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Crop species exhibit an astounding capacity for environmental adaptation, but genetic bottlenecks resulting from intense selection for adaptation and productivity can lead to a genetically vulnerable crop. Improving the genetic resiliency of temperate maize depends upon the use of tropical germplasm, which harbors a rich source of natural allelic diversity. Here, the adaptation process was studied in a tropical maize population subjected to 10 recurrent generations of directional selection for early flowering in a single temperate environment in Iowa, USA. We evaluated the response to this selection across a geographical range spanning from 43.05° (Wl) to 18.00° (PR) latitude. The capacity for an all-tropical maize population to become adapted to a temperate environment was revealed in a marked fashion: on average, families from generation 10 flowered 20 days earlier than families in generation 0, with a nine-day separation between the latest generation 10 family and the earliest generation 0 family. Results suggest that adaptation was primarily due to selection on genetic main effects tailored to temperature-dependent plasticity in flowering time. Genotype-by-environment interactions represented a relatively small component of the phenotypic variation in flowering time, but were sufficient to produce a signature of localized adaptation that radiated latitudinally, in partial association with daylength and temperature, from the original location of selection. Furthermore, the original population exhibited a maladaptive syndrome including excessive ear and plant heights along with later flowering; this was reduced in frequency by selection for flowering time. [ABSTRACT FROM AUTHOR]

Abstract Background A major challenge in the identification and development of superior feedstocks for the production of second generation biofuels is the rapid assessment of biomass composition in a large number of samples. Currently, highly accurate and precise robotic analysis systems are available for the evaluation of biomass composition, on a large number of samples, with a variety of pretreatments. However, the lack of an inexpensive and high-throughput process for large scale sampling of biomass resources is still an important limiting factor. Our goal was to develop a simple mechanical maize stalk core sampling device that can be utilized to collect uniform samples of a dimension compatible with robotic processing and analysis, while allowing the collection of hundreds to thousands of samples per day. Results We have developed a core sampling device (CSD) to collect maize stalk samples compatible with robotic processing and analysis. The CSD facilitates the collection of thousands of uniform tissue cores consistent with high-throughput analysis required for breeding, genetics, and production studies. With a single CSD operated by one person with minimal training, more than 1,000 biomass samples were obtained in an eight-hour period. One of the main advantages of using cores is the high level of homogeneity of the samples obtained and the minimal opportunity for sample contamination. In addition, the samples obtained with the CSD can be placed directly into a bath of ice, dry ice, or liquid nitrogen maintaining the composition of the biomass sample for relatively long periods of time. Conclusions The CSD has been demonstrated to successfully produce homogeneous stalk core samples in a repeatable manner with a throughput substantially superior to the currently available sampling methods. Given the variety of maize developmental stages and the diversity of stalk diameter evaluated, it is expected that the CSD will have utility for other bioenergy crops as well.

Abstract Background Improvement of biofeedstock quality for cellulosic ethanol production will be facilitated by inexpensive and rapid methods of evaluation, such as those already employed in the field of ruminant nutrition. Our objective was to evaluate whether forage quality and compositional measurements could be used to estimate ethanol yield of maize stover as measured by a simplified pretreatment and simultaneous saccharification and fermentation assay. Twelve maize varieties selected to be diverse for stover digestibility and composition were evaluated. Results Variation in ethanol yield was driven by glucan convertibility rather than by glucan content. Convertibility was highly correlated with ruminal digestibility and lignin content. There was no relationship between structural carbohydrate content (glucan and neutral detergent fiber) and ethanol yield. However, when these variables were included in multiple regression equations including convertibility or neutral detergent fiber digestibility, their partial regression coefficients were significant and positive. A regression model including both neutral detergent fiber and its ruminal digestibility explained 95% of the variation in ethanol yield. Conclusion Forage quality and composition measurements may be used to predict cellulosic ethanol yield to guide biofeedstock improvement through agronomic research and plant breeding.

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023, the first open-to-the-public Genomes to Fields initiative Genotype by Environment prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements, and field management notes gathered by the project over 9 years. The competition attracted registrants from around the world with representation from academic, government, industry, and nonprofit institutions as well as unaffiliated. These participants came from diverse disciplines, including plant science, animal science, breeding, statistics, computational biology, and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved 2 models combining machine learning and traditional breeding tools: 1 model emphasized environment using features extracted by random forest, ridge regression, and least squares, and 1 focused on genetics. Other high-performing teams' methods included quantitative genetics, machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics, weather, and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition., Competing Interests: Conflicts of interest: The authors declare no conflicts of interest., (Published by Oxford University Press on behalf of The Genetics Society of America 2024.)

Our own experience of emotional events influences how we approach and react to others' emotions. Here we observe that mice exhibit divergent interindividual responses to others in stress (that is, preference or avoidance) only if they have previously experienced the same aversive event. These responses are estrus dependent in females and dominance dependent in males. Notably, silencing the expression of the corticotropin-releasing factor (CRF) within the medial prefrontal cortex (mPFC) attenuates the impact of stress self-experience on the reaction to others' stress. In vivo microendoscopic calcium imaging revealed that mPFC CRF neurons are activated more toward others' stress only following the same negative self-experience. Optogenetic manipulations confirmed that higher activation of mPFC CRF neurons is responsible for the switch from preference to avoidance of others in stress, but only following stress self-experience. These results provide a neurobiological substrate underlying how an individual's emotional experience influences their approach toward others in a negative emotional state., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The purpose of this study is to conduct a literature review on the current role of computed tomography pulmonary angiography (CTPA) in the diagnosis and prognosis of pulmonary embolism (PE). It addresses key topics such as the quantification of the thrombotic burden, its role as a predictor of mortality, new diagnostic techniques that are available, the possibility of analyzing the thrombus composition to differentiate its evolutionary stage, and the applicability of artificial intelligence (AI) in PE through CTPA. The only finding from CTPA that has been validated as a prognostic factor so far is the right ventricle/left ventricle (RV/LV) diameter ratio being >1, which is associated with a 2.5-fold higher risk of all-cause mortality or adverse events, and a 5-fold higher risk of PE-related mortality. The increasing use of techniques such as dual-energy computed tomography allows for the more accurate diagnosis of perfusion defects, which may go undetected in conventional computed tomography, identifying up to 92% of these defects compared to 78% being detected by CTPA. Additionally, it is essential to explore the latest advances in the application of AI to CTPA, which are currently expanding and have demonstrated a 23% improvement in the detection of subsegmental emboli compared to manual interpretation. With deep image analysis, up to a 95% accuracy has been achieved in predicting PE severity based on the thrombus volume and perfusion deficits. These advancements over the past 10 years significantly contribute to early intervention strategies and, therefore, to the improvement of morbidity and mortality outcomes for these patients.

Background: The pathogenesis of congenital diaphragmatic hernia (CDH) depends on multiple factors. Activation of the DNA-sensing cyclic-GMP-AMP-synthase (cGAS) and Stimulator-of-Interferon-Genes (STING) pathway by double-stranded DNA (dsDNA) links environmental stimuli and inflammation. We hypothesized that nitrofen exposure alters cGAS and STING in human bronchial epithelial cells and fetal rat lungs., Methods: We used the Quant-IT™-PicoGreen™ assay to assess dsDNA concentration in BEAS-2B cells after 24 h of nitrofen-exposure and performed immunofluorescence of cGAS/STING. We used nitrofen to induce CDH and harvested control and CDH lungs at embryonic day E15, E18 and E21 for cGAS/STING immunofluorescence, RT-qPCR and RNA-Scope™ in-situ-hybridization (E18, E21)., Results: We found a higher concentration of dsDNA following nitrofen treatment. Nitrofen-exposure to BEAS-2B cells increased cGAS and STING protein abundance. cGAS abundance was higher in nitrofen lungs at E15, E18 and E21. RNA-Scope in-situ-hybridization showed higher cGAS and STING expression in E18 and E21 lungs. RT-qPCR revealed higher mRNA expression levels of STING in E21 nitrofen-induced lungs., Conclusion: Our data suggest that nitrofen-exposure increases dsDNA content which leads to stimulation of the cGAS/STING pathway in human BEAS-2B cells and the nitrofen rat model of CDH. Consequently, DNA sensing and the cGAS-STING-pathway potentially contribute to abnormal lung development in CDH., Impact Statement: We found an alteration of DNA sensing targets cGAS and STING in human BEAS-2B cells and experimental congenital diaphragmatic hernia with higher protein abundance and mRNA expression in cells and lung sections of nitrofen-treated rat pups. This is the first study to investigate DNA sensing, a potential link between environmental stimuli and inflammation, in experimental CDH. Our study extends the knowledge on the pathogenesis of experimental CDH., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition., Competing Interests: Conflict of Interest The authors declare no conflict of interest.

Elucidating gene regulatory networks is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait loci (eQTL) to construct TF-target networks. In total, we analyzed ∼4.6M interactions to generate four distinct types of TF-target networks: co-expression, PDI, trans -eQTL, and cis -eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.

The US standard for maize commercially grown for grain specifies that yellow corn can contain at maximum 5% corn of other colors. Inbred parents of commercial hybrids typically have clear pericarp, but transgressive segregants in breeding populations can display variation in pericarp pigmentation. We identified 10 doubled haploid biparental populations segregating for pigmented pericarp and evaluated qualitative genetic models using chi-square tests of observed and expected frequencies. Pigmentation ranged from light to dark brown color, and pigmentation intensity was quantitatively measured across 1,327 inbred lines using hue calculated from RGB pixel values. Genetic mapping was used to identify loci associated with pigmentation intensity. For 9 populations, pigmentation inheritance best fit a hypothesis of a 2- or 3-gene epistatic model. Significant differences in pigment intensity were observed across populations. W606S-derived inbred lines with the darkest pericarp often had clear glumes, suggesting the presence of a novel P1-rw allele, a hypothesis supported by a significant quantitative trait locus peak at P1. A separate quantitative trait locus region on chromosome 2 between 221.64 and 226.66 Mbp was identified in LH82-derived populations, and the peak near p1 was absent. A genome-wide association study using 416 inbred lines from the Wisconsin Diversity panel with full genome resequencing revealed 4 significant associations including the region near P1. This study supports that pericarp pigmentation among dent maize inbreds can arise by transgressive segregation when pigmentation in the parental generation is absent and is partially explained by functional allelic variation at the P1 locus., Competing Interests: Conflicts of interest The authors declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

The chitin synthase Chs3 is a multipass membrane protein whose trafficking is tightly controlled. Accordingly, its exit from the endoplasmic reticulum (ER) depends on several complementary mechanisms that ensure its correct folding. Despite its potential failure on its exit, Chs3 is very stable in this compartment, which suggests its poor recognition by ER quality control mechanisms such as endoplasmic reticulum-associated degradation (ERAD). Here we show that proper N-glycosylation of its luminal domain is essential to prevent the aggregation of the protein and its subsequent recognition by the Hrd1-dependent ERAD-L machinery. In addition, the interaction of Chs3 with its chaperone Chs7 seems to mask additional cytosolic degrons, thereby avoiding their recognition by the ERAD-C pathway. On top of that, Chs3 molecules that are not degraded by conventional ERAD can move along the ER membrane to reach the inner nuclear membrane, where they are degraded by the inner nuclear membrane-associated degradation (INMAD) system, which contributes to the intracellular homeostasis of Chs3. These results indicate that Chs3 is an excellent model to study quality control mechanisms in the cell and reinforce its role as a paradigm in intracellular trafficking research.

Source and sink interactions play a critical but mechanistically poorly understood role in the regulation of senescence. To disentangle the genetic and molecular mechanisms underlying source-sink-regulated senescence (SSRS), we performed a phenotypic, transcriptomic, and systems genetics analysis of senescence induced by the lack of a strong sink in maize (Zea mays). Comparative analysis of genotypes with contrasting SSRS phenotypes revealed that feedback inhibition of photosynthesis, a surge in reactive oxygen species, and the resulting endoplasmic reticulum (ER) stress were the earliest outcomes of weakened sink demand. Multienvironmental evaluation of a biparental population and a diversity panel identified 12 quantitative trait loci and 24 candidate genes, respectively, underlying SSRS. Combining the natural diversity and coexpression networks analyses identified 7 high-confidence candidate genes involved in proteolysis, photosynthesis, stress response, and protein folding. The role of a cathepsin B like protease 4 (ccp4), a candidate gene supported by systems genetic analysis, was validated by analysis of natural alleles in maize and heterologous analyses in Arabidopsis (Arabidopsis thaliana). Analysis of natural alleles suggested that a 700-bp polymorphic promoter region harboring multiple ABA-responsive elements is responsible for differential transcriptional regulation of ccp4 by ABA and the resulting variation in SSRS phenotype. We propose a model for SSRS wherein feedback inhibition of photosynthesis, ABA signaling, and oxidative stress converge to induce ER stress manifested as programed cell death and senescence. These findings provide a deeper understanding of signals emerging from loss of sink strength and offer opportunities to modify these signals to alter senescence program and enhance crop productivity., Competing Interests: Conflict of interest statement. The authors declare no conflict of interest., (© American Society of Plant Biologists 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models., (© 2023. The Author(s).)

Objectives: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available., Data Description: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Objectives: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data., Data Description: This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years., (© 2023. The Author(s).)

Key Message: A novel locus was discovered on chromosome 7 associated with a lesion mimic in maize; this lesion mimic had a quantitative and heritable phenotype and was predicted better via subset genomic markers than whole genome markers across diverse environments. Lesion mimics are a phenotype of leaf micro-spotting in maize (Zea mays L.), which can be early signs of biotic or abiotic stresses. Dissecting its inheritance is helpful to understand how these loci behave across different genetic backgrounds. Here, 538 maize recombinant inbred lines (RILs) segregating for a novel lesion mimic were quantitatively phenotyped in Georgia, Texas, and Wisconsin. These RILs were derived from three bi-parental crosses using a tropical pollinator (Tx773) as the common parent crossed with three inbreds (LH195, LH82, and PB80). While this lesion mimic was heritable across three environments based on phenotypic ([Formula: see text] = 0.68) and genomic ([Formula: see text] = 0.91) data, transgressive segregation was observed. A genome-wide association study identified a single novel locus on chromosome 7 (at 70.6 Mb) also covered by a quantitative trait locus interval (69.3-71.0 Mb), explaining 11-15% of the variation, depending on the environment. One candidate gene identified in this region, Zm00001eb308070, is related to the abscisic acid pathway involving in cell death. Genomic predictions were applied to genome-wide markers (39,611 markers) contrasted with a marker subset (51 markers). Population structure explained more variation than environment in genomic prediction, but other substantial genetic background effects were additionally detected. Subset markers explained substantially less genetic variation (24.9%) for the lesion mimic than whole genome markers (55.4%) in the model, yet predicted the lesion mimic better (0.56-0.66 vs. 0.26-0.29). These results indicate this lesion mimic phenotype was less affected by environment than by epistasis and genetic background effects, which explain its transgressive segregation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)