Arabinosylation of cell wall extensin is required for the directional response to salinity in roots.

Soil salinity is a major contributor to crop yield losses. To improve our understanding of root responses to salinity, we developed and exploited a real-time salt-induced tilting assay. This assay follows root growth upon both gravitropic and salt challenges, revealing that root bending upon tilting is modulated by Na+ ions, but not by osmotic stress. Next, we measured this salt-specific response in 345 natural Arabidopsis (Arabidopsis thaliana) accessions and discovered a genetic locus, encoding the cell wall-modifying enzyme EXTENSIN ARABINOSE DEFICIENT TRANSFERASE (ExAD) that is associated with root bending in the presence of NaCl (hereafter salt). Extensins are a class of structural cell wall glycoproteins known as hydroxyproline (Hyp)-rich glycoproteins, which are posttranslationally modified by O-glycosylation, mostly involving Hyp-arabinosylation. We show that salt-induced ExAD-dependent Hyp-arabinosylation influences root bending responses and cell wall thickness. Roots of exad1 mutant seedlings, which lack Hyp-arabinosylation of extensin, displayed increased thickness of root epidermal cell walls and greater cell wall porosity. They also showed altered gravitropic root bending in salt conditions and a reduced salt-avoidance response. Our results suggest that extensin modification via Hyp-arabinosylation is a unique salt-specific cellular process required for the directional response of roots exposed to salinity.

The dimorphic diaspore model Aethionema arabicum (Brassicaceae): Distinct molecular and morphological control of responses to parental and germination temperatures.

Plants in habitats with unpredictable conditions often have diversified bet-hedging strategies that ensure fitness over a wider range of variable environmental factors. A striking example is the diaspore (seed and fruit) heteromorphism that evolved to maximize species survival in Aethionema arabicum (Brassicaceae) in which external and endogenous triggers allow the production of two distinct diaspores on the same plant. Using this dimorphic diaspore model, we identified contrasting molecular, biophysical, and ecophysiological mechanisms in the germination responses to different temperatures of the mucilaginous seeds (M+ seed morphs), the dispersed indehiscent fruits (IND fruit morphs), and the bare non-mucilaginous M- seeds obtained by pericarp (fruit coat) removal from IND fruits. Large-scale comparative transcriptome and hormone analyses of M+ seeds, IND fruits, and M- seeds provided comprehensive datasets for their distinct thermal responses. Morph-specific differences in co-expressed gene modules in seeds, as well as in seed and pericarp hormone contents, identified a role of the IND pericarp in imposing coat dormancy by generating hypoxia affecting ABA sensitivity. This involved expression of morph-specific transcription factors, hypoxia response and cell wall-remodeling genes, as well as altered abscisic acid (ABA) metabolism, transport, and signaling. Parental temperature affected ABA contents and ABA-related gene expression and altered IND pericarp biomechanical properties. Elucidating the molecular framework underlying the diaspore heteromorphism can provide insight into developmental responses to globally changing temperatures.

LeTra: a leaf tracking workflow based on convolutional neural networks and intersection over union.

BACKGROUND: The study of plant photosynthesis is essential for productivity and yield. Thanks to the development of high-throughput phenotyping (HTP) facilities, based on chlorophyll fluorescence imaging, photosynthetic traits can be measured in a reliable, reproducible and efficient manner. In most state-of-the-art HTP platforms, these traits are automatedly analyzed at individual plant level, but information at leaf level is often restricted by the use of manual annotation. Automated leaf tracking over time is therefore highly desired. Methods for tracking individual leaves are still uncommon, convoluted, or require large datasets. Hence, applications and libraries with different techniques are required. New phenotyping platforms are initiated now more frequently than ever; however, the application of advanced computer vision techniques, such as convolutional neural networks, is still growing at a slow pace. Here, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants. We also provide datasets and code for training and testing on both detection and tracking of individual leaves, aiming to stimulate the community to expand the current methodologies on this topic. RESULTS: We tested the results for detection and segmentation on 523 Arabidopsis thaliana leaves at three different stages of development from which we obtained a mean F-score of 0.956 on detection and 0.844 on segmentation overlap through the intersection over union (IoU). On the tracking side, we tested nine different plants with 191 leaves. A total of 161 leaves were tracked without issues, accounting to a total of 84.29% correct tracking, and a Higher Order Tracking Accuracy (HOTA) of 0.846. In our case study, leaf age and leaf order influenced photosynthetic capacity and photosynthetic response to light treatments. Leaf-dependent photosynthesis varies according to the genetic background. CONCLUSION: The method provided is robust for leaf tracking on top-down images. Although one of the strong components of the method is the low requirement in training data to achieve a good base result (based on fine-tuning), most of the tracking issues found could be solved by expanding the training dataset for the Mask R-CNN model.

Green composites based on volcanic red algae Cyanidiales, cellulose, and coffee waste biomass modified with magnetic nanoparticles for the removal of methylene blue.

In this paper, green nanocomposites based on biomass and superparamagnetic nanoparticles were synthesized and used as adsorbents to remove methylene blue (MB) from water with magnetic separation. The adsorbents were synthesized through the wet co-precipitation technique, in which iron-oxide nanoparticles coated the cores based on coffee, cellulose, and red volcanic algae waste. The procedure resulted in materials that could be easily separated from aqueous solutions with magnets. The morphology and chemical composition of the nanocomposites were characterized by SEM, FT-IR, and XPS methods. The adsorption studies of MB removal with UV-vis spectrometry showed that the adsorption performance of the prepared materials strongly depended on their morphology and the type of the organic adsorbent. The adsorption studies presented the highest effectiveness in neutral pH with only a slight effect on ionic strength. The MB removal undergoes pseudo-second kinetics for all adsorbents. The maximal adsorption capacity for the coffee@Fe3O4-2, cellulose@Fe3O4-1, and algae@Fe3O4-1 is 38.23 mg g-1, 41.61 mg g-1, and 48.41 mg g-1, respectively. The mechanism of MB adsorption follows the Langmuir model using coffee@Fe3O4 and cellulose@Fe3O4, while for algae@Fe3O4 the process fits to the Redlich-Peterson model. The removal efficiency analysis based on UV-vis adsorption spectra revealed that the adsorption effectiveness of the nanocomposites increased as follows: coffee@Fe3O4-2 > cellulose@Fe3O4-1 > algae@Fe3O4-1, demonstrating an MB removal efficiency of up to 90%.

Genomic Blocks in Aethionema arabicum Support Arabideae as Next Diverging Clade in Brassicaceae.

The tribe Aethionemeae is sister to all other crucifers, making it a crucial group for unraveling genome evolution and phylogenetic relationships within the crown group Brassicaceae. In this study, we extend the analysis of Brassicaceae genomic blocks (GBs) to Aethionema whereby we identified unique block boundaries shared only with the tribe Arabideae. This was achieved using bioinformatic methods to analyze synteny between the recently updated genome sequence of Aethionema arabicum and other high-quality Brassicaceae genome sequences. We show that compared to the largely conserved genomic structure of most non-polyploid Brassicaceae lineages, GBs are highly rearranged in Aethionema. Furthermore, we detected similarities between the genomes of Aethionema and Arabis alpina, in which also a high number of genomic rearrangements compared to those of other Brassicaceae was found. These similarities suggest that tribe Arabideae, a clade showing conflicting phylogenetic position between studies, may have diverged before diversification of the other major lineages, and highlight the potential of synteny information for phylogenetic inference.

Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana.

Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ PSII ) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both Φ PSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.

Genome Improvement and Genetic Map Construction for Aethionema arabicum, the First Divergent Branch in the Brassicaceae Family.

The genus Aethionema is a sister-group to the core-group of the Brassicaceae family that includes Arabidopsis thaliana and the Brassica crops. Thus, Aethionema is phylogenetically well-placed for the investigation and understanding of genome and trait evolution across the family. We aimed to improve the quality of the reference genome draft version of the annual species Aethionema arabicum Second, we constructed the first Ae. arabicum genetic map. The improved reference genome and genetic map enabled the development of each other. We started with the initially published genome (version 2.5). PacBio and MinION sequencing together with genetic map v2.5 were incorporated to produce the new reference genome v3.0. The improved genome contains 203 MB of sequence, with approximately 94% of the assembly made up of called (non-gap) bases, assembled into 2,883 scaffolds (with only 6% of the genome made up of non-called bases (Ns)). The N50 (10.3 MB) represents an 80-fold increase over the initial genome release. We generated a Recombinant Inbred Line (RIL) population that was derived from two ecotypes: Cyprus and Turkey (the reference genotype. Using a Genotyping by Sequencing (GBS) approach, we generated a high-density genetic map with 749 (v2.5) and then 632 SNPs (v3.0) was generated. The genetic map and reference genome were integrated, thus greatly improving the scaffolding of the reference genome into 11 linkage groups. We show that long-read sequencing data and genetics are complementary, resulting in an improved genome assembly in Ae. arabicum They will facilitate comparative genetic mapping work for the Brassicaceae family and are also valuable resources to investigate wide range of life history traits in Aethionema.

Flowering Locus C (FLC) Is a Potential Major Regulator of Glucosinolate Content across Developmental Stages of Aethionema arabicum (Brassicaceae).

The biochemical defense of plants can change during their life-cycle and impact herbivore feeding and plant fitness. The annual species Aethionema arabicum is part of the sister clade to all other Brassicaceae. Hence, it holds a phylogenetically important position for studying crucifer trait evolution. Glucosinolates (GS) are essentially Brassicales-specific metabolites involved in plant defense. Using two Ae. arabicum accessions (TUR and CYP) we identify substantial differences in glucosinolate profiles and quantities between lines, tissues and developmental stages. We find tissue specific side-chain modifications in aliphatic GS: methylthioalkyl in leaves, methylsulfinylalkyl in fruits, and methylsulfonylalkyl in seeds. We also find large differences in absolute glucosinolate content between the two accessions (up to 10-fold in fruits) that suggest a regulatory factor is involved that is not part of the quintessential glucosinolate biosynthetic pathway. Consistent with this hypothesis, we identified a single major multi-trait quantitative trait locus controlling total GS concentration across tissues in a recombinant inbred line population derived from TUR and CYP. With fine-mapping, we narrowed the interval to a 58 kb region containing 15 genes, but lacking any known GS biosynthetic genes. The interval contains homologs of both the sulfate transporter SULTR2;1 and FLOWERING LOCUS C. Both loci have diverse functions controlling plant physiological and developmental processes and thus are potential candidates regulating glucosinolate variation across the life-cycle of Aethionema. Future work will investigate changes in gene expression of the candidates genes, the effects of GS variation on insect herbivores and the trade-offs between defense and reproduction.

Developmental Control and Plasticity of Fruit and Seed Dimorphism in Aethionema arabicum.

Understanding how plants cope with changing habitats is a timely and important topic in plant research. Phenotypic plasticity describes the capability of a genotype to produce different phenotypes when exposed to different environmental conditions. In contrast, the constant production of a set of distinct phenotypes by one genotype mediates bet hedging, a strategy that reduces the temporal variance in fitness at the expense of a lowered arithmetic mean fitness. Both phenomena are thought to represent important adaptation strategies to unstable environments. However, little is known about the underlying mechanisms of these phenomena, partly due to the lack of suitable model systems. We used phylogenetic and comparative analyses of fruit and seed anatomy, biomechanics, physiology, and environmental responses to study fruit and seed heteromorphism, a typical morphological basis of a bet-hedging strategy of plants, in the annual Brassicaceae species Aethionema arabicum Our results indicate that heteromorphism evolved twice within the Aethionemeae, including once for the monophyletic annual Aethionema clade. The dimorphism of Ae. arabicum is associated with several anatomic, biomechanical, gene expression, and physiological differences between the fruit and seed morphs. However, fruit ratios and numbers change in response to different environmental conditions. Therefore, the life-history strategy of Ae. arabicum appears to be a blend of bet hedging and plasticity. Together with the available genomic resources, our results pave the way to use this species in future studies intended to unravel the molecular control of heteromorphism and plasticity.

A role for seed storage proteins in Arabidopsis seed longevity.

Proteomics approaches have been a useful tool for determining the biological roles and functions of individual proteins and identifying the molecular mechanisms that govern seed germination, vigour and viability in response to ageing. In this work the dry seed proteome of four Arabidopsis thaliana genotypes, that carry introgression fragments at the position of seed longevity quantitative trait loci and as a result display different levels of seed longevity, was investigated. Seeds at two physiological states, after-ripened seeds that had the full germination ability and aged (stored) seeds of which the germination ability was severely reduced, were compared. Aged dry seed proteomes were markedly different from the after-ripened and reflected the seed longevity level of the four genotypes, despite the fact that dry seeds are metabolically quiescent. Results confirmed the role of antioxidant systems, notably vitamin E, and indicated that protection and maintenance of the translation machinery and energy pathways are essential for seed longevity. Moreover, a new role for seed storage proteins (SSPs) was identified in dry seeds during ageing. Cruciferins (CRUs) are the most abundant SSPs in Arabidopsis and seeds of a triple mutant for three CRU isoforms (crua crub cruc) were more sensitive to artificial ageing and their seed proteins were highly oxidized compared with wild-type seeds. These results confirm that oxidation is involved in seed deterioration and that SSPs buffer the seed from oxidative stress, thus protecting important proteins required for seed germination and seedling formation.

Natural variation for seed longevity and seed dormancy are negatively correlated in Arabidopsis.

Dormancy is a state of metabolic arrest that facilitates the survival of organisms during environmental conditions incompatible with their regular course of life. Many organisms have deep dormant stages to promote an extended life span (increased longevity). In contrast, plants have seed dormancy and seed longevity described as two traits. Seed dormancy is defined as a temporary failure of a viable seed to germinate in conditions that favor germination, whereas seed longevity is defined as seed viability after dry storage (storability). In plants, the association of seed longevity with seed dormancy has not been studied in detail. This is surprising given the ecological, agronomical, and economic importance of seed longevity. We studied seed longevity to reveal its genetic regulators and its association with seed dormancy in Arabidopsis (Arabidopsis thaliana). Integrated quantitative trait locus analyses for seed longevity, in six recombinant inbred line populations, revealed five loci: Germination Ability After Storage1 (GAAS1) to GAAS5. GAAS loci colocated with seed dormancy loci, Delay Of Germination (DOG), earlier identified in the same six recombinant inbred line populations. Both GAAS loci and their colocation with DOG loci were validated by near isogenic lines. A negative correlation was observed, deep seed dormancy correlating with low seed longevity and vice versa. Detailed analysis on the collocating GAAS5 and DOG1 quantitative trait loci revealed that the DOG1-Cape Verde Islands allele both reduces seed longevity and increases seed dormancy. To our knowledge, this study is the first to report a negative correlation between seed longevity and seed dormancy.