scholarly journals Identification of Fusarium solani f. sp. pisi (Fsp) responsive genes in Pisum sativum

2020 ◽  
Author(s):  
Bruce A. Williamson-Benavides ◽  
Richard Sharpe ◽  
Grant Nelson ◽  
Eliane T. Bodah ◽  
Lyndon D. Porter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pisum Sativum ◽  
Fusarium Solani ◽  
Root Rot ◽  
Time Course ◽  
Acid Synthesis ◽  
Anthocyanin Synthesis ◽  
Transcriptomic Response ◽  
Sugar Transporters ◽  
Go Enrichment

AbstractPisum sativum (pea) is rapidly emerging as an inexpensive and major contributor to the plant-derived protein market. Due to its nitrogen-fixation capability, short life cycle, and low water usage, pea is a useful cover-and-break crop that requires minimal external inputs. It is critical for sustainable agriculture and indispensable for future food security. Root rot in pea, caused by the fungal pathogen Fusarium solani f. sp. pisi (Fsp), can result in a 15-60% reduction in yield. It is urgent to understand the molecular basis of Fsp interaction in pea to develop root rot tolerant cultivars. A complementary genetics and gene expression approach was undertaken in this study to identify Fsp-responsive genes in four tolerant and four susceptible pea genotypes. Time course RNAseq was performed on both sets of genotypes after Fsp challenge. Analysis of the transcriptome data resulted in the identification of 42,905 differentially expressed contigs (DECs). Interestingly, the vast majority of DECs were overexpressed in the susceptible genotypes at all sampling time points, rather than in the tolerant genotypes. Gene expression and GO enrichment analyses revealed genes coding for receptor-mediated endocytosis, sugar transporters, salicylic acid synthesis and signaling, and cell death were overexpressed in the susceptible genotypes. In the tolerant genotypes, genes involved in exocytosis, and secretion by cell, the anthocyanin synthesis pathway, as well as the DRR230 gene, a pathogenesis-related (PR) gene, were overexpressed. The complementary genetic and RNAseq approach has yielded a set of potential genes that could be targeted for improved tolerance against root rot in P. sativum. Fsp challenge produced a futile transcriptomic response in the susceptible genotypes. This type of response is hypothesized to be related to the speed at which the pathogen infestation advances in the susceptible genotypes, and the preexisting level of disease-preparedness in the tolerant genotypes.

scholarly journals TimesVector-Web: A Web Service for Analysing Time Course Transcriptome Data with Multiple Conditions

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 73
Author(s):  
Jaeyeon Jang ◽  
Inseung Hwang ◽  
Inuk Jung
Keyword(s):  
Gene Expression ◽  
Web Service ◽  
Gene Expression Data ◽  
Time Course ◽  
Expression Patterns ◽  
Differentially Expressed Gene ◽  
Expression Data ◽  
Transcriptomic Response ◽  
Time Course Data ◽  
Multiple Conditions

From time course gene expression data, we may identify genes that modulate in a certain pattern across time. Such patterns are advantageous to investigate the transcriptomic response to a certain condition. Especially, it is of interest to compare two or more conditions to detect gene expression patterns that significantly differ between them. Time course analysis can become difficult using traditional differentially expressed gene (DEG) analysis methods since they are based on pair-wise sample comparison instead of a series of time points. Most importantly, the related tools are mostly available as local Software, requiring technical expertise. Here, we present TimesVector-web, which is an easy to use web service for analysing time course gene expression data with multiple conditions. The web-service was developed to (1) alleviate the burden for analyzing multi-class time course data and (2) provide downstream analysis on the results for biological interpretation including TF, miRNA target, gene ontology and pathway analysis. TimesVector-web was validated using three case studies that use both microarray and RNA-seq time course data and showed that the results captured important biological findings from the original studies.

scholarly journals Whole-genome and time-course dual RNA-Seq analyses reveal chronic pathogenicity-related gene dynamics in the ginseng rusty root rot pathogen Ilyonectria robusta

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yiming Guan ◽  
Meili Chen ◽  
Yingying Ma ◽  
Zhenglin Du ◽  
Na Yuan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Root Rot ◽  
Time Course ◽  
Expression Patterns ◽  
Defense Responses ◽  
Infection Process ◽  
Ginseng Root ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
First Time

Abstract Ilyonectria robusta causes rusty root rot, the most devastating chronic disease of ginseng. Here, we for the first time report the high-quality genome of the I. robusta strain CD-56. Time-course (36 h, 72 h, and 144 h) dual RNA-Seq analysis of the infection process was performed, and many genes, including candidate effectors, were found to be associated with the progression and success of infection. The gene expression profile of CD-56 showed a trend of initial inhibition and then gradually returned to a profile similar to that of the control. Analyses of the gene expression patterns and functions of pathogenicity-related genes, especially candidate effector genes, indicated that the stress response changed to an adaptive response during the infection process. For ginseng, gene expression patterns were highly related to physiological conditions. Specifically, the results showed that ginseng defenses were activated by CD-56 infection and persisted for at least 144 h thereafter but that the mechanisms invoked were not effective in preventing CD-56 growth. Moreover, CD-56 did not appear to fully suppress plant defenses, even in late stages after infection. Our results provide new insight into the chronic pathogenesis of CD-56 and the comprehensive and complex inducible defense responses of ginseng root to I. robusta infection.

Vegetable associated Bacillus spp. suppress the pea (Pisum sativum L.) root rot caused by Fusarium solani

2021 ◽  
Vol 158 ◽  
pp. 104610
Author(s):  
Raheela Riaz ◽  
Asghar Khan ◽  
Wajeeha Jahangir Khan ◽  
Zahra Jabeen ◽  
Humaira Yasmin ◽  
...  
Keyword(s):  
Pisum Sativum ◽  
Fusarium Solani ◽  
Root Rot ◽  
Pisum Sativum L ◽  
Bacillus Spp

Gene expression during two alternative pathways of ovary development in Pisum sativum: fruit development and ovary senescence

1992 ◽  
Vol 85 (1) ◽  
pp. 69-76 ◽  
Author(s):  
Maria-Jose Sanchez-Beltran ◽  
Juan Carbonell ◽  
Jose L. Garcia-Martinez ◽  
Isabel Lopez-Diaz
Keyword(s):  
Gene Expression ◽  
Pisum Sativum ◽  
Fruit Development ◽  
Ovary Development ◽  
Alternative Pathways ◽  
Ovary Senescence

scholarly journals Biological control of Phaseolus vulgaris and Pisum sativum root rot disease using Trichoderma species

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Hammad Abdelwanees Ketta ◽  
Omar Abd El-Raouf Hewedy
Keyword(s):  
Phaseolus Vulgaris ◽  
Pisum Sativum ◽  
Common Bean ◽  
Root Rot ◽  
Fungal Pathogens ◽  
Plant Diseases ◽  
Root Rot Disease ◽  
Pea Root ◽  
Rot Disease ◽  
Pea Root Rot

Abstract Background Root rot pathogens reported to cause considerable losses in both the quality and productivity of common bean (Phaseolus vulgaris L.) and pea (Pisum sativum L.). It is an aggressive crop disease with detriment economic influence caused by Fusarium solani and Rhizoctonia solani among other soil-borne fungal pathogens. Destructive plant diseases such as root rot have been managed in the last decades using synthetic pesticides. Main body Seeking of economical and eco-friendly alternatives to combat aggressive soil-borne fungal pathogens that cause significant yield losses is urgently needed. Trichoderma emerged as promising antagonist that inhibits pathogens including those inducing root rot disease. Detailed studies for managing common bean and pea root rot disease using different Trichoderma species (T. harzianum, T. hamatum, T. viride, T. koningii, T. asperellum, T. atroviridae, T. lignorum, T. virens, T. longibrachiatum, T. cerinum, and T. album) were reported both in vitro and in vivo with promotion of plant growth and induction of systemic defense. The wide scale application of selected metabolites produced by Trichoderma spp. to induce host resistance and/or to promote crop yield, may represent a powerful tool for the implementation of integrated pest management strategies. Conclusions Biological management of common bean and pea root rot-inducing pathogens using various species of the Trichoderma fungus might have taken place during the recent years. Trichoderma species and their secondary metabolites are useful in the development of protection against root rot to bestow high-yielding common bean and pea crops.

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

Sign in / Sign up

Export Citation Format

Share Document