The Transcriptome of Verticillium dahliae Responds Differentially Depending on the Disease Susceptibility Level of the Olive (Olea europaea L.) Cultivar

Among biotic constraints affecting olive trees cultivation worldwide, the soil-borne fungus Verticillium dahliae is considered one of the most serious threats. Olive cultivars display differential susceptibility to the disease, but our knowledge on the pathogen’s responses when infecting varieties differing in susceptibility is scarce. A comparative transcriptomic analysis (RNA-seq) was conducted in olive cultivars Picual (susceptible) and Frantoio (tolerant). RNA samples originated from roots during the first two weeks after inoculation with V. dahliae defoliating (D) pathotype. Verticillium dahliae mRNA amount was overwhelmingly higher in roots of the susceptible cultivar, indicating that proliferation of pathogen biomass is favored in ‘Picual’. A significant larger number of V. dahliae unigenes (11 fold) were only induced in this cultivar. Seven clusters of differentially expressed genes (DEG) were identified according to time-course expression patterns. Unigenes potentially coding for niche-adaptation, pathogenicity, virulence and microsclerotia development were induced in ‘Picual’, while in ‘Frantoio’ expression remained negligible or null. Verticillium dahliae D pathotype transcriptome responses are qualitatively and quantitatively different, and depend on cultivar susceptibility level. The much larger V. dahliae biomass found in ‘Picual’ roots is a consequence of both host and pathogen DEG explaining, to a large extent, the higher aggressiveness exerted over this cultivar.

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Differential Expression Analysis Using A Model-Based Gene Clustering Algorithm for RNA-Seq Data

10.21203/rs.3.rs-86123/v1 ◽

2020 ◽

Author(s):

Takayuki Osabe ◽

Kentaro Shimizu ◽

Koji Kadota

Keyword(s):

Differential Expression ◽

Time Course ◽

Clustering Algorithm ◽

Expression Patterns ◽

Differential Expression Analysis ◽

Real Data ◽

Gene Clustering ◽

Rna Seq ◽

Model Based ◽

Group Data

Abstract Background RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report a model-based clustering algorithm, MBCluster.Seq, that can be implemented using an R package for DE analysis.Results The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.Conclusions MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

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Long Noncoding RNA Derived from LncRNA–mRNA Co-expression Networks Modulates the Locust Phase Change

10.1101/2020.04.11.036848 ◽

2020 ◽

Author(s):

Ting Li ◽

Bing Chen ◽

Pengcheng Yang ◽

Depin Wang ◽

Baozhen Du ◽

...

Keyword(s):

Phase Change ◽

Animal Behavior ◽

Noncoding Rna ◽

Time Course ◽

Empirical Studies ◽

Expression Patterns ◽

Rna Seq ◽

Protein Coding ◽

Migratory Locust ◽

Functional Annotations

AbstractLong noncoding RNAs (lncRNAs) regulate various biological processes from gene expression to animal behavior. Although protein-coding genes, microRNAs, and neuropeptides play important roles in the regulation of phenotypic plasticity in migratory locust, empirical studies on the function of lncRNAs in the process remain limited. Here, we applied high-throughput RNA-seq to characterize the expression patterns of lncRNAs and mRNAs in the time course of the locust phase change. LncRNAs displayed more rapid response at the early stages of the time-course treatments than mRNA expression. Functional annotations demonstrated that early changed lncRNAs employed different pathways in isolation and crowding processes to cope up with the changes in population density. Finally, two overlapping hub lncRNA loci in the crowding and isolation networks were screened to be functionally verified. Experimental validation indicated that LNC1010057 could act as a potential regulator to modulate the locust phase change. This work offers new insights into the mechanism underlying the locust phase change and expands the scope of lncRNA functions in animal behavior.

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SCPattern: A statistical approach to identify and classify expression changes in single cell RNA-seq experiments with ordered conditions

10.1101/046110 ◽

2016 ◽

Cited By ~ 1

Author(s):

Ning Leng ◽

Li-Fang Chu ◽

Jeea Choi ◽

Christina Kendziorski ◽

James A. Thomson ◽

...

Keyword(s):

Single Cell ◽

Cell Fate ◽

Empirical Bayes ◽

Time Course ◽

Expression Patterns ◽

R Package ◽

Embryonic Cell ◽

Rna Seq ◽

Cell Fate Decisions ◽

Probability Estimates

AbstractMotivationWith the development of single cell RNA-seq (scRNA-seq) technology, scRNA-seq experiments with ordered conditions (e.g. time-course) are becoming common. Methods developed for analyzing ordered bulk RNA-seq experiments are not applicable to scRNA-seq, since their distributional assumptions are often violated by additional heterogeneities prevalent in scRNA-seq. Here we present SC-Pattern - an empirical Bayes model to characterize genes with expression changes in ordered scRNA-seq experiments. SCPattern utilizes the non-parametrical Kolmogorov-Smirnov statistic, thus it has the flexibility to identify genes with a wide variety of types of changes. Additionally, the Bayes framework allows SCPattern to classify genes into expression patterns with probability estimates.ResultsSimulation results show that SCPattern is well powered for identifying genes with expression changes while the false discovery rate is well controlled. SCPattern is also able to accurately classify these dynamic genes into directional expression patterns. Applied to a scRNA-seq time course dataset studying human embryonic cell differentiation, SCPattern detected a group of important genes that are involved in mesendoderm and definitive endoderm cell fate decisions, positional patterning, and cell cycle.Availability and ImplementationThe SCPattern is implemented as an R package along with a user-friendly graphical interface, which are available at:https://github.com/lengning/SCPatternContact:[email protected]

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Factors Affecting Pseudomonas savastanoi pv. savastanoi Plant Inoculations and Their Use for Evaluation of Olive Cultivar Susceptibility

Phytopathology ◽

10.1094/phyto-96-0313 ◽

2006 ◽

Vol 96 (3) ◽

pp. 313-319 ◽

Cited By ~ 39

Author(s):

R. Penyalver ◽

A. García ◽

A. Ferrer ◽

E. Bertolini ◽

J. M. Quesada ◽

...

Keyword(s):

Olive Cultivar ◽

Factors Affecting ◽

Pathogen Virulence ◽

Secondary Tumors ◽

Olive Knot Disease ◽

Olive Cultivars ◽

Olive Trees ◽

Severity Of The Disease ◽

Cultivar Susceptibility ◽

Highly Virulent

Pseudomonas savastanoi pv. savastanoi causes olive knot disease, which is present in most countries where olive trees are grown. Although the use of cultivars with low susceptibility may be one of the most appropriate methods of disease control, little information is available from inoculation assays, and cultivar susceptibility assessments have been limited to few cultivars. We have evaluated the effects of pathogen virulence, plant age, the dose/response relationship, and the induction of secondary tumors in olive inoculation assays. Most P. savastanoi pv. savastanoi strains evaluated were highly virulent to olive plants, but interactions between cultivars and strains were found. The severity of the disease in a given cultivar was strongly dependent of the pathogen dose applied at the wound sites. Secondary tumors developed in noninoculated wounds following inoculation at another position on the stem, suggesting the migration of the pathogen within olive plants. Proportion and weight of primary knots and the presence of secondary knots were evaluated in 29 olive cultivars inoculated with two pathogen strains at two inoculum doses, allowing us to rate most of the cultivars as having either high, medium, or low susceptibility to olive knot disease. None of the cultivars were immune to the disease.

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Phenolic Responses of Resistant and Susceptible Olive Cultivars Induced by Defoliating and Nondefoliating Verticillium dahliae Pathotypes

Plant Disease ◽

10.1094/pdis-94-9-1156 ◽

2010 ◽

Vol 94 (9) ◽

pp. 1156-1162 ◽

Cited By ~ 39

Author(s):

Emmanouil A. Markakis ◽

Sotirios E. Tjamos ◽

Polymnia P. Antoniou ◽

Peter A. Roussos ◽

Epaminondas J. Paplomatas ◽

...

Keyword(s):

Verticillium Dahliae ◽

Defense Mechanisms ◽

Defense Mechanism ◽

Quantitative Polymerase Chain Reaction ◽

Positive Association ◽

Limited Information ◽

Vascular Tissues ◽

Olive Cultivars ◽

Fungal Dna ◽

Olive Trees

Verticillium wilt is the most serious olive disease worldwide. The olive-infecting Verticillium dahliae pathotypes have been classified as defoliating (D) and nondefoliating (ND), and the disease is mainly controlled in olive orchards by using resistant or tolerant cultivars. Limited information is available about the nature of resistance in most of the olive cultivars. In the present study, the phenolic responses of the susceptible to V. dahliae olive cv. Amfissis and the resistant cv. Koroneiki upon D and ND V. dahliae infection were monitored in relation to the fungal DNA levels in the vascular tissues with the purpose to explore the defense mechanisms of olive trees against V. dahliae. Quantitative polymerase chain reaction revealed that the decrease in symptom severity shown in Koroneiki trees was associated with significant reduction in the growth of both V. dahliae pathotypes in the vascular tissues compared with Amfissis. In Koroneiki trees, the levels of o-diphenols and verbascoside were positively associated with the DNA levels of the D and ND pathotypes. In addition, a positive association was observed between the levels of verbascoside and the fungal DNA level in Amfissis trees, whereas a negative association was revealed between the fungal DNA level and the total phenols and oleuropein content in both cultivars. The levels of verbascoside were clearly higher in Koroneiki trees compared with Amfissis trees, indicating for the first time in the literature the involvement of verbascoside in the defense mechanism of olive trees against V. dahliae.

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Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

BMC Bioinformatics ◽

10.1186/s12859-021-04438-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Takayuki Osabe ◽

Kentaro Shimizu ◽

Koji Kadota

Keyword(s):

Differential Expression ◽

Time Course ◽

Clustering Algorithm ◽

Expression Patterns ◽

Differential Expression Analysis ◽

Real Data ◽

Gene Clustering ◽

Rna Seq ◽

Model Based ◽

Group Data

Abstract Background RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis. Results The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (PDEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm. Conclusions MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

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Effectiveness of model-based clustering in analyzing Plasmodium falciparum RNA-seq time-course data

F1000Research ◽

10.12688/f1000research.12360.2 ◽

2018 ◽

Vol 6 ◽

pp. 1706

Author(s):

Jelili Oyelade ◽

Itunuoluwa Isewon ◽

Damilare Olaniyan ◽

Solomon O Rotimi ◽

Jumoke Soyemi

Keyword(s):

Plasmodium Falciparum ◽

Time Course ◽

Markov Models ◽

Expression Patterns ◽

Functional Enrichment ◽

Model Parameters ◽

Ratio Test ◽

Rna Seq ◽

Model Based Clustering ◽

Time Course Data

Background: The genomics and microarray technology played tremendous roles in the amount of biologically useful information on gene expression of thousands of genes to be simultaneously observed. This required various computational methods of analyzing these amounts of data in order to discover information about gene function and regulatory mechanisms. Methods: In this research, we investigated the usefulness of hidden markov models (HMM) as a method of clustering Plasmodium falciparum genes that show similar expression patterns. The Baum-Welch algorithm was used to train the dataset to determine the maximum likelihood estimate of the Model parameters. Cluster validation was conducted by performing a likelihood ratio test. Results: The fitted HMM was able to identify 3 clusters from the dataset and sixteen functional enrichment in the cluster set were found. This method efficiently clustered the genes based on their expression pattern while identifying erythrocyte membrane protein 1 as a prominent and diverse protein in P. falciparum. Conclusion: The ability of HMM to identify 3 clusters with sixteen functional enrichment from the 2000 genes makes this a useful method in functional cluster analysis for P. falciparum

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

Scientific Reports ◽

10.1038/s41598-020-58342-7 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 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.

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Effectiveness of model-based clustering in analyzing Plasmodium falciparum RNA-seq time-course data

F1000Research ◽

10.12688/f1000research.12360.1 ◽

2017 ◽

Vol 6 ◽

pp. 1706

Author(s):

Jelili Oyelade ◽

Itunuoluwa Isewon ◽

Damilare Olaniyan ◽

Solomon O Rotimi ◽

Jumoke Soyemi

Keyword(s):

Plasmodium Falciparum ◽

Time Course ◽

Markov Models ◽

Expression Patterns ◽

Functional Enrichment ◽

Model Parameters ◽

Ratio Test ◽

Rna Seq ◽

Model Based Clustering ◽

Time Course Data

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Integration of transcriptomic data identifies key hallmark genes in hypertrophic cardiomyopathy

BMC Cardiovascular Disorders ◽

10.1186/s12872-021-02147-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jing Xu ◽

Xiangdong Liu ◽

Qiming Dai

Keyword(s):

Machine Learning ◽

Hypertrophic Cardiomyopathy ◽

Heart Diseases ◽

Expression Patterns ◽

Support Vector ◽

Rna Seq ◽

Ppi Network ◽

Learning Methods ◽

Transcriptomic Data ◽

Machine Learning Methods

Abstract Background Hypertrophic cardiomyopathy (HCM) represents one of the most common inherited heart diseases. To identify key molecules involved in the development of HCM, gene expression patterns of the heart tissue samples in HCM patients from multiple microarray and RNA-seq platforms were investigated. Methods The significant genes were obtained through the intersection of two gene sets, corresponding to the identified differentially expressed genes (DEGs) within the microarray data and within the RNA-Seq data. Those genes were further ranked using minimum-Redundancy Maximum-Relevance feature selection algorithm. Moreover, the genes were assessed by three different machine learning methods for classification, including support vector machines, random forest and k-Nearest Neighbor. Results Outstanding results were achieved by taking exclusively the top eight genes of the ranking into consideration. Since the eight genes were identified as candidate HCM hallmark genes, the interactions between them and known HCM disease genes were explored through the protein–protein interaction (PPI) network. Most candidate HCM hallmark genes were found to have direct or indirect interactions with known HCM diseases genes in the PPI network, particularly the hub genes JAK2 and GADD45A. Conclusions This study highlights the transcriptomic data integration, in combination with machine learning methods, in providing insight into the key hallmark genes in the genetic etiology of HCM.

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