Biology subjects

Langlade, N.

Publications and source records attributed to Langlade, N..

4 recordsLinked to original sources

bioRxiv2026

A plant single nucleotide polymorphism impacts nectar sugar composition, microbial diversity and pollinator visits

Nectar is a hub for plant-pollinator interactions, yet gene-level causal links between plant genetic variation, pollinator foraging and nectar microbial assembly remain poorly resolved. Using near-isogenic lines, innovative field time-lapse monitoring of pollinator visits and long-read amplicon sequencing of nectar microbiota, we show that a natural single-nucleotide variant at a cell-wall invertase gene (HaCWINV2) controls sunflower nectar chemistry and influences both pollinators and microbes. Plants homozygous for a loss-of-function HaCWINV2 allele produce sucrose-rich nectar, resulting in fewer bee visits under field conditions, while bumblebee visitation remained unaffected. In pollinator-excluded flowers, invertase-deficient plants harboured greater fungal diversity and compositionally distinct communities, indicating that nectar sugar profiles act as ecological filters shaping the nectar microbiome. This loss-of-function allele was found rarely and only at the heterozygote state in wild sunflowers and was fixed in 35% of cultivated lines indicating a positive selection during domestication. Our findings establish a causal link between a single gene and nectar chemistry with cascading ecological effects in a plant-pollinator system, illustrating how subtle genetic changes scale up to alter nectar traits, microbial assembly and pollinator foraging behaviour.

genetics↗

bioRxiv2022

The genomics of linkage drag in sunflower

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here we comprehensively analyzed the genomic and phenotypic impacts of wild introgressions into cultivated sunflower to estimate the impacts of linkage drag. First, we generated new reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge regression model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including > 3,000 new genes. While introgressions reduced genetic load at protein-coding sequences and positively affected traits associated with abiotic stress resistance, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, pre-breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

genomics↗

bioRxiv2020

Optimized cultivar deployment improves the efficiency and stability of sunflower crop production at national scale

Plant breeding programs design new crop cultivars which, while developed for distinct populations of environments, are nevertheless grown over large areas during their time in the market. Over its cultivation area, the crop is exposed to highly diverse stress patterns caused by climatic uncertainty and multiple management options, which often leads to decreased expected crop performance. In this study, we aim is to assess how finer spatial management of genetic resources could reduce the yield variance explained by genotype x environment interactions in a set of cropping environments and ultimately improve the efficiency and stability of crop production. We used modeling and simulation to predict the crop performance resulting from the interaction between cultivar growth and development, climate and soil conditions, and management practices. We designed a computational experiment that evaluated the performance of a collection of commercial sunflower cultivars in a realistic population of cropping conditions in France, built from extensive agricultural surveys. Distinct farming locations sharing similar simulated abiotic stress patterns were clustered together to specify environment types. We then used optimization methods to search for cultivars x environments combinations leading to increased yield expectations. Results showed that a single cultivar choice adapted to the most frequent environment-type in the population is a robust strategy. However, the relevance of cultivar recommendations to specific locations was gradually increasing with the knowledge of pedo-climatic conditions. We argue that this approach while being operational on current genetic material could act synergistically with plant breeding as more diverse material could enable access to cultivars with distinctive traits, more adapted to specific conditions. Key messageCrop simulation helps to analyze environmental impacts on crops and provides year-independent context information. This information is of major importance when deciding which cultivar to choose at sowing time.

plant biology↗

bioRxiv2018

Heliaphen, an outdoor high-throughput phenotyping platform designed to integrate genetics and crop modeling

Heliaphen is an outdoor pot platform designed for high-throughput phenotyping. It allows automated management of drought scenarios and plant monitoring during the whole plant cycle. A robot moving between plants growing in 15L pots monitors plant water status and phenotypes plant or leaf morphology, from which we can compute more complex traits such as the response of leaf expansion (LE) or plant transpiration (TR) to water deficit. Here, we illustrate the platform capabilities for sunflower on two practical cases: a genetic and genomics study for the response to drought of yield-related traits and a simulation study, where we use measured parameters as inputs for a crop simulation model. For the genetic study, classical measurements of thousand-kernel weight (TKW) were done on a sunflower bi-parental population under water stress and control conditions managed automatically. The association study using the TKW drought-response highlighted five genetic markers. A complementary transcriptomic experiment identified closeby candidate genes differentially expressed in the parental backgrounds in drought conditions. For the simulation study, we used the SUNFLO crop simulation model to assess the impact of two traits measured on the platform (LE and TR) on crop yield in a large population of environments. We conducted simulations in 42 contrasted locations across Europe and 21 years of climate data. We defined the pattern of abiotic stresses occurring at this continental scale and identified ideotypes (i.e. genotypes with specific traits values) that are more adapted to specific environment types. This study exemplifies how phenotyping platforms can help with the identification of the genetic architecture of complex response traits and the estimation of eco-physiological model parameters in order to define ideotypes adapted to different environmental conditions.

plant biology↗