Plant height (PH) is an essential trait in maize (Zea mays) that is tightly associated with planting density, biomass, lodging resistance, and grain yield in the field. Dissecting the dynamics of maize plant architecture will be beneficial for ideotype-based maize breeding and prediction, as the genetic basis controlling PH in maize remains largely unknown. In this study, we developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages. Time-resolved i-traits with novel digital phenotypes and complex correlations with agronomic traits were characterized to reveal the dynamics of maize growth. An i-trait-based genome-wide association study identified 4945 trait-associated SNPs, 2603 genetic loci, and 1974 corresponding candidate genes. We found that rapid growth of maize plants occurs mainly at two developmental stages, stage 2 (S2) to S3 and S5 to S6, accounting for the final PH indicators. By integrating the PH-association network with the transcriptome profiles of specific internodes, we revealed 13 hub genes that may play vital roles during rapid growth. The candidate genes and novel i-traits identified at multiple growth stages may be used as potential indicators for final PH in maize. One candidate gene, ZmVATE, was functionally validated and shown to regulate PH-related traits in maize using genetic mutation. Furthermore, machine learning was used to build predictive models for final PH based on i-traits, and their performance was assessed across developmental stages. Moderate, strong, and very strong correlations between predictions and experimental datasets were achieved from the early S4 (tenth-leaf) stage. Colletively, our study provides a valuable tool for dissecting the spatiotemporal formation of specific internodes and the genetic architecture of PH, as well as resources and predictive models that are useful for molecular design breeding and predicting maize varieties with ideal plant architectures.

Synthetic biology plays a pivotal role in improving crop traits and increasing bioproduction through the use of engineering principles that purposefully modify plants through "design, build, test, and learn" cycles, ultimately resulting in improved bioproduction based on an input genetic circuit (DNA, RNA, and proteins). Crop synthetic biology is a new tool that uses circular principles to redesign and create innovative biological components, devices, and systems to enhance yields, nutrient absorption, resilience, and nutritional quality. In the digital age, artificial intelligence (AI) has demonstrated great strengths in design and learning. The application of AI has become an irreversible trend, with particularly remarkable potential for use in crop breeding. However, there has not yet been a systematic review of AI-driven synthetic biology pathways for plant engineering. In this review, we explore the fundamental engineering principles used in crop synthetic biology and their applications for crop improvement. We discuss approaches to genetic circuit design, including gene editing, synthetic nucleic acid and protein technologies, multi-omics analysis, genomic selection, directed protein engineering, and AI. We then outline strategies for the development of crops with higher photosynthetic efficiency, reshaped plant architecture, modified metabolic pathways, and improved environmental adaptability and nutrient absorption; the establishment of trait networks; and the construction of crop factories. We propose the development of SMART (self-monitoring, adapted, and responsive technology) crops through AI-empowered synthetic biotechnology. Finally, we address challenges associated with the development of synthetic biology and offer potential solutions for crop improvement.

Perception and transduction of salt stress signals are critical for plant survival, growth, and propagation. Thus, identification of components of the salt stress-signaling pathway is important for rice (Oryza sativa) molecular breeding of salt stress resistance. Here, we report the identification of an apetala2/ethylene response factor transcription factor INDETERMINATE SPIKELET1 (IDS1) and its roles in the regulation of rice salt tolerance. By genetic screening and phenotype analysis, we demonstrated that IDS1 conferred transcriptional repression activity and acted as a negative regulator of salt tolerance in rice. To identify potential downstream target genes regulated by IDS1, we conducted chromatin immunoprecipitation (ChIP) sequencing and ChIP-quantitative PCR assays and found that IDS1 may directly associate with the GCC-box-containing motifs in the promoter regions of abiotic stress-responsive genes, including LEA1 (LATE EMBRYOGENESIS ABUNDANT PROTEIN1) and SOS1 (SALT OVERLY SENSITIVE1), which are key genes regulating rice salt tolerance. IDS1 physically interacted with the transcriptional corepressor topless-related 1 and the histone deacetylase HDA1, contributing to the repression of LEA1 and SOS1 expression. Analyses of histone H3 acetylation status and RNA polymerase II occupation on the promoters of LEA1 and SOS1 further defined the molecular foundation of the transcriptional repression activity of IDS1. Our findings illustrate an epigenetic mechanism by which IDS1 modulates salt stress signaling as well as salt tolerance in rice.

Plant roots perceive heat stress (HS) and adapt their architecture accordingly, which in turn influence the yield in crops. Investigating their heterogeneity and cell type-specific response to HS is essential for improving crop resilience. Here, we generate single-cell transcriptional landscape of maize (Zea mays) roots in response to HS. We characterize 15 cell clusters corresponding to 9 major cell types and identify cortex as the main root cell type responsive to HS with the most differentially expressed genes and its trajectory being preferentially affected upon HS. We find that cortex size strongly correlated with heat tolerance that is experimentally validated by using inbred lines and genetic mutation analysis of one candidate gene in maize, providing potential HS tolerance indicator and targets for crop improvement. Moreover, interspecies comparison reveals conserved root cell types and core markers in response to HS in plants, which are experimentally validated. These results provide a universal atlas for unraveling the transcriptional programs that specify and maintain the cell identity of maize roots in response to HS at a cell type-specific level.