The additive function of YIGE2 and YIGE1 in regulating maize ear length.

Ear length (EL) is a key trait that greatly contributes to yield in maize. Although dozens of EL quantitative trait loci have been mapped, very few causal genes have been cloned, and the molecular mechanisms remain largely unknown. Our previous study showed that YIGE1 is involved in sugar and auxin pathways to regulate ear inflorescence meristem (IM) development and thus affects EL in maize. Here, we reveal that YIGE2, the paralog of YIGE1, regulates maize ear development and EL through auxin pathway. Knockout of YIGE2 causes a significant decrease of auxin level, IM length, floret number, EL, and grain yield. yige1 yige2 double mutants had even shorter IM and ears implying that these two genes redundantly regulate IM development and EL. The genes controlling auxin levels are differential expressed in yige1 yige2 double mutants, leading to lower auxin level. These results elucidated the critical role of YIGE2 and the redundancy between YIGE2 and YIGE1 in maize ear development, providing a new genetic resource for maize yield improvement.

A spatial transcriptome map of the developing maize ear.

A comprehensive understanding of inflorescence development is crucial for crop genetic improvement, as inflorescence meristems give rise to reproductive organs and determine grain yield. However, dissecting inflorescence development at the cellular level has been challenging owing to a lack of specific marker genes to distinguish among cell types, particularly in different types of meristems that are vital for organ formation. In this study, we used spatial enhanced resolution omics-sequencing (Stereo-seq) to construct a precise spatial transcriptome map of the developing maize ear primordium, identifying 12 cell types, including 4 newly defined cell types found mainly in the inflorescence meristem. By extracting the meristem components for detailed clustering, we identified three subtypes of meristem and validated two MADS-box genes that were specifically expressed at the apex of determinate meristems and involved in stem cell determinacy. Furthermore, by integrating single-cell RNA transcriptomes, we identified a series of spatially specific networks and hub genes that may provide new insights into the formation of different tissues. In summary, this study provides a valuable resource for research on cereal inflorescence development, offering new clues for yield improvement.

Genomic basis determining root system architecture in maize.

KEY MESSAGE: A total of 389 and 344 QTLs were identified by GWAS and QTL mapping explaining accumulatively 32.2-65.0% and 23.7-63.4% of phenotypic variation for 14 shoot-borne root traits using more than 1300 individuals across multiple field trails. Efficient nutrient and water acquisition from soils depends on the root system architecture (RSA). However, the genetic determinants underlying RSA in maize remain largely unexplored. In this study, we conducted a comprehensive genetic analysis for 14 shoot-borne root traits using 513 inbred lines and 800 individuals from four recombinant inbred line (RIL) populations at the mature stage across multiple field trails. Our analysis revealed substantial phenotypic variation for these 14 root traits, with a total of 389 and 344 QTLs identified through genome-wide association analysis (GWAS) and linkage analysis, respectively. These QTLs collectively explained 32.2-65.0% and 23.7-63.4% of the trait variation within each population. Several a priori candidate genes involved in auxin and cytokinin signaling pathways, such as IAA26, ARF2, LBD37 and CKX3, were found to co-localize with these loci. In addition, a total of 69 transcription factors (TFs) from 27 TF families (MYB, NAC, bZIP, bHLH and WRKY) were found for shoot-borne root traits. A total of 19 genes including PIN3, LBD15, IAA32, IAA38 and ARR12 and 19 GWAS signals were overlapped with selective sweeps. Further, significant additive effects were found for root traits, and pyramiding the favorable alleles could enhance maize root development. These findings could contribute to understand the genetic basis of root development and evolution, and provided an important genetic resource for the genetic improvement of root traits in maize.

An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs.

Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently. Studies have shown that GWAS risk loci and expression quantitative trait loci (eQTLs) often have a lot of overlaps, which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS. In this review, we review three types of gene-trait association detection methods of integrating GWAS summary statistics and eQTLs data, namely colocalization methods, transcriptome-wide association study-oriented approaches, and Mendelian randomization-related methods. At the theoretical level, we discussed the differences, relationships, advantages, and disadvantages of various algorithms in the three kinds of gene-trait association detection methods. To further discuss the performance of various methods, we summarize the significant gene sets that influence high-density lipoprotein, low-density lipoprotein, total cholesterol, and triglyceride reported in 16 studies. We discuss the performance of various algorithms using the datasets of the four lipid traits. The advantages and limitations of various algorithms are analyzed based on experimental results, and we suggest directions for follow-up studies on detecting gene-trait associations.

Engineering the future cereal crops with big biological data: toward an intelligence-driven breeding by design.

How to feed 10 billion human populations is one of the challenges that need to be addressed in the following decades, especially under an unpredicted climate change. Crop breeding, initiating from the phenotype-based selection by local farmers and developing into current biotechnology-based breeding, has played a critical role in securing the global food supply. However, regarding the changing environment and ever-increasing human population, can we breed outstanding crop varieties fast enough to achieve high productivity, good quality, and widespread adaptability? This review outlines the recent achievements in understanding cereal crop breeding, including the current knowledge about crop agronomic traits, newly developed techniques, crop big biological data research, and the possibility of integrating them for intelligence-driven breeding by design, which ushers in a new era of crop breeding practice and shapes the novel architecture of future crops. This review focuses on the major cereal crops, including rice, maize, and wheat, to explain how intelligence-driven breeding by design is becoming a reality.

The light and hypoxia induced gene ZmPORB1 determines tocopherol content in the maize kernel.

Tocopherol is an important lipid-soluble antioxidant beneficial for both human health and plant growth. Here, we fine mapped a major QTL-qVE1 affecting γ-tocopherol content in maize kernel, positionally cloned and confirmed the underlying gene ZmPORB1 (por1), as a protochlorophyllide oxidoreductase. A 13.7 kb insertion reduced the tocopherol and chlorophyll content, and the photosynthetic activity by repressing ZmPORB1 expression in embryos of NIL-K22, but did not affect the levels of the tocopherol precursors HGA (homogentisic acid) and PMP (phytyl monophosphate). Furthermore, ZmPORB1 is inducible by low oxygen and light, thereby involved in the hypoxia response in developing embryos. Concurrent with natural hypoxia in embryos, the redox state has been changed with NO increasing and H2O2 decreasing, which lowered γ-tocopherol content via scavenging reactive nitrogen species. In conclusion, we proposed that the lower light-harvesting chlorophyll content weakened embryo photosynthesis, leading to fewer oxygen supplies and consequently diverse hypoxic responses including an elevated γ-tocopherol consumption. Our findings shed light on the mechanism for fine-tuning endogenous oxygen concentration in the maize embryo through a novel feedback pathway involving the light and low oxygen regulation of ZmPORB1 expression and chlorophyll content.

Multi-omics-driven advances in the understanding of triacylglycerol biosynthesis in oil seeds.

Vegetable oils are rich sources of polyunsaturated fatty acids and energy as well as valuable sources of human food, animal feed, and bioenergy. Triacylglycerols, which are comprised of three fatty acids attached to a glycerol backbone, are the main component of vegetable oils. Here, we review the development and application of multiple-level omics in major oilseeds and emphasize the progress in the analysis of the biological roles of key genes underlying seed oil content and quality in major oilseeds. Finally, we discuss future research directions in functional genomics research based on current omics and oil metabolic engineering strategies that aim to enhance seed oil content and quality, and specific fatty acids components according to either human health needs or industrial requirements.

Two teosintes made modern maize.

The origins of maize were the topic of vigorous debate for nearly a century, but neither the current genetic model nor earlier archaeological models account for the totality of available data, and recent work has highlighted the potential contribution of a wild relative, Zea mays ssp. mexicana. Our population genetic analysis reveals that the origin of modern maize can be traced to an admixture between ancient maize and Zea mays ssp. mexicana in the highlands of Mexico some 4000 years after domestication began. We show that variation in admixture is a key component of maize diversity, both at individual loci and for additive genetic variation underlying agronomic traits. Our results clarify the origin of modern maize and raise new questions about the anthropogenic mechanisms underlying dispersal throughout the Americas.

De novo domestication: a new way for crop design and breeding.

The impending global climate change presents significant challenges to agricultural production. It is imperative to find approaches to ensure sustained growth in food production while reducing agricultural input, in order to meet the needs of worldwide people for nutritious food supply. One of the effective strategies to address this challenge is still the development of new crop varieties with high yield, stable yield, environmental friendliness and rich nutrition. The creation of new crop cultivars depends largely on the expansion of genetic resources and the innovation of breeding techniques. De novo domestication is an innovative breeding strategy for developing new crop varieties. It involves utilizing undomesticated or semi-domesticated plants with desirable traits as founder species for breeding. The process involves rapid domestication of wild plants through the redesign of agronomic traits and the introduction of domestication genes to meet diverse human needs. In this review, we overview the history of crop domestication and genetic improvement, clarify the necessity of enriching crop diversity, and emphasize the significance of wild plants' genetic diversity in expanding the scope for crop redesign. Breeding strategy innovation is the key to accelerate crop breeding. We also discuss the feasibility and prospects of rapid developing new crops through de novo domestication.

ZmEREB57 regulates OPDA synthesis and enhances salt stress tolerance through two distinct signalling pathways in Zea mays.

In plant, APETALA2/ethylene-responsive factor (AP2/ERF)-domain transcription factors are important in regulating abiotic stress tolerance. In this study, ZmEREB57 encoding a AP2/ERF transcription factor was identified and its function was investigated in maize. ZmEREB57 is a nuclear protein with transactivation activity induced by several abiotic stress types. Furthermore, two CRISPR/Cas9 knockout lines of ZmEREB57 showed enhanced sensitivity to saline conditions, whereas the overexpression of ZmEREB57 increased salt tolerance in maize and Arabidopsis. DNA affinity purification sequencing (DAP-Seq) analysis revealed that ZmEREB57 notably regulates target genes by binding to promoters containing an O-box-like motif (CCGGCC). ZmEREB57 directly binds to the promoter of ZmAOC2 involved in the synthesis of 12-oxo-phytodienoic acid (OPDA) and jasmonic acid (JA). Transcriptome analysis revealed that several genes involved in regulating stress and redox homeostasis showed differential expression patterns in OPDA- and JA-treated maize seedlings exposed to salt stress compared to those treated with salt stress alone. Analysis of mutants deficient in the biosynthesis of OPDA and JA revealed that OPDA functions as a signalling molecule in the salt response. Our results indicate that ZmEREB57 involves in salt tolerance by regulating OPDA and JA signalling and confirm early observations that OPDA signalling functions independently of JA signalling.

Deep learning in regulatory genomics: from identification to design.

Genomics and deep learning are a natural match since both are data-driven fields. Regulatory genomics refers to functional noncoding DNA regulating gene expression. In recent years, deep learning applications on regulatory genomics have achieved remarkable advances so-much-so that it has revolutionized the rules of the game of the computational methods in this field. Here, we review two emerging trends: (i) the modeling of very long input sequence (up to 200 kb), which requires self-matched modularization of model architecture; (ii) on the balance of model predictability and model interpretability because the latter is more able to meet biological demands. Finally, we discuss how to employ these two routes to design synthetic regulatory DNA, as a promising strategy for optimizing crop agronomic properties.

Complex genetic architecture underlying the plasticity of maize agronomic traits.

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude-related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental variation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by-environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture.

Going broad and deep: sequencing-driven insights into plant physiology, evolution, and crop domestication.

Deep sequencing is a term that has become embedded in the plant genomic literature in recent years and with good reason. A torrent of (largely) high-quality genomic and transcriptomic data has been collected and most of this has been publicly released. Indeed, almost 1000 plant genomes have been reported (www.plabipd.de) and the 2000 Plant Transcriptomes Project has long been completed. The EarthBioGenome project will dwarf even these milestones. That said, massive progress in understanding plant physiology, evolution, and crop domestication has been made by sequencing broadly (across a species) as well as deeply (within a single individual). We will outline the current state of the art in genome and transcriptome sequencing before we briefly review the most visible of these broad approaches, namely genome-wide association and transcriptome-wide association studies, as well as the compilation of pangenomes. This will include both (i) the most commonly used methods reliant on single nucleotide polymorphisms and short InDels and (ii) more recent examples which consider structural variants. We will subsequently present case studies exemplifying how their application has brought insight into either plant physiology or evolution and crop domestication. Finally, we will provide conclusions and an outlook as to the perspective for the extension of such approaches to different species, tissues, and biological processes.

A multi-omics integrative network map of maize.

Networks are powerful tools to uncover functional roles of genes in phenotypic variation at a system-wide scale. Here, we constructed a maize network map that contains the genomic, transcriptomic, translatomic and proteomic networks across maize development. This map comprises over 2.8 million edges in more than 1,400 functional subnetworks, demonstrating an extensive network divergence of duplicated genes. We applied this map to identify factors regulating flowering time and identified 2,651 genes enriched in eight subnetworks. We validated the functions of 20 genes, including 18 with previously unknown connections to flowering time in maize. Furthermore, we uncovered a flowering pathway involving histone modification. The multi-omics integrative network map illustrates the principles of how molecular networks connect different types of genes and potential pathways to map a genome-wide functional landscape in maize, which should be applicable in a wide range of species.

The role of transposon inverted repeats in balancing drought tolerance and yield-related traits in maize.

The genomic basis underlying the selection for environmental adaptation and yield-related traits in maize remains poorly understood. Here we carried out genome-wide profiling of the small RNA (sRNA) transcriptome (sRNAome) and transcriptome landscapes of a global maize diversity panel under dry and wet conditions and uncover dozens of environment-specific regulatory hotspots. Transgenic and molecular studies of Drought-Related Environment-specific Super eQTL Hotspot on chromosome 8 (DRESH8) and ZmMYBR38, a target of DRESH8-derived small interfering RNAs, revealed a transposable element-mediated inverted repeats (TE-IR)-derived sRNA- and gene-regulatory network that balances plant drought tolerance with yield-related traits. A genome-wide scan revealed that TE-IRs associate with drought response and yield-related traits that were positively selected and expanded during maize domestication. These results indicate that TE-IR-mediated posttranscriptional regulation is a key molecular mechanism underlying the tradeoff between crop environmental adaptation and yield-related traits, providing potential genomic targets for the breeding of crops with greater stress tolerance but uncompromised yield.

Genetic basis of resistance to southern corn leaf blight in the maize multi-parent population and diversity panel.

Southern corn leaf blight (SLB), caused by the necrotrophic pathogen Cochliobolus heterostrophus, is one of the maize foliar diseases and poses a great threat to corn production around the world. Identification of genetic variations underlying resistance to SLB is of paramount importance to maize yield and quality. Here, we used a random-open-parent association mapping population containing eight recombinant inbred line populations and one association mapping panel consisting of 513 diversity maize inbred lines with high-density genetic markers to dissect the genetic basis of SLB resistance. Overall, 109 quantitative trait loci (QTLs) with predominantly small or moderate additive effects, and little epistatic effects were identified. We found 35 (32.1%) novel loci in comparison with the reported QTLs. We revealed that resistant alleles were significantly enriched in tropical accessions and the frequency of about half of resistant alleles decreased during the adaptation process owing to the selection of agronomic traits. A large number of annotated genes located in the SLB-resistant QTLs were shown to be involved in plant defence pathways. Integrating genome-wide association study, transcriptomic profiling, resequencing and gene editing, we identified ZmFUT1 and MYBR92 as the putative genes responsible for the major QTLs for resistance to C. heterostrophus. Our results present a comprehensive insight into the genetic basis of SLB resistance and provide resistant loci or genes as direct targets for crop genetic improvement.

A genome-wide association study identifies a transporter for zinc uploading to maize kernels.

The Zn content in cereal seeds is an important trait for crop production as well as for human health. However, little is known about how Zn is loaded to plant seeds. Here, through a genome-wide association study (GWAS), we identify the Zn-NA (nicotianamine) transporter gene ZmYSL2 that is responsible for loading Zn to maize kernels. High promoter sequence variation in ZmYSL2 most likely drives the natural variation in Zn concentrations in maize kernels. ZmYSL2 is specifically localized on the plasma membrane facing the maternal tissue of the basal endosperm transfer cell layer (BETL) and functions in loading Zn-NA into the BETL. Overexpression of ZmYSL2 increases the Zn concentration in the kernels by 31.6%, which achieves the goal of Zn biofortification of maize. These findings resolve the mystery underlying the loading of Zn into plant seeds, providing an efficient strategy for breeding or engineering maize varieties with enriched Zn nutrition.