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.

MpNAC1, a transcription factor from the mangrove associate Millettia pinnata, confers salt and drought stress tolerance in transgenic Arabidopsis and rice.

Pongamia (Millettia pinnata Syn. Pongamia pinnata), a mangrove associate plant, exhibits good stress tolerance, making it a treasure of genetic resources for crop improvement. NAC proteins are plant-specific transcription factors, which have been elucidated to participate in the regulation and tolerance of abiotic stresses (such as salt and drought). Here, we identified a salt-induced gene from Pongamia, MpNAC1, which encodes an NAC factor sharing five highly conserved domains with other NACs and exhibits close homology to AtNAC19/AtNAC55/AtNAC72 in Arabidopsis. MpNAC1 showed nuclear localization and transcriptional activator activity. MpNAC1-overexpressing Arabidopsis exhibited significantly stronger salt and drought tolerance compared with wild-type plants. The expression levels of stress-responsive genes were activated in transgenic Arabidopsis. Furthermore, the heterologous expression of MpNAC1 also enhanced the salt and drought tolerance of transgenic rice. The major agronomic traits, such as plant height and tiller number, panicle length, grain size, and yield, were similar between the transgenic lines and wild type under normal field growth conditions. RNA-Seq analysis revealed that MpNAC1 significantly up-regulated stress-responsive genes and activated the biosynthesis of secondary metabolites such as flavonoids, resulting in increased stress tolerance. Taken together, the MpNAC1 increased salt and drought stress tolerance in transgenic plants and did not retard the plant growth and development under normal growth conditions, suggesting the potential of MpNAC1 in breeding stress-resilient crops.

Comprehensive genome-wide analysis of wheat xylanase inhibitor protein (XIP) genes: unveiling their role in Fusarium head blight resistance and plant immune mechanisms.

In this comprehensive genome-wide study, we identified and classified 83 Xylanase Inhibitor Protein (XIP) genes in wheat, grouped into five distinct categories, to enhance understanding of wheat's resistance to Fusarium head blight (FHB), a significant fungal threat to global wheat production. Our analysis reveals the unique distribution of XIP genes across wheat chromosomes, particularly at terminal regions, suggesting their role in the evolutionary expansion of the gene family. Several XIP genes lack signal peptides, indicating potential alternative secretion pathways that could be pivotal in plant defense against FHB. The study also uncovers the sequence homology between XIPs and chitinases, hinting at a functional diversification within the XIP gene family. Additionally, the research explores the association of XIP genes with plant immune mechanisms, particularly their linkage with plant hormone signaling pathways like abscisic acid and jasmonic acid. XIP-7A3, in particular, demonstrates a significant increase in expression upon FHB infection, highlighting its potential as a key candidate gene for enhancing wheat's resistance to this disease. This research not only enriches our understanding of the XIP gene family in wheat but also provides a foundation for future investigations into their role in developing FHB-resistant wheat cultivars. The findings offer significant implications for wheat genomics and breeding, contributing to the development of more resilient crops against fungal diseases.

Association mapping analysis for cultivated and weedy types of Perilla crop collected from South Korea using morphological characteristics and SSR markers.

There are two cultivated and weedy types of Perilla crop (TCWTPC), and they are widely distributed and cultivated in East Asia, especially in South Korea and Japan. The objective of this study is to create simple sequence repeat (SSR) markers linked to morphological traits that show differences between accessions of the TCWTPC using recently designed SSR primer sets in Perilla crop. Genetic diversity within 52 accessions of the TCWTPC, gathered from South Korea, was assessed using 28 novel Perilla SSR primer sets. Based on the assessment, a collection of 28 Perilla SSR primer sets were shown to exhibit polymorphism and yielded a total of 142 alleles across the 52 accessions of the TCWTPC. Through inspection of a phylogenetic tree and population structure, the 52 accessions of the TCWTPC were classified into three major groups. Although most accessions of the TCWTPC were relatively clearly distinguished, SSR markers failed to distinguish several accessions belonging to the two weedy types of the Perilla crop. By using an association mapping analysis (AMA) of the 28 Perilla SSR markers and seven morphological characteristics in the 52 TCWTPC accessions, we detected that three of the Perilla SSR markers (KNUPF134, KNUPF137, KNUPF149) were associated with plant and seed characteristics. The novel SSR primer sets developed in Perilla crop should be useful in AMA for assessing genetic diversity and relationships between and within TCWTPC accessions, and this information will be helpful for genetic mapping in breeding programs for Perilla crop.

Dissection of rhizosphere microbiome and exploiting strategies for sustainable agriculture.

The rhizosphere microbiome plays critical roles in plant growth and provides promising solutions for sustainable agriculture. While the rhizosphere microbiome frequently fluctuates with the soil environment, recent studies have demonstrated that a small proportion of the microbiome is consistently assembled in the rhizosphere of a specific plant genotype regardless of the soil condition, which is determined by host genetics. Based on these breakthroughs, which involved exploiting the plant-beneficial function of the rhizosphere microbiome, we propose to divide the rhizosphere microbiome into environment-dominated and plant genetic-dominated components based on their different assembly mechanisms. Subsequently, two strategies to explore the different rhizosphere microbiome components for agricultural production are suggested, that is, the precise management of the environment-dominated rhizosphere microbiome by agronomic practices, and the elucidation of the plant genetic basis of the plant genetic-dominated rhizosphere microbiome for breeding microbiome-assisted crop varieties. We finally present the major challenges that need to be overcome to implement strategies for modulating these two components of the rhizosphere microbiome.

Multi-omics assists genomic prediction of maize yield with machine learning approaches.

With the improvement of high-throughput technologies in recent years, large multi-dimensional plant omics data have been produced, and big-data-driven yield prediction research has received increasing attention. Machine learning offers promising computational and analytical solutions to interpret the biological meaning of large amounts of data in crops. In this study, we utilized multi-omics datasets from 156 maize recombinant inbred lines, containing 2496 single nucleotide polymorphisms (SNPs), 46 image traits (i-traits) from 16 developmental stages obtained through an automatic phenotyping platform, and 133 primary metabolites. Based on benchmark tests with different types of prediction models, some machine learning methods, such as Partial Least Squares (PLS), Random Forest (RF), and Gaussian process with Radial basis function kernel (GaussprRadial), achieved better prediction for maize yield, albeit slight difference for method preferences among i-traits, genomic, and metabolic data. We found that better yield prediction may be caused by various capabilities in ranking and filtering data features, which is found to be linked with biological meaning such as photosynthesis-related or kernel development-related regulations. Finally, by integrating multiple omics data with the RF machine learning approach, we can further improve the prediction accuracy of grain yield from 0.32 to 0.43. Our research provides new ideas for the application of plant omics data and artificial intelligence approaches to facilitate crop genetic improvements. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01454-z.

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.

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.

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.

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.

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.

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.

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.

Development of an SNP marker set for marker-assisted backcrossing using genotyping-by-sequencing in tetraploid perilla.

High-quality molecular markers are essential for marker-assisted selection to accelerate breeding progress. Compared with diploid species, recently diverged polyploid crop species tend to have highly similar homeologous subgenomes, which is expected to limit the development of broadly applicable locus-specific single-nucleotide polymorphism (SNP) assays. Furthermore, it is particularly challenging to make genome-wide marker sets for species that lack a reference genome. Here, we report the development of a genome-wide set of kompetitive allele specific PCR (KASP) markers for marker-assisted recurrent selection (MARS) in the tetraploid minor crop perilla. To find locus-specific SNP markers across the perilla genome, we used genotyping-by-sequencing (GBS) to construct linkage maps of two F2 populations. The two resulting high-resolution linkage maps comprised 2326 and 2454 SNP markers that spanned a total genetic distance of 2133 cM across 16 linkage groups and 2169 cM across 21 linkage groups, respectively. We then obtained a final genetic map consisting of 22 linkage groups with 1123 common markers from the two genetic maps. We selected 96 genome-wide markers for MARS and confirmed the accuracy of markers in the two F2 populations using a high-throughput Fluidigm system. We confirmed that 91.8% of the SNP genotyping results from the Fluidigm assay were the same as the results obtained through GBS. These results provide a foundation for marker-assisted backcrossing and the development of new varieties of perilla.

Prime editing: Its systematic optimization and current applications in disease treatment and agricultural breeding.

CRISPR/Cas-mediated genome-editing technology has accelerated the development of the life sciences. Prime editing has raised genome editing to a new level because it allows for all 12 types of base substitutions, targeted insertions and deletions, large DNA fragment integration, and even combinations of these edits without generating DNA double-strand breaks. This versatile and game-changing technology has successfully been applied to human cells and plants, and it currently plays important roles in basic research, gene therapy, and crop breeding. Although prime editing has substantially expanded the range of possibilities for genome editing, its efficiency requires improvement. In this review, we briefly introduce prime editing and highlight recent optimizations that have improved the efficiency of prime editors. We also describe how the dual-pegRNA strategy has expanded current editing capabilities, and we summarize the potential of prime editing in treating mammalian diseases and improving crop breeding. Finally, we discuss the limitations of current prime editors and future prospects for optimizing these editors.