Application and prospect of BSA method based on bibliometrics in crop breeding

In order to understand the progress and frontier in the application of BSA(bulked segregant analysis) method in crop breeding and to reflect objectively the contribution of different countries, institutions and researchers in this field at home and abroad, this study analyzed 2111 items in the WOS (Web of Science) database from 2000 to 2023 and 446 items in the CNKI (China National through Knowledge Infrastructure) database from 2003 to 2023, regarding the researches of the application of BSA in crop breeding, basing on bibliometric analysis methods using CiteSpace software including keyword co-occurrence analysis, highlight word analysis, keyword clustering analysis, clustering timeline analysis and author co-citation. The results showed that there was an consistent increasing trend in the publication number of the application of BSA in crop breeding both in the domestic and foreign journals year by year. Ranking of the top countries according to the number of publications was China, the United States and India. The Huazhong Agricultural University displayed the highest number of publications in the CNKI database, while the Chinese Academy of Agricultural Sciences was found to have the highest number of publications in the WOS database. The published articles related to the application of BSA in crop breeding abroad mainly focused on the disciplines such as plant science, agronomy, horticulture and genetics, while those in China mainly concentrated on such disciplines as plant science, plant protection, horticulture and biology. The top three authors in terms of influence in the field of appling BSA in crop breeding were Michelmore RW, Kosambi DD and Li H, while Michelmore RW, Lander ES and Li H had closer cooperations with other authors. The top three crops relating to the studies of BSA were rice(Oryza sativa), soybean(Glycine max), corn(Zea mays L.) with the hot spot traits of disease resistance and plant height domestically. The top three crops involving the studies of BSA were rice, Arabidopsis thaliana and wheat(Triticum aestivum L.) with hot spot traits of disease resistance abroad. Up to now, BSA was mainly used to localize and functionally verify the candidate genes linking target traits and the mutated genes in crops in the domestical documents, while the foreign published studies based on BSA were mainly focused on the fine mapping and functional verification of target trait genes aiming at the revelation of genetic mechanisms in crops. Research frontier analysis indicated that rice, peanuts(Arachis hypogaea L.), upland cotton(Gossypium hirsutum L.) would be the main objects of studies concerning application of BSA in crop breeding with the hot topics of crop mutants and crop metabolites in the future.

Haploid rhapsody: the molecular and cellular orchestra of in vivo haploid induction in plants

In planta haploid induction (HI), which reduces the chromosome number in the progeny after fertilization, has garnered increasing attention for its significant potential in crop breeding and genetic research. Despite the identification of several natural and synthetic HI systems in different plant species, the molecular and cellular mechanisms underlying these HI systems remain largely unknown. This review synthesizes the current understanding of HI systems in plants (with a focus on genes and molecular mechanisms involved), including the molecular and cellular interactions which orchestrate the HI process. As most HI systems can function across taxonomic boundaries, we particularly discuss the evidence for conserved mechanisms underlying the process. These include mechanisms involved in preserving chromosomal integrity, centromere function, gamete communication and/or fusion, and maintenance of karyogamy. While significant discoveries and advances on haploid inducer systems have arisen over the past decades, we underscore gaps in understanding and deliberate on directions for further research for a more comprehensive understanding of in vivo HI processes in plants.

Recent progress in understanding the cellular and genetic basis of plant responses to low oxygen holds promise for developing flood-resilient crops

With recent progress in active research on flooding and hypoxia/anoxia tolerance in native and agricultural crop plants, vast knowledge has been gained on both individual tolerance mechanisms and the general mechanisms of flooding tolerance in plants. Research on carbohydrate consumption, ethanolic and lactic acid fermentation, and their regulation under stress conditions has been accompanied by investigations on aerenchyma development and the emergence of the radial oxygen loss barrier in some plant species under flooded conditions. The discovery of the oxygen-sensing mechanism in plants and unravelling the intricacies of this mechanism have boosted this very international research effort. Recent studies have highlighted the importance of oxygen availability as a signalling component during plant development. The latest developments in determining actual oxygen concentrations using minute probes and molecular sensors in tissues and even within cells have provided new insights into the intracellular effects of flooding. The information amassed during recent years has been used in the breeding of new flood-tolerant crop cultivars. With the wealth of metabolic, anatomical, and genetic information, novel holistic approaches can be used to enhance crop species and their productivity under increasing stress conditions due to climate change and the subsequent changes in the environment.

Key candidate genes for male sterility in peppers unveiled via transcriptomic and proteomic analyses

This study aimed to enhance the use of male sterility in pepper to select superior hybrid generations. Transcriptomic and proteomic analyses of fertile line 1933A and nucleic male sterility line 1933B of Capsicum annuum L. were performed to identify male sterility-related proteins and genes. The phylogenetic tree, physical and chemical characteristics, gene structure characteristics, collinearity and expression characteristics of candidate genes were analyzed. The study identified 2,357 differentially expressed genes, of which 1,145 and 229 were enriched in the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases, respectively. A total of 7,628 quantifiable proteins were identified and 29 important proteins and genes were identified. It is worth noting that the existence of CaPRX genes has been found in both proteomics and transcriptomics, and 3 CaPRX genes have been identified through association analysis. A total of 66 CaPRX genes have been identified at the genome level, which are divided into 13 subfamilies, all containing typical CaPRX gene conformal domains. It is unevenly distributed across 12 chromosomes (including the virtual chromosome Chr00). Salt stress and co-expression analysis show that male sterility genes are expressed to varying degrees, and multiple transcription factors are co-expressed with CaPRXs, suggesting that they are involved in the induction of pepper salt stress. The study findings provide a theoretical foundation for genetic breeding by identifying genes, metabolic pathways, and molecular mechanisms involved in male sterility in pepper.

Small RNAs: Promising Molecules to Tackle Climate Change Impacts in Coffee Production

Over the centuries, human society has evolved based on the ability to select and use more adapted species for food supply, which means making plant species tastier and more productive in particular environmental conditions. However, nowadays, this scenario is highly threatened by climate change, especially by the changes in temperature and greenhouse gasses that directly affect photosynthesis, which highlights the need for strategic studies aiming at crop breeding and guaranteeing food security. This is especially worrying for crops with complex phenology, genomes with low variability, and the ones that support a large production chain, such as Coffea sp. L. In this context, recent advances shed some light on the genome function and transcriptional control, revealing small RNAs (sRNAs) that are responsible for environmental cues and could provide variability through gene expression regulation. Basically, sRNAs are responsive to environmental changes and act on the transcriptional and post-transcriptional gene silencing pathways that regulate gene expression and, consequently, biological processes. Here, we first discuss the predicted impact of climate changes on coffee plants and coffee chain production and then the role of sRNAs in response to environmental changes, especially temperature, in different species, together with their potential as tools for genetic improvement. Very few studies in coffee explored the relationship between sRNAs and environmental cues; thus, this review contributes to understanding coffee development in the face of climate change and towards new strategies of crop breeding.

Deep learning-empowered crop breeding: intelligent, efficient and promising

Crop breeding is one of the main approaches to increase crop yield and improve crop quality. However, the breeding process faces challenges such as complex data, difficulties in data acquisition, and low prediction accuracy, resulting in low breeding efficiency and long cycle. Deep learning-based crop breeding is a strategy that applies deep learning techniques to improve and optimize the breeding process, leading to accelerated crop improvement, enhanced breeding efficiency, and the development of higher-yielding, more adaptive, and disease-resistant varieties for agricultural production. This perspective briefly discusses the mechanisms, key applications, and impact of deep learning in crop breeding. We also highlight the current challenges associated with this topic and provide insights into its future application prospects.

Redesigning crop varieties to win the race between climate change and food security

Climate change poses daunting challenges to agricultural production and food security. Rising temperatures, shifting weather patterns, and more frequent extreme events have already demonstrated their effects on local, regional, and global agricultural systems. Crop varieties that withstand climate-related stresses and are suitable for cultivation in innovative cropping systems will be crucial to maximize risk avoidance, productivity, and profitability under climate-changed environments. We surveyed 588 expert stakeholders to predict current and novel traits that may be essential for future pearl millet, sorghum, maize, groundnut, cowpea, and common bean varieties, particularly in sub-Saharan Africa. We then review the current progress and prospects for breeding three prioritized future-essential traits for each of these crops. Experts predict that most current breeding priorities will remain important, but that rates of genetic gain must increase to keep pace with climate challenges and consumer demands. Importantly, the predicted future-essential traits include innovative breeding targets that must also be prioritized; for example, (1) optimized rhizosphere microbiome, with benefits for P, N, and water use efficiency, (2) optimized performance across or in specific cropping systems, (3) lower nighttime respiration, (4) improved stover quality, and (5) increased early vigor. We further discuss cutting-edge tools and approaches to discover, validate, and incorporate novel genetic diversity from exotic germplasm into breeding populations with unprecedented precision, accuracy, and speed. We conclude that the greatest challenge to developing crop varieties to win the race between climate change and food security might be our innovativeness in defining and boldness to breed for the traits of tomorrow.

A novel method for maize leaf disease classification using the RGB-D post-segmentation image data

Maize (Zea mays L.) is one of the most important crops, influencing food production and even the whole industry. In recent years, global crop production has been facing great challenges from diseases. However, most of the traditional methods make it difficult to efficiently identify disease-related phenotypes in germplasm resources, especially in actual field environments. To overcome this limitation, our study aims to evaluate the potential of the multi-sensor synchronized RGB-D camera with depth information for maize leaf disease classification. We distinguished maize leaves from the background based on the RGB-D depth information to eliminate interference from complex field environments. Four deep learning models (i.e., Resnet50, MobilenetV2, Vgg16, and Efficientnet-B3) were used to classify three main types of maize diseases, i.e., the curvularia leaf spot [Curvularia lunata (Wakker) Boedijn], the small spot [Bipolaris maydis (Nishik.) Shoemaker], and the mixed spot diseases. We finally compared the pre-segmentation and post-segmentation results to test the robustness of the above models. Our main findings are: 1) The maize disease classification models based on the pre-segmentation image data performed slightly better than the ones based on the post-segmentation image data. 2) The pre-segmentation models overestimated the accuracy of disease classification due to the complexity of the background, but post-segmentation models focusing on leaf disease features provided more practical results with shorter prediction times. 3) Among the post-segmentation models, the Resnet50 and MobilenetV2 models showed similar accuracy and were better than the Vgg16 and Efficientnet-B3 models, and the MobilenetV2 model performed better than the other three models in terms of the size and the single image prediction time. Overall, this study provides a novel method for maize leaf disease classification using the post-segmentation image data from a multi-sensor synchronized RGB-D camera and offers the possibility of developing relevant portable devices.

SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding

Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a deep learning-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its minimal increase in parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP represents improved performance to its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including cotton, maize, rice and tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGP's potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two features: 'Trait Lookup', offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and 'Trait Prediction', allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction, SoyDNGP opens up new possibilities in the quest for optimized soybean breeding.

Innovative approaches to integrating gender into conventional maize breeding: lessons from the Seed Production Technology for Africa project

The integration of gender concerns in crop breeding programs aims to improve the suitability and appeal of new varieties to both women and men, in response to concerns about unequal adoption of improved seed. However, few conventional breeding programs have sought to center social inclusion concerns. This community case study documents efforts to integrate gender into the maize-focused Seed Production Technology for Africa (SPTA) project using innovation history analysis drawing on project documents and the authors' experiences. These efforts included deliberate exploration of potential gendered impacts of project technologies and innovations in the project's approach to variety evaluation, culminating in the use of decentralized on-farm trials using the tricot approach. Through this case study, we illustrate the power of active and respectful collaborations between breeders and social scientists, spurred by donor mandates to address gender and social inclusion. Gender integration in this case was further facilitated by open-minded project leaders and allocation of funding for gender research. SPTA proved to be fertile ground for experimentation and interdisciplinary collaboration around gender and maize breeding, and has provided proof of concept for larger breeding projects seeking to integrate gender considerations.

Water use efficiency across scales: from genes to landscapes

Water scarcity is already set to be one of the main issues of the 21st century, because of competing needs between civil, industrial, and agricultural use. Agriculture is currently the largest user of water, but its share is bound to decrease as societies develop and clearly it needs to become more water efficient. Improving water use efficiency (WUE) at the plant level is important, but translating this at the farm/landscape level presents considerable challenges. As we move up from the scale of cells, organs, and plants to more integrated scales such as plots, fields, farm systems, and landscapes, other factors such as trade-offs need to be considered to try to improve WUE. These include choices of crop variety/species, farm management practices, landscape design, infrastructure development, and ecosystem functions, where human decisions matter. This review is a cross-disciplinary attempt to analyse approaches to addressing WUE at these different scales, including definitions of the metrics of analysis and consideration of trade-offs. The equations we present in this perspectives paper use similar metrics across scales to make them easier to connect and are developed to highlight which levers, at different scales, can improve WUE. We also refer to models operating at these different scales to assess WUE. While our entry point is plants and crops, we scale up the analysis of WUE to farm systems and landscapes.

Assessment of Soybean Lodging Using UAV Imagery and Machine Learning

Plant lodging is one of the most essential phenotypes for soybean breeding programs. Soybean lodging is conventionally evaluated visually by breeders, which is time-consuming and subject to human errors. This study aimed to investigate the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning in assessing the lodging conditions of soybean breeding lines. A UAV imaging system equipped with an RGB (red-green-blue) camera was used to collect the imagery data of 1266 four-row plots in a soybean breeding field at the reproductive stage. Soybean lodging scores were visually assessed by experienced breeders, and the scores were grouped into four classes, i.e., non-lodging, moderate lodging, high lodging, and severe lodging. UAV images were stitched to build orthomosaics, and soybean plots were segmented using a grid method. Twelve image features were extracted from the collected images to assess the lodging scores of each breeding line. Four models, i.e., extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN) and artificial neural network (ANN), were evaluated to classify soybean lodging classes. Five data preprocessing methods were used to treat the imbalanced dataset to improve classification accuracy. Results indicate that the preprocessing method SMOTE-ENN consistently performs well for all four (XGBoost, RF, KNN, and ANN) classifiers, achieving the highest overall accuracy (OA), lowest misclassification, higher F1-score, and higher Kappa coefficient. This suggests that Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) may be a good preprocessing method for using unbalanced datasets and the classification task. Furthermore, an overall accuracy of 96% was obtained using the SMOTE-ENN dataset and ANN classifier. The study indicated that an imagery-based classification model could be implemented in a breeding program to differentiate soybean lodging phenotype and classify lodging scores effectively.

G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction

Genotype-to-phenotype (G2P) prediction has become a mainstream paradigm to facilitate genomic selection (GS)-assisted breeding in the seed industry. Many methods have been introduced for building GS models, but their prediction precision may vary depending on species and specific traits. Therefore, evaluation of multiple models and selection of the appropriate one is crucial to effective GS analysis. Here, we present the G2P container developed for the Singularity platform, which not only contains a library of 16 state-of-the-art GS models and 13 evaluation metrics. G2P works as an integrative environment offering comprehensive, unbiased evaluation analyses of the 16 GS models, which may be run in parallel on high-performance computing clusters. Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. This functionality should further improve the precision of G2P prediction. Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. This functionality is quite useful in practice, as it reduces the cost of phenotyping when constructing training population. The G2P container and source codes are freely accessible at https://g2p-env.github.io/.

CRISPR-Based Genome Editing Tools: An Accelerator in Crop Breeding for a Changing Future

Genome editing is an important strategy to maintain global food security and achieve sustainable agricultural development. Among all genome editing tools, CRISPR-Cas is currently the most prevalent and offers the most promise. In this review, we summarize the development of CRISPR-Cas systems, outline their classification and distinctive features, delineate their natural mechanisms in plant genome editing and exemplify the applications in plant research. Both classical and recently discovered CRISPR-Cas systems are included, detailing the class, type, structures and functions of each. We conclude by highlighting the challenges that come with CRISPR-Cas and offer suggestions on how to tackle them. We believe the gene editing toolbox will be greatly enriched, providing new avenues for a more efficient and precise breeding of climate-resilient crops.

Uncovering genetic control of primary root length variation in Brassica napus using QTL-seq. A commentary on: 'Rapid identification of a major locus qPRL-C06 affecting primary root length in Brassica napus by QTL-seq'

This article comments on:

Wei Wang, Haijiang Liu, Yiwen Xie, Graham John King, Philip John White, Jun Zou, Fangsen Xu, and Lei Shi. Rapid identification of a major locus qPRL-C06 affecting primary root length in Brassica napus by QTL-seq, Annals of Botany, Volume 131, Issue 4, 14 March 2023, Pages 569–583, https://doi.org/10.1093/aob/mcac123

Genomics-driven breeding for local adaptation of durum wheat is enhanced by farmers' traditional knowledge

In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat (Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers' appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker-trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers' traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation.

Translating virome analyses to support biosecurity, on-farm management, and crop breeding

Virome analysis via high-throughput sequencing (HTS) allows rapid and massive virus identification and diagnoses, expanding our focus from individual samples to the ecological distribution of viruses in agroecological landscapes. Decreases in sequencing costs combined with technological advances, such as automation and robotics, allow for efficient processing and analysis of numerous samples in plant disease clinics, tissue culture laboratories, and breeding programs. There are many opportunities for translating virome analysis to support plant health. For example, virome analysis can be employed in the development of biosecurity strategies and policies, including the implementation of virome risk assessments to support regulation and reduce the movement of infected plant material. A challenge is to identify which new viruses discovered through HTS require regulation and which can be allowed to move in germplasm and trade. On-farm management strategies can incorporate information from high-throughput surveillance, monitoring for new and known viruses across scales, to rapidly identify important agricultural viruses and understand their abundance and spread. Virome indexing programs can be used to generate clean germplasm and seed, crucial for the maintenance of seed system production and health, particularly in vegetatively propagated crops such as roots, tubers, and bananas. Virome analysis in breeding programs can provide insight into virus expression levels by generating relative abundance data, aiding in breeding cultivars resistant, or at least tolerant, to viruses. The integration of network analysis and machine learning techniques can facilitate designing and implementing management strategies, using novel forms of information to provide a scalable, replicable, and practical approach to developing management strategies for viromes. In the long run, these management strategies will be designed by generating sequence databases and building on the foundation of pre-existing knowledge about virus taxonomy, distribution, and host range. In conclusion, virome analysis will support the early adoption and implementation of integrated control strategies, impacting global markets, reducing the risk of introducing novel viruses, and limiting virus spread. The effective translation of virome analysis depends on capacity building to make benefits available globally.

Exploring Thrips Preference and Resistance in Flowers, Leaves, and Whole Plants of Ten Capsicum Accessions

Capsicum species grown for pepper production suffer severely from thrips damage, urging the identification of natural resistance. Resistance levels are commonly assessed on leaves. However, Capsicum plants are flower-bearing during most of the production season, and thrips also feed on pollen and flower tissues. In order to obtain a comprehensive estimate of elements contributing to thrips resistance, flower tissues should be considered as well. Therefore, we assessed resistance to Frankliniella occidentalis in flowers, leaves, and whole plants of ten Capsicum accessions. Using choice assays, we found that thrips prefer flowers of certain accessions over others. The preference of adult thrips for flowers was positively correlated to trehalose and fructose concentration in anthers as well as to pollen quantity. Resistance measured on leaf discs and thrips population development on whole plants was significantly and positively correlated. Leaf-based resistance thus translates to reduced thrips population development. Results of the flower assays were not significantly correlated with resistance in leaves or on whole plants. This suggests that both leaves and flowers represent a different part of the resistance spectrum and should both be considered for understanding whole plant resistance and the identification of resistant Capsicum varieties.

Basic Helix-Loop-Helix Transcription Factors: Regulators for Plant Growth Development and Abiotic Stress Responses

Plant basic helix-loop-helix (bHLH) transcription factors are involved in many physiological processes, and they play important roles in the abiotic stress responses. The literature related to genome sequences has increased, with genome-wide studies on the bHLH transcription factors in plants. Researchers have detailed the functionally characterized bHLH transcription factors from different aspects in the model plant Arabidopsis thaliana, such as iron homeostasis and abiotic stresses; however, other important economic crops, such as rice, have not been summarized and highlighted. The bHLH members in the same subfamily have similar functions; therefore, unraveling their regulatory mechanisms will help us to identify and understand the roles of some of the unknown bHLH transcription factors in the same subfamily. In this review, we summarize the available knowledge on functionally characterized bHLH transcription factors according to four categories: plant growth and development; metabolism synthesis; plant signaling, and abiotic stress responses. We also highlight the roles of the bHLH transcription factors in some economic crops, especially in rice, and discuss future research directions for possible genetic applications in crop breeding.

The Roads to Haploid Embryogenesis

Although zygotic embryogenesis is usually studied in the field of seed biology, great attention has been paid to the methods used to generate haploid embryos due to their applications in crop breeding. These mainly include two methods for haploid embryogenesis: in vitro microspore embryogenesis and in vivo haploid embryogenesis. Although microspore culture systems and maize haploid induction systems were discovered in the 1960s, little is known about the molecular mechanisms underlying haploid formation. In recent years, major breakthroughs have been made in in vivo haploid induction systems, and several key factors, such as the matrilineal (MTL), baby boom (BBM), domain of unknown function 679 membrane protein (DMP), and egg cell-specific (ECS) that trigger in vivo haploid embryo production in both the crops and Arabidopsis models have been identified. The discovery of these haploid inducers indicates that haploid embryogenesis is highly related to gamete development, fertilization, and genome stability in ealry embryos. Here, based on recent efforts to identify key players in haploid embryogenesis and to understand its molecular mechanisms, we summarize the different paths to haploid embryogenesis, and we discuss the mechanisms of haploid generation and its potential applications in crop breeding. Although these haploid-inducing factors could assist egg cells in bypassing fertilization to initiate embryogenesis or trigger genome elimination in zygotes after fertilization to form haploid embryos, the fertilization of central cells to form endosperms is a prerequisite step for haploid formation. Deciphering the molecular and cellular mechanisms for haploid embryogenesis, increasing the haploid induction efficiency, and establishing haploid induction systems in other crops are critical for promoting the application of haploid technology in crop breeding, and these should be addressed in further studies.

Epitranscriptomic mRNA modifications governing plant stress responses: underlying mechanism and potential application

Plants inevitably encounter environmental adversities, including abiotic and biotic stresses, which significantly impede plant growth and reduce crop yield. Thus, fine-tuning the fate and function of stress-responsive RNAs is indispensable for plant survival under such adverse conditions. Recently, post-transcriptional RNA modifications have been studied as a potent route to regulate plant gene expression under stress. Among over 160 mRNA modifications identified to date, N⁶ -methyladenosine (m⁶ A) in mRNAs is notable because of its multifaceted roles in plant development and stress response. Recent transcriptome-wide mapping has revealed the distribution and patterns of m⁶ A in diverse stress-responsive mRNAs in plants, building a foundation for elucidating the molecular link between m⁶ A and stress response. Moreover, the identification and characterization of m⁶ A writers, readers and erasers in Arabidopsis and other model crops have offered insights into the biological roles of m⁶ A in plant abiotic stress responses. Here, we review the recent progress of research on mRNA modifications, particularly m⁶ A, and their dynamics, distribution, regulation and biological functions in plant stress responses. Further, we posit potential strategies for breeding stress-tolerant crops by engineering mRNA modifications and propose the future direction of research on RNA modifications to gain a much deeper understanding of plant stress biology.

Pan-genome and multi-parental framework for high-resolution trait dissection in melon (Cucumis melo)

Linking genotype with phenotype is a fundamental goal in biology and requires robust data for both. Recent advances in plant-genome sequencing have expedited comparisons among multiple-related individuals. The abundance of structural genomic within-species variation that has been discovered indicates that a single reference genome cannot represent the complete sequence diversity of a species, leading to the expansion of the pan-genome concept. For high-resolution forward genetics, this unprecedented access to genomic variation should be paralleled and integrated with phenotypic characterization of genetic diversity. We developed a multi-parental framework for trait dissection in melon (Cucumis melo), leveraging a novel pan-genome constructed for this highly variable cucurbit crop. A core subset of 25 diverse founders (MelonCore25), consisting of 24 accessions from the two widely cultivated subspecies of C. melo, encompassing 12 horticultural groups, and 1 feral accession was sequenced using a combination of short- and long-read technologies, and their genomes were assembled de novo. The construction of this melon pan-genome exposed substantial variation in genome size and structure, including detection of ~300 000 structural variants and ~9 million SNPs. A half-diallel derived set of 300 F₂ populations, representing all possible MelonCore25 parental combinations, was constructed as a framework for trait dissection through integration with the pan-genome. We demonstrate the potential of this unified framework for genetic analysis of various melon traits, including rind color intensity and pattern, fruit sugar content, and resistance to fungal diseases. We anticipate that utilization of this integrated resource will enhance genetic dissection of important traits and accelerate melon breeding.

Advances in Crop Breeding Through Precision Genome Editing

The global climate change and unfavourable abiotic and biotic factors are limiting agricultural productivity and therefore intensifying the challenges for crop scientists to meet the rising demand for global food supply. The introduction of applied genetics to agriculture through plant breeding facilitated the development of hybrid varieties with improved crop productivity. However, the development of new varieties with the existing gene pools poses a challenge for crop breeders. Genetic engineering holds the potential to broaden genetic diversity by the introduction of new genes into crops. But the random insertion of foreign DNA into the plant's nuclear genome often leads to transgene silencing. Recent advances in the field of plant breeding include the development of a new breeding technique called genome editing. Genome editing technologies have emerged as powerful tools to precisely modify the crop genomes at specific sites in the genome, which has been the longstanding goal of plant breeders. The precise modification of the target genome, the absence of foreign DNA in the genome-edited plants, and the faster and cheaper method of genome modification are the remarkable features of the genome-editing technology that have resulted in its widespread application in crop breeding in less than a decade. This review focuses on the advances in crop breeding through precision genome editing. This review includes: an overview of the different breeding approaches for crop improvement; genome editing tools and their mechanism of action and application of the most widely used genome editing technology, CRISPR/Cas9, for crop improvement especially for agronomic traits such as disease resistance, abiotic stress tolerance, herbicide tolerance, yield and quality improvement, reduction of anti-nutrients, and improved shelf life; and an update on the regulatory approval of the genome-edited crops. This review also throws a light on development of high-yielding climate-resilient crops through precision genome editing.

Gene rational design: the dawn of crop breeding

Limited natural genetic diversity restricts the creation of excellent crops. Modeling-guided rational design represents a promising protein engineering technology to optimize existing genes for desired agronomic traits. Rational design coupled with other engineering approaches could also be applied in artificial gene improvement for the creation of economically valuable crops.

Covering the Combinatorial Design Space of Multiplex CRISPR/Cas Experiments in Plants

Over the past years, CRISPR/Cas-mediated genome editing has revolutionized plant genetic studies and crop breeding. Specifically, due to its ability to simultaneously target multiple genes, the multiplex CRISPR/Cas system has emerged as a powerful technology for functional analysis of genetic pathways. As such, it holds great potential for application in plant systems to discover genetic interactions and to improve polygenic agronomic traits in crop breeding. However, optimal experimental design regarding coverage of the combinatorial design space in multiplex CRISPR/Cas screens remains largely unexplored. To contribute to well-informed experimental design of such screens in plants, we first establish a representation of the design space at different stages of a multiplex CRISPR/Cas experiment. We provide two independent computational approaches yielding insights into the plant library size guaranteeing full coverage of all relevant multiplex combinations of gene knockouts in a specific multiplex CRISPR/Cas screen. These frameworks take into account several design parameters (e.g., the number of target genes, the number of gRNAs designed per gene, and the number of elements in the combinatorial array) and efficiencies at subsequent stages of a multiplex CRISPR/Cas experiment (e.g., the distribution of gRNA/Cas delivery, gRNA-specific mutation efficiency, and knockout efficiency). With this work, we intend to raise awareness about the limitations regarding the number of target genes and order of genetic interaction that can be realistically analyzed in multiplex CRISPR/Cas experiments with a given number of plants. Finally, we establish guidelines for designing multiplex CRISPR/Cas experiments with an optimal coverage of the combinatorial design space at minimal plant library size.

Into the Shadows and Back into Sunlight: Photosynthesis in Fluctuating Light

Photosynthesis is an important remaining opportunity for further improvement in the genetic yield potential of our major crops. Measurement, analysis, and improvement of leaf CO₂ assimilation (A) have focused largely on photosynthetic rates under light-saturated steady-state conditions. However, in modern crop canopies of several leaf layers, light is rarely constant, and the majority of leaves experience marked light fluctuations throughout the day. It takes several minutes for photosynthesis to regain efficiency in both sun-shade and shade-sun transitions, costing a calculated 10-40% of potential crop CO₂ assimilation. Transgenic manipulations to accelerate the adjustment in sun-shade transitions have already shown a substantial productivity increase in field trials. Here, we explore means to further accelerate these adjustments and minimize these losses through transgenic manipulation, gene editing, and exploitation of natural variation. Measurement andanalysis of photosynthesis in sun-shade and shade-sun transitions are explained. Factors limiting speeds of adjustment and how they could be modified to effect improved efficiency are reviewed, specifically nonphotochemical quenching (NPQ), Rubisco activation, and stomatal responses.

riceExplorer: Uncovering the Hidden Potential of a National Genomic Resource Against a Global Database

Agricultural crop breeding programs, particularly at the national level, typically consist of a core panel of elite breeding cultivars alongside a number of local landrace varieties (or other endemic cultivars) that provide additional sources of phenotypic and genomic variation or contribute as experimental materials (e.g., in GWAS studies). Three issues commonly arise. First, focusing primarily on core development accessions may mean that the potential contributions of landraces or other secondary accessions may be overlooked. Second, elite cultivars may accumulate deleterious alleles away from nontarget loci due to the strong effects of artificial selection. Finally, a tendency to focus solely on SNP-based methods may cause incomplete or erroneous identification of functional variants. In practice, integration of local breeding programs with findings from global database projects may be challenging. First, local GWAS experiments may only indicate useful functional variants according to the diversity of the experimental panel, while other potentially useful loci-identifiable at a global level-may remain undiscovered. Second, large-scale experiments such as GWAS may prove prohibitively costly or logistically challenging for some agencies. Here, we present a fully automated bioinformatics pipeline (riceExplorer) that can easily integrate local breeding program sequence data with international database resources, without relying on any phenotypic experimental procedure. It identifies associated functional haplotypes that may prove more robust in determining the genotypic determinants of desirable crop phenotypes. In brief, riceExplorer evaluates a global crop database (IRRI 3000 Rice Genomes) to identify haplotypes that are associated with extreme phenotypic variation at the global level and recorded in the database. It then examines which potentially useful variants are present in the local crop panel, before distinguishing between those that are already incorporated into the elite breeding accessions and those only found among secondary varieties (e.g., landraces). Results highlight the effectiveness of our pipeline, identifying potentially useful functional haplotypes across the genome that are absent from elite cultivars and found among landraces and other secondary varieties in our breeding program. riceExplorer can automatically conduct a full genome analysis and produces annotated graphical output of chromosomal maps, potential global diversity sources, and summary tables.

Realizing the Potential of Camelina sativa as a Bioenergy Crop for a Changing Global Climate

Camelina sativa (L.) Crantz. is an annual oilseed crop within the Brassicaceae family. C. sativa has been grown since as early as 4000 BCE. In recent years, C. sativa received increased attention as a climate-resilient oilseed, seed meal, and biofuel (biodiesel and renewable or green diesel) crop. This renewed interest is reflected in the rapid rise in the number of peer-reviewed publications (>2300) containing “camelina” from 1997 to 2021. An overview of the origins of this ancient crop and its genetic diversity and its yield potential under hot and dry growing conditions is provided. The major biotic barriers that limit C. sativa production are summarized, including weed control, insect pests, and fungal, bacterial, and viral pathogens. Ecosystem services provided by C. sativa are also discussed. The profiles of seed oil and fatty acid composition and the many uses of seed meal and oil are discussed, including food, fodder, fuel, industrial, and medical benefits. Lastly, we outline strategies for improving this important and versatile crop to enhance its production globally in the face of a rapidly changing climate using molecular breeding, rhizosphere microbiota, genetic engineering, and genome editing approaches.

Market Intelligence and Incentive-Based Trait Ranking for Plant Breeding: A Sweetpotato Pilot in Uganda

Crop breeding programs must accelerate crop improvement, spur widespread adoption of new varieties and increase variety turnover they are to meet the diverse needs of their clients. More comprehensive quantitative approaches are needed to better inform breeding programs about the preferred traits among farmers and other actors. However, the ability of current breeding programs to meet the demands of their clients is limited by the lack of insights about value chain actor preference for individual or packages of traits. Ranking traits based on monetary incentives, rather than subjective values, represents a more comprehensive, consistent, and quantitative approach to inform breeding programs. We conducted a large pilot in Uganda to assess the implementation of a novel approach to trait ranking, using a uniquely large sample of diverse sweetpotato value chain actors. We found meaningful differences in trait ranking and heterogeneity among different actors using this approach. We also show our approach's effectiveness at uncovering unmet demand for root quality traits and at characterizing the substantial trait demand heterogeneity among value chain players. Implementing this approach more broadly for sweetpotato and other crops would increase the effectiveness of breeding programs to improve food security in developing countries.

Gender inclusivity through maize breeding in Africa: A review of the issues and options for future engagement

With the prioritization of social inclusion in agricultural development, donors and research centers have shown growing interest in gender-intentional varietal development and delivery. Breeding maize varieties that respond to gender-based differences in trait preferences now represents a central objective of maize R&D in the CGIAR and elsewhere. Drawing on literature on gender and maize seed adoption, variety preferences, and seed system constraints, we take stock of knowns and unknowns related to gender-responsive and gender-intentional maize breeding. While recent research on farmers' variety preferences across crops has yielded insights into gender-based differences, we find that evidence of gender-differentiated preferences for maize varieties remains inconclusive. Ultimately, we identify several research priorities to support gender-intentional maize breeding, including a more nuanced understanding of gender relations in maize production and maize seed decision-making, new and more gender-responsive approaches to measuring farmer preferences and seed demand more broadly, and research to address operational challenges in gender-intentional breeding. We close by identifying some institutional constraints to achieving impact through gender-intentional maize breeding.

A Rapid Pipeline for Pollen- and Anther-Specific Gene Discovery Based on Transcriptome Profiling Analysis of Maize Tissues

Recently, crop breeders have widely adopted a new biotechnology-based process, termed Seed Production Technology (SPT), to produce hybrid varieties. The SPT does not produce nuclear male-sterile lines, and instead utilizes transgenic SPT maintainer lines to pollinate male-sterile plants for propagation of nuclear-recessive male-sterile lines. A late-stage pollen-specific promoter is an essential component of the pollen-inactivating cassette used by the SPT maintainers. While a number of plant pollen-specific promoters have been reported so far, their usefulness in SPT has remained limited. To increase the repertoire of pollen-specific promoters for the maize community, we conducted a comprehensive comparative analysis of transcriptome profiles of mature pollen and mature anthers against other tissue types. We found that maize pollen has much less expressed genes (>1 FPKM) than other tissue types, but the pollen grain has a large set of distinct genes, called pollen-specific genes, which are exclusively or much higher (100 folds) expressed in pollen than other tissue types. Utilizing transcript abundance and correlation coefficient analysis, 1215 mature pollen-specific (MPS) genes and 1009 mature anther-specific (MAS) genes were identified in B73 transcriptome. These two gene sets had similar GO term and KEGG pathway enrichment patterns, indicating that their members share similar functions in the maize reproductive process. Of the genes, 623 were shared between the two sets, called mature anther- and pollen-specific (MAPS) genes, which represent the late-stage pollen-specific genes of the maize genome. Functional annotation analysis of MAPS showed that 447 MAPS genes (71.7% of MAPS) belonged to genes encoding pollen allergen protein. Their 2-kb promoters were analyzed for cis-element enrichment and six well-known pollen-specific cis-elements (AGAAA, TCCACCA, TGTGGTT, [TA]AAAG, AAATGA, and TTTCT) were found highly enriched in the promoters of MAPS. Interestingly, JA-responsive cis-element GCC box (GCCGCC) and ABA-responsive cis-element-coupling element1 (ABRE-CE1, CCACC) were also found enriched in the MAPS promoters, indicating that JA and ABA signaling likely regulate pollen-specific MAPS expression. This study describes a robust and straightforward pipeline to discover pollen-specific promotes from publicly available data while providing maize breeders and the maize industry a number of late-stage (mature) pollen-specific promoters for use in SPT for hybrid breeding and seed production.

Plant genome editing: ever more precise and wide reaching

Genome-editing technologies consisting of targeted mutagenesis and gene targeting enable us to modify genes of interest rapidly and precisely. The discovery in 2012 of CRISPR/Cas9 systems and their development as sequence-specific nucleases has brought about a paradigm shift in biology. Initially, CRISPR/Cas9 was applied in targeted mutagenesis to knock out a target gene. Thereafter, advances in genome-editing technologies using CRISPR/Cas9 developed rapidly, with base editing systems for transition substitution using a combination of Cas9 nickase and either cytidine or adenosine deaminase being reported in 2016 and 2017, respectively, and later in 2021 bringing reports of transversion substitution using Cas9 nickase, cytidine deaminase and uracil DNA glycosylase. Moreover, technologies for gene targeting and prime editing systems using DNA or RNA as donors have also been developed in recent years. Besides these precise genome-editing strategies, reports of successful chromosome engineering using CRISPR/Cas9 have been published recently. The application of genome editing to crop breeding has advanced in parallel with the development of these technologies. Genome-editing enzymes can be introduced into plant cells, and there are now many examples of crop breeding using genome-editing technologies. At present, it is no exaggeration to say that we are now in a position to be able to modify a gene precisely and rearrange genomes and chromosomes in a predicted way. In this review, we introduce and discuss recent highlights in the field of precise gene editing, chromosome engineering and genome engineering technology in plants.

Detect thy family: Mechanisms, ecology and agricultural aspects of kin recognition in plants

The phenomenon that organisms can distinguish genetically related individuals from strangers (i.e., kin recognition) and exhibit more cooperative behaviours towards their relatives (i.e., positive kin discrimination) has been documented in a wide variety of organisms. However, its occurrence in plants has been considered only recently. Despite the concerns about some methodologies used to document kin recognition, there is sufficient evidence to state that it exists in plants. Effects of kin recognition go well beyond reducing resource competition between related plants and involve interactions with symbionts (e.g., mycorrhizal networks). Kin recognition thus likely has important implications for evolution of plant traits, diversity of plant populations, ecological networks and community structures. Moreover, as kin selection may result in less competitive traits and thus greater population performance, it holds potential promise for crop breeding. Exploration of these evo-ecological and agricultural implications requires adequate control and measurements of relatedness, sufficient replication at genotypic level and comprehensive measurements of performance/fitness effects of kin discrimination. The primary questions that need to be answered are: when, where and by how much positive kin discrimination improves population performance.

Phytosanitary Interventions for Safe Global Germplasm Exchange and the Prevention of Transboundary Pest Spread: The Role of CGIAR Germplasm Health Units

The inherent ability of seeds (orthodox, intermediate, and recalcitrant seeds and vegetative propagules) to serve as carriers of pests and pathogens (hereafter referred to as pests) and the risk of transboundary spread along with the seed movement present a high-risk factor for international germplasm distribution activities. Quarantine and phytosanitary procedures have been established by many countries around the world to minimize seed-borne pest spread by screening export and import consignments of germplasm. The effectiveness of these time-consuming and cost-intensive procedures depends on the knowledge of pest distribution, availability of diagnostic tools for seed health testing, qualified operators, procedures for inspection, and seed phytosanitation. This review describes a unique multidisciplinary approach used by the CGIAR Germplasm Health Units (GHUs) in ensuring phytosanitary protection for the safe conservation and global movement of germplasm from the 11 CGIAR genebanks and breeding programs that acquire and distribute germplasm to and from all parts of the world for agricultural research and food security. We also present the challenges, lessons learned, and recommendations stemming from the experience of GHUs, which collaborate with the national quarantine systems to export and distribute about 100,000 germplasm samples annually to partners located in about 90 to 100 countries. Furthermore, we describe how GHUs adjust their procedures to stay in alignment with evolving phytosanitary regulations and pest risk scenarios. In conclusion, we state the benefits of globally coordinated phytosanitary networks for the prevention of the intercontinental spread of pests that are transmissible through plant propagation materials.

Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding

The functional genes underlying phenotypic variation and their interactions represent "genetic mysteries". Understanding and utilizing these genetic mysteries are key solutions for mitigating the current threats to agriculture posed by population growth and individual food preferences. Due to advances in high-throughput multi-omics technologies, we are stepping into an Interactome Big Data era that is certain to revolutionize genetic research. In this article, we provide a brief overview of current strategies to explore genetic mysteries. We then introduce the methods for constructing and analyzing the Interactome Big Data and summarize currently available interactome resources. Next, we discuss how Interactome Big Data can be used as a versatile tool to dissect genetic mysteries. We propose an integrated strategy that could revolutionize genetic research by combining Interactome Big Data with machine learning, which involves mining information hidden in Big Data to identify the genetic models or networks that control various traits, and also provide a detailed procedure for systematic dissection of genetic mysteries,. Finally, we discuss three promising future breeding strategies utilizing the Interactome Big Data to improve crop yields and quality.

MicroRNA Techniques: Valuable Tools for Agronomic Trait Analyses and Breeding in Rice

MicroRNAs (miRNAs) are a class of small noncoding RNAs that regulate gene expression at the post-transcriptional level. Extensive studies have revealed that miRNAs have critical functions in plant growth, development, and stress responses and may provide valuable genetic resources for plant breeding research. We herein reviewed the development, mechanisms, and characteristics of miRNA techniques while highlighting widely used approaches, namely, the short tandem target mimic (STTM) approach. We described STTM-based advances in plant science, especially in the model crop rice, and introduced the CRISPR-based transgene-free crop breeding. Finally, we discussed the challenges and unique opportunities related to combining STTM and CRISPR technology for crop improvement and agriculture.

Looking for Root Hairs to Overcome Poor Soils

Breeding new cultivars allowing reduced fertilization and irrigation is a major challenge. International efforts towards this goal focus on noninvasive methodologies, platforms for high-throughput phenotyping of large plant populations, and quantitative description of root traits as predictors of crop performance in environments with limited water and nutrient availability. However, these high-throughput analyses ignore one crucial component of the root system: root hairs (RHs). Here, we review current knowledge on RH functions, mainly in the context of plant hydromineral nutrition, and take stock of quantitative genetics data pointing at correlations between RH traits and plant biomass production and yield components.

Exploiting Epigenetic Variations for Crop Disease Resistance Improvement

Pathogen infections seriously threaten plant health and global crop production. Epigenetic processes such as DNA methylation, histone post-translational modifications, chromatin assembly and remodeling play important roles in transcriptional regulation of plant defense responses and could provide a new direction to drive breeding strategies for crop disease resistance improvement. Although past decades have seen unprecedented proceedings in understanding the epigenetic mechanism of plant defense response, most of these advances were derived from studies in model plants like Arabidopsis. In this review, we highlighted the recent epigenetic studies on crop-pathogen interactions and discussed the potentials, challenges, and strategies in exploiting epigenetic variations for crop disease resistance improvement.

A Composite Analysis of Flowering Time Regulation in Lettuce

Plants undergo profound physiological changes when transitioning from vegetative to reproductive growth. These changes affect crop production, as in the case of leafy vegetables. Lettuce is one of the most valuable leafy vegetable crops in the world. Past genetic studies have identified multiple quantitative trait loci (QTLs) that affect the timing of the floral transition in lettuce. Extensive functional molecular studies in the model organism Arabidopsis provide the opportunity to transfer knowledge to lettuce to explore the mechanisms through which genetic variations translate into changes in flowering time. In this review, we integrated results from past genetic and molecular studies for flowering time in lettuce with orthology and functional inference from Arabidopsis. This summarizes the basis for all known genetic variation underlying the phenotypic diversity of flowering time in lettuce and how the genetics of flowering time in lettuce projects onto the established pathways controlling flowering time in plants. This comprehensive overview reveals patterns across experiments as well as areas in need of further study. Our review also represents a resource for developing cultivars with delayed flowering time.

Benefiting others and self: Production of vitamins in plants

Vitamins maintain growth and development in humans, animals, and plants. Because plants serve as essential producers of vitamins, increasing the vitamin contents in plants has become a goal of crop breeding worldwide. Here, we begin with a summary of the functions of vitamins. We then review the achievements to date in elucidating the molecular mechanisms underlying how vitamins are synthesized, transported, and regulated in plants. We also stress the exploration of variation in vitamins by the use of forward genetic approaches, such as quantitative trait locus mapping and genome-wide association studies. Overall, we conclude that exploring the diversity of vitamins could provide new insights into plant metabolism and crop breeding.

Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding

Transgressive segregation and heterosis are the reasons that plant breeding works. Molecular explanations for both phenomena have been suggested and play a contributing role. However, it is often overlooked by molecular genetic researchers that transgressive segregation and heterosis are most simply explained by dispersion of favorable alleles. Therefore, advances in molecular biology will deliver the most impact on plant breeding when integrated with sources of heritable trait variation - and this will be best achieved within a quantitative genetics framework. An example of the power of quantitative approaches is the implementation of genomic selection, which has recently revolutionized animal breeding. Genomic selection is now being applied to both hybrid and inbred crops and is likely to be the major source of improvement in plant breeding practice over the next decade. Breeders' ability to efficiently apply genomic selection methodologies is due to recent technology advances in genotyping and sequencing. Furthermore, targeted integration of additional molecular data (such as gene expression, gene copy number and methylation status) into genomic prediction models may increase their performance. In this review, we discuss and contextualize a suite of established quantitative genetics themes relating to hybrid vigour, transgressive segregation and their central relevance to plant breeding, with the aim of informing crop researchers outside of the quantitative genetics discipline of their relevance and importance to crop improvement. Better understanding between molecular and quantitative disciplines will increase the potential for further improvements in plant breeding methodologies and so help underpin future food security.

PEG-Delivered CRISPR-Cas9 Ribonucleoproteins System for Gene-Editing Screening of Maize Protoplasts

Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 technology allows the modification of DNA sequences in vivo at the location of interest. Although CRISPR-Cas9 can produce genomic changes that do not require DNA vector carriers, the use of transgenesis for the stable integration of DNA coding for gene-editing tools into plant genomes is still the most used approach. However, it can generate unintended transgenic integrations, while Cas9 prolonged-expression can increase cleavage at off-target sites. In addition, the selection of genetically modified cells from millions of treated ones, especially plant cells, is still challenging. In a protoplast system, previous studies claimed that such pitfalls would be averted by delivering pre-assembled ribonucleoprotein complexes (RNPs) composed of purified recombinant Cas9 enzyme and in vitro transcribed guide RNA (gRNA) molecules. We, therefore, aimed to develop the first DNA-free protocol for gene-editing in maize and introduced RNPs into their protoplasts with polyethylene glycol (PEG) 4000. We performed an effective transformation of maize protoplasts using different gRNAs sequences targeting the inositol phosphate kinase gene, and by applying two different exposure times to RNPs. Using a low-cost Sanger sequencing protocol, we observed an efficiency rate of 0.85 up to 5.85%, which is equivalent to DNA-free protocols used in other plant species. A positive correlation was displayed between the exposure time and mutation frequency. The mutation frequency was gRNA sequence- and exposure time-dependent. In the present study, we demonstrated that the suitability of RNP transfection was proven as an effective screening platform for gene-editing in maize. This efficient and relatively easy assay method for the selection of gRNA suitable for the editing of the gene of interest will be highly useful for genome editing in maize, since the genome size and GC-content are large and high in the maize genome, respectively. Nevertheless, the large amplitude of mutations at the target site require scrutiny when checking mutations at off-target sites and potential safety concerns.