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Mohammadi-Dehcheshmeh M, Niazi A, Ebrahimi M, Tahsili M, Nurollah Z, Ebrahimi Khaksefid R, Ebrahimi M, Ebrahimie E. Unified Transcriptomic Signature of Arbuscular Mycorrhiza Colonization in Roots of Medicago truncatula by Integration of Machine Learning, Promoter Analysis, and Direct Merging Meta-Analysis. FRONTIERS IN PLANT SCIENCE 2018; 9:1550. [PMID: 30483277 PMCID: PMC6240842 DOI: 10.3389/fpls.2018.01550] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 10/03/2018] [Indexed: 05/25/2023]
Abstract
Plant root symbiosis with Arbuscular mycorrhizal (AM) fungi improves uptake of water and mineral nutrients, improving plant development under stressful conditions. Unraveling the unified transcriptomic signature of a successful colonization provides a better understanding of symbiosis. We developed a framework for finding the transcriptomic signature of Arbuscular mycorrhiza colonization and its regulating transcription factors in roots of Medicago truncatula. Expression profiles of roots in response to AM species were collected from four separate studies and were combined by direct merging meta-analysis. Batch effect, the major concern in expression meta-analysis, was reduced by three normalization steps: Robust Multi-array Average algorithm, Z-standardization, and quartiling normalization. Then, expression profile of 33685 genes in 18 root samples of Medicago as numerical features, as well as study ID and Arbuscular mycorrhiza type as categorical features, were mined by seven models: RELIEF, UNCERTAINTY, GINI INDEX, Chi Squared, RULE, INFO GAIN, and INFO GAIN RATIO. In total, 73 genes selected by machine learning models were up-regulated in response to AM (Z-value difference > 0.5). Feature weighting models also documented that this signature is independent from study (batch) effect. The AM inoculation signature obtained was able to differentiate efficiently between AM inoculated and non-inoculated samples. The AP2 domain class transcription factor, GRAS family transcription factors, and cyclin-dependent kinase were among the highly expressed meta-genes identified in the signature. We found high correspondence between the AM colonization signature obtained in this study and independent RNA-seq experiments on AM colonization, validating the repeatability of the colonization signature. Promoter analysis of upregulated genes in the transcriptomic signature led to the key regulators of AM colonization, including the essential transcription factors for endosymbiosis establishment and development such as NF-YA factors. The approach developed in this study offers three distinct novel features: (I) it improves direct merging meta-analysis by integrating supervised machine learning models and normalization steps to reduce study-specific batch effects; (II) seven attribute weighting models assessed the suitability of each gene for the transcriptomic signature which contributes to robustness of the signature (III) the approach is justifiable, easy to apply, and useful in practice. Our integrative framework of meta-analysis, promoter analysis, and machine learning provides a foundation to reveal the transcriptomic signature and regulatory circuits governing Arbuscular mycorrhizal symbiosis and is transferable to the other biological settings.
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Affiliation(s)
- Manijeh Mohammadi-Dehcheshmeh
- Australian Centre for Antimicrobial Resistance Ecology, School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, SA, Australia
- Institute of Biotechnology, Shiraz University, Shiraz, Iran
| | - Ali Niazi
- Institute of Biotechnology, Shiraz University, Shiraz, Iran
| | | | | | - Zahra Nurollah
- Department of Biotechnology, Shahrekord University, Shahrekord, Iran
| | - Reyhaneh Ebrahimi Khaksefid
- Department of Biotechnology, Shahrekord University, Shahrekord, Iran
- School of Agriculture Food and Wine, Department of Plant Science, The University of Adelaide, Adelaide, SA, Australia
| | - Mahdi Ebrahimi
- Max-Planck-Institute for Informatics, Saarbrucken, Germany
| | - Esmaeil Ebrahimie
- Australian Centre for Antimicrobial Resistance Ecology, School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, SA, Australia
- Institute of Biotechnology, Shiraz University, Shiraz, Iran
- Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
- Division of Information Technology, Engineering and the Environment, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia
- Faculty of Science and Engineering, School of Biological Sciences, Flinders University, Adelaide, SA, Australia
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Willems P, Mhamdi A, Stael S, Storme V, Kerchev P, Noctor G, Gevaert K, Van Breusegem F. The ROS Wheel: Refining ROS Transcriptional Footprints. PLANT PHYSIOLOGY 2016; 171:1720-33. [PMID: 27246095 PMCID: PMC4936575 DOI: 10.1104/pp.16.00420] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Accepted: 05/30/2016] [Indexed: 05/19/2023]
Abstract
In the last decade, microarray studies have delivered extensive inventories of transcriptome-wide changes in messenger RNA levels provoked by various types of oxidative stress in Arabidopsis (Arabidopsis thaliana). Previous cross-study comparisons indicated how different types of reactive oxygen species (ROS) and their subcellular accumulation sites are able to reshape the transcriptome in specific manners. However, these analyses often employed simplistic statistical frameworks that are not compatible with large-scale analyses. Here, we reanalyzed a total of 79 Affymetrix ATH1 microarray studies of redox homeostasis perturbation experiments. To create hierarchy in such a high number of transcriptomic data sets, all transcriptional profiles were clustered on the overlap extent of their differentially expressed transcripts. Subsequently, meta-analysis determined a single magnitude of differential expression across studies and identified common transcriptional footprints per cluster. The resulting transcriptional footprints revealed the regulation of various metabolic pathways and gene families. The RESPIRATORY BURST OXIDASE HOMOLOG F-mediated respiratory burst had a major impact and was a converging point among several studies. Conversely, the timing of the oxidative stress response was a determining factor in shaping different transcriptome footprints. Our study emphasizes the need to interpret transcriptomic data sets in a systematic context, where initial, specific stress triggers can converge to common, aspecific transcriptional changes. We believe that these refined transcriptional footprints provide a valuable resource for assessing the involvement of ROS in biological processes in plants.
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Affiliation(s)
- Patrick Willems
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Medical Biotechnology Center, VIB, 9000 Ghent, Belgium (P.W., S.S., K.G.);Department of Biochemistry, Ghent University, 9000 Ghent, Belgium (P.W., S.S., K.G.);Institut des Sciences des Plantes de Paris-Saclay, Unité Mixte de Recherche 8618, Centre National de la Recherche Scientifique, Université de Paris-Sud, 91405 Orsay cedex, France (A.M., G.N.); andUnité Mixte de Recherche 9213/Unité Mixte de Recherche 1403, Université Paris-Sud, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Université d'Evry, Université Paris-Diderot, Sorbonne Paris-Cité, 91405 Orsay, France (A.M., G.N.)
| | - Amna Mhamdi
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Medical Biotechnology Center, VIB, 9000 Ghent, Belgium (P.W., S.S., K.G.);Department of Biochemistry, Ghent University, 9000 Ghent, Belgium (P.W., S.S., K.G.);Institut des Sciences des Plantes de Paris-Saclay, Unité Mixte de Recherche 8618, Centre National de la Recherche Scientifique, Université de Paris-Sud, 91405 Orsay cedex, France (A.M., G.N.); andUnité Mixte de Recherche 9213/Unité Mixte de Recherche 1403, Université Paris-Sud, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Université d'Evry, Université Paris-Diderot, Sorbonne Paris-Cité, 91405 Orsay, France (A.M., G.N.)
| | - Simon Stael
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Medical Biotechnology Center, VIB, 9000 Ghent, Belgium (P.W., S.S., K.G.);Department of Biochemistry, Ghent University, 9000 Ghent, Belgium (P.W., S.S., K.G.);Institut des Sciences des Plantes de Paris-Saclay, Unité Mixte de Recherche 8618, Centre National de la Recherche Scientifique, Université de Paris-Sud, 91405 Orsay cedex, France (A.M., G.N.); andUnité Mixte de Recherche 9213/Unité Mixte de Recherche 1403, Université Paris-Sud, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Université d'Evry, Université Paris-Diderot, Sorbonne Paris-Cité, 91405 Orsay, France (A.M., G.N.)
| | - Veronique Storme
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Medical Biotechnology Center, VIB, 9000 Ghent, Belgium (P.W., S.S., K.G.);Department of Biochemistry, Ghent University, 9000 Ghent, Belgium (P.W., S.S., K.G.);Institut des Sciences des Plantes de Paris-Saclay, Unité Mixte de Recherche 8618, Centre National de la Recherche Scientifique, Université de Paris-Sud, 91405 Orsay cedex, France (A.M., G.N.); andUnité Mixte de Recherche 9213/Unité Mixte de Recherche 1403, Université Paris-Sud, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Université d'Evry, Université Paris-Diderot, Sorbonne Paris-Cité, 91405 Orsay, France (A.M., G.N.)
| | - Pavel Kerchev
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Medical Biotechnology Center, VIB, 9000 Ghent, Belgium (P.W., S.S., K.G.);Department of Biochemistry, Ghent University, 9000 Ghent, Belgium (P.W., S.S., K.G.);Institut des Sciences des Plantes de Paris-Saclay, Unité Mixte de Recherche 8618, Centre National de la Recherche Scientifique, Université de Paris-Sud, 91405 Orsay cedex, France (A.M., G.N.); andUnité Mixte de Recherche 9213/Unité Mixte de Recherche 1403, Université Paris-Sud, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Université d'Evry, Université Paris-Diderot, Sorbonne Paris-Cité, 91405 Orsay, France (A.M., G.N.)
| | - Graham Noctor
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Medical Biotechnology Center, VIB, 9000 Ghent, Belgium (P.W., S.S., K.G.);Department of Biochemistry, Ghent University, 9000 Ghent, Belgium (P.W., S.S., K.G.);Institut des Sciences des Plantes de Paris-Saclay, Unité Mixte de Recherche 8618, Centre National de la Recherche Scientifique, Université de Paris-Sud, 91405 Orsay cedex, France (A.M., G.N.); andUnité Mixte de Recherche 9213/Unité Mixte de Recherche 1403, Université Paris-Sud, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Université d'Evry, Université Paris-Diderot, Sorbonne Paris-Cité, 91405 Orsay, France (A.M., G.N.)
| | - Kris Gevaert
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Medical Biotechnology Center, VIB, 9000 Ghent, Belgium (P.W., S.S., K.G.);Department of Biochemistry, Ghent University, 9000 Ghent, Belgium (P.W., S.S., K.G.);Institut des Sciences des Plantes de Paris-Saclay, Unité Mixte de Recherche 8618, Centre National de la Recherche Scientifique, Université de Paris-Sud, 91405 Orsay cedex, France (A.M., G.N.); andUnité Mixte de Recherche 9213/Unité Mixte de Recherche 1403, Université Paris-Sud, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Université d'Evry, Université Paris-Diderot, Sorbonne Paris-Cité, 91405 Orsay, France (A.M., G.N.)
| | - Frank Van Breusegem
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium (P.W., A.M., S.S., V.S., P.K., F.V.B.);Medical Biotechnology Center, VIB, 9000 Ghent, Belgium (P.W., S.S., K.G.);Department of Biochemistry, Ghent University, 9000 Ghent, Belgium (P.W., S.S., K.G.);Institut des Sciences des Plantes de Paris-Saclay, Unité Mixte de Recherche 8618, Centre National de la Recherche Scientifique, Université de Paris-Sud, 91405 Orsay cedex, France (A.M., G.N.); andUnité Mixte de Recherche 9213/Unité Mixte de Recherche 1403, Université Paris-Sud, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Université d'Evry, Université Paris-Diderot, Sorbonne Paris-Cité, 91405 Orsay, France (A.M., G.N.)
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Guilloux JP, Bassi S, Ding Y, Walsh C, Turecki G, Tseng G, Cyranowski JM, Sibille E. Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression. Neuropsychopharmacology 2015; 40:701-10. [PMID: 25176167 PMCID: PMC4289958 DOI: 10.1038/npp.2014.226] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2014] [Revised: 08/05/2014] [Accepted: 08/09/2014] [Indexed: 12/30/2022]
Abstract
Major depressive disorder (MDD) in general, and anxious-depression in particular, are characterized by poor rates of remission with first-line treatments, contributing to the chronic illness burden suffered by many patients. Prospective research is needed to identify the biomarkers predicting nonremission prior to treatment initiation. We collected blood samples from a discovery cohort of 34 adult MDD patients with co-occurring anxiety and 33 matched, nondepressed controls at baseline and after 12 weeks (of citalopram plus psychotherapy treatment for the depressed cohort). Samples were processed on gene arrays and group differences in gene expression were investigated. Exploratory analyses suggest that at pretreatment baseline, nonremitting patients differ from controls with gene function and transcription factor analyses potentially related to elevated inflammation and immune activation. In a second phase, we applied an unbiased machine learning prediction model and corrected for model-selection bias. Results show that baseline gene expression predicted nonremission with 79.4% corrected accuracy with a 13-gene model. The same gene-only model predicted nonremission after 8 weeks of citalopram treatment with 76% corrected accuracy in an independent validation cohort of 63 MDD patients treated with citalopram at another institution. Together, these results demonstrate the potential, but also the limitations, of baseline peripheral blood-based gene expression to predict nonremission after citalopram treatment. These results not only support their use in future prediction tools but also suggest that increased accuracy may be obtained with the inclusion of additional predictors (eg, genetics and clinical scales).
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Affiliation(s)
- Jean-Philippe Guilloux
- Université Paris-Sud EA 3544, Faculté de Pharmacie, Châtenay-Malabry, France,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sabrina Bassi
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA,Instituto de Ciencias Básicas y Medicina Experimental, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Ying Ding
- Joint CMU-Pitt PhD Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chris Walsh
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gustavo Turecki
- McGill Group for Suicide Studies and Depressive Program, Douglas Mental Health Institute, Montréal, QC, Canada
| | - George Tseng
- Joint CMU-Pitt PhD Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA,Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jill M Cyranowski
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA,Chatham University, 1 Woodland Road, Pittsburgh, PA 15232, USA, Tel: +412 365 1568, Fax: +412 365 1130, E-mail:
| | - Etienne Sibille
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA,Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA,Departments of Psychiatry and Pharmacology, University of Toronto, Campbell Family Research Institute, Toronto, ON, Canada M5T 1R8, Tel: +416 535 8501 ext 33542, Fax: +416 979 4704, E-mail:
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