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Rising temperature extremes during critical reproductive periods threaten the yield of major grain and fruit crops. Flowering plant reproduction depends on the ability of pollen grains to generate a pollen tube, which elongates through the pistil to deliver sperm cells to female gametes for double fertilization. We used tomato as amodel fruit croptodeterminehowhigh temperature affects the pollen tube growthphase, takingadvantage of cultivars noted for fruit production in exceptionally hot growing seasons. We found that exposure to high temperature solely during the pollen tube growth phase limits fruit biomass and seed set more significantly in thermosensitive cultivars than in thermotolerant cultivars. Importantly, we found that pollen tubes from the thermotolerantTamaulipas cultivar have enhanced growth in vivo and in vitro under high temperature. Analysis of the pollen tube transcriptome’s response to high temperature allowed us to define two responsemodes (enhanced induction of stress responses and higher basal levels of growth pathways repressed by heat stress) associated with reproductive thermotolerance. Importantly, we define key components of the pollen tube stress response, identifyingenhancedreactive oxygenspecies (ROS)homeostasis andpollen tubecallose synthesis and deposition as important components of reproductive thermotolerance in Tamaulipas. Our work identifies the pollen tube growth phase as a viable target to enhance reproductive thermotolerance and delineates key pathways that are altered in crop varieties capable of fruiting under high-temperature conditions. 

Glyceollins, a family of phytoalexins elicited in legume species, play crucial roles in environmental stress response (e.g., defending against pathogens) and human health. However, little is known about the genetic basis of glyceollin elicitation. In the present study, we employed a metabolite-based genome-wide association (mGWA) approach to identify candidate genes involved in glyceollin elicitation in genetically diverse and understudied wild soybeans subjected to soybean cyst nematode. In total, eight SNPs on chromosomes 3, 9, 13, 15, and 20 showed significant associations with glyceollin elicitation. Six genes fell into two gene clusters that encode glycosyltransferases in the phenylpropanoid pathway and were physically close to one of the significant SNPs (ss715603454) on chromosome 9. Additionally, transcription factors (TFs) genes such asMYBandWRKYwere also found as promising candidate genes within close linkage to significant SNPs on chromosome 9. Notably, four significant SNPs on chromosome 9 show epistasis and a strong signal for selection. The findings describe the genetic foundation of glyceollin biosynthesis in wild soybeans; the identified genes are predicted to play a significant role in glyceollin elicitation regulation in wild soybeans. Additionally, how the epistatic interactions and selection influence glyceollin variation in natural populations deserves further investigation to elucidate the molecular mechanism of glyceollin biosynthesis. 

Elevated temperatures impair pollen performance and reproductive success, resulting in lower crop yields. The tomato (Solanum lycopersicum) anthocyanin reduced (are) mutant harbors a mutation in FLAVANONE 3-HYDROXYLASE (F3H), resulting in impaired flavonol antioxidant biosynthesis. The are mutant has reduced pollen performance and seed set relative to the VF36 parental line, phenotypes that are accentuated at elevated temperatures. Transformation of are with the wild-type F3H gene, or chemical complementation with flavonols, prevented temperature-dependent reactive oxygen species (ROS) accumulation in pollen and restored the reduced viability, germination, and tube elongation of are to VF36 levels. Overexpression of F3H in VF36 prevented temperature-driven ROS increases and impaired pollen performance, revealing that flavonol biosynthesis promotes thermotolerance. Although stigmas of are had reduced flavonol and elevated ROS levels, the growth of are pollen tubes was similarly impaired in both are and VF36 pistils. RNA-seq was performed at optimal and stress temperatures in are, VF36, and the F3H overexpression line at multiple timepoints across pollen tube elongation. The number of differentially expressed genes increased over time under elevated temperatures in all genotypes, with the greatest number in are. These findings suggest potential agricultural interventions to combat the negative effects of heat-induced ROS in pollen that lead to reproductive failure. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age. 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.