Biology subjects

Labonne, J.

Publications and source records attributed to Labonne, J..

4 recordsLinked to original sources

bioRxiv2026

Telomere length can be estimated from fish scales

Telomeres, repetitive DNA sequences at the ends of chromosomes, are increasingly studied as biomarkers of physiological stress as well as ageing processes. In ichthyology, fish scales provide valuable information regarding life history and ageing and are thus often preserved in substantial collections. This study aims to adapt a quantitative Polymerase Chain Reaction (qPCR) method to estimate relative telomere length (RTL) from fish scales archived in such collections. Using the brown trout (Salmo trutta) as a model species, we show that archived scales contain enough DNA to perform qPCR-based RTL measurement. RTLs in scales are on average higher than in fins, however we found no significant correlation between RTL values of both tissues at the individual level. Analysing archived scales originating from the subantarctic Kerguelen Islands introduced brown trout populations, we find substantial variation in RTL between rivers, age classes and body size.

molecular biology↗

bioRxiv2024

Dissecting the nutritional regulations of a whole amino acid transporter family from a complex genome species: A holistic approach turning weaknesses into strengths

Amino acid transporters (AATs) are described as pivotal in maintaining circulating and cellular concentrations of AA via regulation of their expression in response to the cellular environment. Rainbow trout (RT), a complex genome species, is poorly described for AATs roles in controlling its predominant AA-based metabolism, despite representing a major challenge in the aquaculture nutrition field. Therefore, we identified the whole repertoire of AAT found in RT genome (>200), its expression in tissues and its nutritional regulations in vitro. Results garnered revealed the existence of different clusters of AATs, notably due to promoters bearing ATF4-related AA response elements. Moreover, the modeling of each AAT-specific cluster activities disclosed mTOR-related signaling functions of Ile and Phe, yet unknown in RT. Thus, this novel approach herein described should help to better grasp AA homeostasis in most organisms and topics such as fish nutrition and evolution.

cell biology↗

bioRxiv2020

A demogenetic agent based model for the evolution of traits and genome architecture under sexual selection

Sexual selection has long been known to favor the evolution of mating behaviors such as mate preference and competitiveness, and to affect their genetic architecture, for instance by favoring genetic correlation between some traits. Reciprocally, genetic architecture can affect the expression and the evolvability of traits and mating behaviors. But sexual selection is highly context-dependent, making interactions between individuals a central process in evolution, governing the transmission of genotypes to the next generation. This loop between the genetic structure conditioning the expression and evolution of traits and behaviour, and the feedback of this phenotypic evolution on the architecture of the genome in the dynamic context of sexual selection, has yet to be thoroughly investigated. We argue that demogenetic agent-based models (DG-ABM) are especially suited to tackle such a challenge because they allow explicit modelling of both the genetic architecture of traits and the behavioural interactions in a dynamic population context. We here present a DG-ABM able to simultaneously track individual variation in traits (such as gametic investment, preference, competitiveness), fitness and genetic architecture throughout evolution. Using two simulation experiments, we compare various mating systems and show that behavioral interactions during mating triggered some complex feedback in our model, between fitness, population demography, and genetic architecture, placing interactions between individuals at the core of evolution through sexual selection. DG-ABMs can, therefore, relate to theoretical patterns expected at the population level from simpler analytical models in evolutionary biology, and at the same time provide a more comprehensive framework regarding individual trait and behaviour variation, that is usually envisioned separately from genome architecture in behavioural ecology.

evolutionary biology↗

bioRxiv2017

With Our Powers Combined: Integrating Behavioral And Genetic Data To Estimate Mating Success And Sexual Selection

The analysis of sexual selection classically relies on the regression of individual phenotypes against the marginal sums of a males x females matrix of pairwise reproductive success, assessed by genetic parentage analysis. When the matrix is binarized, the marginal sums give the individual mating success. Because such analysis treats male and female mating/reproductive success independently, it ignores that the success of a male x female sexual interaction can be attributable to the phenotype of both individuals. Also, because it is based on genetic data only, it is oblivious to costly yet unproductive matings, which may be documented by behavioral observations. To solve these problems, we propose a statistical model which combines matrices of offspring numbers and behavioral observations. It models reproduction on each mating occasion of a mating season as three stochastic and interdependent pairwise processes, each potentially affected by the phenotype of both individuals and by random individual effect: encounter (Bernoulli), concomitant gamete emission (Bernoulli), and offspring production (Poisson). Applied to data from a mating experiment on brown trout, the model yielded different results from the classical regression analysis, with only a limited effect of male body size on the probability of gamete release and a negative effect of female body size on the probability of encounter and gamete release. Because the general structure of the model can be adapted to other partitioning of the reproductive process, it can be used for a variety of biological systems where behavioral and genetic data are available.

evolutionary biology↗