Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.

Effects of elevated temperature on 8-OHdG expression in the American oyster (Crassostrea virginica): Induction of oxidative stress biomarkers, cellular apoptosis, DNA damage and γH2AX signaling pathways.

Global temperature is increasing due to anthropogenic activities and the effects of elevated temperature on DNA lesions are not well documented in marine organisms. The American oyster (Crassostrea virginica, an edible and commercially important marine mollusk) is an ideal shellfish species to study oxidative DNA lesions during heat stress. In this study, we examined the effects of elevated temperatures (24, 28, and 32 °C for one-week exposure) on heat shock protein-70 (HSP70, a biomarker of heat stress), 8­hydroxy-2'-deoxyguanosine (8-OHdG, a biomarker of pro-mutagenic DNA lesion), double-stranded DNA (dsDNA), γ-histone family member X (γH2AX, a molecular biomarker of DNA damage), caspase-3 (CAS-3, a key enzyme of apoptotic pathway) and Bcl-2-associated X (BAX, an apoptosis regulator) protein and/or mRNA expressions in the gills of American oysters. Immunohistochemical and qRT-PCR results showed that HSP70, 8-OHdG, dsDNA, and γH2AX expressions in gills were significantly increased at high temperatures (28 and 32 °C) compared with control (24°C). In situ TUNEL analysis showed that the apoptotic cells in gill tissues were increased in heat-exposed oysters. Interestingly, the enhanced apoptotic cells were associated with increased CAS-3 and BAX mRNA and/or protein expressions, along with 8-OHdG levels in gills after heat exposure. Moreover, the extrapallial (EP) fluid (i.e., extracellular body fluid) protein concentrations were lower; however, the EP glucose levels were higher in heat-exposed oysters. Taken together, these results suggest that heat shock-driven oxidative stress alters extracellular body fluid conditions and induces cellular apoptosis and DNA damage, which may lead to increased 8-OHdG levels in cells/tissues in oysters.