Biology subjects

Salas, N.

Publications and source records attributed to Salas, N..

5 recordsLinked to original sources

bioRxiv2026

Multispecies Mixtures: An Individual-Centered Quantitative Genetic Framework for Complex Plant Neighborhoods

Mixing crop species or varieties in the same field can raise yield and stability, but performance varies widely among combinations that breeders cannot yet predict. What makes a good neighbor is partly heritable, and quantitative genetics models this as an indirect genetic effect describing a genotype's contribution to its neighbors' phenotypes. Here, we extend existing models with a complementary approach based on continuous neighborhoods, in which diverse genotypes of two species are mixed. In a continuous neighborhood design, several genotypes of each species are grown in a single trial, with every plant georeferenced. Each plant's phenotype is decomposed into direct genetic effects, indirect genetic effects from neighbors of both species, and environmental effects. Breeding values are then assembled for each genotype by combining its three genetic effects. We derived analytical expressions for the variances and covariance of these effects, validated the model, and evaluated its statistical properties through simulations spanning a wide range of parameter settings. For the same number of plants, the continuous neighborhood design estimated indirect effects far more accurately than a pairwise design and, in addition, allowed indirect environmental variance to be estimated. Applied to a wheat-alfalfa experiment comprising 3,840 plants, from 181 wheat pure lines and 106 alfalfa families, the model attributed 8.6% and 8.2% of the variance in wheat grain number and alfalfa biomass to direct genetic effects respectively, and 4.0% and 4.6% to indirect genetic effects from alfalfa. The framework therefore offers an individual-entered basis for analyzing multispecies neighborhoods and their breeding potential.

genetics↗

bioRxiv2025

Factors Influencing Phenomic Prediction: A Case Study on a Large Sorghum BCNAM Population

Plant breeding efficiency is crucial to develop varieties able to cope with climate change and support food and feed value chains. Genomic prediction (GP) has been a major step in increasing this efficiency and is now routinely used in breeding programs. Recently, phenomic prediction (PP) has gained attention as a promising complementary approach to GP, further increasing the breeding programs efficiency. Factors impacting the predictive ability (PA) of PP have been studied on many species but are not fully clarified. In this context, we studied the impacts of spectra pre-processing, prediction methods, population structure, training set size, NIRS acquisition environment and wavelength selection on a large multi-parental sorghum population including 2498 genotypes. Our results show that PP can compete with GP, that it is less affected by population structure, and can reach its maximal PA with smaller training sets than GP, but its performances are trait dependant. We also show that NIRS can be acquired in a reference environment to perform prediction in other environments and that it is possible to randomly select as little as 10 wavelengths to perform predictions. Finally, we show that spectra pre-processing, and statistical methods have a limited and unclear impact on PA. Our study confirms that PP is a relevant trait prediction method that deserves attention to optimize breeding schemes. The main challenges for the future will be to better understand the information contained in the spectra and disentangle their genetic and proxy components to optimize the use of PP in breeding programs. Key messagePhenomic prediction is promising for sorghum breeding. Geneticists methods may not be suited to optimally extract spectral information.

genomics↗

bioRxiv2025

Genotype-specific roles of small extracellular vesicles in modulating metronidazole resistance in Giardia lamblia

Giardia lamblia, a eukaryotic intestinal parasite, produces small extracellular vesicles (sEVs) as a conserved evolutionary mechanism. This study investigates the functional role of sEVs in transferring drug-resistance traits among parasites. sEVs derived from metronidazole (MTZ)-resistant clones are shown to modify the expression of enzymes involved in MTZ metabolism and the production of reactive oxygen species (ROS) in recipient wild-type parasites. These changes significantly alter the drug sensitivity of recipient parasites. The transfer efficiency and phenotypic impact vary depending on the genetic background of the isolates, highlighting a genotype-specific mechanism. Our findings reveal that sEVs act as mediators of phenotypic adaptation in G. lamblia, enhancing parasite survival under drug-induced stress. This study underscores the importance of sEVs in drug-resistance dynamics and provides a basis for exploring therapeutic interventions targeting EV-mediated resistance in giardiasis. Highlights In briefO_LISmall extracellular vesicles (sEVs) from Giardia lamblia mediate genotype-specific MTZ resistance transfer. C_LIO_LISmall extracellular vesicles from drug-resistant clones (RsEVs) alter enzyme expression and reactive oxygen species (ROS) production in recipient trophozoites. C_LIO_LIThe genetic background of G. lamblia isolates influences the effectiveness of resistance transfer. C_LIO_LIFindings provide insights into resistance mechanisms and potential targets for new giardiasis therapies. C_LI

cell biology↗

bioRxiv2024

Performance of phenomic selection in rice: effects of population size and genotype-environment interactions on predictive ability

Phenomic prediction (PP), a novel approach utilizing Near Infrared Spectroscopy (NIRS) data, offers an alternative to genomic prediction (GP) for breeding applications. In PP, a hyperspectral relationship matrix replaces the genomic relationship matrix, potentially capturing both additive and non-additive genetic effects. While PP boasts advantages in cost and throughput compared to GP, the factors influencing its accuracy remain unclear and need to be defined. This study investigated the impact of various factors, namely the training population size, the multi-environment information integration, and the incorporations of genotype x environment (GxE) effects, on PP compared to GP. We evaluated the prediction accuracies for several agronomically important traits (days to flowering, plant height, yield, harvest index, thousand-grain weight, and grain nitrogen content) in a rice diversity panel grown in four distinct environments. Training population size and GxE effects inclusion had minimal influence on PP accuracy. The key factor impacting the accuracy of PP was the number of environments included. Using data from a single environment, GP generally outperformed PP. However, with data from multiple environments, using genotypic random effect and relationship matrix per environment, PP achieved comparable accuracies to GP. Combining PP and GP information did not significantly improve predictions compared to the best model using a single source of information (e.g., average predictive ability of GP, PP, and combined GP and PP for grain yield were of 0.44, 0.42, and 0.44, respectively). Our findings suggest that PP can be as accurate as GP when all genotypes have at least one NIRS measurement, potentially offering significant advantages for rice breeding programs. Authors SummaryThis study explores the interest of phenomic selection within the context of rice breeding. Unlike genomic selection, phenomic selection utilizes near-infrared spectroscopic (NIRS) technology to predict genotypes performance. The importance of this methodology lies in its capacity to reduce the costs and enhance the genetic gains of breeding programs, particularly in developing countries where genomic information is not always easily accessible (cost, availability, ease of use). Also, NIRS technology is often already available, even in resource-constrained breeding programs. By focusing the study on rice, a staple food for billions, our research aims to demonstrate the applicability of phenomic selection compared to genomic selection. By investigating the influence of various factors on phenomic prediction accuracy (training population size, incorporation of multiple environment information, consideration of genotype x environment effects in the prediction models), we are contributing to the optimization of this novel breeding method, which could potentially lead to significant improvements in agricultural productivity and food security.

plant biology↗

bioRxiv2023

Role of cytoneme-like structures and extracellular vesicles in Trichomonas vaginalis parasite: parasite communication

Trichomonas vaginalis, the etiologic agent of the most common non-viral sexually transmitted infection worldwide, colonizes the human urogenital tract where it remains extracellular and adheres to epithelial cells. With an estimated prevalence of 276 million new cases annually, mixed infections with different parasite strains are expected. Although it is considered as obvious that parasites interact with their host to enhance their own survival and transmission, evidence of mixed infection call into question the extent to which unicellular parasites communicate with each other. Here, we demonstrated that different T. vaginalis strains are able to communicate through the formation of cytoneme-like membranous cell connections. We showed that T. vaginalis adherent strains form abundant membrane protrusions and cytonemes formation of an adherent parasite strain (CDC1132) is affected in the presence of a different strain (G3 or B7RC2). Using a cell culture inserts assays, we demonstrated that the effect in cytoneme formation is contact independent and that extracellular vesicles (EVs) are responsible, at least in part, of the communication among strains. In this sense, we found that EVs isolated from G3, B7RC2 and CDC1132 strains contain a highly distinct repertoire of proteins, some of them involved in signaling and communication, among other functions. Finally, we showed that parasite adherence to host cells is affected by this communication between strains as binding of adherent T. vaginalis CDC1132 strain to prostate cells is significantly higher in the presence of G3 or B7RC2 strains. Demonstrating that interaction of isolates with distinct phenotypic characteristics may have significant clinical repercussions, we also observed that a poorly adherent parasite strain (G3) adheres more strongly to prostate cells in the presence of an adherent strain. The study of signaling, sensing and cell communication in parasitic organisms will surely enhance our understanding of the basic biological characteristics of parasites that might have important consequences in pathogenesis.

microbiology↗