Biology subjects

Jullien, M.

Publications and source records attributed to Jullien, M..

3 recordsLinked to original sources

bioRxiv2026

COCOA-Tree: Phylogenetic visualization and comparative analysis of coevolving residues

The evolutionary co-occurrence of amino acid changes between protein residues underlies key structural and functional properties of protein families. Building on these coevolutionary patterns, methods have been developed to identify groups of residues associated with enzyme functionalities, such as Statistical Coupling Analysis (SCA) or Specificity-Determining Position (SDP) methods. These methods and their variations differ in the metrics used to quantify coevolution, residues weighting schemes, and corrections introduced to mitigate noise and phylogenetic biases. Yet, systematic comparisons across methods are rarely performed, and the evolutionary origins of the coevolutionary patterns highlighted by each approach are seldom addressed, limiting our ability to disentangle functional from phylogenetic contributions. To address these issues, we introduce COCOA-Tree, a Python library for SCA-like dimensionality-reduction analyses. COCOA-Tree supports custom metrics and enables visualization of coevolutionary patterns on phylogenetic trees. We also provide guidance to map results onto 3D structures in PyMOL. Using COCOA-Tree, we reanalyze published datasets and uncover previously unnoticed evolutionary properties of groups of coevolving residues detected by SCA, known as sectors. In particular, in the well-studied S1A serine protease family, we show that two of the three known sectors exhibit qualitatively distinct levels of sequence conservation depending on the enzymatic functions and on the phylogenetic clades to which the proteins belong. We further show that different coevolution metrics often identify qualitatively distinct groups of coevolving residues, although they yield consistent results for mildly conserved residues. Finally, as an example of the versatility of COCOA-Tree, we provide an example of visualizing pairs of residues detected by Direct Coupling Analysis (DCA) methods on a phylogenetic tree, highlighting a rich diversity of co-evolutionary patterns. Overall, we expect COCOA-Tree to help identify residues that control protein function and thereby improve our capacity for functional engineering and our understanding of the principles governing protein evolution. COCOA-Tree website: https://tree-timc.github.io/cocoatree

bioinformatics↗

bioRxiv2020

Evolution of flowering time in a selfing annual plant: Roles of adaptation and genetic drift

Resurrection studies are a useful tool to measure how phenotypic traits have changed in populations through time. If these traits modifications correlate with the environmental changes that occurred during the time period, it suggests that the phenotypic changes could be a response to selection. Selfing, through its reduction of effective size, could challenge the ability of a population to adapt to environmental changes. Here, we used a resurrection study to test for adaptation in a selfing population of Medicago truncatula, by comparing the genetic composition and flowering times across 22 generations. We found evidence for evolution towards earlier flowering times by about two days and a peculiar genetic structure, typical of highly selfing populations, where some multilocus genotypes (MLGs) are persistent through time. We used the change in frequency of the MLGs through time as a multilocus fitness measure and built a selection gradient that suggests evolution towards earlier flowering times. Yet, a simulation model revealed that the observed change in flowering time could be explained by drift alone, provided the effective size of the population is small enough (<150). These analyses suffer from the difficulty to estimate the effective size in a highly selfing population, where effective recombination is severely reduced.

evolutionary biology↗

bioRxiv2018

Discontinuities in quinoa biodiversity in the dry Andes: an 18-century perspective based on allelic genotyping

History and environment shape crop biodiversity, particularly in areas with vulnerable human communities and ecosystems. Tracing crop biodiversity over time helps understand how rural societies cope with anthropogenic or climatic changes. Exceptionally well preserved ancient DNA of quinoa (Chenopodium quinoa Willd.) from the cold and arid Andes of Argentina has allowed us to track changes and continuities in quinoa diversity over 18 centuries, by coupling genotyping of 157 ancient and modern seeds by 24 SSR markers with cluster and coalescence analyses. Cluster analyses revealed clear population patterns separating modern and ancient quinoas. Coalescence-based analyses revealed that genetic drift within a single population cannot explain genetic differentiation among ancient and modern quinoas. The hypothesis of a genetic bottleneck related to the Spanish Conquest also does not seem to apply at a local scale. Instead, the most likely scenario is the replacement of preexisting quinoa gene pools with new ones of lower genetic diversity. This process occurred at least twice in the last 18 centuries: first, between the 6th and 12th centuries--a time of agricultural intensification well before the Inka and Spanish conquests--and then between the 13th century and today--a period marked by farming marginalization in the late 19th century likely due to a severe multidecadal drought. While these processes of local gene pool replacement do not imply losses of genetic diversity at the metapopulation scale, they support the view that gene pool replacement linked to social and environmental changes can result from opposite agricultural trajectories.

ecology↗