La cartographie des sites de régulation génétique à partir de données de débalancement allélique

En 1975, Wilson et King ont proposé que l'évolution opère non seulement via des changements affectant la structure des protéines, mais aussi via des mutations qui modifient la régulation génétique. L'étude des éléments régulateurs de l'expression génétique a un rôle important dans la compréhension de l'expression de différentes maladies et de la réponse thérapeutique. Nous avons développé un algorithme bio- informatique qui nous permet rapidement de trouver des sites de régulation génétique à travers tout le génome et pour une grande quantité de gènes. Notre approche consiste à trouver des sites polymorphes (SNPs) qui sont en déséquilibre de liaison avec le débalancement allélique (AI) afin de cartographier la région régulatrice et le site responsable. Notre méthode est avantageuse par rapport à d'autres méthodes, car elle n'a pas besoin des données « phasées». De plus, les données de débalancement allélique ne sont pas affectées par des facteurs externes étant donné qu'ils sont mesurés dans la même cellule. Nous avons démontré que notre approche est fiable et qu'elle peut détecter des sites loin du gène. De plus, il peut être appliqué à des données de génotypage sans avoir besoin de les « phaser » .Wilson and King (1975) proposed that evolution frequently operates through mutations affecting genetic regulation. Likewise, it is expected that genetic variation responsible for inter-individual differences will be due to variation in regulatory sites. Identifying such sites is thus important in the genetic and medical research. We have developed a new bioinformatics algorithm to find genome-wide regulatory sites for a big number of genes. Individuals carrying different alleles at a regulatory site will exhibit allelic imbalance(AI) due to differential expression of the two copies the same locus. Our approach consists of searching polymorphic sites (SNPs) in linkage disequilibrium with AI in order to map regulatory regions. We have detected many SNPs associated to the regulation of different genes pointed in previous studies. We have also found regulatory regions far from the transcription start site (TSS). The major advantage of this method is that phased data is not needed. In addition, AI data has the benefit of not being affected by external factors since it is measured in the same cell. The results show that our approach is reliable and it can detect sites far from the gene

Genotype-Based Test in Mapping Cis-Regulatory Variants from Allele-Specific Expression Data

Identifying and understanding the impact of gene regulatory variation is of considerable importance in evolutionary and medical genetics; such variants are thought to be responsible for human-specific adaptation [1] and to have an important role in genetic disease. Regulatory variation in cis is readily detected in individuals showing uneven expression of a transcript from its two allelic copies, an observation referred to as allelic imbalance (AI). Identifying individuals exhibiting AI allows mapping of regulatory DNA regions and the potential to identify the underlying causal genetic variant(s). However, existing mapping methods require knowledge of the haplotypes, which make them sensitive to phasing errors. In this study, we introduce a genotype-based mapping test that does not require haplotype-phase inference to locate regulatory regions. The test relies on partitioning genotypes of individuals exhibiting AI and those not expressing AI in a 2×3 contingency table. The performance of this test to detect linkage disequilibrium (LD) between a potential regulatory site and a SNP located in this region was examined by analyzing the simulated and the empirical AI datasets. In simulation experiments, the genotype-based test outperforms the haplotype-based tests with the increasing distance separating the regulatory region from its regulated transcript. The genotype-based test performed equally well with the experimental AI datasets, either from genome–wide cDNA hybridization arrays or from RNA sequencing. By avoiding the need of haplotype inference, the genotype-based test will suit AI analyses in population samples of unknown haplotype structure and will additionally facilitate the identification of cis-regulatory variants that are located far away from the regulated transcript

An atlas of over 90.000 conserved noncoding sequences provides insight into crucifer regulatory regions

Despite the central importance of noncoding DNA to gene regulation and evolution, understanding of the extent of selection on plant noncoding DNA remains limited compared to that of other organisms. Here we report sequencing of genomes from three Brassicaceae species (Leavenworthia alabamica, Sisymbrium irio and Aethionema arabicum) and their joint analysis with six previously sequenced crucifer genomes. Conservation across orthologous bases suggests that at least 17% of the Arabidopsis thaliana genome is under selection, with nearly one-quarter of the sequence under selection lying outside of coding regions. Much of this sequence can be localized to approximately 90,000 conserved noncoding sequences (CNSs) that show evidence of transcriptional and post-transcriptional regulation. Population genomics analyses of two crucifer species, A. thaliana and Capsella grandiflora, confirm that most of the identified CNSs are evolving under medium to strong purifying selection. Overall, these CNSs highlight both similarities and several key differences between the regulatory DNA of plants and other species

A Comprehensive Approach to Assess Arabidopsis Survival Phenotype in Water-Limited Condition Using a Non-invasive High-Throughput Phenomics Platform

With the rapid rise in global population and the challenges caused by climate changes, the maximization of plant productivity and the development of sustainable agriculture strategies are vital for food security. One of the resources more affected in this new environment will be the limitation of water. In this study, we describe the use of non-invasive technologies exploiting sensors for visible, fluorescent, and near-infrared lights to accurately screen survival phenotypes in Arabidopsis thaliana exposed to water-limited conditions. We implemented two drought protocols and a robust analysis methodology that enabled us to clearly assess the wilting or dryness status of the plants at different time points using a phenomics platform. In conclusion, our approach has shown to be very accurate and suitable for experiments where hundred of samples have to be screened making a manual evaluation unthinkable. This approach can be used not only in functional genomics studies but also in agricultural applications

DataSheet_1_Integrated web portal for non-destructive salt sensitivity detection of Camelina sativa seeds using fluorescent and visible light images coupled with machine learning algorithms.pdf

Climate change has created unprecedented stresses in the agricultural sector, driving the necessity of adapting agricultural practices and developing novel solutions to the food crisis. Camelina sativa (Camelina) is a recently emerging oilseed crop with high nutrient-density and economic potential. Camelina seeds are rich in essential fatty acids and contain potent antioxidants required to maintain a healthy diet. Camelina seeds are equally amenable to economic applications such as jet fuel, biodiesel and high-value industrial lubricants due to their favorable proportions of unsaturated fatty acids. High soil salinity is one of the major abiotic stresses threatening the yield and usability of such crops. A promising mitigation strategy is automated, non-destructive, image-based phenotyping to assess seed quality in the food manufacturing process. In this study, we evaluate the effectiveness of image-based phenotyping on fluorescent and visible light images to quantify and qualify Camelina seeds. We developed a user-friendly web portal called SeedML that can uncover key morpho-colorimetric features to accurately identify Camelina seeds coming from plants grown in high salt conditions using a phenomics platform equipped with fluorescent and visible light cameras. This portal may be used to enhance quality control, identify stress markers and observe yield trends relevant to the agricultural sector in a high throughput manner. Findings of this work may positively contribute to similar research in the context of the climate crisis, while supporting the implementation of new quality controls tools in the agri-food domain.</p

DataSheet_2_Integrated web portal for non-destructive salt sensitivity detection of Camelina sativa seeds using fluorescent and visible light images coupled with machine learning algorithms.pdf

Climate change has created unprecedented stresses in the agricultural sector, driving the necessity of adapting agricultural practices and developing novel solutions to the food crisis. Camelina sativa (Camelina) is a recently emerging oilseed crop with high nutrient-density and economic potential. Camelina seeds are rich in essential fatty acids and contain potent antioxidants required to maintain a healthy diet. Camelina seeds are equally amenable to economic applications such as jet fuel, biodiesel and high-value industrial lubricants due to their favorable proportions of unsaturated fatty acids. High soil salinity is one of the major abiotic stresses threatening the yield and usability of such crops. A promising mitigation strategy is automated, non-destructive, image-based phenotyping to assess seed quality in the food manufacturing process. In this study, we evaluate the effectiveness of image-based phenotyping on fluorescent and visible light images to quantify and qualify Camelina seeds. We developed a user-friendly web portal called SeedML that can uncover key morpho-colorimetric features to accurately identify Camelina seeds coming from plants grown in high salt conditions using a phenomics platform equipped with fluorescent and visible light cameras. This portal may be used to enhance quality control, identify stress markers and observe yield trends relevant to the agricultural sector in a high throughput manner. Findings of this work may positively contribute to similar research in the context of the climate crisis, while supporting the implementation of new quality controls tools in the agri-food domain.</p

Abiotic Stress Phenotypes Are Associated with Conserved Genes Derived from Transposable Elements

Plant phenomics offers unique opportunities to accelerate our understanding of gene function and plant response to different environments, and may be particularly useful for studying previously uncharacterized genes. One important type of poorly characterized genes is those derived from transposable elements (TEs), which have departed from a mobility-driven lifestyle to attain new adaptive roles for the host (exapted TEs). We used phenomics approaches, coupled with reverse genetics, to analyze T-DNA insertion mutants of both previously reported and novel protein-coding exapted TEs in the model plant Arabidopsis thaliana. We show that mutations in most of these exapted TEs result in phenotypes, particularly when challenged by abiotic stress. We built statistical multi-dimensional phenotypic profiles and compared them to wild-type and known stress responsive mutant lines for each particular stress condition. We found that these exapted TEs may play roles in responses to phosphate limitation, tolerance to high salt concentration, freezing temperatures, and arsenic toxicity. These results not only experimentally validate a large set of putative functional exapted TEs recently discovered through computational analysis, but also uncover additional novel phenotypes for previously well-characterized exapted TEs in A. thaliana

Manhattan plots of <i>p</i>-values from the contingency test.

<p>(<b>A</b>) for all autosomes using HapMap2 polymorphisms and AI data for LRRIQ3; (<b>B</b>) using HapMap3 polymorphisms and AI data for TAPBP; and (<b>C</b>) using 1000 genomes sequences for chromosome 6 and the same AI data for TAPBP.</p

Sets of possible genotypes under complete and incomplete linkage disequilibrium.

<p>Under complete LD for genealogical positions below (<b>A</b>), parallel (<b>B</b>) and above (<b>C</b>), there are always two genotypes characterizing AI-individuals and only one type of A-site homozygote present (AA or aa). Under equilibrium or incomplete linkage disequilibrium (<b>D</b>) all four haplotypes involving R and A sites are present and thus potentially all ten resulting genotypes as well.</p

Four possible mutational pathways creating three distinct sets of three haplotypes.

<p>Depending on the sequence of mutations starting with the ancestral haplotype on the left, we obtain three sets of haplotypes, referred to as below, parallel and above to reflect the position of the <i>A</i>-site vs. <i>R</i>-site mutation on the genealogy shown on the right. These genealogical positions can be modified by recombination. We assume no recurrent mutations.</p