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

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.

Camelina sativa High-Throughput Phenotyping Under Normal and Salt Conditions Using a Plant Phenomics Platform.

Climate change and environmental pollution will have a great impact on food security worldwide. More than 30% of the world's irrigated areas are estimated to be perturbed by high salinity affecting the productivity of crops. Camelina sativa, also known as false flax, is a flowering plant that is mainly cultivated as an oilseed crop that has many potential economic benefits; it can be used in food products, in industrial applications, and in animal feed and converted into biofuel. However, natural disasters due to climate events have led to significant crop losses. In this work, we developed a high-throughput phenotyping protocol to analyze the effects of different concentrations of salt on C. sativa using the McGill Plant Phenomics Platform (MP3). We present an adapted protocol to be applied with phenomics facilities in a greenhouse environment and the most effective way for high-throughput phenotyping.

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.

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.

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.

The Capsella rubella genome and the genomic consequences of rapid mating system evolution.

The shift from outcrossing to selfing is common in flowering plants, but the genomic consequences and the speed at which they emerge remain poorly understood. An excellent model for understanding the evolution of self fertilization is provided by Capsella rubella, which became self compatible <200,000 years ago. We report a C. rubella reference genome sequence and compare RNA expression and polymorphism patterns between C. rubella and its outcrossing progenitor Capsella grandiflora. We found a clear shift in the expression of genes associated with flowering phenotypes, similar to that seen in Arabidopsis, in which self fertilization evolved about 1 million years ago. Comparisons of the two Capsella species showed evidence of rapid genome-wide relaxation of purifying selection in C. rubella without a concomitant change in transposable element abundance. Overall we document that the transition to selfing may be typified by parallel shifts in gene expression, along with a measurable reduction of purifying selection.

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 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.