Biology subjects

Langlade, N. B.

Publications and source records attributed to Langlade, N. B..

5 recordsLinked to original sources

bioRxiv2026

Same visitors, different outcomes: floral phenotype gates pollinator-vectored nectar microbial establishment

The microbiome of nectar has the potential to modify flower chemistry and plant-pollinator interactions. How its assembly differs between diurnal and nocturnal guilds, and how it depends on the flower they visit, remains poorly resolved in crops. We conducted field assays using two commercial sunflower cultivars (Helianthus annuus) that differ markedly in nectar volume and sugar composition, under four pollinator access treatments. Diurnal pollinator access, whether continuous or daytime-only, resulted in a cultivar-dependent microbiome signature. In one cultivar, nectar communities were characterized by the dominance of specialist taxa such as the yeast Metschnikowia and the bacterium Acinetobacter. However, neither taxa established dominance in the nectar of the other cultivar despite receiving substantial insect visits. Instead, continuous access alone lowered overall fungal diversity without restructuring composition, which was more strongly structured by inter-annual variation than by pollinator access treatment. Profiling of the volatile organic compounds in florets revealed clear differences between cultivars, but different access treatment induced only minor changes in the volatile blends. Our findings establish that pollinator guilds shape sunflower nectar microbiota in a genotype-dependent manner. We propose that diurnal insect visits seed nectar with specialist taxa, but whether those specialists establish depends on nectar availability and composition.

microbiology↗

bioRxiv2026

PolliCrop: A high-throughput computer vision pipeline for pollinator monitoring in agroecosystems

Flower-visiting insect populations are declining since the 1990s, especially because of the decrease of floral resources in agricultural settings. Mass flowering crops can help increase resource availability, and plant breeding can be directed towards selecting varieties attracting more flower-visiting insects. This requires the implementation of an automated high-throughput phenotyping tool for assessing the attractiveness of plant genotypes to flower-visiting insects. In this study, (i) we present a procedure to take standardized images of sunflower heads with camera traps continuously at day and night in the field; (ii) we trained two versions of a deep learning model, named PolliCrop, to automatically detect and identify three classes of the main insects visiting sunflower on these images (non-Bombus bees, bumble bees, lepidopterans); (iii) we assessed and validated the ability of PolliCrop to correctly predict the true visitation frequencies of the insect classes on three sunflower genotypes; (iv) we presented two statistical approaches to compare the insect visitation frequencies between plant genotypes, one including weather variables, and the other one without. One PolliCrop version yielded satisfying performance to correctly detect the three insect classes. In particular, it correctly predicted the insect visitation frequencies on two sunflower genotypes in a range of {+/-}10%. The other PolliCrop version can be useful in certain contexts of images and objectives. PolliCrop can be extended in the future to other crop species by training PolliCrop on new images captured in these crops. The field experimental design to set up for comparing the attractiveness between genotypes is also discussed.

animal behavior and cognition↗

bioRxiv2021

Pooled Single-Molecule transcriptomics identifies a giant gene under balancing selection in sunflower

Genes under balancing selection control phenotypes such as immunity, color or sex, but are difficult to identify. Self-incompatibility genes are under negative frequency-dependent selection, a special case of balancing selection, with up to 30 to 50 alleles segregating per population. We developed a method based on pooled Single-Molecule transcriptomics to identify balanced polymorphisms expressed in tissues of interest. We searched for multi-allelic, non-recombining genes causing self-incompatibility in wild sunflower (Helianthus annuus). A diversity scan in pistil identified a gene, Ha7650b,that displayed balanced polymorphism and colocalized with a quantitative trait locus for self-incompatibility. Unexpectedly, Ha7650b displayed gigantism (400 kb), which was caused by increase in intron size as a consequence of suppressed recombination. Ha7650b emerged after a whole-genome duplication (29 millions years ago) followed by tandem duplications and neofunctionalisation. Ha7650b shows expression, genetic location, genomic neighbourhood and predicted function that provide strong evidence that it is involved in self-incompatibility. Pooled Single-Molecule transcriptomics is an affordable and powerful new method that makes it possible to identify diversity and structural outliers simultaneously. It will allow a breakthrough in the discovery of self-incompatibility genes and other expressed genes under balancing selection.

evolutionary biology↗

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↗

bioRxiv2017

Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids

Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

genetics↗