QTL mapping of flowering time and biomass yield in tetraploid alfalfa (Medicago sativa L.)

A population genomics approach to unlock the genetic potential of lablab (Lablab purpureus (L.) Sweet), an underutilized tropical forage crop

BACKGROUND

Lablab is one of the conventionally grown multi-purpose crops that originated in Africa. It is an annual or short-lived perennial forage legume which has versatile uses (as a vegetable and dry seeds, as food or feed, or as green manure) but is yet to receive adequate research attention and hence remains underexploited. To develop new and highly productive lablab varieties, using genomics-assisted selection, the present study aimed to identify quantitative trait loci associated with agronomically important traits in lablab and to assess the stability of these traits across two different agro-ecologies in Ethiopia. Here, we resequenced one hundred and forty-two lablab accessions, utilised whole genome genotyping approaches, and conducted multi-locational phenotyping over two years.

RESULTS

The selected lablab accessions displayed significant agro-morphological variation in eight analysed traits, including plant height, total fresh weight, and total dry weight. Furthermore, the agronomic performance of the accessions was significantly different across locations and years, highlighting substantial genotype-by-environment interactions. The population genetic structure of the lablab accessions, based on ~ 500,000 informative single nucleotide polymorphisms (SNPs), revealed an independent domestication pattern for two-seeded and four-seeded lablab accessions. Finally, based on multi-environmental trial data, a genome-wide association study (GWAS) identified useful SNPs and k-mers for key traits, such as plant height and total dry weight.

CONCLUSIONS

The publicly available genomic tools and field evaluation data from this study will offer a valuable resource for plant breeders and researchers to inform a new cycle of lablab breeding. With the aid of these tools, the breeding cycle will be significantly reduced and livestock farmers will have access to improved lablab varieties in a shorter time-frame.

Pearl millet a promising fodder crop for changing climate: a review

The agricultural sector faces colossal challenges amid environmental changes and a burgeoning human population. In this context, crops must adapt to evolving climatic conditions while meeting increasing production demands. The dairy industry is anticipated to hold the highest value in the agriculture sector in future. The rise in the livestock population is expected to result in an increased demand for fodder feed. Consequently, it is crucial to seek alternative options, as crops demand fewer resources and are resilient to climate change. Pearl millet offers an apposite key to these bottlenecks, as it is a promising climate resilience crop with significantly low energy, water and carbon footprints compared to other crops. Numerous studies have explored its potential as a fodder crop, revealing promising performance. Despite its capabilities, pearl millet has often been overlooked. To date, few efforts have been made to document molecular aspects of fodder-related traits. However, several QTLs and candidate genes related to forage quality have been identified in other fodder crops, which can be harnessed to enhance the forage quality of pearl millet. Lately, excellent genomic resources have been developed in pearl millet allowing deployment of cutting-edge genomics-assisted breeding for achieving a higher rate of genetic gains. This review would facilitate a deeper understanding of various aspects of fodder pearl millet in retrospect along with the future challenges and their solution. This knowledge may pave the way for designing efficient breeding strategies in pearl millet thereby supporting sustainable agriculture and livestock production in a changing world.

Identification of superior haplotypes for flowering time in pigeonpea through candidate gene-based association study of a diverse minicore collection

KEY MESSAGE

In current study candidate gene (261 genes) based association mapping on 144 pigeonpea accessions for flowering time and related traits and 29 MTAs producing eight superior haplotypes were identified. In the current study, we have conducted an association analysis for flowering-associated traits in a diverse pigeonpea mini-core collection comprising 144 accessions using the SNP data of 261 flowering-related genes. In total, 13,449 SNPs were detected in the current study, which ranged from 743 (ICP10228) to 1469 (ICP6668) among the individuals. The nucleotide diversity (0.28) and Watterson estimates (0.34) reflected substantial diversity, while Tajima's D (-0.70) indicated the abundance of rare alleles in the collection. A total of 29 marker trait associations (MTAs) were identified, among which 19 were unique to days to first flowering (DOF) and/or days to fifty percent flowering (DFF), 9 to plant height (PH), and 1 to determinate (Det) growth habit using 3 years of phenotypic data. Among these MTAs, six were common to DOF and/or DFF, and four were common to DOF/DFF along with the PH, reflecting their pleiotropic action. These 29 MTAs spanned 25 genes, among which 10 genes clustered in the protein-protein network analysis, indicating their concerted involvement in floral induction. Furthermore, we identified eight haplotypes, four of which regulate late flowering, while the remaining four regulate early flowering using the MTAs. Interestingly, haplotypes conferring late flowering (H001, H002, and H008) were found to be taller, while those involved in early flowering (H003) were shorter in height. The expression pattern of these genes, as inferred from the transcriptome data, also underpinned their involvement in floral induction. The haplotypes identified will be highly useful to the pigeonpea breeding community for haplotype-based breeding.

The Extraordinary Alkali Bee, Nomia melanderi (Halictidae), the World's Only Intensively Managed Ground-Nesting Bee

Among the ground-nesting bees are several proven crop pollinators, but only the alkali bee (Nomia melanderi) has been successfully managed. In <80 years, it has become the world's most intensely studied ground-nesting solitary bee. In many ways, the bee seems paradoxical. It nests during the torrid, parched midsummer amid arid valleys and basins of the western United States, yet it wants damp nesting soil. In these basins, extensive monocultures of an irrigated Eurasian crop plant, alfalfa (lucerne), subsidize millions of alkali bees. Elsewhere, its polylectic habits and long foraging range allow it to stray into neighboring crops contaminated with insecticides. Primary wild floral hosts are either non-native or poorly known. Kleptoparasitic bees plague most ground nesters, but not alkali bees, which do, however, host other well-studied parasitoids. Building effective nesting beds requires understanding the hydraulic conductivity of silty nesting soils and its important interplay with specific soil mineral salts. Surprisingly, some isolated populations endure inhospitably cold climates by nesting amid hot springs. Despite the peculiarities and challenges associated with its management, the alkali bee remains the second most valuable managed solitary bee for US agriculture and perhaps the world.

CRISPR/Cas9-mediated knockout of a polyester synthase-like gene delays flowering time in alfalfa

KEY MESSAGE

T-DNA and CRISPR/Cas9-mediated knockout of polyester synthase-like genes delays flowering time in Arabidopsis thaliana and Medicago sativa (alfalfa). Thus, we here present the first report of edited alfalfa with delayed flowering.

Combining QTL mapping and RNA-Seq Unravels candidate genes for Alfalfa (Medicago sativa L.) leaf development

BACKGROUND

Leaf size affects crop canopy morphology and photosynthetic efficiency, which can influence forage yield and quality. It is of great significance to mine the key genes controlling leaf development for breeding new alfalfa varieties. In this study, we mapped leaf length (LL), leaf width (LW), and leaf area (LA) in an F1 mapping population derived from a cultivar named ZhongmuNo.1 with larger leaf area and a landrace named Cangzhou with smaller leaf area.

RESULTS

This study showed that the larger LW was more conducive to increasing LA. A total of 24 significant quantitative trait loci (QTL) associated with leaf size were identified on both the paternal and maternal linkage maps. Among them, nine QTL explained about 11.50-22.45% phenotypic variation. RNA-seq analysis identified 2,443 leaf-specific genes and 3,770 differentially expressed genes. Combining QTL mapping, RNA-seq alalysis, and qRT-PCR, we identified seven candidate genes associated with leaf development in five major QTL regions.

CONCLUSION

Our study will provide a theoretical basis for marker-assisted breeding and lay a foundation for further revealing molecular mechanism of leaf development in alfalfa.

A Genome-Wide Association Study Coupled With a Transcriptomic Analysis Reveals the Genetic Loci and Candidate Genes Governing the Flowering Time in Alfalfa (Medicago sativa L.)

The transition to flowering at the right time is very important for adapting to local conditions and maximizing alfalfa yield. However, the understanding of the genetic basis of the alfalfa flowering time remains limited. There are few reliable genes or markers for selection, which hinders progress in genetic research and molecular breeding of this trait in alfalfa. We sequenced 220 alfalfa cultivars and conducted a genome-wide association study (GWAS) involving 875,023 single-nucleotide polymorphisms (SNPs). The phenotypic analysis showed that the breeding status and geographical origin strongly influenced the alfalfa flowering time. Our GWAS revealed 63 loci significantly related to the flowering time. Ninety-five candidate genes were detected at these SNP loci within 40 kb (20 kb up- and downstream). Thirty-six percent of the candidate genes are involved in development and pollen tube growth, indicating that these genes are key genetic mechanisms of alfalfa growth and development. The transcriptomic analysis showed that 1,924, 2,405, and 3,779 differentially expressed genes (DEGs) were upregulated across the three growth stages, while 1,651, 2,613, and 4,730 DEGs were downregulated across the stages. Combining the results of our GWAS and transcriptome analysis, in total, 38 candidate genes (7 differentially expressed during the bud stage, 13 differentially expressed during the initial flowering stage, and 18 differentially expressed during the full flowering stage) were identified. Two SNPs located in the upstream region of the Msa0888690 gene (which is involved in isop renoids) were significantly related to flowering. The two significant SNPs within the upstream region of Msa0888690 existed as four different haplotypes in this panel. The genes identified in this study represent a series of candidate targets for further research investigating the alfalfa flowering time and could be used for alfalfa molecular breeding.

RAD-Seq-Based High-Density Linkage Maps Construction and Quantitative Trait Loci Mapping of Flowering Time Trait in Alfalfa (Medicago sativa L.)

Alfalfa (Medicago sativa L.) is a perennial forage crop known as the "Queen of Forages." To dissect the genetic mechanism of flowering time (FT) in alfalfa, high-density linkage maps were constructed for both parents of an F1 mapping population derived from a cross between Cangzhou (P1) and ZhongmuNO.1 (P2), consisting of 150 progenies. The FT showed a transgressive segregation pattern in the mapping population. A total of 13,773 single-nucleotide polymorphism markers was obtained by using restriction-site associated DNA sequencing and distributed on 64 linkage groups, with a total length of 3,780.49 and 4,113.45 cM and an average marker interval of 0.58 and 0.59 cM for P1 and P2 parent, respectively. Quantitative trait loci (QTL) analyses were performed using the least square means of each year as well as the best linear unbiased prediction values across 4 years. Sixteen QTLs for FT were detected for P1 and 22 QTLs for P2, accounting for 1.40-16.04% of FT variation. RNA-Seq analysis at three flowering stages identified 5,039, 7,058, and 7,996 genes that were differentially expressed between two parents, respectively. Based on QTL mapping, DEGs analysis, and functional annotation, seven candidate genes associated with flowering time were finally detected. This study discovered QTLs and candidate genes for alfalfa FT, making it a useful resource for breeding studies on this essential crop.

Silencing of MsD14 Resulted in Enhanced Forage Biomass through Increasing Shoot Branching in Alfalfa (Medicago sativa L.)

Branching is one of the key determinants of plant architecture that dramatically affects crop yield. As alfalfa is the most important forage crop, understanding the genetic basis of branching in this plant can facilitate breeding for a high biomass yield. In this study, we characterized the strigolactone receptor gene MsD14 in alfalfa and demonstrated that MsD14 was predominantly expressed in flowers, roots, and seedpods. Furthermore, we found that MsD14 expression could significantly respond to strigolactone in alfalfa seedlings, and its protein was located in the nucleus, cytoplasm, and cytomembrane. Most importantly, transformation assays demonstrated that silencing of MsD14 in alfalfa resulted in increased shoot branching and forage biomass. Significantly, MsD14 could physically interact with AtMAX2 and MsMAX2 in the presence of strigolactone, suggesting a similarity between MsD14 and AtD14. Together, our results revealed the conserved D14-MAX2 module in alfalfa branching regulation and provided candidate genes for alfalfa high-yield molecular breeding.

Controlling flowering of Medicago sativa (alfalfa) by inducing dominant mutations

Breeding plants with polyploid genomes is challenging because functional redundancy hampers the identification of loss-of-function mutants. Medicago sativa is tetraploid and obligate outcrossing, which together with inbreeding depression complicates traditional breeding approaches in obtaining plants with a stable growth habit. Inducing dominant mutations would provide an alternative strategy to introduce domestication traits in plants with high gene redundancy. Here we describe two complementary strategies to induce dominant mutations in the M. sativa genome and how they can be relevant in the control of flowering time. First, we outline a genome-engineering strategy that harnesses the use of microProteins as developmental regulators. MicroProteins are small proteins that appeared during genome evolution from genes encoding larger proteins. Genome-engineering allows us to retrace evolution and create microProtein-coding genes de novo. Second, we provide an inventory of genes regulated by microRNAs that control plant development. Making respective gene transcripts microRNA-resistant by inducing point mutations can uncouple microRNA regulation. Finally, we investigated the recently published genomes of M. sativa and provide an inventory of breeding targets, some of which, when mutated, are likely to result in dominant traits.

Genome assembly of alfalfa cultivar zhongmu-4 and identification of SNPs associated with agronomic traits

Alfalfa (Medicago sativa L.) is the most important legume forage crop worldwide with high nutritional value and yield. For a long time, the breeding of alfalfa was hampered by lacking reliable information on the autotetraploid genome and molecular markers linked to important agronomic traits. We herein reported the de novo assembly of the allele-aware chromosome-level genome of Zhongmu-4, a cultivar widely cultivated in China, and a comprehensive database of genomic variations based on resequencing of 220 germplasms. Approximate 2.74 Gb contigs (N50 of 2.06 Mb), accounting for 88.39% of the estimated genome, were assembled, and 2.56 Gb contigs were anchored to 32 pseudo-chromosomes. A total of 34,922 allelic genes were identified from the allele-aware genome. We observed the expansion of gene families, especially those related to the nitrogen metabolism, and the increase of repetitive elements including transposable elements, which probably resulted in the increase of Zhongmu-4 genome compared with Medicago truncatula. Population structure analysis revealed that the accessions from Asia and South America had relatively lower genetic diversity than those from Europe, suggesting that geography may influence alfalfa genetic divergence during local adaption. Genome-wide association studies identified 101 single nucleotide polymorphisms (SNPs) associated with 27 agronomic traits. Two candidate genes were predicted to be correlated with fall dormancy and salt response. We believe that the allele-aware chromosome-level genome sequence of Zhongmu-4 combined with the resequencing data of the diverse alfalfa germplasms will facilitate genetic research and genomics-assisted breeding in variety improvement of alfalfa.

Mapping freezing tolerance QTL in alfalfa: based on indoor phenotyping

BACKGROUND

Winter freezing temperature impacts alfalfa (Medicago sativa L.) persistence and seasonal yield and can lead to the death of the plant. Understanding the genetic mechanisms of alfalfa freezing tolerance (FT) using high-throughput phenotyping and genotyping is crucial to select suitable germplasm and develop winter-hardy cultivars. Several clones of an alfalfa F1 mapping population (3010 x CW 1010) were tested for FT using a cold chamber. The population was genotyped with SNP markers identified using genotyping-by-sequencing (GBS) and the quantitative trait loci (QTL) associated with FT were mapped on the parent-specific linkage maps. The ultimate goal is to develop non-dormant and winter-hardy alfalfa cultivars that can produce extended growth in the areas where winters are often mild.

RESULTS

Alfalfa FT screening method optimized in this experiment comprises three major steps: clone preparation, acclimation, and freezing test. Twenty clones of each genotype were tested, where 10 samples were treated with freezing temperature, and 10 were used as controls. A moderate positive correlation (r ~ 0.36, P < 0.01) was observed between indoor FT and field-based winter hardiness (WH), suggesting that the indoor FT test is a useful indirect selection method for winter hardiness of alfalfa germplasm. We detected a total of 20 QTL associated with four traits; nine for visual rating-based FT, five for percentage survival (PS), four for treated to control regrowth ratio (RR), and two for treated to control biomass ratio (BR). Some QTL positions overlapped with WH QTL reported previously, suggesting a genetic relationship between FT and WH. Some favorable QTL from the winter-hardy parent (3010) were from the potential genic region for a cold tolerance gene CBF. The BLAST alignment of a CBF sequence of M. truncatula, a close relative of alfalfa, against the alfalfa reference showed that the gene's ortholog resides around 75 Mb on chromosome 6.

CONCLUSIONS

The indoor freezing tolerance selection method reported is useful for alfalfa breeders to accelerate breeding cycles through indirect selection. The QTL and associated markers add to the genomic resources for the research community and can be used in marker-assisted selection (MAS) for alfalfa cold tolerance improvement.

Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50-70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green-Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

Genome-Wide Association Study of Maize Aboveground Dry Matter Accumulation at Seedling Stage

Dry matter accumulation and partitioning during the early phases of development could significantly affect crop growth and productivity. In this study, the aboveground dry matter (DM), the DM of different organs, and partition coefficients of a maize association mapping panel of 412 inbred lines were evaluated at the third and sixth leaf stages (V3 and V6). Further, the properties of these phenotypic traits were analyzed. Genome-wide association studies (GWAS) were conducted on the total aboveground biomass and the DM of different organs. Analysis of GWAS results identified a total of 1,103 unique candidate genes annotated by 678 significant SNPs (P value < 1.28e-6). A total of 224 genes annotated by SNPs at the top five of each GWAS method and detected by multiple GWAS methods were regarded as having high reliability. Pathway enrichment analysis was also performed to explore the biological significance and functions of these candidate genes. Several biological pathways related to the regulation of seed growth, gibberellin-mediated signaling pathway, and long-day photoperiodism were enriched. The results of our study could provide new perspectives on breeding high-yielding maize varieties.

Identification and characterization of regulatory pathways involved in early flowering in the new leaves of alfalfa (Medicago sativa L.) by transcriptome analysis

BACKGROUND

Alfalfa (Medicago sativa L.) is a perennial legume extensively planted throughout the world as a high nutritive value livestock forage. Flowering time is an important agronomic trait that contributes to the production of alfalfa hay and seeds. However, the underlying molecular mechanisms of flowering time regulation in alfalfa are not well understood.

RESULTS

In this study, an early-flowering alfalfa genotype 80 and a late-flowering alfalfa genotype 195 were characterized for the flowering phenotype. Our analysis revealed that the lower jasmonate (JA) content in new leaves and the downregulation of JA biosynthetic genes (i.e. lipoxygenase, the 12-oxophytodienoate reductase-like protein, and salicylic acid carboxyl methyltransferase) may play essential roles in the early-flowering phenotype of genotype 80. Further research indicated that genes encode pathogenesis-related proteins [e.g. leucine rich repeat (LRR) family proteins, receptor-like proteins, and toll-interleukin-like receptor (TIR)-nucleotide-binding site (NBS)-LRR class proteins] and members of the signaling receptor kinase family [LRR proteins, kinases domain of unknown function 26 (DUF26) and wheat leucine-rich repeat receptor-like kinase10 (LRK10)-like kinases] are related to early flowering in alfalfa. Additionally, those involved in secondary metabolism (2-oxoglutarate/Fe (II)-dependent dioxygenases and UDP-glycosyltransferase) and the proteasome degradation pathway [really interesting new gene (RING)/U-box superfamily proteins and F-box family proteins] are also related to early flowering in alfalfa.

CONCLUSIONS

Integrated phenotypical, physiological, and transcriptomic analyses demonstrate that hormone biosynthesis and signaling pathways, pathogenesis-related genes, signaling receptor kinase family genes, secondary metabolism genes, and proteasome degradation pathway genes are responsible for the early flowering phenotype in alfalfa. This will provide new insights into future studies of flowering time in alfalfa and inform genetic improvement strategies for optimizing this important trait.

Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.)

The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials.

Characterization of Squamosa-Promoter Binding Protein-Box Family Genes Reveals the Critical Role of MsSPL20 in Alfalfa Flowering Time Regulation

SQUAMOSA Promoter-binding protein-Like (SPL) genes affect a broad range of plant biological processes and show potential application in crop improvement by genetic modification. As the most widely planted forage crop in the world, biomass and abiotic stresses tolerance are important breeding targets for alfalfa (Medicago sativa L.). Nevertheless, the systematic analysis of SPL genes in alfalfa genome remains lacking. In the present study, we characterized 22 putative non-redundant SPL genes in alfalfa genome and uncovered the abundant structural variation among MsSPL genes. The phylogenetic analysis of plant SPL proteins separated them into 10 clades and clade J was an alfalfa-specific clade, suggesting SPL genes in alfalfa might have experienced gene duplication and functional differentiation within the genome. Meanwhile, 11 MsSPL genes with perfect matches to miRNA response elements (MREs) could be degraded by miR156, and the cleavage sites were gene specific. In addition, we investigated the temporal and spatial expression patterns of MsSPL genes and their expression patterns in response to multiple treatments, characterizing candidate SPL genes in alfalfa development and abiotic stress tolerant regulation. More importantly, overexpression of the alfalfa-specific SPL gene (MsSPL20) showed stable delayed flowering time, as well as increased biomass. Further studies indicated that MsSPL20 delayed flowering time by regulating the expression of genes involved in floret development, including HD3A, FTIP1, TEM1, and HST1. Together, our findings provide valuable information for future research and utilization of SPL genes in alfalfa and elucidate a possibly alfalfa-specific flowering time regulation, thereby supplying candidate genes for alfalfa molecular-assisted breeding.

Genome-Wide Identification, Expression Analysis and Functional Study of CCT Gene Family in Medicago truncatula

The control of flowering time has an important impact on biomass and the environmental adaption of legumes. The CCT (CO, COL and TOC1) gene family was elucidated to participate in the molecular regulation of flowering in plants. We identified 36 CCT genes in the M. truncatula genome and they were classified into three distinct subfamilies, PRR (7), COL (11) and CMF (18). Synteny and phylogenetic analyses revealed that CCT genes occurred before the differentiation of monocot and dicot, and CCT orthologous genes might have diversified among plants. The diverse spatial-temporal expression profiles indicated that MtCCT genes could be key regulators in flowering time, as well as in the development of seeds and nodules in M. truncatula. Notably, 22 MtCCT genes with typical circadian rhythmic variations suggested their different responses to light. The response to various hormones of MtCCT genes demonstrated that they participate in plant growth and development via varied hormones dependent pathways. Moreover, six MtCCT genes were dramatically induced by salinity and dehydration treatments, illustrating their vital roles in the prevention of abiotic injury. Collectively, our study provides valuable information for the in-depth investigation of the molecular mechanism of flowering time in M. truncatula, and it also provides candidate genes for alfalfa molecular breeding with ideal flowering time.

Transgenic Breeding Approaches for Improving Abiotic Stress Tolerance: Recent Progress and Future Perspectives

The recent rapid climate changes and increasing global population have led to an increased incidence of abiotic stress and decreased crop productivity. Environmental stresses, such as temperature, drought, nutrient deficiency, salinity, and heavy metal stresses, are major challenges for agriculture, and they lead to a significant reduction in crop growth and productivity. Abiotic stress is a very complex phenomenon, involving a variety of physiological and biochemical changes in plant cells. Plants exposed to abiotic stress exhibit enhanced levels of ROS (reactive oxygen species), which are highly reactive and toxic and affect the biosynthesis of chlorophyll, photosynthetic capacity, and carbohydrate, protein, lipid, and antioxidant enzyme activities. Transgenic breeding offers a suitable alternative to conventional breeding to achieve plant genetic improvements. Over the last two decades, genetic engineering/transgenic breeding techniques demonstrated remarkable developments in manipulations of the genes for the induction of desired characteristics into transgenic plants. Transgenic approaches provide us with access to identify the candidate genes, miRNAs, and transcription factors (TFs) that are involved in specific plant processes, thus enabling an integrated knowledge of the molecular and physiological mechanisms influencing the plant tolerance and productivity. The accuracy and precision of this phenomenon assures great success in the future of plant improvements. Hence, transgenic breeding has proven to be a promising tool for abiotic stress improvement in crops. This review focuses on the potential and successful applications, recent progress, and future perspectives of transgenic breeding for improving abiotic stress tolerance and productivity in plants.

Correction to: QTL mapping of flowering time and biomass yield in tetraploid alfalfa (Medicago sativa L.)

In the article [1], in 'Methods' section and 'G x E and heritability' subsection, there is an error in the formula of heritability (H²).