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Measurements of oxygen isotope enrichment of leaf water above source water (Δ18OLW) can improve our understanding of the interaction between leaf anatomy and physiology on leaf water transport. Models have been developed to predict Δ18OLW such as the string‐of‐lakes model, which describes the mixing of leaf water pools, and the Péclet effect model, which incorporates transpiration rate and the mixing length between unenriched xylem and enriched mesophyll water in the mesophyll (Lm) or veins (Lv). Here we compare measurements and models of Δ18OLW on two cell wall composition mutants grown under two light intensities and relative humidities to evaluate cell wall properties on leaf water transport. In maize (Zea mays), the compromised ultrastructure of the suberin lamellae in the bundle sheath of the ALIPHATIC SUBERIN FERULOYL TRANSFERASE mutant (Zmasft) reduced barriers to apoplastic water movement, resulting in higher E and, potentially, Lv and, consequently, lower Δ18OLW. The difference in Δ18OLW in cellulose synthase‐like F6 (CslF6) mutants and wild‐type of rice (Oryza sativa) grown under two light intensities co‐varied with stomatal density. These results show that cell wall composition and stomatal density influence Δ18OLW and that stable isotopes can facilitate the development of a physiologically and anatomically explicit water transport model. Summary statement: The greater apoplastic permeability in maize (Zea mays) mutants that had a compromised ultrastructure of the suberin lamellae in the bundle sheath led to higher transpiration rates and lower isotopic mixing length than wildtype, resulting in lower oxygen isotope enrichment of leaf water (Péclet effect). In rice (Oryza sativa) wild type and mutants with no mixed linkage glucan in the cell wall, oxygen isotope enrichment in leaf water in response to growth light intensity co‐varied with stomatal density, showing that differences in stomatal density modulate oxygen isotopic enrichment in leaf water. [ABSTRACT FROM AUTHOR]

Certain cultivars of maize show increased tolerance to water deficit conditions by maintenance of root growth. To better understand the molecular mechanisms related to this adaptation, nodal root growth zone samples were collected from the reference inbred line B73 and inbred line FR697, which exhibits a relatively greater ability to maintain root elongation under water deficits. Plants were grown under various water stress levels in both field and controlled environment settings. FR697-specific RNA-Seq datasets were generated and used for a de novo transcriptome assembly to characterize any genotype-specific genetic features. The assembly was aided by an Iso-Seq library of transcripts generated from various FR697 plant tissue samples. The Necklace pipeline was used to combine a Trinity de novo assembly along with a reference guided assembly and the Viridiplantae proteome to generate an annotated consensus "SuperTranscriptome" assembly of 47,915 transcripts with a N50 of 3152 bp in length. The results were compared by Blastn to maize reference genes, a Benchmarking Universal Single-Copy Orthologs (BUSCO) genome completeness report and compared with three maize reference genomes. The resultant 'SuperTranscriptome' was demonstrated to be of high-quality and will serve as an important reference for analysis of the maize nodal root transcriptomic response to environmental perturbations. [ABSTRACT FROM AUTHOR]

Advances in next‐generation sequencing and other high‐throughput technologies have facilitated multiomics research, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics. The resultant emerging multiomics data have brought new challenges as well as opportunities, as seen in the plant and agriculture science domains. We reviewed several bioinformatic and computational methods, models, and platforms, and we have highlighted some of our in‐house developed efforts aimed at multiomics data analysis, integration, and management issues faced by the research community. A case study using multiomics datasets generated from our studies of maize nodal root growth under water deficit stress demonstrates the power of these datasets and some other publicly available tools. This analysis also sheds light on the landscape of such applied bioinformatic tools currently available for plant and crop science studies and introduces emerging trends and how they may affect the future. [ABSTRACT FROM AUTHOR]