Maize (Zea mays) is the main crop in China, and drought is a primary abiotic stress during its growth, resulting in direct reduction in grain yield and quality, thereby posing a threat to food security within the global climate context. Therefore, it is imperative to identify drought-resistant germplasm resources, elucidate the molecular mechanisms of drought stress response, and breed drought-resistant varieties. Here, we review recent advances in the genetic dissection of drought resistance in maize using methods such as genome-wide association study, quantitative trait locus gene cloning and multi-omics analysis. Additionally, we introduce potential strategies for genetic improvement of drought resistance by leveraging the identified genetic resources while discussing future perspectives within this research area.

With the rapid development of biotechnology, gene editing technology has become an important tool in the field of modern agricultural breeding. Especially in maize, a globally important food crop, the application of gene editing technology shows great potential and prospects. In this study, the recent progress and applications of gene editing technology in maize research are reviewed, with a focus on the latest achievements in maize genome editing by systems such as CRISPR/Cas. Firstly, the basic principles and types of gene editing technology are introduced, particularly the working mechanism of the CRISPR/Cas system and its application advantages in maize. Secondly, a detailed review of the research progress of gene editing technology in maize breeding is provided, covering various aspects from basic genome editing to complex multi-gene regulation, aiming at the improvement of key traits such as maize yield, quality, and stress resistance. Finally, the contributions of outstanding researchers in China and the existing issues of gene editing technology in maize breeding are discussed, along with an outlook on future development trends. With the continuous improvement and breakthroughs in gene editing technology, it will play an irreplaceable role in maize breeding, propelling maize research to new heights.

Plants encounter various abiotic stresses throughout their lifecycle, including heat stress, drought, salinity, and others, which have diverse impacts on their growth and development. Global warming has exacerbated the impact of heat stress on crops such as corn, potentially leading to growth retardation and reduced reproductive capacity. As a crucial crop, the yield and quality of corn are severely affected by heat stress. Plants respond to heat stress through complex molecular mechanisms involving multiple signal transduction pathways and gene expression regulation. Using advanced techniques such as genetics, genomics, multi-omics analysis, and high-throughput phenotyping, it is crucial to deeply explore and analyze the genes and loci related to abiotic stress tolerance, including heat stress, in the corn genome. These studies not only enhance our understanding of the biological mechanisms underlying corn stress tolerance but also provide crucial molecular markers and candidate gene resources for breeders to accelerate the development of new corn varieties.

Climate change-induced global average temperature rise poses a severe threat to food production, with maize, one of the world's three major staple crops, being particularly susceptible to high temperatures. High temperatures significantly impact maize at various stages of its growth and development, especially during the reproductive growth stage, which can drastically reduce maize yields. We systematically summarized the effects of high temperatures on maize at different growth stages, including germination, seedling, late vegetative growth, flowering, and grain-filling stages. It thoroughly reviews the main molecular mechanisms by which maize responds to heat stress, including heat shock and unfolded protein responses. Furthermore, we summarize the latest advances in breeding heat-tolerant maize in China. A batch of heat-tolerant hybrids and inbred lines have been identified through artificial high-temperature treatments and field natural high-temperature screening. In addition, we proposed important future research strategies, including new technological methods such as phenomics, genome-wide association studies, and genomic selection breeding, combined with intelligent agricultural management measures. The goal is to cultivate maize varieties with high heat tolerance to cope with the high-temperature challenges brought about by climate change, thereby ensuring global food security.

As a photosynthetic organ of crops, the senescence process of leaves has an important impact on yield formation, but the relationship between leaf senescence and interleaf microorganisms has been less studied. In order to explore the influence of the senescence process of maize leaves on the interleaf bacterial community, this study took three maize varieties of different maturity (early maturity variety Heike Yu 17, H17, medium maturity variety Zhongdan 111, Z111, and late maturity variety Shen Yu 21, S21) in the spring maize area of Northeast China as the experimental materials, and the leaves of the ear position of the three maize varieties were sampled five times starting from the blooming stage of early maturity varieties, and the physiological indexes of senescence were determined, and at the same time The community composition of endogenous and exogenous bacteria in the interleaf was determined based on high-throughput sequencing technology. The results showed that at the late reproductive stage, leaf water content, POD and SOD activities were significantly higher in the mid- and late-maturing varieties than in the early-maturing varieties. At the phylum level, Cyanobacteria were endemic to medium- and late-maturing cultivars; at the genus level, the relative abundance of endogenous shared bacteria Sphingomonas, Methylobacterium, and Deinococcus in maize leaves was significantly reduced during IV and V periods The relative abundance of endogenous bacteria Streptomyces and exogenous bacteria P3OB-42 were significantly enriched in the late senescence period, with similar trends and significant differences in relative abundance among the three species. The relative abundance of endogenous and exogenous bacteria differed significantly, with the top 5 exogenous bacteria accounting for more than 60%, while for endogenous bacteria, the top 5 accounted for only more than 30%. Soluble sugar content, photosynthetic pigment content and SOD activity were significantly correlated with bacterial community structure and abundance. In conclusion, medium- and late-maturing varieties were effective in prolonging leaf greening period, maintaining late leaf physiological activity and delaying senescence. The effects of senescence on the composition and diversity of endogenous bacterial communities were significantly greater than those of exogenous bacteria, and there were significantly differentiated genera among varieties of different maturity stages. Moreover, soluble sugar content, photosynthetic pigment content and SOD activity were the key factors affecting the interleaf bacterial communities as well as the dominant species.

Multi-environment field testing is an important way to select optimize maize yield and yield stability varieties. However, because of its high cost, it has gradually become a challenge in plant breeding. The combination of field sparse testing and genome-wide prediction method can be used to predict untested phenotypes, reduced the effort and cost of field testing. In this experiment, 244 inbred lines of natural populations were planted in Shunyi, Beijing and Mishan, Heilongjiang in 2022 and 2023. Six agronomic traits were studies, including days to anthesis, plant height, ear height, ear length, kernel number per row and ear row number. The effects of four different models (Single, Across, M×E and R-norm), two different cross-validation schemes (CV1 and CV2) and three different training sets sampling ratios (0.5, 0.7 and 0.9) on the prediction accuracy were compared. The results showed that the average prediction accuracy of the six agronomic traits was 0.67, 0.58, 0.50, 0.33, 0.33 and 0.48. In CV1, the average prediction accuracy of each model in six traits ranged from 0.19 to 0.65, and in CV2, the average prediction accuracy ranged from 0.47 to 0.89. The average prediction accuracy of the Single model, Across model, M×E model and R-norm model was 0.36, 0.52, 0.53 and 0.53 for each trait. The comparison of different training set sampling ratios shows that the improvement of the proportion of the training sets has limited improvement in the prediction accuracy of different traits in different models, and the maximum is only 0.05. The results show that the CV2 training set can be used to form a scheme and include phenotypic data from multiple environments in the prediction model to provide good prediction accuracy for multi-environment prediction.