The nuclear pore Y-complex functions as a platform for transcriptional regulation of FLOWERING LOCUS C in Arabidopsis.

The nuclear pore complex (NPC) has multiple functions beyond the nucleo-cytoplasmic transport of large molecules. Subnuclear compartmentalization of chromatin is critical for gene expression in animals and yeast. However, the mechanism by which the NPC regulates gene expression is poorly understood in plants. Here we report that the Y-complex (Nup107-160 complex, a subcomplex of the NPC) self-maintains its nucleoporin homeostasis and modulates FLOWERING LOCUS C (FLC) transcription via changing histone modifications at this locus. We show that Y-complex nucleoporins are intimately associated with FLC chromatin through their interactions with histone H2A at the nuclear membrane. Fluorescence in situ hybridization assays revealed that Nup96, a Y-complex nucleoporin, enhances FLC positioning at the nuclear periphery. Nup96 interacted with HISTONE DEACETYLASE 6 (HDA6), a key repressor of FLC expression via histone modification, at the nuclear membrane to attenuate HDA6-catalyzed deposition at the FLC locus and change histone modifications. Moreover, we demonstrate that Y-complex nucleoporins interact with RNA polymerase II to increase its occupancy at the FLC locus, facilitating transcription. Collectively, our findings identify an attractive mechanism for the Y-complex in regulating FLC expression via tethering the locus at the nuclear periphery and altering its histone modification.

Arabidopsis transcription factor TCP4 controls the identity of the apical gynoecium.

The style and stigma at the apical gynoecium are crucial for flowering plant reproduction. However, the mechanisms underlying specification of the apical gynoecium remain unclear. Here, we demonstrate that Class II TEOSINTE BRANCHED 1/CYCLOIDEA/PCF (TCP) transcription factors are critical for apical gynoecium specification in Arabidopsis (Arabidopsis thaliana). The septuple tcp2 tcp3 tcp4 tcp5 tcp10 tcp13 tcp17 (tcpSEP) and duodecuple tcp2 tcp3 tcp4 tcp5 tcp10 tcp13 tcp17 tcp24 tcp1 tcp12 tcp18 tcp16 (tcpDUO) mutants produce narrower and longer styles, while disruption of TCPs and CRABS CLAW (CRC) or NGATHAs (NGAs) in tcpDUO crc or tcpDUO nga1 nga2 nga4 causes the apical gynoecium to be replaced by lamellar structures with indeterminate growth. TCPs are predominantly expressed in the apex of the gynoecium. TCP4 interacts with CRC to synergistically upregulate the expression level of NGAs, and NGAs further form high-order complexes to control the expression of auxin-related genes in the apical gynoecium by directly interacting with TCP4. Our findings demonstrate that TCP4 physically associates with CRC and NGAs to control auxin biosynthesis in forming fine structures of the apical gynoecium.

RefMetaPlant: a reference metabolome database for plants across five major phyla.

Plants are unique with tremendous chemical diversity and metabolic complexity, which is highlighted by estimates that green plants collectively produce metabolites numbering in the millions. Plant metabolites play crucial roles in all aspects of plant biology, like growth, development, stress responses, etc. However, the lack of a reference metabolome for plants, and paucity of high-quality standard compound spectral libraries and related analytical tools, have hindered the discovery and functional study of phytochemicals in plants. Here, by leveraging an advanced LC-MS platform, we generated untargeted mass spectral data from >150 plant species collected across the five major phyla. Using a self-developed computation protocol, we constructed reference metabolome for 153 plant species. A 'Reference Metabolome Database for Plants' (RefMetaPlant) was built to encompass the reference metabolome, integrated standard compound mass spectral libraries for annotation, and related query and analytical tools like 'LC-MS/MS Query', 'RefMetaBlast' and 'CompoundLibBlast' for searches and profiling of plant metabolome and metabolite identification. Analogous to a reference genome in genomic research, RefMetaPlant provides a powerful platform to support plant genome-scale metabolite analysis to promote knowledge/data sharing and collaboration in the field of metabolomics. RefMetaPlant is freely available at https://www.biosino.org/RefMetaDB/.

CropMetabolome: a comprehensive metabolome database for major crops cross eight categories.

Chemical compositions of crops are of great agronomical importance, as crops serve as resources for nutrition, energy, and medicines for human and livestock. For crop metabolomics research, the lack of crop reference metabolome and high-quality reference compound mass spectra, as well as utilities for metabolic profiling, has hindered the discovery and functional study of phytochemicals in crops. To meet these challenging needs, we have developed the Crop Metabolome database (abbreviated as CropMetabolome) that is dedicated to the construction of crop reference metabolome, repository, and dissemination of crop metabolomic data, and profiling and analytic tools for metabolomics research. CropMetabolome contains a metabolomics database for more than 50 crops (belonging to eight categories) that integrated self-generated raw mass spectral data and public-source datasets. The reference metabolome for 59 crop species was constructed, which have functions that parallel those of reference genome in genomic studies. CropMetabolome also contains 'Standard compound mass spectral library', 'Flavonoids library', 'Pesticide library', and a set of related analytical tools that enable metabolic profiling based on a reference metabolome (CropRefMetaBlast), annotation and identification of new metabolites (CompoundLibBlast), deducing the structure of novel flavonoid derivatives (FlavoDiscover), and detecting possible residual pesticides in crop samples (PesticiDiscover). In addition, CropMetabolome is a repository to share and disseminate metabolomics data and a platform to promote collaborations to develop reference metabolome for more crop species. CropMetabolome is a comprehensive platform that offers important functions in crop metabolomics research and contributes to improve crop breeding, nutrition, and safety. CropMetabolome is freely available at https://www.cropmetabolome.com/.

A transformer-based genomic prediction method fused with knowledge-guided module.

Genomic prediction (GP) uses single nucleotide polymorphisms (SNPs) to establish associations between markers and phenotypes. Selection of early individuals by genomic estimated breeding value shortens the generation interval and speeds up the breeding process. Recently, methods based on deep learning (DL) have gained great attention in the field of GP. In this study, we explore the application of Transformer-based structures to GP and develop a novel deep-learning model named GPformer. GPformer obtains a global view by gleaning beneficial information from all relevant SNPs regardless of the physical distance between SNPs. Comprehensive experimental results on five different crop datasets show that GPformer outperforms ridge regression-based linear unbiased prediction (RR-BLUP), support vector regression (SVR), light gradient boosting machine (LightGBM) and deep neural network genomic prediction (DNNGP) in terms of mean absolute error, Pearson's correlation coefficient and the proposed metric consistent index. Furthermore, we introduce a knowledge-guided module (KGM) to extract genome-wide association studies-based information, which is fused into GPformer as prior knowledge. KGM is very flexible and can be plugged into any DL network. Ablation studies of KGM on three datasets illustrate the efficiency of KGM adequately. Moreover, GPformer is robust and stable to hyperparameters and can generalize to each phenotype of every dataset, which is suitable for practical application scenarios.

MutL homolog 1 participates in interference-sensitive meiotic crossover formation in soybean.

MutL homolog 1 (MLH1), a member of the MutL homolog family, is required for normal recombination in most organisms. However, its role in soybean (Glycine max) remains unclear to date. Here, we characterized the Glycine max female and male sterility 1 (Gmfms1) mutation that reduces pollen grain viability and increases embryo sac abortion in soybean. Map-based cloning revealed that the causal gene of Gmfms1 is Glycine max MutL homolog 1 (GmMLH1), and CRISPR/Cas9 knockout approach further validated that disruption of GmMLH1 confers the female-male sterility phenotype in soybean. Loss of GmMLH1 function disrupted bivalent formation, leading to univalent mis-segregation during meiosis and ultimately to female-male sterility. The Gmmlh1 mutant showed about a 78.16% decrease in meiotic crossover frequency compared to the wild type. The residual chiasmata followed a Poisson distribution, suggesting that interference-sensitive crossover formation was affected in the Gmmlh1 mutant. Furthermore, GmMLH1 could interact with GmMLH3A and GmMLH3B both in vivo and in vitro. Overall, our work demonstrates that GmMLH1 participates in interference-sensitive crossover formation in soybean, and provides additional information about the conserved functions of MLH1 across plant species.

SYNTAXIN OF PLANTS81 regulates root meristem activity and stem cell niche maintenance via ROS signaling.

Root growth and development depend on continuous cell division and differentiation in root tips. In these processes, reactive oxygen species (ROS) play a critical role as signaling molecules. However, few ROS signaling regulators have been identified. In this study, we found knockdown of a syntaxin gene, SYNTAXIN OF PLANTS81 in Arabidopsis thaliana (AtSYP81) resulted in a severe reduction in root meristem activity and disruption of root stem cell niche (SCN) identity. Subsequently, we found AtSYP81 was highly expressed in roots and localized on the endoplasmic reticulum (ER). Interestingly, the reduced expression of AtSYP81 conferred a decreased number of peroxisomes in root meristem cells, raising a possibility that AtSYP81 regulates root development through peroxisome-mediated ROS production. Further transcriptome analysis revealed that class III peroxidases, which are responsible for intracellular ROS homeostasis, showed significantly changed expression in the atsyp81 mutants and AtSYP81 overexpression lines, adding evidence of the regulatory role of AtSYP81 in ROS signaling. Accordingly, rescuing the decreased ROS level via applying ROS donors effectively restored the defects in root meristem activity and SCN identity in the atsyp81 mutants. APETALA2 (AP2) transcription factors PLETHORA1 and 2 (PLT1 and PLT2) were then established as the downstream effectors in this pathway, while potential crosstalk between ROS signaling and auxin signaling was also indicated. Taken together, our findings suggest that AtSYP81 regulates root meristem activity and maintains root SCN identity by controlling peroxisome- and peroxidase-mediated ROS homeostasis, thus both broadening and deepening our understanding of the biological roles of SNARE proteins and ROS signaling.

Residual networks without pooling layers improve the accuracy of genomic predictions.

KEY MESSAGE: Residual neural network genomic selection is the first GS algorithm to reach 35 layers, and its prediction accuracy surpasses previous algorithms. With the decrease in DNA sequencing costs and the development of deep learning, phenotype prediction accuracy by genomic selection (GS) continues to improve. Residual networks, a widely validated deep learning technique, are introduced to deep learning for GS. Since each locus has a different weighted impact on the phenotype, strided convolutions are more suitable for GS problems than pooling layers. Through the above technological innovations, we propose a GS deep learning algorithm, residual neural network for genomic selection (ResGS). ResGS is the first neural network to reach 35 layers in GS. In 15 cases from four public data, the prediction accuracy of ResGS is higher than that of ridge-regression best linear unbiased prediction, support vector regression, random forest, gradient boosting regressor, and deep neural network genomic prediction in most cases. ResGS performs well in dealing with gene-environment interaction. Phenotypes from other environments are imported into ResGS along with genetic data. The prediction results are much better than just providing genetic data as input, which demonstrates the effectiveness of GS multi-modal learning. Standard deviation is recommended as an auxiliary GS evaluation metric, which could improve the distribution of predicted results. Deep learning for GS, such as ResGS, is becoming more accurate in phenotype prediction.

Identification of superior and rare haplotypes to optimize branch number in soybean.

KEY MESSAGE: Using the integrated approach in the present study, we identified eleven significant SNPs, seven stable QTLs and 20 candidate genes associated with branch number in soybean. Branch number is a key yield-related quantitative trait that directly affects the number of pods and seeds per soybean plant. In this study, an integrated approach with a genome-wide association study (GWAS) and haplotype and candidate gene analyses was used to determine the detailed genetic basis of branch number across a diverse set of soybean accessions. The GWAS revealed a total of eleven SNPs significantly associated with branch number across three environments using the five GWAS models. Based on the consistency of the SNP detection in multiple GWAS models and environments, seven genomic regions within the physical distance of ± 202.4 kb were delineated as stable QTLs. Of these QTLs, six QTLs were novel, viz., qBN7, qBN13, qBN16, qBN18, qBN19 and qBN20, whereas the remaining one, viz., qBN12, has been previously reported. Moreover, 11 haplotype blocks, viz., Hap4, Hap7, Hap12, Hap13A, Hap13B, Hap16, Hap17, Hap18, Hap19A, Hap19B and Hap20, were identified on nine different chromosomes. Haplotype allele number across the identified haplotype blocks varies from two to five, and different branch number phenotype is regulated by these alleles ranging from the lowest to highest through intermediate branching. Furthermore, 20 genes were identified underlying the genomic region of ± 202.4 kb of the identified SNPs as putative candidates; and six of them showed significant differential expression patterns among the soybean cultivars possessing contrasting branch number, which might be the potential candidates regulating branch number in soybean. The findings of this study can assist the soybean breeding programs for developing cultivars with desirable branch numbers.

Disruption of CHORISMATE SYNTHASE1 leads to yellow-green variegation in soybean leaves.

Yellow-green variegation leaf phenotype adds more value to ornamental plants, but it is regarded as an undesirable trait in crop plants, affecting their yields. Until recently, the underlying mechanism regulating the yellow-green variegation phenotype has remained largely unexplored in soybean. In the present study, we indentified four Glycine max leaf yellow/green variegation mutants, Gmvar1, Gmvar2, Gmvar3, and Gmvar4, from artificial mutagenesis populations. Map-based cloning, together with the allelic identification test and CRISPR-based gene knockout, proved that mutated GmCS1 controls yellow-green variegation phenotype of the Gmvar mutants. GmCS1 encodes a chorismate synthase in soybean. The content of Phe, Tyr, and Trp were dramatically decreased in Gmcs1 mutants. Exogenous supply of three aromatic amino acid mixtures, or only Phe to Gmvar mutants, leads to recovery of the mutant phenotype. The various biological processes and signalling pathways related to metabolism and biosynthesis were altered in Gmvar mutants. Collectively, our findings provide new insights about the molecular regulatory network of yellow-green variegation leaf phenotype in soybean.

Genetic dissection reveals the complex architecture of amino acid composition in soybean seeds.

KEY MESSAGE: Five loci related to soybean protein and amino acid contents were colocated by performing linkage mapping and GWAS. The haplotype analysis showed that Glyma.08G109100 may be useful to improve the soybean seed composition. Soybean (Glycine max (L.) Merr.) seeds are good protein sources. Although genetic variation is abundant, natural variation in seed amino acids and their derived traits is lacking across soybean accessions. Here, we determined the contents of protein and 17 amino acids, obtained 36 derived traits based on the protein and total amino acid contents, and derived 34 traits based on seven amino acid family groups. Furthermore, we performed a linkage analysis of the contents of 17 amino acids and 73 amino acid-derived traits based on the recombinant inbred line (RIL)-derived Kefeng No. 1 × Nannong 1138-2. Six hundred thirty-nine quantitative trait loci (QTLs) were identified, explaining 6.07-39.00% of the phenotypic variation. Among these loci, five were detected in diverse soybean accessions using a genome-wide association study. A network analysis revealed that some loci that were significantly associated with multiple amino acids were tightly linked on chromosome 8 based on linkage disequilibrium values, which also further confirmed the results of the correlation analysis among amino acid traits. Through a combination of a genome-wide association study, linkage analysis, qRT-PCR, and genomic polymorphism comparison, Glyma.08G109100 on chromosome 8, which may affect amino acid contents, was selected. The haplotype analysis showed that Hap-T of Glyma.08G109100 may be useful to improve the contents of protein and 16 amino acids in soybean. This study provides new insights into the genetic basis of the amino acid composition in soybean seeds and may facilitate marker-based breeding of soybean with improved nutritional value.

Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding.

Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labour intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the nonlinear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to nonlinear modelling approaches in crop breeding, enabling the capture of nonlinear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change.

Genome-wide survey identified superior and rare haplotypes for plant height in the north-eastern soybean germplasm of China.

The proper and efficient utilization of natural genetic diversity can significantly impact crop improvements. Plant height is a quantitative trait governing the plant type as well as the yield and quality of soybean. Here, we used a combined approach including a genome-wide association study (GWAS) and haplotype and candidate gene analyses to explore the genetic basis of plant height in diverse natural soybean populations. For the GWAS analysis, we used the whole-genome resequencing data of 196 diverse soybean cultivars collected from different accumulated temperature zones of north-eastern China to detect the significant single-nucleotide polymorphisms (SNPs) associated with plant height across three environments (E1, E2, and E3). A total of 33 SNPs distributed on four chromosomes, viz., Chr.02, Chr.04, Chr.06, and Chr.19, were identified to be significantly associated with plant height across the three environments. Among them, 23 were consistently detected in two or more environments and the remaining 10 were identified in only one environment. Interestingly, all the significant SNPs detected on the respective chromosomes fell within the physical interval of linkage disequilibrium (LD) decay (± 38.9 kb). Hence, these genomic regions were considered to be four quantitative trait loci (QTLs), viz., qPH2, qPH4, qPH6, and qPH19, regulating plant height. Moreover, the genomic region flanking all significant SNPs on four chromosomes exhibited strong LD. These significant SNPs thus formed four haplotype blocks, viz., Hap-2, Hap-4, Hap-6, and Hap-19. The number of haplotype alleles underlying each block varied from four to six, and these alleles regulate the different phenotypes of plant height ranging from dwarf to extra-tall heights. Nine candidate genes were identified within the four haplotype blocks, and these genes were considered putative candidates regulating soybean plant height. Hence, these stable QTLs, superior haplotypes, and candidate genes (after proper validation) can be deployed for the development of soybean cultivars with desirable plant heights. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01363-7.

Novel Seed Size: A Novel Seed-Developing Gene in Glycine max.

Soybean-seed development is controlled in multiple ways, as in many known regulating genes. Here, we identify a novel gene, Novel Seed Size (NSS), involved in seed development, by analyzing a T-DNA mutant (S006). The S006 mutant is a random mutant of the GmFTL4pro:GUS transgenic line, with phenotypes with small and brown seed coats. An analysis of the metabolomics and transcriptome combined with RT-qPCR in the S006 seeds revealed that the brown coat may result from the increased expression of chalcone synthase 7/8 genes, while the down-regulated expression of NSS leads to small seed size. The seed phenotypes and a microscopic observation of the seed-coat integument cells in a CRISPR/Cas9-edited mutant nss1 confirmed that the NSS gene conferred small phenotypes of the S006 seeds. As mentioned in an annotation on the Phytozome website, NSS encodes a potential DNA helicase RuvA subunit, and no such genes were previously reported to be involved in seed development. Therefore, we identify a novel gene in a new pathway controlling seed development in soybeans.

GmUFO1 Regulates Floral Organ Number and Shape in Soybean.

The UNUSUAL FLORAL ORGANS (UFO) gene is an essential regulatory factor of class B genes and plays a vital role in the process of inflorescence primordial and flower primordial development. The role of UFO genes in soybean was investigated to better understand the development of floral organs through gene cloning, expression analysis, and gene knockout. There are two copies of UFO genes in soybean and in situ hybridization, which have demonstrated similar expression patterns of the GmUFO1 and GmUFO2 genes in the flower primordium. The phenotypic observation of GmUFO1 knockout mutant lines (Gmufo1) showed an obvious alteration in the floral organ number and shape and mosaic organ formation. By contrast, GmUFO2 knockout mutant lines (Gmufo2) showed no obvious difference in the floral organs. However, the GmUFO1 and GmUFO2 double knockout lines (Gmufo1ufo2) showed more mosaic organs than the Gmufo1 lines, in addition to the alteration in the organ number and shape. Gene expression analysis also showed differences in the expression of major ABC function genes in the knockout lines. Based on the phenotypic and expression analysis, our results suggest the major role of GmUFO1 in the regulation of flower organ formation in soybeans and that GmUFO2 does not have any direct effect but might have an interaction role with GmUFO1 in the regulation of flower development. In conclusion, the present study identified UFO genes in soybean and improved our understanding of floral development, which could be useful for flower designs in hybrid soybean breeding.

Soybean GmHY2a encodes a phytochromobilin synthase that regulates internode length and flowering time.

Plant height and flowering time are important agronomic traits that directly affect soybean [Glycine max (L.) Merr.] adaptability and yield. Here, the Glycine max long internode 1 (Gmlin1) mutant was selected from an ethyl methyl sulfonate (EMS)-mutated Williams 82 population due to its long internodes and early flowering. Using bulked segregant analysis (BSA), the Gmlin1 locus was mapped to Glyma.02G304700, a homologue of the Arabidopsis HY2 gene, which encodes a phytochromobilin (PΦB) synthase involved in phytochrome chromophore synthesis. Mutation of GmHY2a results in failure of the de-etiolation response under both red and far-red light. The Gmlin1 mutant exhibits a constitutive shade avoidance response under normal light, and the mutations influence the auxin and gibberellin pathways to promote internode elongation. The Gmlin1 mutant also exhibits decreased photoperiod sensitivity. In addition, the soybean photoperiod repressor gene E1 is down-regulated in the Gmlin1 mutant, resulting in accelerated flowering. The nuclear import of phytochrome A (GmphyA) and GmphyB following light treatment is decreased in Gmlin1 protoplasts, indicating that the weak light response of the Gmlin1 mutant is caused by a decrease in functional phytochrome. Together, these results indicate that GmHY2a plays an important role in soybean phytochrome biosynthesis and provide insights into the adaptability of the soybean plant.

A malectin-like receptor kinase regulates cell death and pattern-triggered immunity in soybean.

Plant cells can sense conserved molecular patterns through pattern recognition receptors (PRRs) and initiate pattern-triggered immunity (PTI). Details of the PTI signaling network are starting to be uncovered in Arabidopsis, but are still poorly understood in other species, including soybean (Glycine max). In this study, we perform a forward genetic screen for autoimmunity-related lesion mimic mutants (lmms) in soybean and identify two allelic mutants, which carry mutations in Glyma.13G054400, encoding a malectin-like receptor kinase (RK). The mutants exhibit enhanced resistance to both bacterial and oomycete pathogens, as well as elevated ROS production upon treatment with the bacterial pattern flg22. Overexpression of GmLMM1 gene in Nicotiana benthamiana severely suppresses flg22-triggered ROS production and oomycete pattern XEG1-induced cell death. We further show that GmLMM1 interacts with the flg22 receptor FLS2 and its co-receptor BAK1 to negatively regulate flg22-induced complex formation between them. Our study identifies an important component in PTI regulation and reveals that GmLMM1 acts as a molecular switch to control an appropriate immune activation, which may also be adapted to other PRR-mediated immune signaling in soybean.

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding.

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other "omics" approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These "omics" approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with "omics" tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

The miR166 mediated regulatory module controls plant height by regulating gibberellic acid biosynthesis and catabolism in soybean.

MicroRNAs (miRNAs) are endogenous small non-coding RNAs that play critical roles in regulating plant growth and development. Here, we used Short Tandem Target Mimic (STTM) technology to generate soybean (Glycine max (L.) Merr.) miRNA knockdown lines and identify miRNAs that regulate plant height, a key agronomic trait that affects yield. STTM166 successfully silenced miR166 in soybean and upregulated the expression of miR166 target genes, such as ATHB14-LIKE. The miR166 knockdown lines (GmSTTM166) displayed a reduced plant height phenotype. Moreover, GmSTTM166 plants contained lower levels of bioactive gibberellic acid (GA3) than wild-type plants, and application of exogenous GA partially rescued the dwarf phenotype of GmSTTM166. Knockdown of miR166 altered the expression of genes involved in GA biosynthesis and catabolism. Further analysis revealed that ATHB14-LIKE directly represses transcription of the GA biosynthesis genes GmGA1 and GmGA2, while activating transcription of the GA catabolic gene GIBBERLLIN 2 OXIDASE 2 (GmGA2ox2). Collectively, these results reveal a pivotal role for miR166 in the genetic control of plant height in soybean, thereby providing invaluable insights for molecular breeding to improve soybean yield.

GmCCD4 controls carotenoid content in soybeans.

To better understand the mechanisms regulating plant carotenoid metabolism in staple crop, we report the map-based cloning and functional characterization of the Glycine max carotenoid cleavage dioxygenase 4 (GmCCD4) gene, which encodes a carotenoid cleavage dioxygenase enzyme involved in metabolizing carotenoids into volatile ß-ionone. Loss of GmCCD4 protein function in four Glycine max increased carotenoid content (gmicc) mutants resulted in yellow flowers due to excessive accumulation of carotenoids in flower petals. The carotenoid contents also increase three times in gmicc1 seeds. A genome-wide association study indicated that the GmCCD4 locus was one major locus associated with carotenoid content in natural population. Further analysis indicated that the haplotype-1 of GmCCD4 gene was positively associated with higher carotenoid levels in soybean cultivars and accumulated more ß-carotene in engineered E. coli with ectopic expression of different GmCCD4 haplotypes. These observations uncovered that GmCCD4 was a negative regulator of carotenoid content in soybean, and its various haplotypes provide useful resources for future soybean breeding practice.