological features, targetable genetic alterations and survival. Despite its prognostic and therapeutic value, the classification is not routinely used in the clinic because of technical limitations, including insufficient tissue material or the cost of molecular analyses. Here, we assessed a self-supervised learning (SSL) model for predicting iCCA transcriptomic classes on whole-slide digital histological images (WSIs) DesignTranscriptomic classes defined from RNAseq data were available for all samples. The SSL method, called Giga-SSL, was used to train our model on a discovery set of 766 biopsy slides (n=137 cases) and surgical samples (n=109 cases) from 246 patients in a five-fold cross-validation scheme. The model was validated in The Cancer Genome Atlas (TCGA) (n= 29) and a French external validation set (n=32). ResultsOur model showed good to very good performance in predicting the four most frequent transcriptomic class in the discovery set (area under the curve [AUC]: 0.63-0.84), especially for the hepatic stem-like class (37% of cases, AUC 0.84). The model performed equally well in predicting these four transcriptomic classes in the two validation sets, with AUCs ranging from 0.76 to 0.80 in the TCGA set and 0.62 to 0.92 in the French external set. ConclusionWe developed and validated an SSL-based model for predicting iCCA transcriptomic classes on routine histological slides of biopsy and surgical samples, which may impact iCCA management by predicting prognosis and guiding the treatment strategy.

cancer biology↗

bioRxiv2024

The interplay of facilitation and competition drives the emergence of multistability in dryland plant communities

Species are wrapped in a set of feedbacks within communities and with their abiotic environment, which can can generate alternative stable states. So far, research on alternative stable states has mostly focused on systems with a small number of species and a limited diversity of interaction types. Here, we analyze a spatial model of plant community dynamics in drylands, where each species is characterized by a strategy, and interact through facilitation and competition. Our work identifies three different types of multistability emerging from the interplay of competition and facilitation. Under low-stress levels, the community organizes in small groups of coexisting species maintained by space and facilitation, while under higher stress levels, positive feedbacks from competition and facilitation lead to the dominance of a single species before desertification happens. Our study paves the way for bridging community ecology and alternative states theory in a common framework.

ecology↗

bioRxiv2023

Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure

O_LICharacterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology, and conservation. A highly debated question is whether and how the structure of a bipartite ecological network differs between antagonistic (e.g., herbivory) and mutualistic (e.g., pollination) interaction types. C_LIO_LIHere, we tackle this question by using a multiscale characterization of network structure, machine learning tools, and a large database of empirical and simulated bipartite networks. C_LIO_LIContrary to previous studies focusing on global structural metrics, such as nestedness and modularity, which concluded that antagonistic and mutualistic networks cannot be told apart from only their structure, we find that they can be told apart by combining a meso-scale characterization of their structure and supervised machine learning. Motif frequencies appear particularly informative, with an over-representation of densely connected motifs in antagonistic networks and of motifs with asymmetrical specialization in mutualistic networks. These characteristics can be used to predict interaction types with relatively good confidence. Beyond this classical mutualism/antagonism dichotomy, we also find significant structural uniqueness linked to specific ecologies (e.g., pollination, parasitism). C_LIO_LIOur results clarify structural differences between antagonistic and mutualistic networks and suggest the investigation of the structural uniqueness of specific ecologies as a promising approach for characterizing interactions beyond the coarse antagonistic/mutualistic dichotomy. C_LI

ecology↗

bioRxiv2023

Quality matters: stoichiometry of resources modulates spatial feedbacks in aquatic-terrestrial meta-ecosystems

Species dispersal and resource spatial flows greatly affect the dynamics of connected ecosystems. So far, research on meta-ecosystems has mainly focused on the quantitative effect of subsidy flows. Yet, resource exchanges at heterotrophic-autotrophic (e.g., aquatic-terrestrial) ecotones display a stoichiometric asymmetry that likely matters for functioning. Here, we joined ecological stoichiometry and the meta-ecosystem framework to understand how subsidy stoichiometry mediates the response of the meta-ecosystem to subsidy flows. Our model results demonstrate that resource flows between ecosystems can induce a positive spatial feedback loop, leading to higher production at the meta-ecosystem scale by relaxing local ecosystem limitations ("spatial complementarity"). Furthermore, we show that spatial flows can also have an unexpected negative impact on production when accentuating the stoichiometric mismatch between local resources and basal species needs. This study paves the way for studies on the interdependancy of ecosystems at the landscape extent. Data: The code and the data, as well as a small tutorial to run the model are available on Github via Zenodo: https://doi.org/10.5281/zenodo.7733880

ecology↗

bioRxiv2021

Landscape structure and dispersal rate drive large scale catastrophic shifts in spatially explicit metapopulations

Even when environments deteriorate gradually, ecosystems may shift abruptly from one state to another. Such catastrophic shifts are difficult to predict and reverse (hysteresis). While well studied in simplified contexts, we lack a general understanding of how catastrophic shifts spread in realistic spatial contexts. For different types of landscape structure, including typical terrestrial modular and riverine dendritic networks, we here investigate landscape-scale stability in metapopulations made of bistable patches. We find that such metapopulations usually exhibit large scale catastrophic shifts and hysteresis, and that the properties of these shifts depend strongly on metapopulation spatial structure and dispersal rate: intermediate dispersal rates and a riverine spatial structure can largely reduce hysteresis size. Interestingly, our study suggests that large-scale restoration is easier with spatially clustered restoration efforts and in populations characterized by an intermediate dispersal rate.

ecology↗

bioRxiv2019

Genomic and epidemiological evidence of a dominant Panton-Valentine leucocidin-positive Methicillin Resistant Staphylococcus aureus lineage in Sri Lanka with spread to the United Kingdom and Australia

ObjectiveTo undertake the first detailed genomic analysis of methicillin-resistant Staphylococcus aureus (MRSA) isolated in Sri Lanka.\n\nMethodsA prospective observational study was performed on 94 MRSA isolates collected over a four month period from the Anuradhapura Teaching Hospital, Sri Lanka. Screening for mecA, mecC and the Panton-Valentine leucocidin (PVL)-associated lukS-PV/lukF-PV genes and molecular characterisation by spa typing was undertaken. Whole genome sequencing (WGS) and phylogenetic analysis was performed on selected multilocus sequence type (MLST) clonal complex 5 (CC5) isolates from Sri Lanka, England, Australia and Argentina.\n\nResultsAll 94 MRSA harboured the mecA gene. Nineteen spa types associated with nine MLST clonal complexes were identified. Most isolates were from skin and soft tissue infections (76.9%), with the remainder causing more invasive disease. Sixty two (65.9%) of isolates were PVL positive with the majority (56 isolates; 90.3%) belonging to a dominant CC5 lineage. This lineage, PVL-positive ST5-MRSA-IVc, was associated with community and hospital-onset infections. Based on WGS, representative PVL-positive ST5-MRSA-IVc isolates from Sri Lanka, England and Australia formed a single phylogenetic clade, suggesting wide geographical circulation.\n\nConclusionsWe present the most detailed genomic analysis of MRSA isolated in Sri Lanka to date. The analysis identified a PVL-positive ST5-MRSA-IVc that dominates MRSA clinical infections in Sri Lanka. Furthermore, transmission of the strain has occurred in the United Kingdom and Australia.

microbiology↗

bioRxiv2017

Methicillin resistant Staphylococcus aureus emerged long before the introduction of methicillin in to clinical practice

The spread of drug-resistant bacterial pathogens pose a major threat to global health. It is widely recognised that the widespread use of antibiotics has generated selective pressures that have driven the emergence of resistant strains. Methicillin-resistant Staphylococcus aureus (MRSA) was first observed in 1960, less than one year after the introduction of this second generation {beta}-lactam antibiotic into clinical practice. Epidemiological evidence has always suggested that resistance arose around this period, when the mecA gene encoding methicillin resistance carried on an SCCmec element, was horizontally transferred to an intrinsically sensitive strain of S. aureus. Whole genome sequencing a collection of the very first MRSA isolates allowed us to reconstruct the evolutionary history of the archetypal MRSA. Bayesian phylogenetic reconstruction was applied to infer the time point at which this early MRSA lineage arose and when SCCmec was acquired. MRSA emerged in the mid 1940s, following the acquisition of an ancestral type I SCCmec element, some fourteen years prior to the first therapeutic use of methicillin. Methicillin use was not the original driving factor in the evolution of MRSA as previously thought. Rather it was the widespread use of first generation {beta}-lactams such as penicillin in the years prior to the introduction of methicillin, which selected for S. aureus strains carrying the mecA determinant. Crucially this highlights how new drugs, introduced to circumvent known resistance mechanisms, can be rendered ineffective by unrecognised adaptations in the bacterial population due to the historic selective landscape created by the widespread use of other antibiotics.

microbiology↗