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

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.

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Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

Journal of Integrative Bioinformatics ◽

10.1515/jib-2017-0011 ◽

2017 ◽

Vol 14 (2) ◽

Author(s):

Qihua Tan ◽

Mads Thomassen ◽

Mark Burton ◽

Kristian Fredløv Mose ◽

Klaus Ejner Andersen ◽

...

Keyword(s):

Gene Expression ◽

Correlation Coefficient ◽

Parametric Analysis ◽

Time Course ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Microarray Time Course ◽

Time Course Data ◽

Generalized Correlation ◽

Non Parametric

AbstractModeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

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RNA-Seq Analysis of Spatiotemporal Gene Expression Patterns During Fruit Development Revealed Reference Genes for Transcript Normalization in Plums

Plant Molecular Biology Reporter ◽

10.1007/s11105-015-0860-3 ◽

2015 ◽

Vol 33 (6) ◽

pp. 1634-1649 ◽

Cited By ~ 16

Author(s):

Ho-Youn Kim ◽

Prasenjit Saha ◽

Macarena Farcuh ◽

Bosheng Li ◽

Avi Sadka ◽

...

Keyword(s):

Gene Expression ◽

Fruit Development ◽

Reference Genes ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Rna Seq

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A clustering model for identification of time course gene expression patterns

2016 1st International Conference on Biomedical Engineering (IBIOMED) ◽

10.1109/ibiomed.2016.7869819 ◽

2016 ◽

Author(s):

Peter Juma Ochieng ◽

Sri Ita Tarigan ◽

Hendrik Didik

Keyword(s):

Gene Expression ◽

Time Course ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Clustering Model

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Raw sequence to target gene prediction: An integrated inference pipeline for ChIP-seq and RNA-seq datasets

10.1101/220152 ◽

2017 ◽

Author(s):

Nisar Wani ◽

Khalid Raza

Keyword(s):

Gene Expression ◽

Target Gene ◽

Target Genes ◽

Gene Prediction ◽

Expression Patterns ◽

Dna Binding Proteins ◽

Gene Expression Patterns ◽

Rna Seq ◽

Cellular Processes ◽

Regulatory Effects

AbstractGene expression patterns determine the manner whereby organisms regulate various cellular processes and therefore their organ functions.These patterns do not emerge on their own, but as a result of diverse regulatory factors such as, DNA binding proteins known as transcription factors (TF), chromatin structure and various other environmental factors. TFs play a pivotal role in gene regulation by binding to different locations on the genome and influencing the expression of their target genes. Therefore, predicting target genes and their regulation becomes an important task for understanding mechanisms that control cellular processes governing both healthy and diseased cells.In this paper, we propose an integrated inference pipeline for predicting target genes and their regulatory effects for a specific TF using next-generation data analysis tools.

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Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

10.1101/2020.09.15.299123 ◽

2020 ◽

Author(s):

Timothy J. Durham ◽

Riza M. Daza ◽

Louis Gevirtzman ◽

Darren A. Cusanovich ◽

William Stafford Noble ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Patterns ◽

Cell Types ◽

Chromatin Accessibility ◽

Gene Expression Patterns ◽

Rna Seq ◽

Cell Type ◽

Tissue Specific ◽

C Elegans

AbstractRecently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

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Discovering gene expression patterns in time course microarray experiments by ANOVA–SCA

Bioinformatics ◽

10.1093/bioinformatics/btm251 ◽

2007 ◽

Vol 23 (14) ◽

pp. 1792-1800 ◽

Cited By ~ 50

Author(s):

María José Nueda ◽

Ana Conesa ◽

Johan A. Westerhuis ◽

Huub C. J. Hoefsloot ◽

Age K. Smilde ◽

...

Keyword(s):

Gene Expression ◽

Time Course ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Microarray Experiments

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Differences in gene expression patterns, revealed by RNA-seq analysis, between various Sanfilippo and Morquio disease subtypes

Gene ◽

10.1016/j.gene.2021.146090 ◽

2021 ◽

pp. 146090

Author(s):

Karolina Wiśniewska ◽

Lidia Gaffke ◽

Karolina Krzelowska ◽

Grzegorz Węgrzyn ◽

Karolina Pierzynowska

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Rna Seq ◽

Morquio Disease ◽

Disease Subtypes

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Transcriptomic analysis reveals distinct resistant response by physcion and chrysophanol against cucumber powdery mildew

PeerJ ◽

10.7717/peerj.1991 ◽

2016 ◽

Vol 4 ◽

pp. e1991 ◽

Cited By ~ 9

Author(s):

Yanping Li ◽

Shilin Tian ◽

Xiaojun Yang ◽

Xin Wang ◽

Yuhai Guo ◽

...

Keyword(s):

Gene Expression ◽

Disease Resistance ◽

Powdery Mildew ◽

Differentially Expressed Genes ◽

Expression Patterns ◽

Defense Responses ◽

Differentially Expressed ◽

Rna Seq ◽

Sequencing Data ◽

Transcriptional Change

Physcion and chrysophanol induce defense responses against powdery mildew in cucumbers. The combination of these two compounds has synergistic interaction against the disease. We performed RNA-seq on cucumber leaf samples treated with physcion and chrysophanol alone and with their combination. We generated 17.6 Gb of high-quality sequencing data (∼2 Gb per sample) and catalogued the expressions profiles of 12,293 annotated cucumber genes in each sample. We identified numerous differentially expressed genes that exhibited distinct expression patterns among the three treatments. The gene expression patterns of the Chr and Phy treatments were more similar to each other than to the Phy × Chr treatment. The Phy × Chr treatment induced the highest number of differentially expressed genes. This dramatic transcriptional change after Phy × Chr treatment leaves reflects that physcion combined with chrysophanol treatment was most closely associated with induction of disease resistance. The analysis showed that the combination treatment caused expression changes of numerous defense-related genes. These genes have known or potential roles in structural, chemical and signaling defense responses and were enriched in functional gene categories potentially responsible for cucumber resistance. These results clearly demonstrated that disease resistance in cucumber leaves was significantly influenced by the combined physcion and chrysophanol treatment. Thus, physcion and chrysophanol are appealing candidates for further investigation of the gene expression and associated regulatory mechanisms related to the defense response.

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