A superior gene allele involved in abscisic acid signaling enhances drought tolerance and yield in chickpea.
Plant Physiol
; 191(3): 1884-1912, 2023 03 17.
Article
em En
| MEDLINE
| ID: mdl-36477336
ABSTRACT
Identifying potential molecular tags for drought tolerance is essential for achieving higher crop productivity under drought stress. We employed an integrated genomics-assisted breeding and functional genomics strategy involving association mapping, fine mapping, map-based cloning, molecular haplotyping and transcript profiling in the introgression lines (ILs)- and near isogenic lines (NILs)-based association panel and mapping population of chickpea (Cicer arietinum). This combinatorial approach delineated a bHLH (basic helix-loop-helix) transcription factor, CabHLH10 (Cicer arietinum bHLH10) underlying a major QTL, along with its derived natural alleles/haplotypes governing yield traits under drought stress in chickpea. CabHLH10 binds to a cis-regulatory G-box promoter element to modulate the expression of RD22 (responsive to desiccation 22), a drought/abscisic acid (ABA)-responsive gene (via a trans-expression QTL), and two strong yield-enhancement photosynthetic efficiency (PE) genes. This, in turn, upregulates other downstream drought-responsive and ABA signaling genes, as well as yield-enhancing PE genes, thus increasing plant adaptation to drought with reduced yield penalty. We showed that a superior allele of CabHLH10 introgressed into the NILs improved root and shoot biomass and PE, thereby enhancing yield and productivity during drought without compromising agronomic performance. Furthermore, overexpression of CabHLH10 in chickpea and Arabidopsis (Arabidopsis thaliana) conferred enhanced drought tolerance by improving root and shoot agro-morphological traits. These findings facilitate translational genomics for crop improvement and the development of genetically tailored, climate-resilient, high-yielding chickpea cultivars.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Cicer
/
Locos de Características Quantitativas
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article