Unlocking Soil Microbiome’s Role in Suppressing Striga Infection in Sorghum: Towards Sustainable Management Strategies
Soil microbiomes can suppress Striga infection in sorghum roots through alterations in host-parasite signaling and root anatomy.
This research identifies novel recessive alleles within the Tannin1 and Tannin2 genes in sorghum, offering insights into tannin regulation and providing practical tools for breeding programs aiming to modulate tannin content.
Researchers identified novel quantitative trait nucleotides (QTNs) associated with root system architecture (RSA) traits in Ethiopian sorghum accessions, offering insights into enhancing sorghum’s drought tolerance through genetic improvement of its root development.
Researchers investigated the genetic basis of sorghum plant color, revealing complex interactions between multiple loci and candidate genes associated with phenotypic traits and fungal resistance.
Fu et al. utilize laser capture microdissection to dissect and analyze transcriptomes from distinct cell types in bioenergy sorghum stems, revealing intricate gene regulatory networks and molecular mechanisms governing stem development and secondary cell wall formation.
A comprehensive analysis identified 112 Sorghum NAC genes, characterized their phylogenetic distribution and structural diversity, and uncovered nine greenbug-inducible SbNAC genes, shedding light on their potential roles in sorghum defense against aphids.
HPC-GVCW offers a high-performance computing-based workflow for rapid SNP detection in major crops, significantly reducing execution times and facilitating the exploration of genetic diversity essential for molecular-assisted selection breeding programs.
This study on Sorghum bicolor and Arabidopsis thaliana extracellular vesicles reveals partial conservation in protein content, providing crucial insights into the dynamic nature of intercellular communication and defense responses in plant cells.
Spike-in normalization improves the accuracy and reliability of differential gene expression analysis in plant RNA-Seq experiments, particularly under conditions where experimental factors significantly influence gene expression dynamics.