Biology subjects

Foroud, N. A.

Publications and source records attributed to Foroud, N. A..

3 recordsLinked to original sources

bioRxiv2021

Genome-wide survey of the F-box/Kelch (FBK) members and molecular identification of a novel FBK gene TaAFR in wheat

F-box proteins play critical roles in plant responses to biotic/abiotic stresses. In the present study, a total of 68 wheat F-box/Kelch (TaFBK) gene sequences encoding for 74 proteins were obtained in a genome-wide survey against EnsemblPlants. The 74 TaFBK proteins were divided into 5 categories based on their domain structures. The FBK proteins from wheat, Arabidopsis, and three other cereal species were grouped into 7 clades, and the number of Kelch domains present was their key clustering criterion. Sixty-eight TaFBK genes were unevenly distributed on 21 chromosomes. Most of TaFBKs were predicted to localize in the nucleus and cytoplasm. In silico analysis of a digital PCR revealed that TaFBKs were expressed at multiple developmental stages and tissues, and in response to drought and/or heat stresses. The TaFBK19 gene, a homologous to the Attenuated Far-Red Response (AFR) genes in other plant species, and hence named TaAFR, was selected for further analysis. The gene was isolated from the wheat line TcLr15 and its expression evaluated by quantitative real-time PCR. TaAFR transcripts were primarily detected in wheat leaves, and its expression was found to be regulated by various abiotic and biotic stresses as well as plant signaling hormones. Of particular interest, TaAFR expression was differentially regulated in the compatible vs incompatible wheat leaf rust reaction. Subcellular localization studies showed that TaAFR accumulates in the nucleus and cytoplasm. Three TaAFR-interacting proteins were identified experimentally: Skp1/ASK1-like protein (Skp1), ADP-ribosylation factor 2-like isoform X1 (ARL2) and phenylalanine ammonia-lyase (PAL). Further analysis revealed that the Skp1 protein interacted specifically with the F-box domain of TaAFR, while ARL2 and PAL were recognized by the Kelch domain. The data presented herein provides a solid foundation from which the function and metabolic network of TaAFR and other wheat FBKs can be further explored.

molecular biology↗

bioRxiv2020

Wheat transcriptome profiling reveals abscisic and gibberellic acid treatments regulate early-stage phytohormone defense signaling, cell wall fortification, and metabolic switches following Fusarium graminearum-challenge

BackgroundApplication of the wheat phytohormones abscisic acid (ABA) or gibberellic acid (GA) affect Fusarium head blight (FHB) disease severity; however, the molecular underpinnings of the elicited phenotypes remain unclear. Herein, the transcriptomic responses of an FHB-susceptible wheat cultivar Fielder were characterized upon treatment with ABA, an ABA receptor antagonist (AS6), or GA in the presence or absence of Fusarium graminearum (Fg) challenge. ResultsA total of 30,876 differentially expressed genes (DEGs) where identified in Fielder (26,004) and Fg (4,872). Fg challenge alone resulted in the most substantial wheat DEGs contributing to 57.2% of the total transcriptomic variation. Using a combination of topology overlap and correlation analyses, 9,689 Fg-related wheat DEGs were defined. Further enrichment analysis of the top 1% networked wheat DEGs identified critical expression changes within defense responses, cell structural metabolism, molecular transport, and membrane/lipid metabolism. Fg-challenged conditions also included the expression of a putative Fg ABA-biosynthetic cytochrome P450 and repression of wheat FUS3 for dysregulating ABA and GA crosstalk. ABA treatment alone elicited 4536 (32%) wheat DEGs common to those of the Fg-challenge, and Fg+ABA further enhanced 888 (12.5%) of them. These ABA elicited DEGs are involved in defense through both classical and non-classical phytohormone signaling and regulating cell wall structures including polyphenolic metabolism. Conversely, Fg+GA opposed 2239 (33%) Fg-elicited wheat DEGs, including modulating primary and secondary metabolism, defense responses, and flowering genes. ABA and jointly ABAFg[Fg+ABA] treatments repressed, while Fg+GA induced an over-representation of wheat DEGs mapping to chromosome 6BL. Finally, compared to Fg+ABA, co-application of Fg+AS6 did not antagonize ABA biosynthesis or signal but rather elicited antagonistic Fg (557) and wheat (11) DEGs responses directly tied to stress responses, phytohormone transport, and FHB. ConclusionsComparative transcriptomics highlight the effects of wheat phytohormones on individual pathway and global metabolism simultaneously. Application of ABA may reduce FHB severity through misregulating defense mechanisms and cell wall fortification pathways. GA application may alter primary and secondary metabolism, creating a metabolic shift to ultimately reduce FHB severity. By comparing these findings to those previously reported for four additional plant genotypes, an additive model of the wheat-Fg interaction is proposed.

genomics↗

bioRxiv2019

Differential expression feature extraction (DEFE) and its application in RNA-seq data analysis

In differential gene expression data analysis, one objective is to identify groups of co-expressed genes from a large dataset to detect the association between such a group of genes and a phenotypic trait. This is often done through a clustering approach, such as k-means or bipartition hierarchical clustering, based on particular similarity measures in the grouping process. In such a dataset, the gene differential expression itself is an innate attribute that can be used in the feature extraction process. For example, in a dataset consisting of multiple treatments versus their controls, the expression of a gene in each treatment would have three possible behaviors, up-, down- regulated, or unchanged. We propose here a differential expression feature extraction (DEFE) method by using a string consisting of three numerical values at each character to denote such behavior, i.e. 1=up, 2=down, and 0=unchanged, which results in up to 3B differential expression patterns across all B comparisons. This approach has been successfully applied in many datasets, of which we present in this study two sets of RNA-sequencing (RNA-seq) data on wheat challenged with stress related phytohormones or Fusarium graminearum, the causal agent of fusarium head blight (FHB), a devastating wheat disease to illustrate the algorithm. Combinations of multiple schemes of DEFE patterns revealed groups of genes putatively associated with resistance or susceptibility to FHB. DEFE enabled discovery of genes closely associated with defense related signaling molecules such as JAZ10, shikimate and chorismate biosynthesis pathway and groups of wheat genes with differential effects between more or less virulent strains of Fusarium graminearum.

genomics↗