Integrating QTL mapping and transcriptomics identifies candidate genes underlying QTLs associated with soybean tolerance to low-phosphorus stress

Abstract

Soybean is a high phosphorus (P) demand species that is sensitive to low-P stress. Although many quantitative trait loci (QTL) for P efficiency have been identified in soybean, but few of these have been cloned and agriculturally applied mainly due to various limitations on identifying suitable P efficiency candidate genes. Here, we combined QTL mapping, transcriptome profiling, and plant transformation to identify candidate genes underlying QTLs associated with low-P tolerance and response mechanisms to low-P stress in soybean. By performing QTL linkage mapping using 152 recombinant inbred lines (RILs) that were derived from a cross between a P-efficient variety, Nannong 94-156, and P-sensitive Bogao, we identified four major QTLs underlying P efficiency. Within these four QTL regions, 34/81 candidate genes in roots/leaves were identified using comparative transcriptome analysis between two transgressive RILs, low-P tolerant genotype B20 and sensitive B18. A total of 22 phosphatase family genes were up-regulated significantly under low-P condition in B20. Overexpression of an acid phosphatase candidate gene, GmACP2, in soybean hairy roots increased P efficiency by 15.43-24.54 % compared with that in controls. Our results suggest that integrating QTL mapping and transcriptome profiling could be useful for rapidly identifying candidate genes underlying complex traits, and phosphatase-encoding genes, such as GmACP2, play important roles involving in low-P stress tolerance in soybean.

Transcriptome-wide identification of novel circular RNAs in soybean in response to low-phosphorus stress

“…The sustainability of the use of P fertilizers to optimize crop yields is in jeopardy due to the crisis of the global reserve of rock phosphate and P pollution. Compared with other crop species, soybean needs more P for growth and development; thus, low-P (LP) stress has become a major factor limiting soybean production [2]. Therefore, elucidating the response and adaptation mechanism of soybean to LP stress is of great urgency and importance.…”

“…Using genome-wide high-throughput RNA sequencing (RNA-seq) technology, we then identified all circRNAs in the roots of soybean seedlings and validated them by real-time PCR. Our study specifically (1) identified and characterized soybean root circRNAs that are responsive to LP stress; (2) identified and characterized the possible roles of the cir-cRNA-host genes of differentially expressed (DE) circRNAs in regulating soybean tolerance to LP stress via Gene Ontology (GO) enrichment analysis; and (3) predicted and discussed the sponge action of DE circRNAs and miRNA-targeted genes.…”

Transcriptome Dataset of Soybean (Glycine max) Grown under Phosphorus-Deficient and -Sufficient Conditions

“…The sequencing and preprocessing results are summarized in Table 1. An initial comparative analysis of the transcriptome dataset has revealed that acid phosphatases might be involved in enhancing P efficiency in low-P tolerance in soybean, and gene expression analysis and functional analysis has indicated the robustness of the dataset [11]. However, further analyses using the data is needed-for example, identifying the underlying alternative splicing genes and regulatory network as well as identifying the differentially expressed genes systemically involved in low-P tolerance in roots and leaves-which will further improve our comprehensive understanding of the molecular mechanism underlying plant adaptations to phosphate deficiency.…”

“…Soybean treatment for phosphorus-deficient and -sufficient conditions were followed as previously described [5,11]. Briefly, seeds were surface-sterilized with 0.5% sodium hypochlorite for no more than 4 minutes, rinsed twice with sterile water, and then germinated in sterile vermiculite.…”

Unraveling lipid metabolism in maize with time‐resolved multi‐omics data

“…The combination of quantitative genetics and co‐expression studies provides a powerful framework for functional characterization of genes (Mackay et al ., ). For instance, this strategy has been used to identify genes controlling glucosinolates and anti‐herbivore defense in Arabidopsis (Chan et al ., ), micronutrients in chickpea (Upadhyaya et al ., ), leaf morphology in oilseed rape (Jian et al ., ) and phosphorus stress tolerance in soybean (Zhang et al ., ). In this class of approaches, one may utilize a single data set to determine loci associated with a trait of interest and then narrow down the search to those whose respective gene transcripts are correlated with the trait (Chen et al ., ); moreover, streamlining the list can be carried out by investigation of gene co‐expression based on publically available data sets (Serin et al ., ).…”