Integration of high-throughput phenotyping, GWAS, and predictive models reveals the genetic architecture of plant height in maize

ZmHPAT2 Regulates Maize Growth and Development and Mycorrhizal Symbiosis

Hydroxyproline O-arabinosyltransferase (HPAT), a critical enzyme in plant glycosylation pathways, catalyzes the transfer of arabinose to the hydroxyl group of hydroxyproline residues. This enzyme contains a canonical GT95 glycosyltransferase, a structural hallmark of this carbohydrate-active enzyme family. HPAT mediates arabinosylation of diverse cellular targets, including cell wall extension and small signaling peptides. Emerging evidence has shown that HPAT orthologs regulate plant development and symbiotic interactions through post-translational modification of CLV1/LRR Extracellular (CLE) peptides. Although the molecular functions of HPAT genes have been characterized in model plants such as Arabidopsis thaliana and Lotus japonicus, their roles remain unexplored in Zea mays L. In this study, we used ZmHPAT2 homozygous mutants to explore the function of the maize HPAT gene. Sequence analysis identified a N-terminal signal peptide targeting the Golgi apparatus and promoter elements responsive to AM fungal colonization. Phenotypic analysis revealed its negative regulatory role: zmhpat2 promotes vegetative growth (increased plant height and accelerated flowering) and enhances AM symbiosis (increased colonization rate). Mechanistic studies demonstrated that ZmHPAT2 possesses dual regulatory functions-the activation of auxin signaling and repression of ZmMYB1-mediated arbuscular degradation pathways. In addition, overexpression of ZmHPAT2 in Lotus japonicus inhibits growth (reduced plant height) and impairs symbiotic interactions. Our findings establish ZmHPAT2 as a critical node to regulate auxin and symbiotic signaling, providing novel insights into plant glycosylation-mediated development. This work not only advances our understanding of maize growth regulation but also identifies potential targets for crop improvement through arabinosylation pathway manipulation.

Advancing radiation-induced mutant screening through high-throughput technology: a preliminary evaluation of mutant screening in Arabidopsis thaliana

Identifying mutant traits is essential for improving crop yield, quality, and stress resistance in plant breeding. Historically, the efficiency of breeding has been constrained by throughput and accuracy. Recent significant advancements have been made through the development of automated, high-accuracy, and high-throughput equipment. However, challenges remain in the post-processing of large-scale image data and its practical application and evaluation in breeding. This study presents a comparative analysis of human and machine recognition, with validation of a randomly selected mutant at the physiological level performed on wild-type Arabidopsis thaliana and a candidate mutant of the M3 generation, which was generated through mutagenesis with heavy ion beams (HIBs) and 60Co-γ radiation. The mutant populations were subjected to image acquisition and automated screening using the High-throughput Plant Imaging System (HTPIS), generating approximately 10 GB of data (4,635 image datasets). We performed Principal Components Analysis (PCA), scatter matrix clustering, and Logistic Growth Curve (LGC) analyses, and compared these results with those obtained from traditional manual screening based on human visual assessment, and randomly selected #197 candidate mutants for validation in terms of growth and development, chlorophyll fluorescence, and subcellular structure. Our findings demonstrate that as the confidence interval level increases from 75 to 99.9%, the accuracy of machine-based mutant identification decreases from 1 to 0.446, while the false positive rate decreases from 0.817 to 0.118, and the false negative rate increases from 0 to 0.554. Nevertheless, machine-based screening remains more accurate and efficient than human assessment. This study evaluated and validated the efficiency (greater than 80%) of high-throughput techniques for screening mutants in complex populations of radiation-induced progeny, and presented a graphical data processing procedure for high-throughput screening of mutants, providing a basis for breeding techniques utilizing HIBs and γ-ray radiation, and offering innovative approaches and methodologies for radiation-induced breeding in the context of high-throughput big data.

Phenotyping, genome-wide dissection, and prediction of maize root architecture for temperate adaptability

Root System Architecture (RSA) plays an essential role in influencing maize yield by enhancing anchorage and nutrient uptake. Analyzing maize RSA dynamics holds potential for ideotype-based breeding and prediction, given the limited understanding of the genetic basis of RSA in maize. Here, we obtained 16 root morphology-related traits (R-traits), 7 weight-related traits (W-traits), and 108 slice-related microphenotypic traits (S-traits) from the meristem, elongation, and mature zones by cross-sectioning primary, crown, and lateral roots from 316 maize lines. Significant differences were observed in some root traits between tropical/subtropical and temperate lines, such as primary and total root diameters, root lengths, and root area. Additionally, root anatomy data were integrated with genome-wide association study (GWAS) to elucidate the genetic architecture of complex root traits. GWAS identified 809 genes associated with R-traits, 261 genes linked to W-traits, and 2577 key genes related to 108 slice-related traits. We confirm the function of a candidate gene, fucosyltransferase5 (FUT5), in regulating root development and heat tolerance in maize. The different FUT5 haplotypes found in tropical/subtropical and temperate lines are associated with primary root features and hold promising applications in molecular breeding. Furthermore, we performed machine learning prediction models of RSA using root slice traits, achieving high prediction accuracy. Collectively, our study offers a valuable tool for dissecting the genetic architecture of RSA, along with resources and predictive models beneficial for molecular design breeding and genetic enhancement.

Genome-wide association analysis reveals the function of DgSAUR71 in plant height improvement

BACKGROUND

Orchardgrass (Dactylis glomerata L.) is one of the four most economically important forage grasses cultivated globally and serves as an excellent perennial forage with high ecological value. Plant height is a key determinant of both biomass and grain yield. While numerous genes regulating plant height have been identified in annual crops, no such genes have been reported for orchardgrass.

RESULTS

In this study, we analyzed the relationship between plant height and biomass yield in a natural population of 264 orchardgrass genotypes and found that a plant height of 90-110 cm contributed to the maximum biomass yield. Genome-wide association analysis (GWAS) identified 23 candidate loci associated with plant height, corresponding to 62 candidate genes. Among these, DgSAUR71, a member of the small auxin-up RNA (SAUR) gene family, emerged as a novel candidate gene associated with plant height. Functional analysis revealed that DgSAUR71 slightly reduced plant height in rice (Oryza sativa L.) and was involved in regulating plant height in orchardgrass.

CONCLUSIONS

This study demonstrates that plant height is an important contributor for optimizing biomass yield in orchardgrass, with an optimal range identified. DgSAUR71 was identified as a gene associated with plant height through GWAS and shown to negatively regulate plant height. These findings provide new insights into plant height regulation in orchardgrass and contribute to advancing crop height diversification research.

Genome-wide association study revealed candidate genes associated with leaf size in alfalfa (Medicago sativa L.)

BACKGROUND

Alfalfa (Medicago sativa L.) is one of the most widely cultivated perennial leguminous forages globally, known for its high yield and quality. Leaf size plays a crucial role in influencing its photosynthetic capacity, forage yield, and quality. Therefore, understanding the genetic factors regulating leaf size is of great importance for breeding new alfalfa varieties with improved yield and quality. In this study, we performed a genome-wide association study on four leaf size-related traits in 176 alfalfa germplasm resources to identify candidate genes associated with leaf size.

RESULTS

Phenotypic analysis revealed varying degrees of variation among the four traits, with coefficients of variation ranging from 3.43 to 36.84%. The broad sense heritability of these traits was found to be between 38.30% and 53.23%. Correlation analysis showed a significant positive correlation among the four traits (P < 0.01). The GWAS identified 39 SNPs associated with leaf size, distributed across eight chromosomes, of which 9 SNPs were linked to multiple traits. Haplotype analysis further confirmed that the number of superior alleles in each material was positively correlated with leaf area. Finally, we identified five genes near these 39 significant SNPs that are associated with leaf size or development.

CONCLUSION

Our findings provide new molecular markers for marker-assisted selection in alfalfa breeding programs. Moreover, this study provides a solid foundation for subsequent functional verification and genetic improvement in alfalfa.

Synthetic biology and artificial intelligence in crop improvement

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.

An epiallele of a gene encoding a PfkB-type carbohydrate kinase affects plant architecture in maize

Plant architecture greatly contributes to grain yield, but the epigenetic regulation of plant architecture remains elusive. Here, we identified the maize (Zea mays L.) mutant plant architecture 1 (par1), which shows reduced plant height, shorter and narrower leaves, and larger leaf angles than the wild type. Interestingly, par1 is an epiallele harbouring a de novo CACTA insertion in the intron of the Par1 gene. High DNA methylation levels of the CACTA insertion are associated with strong Par1 expression and normal phenotypes. In contrast, low DNA methylation levels of this insertion are associated with weak Par1 expression and a mutant-like phenotype. The Par1 gene encodes a PfkB-type carbohydrate kinase that converts nucleosides to nucleoside monophosphates both in vitro and in vivo. Additional analyses showed that genes differentially expressed in the par1 mutant are enriched in jasmonic acid (JA) metabolism, and levels of JA metabolites were significantly higher in the mutant than in the wild type. Treatment with either nucleoside monophosphates or a synthetic inhibitor of JA biosynthesis reduced JA levels and partially rescued the mutant phenotype. In summary, we identified an epiallele of a gene encoding a PfkB-type carbohydrate kinase that might affect nucleoside monophosphate and JA levels, thus affecting maize growth.

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

Plant height can be an indicator of plant health across environments and used to identify superior genotypes. Typically plant height is measured at a single timepoint when plants reach terminal height. Evaluating plant height using unoccupied aerial vehicles allows for measurements throughout the growing season, facilitating a better understanding of plant-environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected from planting until terminal height at anthesis (14 flights 2018, 27 in 2019, 12 in 2020, and 11 in 2021) for a panel of ~500 diverse maize inbred lines. The percent variance explained in plant height throughout the season was significantly explained by genotype (9-48%), year (4-52%), and genotype-by-year interactions (14-36%) to varying extents throughout development. Genome-wide association studies revealed 717 significant single nucleotide polymorphisms associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth. When plant height growth curves were compared to growth curves estimated from canopy cover, greater Fréchet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits.

New perspectives of post-GWAS analyses: From markers to causal genes for more precise crop breeding

Crop breeding advancement is hindered by the imperfection of methods to reveal genes underlying key traits. Genome-wide Association Study (GWAS) is one such method, identifying genomic regions linked to phenotypes. Post-GWAS analyses predict candidate genes and assist in causative mutation (CM) recognition. Here, we assess post-GWAS approaches, address limitations in omics data integration and stress the importance of evaluating associated variants within a broader context of publicly available datasets. Recent advances in bioinformatics tools and genomic strategies for CM identification and allelic variation exploration are reviewed. We discuss the role of markers and marker panel development for more precise breeding. Finally, we highlight the perspectives and challenges of GWAS-based CM prediction for complex quantitative traits.

Genome-Wide Development of InDel-SSRs and Association Analysis of Important Agronomic Traits of Taro (Colocasia esculenta) in China

Taro (Colocasia esculenta (L.) Schott) is a tropical tuber crop whose underground corms are used as an important staple food. However, due to a lack of molecular markers, the genetic diversity, germplasm identification, and molecular breeding of taro are greatly limited. In this study, high-density InDel-SSR molecular markers covering the whole genome were developed based on the resequencing data of taro core germplasm. A total of 1,805,634 InDel-SSR loci were identified, and 219 highly polymorphic markers with an average polymorphism information content PIC value of 0.428 were screened. Furthermore, a genetic diversity analysis of 121 taro germplasm resources was conducted based on 219 markers, dividing the resources into three groups. In addition, an association analysis showed that, of the multiple InDel-SSR markers, g13.52 and g12.82 were significantly associated with leaf area and average cormel weight, respectively; the candidate genes CeARF17 (EVM0014444) and CeGA20ox (EVM0001890) were related to cormel expansion; and we excavated the candidate genes CeXXT2 (EVM0016820) and CeLOG1 (EVM0017064), which regulate leaf development. The InDel-SSRs and candidate genes identified in this study are expected to provide important support for genetically improving and breeding new varieties of taro.

Molecular markers and molecular basis of plant type related traits in maize

Maize, belonging to the Poaceae family and the Zea L. genus, stands as an excellent food crop. The plant type has a significant impact on crop growth, photosynthesis, lodging resistance, planting density, and final yield. In this study, 160 maize inbred lines were selected as experimental materials to conduct molecular markers research on maize plant type traits through the measurement of plant type-related traits, population structure, and genome-wide association analysis. The phenotypic data revealed differences in plant type-related traits among maize inbred lines grown in the Xinzhou and Jinzhong regions. The frequency distribution of plant height, ear height, spindle length of tassel, and first-order branch number of tassel traits in the 160 maize inbred lines previously studied generally conformed to a normal distribution. We identified 42,240 high-quality single nucleotide polymorphisms (SNPs) using the Affymetrix Axiom chip. The 160 maize inbred lines were categorized into six subgroups, each exhibiting an average gene diversity of 0.356 and an average polymorphism information content of 0.245. We identified 9, 23, 18, 8 and 32 loci that were significantly associated with first-order branch number of tassel, spindle length of tassel, ear height, plant height, and ear height/plant height ratio, respectively. At the same time, 6, 22, 14, 2, and 37 genes were identified as significantly associated with first-order branch number of tassel, spindle length of tassel, ear height, plant height, and ear height/plant height ratio, respectively. This study comprehensively delved into the genetic information of maize plant type-related traits, offering valuable genetic resources and a solid theoretical foundation for the breeding of novel maize varieties with optimized plant types.

GPCR-like Protein ZmCOLD1 Regulate Plant Height in an ABA Manner

G protein-coupled receptors (GPCRs) are sensors for the G protein complex to sense changes in environmental factors and molecular switches for G protein complex signal transduction. In this study, the homologous gene of GPCR-like proteins was identified from maize and named as ZmCOLD1. Subcellular analysis showed that the ZmCOLD1 protein is localized to the cell membrane and endoplasmic reticulum. A CRISPR/Cas9 knock-out line of ZmCOLD1 was further created and its plant height was significantly lower than the wild-type maize at both the seedling and adult stages. Histological analysis showed that the increased cell number but significantly smaller cell size may result in dwarfing of zmcold1, indicating that the ZmCOLD1 gene could regulate plant height development by affecting the cell division process. Additionally, ZmCOLD1 was verified to interact with the maize Gα subunit, ZmCT2, though the central hydrophilic loop domain by in vivo and in vitro methods. Abscisic acid (ABA) sensitivity analysis by seed germination assays exhibited that zmcold1 were hypersensitive to ABA, indicating its important roles in ABA signaling. Finally, transcriptome analysis was performed to investigate the transcriptional change in zmcold1 mutant. Overall, ZmCOLD1 functions as a GPCR-like protein and an important regulator to plant height.

Genome-wide association study reveals genetic basis and candidate genes for chlorophyll content of leaves in maize (Zea mays L.)

The chlorophyll content (CC) directly affects photosynthesis, growth, and yield. However, the genetic basis of CC is still unclear in maize (Zea mays L.). Here, we conducted a genome-wide association study using mixed linear model for CC of the fifth leaves at seedling stage (CCFSS) and the ear leaves at filling stage (CCEFS) for 334 maize inbred lines. The heritability estimates for CCFSS and CCEFS, obtained via variance components analysis using the lme4 package in R, were 70.84% and 78.99%, respectively, indicating that the CC of leaves is primarily controlled by genetic factors. A total of 15 CC-related SNPs and 177 candidate genes were identified with a p-value < 4.49 × 10-5, which explained 4.98-7.59% of the phenotypic variation. Lines with more favorable gene variants showed higher CC. Meanwhile, Gene Ontology (GO) analysis implied that these candidate genes were probably related to chlorophyll biosynthesis. In addition, gene-based association analyses revealed that six variants in GRMZM2G037152, GRMZM5G816561, GRMZM2G324462, and GRMZM2G064657 genes were significantly (p-value < 0.01) correlated with CC, of which GRMZM2G064657 (encodes a phosphate transporter protein) and GRMZM5G816561 (encodes a cytochrome P450 protein) were specifically highly expressed in leaves tissues. Interestingly, these candidate genes were previously reported to involve in the regulation of the contents of chlorophyll in plants or Chlamydomonas. These results may contribute to the understanding of genetic basis and molecular mechanisms of maize CC and the selection of maize varieties with improved CC.

Recognition and Localization of Maize Leaf and Stalk Trajectories in RGB Images Based on Point-Line Net

Plant phenotype detection plays a crucial role in understanding and studying plant biology, agriculture, and ecology. It involves the quantification and analysis of various physical traits and characteristics of plants, such as plant height, leaf shape, angle, number, and growth trajectory. By accurately detecting and measuring these phenotypic traits, researchers can gain insights into plant growth, development, stress tolerance, and the influence of environmental factors, which has important implications for crop breeding. Among these phenotypic characteristics, the number of leaves and growth trajectory of the plant are most accessible. Nonetheless, obtaining these phenotypes is labor intensive and financially demanding. With the rapid development of computer vision technology and artificial intelligence, using maize field images to fully analyze plant-related information can greatly eliminate repetitive labor and enhance the efficiency of plant breeding. However, it is still difficult to apply deep learning methods in field environments to determine the number and growth trajectory of leaves and stalks due to the complex backgrounds and serious occlusion problems of crops in field environments. To preliminarily explore the application of deep learning technology to the acquisition of the number of leaves and stalks and the tracking of growth trajectories in field agriculture, in this study, we developed a deep learning method called Point-Line Net, which is based on the Mask R-CNN framework, to automatically recognize maize field RGB images and determine the number and growth trajectory of leaves and stalks. The experimental results demonstrate that the object detection accuracy (mAP50) of our Point-Line Net can reach 81.5%. Moreover, to describe the position and growth of leaves and stalks, we introduced a new lightweight "keypoint" detection branch that achieved a magnitude of 33.5 using our custom distance verification index. Overall, these findings provide valuable insights for future field plant phenotype detection, particularly for datasets with dot and line annotations.

Genetic response of a perennial grass to warm and wet environments interacts and is associated with trait means as well as plasticity

The potential for rapid evolution is an important mechanism allowing species to adapt to changing climatic conditions. Although such potential has been largely studied in various short-lived organisms, to what extent we can observe similar patterns in long-lived plant species, which often dominate natural systems, is largely unexplored. We explored the potential for rapid evolution in Festuca rubra, a long-lived grass with extensive clonal growth dominating in alpine grasslands. We used a field sowing experiment simulating expected climate change in our model region. Specifically, we exposed seeds from five independent seed sources to novel climatic conditions by shifting them along a natural climatic grid and explored the genetic profiles of established seedlings after 3 years. Data on genetic profiles of plants selected under different novel conditions indicate that different climate shifts select significantly different pools of genotypes from common seed pools. Increasing soil moisture was more important than increasing temperature or the interaction of the two climatic factors in selecting pressure. This can indicate negative genetic interaction in response to the combined effects or that the effects of different climates are interactive rather than additive. The selected alleles were found in genomic regions, likely affecting the function of specific genes or their expression. Many of these were also linked to morphological traits (mainly to trait plasticity), suggesting these changes may have a consequence on plant performance. Overall, these data indicate that even long-lived plant species may experience strong selection by climate, and their populations thus have the potential to rapidly adapt to these novel conditions.

Dissection of Common Rust Resistance in Tropical Maize Multiparent Population through GWAS and Linkage Studies

Common rust (CR), caused by Puccina sorghi, is a major foliar disease in maize that leads to quality deterioration and yield losses. To dissect the genetic architecture of CR resistance in maize, this study utilized the susceptible temperate inbred line Ye107 as the male parent crossed with three resistant tropical maize inbred lines (CML312, D39, and Y32) to generate 627 F7 recombinant inbred lines (RILs), with the aim of identifying maize disease-resistant loci and candidate genes for common rust. Phenotypic data showed good segregation between resistance and susceptibility, with varying degrees of resistance observed across different subpopulations. Significant genotype effects and genotype × environment interactions were observed, with heritability ranging from 85.7% to 92.2%. Linkage and genome-wide association analyses across the three environments identified 20 QTLs and 62 significant SNPs. Among these, seven major QTLs explained 66% of the phenotypic variance. Comparison with six SNPs repeatedly identified across different environments revealed overlap between qRUST3-3 and Snp-203,116,453, and Snp-204,202,469. Haplotype analysis indicated two different haplotypes for CR resistance for both the SNPs. Based on LD decay plots, three co-located candidate genes, Zm00001d043536, Zm00001d043566, and Zm00001d043569, were identified within 20 kb upstream and downstream of these two SNPs. Zm00001d043536 regulates hormone regulation, Zm00001d043566 controls stomatal opening and closure, related to trichome, and Zm00001d043569 is associated with plant disease immune responses. Additionally, we performed candidate gene screening for five additional SNPs that were repeatedly detected across different environments, resulting in the identification of five candidate genes. These findings contribute to the development of genetic resources for common rust resistance in maize breeding programs.

Plant microphenotype: from innovative imaging to computational analysis

The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.

Genomic selection in plant breeding: Key factors shaping two decades of progress

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.

Effects of exogenous glycine betaine on growth and development of tomato seedlings under cold stress

Low temperature is a type of abiotic stress affecting the tomato (Solanum lycopersicum) growth. Understanding the mechanisms and utilization of exogenous substances underlying plant tolerance to cold stress would lay the foundation for improving temperature resilience in this important crop. Our study is aiming to investigate the effect of exogenous glycine betaine (GB) on tomato seedlings to increase tolerance to low temperatures. By treating tomato seedlings with exogenous GB under low temperature stress, we found that 30 mmol/L exogenous GB can significantly improve the cold tolerance of tomato seedlings. Exogenous GB can influence the enzyme activity of antioxidant defense system and ROS levels in tomato leaves. The seedlings with GB treatment presented higher Fv/Fm value and photochemical activity under cold stress compared with the control. Moreover, analysis of high-throughput plant phenotyping of tomato seedlings also supported that exogenous GB can protect the photosynthetic system of tomato seedlings under cold stress. In addition, we proved that exogenous GB significantly increased the content of endogenous abscisic acid (ABA) and decreased endogenous gibberellin (GA) levels, which protected tomatoes from low temperatures. Meanwhile, transcriptional analysis showed that GB regulated the expression of genes involved in antioxidant capacity, calcium signaling, photosynthesis activity, energy metabolism-related and low temperature pathway-related genes in tomato plants. In conclusion, our findings indicated that exogenous GB, as a cryoprotectant, can enhance plant tolerance to low temperature by improving the antioxidant system, photosynthetic system, hormone signaling, and cold response pathway and so on.

Evidence that variation in root anatomy contributes to local adaptation in Mexican native maize

Mexican native maize (Zea mays ssp. mays) is adapted to a wide range of climatic and edaphic conditions. Here, we focus specifically on the potential role of root anatomical variation in this adaptation. Given the investment required to characterize root anatomy, we present a machine-learning approach using environmental descriptors to project trait variation from a relatively small training panel onto a larger panel of genotyped and georeferenced Mexican maize accessions. The resulting models defined potential biologically relevant clines across a complex environment that we used subsequently for genotype-environment association. We found evidence of systematic variation in maize root anatomy across Mexico, notably a prevalence of trait combinations favoring a reduction in axial hydraulic conductance in varieties sourced from cooler, drier highland areas. We discuss our results in the context of previously described water-banking strategies and present candidate genes that are associated with both root anatomical and environmental variation. Our strategy is a refinement of standard environmental genome-wide association analysis that is applicable whenever a training set of georeferenced phenotypic data is available.

Functions of Phytochrome Interacting Factors (PIFs) in Adapting Plants to Biotic and Abiotic Stresses

Plants possess the remarkable ability to sense detrimental environmental stimuli and launch sophisticated signal cascades that culminate in tailored responses to facilitate their survival, and transcription factors (TFs) are closely involved in these processes. Phytochrome interacting factors (PIFs) are among these TFs and belong to the basic helix-loop-helix family. PIFs are initially identified and have now been well established as core regulators of phytochrome-associated pathways in response to the light signal in plants. However, a growing body of evidence has unraveled that PIFs also play a crucial role in adapting plants to various biological and environmental pressures. In this review, we summarize and highlight that PIFs function as a signal hub that integrates multiple environmental cues, including abiotic (i.e., drought, temperature, and salinity) and biotic stresses to optimize plant growth and development. PIFs not only function as transcription factors to reprogram the expression of related genes, but also interact with various factors to adapt plants to harsh environments. This review will contribute to understanding the multifaceted functions of PIFs in response to different stress conditions, which will shed light on efforts to further dissect the novel functions of PIFs, especially in adaption to detrimental environments for a better survival of plants.

OPIA: an open archive of plant images and related phenotypic traits

High-throughput plant phenotype acquisition technologies have been extensively utilized in plant phenomics studies, leading to vast quantities of images and image-based phenotypic traits (i-traits) that are critically essential for accelerating germplasm screening, plant diseases identification and biotic & abiotic stress classification. Here, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms. Currently, OPIA houses 56 datasets across 11 plants, comprising a total of 566 225 images with 2 417 186 labeled instances. Notably, it incorporates 56 i-traits of 93 rice and 105 wheat cultivars based on 18 644 individual RGB images, and these i-traits are further annotated based on the Plant Phenotype and Trait Ontology (PPTO) and cross-linked with GWAS Atlas. Additionally, each dataset in OPIA is assigned an evaluation score that takes account of image data volume, image resolution, and the number of labeled instances. More importantly, OPIA is equipped with useful tools for online image pre-processing and intelligent prediction. Collectively, OPIA provides open access to valuable datasets, pre-trained models, and phenotypic traits across diverse plants and thus bears great potential to play a crucial role in facilitating artificial intelligence-assisted breeding research.

Identification and Characterization of ABCG15-A Gene Required for Exocarp Color Differentiation in Pear

Exocarp color is a commercially essential quality for pear which can be divided into two types: green and russet. The occurrence of russet color is associated with deficiencies and defects in the cuticular and epidermal layers, which affect the structure of the cell wall and the deposition of suberin. Until now, the genetic basics triggering this trait have not been well understood, and limited genes have been identified for the trait. To figure out the gene controlling the trait of exocarp color, we perform a comprehensive genome-wide association study, and we describe the candidate genes. One gene encoding the ABCG protein has been verified to be associated with the trait, using an integrative analysis of the metabolomic and transcriptomic data. This review covers a variety of omics resources, which provide a valuable resource for identifying gene-controlled traits of interest. The findings in this study help to elucidate the genetic components responsible for the trait of exocarp color in pear, and the implications of these findings for future pear breeding are evaluated.

Machine learning for image-based multi-omics analysis of leaf veins

Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form and function of veins requires a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning have facilitated the creation of algorithms that can identify vein networks and explore their developmental progression. Here, we review the functional, environmental, and genetic factors associated with vein networks, along with the current status of research on image analysis. In addition, we discuss the methods of venous phenotype extraction and multi-omics association analysis using machine learning technology, which could provide a theoretical basis for improving crop productivity by optimizing the vein network architecture.

Identification of QTL for kernel weight and size and analysis of the pentatricopeptide repeat (PPR) gene family in cultivated peanut (Arachis hypogaea L.)

Peanut (Arachis hypogaea L.) is an important oilseed crop worldwide. Improving its yield is crucial for sustainable peanut production to meet increasing food and industrial requirements. Deciphering the genetic control underlying peanut kernel weight and size, which are essential components of peanut yield, would facilitate high-yield breeding. A high-density single nucleotide polymorphism (SNP)-based linkage map was constructed using a recombinant inbred lines (RIL) population derived from a cross between the variety Yuanza9102 and a germplasm accession wt09-0023. Kernel weight and size quantitative trait loci (QTLs) were co-localized to a 0.16 Mb interval on Arahy07 using inclusive composite interval mapping (ICIM). Analysis of SNP, and Insertion or Deletion (INDEL) markers in the QTL interval revealed a gene encoding a pentatricopeptide repeat (PPR) superfamily protein as a candidate closely linked with kernel weight and size in cultivated peanut. Examination of the PPR gene family indicated a high degree of collinearity of PPR genes between A. hypogaea and its diploid progenitors, Arachis duranensis and Arachis ipaensis. The candidate PPR gene, Arahy.JX1V6X, displayed a constitutive expression pattern in developing seeds. These findings lay a foundation for further fine mapping of QTLs related to kernel weight and size, as well as validation of candidate genes in cultivated peanut.

Harnessing intra-varietal variation for agro-morphological and nutritional traits in a popular rice landrace for sustainable food security in tropical islands

Introduction

Rice crop meets the calorie and nutritional requirements of a larger segment of the global population. Here, we report the occurrence of intra-varietal variation in a popular rice landrace C14-8 traditionally grown under the geographical isolation of the Andaman Islands.

Methods

Based on grain husk color, four groups were formed, wherein the extent of intra-varietal variation was studied by employing 22 agro-morphological and biochemical traits.

Results

Among the traits studied, flavonoid and anthocyanin contents and grain yield exhibited a wider spectrum of variability due to more coefficients of variation (>25%). The first five principal components (PCs) of principal components analysis explained a significant proportion of the variation (91%) and the first two PCs explained 63.3% of the total variation, with PC1 and PC2 explaining 35.44 and 27.91%, respectively. A total of 50 highly variable SSR (HvSSR) markers spanning over 12 chromosomes produced 314 alleles, which ranged from 1 to 15 alleles per marker, with an average of 6.28. Of the 314 alleles, 64 alleles were found to be rare among the C14-8 selections. While 62% of HvSSR markers exhibited polymorphism among the C14-8 population, chromosomes 2, 7, 9, and 11 harbored the most polymorphic loci. The group clustering of the selections through HvSSR markers conformed to the grouping based on grain husk coloration.

Discussion

Our studies on the existence and pertinence of intra-varietal variations are expected to be of significance in the realms of evolutionary biology and sustainable food and nutritional security under the changing climate.