IR64 is an elite Xian/indica variety developed by International Rice Research Institute(IRRl)in 1985,which has been the most widely grown variety and core breeding parent in South/Southeast Asia(Mackill and Khush,2018...IR64 is an elite Xian/indica variety developed by International Rice Research Institute(IRRl)in 1985,which has been the most widely grown variety and core breeding parent in South/Southeast Asia(Mackill and Khush,2018).IR64 has been utilized to develop stress-tolerant(such as drought-adapted and submergenceresistant)near-isogenic lines,underscoring its great potential in agricultural genomics(Tanaka et al.,2020).展开更多
Inoculation of starter culture is a viable method to improve the quality of fermented foods,but its effect on the flavor metabolite profiles and the underlying mechanisms are still unclear.This study aimed to elucidat...Inoculation of starter culture is a viable method to improve the quality of fermented foods,but its effect on the flavor metabolite profiles and the underlying mechanisms are still unclear.This study aimed to elucidate the effects of starters(Lactiplantibacillus plantarum(LP)and Staphylococcus simulans(SS)individually or in combination(LS))on the flavor metabolite profiles of fermented sausages via metabolomics and genomics.L.plantarum markedly modified the composition of bacterial communities and made Lactobacillus spp.dominant in sausages(98.29%and 85.03%in LP and LS groups,respectively).Additionally,inoculation with a single starter,L.plantarum,and a mixed starter yielded similar non-volatile flavor metabolites,which were mainly characterized at the amino acid and peptide levels(relative intensities of 349.65 and 348.62 for the LP and LS groups,respectively).Meanwhile,the mixed starter group had the most volatile flavor metabolites(relative intensity of 34728.67),some of which were contributed by L.plantarum,such as ethyl acetate(relative intensities of 583.33 and 588.33 for the LP and LS groups,respectively)and benzaldehyde(relative intensities of 786.67 and 909.00 for the LP and LS groups,respectively),and several of which were generated by S.simulans,such as ethyl propionate(relative intensities of 214.67 and 136.67 for the SS and LS groups,respectively)and benzyl alcohol(relative intensities of 720.00 and 656.00 for the SS and LS groups,respectively).Furthermore,L.plantarum was found to possess more genes encoding peptidases(48)and carbohydrate-active enzymes(124),while S.simulans had more genes related to lipid hydrolysis(12).In conclusion,differences in the properties and combinations of indigenous strains play a crucial role in the generation of flavor metabolites in sausages.展开更多
The genus Clematis(Ranunculaceae)comprises over 300 species with remarkable morphological and ecological diversity worldwide.Despite its horticultural,medicinal,and ecological importance,a well-resolved phylogeny and ...The genus Clematis(Ranunculaceae)comprises over 300 species with remarkable morphological and ecological diversity worldwide.Despite its horticultural,medicinal,and ecological importance,a well-resolved phylogeny and coherent infrageneric classification are still lacking.Here,we reconstruct a robust phylogeny for Clematis using a phylogenomic approach and revise its infrageneric taxonomy.We incorporated 198 samples representing 151 species,two subspecies,and 12 varieties,covering all subgenera and most sections worldwide,obtained from both fresh and herbarium material.Nuclear single nucleotide polymorphisms(SNPs)and complete plastid genomes were assembled for phylogenetic analyses.We also prepared a nuclear ribosomal ITS(nrITS)dataset comprising 171 species,two subspecies,and 12 varieties(217 samples)to include as many species as possible for phylogenetic inference.Phylogenies based on plastid genomes and nrITS exhibited limited resolution and modest support,highlighting challenges in resolving certain relationships.Nuclear SNP analyses yielded a robust phylogenetic tree with 22 well-supported clades corresponding to 22 sections,with most previously recognized subgenera and sections not recovered as monophyletic.Ancestral state reconstruction of 12 key morphological characters revealed multiple independent origins of character states.This study presents the first comprehensive sectional classification for Clematis based on robust phylogenomic evidence,redefines morphological characteristics for each section,and resolves long-standing taxonomic ambiguities.Our results establish a framework for future studies on the evolution,ecology,and horticultural potential of this globally significant genus.展开更多
Modern oilseed breeding faces the complex challenge of simultaneously improving yield,nutritional quality,and stress resilience within a sustainable agricultural framework.A predictive,systems-oriented strategy offers...Modern oilseed breeding faces the complex challenge of simultaneously improving yield,nutritional quality,and stress resilience within a sustainable agricultural framework.A predictive,systems-oriented strategy offers a solution by using predictive modeling and precise genetic intervention to engineer target ideotypes.This review synthesizes the integration of genomic selection(GS),genome editing,and epigenetic regulation to operationalize a precision breeding strategy in major oilseed crops.We document how GS,utilizing high-density SNP arrays and sequencing data,has reduced breeding cycles by up to 50%in soybean,enabling rapid selection for complex traits like seed protein content.We highlight the precision of CRISPR-Cas systems in executing design goals,such as creating commercial-grade high-oleic soybeans(>80%oleic acid)by knocking out FAD2 genes.Similarly,editing glucosinolate biosynthesis genes in rapeseed has directly improved meal quality.Furthermore,we explore the emerging role of epigenetic regulation as a tunable layer in trait optimization,where DNA methylation patterns in sunflower are linked to drought stress memory and flowering time.Finally,we present an integrated molecular framework,which synergizes these technologies to develop ideotypes with optimized architectures and composition.Despite challenges in phenotyping and global regulation,the strategic implementation of this molecular toolkit is pioneering a new era of precision breeding for the sustainable intensification of oilseed production.展开更多
The plastid genome(plastome)represents an indispensable molecular resource for studying plant phylogeny and evolution.Although plastome size is much smaller than that of nuclear genomes,accurately and efficientlyannot...The plastid genome(plastome)represents an indispensable molecular resource for studying plant phylogeny and evolution.Although plastome size is much smaller than that of nuclear genomes,accurately and efficientlyannotating and utilizing plastome sequences remain challenging.Therefore,a streamlined phylogenomic pipeline spanning plastome annotation,phylogenetic reconstruction and comparative genomics would greatly facilitate research utilizing this important organellar genome.Here,we develop PlastidHub,a novel web application employing innovative tools to analyze plastome sequences.In comparison with existing tools,key novel functionalities in PlastidHub include:(1)standardization of quadripartite structure;(2)improvement of annotation flexibility and consistency;(3)quantitative assessment of annotation completeness;(4)diverse extraction modes for canonical and specialized sequences;(5)intelligent screening of molecular markers for biodiversity studies;(6)genelevel visual comparison of structural variations and annotation completeness.PlastidHub features cloud-based web applications that do not require users to install,update,or maintain tools;detailed help documents including user guides,test examples,a static pop-up prompt box,and dynamic pop-up warning prompts when entering unreasonable parameter values;batch processing capabilities for all tools;intermediate results for secondary use;and easy-to-operate task flows between fileupload and download.A key feature of PlastidHub is its interrelated task-based user interface design.Give that PlastidHub is easy to use without specialized computational skills or resources,this new platform should be widely used among botanists and evolutionary biologists,improving and expediting research employing the plastome.PlastidHub is available at https://www.plastidhub.cn.展开更多
Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic dat...Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis,thus providing strong support for personalized treatments.In radiomics,AI can analyze high-dimensional features from computed tomography(CT),magnetic resonance imaging(MRI),and positron emission tomography/computed tomography(PET/CT)images to discover imaging biomarkers associated with tumor heterogeneity,treatment response,and disease progression,thereby enabling non-invasive,real-time assessments for personalized therapy.Pathomics leverages AI for deep analysis of digital pathology images,and can uncover subtle changes in tissue microenvironments,cellular characteristics,and morphological features,and offer unique insights into immunotherapy response prediction and biomarker discovery.These AI-driven technologies not only enhance the speed,accuracy,and robustness of biomarker discovery but also significantly improve the precision,personalization,and effectiveness of clinical treatments,and are driving a shift from empirical to precision medicine.Despite challenges such as data quality,model interpretability,integration of multi-modal data,and privacy protection,the ongoing advancements in AI,coupled with interdisciplinary collaboration,are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction.These improvements are expected to lead to more accurate,personalized treatment strategies and ultimately better patient outcomes,marking a significant step forward in the evolution of precision medicine.展开更多
As a high-value eudicot family,many famous horticultural crop genomes have been deciphered in Oleaceae.However,there are currently no bioinformatics platforms focused on empowering genome research in Oleaceae.Herein,w...As a high-value eudicot family,many famous horticultural crop genomes have been deciphered in Oleaceae.However,there are currently no bioinformatics platforms focused on empowering genome research in Oleaceae.Herein,we developed the first comprehensive Oleaceae Genome Research Platform(OGRP,https://oleaceae.cgrpoee.top/).In OGRP,70 genomes of 10 Oleaceae species and 46 eudicots and 366 transcriptomes involving 18 Oleaceae plant tissues can be obtained.We built 34 window-operated bioinformatics tools,collected 38 professional practical software programs,and proposed 3 new pipelines,namely ancient polyploidization identification,ancestral karyotype reconstruction,and gene family evolution.Employing these pipelines to reanalyze the Oleaceae genomes,we clarified the polyploidization,reconstructed the ancestral karyotypes,and explored the effects of paleogenome evolution on genes with specific biological regulatory roles.Significantly,we generated a series of comparative genomic resources focusing on the Oleaceae,comprising 108 genomic synteny dot plots,1952225 collinear gene pairs,multiple genome alignments,and imprints of paleochromosome rearrangements.Moreover,in Oleaceae genomes,researchers can efficiently search for 1785987 functional annotations,22584 orthogroups,29582 important trait genes from 74 gene families,12664 transcription factor-related genes,9178872 transposable elements,and all involved regulatory pathways.In addition,we provided downloads and usage instructions for the tools,a species encyclopedia,ecological resources,relevant literatures,and external database links.In short,ORGP integrates rich data resources and powerful analytical tools with the characteristic of continuous updating,which can efficiently empower genome research and agricultural breeding in Oleaceae and other plants.展开更多
Background:Neoadjuvant chemotherapy(NAC)significantly enhances clinical outcomes in patients with triple-negative breast cancer(TNBC);however,chemoresistance frequently results in treatment failure.Consequently,unders...Background:Neoadjuvant chemotherapy(NAC)significantly enhances clinical outcomes in patients with triple-negative breast cancer(TNBC);however,chemoresistance frequently results in treatment failure.Consequently,understanding the mechanisms underlying resistance and accurately predicting this phenomenon are crucial for improving treatment efficacy.Methods:Ultrasound images from 62 patients,taken before and after neoadjuvant therapy,were collected.Mitochondrial-related genes were extracted from a public database.Ultrasound features associated with NAC resistance were identified and correlated with significant mitochondrial-related genes.Subsequently,a prognostic model was developed and evaluated using the GSE58812 dataset.We also assessed this model alongside clinical factors and its ability to predict immunotherapy response.Results:A total of 32 significant differentially expressed genes in TNBC across three groups indicated a strong correlation with ultrasound features.Univariate and multivariate Cox regression analyses identified six genes as independent risk factors for TNBC prognosis.Based on these six mitochondrial-related genes,we constructed a TNBC prognostic model.The model’s risk scores indicated that high-risk patients generally have a poorer prognosis compared to low-risk patients,with the model demonstrating high predictive performance(p=0.002,AUC=0.745).This conclusion was further supported in the test set(p=0.026,AUC=0.718).Additionally,we found that high-risk patients exhibited more advanced tumor characteristics,while low-risk patients were more sensitive to common chemotherapy drugs and immunotherapy.The signature-related genes also predicted immunotherapy response with a high accuracy of 0.765.Conclusion:We identified resistance-related features from ultrasound images and integrated them with genomic data,enabling effective risk stratification of patients and prediction of the efficacy of neoadjuvant chemotherapy and immunotherapy in patients with TNBC.展开更多
With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with part...With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with particular promise in plant genomics.This review systematically examines the progress of DL applications in DNA,RNA,and protein sequence analysis,covering key tasks such as gene regulatory element identification,gene function annotation,and protein structure prediction,and highlighting how these DL applications illuminate research of plants,including horticultural plants.We evaluate the advantages of different neural network architectures and their applications in different biology studies,as well as the development of large language models(LLMs)in genomic modelling,such as the plantspecific models PDLLMs and AgroNT.We also briefly introduce the general workflow of the basic DL model for plant genomics study.While DL has significantly improved prediction accuracy in plant genomics,its broader application remains constrained by several challenges,including the limited availability of well-annotated data,computational capacity,innovative model architectures adapted to plant genomes,and model interpretability.Future advances will require interdisciplinary collaborations to develop DL applications for intelligent plant genomic research frameworks with broader applicability.展开更多
Rice cultivation contributes up to 12%of global anthropogenic methane(CH4)emissions,making it a significant climate concern.With rice demand projected to double by 2050,achieving the required 2.4%annual genetic gain m...Rice cultivation contributes up to 12%of global anthropogenic methane(CH4)emissions,making it a significant climate concern.With rice demand projected to double by 2050,achieving the required 2.4%annual genetic gain must be balanced with emission reduction.This review synthesizes recent progress in three key areas:(1)mitigation strategies such as alternate wetting and drying and direct-seeded rice,which can reduce CH4 emissions by 30%-40%;(2)identification of physiological and molecular traits,such as short duration,high harvest index,improved nitrogen use efficiency,optimized root architecture,and stress tolerance with reduced greenhouse gas(GHG)footprints;and(3)the potential of genomics-assisted breeding and high-throughput phenotyping to accelerate the development of climate-resilient rice varieties with lower CH4 emissions.Specifically,we highlight how the synergistic integration of high-throughput phenotyping,genomic selection,and marker-assisted breeding can substantially improve the efficiency and precision of breeding programs targeting the development of climate-resilient rice varieties with reduced CH4 emissions.This is exemplified through successful case studies utilizing multi-omics approaches,including the development of Green Super Rice varieties(GSR 2 and GSR 8),which have demonstrated up to a 37%reduction in GHG emissions.Crucially,we propose a stratified trait profile for low-GHG rice development and provide guidelines and metrics for integrating these traits into mainstream breeding pipelines.We conclude by proposing a strategic framework integrating carbon-efficient breeding,climate-adapted agronomy,and policy support,which is essential for scaling low-GHG rice systems globally.展开更多
The low egg production of goose greatly limits the development of the industry.China possesses the most abundant goose breeds resources.In this study,genome resequencing data of swan goose(Anser cygnoides)and domestic...The low egg production of goose greatly limits the development of the industry.China possesses the most abundant goose breeds resources.In this study,genome resequencing data of swan goose(Anser cygnoides)and domesticated high and low laying goose breeds(Anser cygnoides domestiation)were used to identify key genes related to egg laying ability in geese and verify their functions.Selective sweep analyses revealed 416 genes that were specifically selected during the domestication process from swan geese to high laying geese.Furthermore,SNPs and Indels markers were used in GWAS analyses between high and low laying breed geese.The results showed that RTCB,BPIFC,SYN3,SYNE1,VIP,and ESR1 may be related to the differences in laying ability of geese.Notably,only ESR1 was identified simultaneously by GWAS and selective sweep analysis.The genotype of Indelchr3:54429172,located downstream of ESR1,was confirmed to affect the expression of ESR1 in the ovarian stroma and showed significant correlation with body weight at first egg and laying frequency of geese.CCK-8,EdU,and flow cytometry confirmed that ESR1 can promote the apoptosis of goose pre-hierarchical follicles ganulosa cells(phGCs)and inhibit their proliferation.Combined with transcriptome data,it was found ESR1 involved in the function of goose phGCs may be related to MAPK and TGF-beta signaling pathways.Overall,our study used genomic information from different goose breeds to identify an indel located in the downstream of ESR1 associated with goose laying ability.The main pathways and biological processes of ESR1 involved in the regulation of goose laying ability were identified by cell biology and transcriptomics methods.These results are helpful to further understand the laying ability characteristics of goose and improve the egg production of geese.展开更多
The Beijing Institute of Genomics(BIG)of the Chinese Academy of Sciences,as the leading Institute in Genomics,has walked through 20 year’s journey since being founded in November 2003.From participating in the Human ...The Beijing Institute of Genomics(BIG)of the Chinese Academy of Sciences,as the leading Institute in Genomics,has walked through 20 year’s journey since being founded in November 2003.From participating in the Human Genome Project(HGP)in completing the“1%task”to independently accomplishing the super-hybrid rice genome and other several national and international genome projects,BIG has made tremendous contributions in genomics research and development in China.In 2024,bearing great ambition and responsibility,BIG is transformed to the China National Center for Bioinformation(CNCB),aiming to become a global hub in bioinformatics big data services,innovation,and entrepreneurship.With the completion of its new infrastructure in 2027,CNCB is looking into a brighter future.展开更多
Sorghum,renowned for its substantial biomass production and remarkable tolerance to various stresses,possesses extensive gene resources and phenotypic variations.A comprehensive understanding of the genetic basis unde...Sorghum,renowned for its substantial biomass production and remarkable tolerance to various stresses,possesses extensive gene resources and phenotypic variations.A comprehensive understanding of the genetic basis underlying complex agronomic traits is essential for unlocking the potential of sorghum in addressing food and feed security and utilizing marginal lands.In this context,we provide an overview of the major trends in genomic resource studies focusing on key agronomic traits over the past decade,accompanied by a summary of functional genomic platforms.We also delve into the molecular functions and regulatory networks of impactful genes for important agricultural traits.Lastly,we discuss and synthesize the current challenges and prospects for advancing molecular design breeding by gene-editing and polymerization of the excellent alleles,with the aim of accelerating the development of desired sorghum varieties.展开更多
Plant germplasm underpins much of crop genetic improvement. Millions of germplasm accessions have been collected and conserved ex situ and/or in situ, and the major challenge is now how to exploit and utilize this abu...Plant germplasm underpins much of crop genetic improvement. Millions of germplasm accessions have been collected and conserved ex situ and/or in situ, and the major challenge is now how to exploit and utilize this abundant resource. Genomics-based plant germplasm research （GPGR） or ＂Cenoplasmics＂ is a novel cross-disciplinary research field that seeks to apply the principles and techniques of genomics to germplasm research. We describe in this paper the concept, strategy, and approach behind GPGR, and summarize current progress in the areas of the definition and construction of core collections, enhancement of germplasm with core collections, and gene discovery from core collections. GPGR is opening a new era in germplasm research. The contribution, progress and achievements of GPGR in the future are predicted.展开更多
Horseshoe bats(genus Rhinolophus,family Rhinolophidae)represent an important group within chiropteran phylogeny due to their distinctive traits,including constant high-frequency echolocation,rapid karyotype evolution,...Horseshoe bats(genus Rhinolophus,family Rhinolophidae)represent an important group within chiropteran phylogeny due to their distinctive traits,including constant high-frequency echolocation,rapid karyotype evolution,and unique immune system.Advances in evolutionary biology,supported by high-quality reference genomes and comprehensive whole-genome data,have significantly enhanced our understanding of species origins,speciation mechanisms,adaptive evolutionary processes,and phenotypic diversity.However,genomic research and understanding of the evolutionary patterns of Rhinolophus are severely constrained by limited data,with only a single published genome of R.ferrumequinum currently available.In this study,we constructed a high-quality chromosome-level reference genome for the intermediate horseshoe bat(R.affinis).Comparative genomic analyses revealed potential genetic characteristics associated with virus tolerance in Rhinolophidae.Notably,we observed expansions in several immune-related gene families and identified various genes functionally associated with the SARS-CoV-2 signaling pathway,DNA repair,and apoptosis,which displayed signs of rapid evolution.In addition,we observed an expansion of the major histocompatibility complex class II(MHC-II)region and a higher copy number of the HLA-DQB2 gene in horseshoe bats compared to other chiropteran species.Based on whole-genome resequencing and population genomic analyses,we identified multiple candidate loci(e.g.,GLI3)associated with variations in echolocation call frequency across R.affinis subspecies.This research not only expands our understanding of the genetic characteristics of the Rhinolophus genus but also establishes a valuable foundation for future research.展开更多
基金supported by the Natural Science Foundation of Anhui Province(2408085MC058 and 2308085QC91)National Natural Science Foundation of China(32301783and U21A20214)+5 种基金Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS CSIAF-202303)Nanfan special project,CAAS(YYLH2309,YBXM2322,YYLH2401)Scientific Innovation 2030 Project(2022ZD0401703)CAAS Innovative Team Award,Science and Technology of Innovative research program of Anhui Province(202423m1005002)National Key Research and Development Program of China(2023YFD1200900)the Natural Science Foundation General Program of Hebei Province(C2024204242).
文摘IR64 is an elite Xian/indica variety developed by International Rice Research Institute(IRRl)in 1985,which has been the most widely grown variety and core breeding parent in South/Southeast Asia(Mackill and Khush,2018).IR64 has been utilized to develop stress-tolerant(such as drought-adapted and submergenceresistant)near-isogenic lines,underscoring its great potential in agricultural genomics(Tanaka et al.,2020).
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institution(PAPD).
文摘Inoculation of starter culture is a viable method to improve the quality of fermented foods,but its effect on the flavor metabolite profiles and the underlying mechanisms are still unclear.This study aimed to elucidate the effects of starters(Lactiplantibacillus plantarum(LP)and Staphylococcus simulans(SS)individually or in combination(LS))on the flavor metabolite profiles of fermented sausages via metabolomics and genomics.L.plantarum markedly modified the composition of bacterial communities and made Lactobacillus spp.dominant in sausages(98.29%and 85.03%in LP and LS groups,respectively).Additionally,inoculation with a single starter,L.plantarum,and a mixed starter yielded similar non-volatile flavor metabolites,which were mainly characterized at the amino acid and peptide levels(relative intensities of 349.65 and 348.62 for the LP and LS groups,respectively).Meanwhile,the mixed starter group had the most volatile flavor metabolites(relative intensity of 34728.67),some of which were contributed by L.plantarum,such as ethyl acetate(relative intensities of 583.33 and 588.33 for the LP and LS groups,respectively)and benzaldehyde(relative intensities of 786.67 and 909.00 for the LP and LS groups,respectively),and several of which were generated by S.simulans,such as ethyl propionate(relative intensities of 214.67 and 136.67 for the SS and LS groups,respectively)and benzyl alcohol(relative intensities of 720.00 and 656.00 for the SS and LS groups,respectively).Furthermore,L.plantarum was found to possess more genes encoding peptidases(48)and carbohydrate-active enzymes(124),while S.simulans had more genes related to lipid hydrolysis(12).In conclusion,differences in the properties and combinations of indigenous strains play a crucial role in the generation of flavor metabolites in sausages.
基金funded by the National Natural Science Foundation of China(grant no.31670207).
文摘The genus Clematis(Ranunculaceae)comprises over 300 species with remarkable morphological and ecological diversity worldwide.Despite its horticultural,medicinal,and ecological importance,a well-resolved phylogeny and coherent infrageneric classification are still lacking.Here,we reconstruct a robust phylogeny for Clematis using a phylogenomic approach and revise its infrageneric taxonomy.We incorporated 198 samples representing 151 species,two subspecies,and 12 varieties,covering all subgenera and most sections worldwide,obtained from both fresh and herbarium material.Nuclear single nucleotide polymorphisms(SNPs)and complete plastid genomes were assembled for phylogenetic analyses.We also prepared a nuclear ribosomal ITS(nrITS)dataset comprising 171 species,two subspecies,and 12 varieties(217 samples)to include as many species as possible for phylogenetic inference.Phylogenies based on plastid genomes and nrITS exhibited limited resolution and modest support,highlighting challenges in resolving certain relationships.Nuclear SNP analyses yielded a robust phylogenetic tree with 22 well-supported clades corresponding to 22 sections,with most previously recognized subgenera and sections not recovered as monophyletic.Ancestral state reconstruction of 12 key morphological characters revealed multiple independent origins of character states.This study presents the first comprehensive sectional classification for Clematis based on robust phylogenomic evidence,redefines morphological characteristics for each section,and resolves long-standing taxonomic ambiguities.Our results establish a framework for future studies on the evolution,ecology,and horticultural potential of this globally significant genus.
基金sponsored by the Inner Mongolia Natural Science Foundation(2025MS03134)National Natural Science Foundation of China(32372566)+3 种基金the Department of Agriculture and Rural Affairs of Zhejiang Province(2025ZDXT02-6)Sichuan rapeseed innovation team(SCCXTD-2024-3)Key Support Projects for Foreign Experts(D20240074)Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry(CIC-MCP)。
文摘Modern oilseed breeding faces the complex challenge of simultaneously improving yield,nutritional quality,and stress resilience within a sustainable agricultural framework.A predictive,systems-oriented strategy offers a solution by using predictive modeling and precise genetic intervention to engineer target ideotypes.This review synthesizes the integration of genomic selection(GS),genome editing,and epigenetic regulation to operationalize a precision breeding strategy in major oilseed crops.We document how GS,utilizing high-density SNP arrays and sequencing data,has reduced breeding cycles by up to 50%in soybean,enabling rapid selection for complex traits like seed protein content.We highlight the precision of CRISPR-Cas systems in executing design goals,such as creating commercial-grade high-oleic soybeans(>80%oleic acid)by knocking out FAD2 genes.Similarly,editing glucosinolate biosynthesis genes in rapeseed has directly improved meal quality.Furthermore,we explore the emerging role of epigenetic regulation as a tunable layer in trait optimization,where DNA methylation patterns in sunflower are linked to drought stress memory and flowering time.Finally,we present an integrated molecular framework,which synergizes these technologies to develop ideotypes with optimized architectures and composition.Despite challenges in phenotyping and global regulation,the strategic implementation of this molecular toolkit is pioneering a new era of precision breeding for the sustainable intensification of oilseed production.
基金the Natural Science Foundation of Shandong Province(ZR2020QC022)the Science and Technology Basic Resources Investigation Program of China(No.2019FY100900)+2 种基金the Major Program for Basic Research Project of Yunnan Province(202401BC070001)Yunnan Revitalization Talent Support Program:Yunling Scholar Project to Tingshuang Yithe open research project of“Cross Cooperative Team”of the Germplasm Bank of Wild Species,Kunming Institute of Botany,Chinese Academy of Sciences.
文摘The plastid genome(plastome)represents an indispensable molecular resource for studying plant phylogeny and evolution.Although plastome size is much smaller than that of nuclear genomes,accurately and efficientlyannotating and utilizing plastome sequences remain challenging.Therefore,a streamlined phylogenomic pipeline spanning plastome annotation,phylogenetic reconstruction and comparative genomics would greatly facilitate research utilizing this important organellar genome.Here,we develop PlastidHub,a novel web application employing innovative tools to analyze plastome sequences.In comparison with existing tools,key novel functionalities in PlastidHub include:(1)standardization of quadripartite structure;(2)improvement of annotation flexibility and consistency;(3)quantitative assessment of annotation completeness;(4)diverse extraction modes for canonical and specialized sequences;(5)intelligent screening of molecular markers for biodiversity studies;(6)genelevel visual comparison of structural variations and annotation completeness.PlastidHub features cloud-based web applications that do not require users to install,update,or maintain tools;detailed help documents including user guides,test examples,a static pop-up prompt box,and dynamic pop-up warning prompts when entering unreasonable parameter values;batch processing capabilities for all tools;intermediate results for secondary use;and easy-to-operate task flows between fileupload and download.A key feature of PlastidHub is its interrelated task-based user interface design.Give that PlastidHub is easy to use without specialized computational skills or resources,this new platform should be widely used among botanists and evolutionary biologists,improving and expediting research employing the plastome.PlastidHub is available at https://www.plastidhub.cn.
基金supported by grants from the National Natural Science Foundation of China(Grant No.82272008)The Science&Technology Development Fund of Tianjin Education Commission for Higher Education(Grant No.2021KJ194)Tianjin Key Medical Discipline(Specialty)Construction Project(Grant No.TJYXZDXK-009A).
文摘Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis,thus providing strong support for personalized treatments.In radiomics,AI can analyze high-dimensional features from computed tomography(CT),magnetic resonance imaging(MRI),and positron emission tomography/computed tomography(PET/CT)images to discover imaging biomarkers associated with tumor heterogeneity,treatment response,and disease progression,thereby enabling non-invasive,real-time assessments for personalized therapy.Pathomics leverages AI for deep analysis of digital pathology images,and can uncover subtle changes in tissue microenvironments,cellular characteristics,and morphological features,and offer unique insights into immunotherapy response prediction and biomarker discovery.These AI-driven technologies not only enhance the speed,accuracy,and robustness of biomarker discovery but also significantly improve the precision,personalization,and effectiveness of clinical treatments,and are driving a shift from empirical to precision medicine.Despite challenges such as data quality,model interpretability,integration of multi-modal data,and privacy protection,the ongoing advancements in AI,coupled with interdisciplinary collaboration,are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction.These improvements are expected to lead to more accurate,personalized treatment strategies and ultimately better patient outcomes,marking a significant step forward in the evolution of precision medicine.
基金supported by the National Natural Science Foundation of China(32470676 and 32170236)Central Guidance on Local Science and Technology Development Fund of Hebei Province(246Z2508G)+2 种基金Hebei Natural Science Foundation(C2020209064)Tangshan Science and Technology Program Project(21130217C)Key research project of North China University of Science and Technology(ZD-YG-202313-23).
文摘As a high-value eudicot family,many famous horticultural crop genomes have been deciphered in Oleaceae.However,there are currently no bioinformatics platforms focused on empowering genome research in Oleaceae.Herein,we developed the first comprehensive Oleaceae Genome Research Platform(OGRP,https://oleaceae.cgrpoee.top/).In OGRP,70 genomes of 10 Oleaceae species and 46 eudicots and 366 transcriptomes involving 18 Oleaceae plant tissues can be obtained.We built 34 window-operated bioinformatics tools,collected 38 professional practical software programs,and proposed 3 new pipelines,namely ancient polyploidization identification,ancestral karyotype reconstruction,and gene family evolution.Employing these pipelines to reanalyze the Oleaceae genomes,we clarified the polyploidization,reconstructed the ancestral karyotypes,and explored the effects of paleogenome evolution on genes with specific biological regulatory roles.Significantly,we generated a series of comparative genomic resources focusing on the Oleaceae,comprising 108 genomic synteny dot plots,1952225 collinear gene pairs,multiple genome alignments,and imprints of paleochromosome rearrangements.Moreover,in Oleaceae genomes,researchers can efficiently search for 1785987 functional annotations,22584 orthogroups,29582 important trait genes from 74 gene families,12664 transcription factor-related genes,9178872 transposable elements,and all involved regulatory pathways.In addition,we provided downloads and usage instructions for the tools,a species encyclopedia,ecological resources,relevant literatures,and external database links.In short,ORGP integrates rich data resources and powerful analytical tools with the characteristic of continuous updating,which can efficiently empower genome research and agricultural breeding in Oleaceae and other plants.
基金supported by Wu Jieping Medical Foundation(320.6750.2022-19-40 and 320.6750.2024-18-41)Guangxi University Young and Middle-Aged Teachers Research Basic Ability Improvement Project(2024KY0510)Guangxi Health Commission Self-Funded Research Project(Z-C20231002).
文摘Background:Neoadjuvant chemotherapy(NAC)significantly enhances clinical outcomes in patients with triple-negative breast cancer(TNBC);however,chemoresistance frequently results in treatment failure.Consequently,understanding the mechanisms underlying resistance and accurately predicting this phenomenon are crucial for improving treatment efficacy.Methods:Ultrasound images from 62 patients,taken before and after neoadjuvant therapy,were collected.Mitochondrial-related genes were extracted from a public database.Ultrasound features associated with NAC resistance were identified and correlated with significant mitochondrial-related genes.Subsequently,a prognostic model was developed and evaluated using the GSE58812 dataset.We also assessed this model alongside clinical factors and its ability to predict immunotherapy response.Results:A total of 32 significant differentially expressed genes in TNBC across three groups indicated a strong correlation with ultrasound features.Univariate and multivariate Cox regression analyses identified six genes as independent risk factors for TNBC prognosis.Based on these six mitochondrial-related genes,we constructed a TNBC prognostic model.The model’s risk scores indicated that high-risk patients generally have a poorer prognosis compared to low-risk patients,with the model demonstrating high predictive performance(p=0.002,AUC=0.745).This conclusion was further supported in the test set(p=0.026,AUC=0.718).Additionally,we found that high-risk patients exhibited more advanced tumor characteristics,while low-risk patients were more sensitive to common chemotherapy drugs and immunotherapy.The signature-related genes also predicted immunotherapy response with a high accuracy of 0.765.Conclusion:We identified resistance-related features from ultrasound images and integrated them with genomic data,enabling effective risk stratification of patients and prediction of the efficacy of neoadjuvant chemotherapy and immunotherapy in patients with TNBC.
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A20210,31722048,and 32102382)Central Public-interest Scientific Institution Basal Research Fund,The Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Horticultural Crops,Ministry of Agriculture and Rural Affairs,China.
文摘With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with particular promise in plant genomics.This review systematically examines the progress of DL applications in DNA,RNA,and protein sequence analysis,covering key tasks such as gene regulatory element identification,gene function annotation,and protein structure prediction,and highlighting how these DL applications illuminate research of plants,including horticultural plants.We evaluate the advantages of different neural network architectures and their applications in different biology studies,as well as the development of large language models(LLMs)in genomic modelling,such as the plantspecific models PDLLMs and AgroNT.We also briefly introduce the general workflow of the basic DL model for plant genomics study.While DL has significantly improved prediction accuracy in plant genomics,its broader application remains constrained by several challenges,including the limited availability of well-annotated data,computational capacity,innovative model architectures adapted to plant genomes,and model interpretability.Future advances will require interdisciplinary collaborations to develop DL applications for intelligent plant genomic research frameworks with broader applicability.
基金supported by the Consultative Group of International Agricultural Research,Science Programme-Breeding for Tomorrow,India(Grant No.SP01/B4T/AoW02).
文摘Rice cultivation contributes up to 12%of global anthropogenic methane(CH4)emissions,making it a significant climate concern.With rice demand projected to double by 2050,achieving the required 2.4%annual genetic gain must be balanced with emission reduction.This review synthesizes recent progress in three key areas:(1)mitigation strategies such as alternate wetting and drying and direct-seeded rice,which can reduce CH4 emissions by 30%-40%;(2)identification of physiological and molecular traits,such as short duration,high harvest index,improved nitrogen use efficiency,optimized root architecture,and stress tolerance with reduced greenhouse gas(GHG)footprints;and(3)the potential of genomics-assisted breeding and high-throughput phenotyping to accelerate the development of climate-resilient rice varieties with lower CH4 emissions.Specifically,we highlight how the synergistic integration of high-throughput phenotyping,genomic selection,and marker-assisted breeding can substantially improve the efficiency and precision of breeding programs targeting the development of climate-resilient rice varieties with reduced CH4 emissions.This is exemplified through successful case studies utilizing multi-omics approaches,including the development of Green Super Rice varieties(GSR 2 and GSR 8),which have demonstrated up to a 37%reduction in GHG emissions.Crucially,we propose a stratified trait profile for low-GHG rice development and provide guidelines and metrics for integrating these traits into mainstream breeding pipelines.We conclude by proposing a strategic framework integrating carbon-efficient breeding,climate-adapted agronomy,and policy support,which is essential for scaling low-GHG rice systems globally.
基金supported by the China Agriculture Research System of MOF and MARA(CARS-42-4)School Cooperation Project of Ya’an(21SXHZ0028)the Key Technology Support Program of Sichuan Province,China(2021YFYZ0014),for the financial support。
文摘The low egg production of goose greatly limits the development of the industry.China possesses the most abundant goose breeds resources.In this study,genome resequencing data of swan goose(Anser cygnoides)and domesticated high and low laying goose breeds(Anser cygnoides domestiation)were used to identify key genes related to egg laying ability in geese and verify their functions.Selective sweep analyses revealed 416 genes that were specifically selected during the domestication process from swan geese to high laying geese.Furthermore,SNPs and Indels markers were used in GWAS analyses between high and low laying breed geese.The results showed that RTCB,BPIFC,SYN3,SYNE1,VIP,and ESR1 may be related to the differences in laying ability of geese.Notably,only ESR1 was identified simultaneously by GWAS and selective sweep analysis.The genotype of Indelchr3:54429172,located downstream of ESR1,was confirmed to affect the expression of ESR1 in the ovarian stroma and showed significant correlation with body weight at first egg and laying frequency of geese.CCK-8,EdU,and flow cytometry confirmed that ESR1 can promote the apoptosis of goose pre-hierarchical follicles ganulosa cells(phGCs)and inhibit their proliferation.Combined with transcriptome data,it was found ESR1 involved in the function of goose phGCs may be related to MAPK and TGF-beta signaling pathways.Overall,our study used genomic information from different goose breeds to identify an indel located in the downstream of ESR1 associated with goose laying ability.The main pathways and biological processes of ESR1 involved in the regulation of goose laying ability were identified by cell biology and transcriptomics methods.These results are helpful to further understand the laying ability characteristics of goose and improve the egg production of geese.
文摘The Beijing Institute of Genomics(BIG)of the Chinese Academy of Sciences,as the leading Institute in Genomics,has walked through 20 year’s journey since being founded in November 2003.From participating in the Human Genome Project(HGP)in completing the“1%task”to independently accomplishing the super-hybrid rice genome and other several national and international genome projects,BIG has made tremendous contributions in genomics research and development in China.In 2024,bearing great ambition and responsibility,BIG is transformed to the China National Center for Bioinformation(CNCB),aiming to become a global hub in bioinformatics big data services,innovation,and entrepreneurship.With the completion of its new infrastructure in 2027,CNCB is looking into a brighter future.
基金the National Science Foundation for Young Scientists of China(32201780)the Fundamental Research Fund for the Central Universities(77000–12240011)+2 种基金Shenzhen Postdoctoral Funding Project(szbo202410)the National Natural Science Foundation of China(32241045 and 32241038)the National Key Research and Development Program of China(2022YFD1500503,2023YFD1200700,and 2023YFD1200704).
文摘Sorghum,renowned for its substantial biomass production and remarkable tolerance to various stresses,possesses extensive gene resources and phenotypic variations.A comprehensive understanding of the genetic basis underlying complex agronomic traits is essential for unlocking the potential of sorghum in addressing food and feed security and utilizing marginal lands.In this context,we provide an overview of the major trends in genomic resource studies focusing on key agronomic traits over the past decade,accompanied by a summary of functional genomic platforms.We also delve into the molecular functions and regulatory networks of impactful genes for important agricultural traits.Lastly,we discuss and synthesize the current challenges and prospects for advancing molecular design breeding by gene-editing and polymerization of the excellent alleles,with the aim of accelerating the development of desired sorghum varieties.
基金supported by the National Basic Research Program of China(No.2004CB117200)the National Natural Science Foundation of China(No.31261140368)
文摘Plant germplasm underpins much of crop genetic improvement. Millions of germplasm accessions have been collected and conserved ex situ and/or in situ, and the major challenge is now how to exploit and utilize this abundant resource. Genomics-based plant germplasm research （GPGR） or ＂Cenoplasmics＂ is a novel cross-disciplinary research field that seeks to apply the principles and techniques of genomics to germplasm research. We describe in this paper the concept, strategy, and approach behind GPGR, and summarize current progress in the areas of the definition and construction of core collections, enhancement of germplasm with core collections, and gene discovery from core collections. GPGR is opening a new era in germplasm research. The contribution, progress and achievements of GPGR in the future are predicted.
基金supported by the China Postdoctoral Science Foundation(2022M722020)to Z.L.Key Project of Scientific Research Program of Shaanxi Provincial Education Department(23JY020)to Z.L.+5 种基金Natural Science Basic Research Program of Shaanxi(2024JCYBMS-152)to Z.L.Key Projects of Shaanxi University of Technology(SLGKYXM2302)to Z.L.Opening Foundation of Shaanxi University of Technology(SLGPT2019KF02-02)to Z.L.Natural Science Basic Research Program of Shaanxi(2020JM-280)to G.L.Fundamental Research Funds for the Central Universities(GK201902008)to G.LNational Natural Science Foundation of China(31570378)to X.M.
文摘Horseshoe bats(genus Rhinolophus,family Rhinolophidae)represent an important group within chiropteran phylogeny due to their distinctive traits,including constant high-frequency echolocation,rapid karyotype evolution,and unique immune system.Advances in evolutionary biology,supported by high-quality reference genomes and comprehensive whole-genome data,have significantly enhanced our understanding of species origins,speciation mechanisms,adaptive evolutionary processes,and phenotypic diversity.However,genomic research and understanding of the evolutionary patterns of Rhinolophus are severely constrained by limited data,with only a single published genome of R.ferrumequinum currently available.In this study,we constructed a high-quality chromosome-level reference genome for the intermediate horseshoe bat(R.affinis).Comparative genomic analyses revealed potential genetic characteristics associated with virus tolerance in Rhinolophidae.Notably,we observed expansions in several immune-related gene families and identified various genes functionally associated with the SARS-CoV-2 signaling pathway,DNA repair,and apoptosis,which displayed signs of rapid evolution.In addition,we observed an expansion of the major histocompatibility complex class II(MHC-II)region and a higher copy number of the HLA-DQB2 gene in horseshoe bats compared to other chiropteran species.Based on whole-genome resequencing and population genomic analyses,we identified multiple candidate loci(e.g.,GLI3)associated with variations in echolocation call frequency across R.affinis subspecies.This research not only expands our understanding of the genetic characteristics of the Rhinolophus genus but also establishes a valuable foundation for future research.
