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.

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.

Halogenating enzymes have evolved in considerable mechanistic diversity. The apparent need for secondary metabolism coincides with the current need to introduce halogens in synthetic products. The potential of halogenating enzymes and, especially, vanadate-dependent haloperoxidases has been insufficiently exploited for synthetic purposes. In this work, we identified potential halogenase sequences by screening algal, fungal, and protobacterial sequence databases, structural modeling of putative halogenases, and mapping and comparing active sites. In a final step, individual haloperoxidases were expressed and kinetically characterized. A vanadate-dependent haloperoxidase from Rhodoplanes roseus was heterologously expressible by E. coli and could be purified to homogeneity. The kinetic data revealed a higher turnover number than the known V_ClPO-CI and no inhibitory effect from bromide, rendering this enzyme a promising biocatalyst. Other predicted haloperoxidases were not expressed successfully yet but these enzymes were predicted to be present in a wide taxonomic variety.