Alfalfa(Medicago sativa L.)is one of the most important and widely cultivated forage crops.It is commonly used as a vegetable and medicinal herb because of its excellent nutritional quality and significant economic va...Alfalfa(Medicago sativa L.)is one of the most important and widely cultivated forage crops.It is commonly used as a vegetable and medicinal herb because of its excellent nutritional quality and significant economic value.Based on Illumina,Nanopore and Hi-C data,we assembled a chromosome-scale assembly of Medicago sativa spp.caerulea(voucher PI464715),the direct diploid progenitor of autotetraploid alfalfa.The assembled genome comprises 793.2 Mb of genomic sequence and 47,202 annotated protein-coding genes.The contig N50 length is 3.86 Mb.This genome is almost twofold larger and contains more annotated protein-coding genes than that of its close relative,Medicago truncatula(420 Mb and 44,623 genes).The more expanded gene families compared with those in M.truncatula and the expansion of repetitive elements rather than whole-genome duplication(i.e.,the two species share the ancestral Papilionoideae whole-genome duplication event)may have contributed to the large genome size of M.sativa spp.caerulea.Comparative and evolutionary analyses revealed that M.sativa spp.caerulea diverged from M.truncatula~5.2 million years ago,and the chromosomal fissions and fusions detected between the two genomes occurred during the divergence of the two species.In addition,we identified 489 resistance(R)genes and 82 and 85 candidate genes involved in the lignin and cellulose biosynthesis pathways,respectively.The near-complete and accurate diploid alfalfa reference genome obtained herein serves as an important complement to the recently assembled autotetraploid alfalfa genome and will provide valuable genomic resources for investigating the genomic architecture of autotetraploid alfalfa as well as for improving breeding strategies in alfalfa.展开更多
Freeze injury during the seedling stage significantly impacts wheat growth and yield,making the development of freeze-tolerant varieties crucial for ensuring stable yields.To identify key genetic factors for wheat fre...Freeze injury during the seedling stage significantly impacts wheat growth and yield,making the development of freeze-tolerant varieties crucial for ensuring stable yields.To identify key genetic factors for wheat freeze tolerance,an accurate assessment of freeze tolerance is necessary.However,traditional methods,such as visual inspection,are subjective and can vary significantly among observers.In this study,we developed FreezeNet,a lightweight deep learning model designed to accurately quantify freeze injury using an image-based phenotyping method.Freeze tolerance traits,including vegetation area(VA),green vegetation area(GVA),yellow vegetation fraction(YVF),and mean hue value(mHue),were extracted for freeze tolerance assessment.We captured standardized images with a smartphone and used FreezeNet to extract the freeze tolerance traits for 220 wheat accessions.These traits were strongly correlated with traditional injury scores estimated through visual in-spection.Moreover,they presented relatively high heritability.Using these traits,we conducted genome-wide association studies(GWASs)to identify genetic loci associated with freeze tolerance.Eleven significant QTLs associated with freeze tolerance were identified,including 8 novel loci.By integrating four of these loci into a wheat germplasm that lacked any of the 11 QTLs,we significantly enhanced its freeze resistance,demonstrating the practical application of these genetic loci in breeding for improved freeze tolerance.Our results highlight FreezeNet as an advanced tool for assessing wheat freeze injury and identifying the genetic factors responsible for freeze tolerance,with the potential to guide breeding efforts toward the development of more resilient wheat varieties.展开更多
基金supported equally by the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(2019QZKK0502)the National Natural Science Foundation of China(31971391)further supported by the National Natural Science Foundation of China(41901056 and 31722055).
文摘Alfalfa(Medicago sativa L.)is one of the most important and widely cultivated forage crops.It is commonly used as a vegetable and medicinal herb because of its excellent nutritional quality and significant economic value.Based on Illumina,Nanopore and Hi-C data,we assembled a chromosome-scale assembly of Medicago sativa spp.caerulea(voucher PI464715),the direct diploid progenitor of autotetraploid alfalfa.The assembled genome comprises 793.2 Mb of genomic sequence and 47,202 annotated protein-coding genes.The contig N50 length is 3.86 Mb.This genome is almost twofold larger and contains more annotated protein-coding genes than that of its close relative,Medicago truncatula(420 Mb and 44,623 genes).The more expanded gene families compared with those in M.truncatula and the expansion of repetitive elements rather than whole-genome duplication(i.e.,the two species share the ancestral Papilionoideae whole-genome duplication event)may have contributed to the large genome size of M.sativa spp.caerulea.Comparative and evolutionary analyses revealed that M.sativa spp.caerulea diverged from M.truncatula~5.2 million years ago,and the chromosomal fissions and fusions detected between the two genomes occurred during the divergence of the two species.In addition,we identified 489 resistance(R)genes and 82 and 85 candidate genes involved in the lignin and cellulose biosynthesis pathways,respectively.The near-complete and accurate diploid alfalfa reference genome obtained herein serves as an important complement to the recently assembled autotetraploid alfalfa genome and will provide valuable genomic resources for investigating the genomic architecture of autotetraploid alfalfa as well as for improving breeding strategies in alfalfa.
基金This work was supported by the National Key Research and Development Program of China(2023YFF1000100)the Biological Breeding-National Science and Technology Major Project(2023ZD04076).
文摘Freeze injury during the seedling stage significantly impacts wheat growth and yield,making the development of freeze-tolerant varieties crucial for ensuring stable yields.To identify key genetic factors for wheat freeze tolerance,an accurate assessment of freeze tolerance is necessary.However,traditional methods,such as visual inspection,are subjective and can vary significantly among observers.In this study,we developed FreezeNet,a lightweight deep learning model designed to accurately quantify freeze injury using an image-based phenotyping method.Freeze tolerance traits,including vegetation area(VA),green vegetation area(GVA),yellow vegetation fraction(YVF),and mean hue value(mHue),were extracted for freeze tolerance assessment.We captured standardized images with a smartphone and used FreezeNet to extract the freeze tolerance traits for 220 wheat accessions.These traits were strongly correlated with traditional injury scores estimated through visual in-spection.Moreover,they presented relatively high heritability.Using these traits,we conducted genome-wide association studies(GWASs)to identify genetic loci associated with freeze tolerance.Eleven significant QTLs associated with freeze tolerance were identified,including 8 novel loci.By integrating four of these loci into a wheat germplasm that lacked any of the 11 QTLs,we significantly enhanced its freeze resistance,demonstrating the practical application of these genetic loci in breeding for improved freeze tolerance.Our results highlight FreezeNet as an advanced tool for assessing wheat freeze injury and identifying the genetic factors responsible for freeze tolerance,with the potential to guide breeding efforts toward the development of more resilient wheat varieties.
