Biomedical subjects
Publications and source records attributed to Jingjing Zhai.
Dr. Laura Zahn asked the authors, Dr. Jingjing Zhai and Dr. Edward (Ed) S. Buckler, to tell us about their research relating to their Cell Genomics paper, "PlantCAD2: A DNA foundation model for interpreting genomes across flowering plants."
Despite the plethora of knowledge about the benefits of meiotic recombination and numerous theoretical studies examining how recombination rates evolve, there is a general lack of empirical support and consensus across species. To fill this knowledge gap, we characterized the evolution of recombination landscape in maize during its domestication from teosinte and related the observed changes to established theoretical frameworks. Through examining recombination in experimental populations of maize and teosinte and the population genomics approach of identifying historical recombination events using ancestral recombination graph inference to generate saturated maize and teosinte recombination maps, we found that during domestication, maize experienced a 12% increase in its genome-wide recombination rate. Furthermore, maize evolved higher recombination rates on the long arms of chromosomes in regions closer to centromeres, where recombination is generally very low. The repatterning of crossover events came from changes in global crossover positioning rather than alterations in cis-acting chromatin factors. Consequently, we found evidence of selection acting on trans-acting recombination modifiers affecting crossover interference and controlling the interference-dependent class I crossover pathway. We show that CO repatterning was likely beneficial for maize fitness, as significant recombination rate increases were predominantly in gene-rich regions, which harbor domestication-related variation. This work suggests genomic and mechanistic processes leading to the evolution of meiotic recombination landscape in response to directional selection pressure and provides evidence for the evolutionary advantage of recombination.
Historical plant breeding, which optimizes phenotypes through selective crossing guided by phenotypic evaluation and molecular markers, is limited by evolutionary constraints that hinder rapid crop improvement. A new paradigm, precision breeding, circumvents these limitations by targeting genetic variants through functional molecular knowledge. To generate this knowledge at scale, sequence-based deep learning leverages high-quality genome sequence data to predict variant effects at base-pair resolution. When linked to agronomically important traits, these predictions enable breeders to prioritize variants for precision selection or editing. Although it is still in the early stages of development, we foresee three key applications for this approach: introgressing genes from distant breeding pools, purging deleterious mutations and designing new plant ideotypes. Looking ahead, refined computational models will facilitate targeted editing and the systematic redesign of complex physiological processes to address emerging breeding goals under shifting environmental conditions.
Compared with other transcription factors, much less studies have been performed on paclobutrazol-resistance (PRE), a subgroup of the extensive bHLH transcription factor gene family, and the research in cotton was also limited. By utilizing the PRE genes and their conserved domains identified in Arabidopsis, a total of 23, 22, 11, and 12 PRE genes were identified from two major cultivated cotton species and their two ancestors, respectively. The cotton PRE gene family was categorized into three subgroups based on evolutionary tree analysis. Motif and intron analyses indicated that the PRE gene has remained highly conserved throughout evolution. Collinearity analysis indicated that gene duplication, particularly through fragment replication, has significantly contributed to the expansion of the cotton PRE family. An exploration of the conserved elements within the PRE gene family uncovered numerous elements associated with plant stress resistance. Additionally, cotton transcriptome and qRT-PCR analysis showed that PRE genes were associated with a variety of abiotic stresses, including salt, drought, and cold treatments. Subcellular localization experiments indicated that the GhPRE3 gene is associated with membrane proteins. Finally, we selected the GhPRE3 gene for a VIGS experiment, which revealed that under salt stress and drought stress conditions, the wilting of leaves in the GhPRE3-silenced plants was significantly more severe than that observed in the control group, with T-AOC levels notably lower and MDA levels significantly higher. Overexpression of GhPRE3 enhanced seed germination and root development in transgenic Arabidopsis thaliana under salt stress and drought stresses. This suggests that GhPRE3 plays a positive regulatory role in cotton tolerance to salt and drought stressed, providing a reference for molecular genetic breeding of cotton with salt and drought tolerance.
Assembled genomes and their associated annotations have transformed our study of gene function. However, each new annotated assembly generates new gene models. Inconsistencies between annotations likely arise from biological and technical causes, including pseudogene misclassification, transposon activity, and intron retention from sequencing of unspliced transcripts. To evaluate gene model predictions, we developed reelGene, a pipeline of machine learning models focused on (1) transcription boundaries, (2) mRNA integrity, and (3) protein structure. The first two models leverage sequence characteristics and evolutionary conservation across related taxa to learn the grammar of conserved transcription boundaries and mRNA sequences, while the third uses the conserved evolutionary grammar of protein sequences to predict whether a gene can produce a protein. Evaluating 1.8 million transcript models in Zea mays ssp. mays (maize), reelGene classified 28% as incorrectly annotated or non-functional. We find that reelGene classifies 92.2% of genes in the maize proteome and 99.2% of genes within the maize classical gene list as functional. reelGene also provides a way to further investigate genome biology- for instance, reelGene indicates that 10.3% of dispensable genes in B73 are functional, and within retained duplicate genes, reelGene identifies a 30% bias toward the retention of the M1 subgenome when one copy is functional and the other is non-functional. As an annotation-evaluating tool, reelGene is directly applicable to species of the Andropogoneae tribe, including other important crops like sorghum and miscanthus. As a community resource, reelGene has been integrated onto MaizeGDB both as a browser track and as an individual Shiny App, allowing researchers to evaluate gene model accuracy and further investigate genome biology.