Identification of candidate genes involved in early iron deficiency chlorosis signaling in soybean (Glycine max) roots and leaves

Abstract

BackgroundIron is an essential micronutrient for all living things, required in plants for photosynthesis, respiration and metabolism. A lack of bioavailable iron in soil leads to iron deficiency chlorosis (IDC), causing a reduction in photosynthesis and interveinal yellowing of leaves. Soybeans (Glycine max (L.) Merr.) grown in high pH soils often suffer from IDC, resulting in substantial yield losses. Iron efficient soybean cultivars maintain photosynthesis and have higher yields under IDC-promoting conditions than inefficient cultivars.ResultsTo capture signaling between roots and leaves and identify genes acting early in the iron efficient cultivar Clark, we conducted a RNA-Seq study at one and six hours after replacing iron sufficient hydroponic media (100 μM iron(III) nitrate nonahydrate) with iron deficient media (50 μM iron(III) nitrate nonahydrate). At one hour of iron stress, few genes were differentially expressed in leaves but many were already changing expression in roots. By six hours, more genes were differentially expressed in the leaves, and a massive shift was observed in the direction of gene expression in both roots and leaves. Further, there was little overlap in differentially expressed genes identified in each tissue and time point.ConclusionsGenes involved in hormone signaling, regulation of DNA replication and iron uptake utilization are key aspects of the early iron-efficiency response. We observed dynamic gene expression differences between roots and leaves, suggesting the involvement of many transcription factors in eliciting rapid changes in gene expression. In roots, genes involved iron uptake and development of Casparian strips were induced one hour after iron stress. In leaves, genes involved in DNA replication and sugar signaling responded to iron deficiency. The differentially expressed genes (DEGs) and signaling components identified here represent new targets for soybean improvement.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-702) contains supplementary material, which is available to authorized users.

Genetic variants in root architecture-related genes in a Glycine soja accession, a potential resource to improve cultivated soybean

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Transcriptome response of Lolium arundinaceum to its fungal endophyte Epichloë coenophiala

“…Informative B last X hits were identified by removing hits containing the key words predicted, hypothetical, related, expressed, ORF and scaffold. Custom Perl scripts were then used to assign gene ontology (GO) terms for biological processes and molecular functions (Berardini et al ., ) inferred from the top A. thaliana hit to each TF transcript (Atwood et al ., ; Moran Lauter et al ., ). Note that single contigs can be associated with multiple GO terms.…”

A real-time phenotyping framework using machine learning for plant stress severity rating in soybean

“…Selection for desirable soybean genotype (with IDC tolerance) is done either through phenotyping in the field or in greenhouses [7], or genotyping with molecular markers linked to genes that improve IDC tolerance. More than 10 genes have been reported to be associated with improving IDC tolerance [8, 9] making genotyping approaches onerous where a breeding program may be working to select for several other traits. Phenotyping is most suitable as it allows identification of soybean genotypes that have an acceptable IDC tolerance.…”