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Lopes JML, Nascimento LSDQ, Souza VC, de Matos EM, Fortini EA, Grazul RM, Santos MO, Soltis DE, Soltis PS, Otoni WC, Viccini LF. Water stress modulates terpene biosynthesis and morphophysiology at different ploidal levels in Lippia alba (Mill.) N. E. Brown (Verbenaceae). PROTOPLASMA 2024; 261:227-243. [PMID: 37665420 DOI: 10.1007/s00709-023-01890-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 08/18/2023] [Indexed: 09/05/2023]
Abstract
Monoterpenes are the main component in essential oils of Lippia alba. In this species, the chemical composition of essential oils varies with genome size: citral (geraniol and neral) is dominant in diploids and tetraploids, and linalool in triploids. Because environmental stress impacts various metabolic pathways, we hypothesized that stress responses in L. alba could alter the relationship between genome size and essential oil composition. Water stress affects the flowering, production, and reproduction of plants. Here, we evaluated the effect of water stress on morphophysiology, essential oil production, and the expression of genes related to monoterpene synthesis in diploid, triploid, and tetraploid accessions of L. alba cultivated in vitro for 40 days. First, using transcriptome data, we performed de novo gene assembly and identified orthologous genes using phylogenetic and clustering-based approaches. The expression of candidate genes related to terpene biosynthesis was estimated by real-time quantitative PCR. Next, we assessed the expression of these genes under water stress conditions, whereby 1% PEG-4000 was added to MS medium. Water stress modulated L. alba morphophysiology at all ploidal levels. Gene expression and essential oil production were affected in triploid accessions. Polyploid accessions showed greater growth and metabolic tolerance under stress compared to diploids. These results confirm the complex regulation of metabolic pathways such as the production of essential oils in polyploid genomes. In addition, they highlight aspects of genotype and environment interactions, which may be important for the conservation of tropical biodiversity.
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Affiliation(s)
- Juliana Mainenti Leal Lopes
- Department of Biology, Insitute of Biological Science, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, 36036-900, Brazil
- School of Life Science and Environment, Department of Genetic and Biotechnology, University of Trás-Os-Montes and Alto Douro, 5001-801, Vila Real, Portugal
- BioISI - Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisboa, 1649-004, Lisbon, Portugal
| | | | - Vinicius Carius Souza
- Department of Biology, Insitute of Biological Science, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, 36036-900, Brazil
| | - Elyabe Monteiro de Matos
- Department of Biology, Insitute of Biological Science, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, 36036-900, Brazil
| | - Evandro Alexandre Fortini
- Laboratory of Plant Tissue Culture (LCTII), Department of Plant Biology/BIOAGRO, Universidade Federal de Viçosa, Av. P.H. Rolfs S/N, Campus Universitário, Viçosa, MG, 36570-000, Brazil
| | | | - Marcelo Oliveira Santos
- Department of Biology, Insitute of Biological Science, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, 36036-900, Brazil
| | - Douglas E Soltis
- Florida Museum of Natural History, University of Florida, Gainesville, FL, 32611, USA
- Department of Biology, University of Florida, Gainesville, FL, 32611, USA
| | - Pamela S Soltis
- Florida Museum of Natural History, University of Florida, Gainesville, FL, 32611, USA
| | - Wagner Campos Otoni
- Laboratory of Plant Tissue Culture (LCTII), Department of Plant Biology/BIOAGRO, Universidade Federal de Viçosa, Av. P.H. Rolfs S/N, Campus Universitário, Viçosa, MG, 36570-000, Brazil
| | - Lyderson Facio Viccini
- Department of Biology, Insitute of Biological Science, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, 36036-900, Brazil.
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Koh E, Goh W, Julca I, Villanueva E, Mutwil M. PEO: Plant Expression Omnibus - a comparative transcriptomic database for 103 Archaeplastida. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:1592-1603. [PMID: 38050352 DOI: 10.1111/tpj.16566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/16/2023] [Indexed: 12/06/2023]
Abstract
The Plant Expression Omnibus (PEO) is a web application that provides biologists with access to gene expression insights across over 100 plant species, ~60 000 manually annotated RNA-seq samples, and more than 4 million genes. The tool allows users to explore the expression patterns of genes across different organs, identify organ-specific genes, and discover top co-expressed genes for any gene of interest. PEO also provides functional annotations for each gene, allowing for the identification of genetic modules and pathways. PEO is designed to facilitate comparative kingdom-wide gene expression analysis and provide a valuable resource for plant biology research. We provide two case studies to demonstrate the utility of PEO in identifying candidate genes in pollen coat biosynthesis in Arabidopsis and investigating the biosynthetic pathway components of capsaicin in Capsicum annuum. The database is freely available at https://expression.plant.tools/.
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Affiliation(s)
- Eugene Koh
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - William Goh
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Irene Julca
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Erielle Villanueva
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Marek Mutwil
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
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Raza A, Salehi H, Bashir S, Tabassum J, Jamla M, Charagh S, Barmukh R, Mir RA, Bhat BA, Javed MA, Guan DX, Mir RR, Siddique KHM, Varshney RK. Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity. PLANT CELL REPORTS 2024; 43:80. [PMID: 38411713 PMCID: PMC10899315 DOI: 10.1007/s00299-024-03153-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/05/2024] [Indexed: 02/28/2024]
Abstract
The escalating challenges posed by metal(loid) toxicity in agricultural ecosystems, exacerbated by rapid climate change and anthropogenic pressures, demand urgent attention. Soil contamination is a critical issue because it significantly impacts crop productivity. The widespread threat of metal(loid) toxicity can jeopardize global food security due to contaminated food supplies and pose environmental risks, contributing to soil and water pollution and thus impacting the whole ecosystem. In this context, plants have evolved complex mechanisms to combat metal(loid) stress. Amid the array of innovative approaches, omics, notably transcriptomics, proteomics, and metabolomics, have emerged as transformative tools, shedding light on the genes, proteins, and key metabolites involved in metal(loid) stress responses and tolerance mechanisms. These identified candidates hold promise for developing high-yielding crops with desirable agronomic traits. Computational biology tools like bioinformatics, biological databases, and analytical pipelines support these omics approaches by harnessing diverse information and facilitating the mapping of genotype-to-phenotype relationships under stress conditions. This review explores: (1) the multifaceted strategies that plants use to adapt to metal(loid) toxicity in their environment; (2) the latest findings in metal(loid)-mediated transcriptomics, proteomics, and metabolomics studies across various plant species; (3) the integration of omics data with artificial intelligence and high-throughput phenotyping; (4) the latest bioinformatics databases, tools and pipelines for single and/or multi-omics data integration; (5) the latest insights into stress adaptations and tolerance mechanisms for future outlooks; and (6) the capacity of omics advances for creating sustainable and resilient crop plants that can thrive in metal(loid)-contaminated environments.
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Affiliation(s)
- Ali Raza
- Guangdong Key Laboratory of Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, 518060, China
| | - Hajar Salehi
- Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122, Piacenza, Italy
| | - Shanza Bashir
- Institute of Environmental Sciences and Engineering, School of Civil and Environmental Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Javaria Tabassum
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore, Pakistan
| | - Monica Jamla
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Ganeshkhind, Pune, 411016, India
| | - Sidra Charagh
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Hangzhou, China
| | - Rutwik Barmukh
- WA State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, 6150, Australia
| | - Rakeeb Ahmad Mir
- Department of Biotechnology, School of Life Sciences, Central University of Kashmir, Ganderbal, India
| | - Basharat Ahmad Bhat
- Department of Bio-Resources, Amar Singh College Campus, Cluster University Srinagar, Srinagar, JK, India
| | - Muhammad Arshad Javed
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore, Pakistan
| | - Dong-Xing Guan
- Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Faculty of Agriculture, Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST), Srinagar, Kashmir, India
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia.
| | - Rajeev K Varshney
- WA State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, 6150, Australia.
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Haldar A, Oza VH, DeVoss NS, Clark AD, Lasseigne BN. CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis. Bioinformatics 2023; 39:btad759. [PMID: 38109675 PMCID: PMC10749757 DOI: 10.1093/bioinformatics/btad759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 10/30/2023] [Accepted: 12/16/2023] [Indexed: 12/20/2023] Open
Abstract
SUMMARY High-throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), a Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics. AVAILABILITY AND IMPLEMENTATION https://github.com/lasseignelab/CoSIA.
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Affiliation(s)
- Anisha Haldar
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Vishal H Oza
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Nathaniel S DeVoss
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Amanda D Clark
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Brittany N Lasseigne
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States
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Chao H, Zhang S, Hu Y, Ni Q, Xin S, Zhao L, Ivanisenko VA, Orlov YL, Chen M. Integrating omics databases for enhanced crop breeding. J Integr Bioinform 2023; 20:jib-2023-0012. [PMID: 37486120 PMCID: PMC10777369 DOI: 10.1515/jib-2023-0012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 06/12/2023] [Indexed: 07/25/2023] Open
Abstract
Crop plant breeding involves selecting and developing new plant varieties with desirable traits such as increased yield, improved disease resistance, and enhanced nutritional value. With the development of high-throughput technologies, such as genomics, transcriptomics, and metabolomics, crop breeding has entered a new era. However, to effectively use these technologies, integration of multi-omics data from different databases is required. Integration of omics data provides a comprehensive understanding of the biological processes underlying plant traits and their interactions. This review highlights the importance of integrating omics databases in crop plant breeding, discusses available omics data and databases, describes integration challenges, and highlights recent developments and potential benefits. Taken together, the integration of omics databases is a critical step towards enhancing crop plant breeding and improving global food security.
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Affiliation(s)
- Haoyu Chao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Shilong Zhang
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Yueming Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Qingyang Ni
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Saige Xin
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Liang Zhao
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
| | - Vladimir A. Ivanisenko
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk630090, Russia
| | - Yuriy L. Orlov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk630090, Russia
- Agrarian and Technological Institute, Peoples’ Friendship University of Russia, Moscow117198, Russia
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health (Sechenov University), Moscow119991, Russia
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou310058, China
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Dwivedi SL, Quiroz LF, Reddy ASN, Spillane C, Ortiz R. Alternative Splicing Variation: Accessing and Exploiting in Crop Improvement Programs. Int J Mol Sci 2023; 24:15205. [PMID: 37894886 PMCID: PMC10607462 DOI: 10.3390/ijms242015205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
Alternative splicing (AS) is a gene regulatory mechanism modulating gene expression in multiple ways. AS is prevalent in all eukaryotes including plants. AS generates two or more mRNAs from the precursor mRNA (pre-mRNA) to regulate transcriptome complexity and proteome diversity. Advances in next-generation sequencing, omics technology, bioinformatics tools, and computational methods provide new opportunities to quantify and visualize AS-based quantitative trait variation associated with plant growth, development, reproduction, and stress tolerance. Domestication, polyploidization, and environmental perturbation may evolve novel splicing variants associated with agronomically beneficial traits. To date, pre-mRNAs from many genes are spliced into multiple transcripts that cause phenotypic variation for complex traits, both in model plant Arabidopsis and field crops. Cataloguing and exploiting such variation may provide new paths to enhance climate resilience, resource-use efficiency, productivity, and nutritional quality of staple food crops. This review provides insights into AS variation alongside a gene expression analysis to select for novel phenotypic diversity for use in breeding programs. AS contributes to heterosis, enhances plant symbiosis (mycorrhiza and rhizobium), and provides a mechanistic link between the core clock genes and diverse environmental clues.
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Affiliation(s)
| | - Luis Felipe Quiroz
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, University Road, H91 REW4 Galway, Ireland
| | - Anireddy S N Reddy
- Department of Biology and Program in Cell and Molecular Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Charles Spillane
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, University Road, H91 REW4 Galway, Ireland
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, 23053 Alnarp, SE, Sweden
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Haldar A, Oza VH, DeVoss NS, Clark AD, Lasseigne BN. CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.21.537877. [PMID: 37163017 PMCID: PMC10168259 DOI: 10.1101/2023.04.21.537877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
High throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), an Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics.
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Affiliation(s)
- Anisha Haldar
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Vishal H. Oza
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Nathaniel S. DeVoss
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Amanda D. Clark
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Brittany N. Lasseigne
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
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Wang Z, Zhu Y, Liu Z, Li H, Tang X, Jiang Y. Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest. Front Genet 2023; 14:1190887. [PMID: 37229198 PMCID: PMC10203421 DOI: 10.3389/fgene.2023.1190887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 04/17/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction: With the advancement of RNA-seq technology and machine learning, training large-scale RNA-seq data from databases with machine learning models can generally identify genes with important regulatory roles that were previously missed by standard linear analytic methodologies. Finding tissue-specific genes could improve our comprehension of the relationship between tissues and genes. However, few machine learning models for transcriptome data have been deployed and compared to identify tissue-specific genes, particularly for plants. Methods: In this study, an expression matrix was processed with linear models (Limma), machine learning models (LightGBM), and deep learning models (CNN) with information gain and the SHAP strategy based on 1,548 maize multi-tissue RNA-seq data obtained from a public database to identify tissue-specific genes. In terms of validation, V-measure values were computed based on k-means clustering of the gene sets to evaluate their technical complementarity. Furthermore, GO analysis and literature retrieval were used to validate the functions and research status of these genes. Results: Based on clustering validation, the convolutional neural network outperformed others with higher V-measure values as 0.647, indicating that its gene set could cover as many specific properties of various tissues as possible, whereas LightGBM discovered key transcription factors. The combination of three gene sets produced 78 core tissue-specific genes that had previously been shown in the literature to be biologically significant. Discussion: Different tissue-specific gene sets were identified due to the distinct interpretation strategy for machine learning models and researchers may use multiple methodologies and strategies for tissue-specific gene sets based on their goals, types of data, and computational resources. This study provided comparative insight for large-scale data mining of transcriptome datasets, shedding light on resolving high dimensions and bias difficulties in bioinformatics data processing.
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Affiliation(s)
- Zijie Wang
- School of Agriculture, Sun Yat-sen University, Shenzhen, China
| | - Yuzhi Zhu
- School of Agriculture, Sun Yat-sen University, Shenzhen, China
| | - Zhule Liu
- School of Agriculture, Sun Yat-sen University, Shenzhen, China
| | - Hongfu Li
- School of Agriculture, Sun Yat-sen University, Shenzhen, China
| | - Xinqiang Tang
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China
| | - Yi Jiang
- School of Agriculture, Sun Yat-sen University, Shenzhen, China
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