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A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.

ABSTRACT: In terms of making genes expression data more interpretable and comprehensible, there exists a significant superiority on sparse methods. Many sparse methods, such as penalized matrix decomposition (PMD) and sparse principal component analysis (SPCA), have been applied to extract plants core genes. Supervised algorithms, especially the support vector machine-recursive feature elimination (SVM-RFE) method, always have good performance in gene selection. In this paper, we draw into class information via the total scatter matrix and put forward a class-information-based penalized matrix decomposition (CIPMD) method to improve the gene identification performance of PMD-based method. Firstly, the total scatter matrix is obtained based on different samples of the gene expression data. Secondly, a new data matrix is constructed by decomposing the total scatter matrix. Thirdly, the new data matrix is decomposed by PMD to obtain the sparse eigensamples. Finally, the core genes are identified according to the nonzero entries in eigensamples. The results on simulation data show that CIPMD method can reach higher identification accuracies than the conventional gene identification methods. Moreover, the results on real gene expression data demonstrate that CIPMD method can identify more core genes closely related to the abiotic stresses than the other methods.

SUBMITTER: Liu JX

PROVIDER: S-EPMC4152128 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.

Liu Jin-Xing JX Liu Jian J Gao Ying-Lian YL Mi Jian-Xun JX Ma Chun-Xia CX Wang Dong D

PloS one 20140902 9

In terms of making genes expression data more interpretable and comprehensible, there exists a significant superiority on sparse methods. Many sparse methods, such as penalized matrix decomposition (PMD) and sparse principal component analysis (SPCA), have been applied to extract plants core genes. Supervised algorithms, especially the support vector machine-recursive feature elimination (SVM-RFE) method, always have good performance in gene selection. In this paper, we draw into class informati ...[more]

PMID: 25180509

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Project description:Chloroplasts are organelles subjected to extreme oxidative stress conditions. Biomolecules produced in the chloroplasts act as signals guiding plant metabolism toward stress tolerance and play a major role in regulating gene expression in the nucleus. Herein, we used transplastomic plants as an alternative approach to expression of transgenes in the nucleus for conferring stress tolerance to abiotic stresses and herbicides. To investigate the morphophysiological and molecular mechanisms and the role of plastid expressed GSTs in tobacco stress detoxification and stress tolerance, we used transplastomic tobacco lines overexpressing a theta class glutathione transferase (GST) in chloroplasts. The transplastomic plants were tested under drought (0, 100, and 200 mM mannitol) and salinity (0, 150, and 300 mM NaCl) in vitro, and under herbicide stress (Diquat). Our results suggest that pt AtGSTT lines were tolerant to herbicide-induced oxidative and salinity stresses and showed enhanced response tolerance to mannitol-induced osmotic stress compared to WT plants. Overexpression of the Arabidopsis thaliana AtGSTT in the chloroplasts resulted in enhanced photo-tolerance and turgor maintenance under stress. Whole-genome transcriptome analysis revealed that genes related to stress tolerance, were upregulated in pt AtGSTT2a line under both control and high mannitol stress conditions. Transplastomic plants overexpressing the pt AtGSTT2a in the chloroplast showed a state of acclimation to stress, as only limited number of genes were upregulated in the pt AtGSTT2a transplastomic line compared to WT under stress conditions while at the same time genes related to stress tolerance were upregulated in pt AtGSTT2a plants compared to WT in stress-free conditions. In parallel, the metabolic profile indicated limited perturbations of the metabolic homeostasis in the transplastomic lines and greater accumulation of mannitol, and soluble sugars under high mannitol stress. Therefore, transplastomic lines seem to be in a state of acclimation to stress under stress-free conditions, which was maintained even under high mannitol stress. The results help to elucidate the role of GSTs in plant abiotic stress tolerance and the underlying mechanisms of the GSTs expressed in the chloroplast, toward environmental resilience of cultivated crops.

Project description:BackgroundTo develop a set of transcriptome sequences to support research on environmental stress responses in green ash (Fraxinus pennsylvanica), we undertook deep RNA sequencing of green ash tissues under various stress treatments. The treatments, including emerald ash borer (EAB) feeding, heat, drought, cold and ozone, were selected to mimic the increasing threats of climate change and invasive pests faced by green ash across its native habitat.ResultsWe report the generation and assembly of RNA sequences from 55 green ash samples into 107,611 putative unique transcripts (PUTs). 52,899 open reading frames were identified. Functional annotation of the PUTs by comparison to the Uniprot protein database identified matches for 63 % of transcripts and for 98 % of transcripts with ORFs. Further functional annotation identified conserved protein domains and assigned gene ontology terms to the PUTs. Examination of transcript expression across different RNA libraries revealed that expression patterns clustered based on tissues regardless of stress treatment. The transcripts from stress treatments were further examined to identify differential expression. Tens to hundreds of differentially expressed PUTs were identified for each stress treatment. A set of 109 PUTs were found to be consistently up or down regulated across three or more different stress treatments, representing basal stress response candidate genes in green ash. In addition, 1956 simple sequence repeats were identified in the PUTs, of which we identified 465 high quality DNA markers and designed flanking PCR primers.ConclusionsNorth American native ash trees have suffered extensive mortality due to EAB infestation, creating a need to breed or select for resistant green ash genotypes. Stress from climate change is an additional concern for longevity of native ash populations. The use of genomics could accelerate management efforts. The green ash transcriptome we have developed provides important sequence information, genetic markers and stress-response candidate genes.

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A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.

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A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.

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