Biology subjects

Cotto, O.

Publications and source records attributed to Cotto, O..

4 recordsLinked to original sources

bioRxiv2026

Nemo2.4: fast and accurate quantitative genetics forward-time simulations

We present Nemo 2.4, an advanced forward-time individual-based simulation framework designed to model the complex eco-evolutionary dynamics and genetic basis of quantitative traits. This tool addresses current challenges in evolutionary quantitative genetics by providing unprecedented flexibility and computational efficiency. Nemo 2.4s modular architecture allows researchers to design custom life cycles by combining specialized Life Cycle Event (LCE) modules, from reproduction and dispersal to selection, crossing, and phenotype expression. The software supports diverse population models, including both Wright-Fisher (WF) and non-WF dynamics, spatially explicit models, and varying demography. Nemo 2.4 handles a wide range of genetic architectures, including both multi-allelic Quantitative Trait Loci (QTL) for general trait studies, and dense di-allelic Quantitative Trait Nucleotides (QTN) implemented with highly optimized bit-wise data structures. Crucially, it allows the simulation of QTNs on comprehensive genetic maps that incorporate other genetic elements, providing genomic-scale resolution. Key biological complexities are integrated natively: the model accommodates modular pleiotropy, dominance, and pairwise epistasis across multiple traits, facilitating the study of complex genotype-phenotype mappings. Furthermore, Nemo 2.4 models phenotypic plasticity through reaction norms and incorporates underlying liability thresholds, enabling the simulation of environmental influences on trait evolution with various forms of selection (e.g., Gaussian, linear, truncation). Due to its compiled design and memory-efficient data representations for large numbers of loci, Nemo provides a robust platform for running high-throughput simulations critical for testing theoretical predictions in polygenic adaptation and understanding evolutionary responses to changing environments.

evolutionary biology↗

bioRxiv2025

Symptom observation underestimates co-infections: insight from viral and bacterial diseases in rice fields in Burkina Faso

Co-occurrence of multiple diseases and co-infection of individual plants by various pathogens have potential epidemiological and evolutionary implications. Based on previous information on the co-occurrence of the rice yellow mottle disease (caused by the rice yellow mottle virus, RYMV) and bacterial leaf streak (BLS, due to Xanthomonas oryzae pv. oryzicola, Xoc) in Burkina Faso, and experimental evidence of interactions between the pathogens causing these two diseases, we aimed to monitor the two pathogens more intensively in farmers rice fields. To this purpose, we selected fields showing both types of symptoms to maximize the chance of observing co-infections at the plant scale. We performed observations and sampling in two sites over two consecutive years. Over a global dataset of 1666 samples, 1341 were symptomatic. Although the sampling design aimed to observe co-infections, only 37 of these samples (2.8%) were annotated as presenting both yellow mottle and BLS symptoms. The samples were then subjected to a newly designed molecular detection test that specifically amplifies both the virus (RYMV) and the bacteria (Xo). This revealed that 166 samples, i.e. 12.4% of symptomatic samples, were co-infected by RYMV and Xo, hence showing that symptom observation in the field greatly underestimates co-infection levels. Combining these data with a previously published dataset, we estimated that up to 1-4% of all plants in disease hotspots are simultaneously infected by the two pathogens. Further research on multiple infections would benefit from longitudinal surveys over the crop growing season rather than such a cross-sectional study.

plant biology↗

bioRxiv2023

Persistence-colonization trade-off and niche differentiation enable the coexistence of E. coli phylogroups

Despite extensive literature on the pathogenicity and virulence of the opportunistic pathogen Escherichia coli, much less is known about its ecological and evolutionary dynamics as a commensal in healthy hosts. Based on two detailed longitudinal datasets on the gut microbiota of healthy adult individuals followed over months to years in France and in the USA, we identified a robust trade-off between the ability to establish in a new host (colonization) and the ability to remain in the host (residence). Major E. coli lineages (phylogroups) exhibited similar fitness but a diversity of strategies, from strong colonizers residing for a few days in the gut, to poor colonizers residing for years. Strains with the largest number of extra-intestinal virulence associated genes and highest pathogenicity resided for longest in hosts. Moreover, the residence time of a strain was reduced more strongly when it competed with other strains of the same phylogroup than of different phylogroups, suggesting niche differentiation between them. To investigate the consequences of the trade-off and niche differentiation for coexistence between strains, we developed a discrete-state Markov model describing the dynamics of E. coli in a population of hosts. We found that the trade-off and niche differentiation acted together as equalizing and stabilizing mechanisms enabling the coexistence of phylogroups over extended periods of time. Our model predicted that a reduction in transmission (e.g. better hygiene) would not alter the balance between phylogroups, while disturbance of the microbiome (e.g. antibiotics) would hinder residents strains such as those of the extra-intestinal pathogenic phylogroup B2.3.

evolutionary biology↗

bioRxiv2022

Adaptation to a changing environment: what me Normal?

Predicting the adaptation of populations to a changing environment is crucial to assess the impact of human activities on biodiversity. Many theoretical studies have tackled this issue by modeling the evolution of quantitative traits subject to stabilizing selection around an optimal phenotype, whose value is shifted continuously through time. In this context, the population fate results from the equilibrium distribution of the trait, relative to the moving optimum. Such a distribution may vary with the shape of selection, the system of reproduction, the number of loci, the mutation kernel or their interactions. Here, we develop a methodology that provides quantitative measures of population maladaptation and potential of survival directly from the entire profile of the phenotypic distribution, without any a priori on its shape. We investigate two different systems of reproduction (asexual and infinitesimal sexual models of inheritance), with various forms of selection. In particular, we recover that fitness functions such that selection weakens away from the optimum lead to evolutionary tipping points, with an abrupt collapse of the population when the speed of environmental change is too high. Our unified framework allows deciphering the mechanisms that lead to this phenomenon. More generally, it allows discussing similarities and discrepancies between the two systems of reproduction, which are ultimately explained by different constraints on the evolution of the phenotypic variance. We demonstrate that the mean fitness in the population crucially depends on the shape of the selection function in the infinitesimal sexual model, in contrast with the asexual model. In the asexual model, we also investigate the effect of the mutation kernel and we show that kernels with higher kurtosis tend to reduce maladaptation and improve fitness, especially in fast changing environments. HighlightsO_LIAdaptation to a changing environment may generate non Normal phenotypic distribution. C_LIO_LIThe phenotypic variance at equilibrium truly depends on reproduction model; C_LIO_LISelection shapes mean fitness only in sexual infinitesimal model; C_LIO_LIWeak selection away from the optimum leads to evolutionary tipping points with fast changes; C_LIO_LIFrequent mutations with large effects reduce maladaptation and improve fitness. C_LI

evolutionary biology↗