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Doubled haploid technology and synthetic apomixis:Recent advances and applications in future crop breeding

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Molecular Plant 2024年第7期17卷 1005-1018页

作者： yanzhi Qu Alisdair R.Fernie Jie Liu jianbing yan National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China Hubei Hongshan Laboratory Wuhan 430070China Department of Molecular Physiology Max-Planck-Institute of Molecular Plant Physiology14476 Potsdam-GolmGermany Center of Plant Systems Biology and Biotechnology 4000 PlovdivBulgaria azhouwan National Laboratory Sanya 572024China

Doubled haploid(DH)technology and synthetic apomixis approaches can considerably shorten breeding cycles and enhance breeding *** with traditional breeding methods,DH technology offers the advantage of rapidly generating inbred lines,while synthetic apomixis can effectively fix hybrid *** this review,we focus on(i)recent advances in identifying and characterizing genes responsible for haploid induction(Hl),(ii)the molecular mechanisms of Hl,(ili)spontaneous haploid genome doubling,and(iv)crop synthetic *** also discuss the challenges and potential solutions for future crop breeding programs utilizing DH technology and synthetic ***,we provide our perspectives about how to integrate DH and synthetic apomixis for precision breeding and de novo domestication.

关键词： maize doubled haploid technology haploid induction synthetic apomixis precision breeding

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

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Science China(Life Sciences) 2024年第6期67卷 1133-1154页

作者： yang Zhang Mengyao Wang Zhenguo Li Xuan yang Keqin Li Ao Xie Fang Dong Shihan Wang jianbing yan Jianxiao Liu National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China Key Laboratory of Smart Farming for Agricultural Animals Huazhong Agricultural UniversityWuhan 430070China Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural UniversityWuhan 430070China College of Informatics Huazhong Agricultural UniversityWuhan 430070China College of Life Sciences Nankai UniversityTianjin 300071China

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

关键词： gene-trait association GWAS eQTL colocalization transcriptome-wide association study(TWAS) Mendelian randomization(MR)

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

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Science China(Life Sciences) 2024年第3期67卷 435-448页

作者： Nannan Liu Yuanhao Du Shijuan yan Wei Chen Min Deng Shutu Xu Hong Wang Wei Zhan Wenjie Huang yan Yin Xiaohong yang Qiao Zhao Alisdair R.Fernie jianbing yan National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China Hubei Hongshan Laboratory Wuhan 430070China Agro-biological Gene Research Center Guangdong Academy of Agricultural SciencesGuangzhou 510640China College of Agronomy Hunan Agricultural UniversityChangsha 410128China College of Agronomy Northwest A&F UniversityXi'an 710000China State Key Laboratory of North China Crop Improvement and Regulation Hebei Sub-center of National Maize Improvement Center of ChinaCollege of AgronomyHebei Agricultural UniversityBaoding 071001China College of Life Sciences South-Central Minzu UniversityWuhan 430070China Plant Science Facility of the Institute of Botany Chinese Academy of SciencesBeijing 100093China National Maize Improvement Center of China Beijing Key Laboratory of Crop Genetic ImprovementChina Agricultural UniversityBeijing 100093China Shenzhen Key Laboratory of Synthetic Genomics Guangdong Provincial Key Laboratory of Synthetic GenomicsShenzhen Institute of Synthetic BiologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Max Planck Institute of Molecular Plant Physiology Potsdam-Golm 14476Germany

Tocopherol is an important lipid-soluble antioxidant beneficial for both human health and plant growth. Here, we fine mapped a major QTLqVE1 affecting γ-tocopherol content in maize kernel, positionally cloned and confirmed the underlying gene ZmPORB1(por1), as a protochlorophyllide oxidoreductase. A 13.7 kb insertion reduced the tocopherol and chlorophyll content, and the photosynthetic activity by repressing ZmPORB1 expression in embryos of NIL-K22, but did not affect the levels of the tocopherol precursors HGA(homogentisic acid)and PMP(phytyl monophosphate). Furthermore, ZmPORB1 is inducible by low oxygen and light, thereby involved in the hypoxia response in developing embryos. Concurrent with natural hypoxia in embryos, the redox state has been changed with NO increasing and H_(2)O_(2) decreasing, which lowered γ-tocopherol content via scavenging reactive nitrogen species. In conclusion, we proposed that the lower lightharvesting chlorophyll content weakened embryo photosynthesis, leading to fewer oxygen supplies and consequently diverse hypoxic responses including an elevated γ-tocopherol consumption. Our findings shed light on the mechanism for fine-tuning endogenous oxygen concentration in the maize embryo through a novel feedback pathway involving the light and low oxygen regulation of ZmPORB1 expression and chlorophyll content.

关键词： maize tocopherol hypoxia photosynthesis ZmPORB1

DeepCBA:A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions

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Plant Communications 2024年第9期5卷 38-53页

作者： Zhenye Wang Yong Peng Jie Li Jiying Li Hao Yuan Shangpo yang Xinru Ding Ao Xie Jiangling Zhang Shouzhe Wang Keqin Li Jiaqi Shi Guangjie Xing Weihan Shi jianbing yan Jianxiao Liu National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural UniversityWuhan 430070China College of Informatics Huazhong Agricultural UniversityWuhan 430070China Hubei Hongshan Laboratory Wuhan 430070China Microsoft Corporation RedmondWA 98052USA WIMI Biotechnology Co. Ltd.Changzhou 213000China

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome,which has an important impact on gene expression,transcriptional regulation,and phenotypic *** date,several methods have been developed for predicting gene ***,existing methods do not take into consideration the effect of chromatin interactions on target gene expression,thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory el*** this study,we developed a highly accurate deep learning-based gene expression prediction model(DeepCBA)based on maize chromatin interaction *** with existing models,DeepCBA exhibits higher accuracy in expression classification and expression value *** average Pearson correlation coefficients(PCCs)for predicting gene expression using gene promoter proximal interactions,proximaldistal interactions,and both proximal and distal interactions were 0.818,0.625,and 0.929,respectively,representing an increase of 0.357,0.16,and 0.469 over the PCCs obtained with traditional methods that use only gene proximal *** important motifs were identified through DeepCBA;they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue ***tantly,experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene ***,promoter editing and verification of two reported genes(ZmCLE7 and ZmVTE4)demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent *** is available at http://***/or http://124.***.197.196/.

关键词： maize gene expression prediction chromatin interactions deep learning promoter editing regulatory elements and motifs

Disease Resistance in Maize and the Role of Molecular Breeding in Defending Against Global Threat

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Journal of Integrative Plant Biology 2012年第3期54卷 134-151页

作者： Farhan Ali jianbing yan National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University Wuhan 430070 China Cereal Crop Research Institute （CCRI） Pirsabak Nowshehra Pakistan

Diseases are a potential threat to global food security but plants have evolved an extensive array of methodologies to cope with the invading pathogens. Non-host resistance and quantitative re- sistance are broad spectrum forms of resistance, and all kinds of resistances are controlled by extremely diverse genes called ＂R- genes＂. R-genes follow different mechanisms to defend plants and PAMP-induced defenses in susceptible host plants are referred to as basal resistance. Genetic and phenotypic diversity are vital in maize （Zea mays L.）; as such, genome wide association study （GWAS） along with certain other methodologies can explore the maximum means of genetic diversity. Exploring the complete genetic archi- tecture to manipulate maize genetically reduces the losses from hazardous diseases. Genomic studies can reveal the interaction be- tween different genes and their pathways. By confirming the specific role of these genes and protein-protein interaction （proteomics） via advanced molecular and bioinformatics tools, we can shed a light on the most complicated and abstruse phenomena of resistance.

关键词： Maize concept of pathology R-genes types of resistance genetic diversity genome wide association study.

De Novo Domestication:An Alternative Route toward New Crops for the Future

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Molecular Plant 2019年第5期12卷 615-631页

作者： Alisdair R.Fernie jianbing yan Department of Molecular Physiology Max-Planck-lnstitute of Molecular Plant PhysiologyAm Muhlenberg 114476 Potsdam-GolmGermany National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China

Current global agricultural production must feed over 7 billion ***,productivity varies greatly across the globe and is under threat from both increased competitions for land and climate change and associated environmental ***,the increase in human population size and dietary changes are putting an ever greater burden on *** majority of this burden is met by the cultivation of a very small number of species,largely in locations that differ from their origin of *** technological advances have raised the possibility of de novo domestication of wild plants as a viable solution for designing ideal crops while maintaining food security and a more sustainable lowinput *** we discuss how the discovery of multiple key domestication genes alongside the development of technologies for accurate manipulation of several target genes simultaneously renders de novo domestication a route toward crops for the future.

关键词： De Novo Domestication Redomestication New Crops Genome Editing Food Security Sustainable Agriculture

Structural variation in complex genome: detection, integration and function

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Science China(Life Sciences) 2019年第8期62卷 1098-1100页

作者： Ning yang Shenshen Wu jianbing yan National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University

Structural variations (SVs) are mutations with large-scale changes (generally>50 bp) in the genome. SVs are major sources of the genetic diversity of organisms and thus are of high relevance to phenotype variations, gene dosage and evolutionary genetics. Except detecting SVs through comparative genetic analyses, dozens of software had been developed based on the alignment of short-reads to a single linear genome in the past decades (Guan and Sung, 2016).

关键词： pro Structural variation in complex genome integration and function

Application of deep learning in genomics

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Science China(Life Sciences) 2020年第12期63卷 1860-1878页

作者： Jianxiao Liu Jiying Li Hai Wang jianbing yan National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China College of Informatics Huazhong Agricultural UniversityWuhan 430070China Microsoft Corporation Redmond 98052USA National Maize Improvement Center Key Laboratory of Crop Heterosis and UtilizationJoint Laboratory for International Cooperation in Crop Molecular BreedingChina Agricultural UniversityBeijing 100193China

In recent years, deep learning has been widely used in diverse fields of research, such as speech recognition, image classification,autonomous driving and natural language processing. Deep learning has showcased dramatically improved performance in complex classification and regression problems, where the intricate structure in the high-dimensional data is difficult to discover using conventional machine learning algorithms. In biology, applications of deep learning are gaining increasing popularity in predicting the structure and function of genomic elements, such as promoters, enhancers, or gene expression levels. In this review paper, we described the basic concepts in machine learning and artificial neural network, followed by elaboration on the workflow of using convolutional neural network in genomics. Then we provided a concise introduction of deep learning applications in genomics and synthetic biology at the levels of DNA, RNA and protein. Finally, we discussed the current challenges and future perspectives of deep learning in genomics.

关键词： deep learning genomics convolutional neural network

Maize biology: From functional genomics to breeding application

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Journal of Integrative Plant Biology 2019年第6期61卷 654-657页

作者： jianbing yan Bao-Cai Tan National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University(HZAU) School of Life Sciences Shandong University

Maize (Zea mays L.) is one of the most important crops globally for food, feed, and fuel. Maize grain production has increased more than eight-fold in the past century to a current annual global production of one billion tons (http://***/). By 2050, it will require 70% more food than today's consumma-tion since the population size will increase to 9 billions. It is estimated that maize will contribute more than half of the increased demand for cereals in the future. Thus, there is a tremendous innovation stream for maize breeders to utilize in their attempts to substantially increase maize productivity in an en-vironmentally sensitive way (FAO 2009).

关键词： Maize biology functional genomics breeding application^FA