Biology subjects

Valentini, A.

Publications and source records attributed to Valentini, A..

10 recordsLinked to original sources

bioRxiv2026

Environmental DNA enables rapid detection of invasive coypu and complements camera trapping

Effective management of invasive species requires surveillance methods that reliably detect populations while minimizing field effort. Environmental DNA (eDNA) offers a potentially rapid alternative to conventional monitoring, but direct comparisons with other non-invasive methods remain limited for invasive mammals. Here, we compared eDNA water sampling and camera trapping for detecting established populations of coypu (Myocastor coypus), an invasive semi-aquatic rodent. Using replicated data collected across Mediterranean river systems in southern France, we estimated detection probabilities conditional on coypu presence using Bayesian generalized linear mixed models and quantified the sampling effort required to achieve high cumulative detection probability. Detection probability was 93.0% (95% credible interval: 72.1-98.9%) for a single eDNA water sample, compared with 52.8% (22.6-91.1%) for 30 camera-trap days, with a 96.9% posterior probability that eDNA detection was higher. Two eDNA samples were sufficient to achieve a median cumulative detection probability above 95%, compared with four 30-day camera-trapping periods. Detection also varied at different spatial scales, with camera-trap detectability being heterogeneous among local camera locations and eDNA detectability varying more among catchments. These results show that eDNA provides a rapid and reliable approach for confirming the presence of established coypu populations, while camera traps provide complementary information on activity, behaviour and habitat use. Combining rapid eDNA screening with targeted camera trapping may therefore provide an efficient surveillance strategy for invasive semi-aquatic mammals.

ecology↗

bioRxiv2026

Sequence-Based Therapeutic Peptide Classification with Augmented Negative Sampling

Therapeutic peptides offer high target specificity, low toxicity, and the ability to modulate protein-protein interactions, yet experimental functional characterization remains costly and slow. Computational prediction of therapeutic function directly from sequence could accelerate peptide screening and enable generative design pipelines, but requires reliable discrimination between therapeutic and non-therapeutic peptides. Existing multi-label predictors cover few functions, rely on limited datasets, and exhibit high False Positive Rates (FPRs), limiting their practical utility. We present a lightweight CNN classifier trained on the most comprehensive therapeutic peptide database to date (54,655 peptides, 48 functional categories). A key contribution is a statistically motivated negative sampling strategy using Markov models to generate diverse synthetic decoys at multiple difficulty levels. When evaluated on this controlled decoy benchmark, the FPR is reduced from over 60% for previous models to 2.1% for our approach. On positive therapeutic samples, our fine-tuned five-model ensemble achieves 79.9% Micro F1 and 54.6% Macro F1 while requiring only amino acid sequences as inputs. Analysis using a sparse L1-constrained variant of our model shows that convolutional filters capture conserved functional motifs and statistically improbable non-therapeutic patterns, with downstream layers combining these signals, providing mechanistic evidence that the network learns biologically meaningful structure. On an external generalization benchmark derived from TPpred-LE, our model achieves 55.3% Micro F1 and 38.6% Macro F1 on the 12 shared labels, close to the benchmark-specific baseline (57.9%/38.1%), while retaining substantially broader therapeutic label coverage. Code and models will be made available at https://github.com/terra-quantum-public/tq-therapep-ai.

bioinformatics↗

bioRxiv2026

Comparative efficiency of eDNA, camera traps and scat surveys to detect a semi-aquatic mammal across multiple catchments

Semi-aquatic mammals lie at the intersection of several key conservation issues such as wetland deterioration or species invasions, and monitoring their distribution in space and time is essential to inform conservation strategies. However, gathering information about their presence is challenging due to their elusive lifestyle and generally low abundance. The Eurasian otter (Lutra lutra), a near-threatened and strictly protected species in Europe, is currently recolonizing part of its historical range. Its high conservation interest, combined with a dynamic more commonly associated with range-expanding or invasive species, makes it a particularly compelling case study. Otter monitoring has traditionally relied on scat surveys, but recent environmental DNA (eDNA) and camera-trapping initiatives have emerged offering promising complementary tools. Yet, these approaches have rarely been formally compared, either to one another or across regions. Here, we compared the efficiency of spraint surveys, camera traps, and eDNA for detecting otters, and assessed how their performance varied among four catchments in southern France where the species is known to be present. All three methods provided otter detections with varying efficiency. Scat surveys were the most effective method, with an average detection probability of 0.71 and no strong variability between catchments. Although camera-traps had the lowest detection rate, they provided detections at two of the four sites where no spraint was found, highlighting the complementarity of these two approaches. Detection rates varied greatly between individual cameras rather than between catchments, underscoring sensitivity to camera-placement. eDNA showed important variability between catchments, with detection probabilities differing by roughly sixfold across regions. All in all, our results highlight differences in efficiency between methods and across environmental conditions, and show the value of combining approaches for future monitoring programs.

ecology↗

bioRxiv2026

Ecological Drivers of Nontuberculous Mycobacteria in Aquatic Systems: Biodiversity, Niche Competition, and Pathogen Emergence

Microbial diversity remains largely unexplored across environments and scales, notably because at local scales many microbial taxa exist under a dormant state. Microbial biogeography is shaped by edaphic and ecological drivers, and shifts in microbial community composition are frequently associated with host community structure and health. Nontuberculous mycobacteria represent a striking example of environmental microorganisms with opportunistic pathogenic potential. Unfortunately, data on their diversity, distribution, and ecological interactions in aquatic environments remain limited. However, understanding competition for niche space and the role of abiotic and biotic factors shaping their biogeography is crucial for predicting disease emergence and transmission. Here we aimed at i) identifying microhabitat abiotic and biotic drivers influencing their distribution, ii) assessing the predictability of their diversity and distribution across continents, and iii) examining potential exclusion or associations between pathogenic and nonpathogenic mycobacterial species. By deploying an eDNA-based metabarcoding approach from freshwater samples collected in urban and rural sites in French Guiana and Cote dIvoire, we have boosted our understanding of environmental mycobacteria ecology by highlighting the influence of habitat type, abiotic factors, and microbial interactions on mycobacterial distribution. In addition, the detection of pathogenic species further highlighted the importance of environmental reservoirs in mycobacterial disease transmission.

ecology↗

bioRxiv2025

NeMO: a flexible R package for nested multi-species occupancy modelling and eDNA study optimization

Biodiversity monitoring using environmental DNA (eDNA) metabarcoding has expanded rapidly, providing a non-invasive tool widely adopted by ecologists and stakeholders. However, eDNA surveys are prone to imperfect detection, and non-detections are often misinterpreted as true absences - a critical issue when monitoring rare or elusive species. Despite its implications for biodiversity assessments, detection uncertainty is rarely quantified in eDNA-based studies. Occupancy modeling offers a powerful solution to this limitation but remains underused, partly due to a lack of accessible and flexible tools. We developed NeMO (Nested eDNA Metabarcoding Occupancy), a user-friendly R package for fitting multi-species occupancy models in a Bayesian framework. NeMO explicitly accounts for the nested structure of eDNA metabarcoding workflows - typically involving multiple replication steps such as field samples and PCR replicates - while accommodating presence/absence or read-count data. The framework estimates species occupancy, eDNA collection probability, amplification probability, and expected read counts, and allows users to assess the influence of environmental or methodological covariates on each process. Crucially, NeMO helps rigorously assess detectability and optimize resource allocation in eDNA surveys. It estimates the minimum number of eDNA samples, PCR replicates, and sequencing depth required to reliably detect species when present, thereby guiding study design. We illustrate its utility using a fish biodiversity dataset from the Rhone River (France). NeMO integrates key modelling features into a single streamlined framework, providing researchers and practitioners with an accessible and effective tool to assess detectability and optimize resource allocation in eDNA metabarcoding surveys. Our results highlight the importance of quantifying detection uncertainty, which has major implications for conservation monitoring and for designing cost-effective and reliable eDNA strategies. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/655794v3_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@3a8f2borg.highwire.dtl.DTLVardef@8d8375org.highwire.dtl.DTLVardef@b1976forg.highwire.dtl.DTLVardef@14cfde5_HPS_FORMAT_FIGEXP M_FIG NeMO provides a flexible framework for modeling multi-species site occupancy from eDNA data, estimating species presence and detectability despite imperfect detection. It supports diverse experimental designs, enables model comparison, and guides study design optimization by calculating minimal sampling effort to detect species, helping ecologists balance detection accuracy and resource allocation. C_FIG

ecology↗

bioRxiv2025

An in vivo CRISPR screen unveils top target genes to efficiently improve CAR-T cell persistence in a solid tumour model of lung adenocarcinoma

CAR-T cell therapies are revolutionizing the treatment of refractory and relapsed haematological malignancies, but many patients do not exhibit long-term responses, and these therapies are less effective against solid tumors. Poor persistence of CAR-T cells in patients is associated with therapeutic failure, highlighting the need to identify strategies promoting in vivo expansion. Here, we developed an in vivo competitive screening method to identify genes whose inactivation confers a selective advantage to CAR-T cells. Inactivation of 50 genes in a heterogeneous population of T cells expressing an EGFR-targeting CAR revealed that disruption of REGNASE-1, SOCS1, PTPN2, and P16/NK4A conferred a selective advantage to CAR-T cells in human lung tumor-bearing mice. Consistently, inactivation of these genes improved tumor eradication by CAR-T cells. Interestingly disruption of other genes, described to improve CAR-T cell function in other contexts, had a negative impact in this orthotopic lung tumor model. Further evaluation of long-term effects in a subcutaneous model, highlighted SOCS1 ablation as the most promising strategy for in vivo CAR-T cell amelioration. These results support the importance of evaluating CAR-T cell editing strategies in tumor-specific models and highlight the versatility of our screening approach as a pre-clinical tool for context-specific studies on CAR-T cells amelioration.

immunology↗

bioRxiv2024

Multi-Coloured Sequential Resonance Energy Transfer for Simultaneous Ligand Binding at G Protein-Coupled Receptors

G protein coupled receptors (GPCRs) are the largest family of signalling proteins and highly successful drug targets. Most GPCR drugs interact with a binding pocket for the natural ligand, typically near the extracellular region of the transmembrane domains. Advancements in structural biology have identified additional allosteric binding sites in other parts of these receptors. Allosteric sites provide theoretical advantages, including the ability to modulate natural ligand function, and there is a need for better ways to study how ligands interact with these binding sites. We have developed an approach to study multiple ligands binding to the same receptor at the same time based on sequential resonance energy transfer between two fluorescent ligands bound to a GPCR. We use this approach to identify novel ligand pharmacology and understand binding kinetics to the FFA1 free fatty acid receptor, a clinically relevant receptor. This novel method will aid development of new GPCR drugs.

pharmacology and toxicology↗

bioRxiv2021

How many replicates to accurately estimate fish biodiversity using environmental DNA on coral reefs?

Quantifying the diversity of species in rich tropical marine environments remains challenging. Environmental DNA (eDNA) metabarcoding is a promising tool to face this challenge through the filtering, amplification, and sequencing of DNA traces from water samples. However, the reliability of biodiversity detection from eDNA samples can be low in marine environments because eDNA density is low and certainly patchy in this vast, heterogenous and dynamic environment. So, the number of sampling replicates and filtered volume necessary to obtain accurate estimates of biodiversity in rich tropical marine environments using eDNA metabarcoding is still unknown. Here, we used a paired sampling design of 30L per replicate on 68 reef transects from 8 sites in three tropical regions and identified fish Molecular Taxonomic Units (MOTUs) using a 12S marker. We quantified local biodiversity variation as MOTU richness, compositional turnover and compositional nestedness between replicated pairs of seawater samples. We report strong turnover of MOTUs between replicated pairs of samples undertaken in the same location, time, and conditions. Paired samples contained non-overlapping assemblages rather than subsets of one-another. As a result, localised diversity accumulation curves showed that even 6 replicates (180L) in the same location underestimated local diversity (for an area <1km). However, sampling of regional diversity using [~]25 replicates in variable locations (often covering 10s of km) achieved saturation of biodiversity accumulation curves. Our results demonstrate high variability of diversity estimates perhaps arising from heterogeneous and local distribution of eDNA distribution in seawater or highly skewed frequencies of eDNA traces. This high compositional variability has consequences for using eDNA to monitor temporal and spatial biodiversity changes of local assemblages. Future biomonitoring efforts could be strongly undermined by a high level of false-negative detections under low replication protocols. We reveal the need to increase replicates or increase sampled water volume to better inform management of marine biodiversity using eDNA.

ecology↗

bioRxiv2021

Fast processing of environmental DNA metabarcoding sequence data using convolutional neural networks

1The intensification of anthropogenic pressures have increased consequences on biodiversity and ultimately on the functioning of ecosystems. To monitor and better understand biodiversity responses to environmental changes using standardized and reproducible methods, novel high-throughput DNA sequencing is becoming a major tool. Indeed, organisms shed DNA traces in their environment and this "environmental DNA" (eDNA) can be collected and sequenced using eDNA metabarcoding. The processing of large volumes of eDNA metabarcoding data remains challenging, especially its transformation to relevant taxonomic lists that can be interpreted by experts. Speed and accuracy are two major bottlenecks in this critical step. Here, we investigate whether convolutional neural networks (CNN) can optimize the processing of short eDNA sequences. We tested whether the speed and accuracy of a CNN are comparable to that of the frequently used OBITools bioinformatic pipeline. We applied the methodology on a massive eDNA dataset collected in Tropical South America (French Guiana), where freshwater fishes were targeted using a small region (60pb) of the 12S ribosomal RNA mitochondrial gene. We found that the taxonomic assignments from the CNN were comparable to those of OBITools, with high correlation levels and a similar match to the regional fish fauna. The CNN allowed the processing of raw fastq files at a rate of approximately 1 million sequences per minute which was 150 times faster than with OBITools. Once trained, the application of CNN to new eDNA metabarcoding data can be automated, which promises fast and easy deployment on the cloud for future eDNA analyses.

bioinformatics↗

bioRxiv2020

Characterizing the spatial signal of environmental DNA in river systems using a community ecology approach

Environmental DNA (eDNA) is gaining a growing popularity among scientists but its applicability to biodiversity research and management remains limited in river systems by the lack of knowledge about the spatial extent of the downstream transport of eDNA. Here, we assessed the ability of eDNA inventories to retrieve spatial patterns of fish assemblages along two large and species rich Neotropical rivers. We first examined overall community variation with distance through the distance decay of similarity and compared this pattern to capture-based samples. We then considered previous knowledge on individual species distributions, and compared it to the eDNA inventories for a set of 53 species. eDNA collected from 28 sites in the Maroni and 25 sites in the Oyapock rivers permitted to retrieve a decline of species similarity with distance between sites. The distance decay of similarity derived from eDNA was similar, and even more pronounced, than that obtained with capture-based methods (gil-nets). In addition, the species upstream-downstream distribution range derived from eDNA matched to the known distribution of most species. Our results demonstrate that environmental DNA does not represent an integrative measure of biodiversity across the whole upstream river basin but provide a relevant picture of local fish assemblages. Importantly, the spatial signal gathered from eDNA was therefore comparable to that gathered with local capture based methods, which describes fish fauna over a few hundred metres.

ecology↗