Dataset Information

Transcriptomic Analysis of Flowering Time Genes in Cultivated Chickpea and Wild Cicer.

ABSTRACT: Chickpea (Cicer arietinum L.) is a major grain legume and a good source of plant-based protein. However, comprehensive knowledge of flowering time control in Cicer is lacking. In this study, we acquire high-throughput transcriptome sequencing data and analyze changes in gene expression during floral transition in the early flowering cultivar ICCV 96029, later flowering C. arietinum accessions, and two wild species, C. reticulatum and C. echinospermum. We identify Cicer orthologs of A. thaliana flowering time genes and analyze differential expression of 278 genes between four species/accessions, three tissue types, and two conditions. Our results show that the differences in gene expression between ICCV 96029 and other cultivated chickpea accessions are vernalization-dependent. In addition, we highlight the role of FTa3, an ortholog of FLOWERING LOCUS T in Arabidopsis, in the vernalization response of cultivated chickpea. A common set of differentially expressed genes was found for all comparisons between wild species and cultivars. The direction of expression change for different copies of the FT-INTERACTING PROTEIN 1 gene was variable in different comparisons, which suggests complex mechanisms of FT protein transport. Our study makes a contribution to the understanding of flowering time control in Cicer, and can provide genetic strategies to further improve this important agronomic trait.

SUBMITTER: Gretsova M

PROVIDER: S-EPMC9916832 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Transcriptomic Analysis of Flowering Time Genes in Cultivated Chickpea and Wild Cicer.

Gretsova Maria M Surkova Svetlana S Kanapin Alexander A Samsonova Anastasia A Logacheva Maria M Shcherbakov Andrey A Logachev Anton A Bankin Mikhail M Nuzhdin Sergey S Samsonova Maria M

International journal of molecular sciences 20230131 3

Chickpea (Cicer arietinum L.) is a major grain legume and a good source of plant-based protein. However, comprehensive knowledge of flowering time control in Cicer is lacking. In this study, we acquire high-throughput transcriptome sequencing data and analyze changes in gene expression during floral transition in the early flowering cultivar ICCV 96029, later flowering C. arietinum accessions, and two wild species, C. reticulatum and C. echinospermum. We identi ...[more]

PMID: 36769014

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Project description:Mast flowering is synchronised highly variable flowering by a population of perennial plants over a wide geographical area. High seeding years are seen as a threat to native and endangered species due to high predator density caused by the abundance of seed. An understanding of the molecular pathways that influence masting behaviour in plants could provide better prediction of a forthcoming masting season and enable conservation strategies to be deployed. The goal of this study was to identify candidate flowering genes that might be involved in regulating mast flowering. To achieve this, high-throughput large-scale RNA-sequencing was performed on two masting plant species, Celmisia lyallii (Asteraceae), and Chionochloa pallens (Poaceae) to develop a reference transcriptome for functional and molecular analysis. An average total of 33 million 150 base-paired reads, for both species, were assembled using the Trinity pipeline, resulting in 151,803 and 348,649 transcripts respectively for C. lyallii and C. pallens. For both species, about 56% of the unigenes were annotated with gene descriptions to known proteins followed by Gene Ontology analysis, categorising them on the basis of putative biological processes, molecular function, and cellular localization. A total of 543 transcripts from C. lyallii and 470 transcripts from C. pallens were also mapped to unique flowering-time proteins identified in Arabidopsis thaliana, suggesting the conservation of the flowering network in these wild alpine plants growing in natural field conditions. Expression analysis of several selected homologous flowering-pathway genes showed seasonal and photoperiodic variations. These genes can further be analysed to understand why seasonal cues, such as the increasing photoperiod in spring, that triggers the annual flowering of most plants, are insufficient to always trigger flowering in masting plants and to uncover the molecular basis of how additional cues (such as temperature during the previous growing seasons) then determines flowering in mast years.

Project description:BackgroundAccurate prediction of crop flowering time is required for reaching maximal farm efficiency. Several models developed to accomplish this goal are based on deep knowledge of plant phenology, requiring large investment for every individual crop or new variety. Mathematical modeling can be used to make better use of more shallow data and to extract information from it with higher efficiency. Cultivars of chickpea, Cicer arietanum, are currently being improved by introgressing wild C. reticulatum biodiversity with very different flowering time requirements. More understanding is required for how flowering time will depend on environmental conditions in these cultivars developed by introgression of wild alleles.ResultsWe built a novel model for flowering time of wild chickpeas collected at 21 different sites in Turkey and grown in 4 distinct environmental conditions over several different years and seasons. We propose a general approach, in which the analytic forms of dependence of flowering time on climatic parameters, their regression coefficients, and a set of predictors are inferred automatically by stochastic minimization of the deviation of the model output from data. By using a combination of Grammatical Evolution and Differential Evolution Entirely Parallel method, we have identified a model that reflects the influence of effects of day length, temperature, humidity and precipitation and has a coefficient of determination of R2=0.97.ConclusionsWe used our model to test two important hypotheses. We propose that chickpea phenology may be strongly predicted by accession geographic origin, as well as local environmental conditions at the site of growth. Indeed, the site of origin-by-growth environment interaction accounts for about 14.7% of variation in time period from sowing to flowering. Secondly, as the adaptation to specific environments is blueprinted in genomes, the effects of genes on flowering time may be conditioned on environmental factors. Genotype-by-environment interaction accounts for about 17.2% of overall variation in flowering time. We also identified several genomic markers associated with different reactions to climatic factor changes. Our methodology is general and can be further applied to extend existing crop models, especially when phenological information is limited.

Dataset Information

Transcriptomic Analysis of Flowering Time Genes in Cultivated Chickpea and Wild Cicer.

Publications

Transcriptomic Analysis of Flowering Time Genes in Cultivated Chickpea and Wild <i>Cicer</i>.

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