Biology subjects

Barbero, F.

Publications and source records attributed to Barbero, F..

3 recordsLinked to original sources

bioRxiv2025

Ongoing coevolution between reintroduced Phengaris teleius butterflies and their Myrmica host ants

Coevolutionary interactions between parasites and hosts are key drivers of biological adaptation. In this study, we explore the evolutionary response of the social parasitic butterfly Phengaris teleius to its host ant, Myrmica scabrinodis, taking advantage of a unique opportunity: the reintroduction of the butterfly in the Netherlands thirty years ago. We compared the degree of host mimicry and behavioural performance of caterpillars between the reintroduced and the Polish source population. After about thirty generations, chemical and vibroacoustical signal profiles have diverged. Chemical mimicry remained limited during the pre-adoption phase for both groups; however, in the post-adoption phase, the source population showed significantly higher chemical similarity to their local hosts. In contrast, reintroduced pre-adoption caterpillars evolved vibroacoustic signals closely resembling local hosts, also resulting in a stronger response from their local host ants. This suggests that adoption is driven by acoustics and subsequently serves as a selective filter promoting post-entry chemical refinement. Behavioural data evince that despite the differences between the different communication channels, the combination of signals remains sufficient to ensure recognition and integration in both host-parasite systems. These results illustrate how social parasites involved in multisensory mimicry can rapidly recalibrate strategies to remain functional in new ecological contexts.

ecology↗

bioRxiv2025

PlantCV v4: Image analysis software for high-throughput plant phenotyping

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection. CORE IDEASO_LIPlantCV is an open-source, open-development, Python-based software package that has a new release for improved functionality and usability to make image analysis flexible and easier for researchers without a coding background. C_LIO_LIPlantCV is now capable of handling new data types that are relevant to researchers, such as thermal and hyperspectral, and has built in functionality for extracting information from these image types. C_LIO_LIThe software project aims to lower the barrier to entry into image analysis for researchers by providing numerous, versioned, interactive tutorials that cover most common use cases, particularly in plant science. C_LI

plant biology↗

bioRxiv2024

Scalable emulation of protein equilibrium ensembles with generative deep learning

Following the sequence and structure revolutions, predicting the dynamical mechanisms of proteins that implement biological function remains an outstanding scientific challenge. Several experimental techniques and molecular dynamics (MD) simulations can, in principle, determine conformational states, binding configurations and their probabilities, but suffer from low throughput. Here we develop a Biomolecular Emulator (BioEmu), a generative deep learning system that can generate thousands of statistically independent samples from the protein structure ensemble per hour on a single graphical processing unit. By leveraging novel training methods and vast data of protein structures, over 200 milliseconds of MD simulation, and experimental protein stabilities, BioEmus protein ensembles represent equilibrium in a range of challenging and practically relevant metrics. Qualitatively, BioEmu samples many functionally relevant conformational changes, ranging from formation of cryptic pockets, over unfolding of specific protein regions, to large-scale domain rearrangements. Quantitatively, BioEmu samples protein conformations with relative free energy errors around 1 kcal/mol, as validated against millisecond-timescale MD simulation and experimentally-measured protein stabilities. By simultaneously emulating structural ensembles and thermodynamic properties, BioEmu reveals mechanistic insights, such as the causes for fold destabilization of mutants, and can efficiently provide experimentally-testable hypotheses.

molecular biology↗