The remediation of lead (Pb)-contaminated soils through eco-friendly strategies is critical for sustainable agriculture. This study investigated the role of arbuscular mycorrhizal fungi (AMF) in enhancing maize tolerance to Pb stress and modulating rhizosphere microbial communities. A pot experiment was conducted with maize (Baiyu833) under four Pb concentrations (0, 900, 1800, 2700 mg·kg⁻¹) and three AMF treatments: non-inoculation (Non), Funneliformis mosseae (Fm), or Rhizophagus intraradices (Ri). The results demonstrated that AMF inoculation significantly increased plant biomass, boosted antioxidant enzyme activities (SOD, POD), and reduced malondialdehyde (MDA) levels, mitigating Pb-induced oxidative stress. AMF restricted Pb translocation to aerial parts, with root Pb accumulation reaching 2110.76 mg·kg⁻¹ (Fm) and 2090.56 mg·kg⁻¹ (Ri) under Pb2700, enhancing phytostabilization. High-throughput sequencing revealed that AMF inoculation enriched α-diversity indices and restructured bacterial communities, favoring beneficial taxa like Promicromonospora, which are linked to heavy metal resistance and plant growth promotion. Principal coordinate analysis highlighted distinct clustering of microbial communities driven by AMF, emphasizing their role in alleviating Pb toxicity. These findings underscore that AMF enhance maize resilience to Pb by regulating antioxidant defense, immobilizing Pb in roots, and recruiting stress-tolerant rhizosphere microbiomes. This study provides insights into AMF-assisted phytoremediation as a sustainable strategy for Pb-contaminated soils.

Genomic prediction (GP) across different populations and environments should be enhanced to increase the efficiency of crop breeding. In this study, four populations were constructed and genotyped with DNA chips containing 55,000 SNPs. These populations were testcrossed to a common tester, generating four hybrid populations. Yields of the four hybrid populations were evaluated in three environments. We demonstrated by using real data that the prediction accuracies of GP across structured hybrid populations were lower than those of within-population GP. Including relatives of the validation population in the training population could increase the prediction accuracies of GP across structured hybrid populations drastically. G × E models (including main and genotype-by-environment effect) had better performance than single environment (within environment) and across environment (including only main effect) GP models in the structured hybrid population, especially in the environment where yields had higher heritability. GP by implementing G × E models in two cross-validation schemes indicated that, to increase the prediction accuracy of a new hybrid line, it would be better to field-test the hybrid line in at least one environment. Our results would be helpful for designing training population and planning field testing in hybrid breeding.

Dissecting the genetic basis of yield traits in hybrid populations and identifying the candidate genes are important for molecular crop breeding. In this study, a BC1F3:4 population, the line per se (LPS) population, was constructed by using elite inbred lines Zheng58 and PH4CV as the parental lines. The population was genotyped with 55,000 SNPs and testcrossed to Chang7-2 and PH6WC (two testers) to construct two testcross (TC) populations. The three populations were evaluated for hundred kernel weight (HKW) and yield per plant (YPP) in multiple environments. Marker–trait association analysis (MTA) identified 24 to 151 significant SNPs in the three populations. Comparison of the significant SNPs identified common and specific quantitative trait locus/loci (QTL) in the LPS and TC populations. Genetic feature analysis of these significant SNPs proved that these SNPs were associated with the tested traits and could be used to predict trait performance of both LPS and TC populations. RNA-seq analysis was performed using maize hybrid varieties and their parental lines, and differentially expressed genes (DEGs) between hybrid varieties and parental lines were identified. Comparison of the chromosome positions of DEGs with those of significant SNPs detected in the TC population identified potential candidate genes that might be related to hybrid performance. Combining RNA-seq analysis and MTA results identified candidate genes for hybrid performance, providing information that could be useful for maize hybrid breeding.

Cadmium (Cd) significantly influences the morphological, physiological traits, and transport capacity of plants, but the underlying mechanism of Cd stress still remains to be further studied. In this study, physiological, transcriptomic, and metabolomic analyses were conducted to examine the morphological and physiological traits of two elite maize inbred lines, Chang7_2 (C7_2, a Cd-resistant line) and Zheng58 (Z58, a Cd-sensitive line) under control and Cd stress conditions. The results of morphological traits indicated that C7_2 reduced by 9.50–29.60% under Cd stress, whereas Z58 displayed more pronounced morphological changes ranging from 10.12 to 41.72% under Cd stress. Physiological assessments revealed that C7_2 maintained relatively stable antioxidant enzyme activity, while Z58 demonstrated more rapid alterations in the antioxidant system under Cd stress. Transcriptomic analysis identified 3030 differentially expressed genes (DEGs) unique to C7_2 and 4298 DEGs unique to Z58, with 1746 common DEGs shared between the two lines. Functional annotation revealed that the unique DEGs in C7_2 were mainly enriched in plant hormone signal transduction, plant–pathogen interactions, and the MAPK signaling pathway, while the unique DEGs in Z58 were mainly enriched in ribosome-related functions, plant hormone signal transduction, and phenylpropanoid biosynthesis. Metabolomic analysis identified 12 superclasses encompassing 896 metabolites in C7_2 and Z58, primarily including lipids and lipid-like molecules, organic acids and derivatives, as well as organoheterocyclic compounds. Analysis of differentially accumulated metabolites (DAMs) revealed fewer DAMs were accumulated in C7_2 under Cd stress. Further analysis identified that the three pathways of GPI anchor biosynthesis, glycerophospholipid metabolism, and purine metabolism were among the top 10 metabolic pathways in C7_2 and Z58. The integrative analysis highlighted the crucial roles of phenylpropanoid biosynthesis and zeatin biosynthesis in C7_2 for resistance to Cd stress. This study provides novel insights into the molecular and metabolic pathways underlying Cd tolerance in maize by integrating transcriptomic and metabolomic analyses of two contrasting inbred lines, providing a theoretical foundation for the future breeding of Cd-tolerant varieties.

The continuous increase in maize yield has become increasingly dependent on high-density planting, rendering plant height (PH) and plant architecture core traits that balance light interception efficiency and lodging resistance. Although many PH regulators have been well characterized, we are still confused on how to handle these regulators for maize breeding. At the same time, biotechnological advances have provided us with tools to address this issue. This review presents perspectives on integrating molecular information underlying maize PH regulation with these biotechnological tools to improve high-density tolerance in maize. We first compile an inventory of genes associated with maize PH and classify them into hormone-related and non-hormonal pathways, and reveal that loss-of-function mutations in most of these genes exhibit prominent allelic characteristics, including extreme dwarfism and pleiotropy. We discuss the underlying mechanisms of pleiotropy, which is important for successful editing aimed at achieving moderate dwarfism. Then, we discuss current strategies for high-density tolerance improvement, including genome editing strategies, introgression of dominance genes, multi-gene stacking, and phenotypic prediction, and offer key insights for their practical implementation. We offer insights and perspectives for future high-density tolerance improvement, which requires cell-resolution gene regulatory networks, AI-enabled precision genome editing, and phenotypic prediction models incorporating all environmental factors influencing plant growth. This review aims to provide guidance for researchers and breeders in translating molecular knowledge into improved high-density tolerance, lodging resistance, and yield gain.

Deciphering the molecular basis of heterosis would yield genes and markers for designing improved maize-hybrid varieties. In this study, 481 BC₁F₃ lines derived from Zheng58 and PH4CV were genotyped with 15,386 polymorphic SNPs markers and testcrossed with two testers (Chang7-2 and PH6WC) to generate 962 testcross lines. The yield of these testcross lines and their parental lines was evaluated across multiple environments. Genetic analysis revealed that dominance is the primary contributor to heterosis. Mapping of midparent heterosis (MPH) identified two dominant QTL, six additive-by-additive interactions, eighteen additive-by-dominance interactions, and fifty-four dominance-by-dominance interactions. These interactions encompassed 104 genetic blocks, including 24 genetic blocks that explained >1% of phenotypic variances for both MPH and hybrid performance. We compared the locations of the allele-specific expression genes (ASEGs) identified from the expression data of two hybrid lines and their parental lines with those of the 24 genetic blocks and found 15 ASEGs related to yield or biomass regulation, including two known genes BT2 and ZmNF-YC4. Fisher’s exact test analysis indicated a significant enrichment of these ASEGs in the 24 blocks, affirming the reliability of the MPH-mapping results. The co-expression network of six ASEGs, including BT2 and ZmNF-YC4, contained many genes related to yield or biomass regulation. This study unravels potential candidate genes and regulatory networks associated with maize heterosis.