Comparative Time Series RNA-seq Analysis of Pigeonpea Root Tissues in Response to Fusarium udum Infection
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ABSTRACT:
SUBMITTER: Soren K
PROVIDER: S-EPMC10512240 | biostudies-literature | 2021 Jan
REPOSITORIES: biostudies-literature
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Project description:Fusarium wilt (Fusarium udum Butler) is an important biotic constraint to pigeonpea (Cajanus cajan L.) production worldwide. Breeding for fusarium wilt resistance continues to be an integral part of genetic improvement of pigeonpea. Therefore, the study was aimed at identifying and validating resistant genotypes to fusarium wilt and determining the magnitude of genotype × environment (G × E) interactions through multi-environment and multi-year screening. A total of 976 genotypes including germplasm and breeding lines were screened against wilt using wilt sick plot at Patancheru, India. Ninety two genotypes resistant to wilt were tested for a further two years using wilt sick plot at Patancheru. A Pigeonpea Wilt Nursery (PWN) comprising of 29 genotypes was then established. PWN was evaluated at nine locations representing different agro-climatic zones of India for wilt resistance during two crop seasons 2007/08 and 2008/09. Genotypes (G), environment (E), and G × E interactions were examined by biplot which partitioned the main effect into G, E, and G × E interactions with significant levels (p ≤ 0.001) being obtained for wilt incidence. The genotype contributed 36.51% of resistance variation followed by the environment (29.32%). A GGE biplot integrated with a boxplot and multiple comparison tests enabled us to identify seven stable genotypes (ICPL 20109, ICPL 20096, ICPL 20115, ICPL 20116, ICPL 20102, ICPL 20106, and ICPL 20094) based on their performance across diverse environments. These genotypes have broad based resistance and can be exploited in pigeonpea breeding programs.
| S-EPMC4779891 | biostudies-literature
Project description:Vascular wilt caused by Fusarium udum Butler is the most important disease of pigeonpea throughout the world. F. udum isolate MTCC 2204 (M1) inoculated pigeonpea plants of susceptible (ICP 2376) and resistant (ICP 8863) cultivars were taken at invasion stage of pathogenesis process for transcriptomic profiling to understand defense signaling reactions that interplay at early stage of this plant-pathogen encounter. Differential transcriptomic profiles were generated through cDNA-AFLP from M1 inoculated resistant and susceptible pigeonpea root tissues. Twenty five percent of transcript derived fragments (TDFs) were found to be pathogen induced. Among them 73 TDFs were re-amplified and sequenced. Homology search of the TDFs in available databases and thorough study of scientific literature identified several pathways, which could play crucial role in defense responses of the F. udum inoculated resistant plants. Some of the defense responsive pathways identified to be active during this interaction are, jasmonic acid and salicylic acid mediated defense responses, cell wall remodeling, vascular development and pattering, abscisic acid mediated responses, effector triggered immunity, and reactive oxygen species mediated signaling. This study identified important wilt responsive regulatory pathways in pigeonpea which will be helpful for further exploration of these resistant components for pigeonpea improvement.
| S-EPMC8595609 | biostudies-literature
Project description:BackgroundFusarium wilt (Fusarium udum Butler), an important soil-borne disease of pigeonpea [Cajanus cajan (L.)], causes significant yield losses across the major pigeonpea production regions. Widespread and high diversity in F. udum hampers the breeding for pigeonpea wilt resistance. The study aimed to elucidate the pathogenic diversity and distribution of F. udum variants in major pigeonpea growing regions of India.ResultsThe roving survey was conducted in major pigeonpea-growing states of India to collect the F. udum isolates. Pathogenic variability of 60 F. udum isolates which are selected from diverse geographical locations and pathogenicity test were performed against 11 pigeonpea host differentials cultivars [ICP 8858, ICP 8859, ICP 8862, ICP 8863, ICP 9174, C 11, BDN 1, BDN 2, LRG 30, ICP 2376 and Bahar (ICP 7197)]. The current study indicated distribution of F. udum isolates into nine variants (0, 1, 2, 3, 4, 5, 6, 7 and 8). Variant-2 and 3 were found to be widespread and predominant in most pigeonpea producing regions. Variant-7 (Karnataka) and Variant-8 (Madhya Pradesh and Maharashtra) were found highly virulent, as most of the host differentials were susceptible to these variants. Three host differential cultivars namely ICP 9174, BDN-2 and Bahar (ICP 7197) were found resistant to most of the F. udum isolates.ConclusionThe present study generated significant information in terms of variants of F. udum which could be used further for the deployment of location-specific wilt resistant cultivars for optimized disease-management strategies. Study is also useful for development of broad-based wilt resistant cultivars to curtail the possible epidemics.
| S-EPMC8962003 | biostudies-literature
Project description:Pigeonpea is an important economic crop in the world and is mainly distributed in tropical and subtropical regions. In order to further expand the scope of planting, one of the problems that must be solved is the impact of soil acidity on plants in these areas. Based on our previous work, we constructed a time series RNA sequencing (RNA-seq) analysis under aluminum (Al) stress in pigeonpea. Through a comparison analysis, 11,425 genes were found to be differentially expressed among all the time points. After clustering these genes by their expression patterns, 12 clusters were generated. Many important functional pathways were identified by gene ontology (GO) analysis, such as biological regulation, localization, response to stimulus, metabolic process, detoxification, and so on. Further analysis showed that metabolic pathways played an important role in the response of Al stress. Thirteen out of the 23 selected genes related to flavonoids and phenols were downregulated in response to Al stress. In addition, we verified these key genes of flavonoid- and phenol-related metabolism pathways by qRT-PCR. Collectively, our findings not only revealed the regulation mechanism of pigeonpea under Al stress but also provided methodological support for further exploration of plant stress regulation mechanisms.
| S-EPMC7230159 | biostudies-literature
Project description: Not available
| S-EPMC9581238 | biostudies-literature
Project description:Fusarium udum F02845 is a destructive fungal pathogen which causes pigeonpea (Cajanus cajan L. Millspaugh) wilt. Here we report the first de novo draft assembly of Fusarium udum F02845, isolated from an infected pigeonpea stem. The genome was determined to be 56.38 Mb in size, with a G+C content of 42.44%, and predicted to have 712 scaffolds with a total number of 11,829 genes.
| S-EPMC6256552 | biostudies-literature
Project description:Fusarium wilt (FW), caused by Fusarium udum Butler (FU), is among the challenging factors in the production of pigeonpea. Therefore, exploring a superior pigeonpea genotype from landraces or local cultivars through the selection of innate resistance to FW using different biological and molecular approaches, and validating its resistance response, could be an alternative to sustainable crop improvement. Five distinct pigeonpea genotypes, with resistant (ICP2894) and susceptible (ICP2376) controls, were selected on the basis of the incidence percentage of FW, from three different states of India. Among them, the cultivar Richa, which displayed low incidence of FW (10.0%) during the genotype evaluation, was further examined for its innate resistance to FW. Molecular characterization of antioxidant (AO) enzyme [APX and SOD] and pathogenesis-related (PR) protein [CHS and β-1, 3-glucanase] families were performed. The obtained results of reverse transcription-polymerase chain reaction-based expression study and in silico analysis showed a higher level of induction of PR and AO genes, and the strong interaction of their putative proteins with fungal cellobiohydrolase-c protein established their antifungal activity, conferring early plant defense responses to FU in Richa. Our study demonstrated a strong and combinatorial approach involving biological assay, molecular experiments, and in silico analysis to identify a superior pigeonpea genotype that was resistant to FW across a major biogeographic region.
| S-EPMC7468401 | biostudies-literature
Project description:RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.
| S-EPMC3622290 | biostudies-literature
Project description:BackgroundDynamic expression data, nowadays obtained using high-throughput RNA sequencing, are essential to monitor transient gene expression changes and to study the dynamics of their transcriptional activity in the cell or response to stimuli. Several methods for data selection, clustering and functional analysis are available; however, these steps are usually performed independently, without exploiting and integrating the information derived from each step of the analysis.MethodsHere we present FunPat, an R package for time series RNA sequencing data that integrates gene selection, clustering and functional annotation into a single framework. FunPat exploits functional annotations by performing for each functional term, e.g. a Gene Ontology term, an integrated selection-clustering analysis to select differentially expressed genes that share, besides annotation, a common dynamic expression profile.ResultsFunPat performance was assessed on both simulated and real data. With respect to a stand-alone selection step, the integration of the clustering step is able to improve the recall without altering the false discovery rate. FunPat also shows high precision and recall in detecting the correct temporal expression patterns; in particular, the recall is significantly higher than hierarchical, k-means and a model-based clustering approach specifically designed for RNA sequencing data. Moreover, when biological replicates are missing, FunPat is able to provide reproducible lists of significant genes. The application to real time series expression data shows the ability of FunPat to select differentially expressed genes with high reproducibility, indirectly confirming high precision and recall in gene selection. Moreover, the expression patterns obtained as output allow an easy interpretation of the results.ConclusionsA novel analysis pipeline was developed to search the main temporal patterns in classes of genes similarly annotated, improving the sensitivity of gene selection by integrating the statistical evidence of differential expression with the information on temporal profiles and the functional annotations. Significant genes are associated to both the most informative functional terms, avoiding redundancy of information, and the most representative temporal patterns, thus improving the readability of the results. FunPat package is provided in R/Bioconductor at link: http://sysbiobig.dei.unipd.it/?q=node/79.
| S-EPMC4460925 | biostudies-literature
Project description:BackgroundRecent development of bioinformatics tools for Next Generation Sequencing data has facilitated complex analyses and prompted large scale experimental designs for comparative genomics. When combined with the advances in network inference tools, this can lead to powerful methodologies for mining genomics data, allowing development of pipelines that stretch from sequence reads mapping to network inference. However, integrating various methods and tools available over different platforms requires a programmatic framework to fully exploit their analytic capabilities. Integrating multiple genomic analysis tools faces challenges from standardization of input and output formats, normalization of results for performing comparative analyses, to developing intuitive and easy to control scripts and interfaces for the genomic analysis pipeline.ResultsWe describe here NetSeekR, a network analysis R package that includes the capacity to analyze time series of RNA-Seq data, to perform correlation and regulatory network inferences and to use network analysis methods to summarize the results of a comparative genomics study. The software pipeline includes alignment of reads, differential gene expression analysis, correlation network analysis, regulatory network analysis, gene ontology enrichment analysis and network visualization of differentially expressed genes. The implementation provides support for multiple RNA-Seq read mapping methods and allows comparative analysis of the results obtained by different bioinformatics methods.ConclusionOur methodology increases the level of integration of genomics data analysis tools to network inference, facilitating hypothesis building, functional analysis and genomics discovery from large scale NGS data. When combined with network analysis and simulation tools, the pipeline allows for developing systems biology methods using large scale genomics data.
| S-EPMC8796424 | biostudies-literature