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Ebrahimie E, Rahimirad S, Tahsili M, Mohammadi-Dehcheshmeh M. Alternative RNA splicing in stem cells and cancer stem cells: Importance of transcript-based expression analysis. World J Stem Cells 2021; 13:1394-1416. [PMID: 34786151 PMCID: PMC8567453 DOI: 10.4252/wjsc.v13.i10.1394] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/21/2021] [Accepted: 09/14/2021] [Indexed: 02/06/2023] Open
Abstract
Alternative ribonucleic acid (RNA) splicing can lead to the assembly of different protein isoforms with distinctive functions. The outcome of alternative splicing (AS) can result in a complete loss of function or the acquisition of new functions. There is a gap in knowledge of abnormal RNA splice variants promoting cancer stem cells (CSCs), and their prospective contribution in cancer progression. AS directly regulates the self-renewal features of stem cells (SCs) and stem-like cancer cells. Notably, octamer-binding transcription factor 4A spliced variant of octamer-binding transcription factor 4 contributes to maintaining stemness properties in both SCs and CSCs. The epithelial to mesenchymal transition pathway regulates the AS events in CSCs to maintain stemness. The alternative spliced variants of CSCs markers, including cluster of differentiation 44, aldehyde dehydrogenase, and doublecortin-like kinase, α6β1 integrin, have pivotal roles in increasing self-renewal properties and maintaining the pluripotency of CSCs. Various splicing analysis tools are considered in this study. LeafCutter software can be considered as the best tool for differential splicing analysis and identification of the type of splicing events. Additionally, LeafCutter can be used for efficient mapping splicing quantitative trait loci. Altogether, the accumulating evidence re-enforces the fact that gene and protein expression need to be investigated in parallel with alternative splice variants.
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Affiliation(s)
- Esmaeil Ebrahimie
- School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide 5005, South Australia, Australia
- La Trobe Genomics Research Platform, School of Life Sciences, College of Science, Health and Engineering, La Trobe University, Melbourne 3086, Australia
- School of Biosciences, The University of Melbourne, Melbourne 3010, Australia,
| | - Samira Rahimirad
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology, Tehran 1497716316, Iran
- Division of Urology, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal H4A 3J1, Quebec, Canada
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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. Front Plant Sci 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] [What about the content of this article? (0)] [Affiliation(s)] [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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