Copper (Cu²⁺) toxicity can inhibit plant growth and development. It has been shown that silicon (Si) can relieve Cu²⁺ stress. However, it is unclear how Si-nanoparticles (SiNPs) relieve Cu²⁺ stress in wheat seedlings. Therefore, the current study was conducted by setting up four treatments: CK, SiNP: (2.5 mM), Cu²⁺: (500 µM), and SiNP+Cu²⁺: (2.5 mM SiNP+500 µM Cu²⁺) to explore whether SiNPs can alleviate Cu²⁺ toxicity in wheat seedlings. The results showed that Cu²⁺ stress hampered root and shoot growth and accumulated high Cu²⁺ concentrations in roots (45.35 mg/kg) and shoots (25.70 mg/kg) of wheat as compared to control treatment. Moreover, Cu²⁺ treatment inhibited photosynthetic traits and chlorophyll contents as well as disturbed the antioxidant defense system by accumulating malondialdehyde (MDA) and hydrogen peroxidase (H₂O₂) contents. However, SiNPs treatment increased root length and shoot height by 15.1% and 22%, respectively, under Cu²⁺ toxicity. Moreover, SiNPs application decreased MDA and H₂O₂ contents by 31.25% and 19.25%, respectively. SiNPs increased non-enzymatic compounds such as ascorbic acid-glutathione (AsA-GSH) and enhanced superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbic peroxidase (APX) activities by 77.5%, 141.7%, 68%, and 80%, respectively. Furthermore, SiNPs decreased Cu²⁺ concentrations in shoots by 26.2%, as compared to Cu²⁺ treatment alone. The results concluded that SiNPs could alleviate Cu²⁺ stress in wheat seedlings. The present investigation may help to increase wheat production in Cu²⁺ contaminated soils.

Maize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.

Eucalyptus cladocalyx F. Muell is a tree species suitable for low-rainfall sites, even with annual average precipitation as low as 150 mm per year. Its wood is classified as highly durable and its permanence in soil is longer than 25 years, so it can be used for multiple applications. Given that about 41% of the world’s land area is classified as drylands, added to the impact of climate change on the availability of water resources, it becomes necessary to use plant species that can tolerate environments with low water availability. In this study, a Bayesian analysis of genetic parameters showed that wood density (WD) was moderately heritable, with a posterior mean of h² = 0.29 and a Bayesian credibility region (90%) of 0.06–0.74, while the slenderness coefficient (SC) was highly heritable, with a posterior mean of h² = 0.48 and a Bayesian credibility region (90%) of 0.11–0.87. Through Bayesian regression analysis, we identified four and three significant associations for WD and SC, respectively. Another important finding of the bi-trait Bayesian analysis was the detection of three large-effect pleiotropic QTLs located on LG4 at 52 cM, on LG2 at 125 cM, and on LG6 at 81 cM. Bayesian bi-trait regression and the posterior probability of association indicated that three QTLs presented strong evidence of association with WD and SC. This provides convincing evidence that the loci qtlWD130/qtlSC130, qtlWD195/qtlSC195, and qtlWD196/qtlSC196 have a significant pleiotropic effect. The association mapping based on multivariate Bayesian regression was useful for the identification of genomic regions with pleiotropic effects. These loci can be used in molecular marker-assisted breeding to select trees with better wood density.

Insect-vectored plant viruses pose a serious threat to sustainable production of economically important crops worldwide. This demands a continuous search for environmentally-friendly, sustainable and efficient approaches based on biological agents to address the mounting challenges of viral disease management. To date, the efficacy of actinomycetes bacteria against DNA plant viruses remains unknown. Here, through comparative analyses, we demonstrate that the RFS-23 strain of Streptomyces cellulase possesses protective activity as it positively regulated the plant growth and development. and diminished the severity, of disease symptoms, together with reduced accumulation of Tomato yellow leaf curl virus (TYLCV) DNA. The RFS-23 strain maintained relative chlorophyll contents by promoting the expression of genes (CLH1, HEMA1 and PORA) associated with chlorophyll biogenesis. As compared to another strain, CTF-20, the RSF-23 induced a significantly higher expression of plant defense-related genes (NbCIS and NbNCED) associated with biogenesis and accumulation of salicylic acid and abscisic acid. Additionally, the activity of antioxidant enzymes (SOD, CAT, POD and MDA) was significantly enhanced by RSF-23 treatment, despite the presence of viral infection. These findings suggest that RSF-23 is a novel biocontrol agent with protective activity, and it could be a potential candidate for the management of plant viral infections.

In many agricultural areas, crop production has decreased due to a lack of water availability, which is having a negative impact on sustainability and putting food security at risk. In plants, the plasticity of the root system architecture (RSA) is considered to be a key trait driving the modification of the growth and structure of roots in response to water deficits. The purpose of this study was to examine the plasticity of the RSA traits (mean root diameter, MRD; root volume, RV; root length, RL; and root surface area, SA) associated with drought tolerance in eight Lagenaria siceraria (Mol. Standl) genotypes, representing three different geographical origins: South Africa (BG-58, BG-78, and GC), Asia (Philippines and South Korea), and Chile (Illapel, Chepica, and Osorno). The RSA changes were evaluated at four substrate depths (from 0 to 40 cm). Bottle gourd genotypes were grown in 20 L capacity pots under two contrasting levels of irrigation (well-watered and water-deficit conditions). The results showed that the water productivity (WP) had a significant effect on plasticity values, with the Chilean accessions having the highest values. Furthermore, Illapel and Chepica genotypes presented the highest WP, MRD, and RV values under water-deficit conditions, in which MRD and RV were significant in the deeper layers (20–30 and 30–40 cm). Biplot analysis showed that the Illapel and Chepica genotypes presented a high WP, MRD, and RV, which confirmed that these may be promising drought-tolerant genotypes. Consequently, increased root diameter and volume in bottle gourd may constitute a response to a water deficit. The RSA traits studied here can be used as selection criteria in bottle gourd breeding programs under water-deficit conditions.

The method of regional heritability mapping (RHM) has become an important tool in the identification of quantitative trait loci (QTLs) controlling traits of interest in plants. Here, RHM was first applied in a breeding population of popcorn, to identify the QTLs and candidate genes involved in grain yield, plant height, kernel popping expansion, and first ear height, as well as determining the heritability of each significant genomic region. The study population consisted of 98 S1 families derived from the 9th recurrent selection cycle (C-9) of the open-pollinated variety UENF-14, which were genetically evaluated in two environments (ENV1 and ENV2). Seventeen and five genomic regions were mapped by the RHM method in ENV1 and ENV2, respectively. Subsequent genome-wide analysis based on the reference genome B73 revealed associations with forty-six candidate genes within these genomic regions, some of them are considered to be biologically important due to the proteins that they encode. The results obtained by the RHM method have the potential to contribute to knowledge on the genetic architecture of the growth and yield traits of popcorn, which might be used for marker-assisted selection in breeding programs.