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SUMMARY: Maize plastid terminal oxidase1 (ZmPTOX1) plays a pivotal role in seed development by upholding redox balance within seed plastids. This study focuses on characterizing the white kernel mutant 3735 (wk3735) mutant, which yields pale‐yellow seeds characterized by heightened protein but reduced carotenoid levels, along with delayed germination compared to wild‐type (WT) seeds. We successfully cloned and identified the target gene ZmPTOX1, responsible for encoding maize PTOX—a versatile plastoquinol oxidase and redox sensor located in plastid membranes. While PTOX's established role involves regulating redox states and participating in carotenoid metabolism in Arabidopsis leaves and tomato fruits, our investigation marks the first exploration of its function in storage organs lacking a photosynthetic system. Through our research, we validated the existence of plastid‐localized ZmPTOX1, existing as a homomultimer, and established its interaction with ferredoxin‐NADP+ oxidoreductase 1 (ZmFNR1), a crucial component of the electron transport chain (ETC). This interaction contributes to the maintenance of redox equilibrium within plastids. Our findings indicate a propensity for excessive accumulation of reactive oxygen species (ROS) in wk3735 seeds. Beyond its known role in carotenoids' antioxidant properties, ZmPTOX1 also impacts ROS homeostasis owing to its oxidizing function. Altogether, our results underscore the critical involvement of ZmPTOX1 in governing seed development and germination by preserving redox balance within the seed plastids. Significance Statement: Aside from its established role as a vital regulator of redox states and its involvement in carotenoid metabolism, as observed in Arabidopsis leaves and tomato fruits, this study marks the inaugural exploration into PTOX's function within storage organs lacking a photosynthetic system. Our findings elucidate how ZmPTOX1 significantly impacts protein and starch accumulation in seeds, along with the pace of seed germination. Moreover, it actively maintains redox equilibrium across various stages of seed development and germination. [ABSTRACT FROM AUTHOR]

AIIRA seeks to transform agriculture by creating a new AI‐driven framework for modeling plants at various agronomically relevant scales. We accomplish this by designing and deploying AI‐driven predictive models that fuse diverse data with siloed domain knowledge. AIIRA's vision, illustrated in Figure 1, consists of four technical thrusts with cross‐cutting education, training, and outreach activities. Our activities are focused on theory, algorithms, and tools for the principled creation of goal‐oriented AI tools deployed at plant and field scales. Our use‐inspired AI developments are tightly integrated with USDA‐relevant challenges in crop improvement and sustainable crop production. Our strong social science focus ensures sustained AI adoption across the ag value chain. Our cyberinfrastructure (CI) efforts ensure cohesive, sustainable, and extensible CI to reproducibly share and manage data assets and analysis workflows to a diverse spectrum of the Ag community. Taken together, this will ensure long‐term payoffs in AI and agriculture. AIIRA has established a new field of Cyber Agricultural Systems at the intersection of plant science, agronomics, and AI. Our signature activities build the workforce for this new field through formal and informal educational activities. Through these activities, AIIRA creates accessible pathways for underrepresented groups, especially Native Americans and women. [ABSTRACT FROM AUTHOR]

Three subfamilies of grasses, the Erhardtoideae (rice), the Panicoideae (maize, sorghum, sugar cane and millet), and the Pooideae (wheat, barley and cool season forage grasses) provide the basis of human nutrition and are poised to become major sources of renewable energy. Here we describe the complete genome sequence of the wild grass Brachypodium distachyon (Brachypodium), the first member of the Pooideae subfamily to be completely sequenced. Comparison of the Brachypodium, rice and sorghum genomes reveals a precise sequence- based history of genome evolution across a broad diversity of the grass family and identifies nested insertions of whole chromosomes into centromeric regions as a predominant mechanism driving chromosome evolution in the grasses. The relatively compact genome of Brachypodium is maintained by a balance of retroelement replication and loss. The complete genome sequence of Brachypodium, coupled to its exceptional promise as a model system for grass research, will support the development of new energy and food crops

Most of our understanding of plant genome structure and evolution has come from the careful annotation of small (e.g., 100 kb) sequenced genomic regions or from automated annotation of complete genome sequences. Here, we sequenced and carefully annotated a contiguous 22 Mb region of maize chromosome 4 using an improved pseudomolecule for annotation. The sequence segment was comprehensively ordered, oriented, and confirmed using the maize optical map. Nearly 84% of the sequence is composed of transposable elements (TEs) that are mostly nested within each other, of which most families are low-copy. We identified 544 gene models using multiple levels of evidence, as well as five miRNA genes. Gene fragments, many captured by TEs, are prevalent within this region. Elimination of gene redundancy from a tetraploid maize ancestor that originated a few million years ago is responsible in this region for most disruptions of synteny with sorghum and rice. Consistent with other sub-genomic analyses in maize, small RNA mapping showed that many small RNAs match TEs and that most TEs match small RNAs. These results, performed on approximately 1% of the maize genome, demonstrate the feasibility of refining the B73 RefGen_v1 genome assembly by incorporating optical map, high-resolution genetic map, and comparative genomic data sets. Such improvements, along with those of gene and repeat annotation, will serve to promote future functional genomic and phylogenomic research in maize and other grasses.

Few microarrays have been quantitatively calibrated to identify optimal hybridization conditions because it is difficult to precisely determine the hybridization characteristics of a microarray using biologically variable cDNA samples.Using synthesized samples with known concentrations of specific oligonucleotides, a series of microarray experiments was conducted to evaluate microarrays designed by PICKY, an oligo microarray design software tool, and to test a direct microarray calibration method based on the PICKY-predicted, thermodynamically closest nontarget information. The complete set of microarray experiment results is archived in the GEO database with series accession number GSE14717. Additional data files and Perl programs described in this paper can be obtained from the website http://www.complex.iastate.edu under the PICKY Download area.PICKY-designed microarray probes are highly reliable over a wide range of hybridization temperatures and sample concentrations. The microarray calibration method reported here allows researchers to experimentally optimize their hybridization conditions. Because this method is straightforward, uses existing microarrays and relatively inexpensive synthesized samples, it can be used by any lab that uses microarrays designed by PICKY. In addition, other microarrays can be reanalyzed by PICKY to obtain the thermodynamically closest nontarget information for calibration.

Studies of gene function are often hampered by gene-redundancy, especially in organisms with large genomes such as rice (Oryza sativa). We present an approach for using transcriptomics data to focus functional studies and address redundancy. To this end, we have constructed and validated an inexpensive and publicly available rice oligonucleotide near-whole genome array, called the rice NSF45K array. We generated expression profiles for light- vs. dark-grown rice leaf tissue and validated the biological significance of the data by analyzing sources of variation and confirming expression trends with reverse transcription polymerase chain reaction. We examined trends in the data by evaluating enrichment of gene ontology terms at multiple false discovery rate thresholds. To compare data generated with the NSF45K array with published results, we developed publicly available, web-based tools (www.ricearray.org). The Oligo and EST Anatomy Viewer enables visualization of EST-based expression profiling data for all genes on the array. The Rice Multi-platform Microarray Search Tool facilitates comparison of gene expression profiles across multiple rice microarray platforms. Finally, we incorporated gene expression and biochemical pathway data to reduce the number of candidate gene products putatively participating in the eight steps of the photorespiration pathway from 52 to 10, based on expression levels of putatively functionally redundant genes. We confirmed the efficacy of this method to cope with redundancy by correctly predicting participation in photorespiration of a gene with five paralogs. Applying these methods will accelerate rice functional genomics.

A primary reason for using two-color microarrays is that the use of two samples labeled with different dyes on the same slide, that bind to probes on the same spot, is supposed to adjust for many factors that introduce noise and errors into the analysis. Most users assume that any differences between the dyes can be adjusted out by standard methods of normalization, so that measures such as log ratios on the same slide are reliable measures of comparative expression. However, even after the normalization, there are still probe specific dye and slide variation among the data. We define a method to quantify the amount of the dye-by-probe and slide-by-probe interaction. This serves as a diagnostic, both visual and numeric, of the existence of probe-specific dye bias. We show how this improved the performance of two-color array analysis for arrays for genomic analysis of biological samples ranging from rice to human tissue.We develop a procedure for quantifying the extent of probe-specific dye and slide bias in two-color microarrays. The primary output is a graphical diagnostic of the extent of the bias which called ECDF (Empirical Cumulative Distribution Function), though numerical results are also obtained.We show that the dye and slide biases were high for human and rice genomic arrays in two gene expression facilities, even after the standard intensity-based normalization, and describe how this diagnostic allowed the problems causing the probe-specific bias to be addressed, and resulted in important improvements in performance. The R package LMGene which contains the method described in this paper has been available to download from Bioconductor.

Quantifying historical changes in maize leaf angle and factors affecting it can enhance our understanding of canopy architecture and light capture, and hence crop productivity. Our objectives were to (1) quantify leaf angle genetic gain per canopy position in Bayer's legacy maize (Zea mays L.) hybrids; (2) dissect the contribution of breeding from plant density on historical changes in leaf angle; and (3) synthesize our findings with literature to determine leaf angle changes over a century of breeding. We measured leaf angle in 78 maize hybrids released between 1980 and 2020 across eight environments in the US Corn Belt. We found that new hybrids had on average 6° more erect leaves than old hybrids. The leaf angle genetic gain (toward more erect leaves) was on average 0.08% year−1 for the middle canopy leaves and eightfold larger for the flag leaf. Our results revealed a synergistic effect with similar contributions of maize breeding and plant density on historical leaf angle changes in the middle canopy. However, changes in the bottom and top canopy leaves were due to breeding. Our results, combined with literature, revealed consistent trends toward more vertical leaves over a century of maize breeding, but the leaf angle genetic gain is slowing down in the last decades. This suggests that leaf angle may have reached near‐optimum levels and that multiple ways to maintain the grain yield genetic gain have been functioning in maize breeding. Our study provides prospects to inform breeders and crop modelers to better understand maize leaf architecture and crop yields. Core Ideas: Leaf angle genetic gain is not consistent across canopy positions.Breeding and plant density have similarly contributed to leaf angle changes in the middle canopy.Leaf angle changes in the bottom and top canopy were mainly attributed to breeding.Vertical leaves at the top and horizontal leaves at the bottom were beneficial for yields of modern hybrids.Maize leaf angle genetic gain is slowing down compared to earlier years (1930–1980). [ABSTRACT FROM AUTHOR]

Maize breeding programs have indirectly altered many plant traits; however, our knowledge of some important phenological traits remains unexplored. One such trait is leaf appearance rate, which is crucial for predicting maize development. We studied 40 short‐season (103‐day) and 38 long‐season (111‐day) hybrids released from 1980 to 2020 by Bayer Crop Science. Measurements included weekly counting of collared leaves across 13 experiments in the US Corn Belt. The progression of leaf number was expressed as a function of thermal time and described with a trilinear model. Results indicated that new 111‐day hybrids produce leaves faster than old hybrids throughout the vegetative phase (7.4% and 3.1% faster before and after the ninth leaf stage, respectively), whereas new 103‐day hybrids produce leaves faster only after the ninth leaf stage (9.4%). Thermal time to silking and anthesis decreased by about 1 and 0.56°C day year−1, respectively. Our data revealed that silking and anthesis can precede the final collared leaf by 96°C day (3.3 days under optimal conditions), which indicates an overlap between vegetative and reproductive phases. We concluded that maize breeding has indirectly altered the rate of vegetative development of maize hybrids without affecting the final leaf number. Present results expand our knowledge base on the genotypic variability in maize development traits, which can improve empirical and process‐based models used for crop stage and yield prediction. Core Ideas: We tested 78 maize era hybrids across 12 US Corn Belt environments.New hybrids (2020) produce leaves faster than old hybrids (1980).Silks appear earlier in new hybrids leading to a decreased anthesis‐silking interval.Maize breeding did not affect the final leaf number.Reproductive and vegetative phases overlap in new maize hybrids. [ABSTRACT FROM AUTHOR]

Maize (Zea mays L.) is one of the three major cereal crops in the world. Leaf angle is an important architectural trait of crops due to its substantial role in light interception by the canopy and hence photosynthetic efficiency. Traditionally, leaf angle has been measured using a protractor, a process that is both slow and laborious. Efficiently measuring leaf angle under field conditions via imaging is challenging due to leaf density in the canopy and the resulting occlusions. However, advances in imaging technologies and machine learning have provided new tools for image acquisition and analysis that could be used to characterize leaf angle using three‐dimensional (3D) models of field‐grown plants. In this study, PhenoBot 3.0, a robotic vehicle designed to traverse between pairs of agronomically spaced rows of crops, was equipped with multiple tiers of PhenoStereo cameras to capture side‐view images of maize plants in the field. PhenoStereo is a customized stereo camera module with integrated strobe lighting for high‐speed stereoscopic image acquisition under variable outdoor lighting conditions. An automated image processing pipeline (AngleNet) was developed to measure leaf angles of nonoccluded leaves. In this pipeline, a novel representation form of leaf angle as a triplet of keypoints was proposed. The pipeline employs convolutional neural networks to detect each leaf angle in two‐dimensional images and 3D modeling approaches to extract quantitative data from reconstructed models. Satisfactory accuracies in terms of correlation coefficient (r) and mean absolute error (MAE) were achieved for leaf angle (r>0.87, MAE<5° $r\gt 0.87,\unicode{x02007}MAE\lt \phantom{\rule{}{0ex}}{5}^{^\circ }$) and internode heights (r>0.99, MAE<3.5 cm $r\gt 0.99,\unicode{x02007}MAE\lt \phantom{\rule{}{0ex}}3.5\unicode{x0200A}\mathrm{cm}$). Our study demonstrates the feasibility of using stereo vision to investigate the distribution of leaf angles in maize under field conditions. The proposed system is an efficient alternative to traditional leaf angle phenotyping and thus could accelerate breeding for improved plant architecture. [ABSTRACT FROM AUTHOR]

Global climate change is increasing both average temperatures and the frequencies of extreme high temperatures. Past studies have documented a strong negative effect of exposures to temperatures >30°C on hybrid maize yields. However, these studies could not disentangle genetic adaptation via artificial selection from changes in agronomic practices. Because most of the earliest maize hybrids are no longer available, side-by-side comparisons with modern hybrids under current field conditions are generally impossible. Here, we report on the collection and curation of 81 years of public yield trial records covering 4,730 maize hybrids, which enabled us to model genetic variation for temperature responses among maize hybrids. We show that selection may have indirectly and inconsistently contributed to the genetic adaptation of maize to moderate heat stress over this time period while preserving genetic variance for continued adaptation. However, our results reveal the existence of a genetic tradeoff for tolerance to moderate and severe heat stress, leading to a decrease in tolerance to severe heat stress over the same time period. Both trends are particularly conspicuous since the mid-1970s. Such a tradeoff poses challenges to the continued adaptation of maize to warming climates due to a projected increase in the frequency of extreme heat events. Nevertheless, given recent advances in phenomics, enviromics, and physiological modeling, our results offer a degree of optimism for the capacity of plant breeders to adapt maize to warming climates, assuming appropriate levels of R&D investment. Author summary: As climate change increases average temperatures, the heat tolerance of major crops becomes more important to global food security. Past studies in maize have documented strongly negative effects of heat stress on maize yields but without adequate characterization of changes to heat tolerance over time. In this study, we explored changes in maize heat tolerance over the past 80 years of hybrid maize breeding using data from public yield trials. We showed that tolerance to moderate heat stress has increased, but that tolerance to severe heat stress has decreased. Because climate change is expected to increase the incidence of severe heat stress in particular, these findings indicate the need for more detailed genetic and physiological studies of heat tolerance and their incorporation into plant breeding efforts. They also highlight the past successes of plant breeders at adapting maize to moderate heat stress. Overall, our findings suggest directions for further research to mitigate the negative effects of climate change and a tempered optimism for their application. [ABSTRACT FROM AUTHOR]