QTG-Miner aids rapid dissection of the genetic base of tassel branch number in maize

Comparative transcriptome analysis identified candidate genes associated with kernel row number in maize

Kernel row number (KRN) is a crucial trait in maize that has a high impact on yield. However, KRN is a typical quantitative trait with only a few genes being verified so far. Here, two maize inbred lines with contrasting KRN were used to perform transcriptome analysis at five early ear developmental stages. Pairwise differential gene expression analyses were performed, and a total of 11,897 line-specific differentially expressed genes (DEGs) were detected between the two lines across the five development stages. Clustering analysis of line-specific DEGs revealed that the trends of gene expression changed significantly in the five stages, thus the five stages were further divided into two development phases: Phase I (V6-V8) and Phase II (V9-V10). Gene ontology enrichment analysis revealed that different transcriptional pathways were activated in the two phases. DEGs in Phase I were significantly enriched in morphogenesis and differentiation processes and hormone regulation. Of the 5,850 line-specific DEGs in Phase I, 2,132 genes were in known quantitative trait loci (QTLs) or flanking regions of quantitative trait nucleotides (QTNs), of which 92 were repeatedly detected in QTLs where QTNs also exist. The 92 high-probability candidate genes included development-related transcription factors (SBP-box and AP2/EREBP TFs) as well as genes involved in hormone homeostasis and signaling. Our study provides genetic resources for the elucidation of the molecular mechanisms of KRN development and reference for the cloning of candidate genes.

Big data and artificial intelligence-aided crop breeding: Progress and prospects

The past decade has witnessed rapid developments in gene discovery, biological big data (BBD), artificial intelligence (AI)-aided technologies, and molecular breeding. These advancements are expected to accelerate crop breeding under the pressure of increasing demands for food. Here, we first summarize current breeding methods and discuss the need for new ways to support breeding efforts. Then, we review how to combine BBD and AI technologies for genetic dissection, exploring functional genes, predicting regulatory elements and functional domains, and phenotypic prediction. Finally, we propose the concept of intelligent precision design breeding (IPDB) driven by AI technology and offer ideas about how to implement IPDB. We hope that IPDB will enhance the predictability, efficiency, and cost of crop breeding compared with current technologies. As an example of IPDB, we explore the possibilities offered by CropGPT, which combines biological techniques, bioinformatics, and breeding art from breeders, and presents an open, shareable, and cooperative breeding system. IPDB provides integrated services and communication platforms for biologists, bioinformatics experts, germplasm resource specialists, breeders, dealers, and farmers, and should be well suited for future breeding.

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

Cultivating potential: Harnessing plant stem cells for agricultural crop improvement

Meristems are stem cell-containing structures that produce all plant organs and are therefore important targets for crop improvement. Developmental regulators control the balance and rate of cell divisions within the meristem. Altering these regulators impacts meristem architecture and, as a consequence, plant form. In this review, we discuss genes involved in regulating the shoot apical meristem, inflorescence meristem, axillary meristem, root apical meristem, and vascular cambium in plants. We highlight several examples showing how crop breeders have manipulated developmental regulators to modify meristem growth and alter crop traits such as inflorescence size and branching patterns. Plant transformation techniques are another innovation related to plant meristem research because they make crop genome engineering possible. We discuss recent advances on plant transformation made possible by studying genes controlling meristem development. Finally, we conclude with discussions about how meristem research can contribute to crop improvement in the coming decades.