Ontologies for increasing the FAIRness of plant research data.

The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human- and machine- interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms.

A Solanum lycopersicoides reference genome facilitates insights into tomato specialized metabolism and immunity.

Wild relatives of tomato are a valuable source of natural variation in tomato breeding, as many can be hybridized to the cultivated species (Solanum lycopersicum). Several, including Solanum lycopersicoides, have been crossed to S. lycopersicum for the development of ordered introgression lines (ILs), facilitating breeding for desirable traits. Despite the utility of these wild relatives and their associated ILs, few finished genome sequences have been produced to aid genetic and genomic studies. Here we report a chromosome-scale genome assembly for S. lycopersicoides LA2951, which contains 37 938 predicted protein-coding genes. With the aid of this genome assembly, we have precisely delimited the boundaries of the S. lycopersicoides introgressions in a set of S. lycopersicum cv. VF36 × LA2951 ILs. We demonstrate the usefulness of the LA2951 genome by identifying several quantitative trait loci for phenolics and carotenoids, including underlying candidate genes, and by investigating the genome organization and immunity-associated function of the clustered Pto gene family. In addition, syntenic analysis of R2R3MYB genes sheds light on the identity of the Aubergine locus underlying anthocyanin production. The genome sequence and IL map provide valuable resources for studying fruit nutrient/quality traits, pathogen resistance, and environmental stress tolerance. We present a new genome resource for the wild species S. lycopersicoides, which we use to shed light on the Aubergine locus responsible for anthocyanin production. We also provide IL boundary mappings, which facilitated identifying novel carotenoid quantitative trait loci of which one was likely driven by an uncharacterized lycopene ß-cyclase whose function we demonstrate.

Morphological and Physiological Traits Associated with Yield under Reduced Irrigation in Chilean Coastal Lowland Quinoa.

Quinoa (Chenopodium quinoa Willd.) is a genetically diverse crop that has gained popularity in recent years due to its high nutritional content and ability to tolerate abiotic stresses such as salinity and drought. Varieties from the coastal lowland ecotype are of particular interest due to their insensitivity to photoperiod and their potential to be cultivated in higher latitudes. We performed a field experiment in the southern Atacama Desert in Chile to investigate the responses to reduced irrigation of nine previously selected coastal lowland self-pollinated (CLS) lines and the commercial cultivar Regalona. We found that several lines exhibited a yield and seed size superior to Regalona, also under reduced irrigation. Plant productivity data were analyzed together with morphological and physiological traits measured at the visible inflorescence stage to estimate the contribution of these traits to differences between the CLS lines and Regalona under full and reduced irrigation. We applied proximal sensing methods and found that thermal imaging provided a promising means to estimate variation in plant water use relating to yield, whereas hyperspectral imaging separated lines in a different way, potentially related to photosynthesis as well as water use.

Oxford Nanopore sequencing: new opportunities for plant genomics?

DNA sequencing was dominated by Sanger's chain termination method until the mid-2000s, when it was progressively supplanted by new sequencing technologies that can generate much larger quantities of data in a shorter time. At the forefront of these developments, long-read sequencing technologies (third-generation sequencing) can produce reads that are several kilobases in length. This greatly improves the accuracy of genome assemblies by spanning the highly repetitive segments that cause difficulty for second-generation short-read technologies. Third-generation sequencing is especially appealing for plant genomes, which can be extremely large with long stretches of highly repetitive DNA. Until recently, the low basecalling accuracy of third-generation technologies meant that accurate genome assembly required expensive, high-coverage sequencing followed by computational analysis to correct for errors. However, today's long-read technologies are more accurate and less expensive, making them the method of choice for the assembly of complex genomes. Oxford Nanopore Technologies (ONT), a third-generation platform for the sequencing of native DNA strands, is particularly suitable for the generation of high-quality assemblies of highly repetitive plant genomes. Here we discuss the benefits of ONT, especially for the plant science community, and describe the issues that remain to be addressed when using ONT for plant genome sequencing.

Water Deficit Elicits a Transcriptional Response of Genes Governing d-pinitol Biosynthesis in Soybean (Glycine max).

d-pinitol is the most commonly accumulated sugar alcohol in the Leguminosae family and has been observed to increase significantly in response to abiotic stress. While previous studies have identified genes involved in d-pinitol synthesis, no study has investigated transcript expression in planta. The present study quantified the expression of several genes involved in d-pinitol synthesis in different plant tissues and investigated the accumulation of d-pinitol, myo-inositol and other metabolites in response to a progressive soil drought in soybean (Glycine max). Expression of myo-inositol 1-phosphate synthase (INPS), the gene responsible for the conversion of glucose-6-phosphate to myo-inositol-1-phosphate, was significantly up regulated in response to a water deficit for the first two sampling weeks. Expression of myo-inositol O-methyl transferase (IMT1), the gene responsible for the conversion of myo-inositol into d-ononitol was only up regulated in stems at sampling week 3. Assessment of metabolites showed significant changes in their concentration in leaves and stems. d-Pinitol concentration was significantly higher in all organs sampled from water deficit plants for all three sampling weeks. In contrast, myo-inositol, had significantly lower concentrations in leaf samples despite up regulation of INPS suggesting the transcriptionally regulated flux of carbon through the myo-inositol pool is important during water deficit.

Computational aspects underlying genome to phenome analysis in plants.

Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait-trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features.

Post photosynthetic carbon partitioning to sugar alcohols and consequences for plant growth.

The occurrence of sugar alcohols is ubiquitous among plants. Physiochemical properties of sugar alcohols suggest numerous primary and secondary functions in plant tissues and are often well documented. In addition to functions arising from physiochemical properties, the synthesis of sugar alcohols may have significant influence over photosynthetic, respiratory, and developmental processes owing to their function as a large sink for photosynthates. Sink strength is demonstrated by the high concentrations of sugar alcohols found in plant tissues and their ability to be readily transported. The plant scale distribution and physiochemical function of these compounds renders them strong candidates for functioning as stress metabolites. Despite this, several aspects of sugar alcohol biosynthesis and function are poorly characterised namely: 1) the quantitative characterisation of carbon flux into the sugar alcohol pool; 2) the molecular control governing sugar alcohol biosynthesis on a quantitative basis; 3) the role of sugar alcohols in plant growth and ecology; and 4) consequences of sugar alcohol synthesis for yield production and yield quality. We highlight the need to adopt new approaches to investigating sugar alcohol biosynthesis using modern technologies in gene expression, metabolic flux analysis and agronomy. Combined, these approaches will elucidate the impact of sugar alcohol biosynthesis on growth, stress tolerance, yield and yield quality.