Biology subjects

Durufle, H.

Publications and source records attributed to Durufle, H..

6 recordsLinked to original sources

bioRxiv2026

Studying the role of climate and sex in flowering dynamics of black poplar across time and space

Global change has a major impact on rivers and their associated terrestrial compartment, the riparian forest. The black poplar (Populus nigra L.), the dominant species of the European riparian forest, is considered an indicator of the biodiversity dynamics of these ecosystems, but is threatened with extinction in several parts of its range. The species is dioecious and shows wide variability in many traits, enabling it to adapt to a broad range of environmental conditions. A detailed characterization of this natural variability is therefore a key issue both for the conservation of natural populations and for biological engineering aimed at restoring riverbanks. Phenology is both an indicator of climate change and an adaptation lever for populations, particularly in perennial species such as trees. In this study, we characterized the spatial and temporal variability in flowering phenology by studying 13 natural black poplar populations along the Loire River, France. Flowering date and duration showed substantial variability both within and between populations, and males generally flowered earlier than females. Despite this variability, the ranking of individual trees' flowering dates was highly consistent across years. Most male-female pairs shared several days of overlapping flowering each year, and this overlap was moderately stable across years. Flowering dates advanced by about five to six days per degree Celsius during early spring, with no detectable difference in sensitivity between sexes. This work contributes to the characterization of flowering variability in an economically important tree species and may inform both the conservation of genetic resources and breeding.

plant biology↗

bioRxiv2025

Imputation strategy for population DNA methylation sequencing data

BackgroundDNA methylation is a central epigenetic mechanism involved in regulating gene expression and responses to environmental factors. Although it can sometimes be passed down through generations, its heritability remains variable depending on the species and biological context. These characteristics make it a key marker for studying genotype-environment interactions. However, whole-genome sequencing for DNA methylation analysis remains costly when applied to large numbers of individuals, prompting researchers to focus on specific regions of interest. This targeted approach often results in data matrices with missing values for some individuals, which can hinder downstream analyses. ResultsOur study used 200 and 189 poplar and oak individuals from natural populations, respectively. We tested and compared seven methods for missing data imputation in the specific context of targeted DNA methylation sequencing data obtained in the three different DNA methylation contexts in plants (CpG, CHG, and CHH). The comparison of the different imputation result allows to evaluate their performance to determine the most suitable approach for this type of data. Among them, NIPALS, MissForest, and LOESS provided the highest accuracy. NIPALS delivered the best overall performance but with moderate computational cost, MissForest achieved similar accuracy with faster computation, and LOESS offered competitive results suitable for large datasets. ConclusionsOur results provide a reference for the selection of imputation strategies in targeted sequencing studies, improving the reliability of DNA methylation analyses and broadening the applicability of this type of data in epigenomic research. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/644107v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1c98f47org.highwire.dtl.DTLVardef@1dd9cfborg.highwire.dtl.DTLVardef@6d3a43org.highwire.dtl.DTLVardef@10c1d8b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Natural variation in chalcone isomerase defines a major locus controlling growth variation between Populus nigra populations

Poplar is a promising resource for wood production and the development of lignocellulosic biomass, but currently available varieties have not been optimized for these purposes. Therefore, it is critical to investigate the genetic variability and mechanisms underlying traits that affect biomass yield. Previous studies have shown that target traits in different poplar species are complex, with a small number of genetic factors having relatively low effects compared to medium to high heritability. In this study, a systems biology approach was implemented, combining genomic, transcriptomic, and phenotypic information from a large collection of individuals from natural populations of black poplar from Western Europe. Such an approach identified a QTL and a gene, coding for chalcone isomerase (CHI), as a candidate for controlling radial growth. Additionally, analysis of the structure and diversity of traits as well as CHI gene expression revealed a high allelic fixation index, linked to the geographical origin of the natural populations under study. These findings provide insights into how adaptive traits arise, are selected, and maintained in the populations. Overall, this study contributes to enhancing the use of poplar as a valuable resource for sustainable biomass production.

genomics↗

bioRxiv2021

eQTLs are key players in the integration of genomic and transcriptomic data for phenotype prediction

Multi-omics represent a promising link between phenotypes and genome variation. Few studies yet address their integration to understand genetic architecture and improve predictability. Our study used 241 poplar genotypes, phenotyped in two common gardens, with their xylem and cambium RNA sequenced at one site, yielding large phenotypic, genomic and transcriptomic datasets. For each trait, prediction models were built with genotypic or transcriptomic data and compared to concatenation integrating both omics. The advantage of integration varied across traits and, to understand such differences, we made an eQTL analysis to characterize the interplay between the genome and the transcriptome and classify the predicting features into CIS or TRANS relationships. A strong and significant negative correlation was found between the change in predictability and the change in predictor importance for eQTLs (both TRANS and CIS effects) and CIS regulated transcripts, and mostly for traits showing beneficial integration and evaluated in the site of transcriptomic sampling. Consequently, beneficial integration happens when redundancy of predictors is decreased, leaving the stage to other less prominent but complementary predictors. An additional GO enrichment analysis appeared to corroborate such statistical output. To our knowledge, this is a novel finding delineating a promising way to explore data integration. One-sentence summarySuccessful multi-omics integration when predicting phenotypes makes redundant the predictors that are linked to ubiquitous connections between the omics, according to biological and statistical approaches

genetics↗

bioRxiv2020

Proteomic data from leaves of twenty-four sunflower genotypes underwater deficit

This article describes how the proteomic data were produced on sunflower plants subjected to water deficit. Twenty-four sunflower genotypes were selected to represent genetic diversity within cultivated sunflower. They included both inbred lines and their hybridsWater deficit was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen. Here, we provide proteomic data from sunflower leaves corresponding to the identification of 3062 proteins and the quantification of 1211 of them in these 24 genotypes grown in two watering conditions. These data differentiate both treatment and the different genotypes and constitute a valuable resource to the community to study adaptation of crops to drought and the molecular basis of heterosis.Competing Interest StatementThe authors have declared no competing interest.View Full Text

plant biology↗

bioRxiv2018

A powerful framework for an integrative study with heterogeneous omics data: from univariate statistics to multi-block analysis

The high-throughput data generated by new biotechnologies used in biological studies require specific and adapted statistical treatments. In this work, we propose a novel and powerful framework to manage and analyse multi-omics heterogeneous data to carry out an integrative analysis. We illustrate it using the package mixOmics for the R software as it specifically addresses data integration issues. Our work also aims at confronting the most recent functionalities of mixOmics to real data sets because, even if multi-block integrative methodologies exist, they still have to be used to enlarge our know-how and to provide an operational framework to biologists. Natural populations of the model plant Arabidopsis thaliana are employed in this work but the framework proposed is not limited to this plant and can be deployed whatever the organisms of interest and the biological question. Four omics data sets (phenomics, metabolomics, cell wall proteomics and transcriptomics) have been collected, analysed and integrated in order to study the cell wall plasticity of plants exposed to sub-optimal temperature growth conditions. The methodologies presented start from basic univariate statistics and lead to multi-block integration analysis, and we highlight the fact that each method is associated to one biological issue. Using this powerful framework led us to novel biological conclusions that could not have been reached using standard statistical approaches.

systems biology↗