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Vandepoele K, Thierens S, Van Bel M. Application of orthology and network biology to infer gene functions in non-model plants. PHYSIOLOGIA PLANTARUM 2024; 176:e14441. [PMID: 39019770 DOI: 10.1111/ppl.14441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/12/2024] [Accepted: 02/13/2024] [Indexed: 07/19/2024]
Abstract
Approximately 60% of the genes and gene products in the model species Arabidopsis thaliana have been functionally characterized. In non-model plant species, the functional annotation of the gene space is largely based on homology, with the assumption that genes with shared common ancestry have conserved functions. However, the wide variety in possible morphological, physiological, and ecological differences between plant species gives rise to many species- and clade-specific genes, for which this transfer of knowledge is not possible. Other complications, such as difficulties with genetic transformation, the absence of large-scale mutagenesis methods, and long generation times, further lead to the slow characterization of genes in non-model species. Here, we discuss different resources that integrate plant gene function information. Different approaches that support the functional annotation of gene products, based on orthology or network biology, are described. While sequence-based tools to characterize the functional landscape in non-model species are maturing and becoming more readily available, easy-to-use network-based methods inferring plant gene functions are not as prevalent and have limited functionality.
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Affiliation(s)
- Klaas Vandepoele
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, VIB, Ghent, Belgium
- VIB Center for AI & Computational Biology, VIB, Ghent, Belgium
| | - Sander Thierens
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, VIB, Ghent, Belgium
| | - Michiel Van Bel
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, VIB, Ghent, Belgium
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2
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Jin G, Deng Z, Wang H, Zhang Y, Fu R. EpMYB2 positively regulates chicoric acid biosynthesis by activating both primary and specialized metabolic genes in purple coneflower. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 119:252-265. [PMID: 38596892 DOI: 10.1111/tpj.16759] [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: 11/07/2023] [Revised: 03/20/2024] [Accepted: 03/25/2024] [Indexed: 04/11/2024]
Abstract
Chicoric acid is the major active ingredient of the world-popular medicinal plant purple coneflower (Echinacea purpurea (L.) Menoch). It is recognized as the quality index of commercial hot-selling Echinacea products. While the biosynthetic pathway of chicoric acid in purple coneflower has been elucidated recently, its regulatory network remains elusive. Through co-expression and phylogenetic analysis, we found EpMYB2, a typical R2R3-type MYB transcription factor (TF) responsive to methyl jasmonate (MeJA) simulation, is a positive regulator of chicoric acid biosynthesis. In addition to directly regulating chicoric acid biosynthetic genes, EpMYB2 positively regulates genes of the upstream shikimate pathway. We also found that EpMYC2 could activate the expression of EpMYB2 by binding to its G-box site, and the EpMYC2-EpMYB2 module is involved in the MeJA-induced chicoric acid biosynthesis. Overall, we identified an MYB TF that positively regulates the biosynthesis of chicoric acid by activating both primary and specialized metabolic genes. EpMYB2 links the gap between the JA signaling pathway and chicoric acid biosynthesis. This work opens a new direction toward engineering purple coneflower with higher medicinal qualities.
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Affiliation(s)
- Ge Jin
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, People's Republic of China
| | - Zongbi Deng
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, People's Republic of China
| | - Hsihua Wang
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, People's Republic of China
| | - Yang Zhang
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, People's Republic of China
| | - Rao Fu
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, People's Republic of China
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3
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Hodgson L, Li Y, Iturria-Medina Y, Stratton JA, Wolf G, Krishnaswamy S, Bennett DA, Bzdok D. Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer's disease progression. Commun Biol 2024; 7:591. [PMID: 38760483 PMCID: PMC11101463 DOI: 10.1038/s42003-024-06273-8] [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: 12/18/2023] [Accepted: 05/01/2024] [Indexed: 05/19/2024] Open
Abstract
Late onset Alzheimer's disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface. AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain's major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject's progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci.
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Affiliation(s)
- Liam Hodgson
- School of Computer Science, McGill University, Montréal, QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Yue Li
- School of Computer Science, McGill University, Montréal, QC, Canada
| | - Yasser Iturria-Medina
- McConnell Brain Imaging Centre (BIC), MNI, Faculty of Medicine, McGill University, Montréal, Canada
- Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montréal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montréal, Canada
| | - Jo Anne Stratton
- Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montréal, Canada
| | - Guy Wolf
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada
- Department of Mathematics & Statistics, Université de Montréal, Montréal, Canada
| | - Smita Krishnaswamy
- Department of Computer Science, Department of Genetics, Yale University, New Haven, CT, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Danilo Bzdok
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada.
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montréal, QC, Canada.
- The Neuro - Montréal Neurological Institute, McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, Montréal, QC, Canada.
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Bonthala VS, Stich B. StCoExpNet: a global co-expression network analysis facilitates identifying genes underlying agronomic traits in potatoes. PLANT CELL REPORTS 2024; 43:117. [PMID: 38622429 PMCID: PMC11018665 DOI: 10.1007/s00299-024-03201-2] [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: 11/29/2023] [Accepted: 03/18/2024] [Indexed: 04/17/2024]
Abstract
KEY MESSAGE We constructed a gene expression atlas and co-expression network for potatoes and identified several novel genes associated with various agronomic traits. This resource will accelerate potato genetics and genomics research. Potato (Solanum tuberosum L.) is the world's most crucial non-cereal food crop and ranks third in food production after wheat and rice. Despite the availability of several potato transcriptome datasets at public databases like NCBI SRA, an effort has yet to be put into developing a global transcriptome atlas and a co-expression network for potatoes. The objectives of our study were to construct a global expression atlas for potatoes using publicly available transcriptome datasets, identify housekeeping and tissue-specific genes, construct a global co-expression network and identify co-expression clusters, investigate the transcriptional complexity of genes involved in various essential biological processes related to agronomic traits, and provide a web server (StCoExpNet) to easily access the newly constructed expression atlas and co-expression network to investigate the expression and co-expression of genes of interest. In this study, we used data from 2299 publicly available potato transcriptome samples obtained from 15 different tissues to construct a global transcriptome atlas. We found that roughly 87% of the annotated genes exhibited detectable expression in at least one sample. Among these, we identified 281 genes with consistent and stable expression levels, indicating their role as housekeeping genes. Conversely, 308 genes exhibited marked tissue-specific expression patterns. We exemplarily linked some co-expression clusters to important agronomic traits of potatoes, such as self-incompatibility, anthocyanin biosynthesis, tuberization, and defense responses against multiple pathogens. The dataset compiled here constitutes a new resource (StCoExpNet), which can be accessed at https://stcoexpnet.julius-kuehn.de . This transcriptome atlas and the co-expression network will accelerate potato genetics and genomics research.
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Affiliation(s)
- Venkata Suresh Bonthala
- Institute of Quantitative Genetics and Genomics of Plants, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany.
| | - Benjamin Stich
- Institute of Quantitative Genetics and Genomics of Plants, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
- Julius Kühn-Institut (JKI), Institute for Breeding Research On Agricultural Crops, Rudolf-Schick-Platz 3a, OT Groß Lüsewitz, 18190, Sanitz, Germany
- Max Planck Institute for Plant Breeding Research, Köln, Germany
- Cluster of Excellence On Plant Sciences, From Complex Traits Towards Synthetic Modules, Düsseldorf, Germany
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da Silva Rosa SC, Barzegar Behrooz A, Guedes S, Vitorino R, Ghavami S. Prioritization of genes for translation: a computational approach. Expert Rev Proteomics 2024; 21:125-147. [PMID: 38563427 DOI: 10.1080/14789450.2024.2337004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 02/21/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation. AREAS COVERED In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes. EXPERT OPINION Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.
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Affiliation(s)
- Simone C da Silva Rosa
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
| | - Amir Barzegar Behrooz
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
- Electrophysiology Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Sofia Guedes
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Rui Vitorino
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal
- Department of Medical Sciences, Institute of Biomedicine-iBiMED, University of Aveiro, Aveiro, Portugal
- UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Saeid Ghavami
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
- Faculty of Medicine in Zabrze, Academia of Silesia, Katowice, Poland
- Research Institute of Oncology and Hematology, Cancer Care Manitoba, University of Manitoba, Winnipeg, Canada
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Wang D, Quan M, Qin S, Fang Y, Xiao L, Qi W, Jiang Y, Zhou J, Gu M, Guan Y, Du Q, Liu Q, El‐Kassaby YA, Zhang D. Allelic variations of WAK106-E2Fa-DPb1-UGT74E2 module regulate fibre properties in Populus tomentosa. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:970-986. [PMID: 37988335 PMCID: PMC10955495 DOI: 10.1111/pbi.14239] [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: 05/31/2023] [Revised: 10/13/2023] [Accepted: 10/27/2023] [Indexed: 11/23/2023]
Abstract
Wood formation, intricately linked to the carbohydrate metabolism pathway, underpins the capacity of trees to produce renewable resources and offer vital ecosystem services. Despite their importance, the genetic regulatory mechanisms governing wood fibre properties in woody plants remain enigmatic. In this study, we identified a pivotal module comprising 158 high-priority core genes implicated in wood formation, drawing upon tissue-specific gene expression profiles from 22 Populus samples. Initially, we conducted a module-based association study in a natural population of 435 Populus tomentosa, pinpointing PtoDPb1 as the key gene contributing to wood formation through the carbohydrate metabolic pathway. Overexpressing PtoDPb1 led to a 52.91% surge in cellulose content, a reduction of 14.34% in fibre length, and an increment of 38.21% in fibre width in transgenic poplar. Moreover, by integrating co-expression patterns, RNA-sequencing analysis, and expression quantitative trait nucleotide (eQTN) mapping, we identified a PtoDPb1-mediated genetic module of PtoWAK106-PtoDPb1-PtoE2Fa-PtoUGT74E2 responsible for fibre properties in Populus. Additionally, we discovered the two PtoDPb1 haplotypes that influenced protein interaction efficiency between PtoE2Fa-PtoDPb1 and PtoDPb1-PtoWAK106, respectively. The transcriptional activation activity of the PtoE2Fa-PtoDPb1 haplotype-1 complex on the promoter of PtoUGT74E2 surpassed that of the PtoE2Fa-PtoDPb1 haplotype-2 complex. Taken together, our findings provide novel insights into the regulatory mechanisms of fibre properties in Populus, orchestrated by PtoDPb1, and offer a practical module for expediting genetic breeding in woody plants via molecular design.
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Affiliation(s)
- Dan Wang
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Mingyang Quan
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Shitong Qin
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Yuanyuan Fang
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Liang Xiao
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Weina Qi
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Yongsen Jiang
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Jiaxuan Zhou
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Mingyue Gu
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Yicen Guan
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Qingzhang Du
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Qing Liu
- CSIRO Agriculture and FoodBlack MountainCanberraACTAustralia
| | - Yousry A. El‐Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, Forest Sciences CentreUniversity of British ColumbiaVancouverBCCanada
| | - Deqiang Zhang
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
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Liu F, Fernie AR, Zhang Y. Plant gene co-expression defines the biosynthetic pathway of neuroactive alkaloids. MOLECULAR PLANT 2024; 17:372-374. [PMID: 38321739 DOI: 10.1016/j.molp.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 02/02/2024] [Accepted: 02/02/2024] [Indexed: 02/08/2024]
Affiliation(s)
- Fang Liu
- Key Laboratory of Seed Innovation, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, 100101 Beijing, China
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany; Center for Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria.
| | - Youjun Zhang
- Key Laboratory of Seed Innovation, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, 100101 Beijing, China; University of Chinese Academy of Sciences, 100049 Beijing, China.
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Koutrouli M, Nastou K, Piera Líndez P, Bouwmeester R, Rasmussen S, Martens L, Jensen LJ. FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data. Bioinformatics 2024; 40:btae010. [PMID: 38192003 PMCID: PMC10868155 DOI: 10.1093/bioinformatics/btae010] [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: 09/16/2023] [Revised: 12/07/2023] [Accepted: 01/05/2024] [Indexed: 01/10/2024] Open
Abstract
MOTIVATION Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex. RESULTS To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4210 interactions between 1039 understudied proteins. Our findings showcase FAVA's capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source. AVAILABILITY AND IMPLEMENTATION Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.
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Affiliation(s)
- Mikaela Koutrouli
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Katerina Nastou
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Pau Piera Líndez
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
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9
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Vega-Arroy JD, Herrera-Estrella A, Ovando-Vázquez C, Casas-Flores S. Inferring co-expression networks of Arabidopsis thaliana genes during their interaction with Trichoderma spp. Sci Rep 2024; 14:2466. [PMID: 38291044 PMCID: PMC10827721 DOI: 10.1038/s41598-023-48332-w] [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: 06/07/2023] [Accepted: 11/25/2023] [Indexed: 02/01/2024] Open
Abstract
Fungi of the Trichoderma genus are called "biostimulants" because they promote plant growth and development and induce disease resistance. We used conventional transcriptome and gene co-expression analyses to understand the molecular response of the plant Arabidopsis thaliana to inoculation with Trichoderma atroviride or Trichoderma virens. The transcriptional landscape of the plant during the interaction with these fungi showed a reduction in functions such as reactive oxygen species production, defense mechanisms against pathogens, and hormone signaling. T. virens, as opposed to T. atroviride, was more effective at downregulating genes related to terpenoid metabolism, root development, and chemical homeostasis. Through gene co-expression analysis, we found functional gene modules that closely link plant defense with hypoxia. Notably, we found a transcription factor (locus AT2G47520) with two functional domains of interest: a DNA-binding domain and an N-terminal cysteine needed for protein stability under hypoxia. We hypothesize that the transcription factor can bind to the promoter sequence of the GCC-box that is connected to pathogenesis by positioned weight matrix analysis.
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Affiliation(s)
- Javier-David Vega-Arroy
- IPICYT, División de Biología Molecular, Laboratorio de Genómica Funcional y Comparativa, Camino a la Presa San José 2055. Col. Lomas 4 Sección, 78216, San Luis Potosí, SLP, Mexico
- IPICYT, CONAHCYT, Centro Nacional de Supercomputo, Laboratorio de Inteligencia Artificial y Bioinformática, Camino a la Presa San José 2055. Col. Lomas 4 sección, 78216, San Luis Potosí, SLP, Mexico
| | - Alfredo Herrera-Estrella
- Centro de Investigación y de Estudios Avanzados del IPN, unidad de Genómica Avanzada-Langebio, Libramiento Norte carretera Irapuato-León km 9.6, 36824, Irapuato, GTO, Mexico
| | - Cesaré Ovando-Vázquez
- IPICYT, CONAHCYT, Centro Nacional de Supercomputo, Laboratorio de Inteligencia Artificial y Bioinformática, Camino a la Presa San José 2055. Col. Lomas 4 sección, 78216, San Luis Potosí, SLP, Mexico.
| | - Sergio Casas-Flores
- IPICYT, División de Biología Molecular, Laboratorio de Genómica Funcional y Comparativa, Camino a la Presa San José 2055. Col. Lomas 4 Sección, 78216, San Luis Potosí, SLP, Mexico.
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10
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Singh P, Sundaram KT, Vinukonda VP, Venkateshwarlu C, Paul PJ, Pahi B, Gurjar A, Singh UM, Kalia S, Kumar A, Singh VK, Sinha P. Superior haplotypes of key drought-responsive genes reveal opportunities for the development of climate-resilient rice varieties. Commun Biol 2024; 7:89. [PMID: 38216712 PMCID: PMC10786901 DOI: 10.1038/s42003-024-05769-7] [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: 03/24/2023] [Accepted: 01/02/2024] [Indexed: 01/14/2024] Open
Abstract
Haplotype-based breeding is an emerging and innovative concept that enables the development of designer crop varieties by exploiting and exploring superior alleles/haplotypes among target genes to create new traits in breeding programs. In this regard, whole-genome re-sequencing of 399 genotypes (landraces and breeding lines) from the 3000 rice genomes panel (3K-RG) is mined to identify the superior haplotypes for 95 drought-responsive candidate genes. Candidate gene-based association analysis reveals 69 marker-trait associations (MTAs) in 16 genes for single plant yield (SPY) under drought stress. Haplo-pheno analysis of these 16 genes identifies superior haplotypes for seven genes associated with the higher SPY under drought stress. Our study reveals that the performance of lines possessing superior haplotypes is significantly higher (p ≤ 0.05) as measured by single plant yield (SPY), for the OsGSK1-H4, OsDSR2-H3, OsDIL1-H22, OsDREB1C-H3, ASR3-H88, DSM3-H4 and ZFP182-H4 genes as compared to lines without the superior haplotypes. The validation results indicate that a superior haplotype for the DREB transcription factor (OsDREB1C) is present in all the drought-tolerant rice varieties, while it was notably absent in all susceptible varieties. These lines carrying the superior haplotypes can be used as potential donors in haplotype-based breeding to develop high-yielding drought-tolerant rice varieties.
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Affiliation(s)
- Preeti Singh
- International Rice Research Institute (IRRI), South-Asia Hub, Hyderabad, India
| | - Krishna T Sundaram
- International Rice Research Institute (IRRI), South-Asia Hub, Hyderabad, India
| | | | | | - Pronob J Paul
- International Rice Research Institute (IRRI), South-Asia Hub, Hyderabad, India
| | - Bandana Pahi
- International Rice Research Institute (IRRI), South-Asia Hub, Hyderabad, India
| | - Anoop Gurjar
- International Rice Research Institute, South Asia Regional Centre (ISARC), Varanasi, India
| | - Uma Maheshwar Singh
- International Rice Research Institute, South Asia Regional Centre (ISARC), Varanasi, India
| | - Sanjay Kalia
- Department of Biotechnology, CGO Complex, Lodhi Road, New Delhi, India
| | - Arvind Kumar
- International Rice Research Institute, South Asia Regional Centre (ISARC), Varanasi, India
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Vikas K Singh
- International Rice Research Institute (IRRI), South-Asia Hub, Hyderabad, India.
| | - Pallavi Sinha
- International Rice Research Institute (IRRI), South-Asia Hub, Hyderabad, India.
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11
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Chetty A, Blekhman R. Multi-omic approaches for host-microbiome data integration. Gut Microbes 2024; 16:2297860. [PMID: 38166610 PMCID: PMC10766395 DOI: 10.1080/19490976.2023.2297860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.
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Affiliation(s)
- Ashwin Chetty
- Committee on Genetics, Genomics and Systems Biology, The University of Chicago, Chicago, IL, USA
| | - Ran Blekhman
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
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12
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Soto-Cardinault C, Childs KL, Góngora-Castillo E. Network Analysis of Publicly Available RNA-seq Provides Insights into the Molecular Mechanisms of Plant Defense against Multiple Fungal Pathogens in Arabidopsis thaliana. Genes (Basel) 2023; 14:2223. [PMID: 38137044 PMCID: PMC10743233 DOI: 10.3390/genes14122223] [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: 11/10/2023] [Revised: 12/06/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023] Open
Abstract
Fungal pathogens can have devastating effects on global crop production, leading to annual economic losses ranging from 10% to 23%. In light of climate change-related challenges, researchers anticipate an increase in fungal infections as a result of shifting environmental conditions. However, plants have developed intricate molecular mechanisms for effective defense against fungal attacks. Understanding these mechanisms is essential to the development of new strategies for protecting crops from multiple fungi threats. Public omics databases provide valuable resources for research on plant-pathogen interactions; however, integrating data from different studies can be challenging due to experimental variation. In this study, we aimed to identify the core genes that defend against the pathogenic fungi Colletotrichum higginsianum and Botrytis cinerea in Arabidopsis thaliana. Using a custom framework to control batch effects and construct Gene Co-expression Networks in publicly available RNA-seq dataset from infected A. thaliana plants, we successfully identified a gene module that was responsive to both pathogens. We also performed gene annotation to reveal the roles of previously unknown protein-coding genes in plant defenses against fungal infections. This research demonstrates the potential of publicly available RNA-seq data for identifying the core genes involved in defending against multiple fungal pathogens.
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Affiliation(s)
- Cynthia Soto-Cardinault
- Unidad de Biotecnología, Centro de Investigación Científica de Yucatán, Mérida 97205, Mexico;
| | - Kevin L. Childs
- Plant Biology Department, Michigan State University, East Lansing, MI 48824, USA;
| | - Elsa Góngora-Castillo
- CONAHCYT-Unidad de Biotecnología, Centro de Investigación Científica de Yucatán, Mérida 97205, Mexico
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13
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Trujillo-Ortíz R, Espinal-Enríquez J, Hernández-Lemus E. The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes. Int J Mol Sci 2023; 24:17564. [PMID: 38139393 PMCID: PMC10743684 DOI: 10.3390/ijms242417564] [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: 11/03/2023] [Revised: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.
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Affiliation(s)
- Rodrigo Trujillo-Ortíz
- Computational Genomics Division, Instituto Nacional de Medicina Genómica, Mexico City 14610, Mexico;
| | - Jesús Espinal-Enríquez
- Computational Genomics Division, Instituto Nacional de Medicina Genómica, Mexico City 14610, Mexico;
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City 01010, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, Instituto Nacional de Medicina Genómica, Mexico City 14610, Mexico;
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City 01010, Mexico
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14
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Li S, Nakayama H, Sinha NR. How to utilize comparative transcriptomics to dissect morphological diversity in plants. CURRENT OPINION IN PLANT BIOLOGY 2023; 76:102474. [PMID: 37804608 DOI: 10.1016/j.pbi.2023.102474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/09/2023]
Abstract
Comparative transcriptomics has emerged as a powerful approach that allows us to unravel the genetic basis of organ morphogenesis and its diversification processes during evolution. However, the application of comparative transcriptomics in studying plant morphological diversity addresses challenges such as identifying homologous gene pairs, selecting appropriate developmental stages for comparison, and extracting biologically meaningful networks. Methods such as phylostratigraphy, clustering, and gene co-expression networks are explored to identify functionally equivalent genes, align developmental stages, and uncover gene regulatory relationships. In the current review, we highlight the importance of these approaches in overcoming the complexity of plant genomes, the impact of heterochrony on stage alignment, and the integration of gene networks with additional data for a comprehensive understanding of morphological evolution.
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Affiliation(s)
- Siyu Li
- Department of Plant Biology, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Hokuto Nakayama
- Department of Plant Biology, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA; Graduate School of Science, Department of Biological Sciences, The University of Tokyo, Science Build. #2, 7-3-1 Hongo Bunkyo-ku Tokyo, 113-0033, Japan
| | - Neelima R Sinha
- Department of Plant Biology, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA.
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15
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Cingiz MÖ. k- Strong Inference Algorithm: A Hybrid Information Theory Based Gene Network Inference Algorithm. Mol Biotechnol 2023:10.1007/s12033-023-00929-2. [PMID: 37950851 DOI: 10.1007/s12033-023-00929-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 10/05/2023] [Indexed: 11/13/2023]
Abstract
Gene networks allow researchers to understand the underlying mechanisms between diseases and genes while reducing the need for wet lab experiments. Numerous gene network inference (GNI) algorithms have been presented in the literature to infer accurate gene networks. We proposed a hybrid GNI algorithm, k-Strong Inference Algorithm (ksia), to infer more reliable and robust gene networks from omics datasets. To increase reliability, ksia integrates Pearson correlation coefficient (PCC) and Spearman rank correlation coefficient (SCC) scores to determine mutual information scores between molecules to increase diversity of relation predictions. To infer a more robust gene network, ksia applies three different elimination steps to remove redundant and spurious relations between genes. The performance of ksia was evaluated on microbe microarrays database in the overlap analysis with other GNI algorithms, namely ARACNE, C3NET, CLR, and MRNET. Ksia inferred less number of relations due to its strict elimination steps. However, ksia generally performed better on Escherichia coli (E.coli) and Saccharomyces cerevisiae (yeast) gene expression datasets due to F- measure and precision values. The integration of association estimator scores and three elimination stages slightly increases the performance of ksia based gene networks. Users can access ksia R package and user manual of package via https://github.com/ozgurcingiz/ksia .
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Affiliation(s)
- Mustafa Özgür Cingiz
- Computer Engineering Department, Faculty of Engineering and Natural Sciences, Bursa Technical University, Mimar Sinan Campus, Yildirim, 16310, Bursa, Turkey.
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16
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Gao Q, Yin X, Wang F, Zhang C, Xiao F, Wang H, Hu S, Liu W, Zhou S, Chen L, Dai X, Liang M. Jacalin-related lectin 45 (OsJRL45) isolated from 'sea rice 86' enhances rice salt tolerance at the seedling and reproductive stages. BMC PLANT BIOLOGY 2023; 23:553. [PMID: 37940897 PMCID: PMC10634080 DOI: 10.1186/s12870-023-04533-z] [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: 09/28/2022] [Accepted: 10/17/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Rice (Oryza sativa L.) is one of the most widely cultivated grain crops in the world that meets the caloric needs of more than half the world's population. Salt stress seriously affects rice production and threatens food security. Therefore, mining salt tolerance genes in salt-tolerant germplasm and elucidating their molecular mechanisms in rice are necessary for the breeding of salt tolerant cultivars. RESULTS In this study, a salt stress-responsive jacalin-related lectin (JRL) family gene, OsJRL45, was identified in the salt-tolerant rice variety 'sea rice 86' (SR86). OsJRL45 showed high expression level in leaves, and the corresponding protein mainly localized to the endoplasmic reticulum. The knockout mutant and overexpression lines of OsJRL45 revealed that OsJRL45 positively regulates the salt tolerance of rice plants at all growth stages. Compared with the wild type (WT), the OsJRL45 overexpression lines showed greater salt tolerance at the reproductive stage, and significantly higher seed setting rate and 1,000-grain weight. Moreover, OsJRL45 expression significantly improved the salt-resistant ability and yield of a salt-sensitive indica cultivar, L6-23. Furthermore, OsJRL45 enhanced the antioxidant capacity of rice plants and facilitated the maintenance of Na+-K+ homeostasis under salt stress conditions. Five proteins associated with OsJRL45 were screened by transcriptome and interaction network analysis, of which one, the transmembrane transporter Os10g0210500 affects the salt tolerance of rice by regulating ion transport-, salt stress-, and hormone-responsive proteins. CONCLUSIONS The OsJRL45 gene isolated from SR86 positively regulated the salt tolerance of rice plants at all growth stages, and significantly increased the yield of salt-sensitive rice cultivar under NaCl treatment. OsJRL45 increased the activity of antioxidant enzyme of rice and regulated Na+/K+ dynamic equilibrium under salinity conditions. Our data suggest that OsJRL45 may improve the salt tolerance of rice by mediating the expression of ion transport-, salt stress response-, and hormone response-related genes.
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Affiliation(s)
- Qinmei Gao
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China
- College of Chemistry and Chemical Engineering, Jishou University, Hunan, 416000, China
| | - Xiaolin Yin
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China
| | - Feng Wang
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China
| | - Congzhi Zhang
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China
| | - Feicui Xiao
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China
| | - Hongyan Wang
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China
| | - Shuchang Hu
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China
| | - Weihao Liu
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China
| | - Shiqi Zhou
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China
| | - Liangbi Chen
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China
| | - Xiaojun Dai
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China.
| | - Manzhong Liang
- Hunan Province Key Laboratory of Crop Sterile Germplasm Resource Innovation and Application, College of Life Science, Hunan Normal University, Changsha, 410081, China.
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17
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Kumar N, Mukhtar MS. Integrated Systems Biology Pipeline to Compare Co-Expression Networks in Plants and Elucidate Differential Regulators. PLANTS (BASEL, SWITZERLAND) 2023; 12:3618. [PMID: 37896081 PMCID: PMC10610404 DOI: 10.3390/plants12203618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 10/08/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
To identify sets of genes that exhibit similar expression characteristics, co-expression networks were constructed from transcriptome datasets that were obtained from plant samples at various stages of growth and development or treated with diverse biotic, abiotic, and other environmental stresses. In addition, co-expression network analysis can provide deeper insights into gene regulation when combined with transcriptomics. The coordination and integration of all these complex networks to deduce gene regulation are major challenges for plant biologists. Python and R have emerged as major tools for managing complex scientific data over the past decade. In this study, we describe a reproducible protocol POTFUL (pant co-expression transcription factor regulators), implemented in Python 3, for integrating co-expression and transcription factor target protein networks to infer gene regulation.
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Affiliation(s)
| | - M. Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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18
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Zhu X, Wu Y, Liao L, Huang W, Yuan L, Huang J, Zhan Y, Liu L. Expression Profile and Gene Regulation Network of NUSAP1 in Pan Cancers Based on Integrated Bioinformatics Analysis. Int J Gen Med 2023; 16:4235-4248. [PMID: 37745137 PMCID: PMC10516127 DOI: 10.2147/ijgm.s414270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 08/31/2023] [Indexed: 09/26/2023] Open
Abstract
Background Nucleolar and spindle-associated protein 1 (NUSAP1) plays key roles in microtubules and chromosomes in normal cells both structurally and functionally. In malignancies, NUSAP1 is frequently dysregulated and mutated. However, the expression profiles and biological functions of NUSAP1 in tumors remain unclear. Methods NUSAP1 expression in BALB/c mice and human normal or tumor tissues was examined using immunohistochemistry. Kaplan-Meier survival analysis was utilized to assess the prognostic significance of NUSAP1 in tumors, and principal component analysis and co-expression analysis were performed to explore the unique roles of NUSAP1. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed with DAVID. The relevance between NUSAP1 and tumor-infiltrating immune cells was investigated using TIMER. A transcriptional regulation network was constructed using data from The Cancer Genome Atlas. Results NUSAP1 expression levels in various mice tissues were different. Compared with normal tissues, NUSAP1 was strongly expressed in several human tumor tissues. We believe that NUSAP1 distinctly impacts the prognosis of several cancers and plays various roles in thymoma and testicular germ cell tumors. Further, NUSAP1 expression levels were significantly positively associated with diverse infiltrating levels of immune cells, including B cells, CD4+ and CD8+ T cells, dendritic cells, and macrophages, in thymoma. The expression level of NUSAP1 demonstrated strong relevance with various immune markers in thymoma. Finally, the miR-1236-5p-NUSAP1 and TCF3-NUSAP1 network revealed the tumor-promoting role of NUSAP1 and pertinent underlying mechanisms in human liver hepatocellular carcinoma. Conclusion NUSAP1 may be regarded as a therapeutic target or potential prognostic biomarker for various cancer types.
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Affiliation(s)
- Xiaodi Zhu
- Chronic Airways Diseases Laboratory, Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China
| | - Yuting Wu
- Blood Transfusion Department, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, Jiangxi, 341000People’s Republic of China
| | - Liwei Liao
- Chronic Airways Diseases Laboratory, Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China
| | - Wenqi Huang
- Chronic Airways Diseases Laboratory, Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China
| | - Lu Yuan
- Chronic Airways Diseases Laboratory, Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China
| | - Jihong Huang
- Chronic Airways Diseases Laboratory, Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China
| | - Yongzhong Zhan
- Chronic Airways Diseases Laboratory, Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China
| | - Laiyu Liu
- Chronic Airways Diseases Laboratory, Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China
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19
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Upton RN, Correr FH, Lile J, Reynolds GL, Falaschi K, Cook JP, Lachowiec J. Design, execution, and interpretation of plant RNA-seq analyses. FRONTIERS IN PLANT SCIENCE 2023; 14:1135455. [PMID: 37457354 PMCID: PMC10348879 DOI: 10.3389/fpls.2023.1135455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 06/12/2023] [Indexed: 07/18/2023]
Abstract
Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.
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20
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Shu P, Zhang Z, Wu Y, Chen Y, Li K, Deng H, Zhang J, Zhang X, Wang J, Liu Z, Xie Y, Du K, Li M, Bouzayen M, Hong Y, Zhang Y, Liu M. A comprehensive metabolic map reveals major quality regulations in red-flesh kiwifruit (Actinidia chinensis). THE NEW PHYTOLOGIST 2023; 238:2064-2079. [PMID: 36843264 DOI: 10.1111/nph.18840] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/12/2023] [Indexed: 05/04/2023]
Abstract
Kiwifruit (Actinidia chinensis) is one of the popular fruits world-wide, and its quality is mainly determined by key metabolites (sugars, flavonoids, and vitamins). Previous works on kiwifruit are mostly done via a single omics approach or involve only limited metabolites. Consequently, the dynamic metabolomes during kiwifruit development and ripening and the underlying regulatory mechanisms are poorly understood. In this study, using high-resolution metabolomic and transcriptomic analyses, we investigated kiwifruit metabolic landscapes at 11 different developmental and ripening stages and revealed a parallel classification of 515 metabolites and their co-expressed genes into 10 distinct metabolic vs gene modules (MM vs GM). Through integrative bioinformatics coupled with functional genomic assays, we constructed a global map and uncovered essential transcriptomic and transcriptional regulatory networks for all major metabolic changes that occurred throughout the kiwifruit growth cycle. Apart from known MM vs GM for metabolites such as soluble sugars, we identified novel transcription factors that regulate the accumulation of procyanidins, vitamin C, and other important metabolites. Our findings thus shed light on the kiwifruit metabolic regulatory network and provide a valuable resource for the designed improvement of kiwifruit quality.
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Affiliation(s)
- Peng Shu
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Zixin Zhang
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Yi Wu
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Yuan Chen
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Kunyan Li
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Heng Deng
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Jing Zhang
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Xin Zhang
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Jiayu Wang
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Zhibin Liu
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Yue Xie
- Key Laboratory of Breeding and Utilization of Kiwifruit in Sichuan Province, Sichuan Provincial Academy of Natural Resource Sciences, Chengdu, 610213, Sichuan, China
| | - Kui Du
- Key Laboratory of Breeding and Utilization of Kiwifruit in Sichuan Province, Sichuan Provincial Academy of Natural Resource Sciences, Chengdu, 610213, Sichuan, China
| | - Mingzhang Li
- Key Laboratory of Breeding and Utilization of Kiwifruit in Sichuan Province, Sichuan Provincial Academy of Natural Resource Sciences, Chengdu, 610213, Sichuan, China
| | - Mondher Bouzayen
- GBF Laboratory, Université de Toulouse, INRA, Castanet-Tolosan, 31320, France
| | - Yiguo Hong
- School of Life Sciences, University of Warwick, Warwick, CV4 7AL, UK
- School of Science and the Environment, University of Worcester, Worcester, WR2 6AJ, UK
- Research Centre for Plant RNA Signaling, College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yang Zhang
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Mingchun Liu
- Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610065, Sichuan, China
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21
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Lu S, Keleş S. Debiased personalized gene coexpression networks for population-scale scRNA-seq data. Genome Res 2023; 33:932-947. [PMID: 37295843 PMCID: PMC10519377 DOI: 10.1101/gr.277363.122] [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: 09/28/2022] [Accepted: 06/07/2023] [Indexed: 06/12/2023]
Abstract
Population-scale single-cell RNA-seq (scRNA-seq) data sets create unique opportunities for quantifying expression variation across individuals at the gene coexpression network level. Estimation of coexpression networks is well established for bulk RNA-seq; however, single-cell measurements pose novel challenges owing to technical limitations and noise levels of this technology. Gene-gene correlation estimates from scRNA-seq tend to be severely biased toward zero for genes with low and sparse expression. Here, we present Dozer to debias gene-gene correlation estimates from scRNA-seq data sets and accurately quantify network-level variation across individuals. Dozer corrects correlation estimates in the general Poisson measurement model and provides a metric to quantify genes measured with high noise. Computational experiments establish that Dozer estimates are robust to mean expression levels of the genes and the sequencing depths of the data sets. Compared with alternatives, Dozer results in fewer false-positive edges in the coexpression networks, yields more accurate estimates of network centrality measures and modules, and improves the faithfulness of networks estimated from separate batches of the data sets. We showcase unique analyses enabled by Dozer in two population-scale scRNA-seq applications. Coexpression network-based centrality analysis of multiple differentiating human induced pluripotent stem cell (iPSC) lines yields biologically coherent gene groups that are associated with iPSC differentiation efficiency. Application with population-scale scRNA-seq of oligodendrocytes from postmortem human tissues of Alzheimer's disease and controls uniquely reveals coexpression modules of innate immune response with distinct coexpression levels between the diagnoses. Dozer represents an important advance in estimating personalized coexpression networks from scRNA-seq data.
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Affiliation(s)
- Shan Lu
- Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA
| | - Sündüz Keleş
- Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA;
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53706, USA
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22
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Abulfaraj AA. Relationships between some transcription factors and concordantly expressed drought stress-related genes in bread wheat. Saudi J Biol Sci 2023; 30:103652. [PMID: 37206446 PMCID: PMC10189290 DOI: 10.1016/j.sjbs.2023.103652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/18/2023] [Accepted: 04/09/2023] [Indexed: 05/21/2023] Open
Abstract
The challenge of climate change makes it mandatory to improve tolerance to drought stress in bread wheat (Triticum aestivum) via biotechnological approaches. Drought stress experiment was conducted followed by RNA-Seq analysis for leaves of two wheat cultivars namely Giza 168 and Gemmiza 10 with contrasting genotypes. Expression patterns of the regulated stress-related genes and concordantly expressed TFs were detected, then, validated via qPCR for two loss-of-function mutants in Arabidopsis background harboring mutated genes analogue to those in wheat. Drought-stress related genes were searched for concordantly expressed TFs and a total of eight TFs were shown to coexpress with 14 stress-related genes. Among these genes, one TF belongs to the zinc finger protein CONSTANS family and proved via qPCR to drive expression of a gene encoding a speculative TF namely zinc transporter 3-like and two other stress related genes encoding tryptophan synthase alpha chain and asparagine synthetase. Known functions of the two TFs under drought stress complement those of the two concordantly expressed stress-related genes, thus, it is likely that they are related. This study highlights the possibility to utilize metabolic engineering approaches to decipher and incorporate existing regulatory frameworks under drought stress in future breeding programs of bread wheat.
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Saidi MN, Mahjoubi H, Yacoubi I. Transcriptome meta-analysis of abiotic stresses-responsive genes and identification of candidate transcription factors for broad stress tolerance in wheat. PROTOPLASMA 2023; 260:707-721. [PMID: 36063229 DOI: 10.1007/s00709-022-01807-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
Under field conditions, wheat is subjected to single or multiple stress conditions. The elucidation of the molecular mechanism of stress response is a key step to identify candidate genes for stress resistance in plants. In this study, RNA-seq data analysis identified 17.324, 10.562, 5.510, and 8.653 differentially expressed genes (DEGs) under salt, drought, heat, and cold stress, respectively. Moreover, the comparison of DEGs from each stress revealed 2374 shared genes from which 40% showed highly conserved expression patterns. Moreover, co-expression network analysis and GO enrichment revealed co-expression modules enriched with genes involved in transcription regulation, protein kinase pathway, and genes responding to phytohormones or modulating hormone levels. The expression of 15 selected transcription factor encoding genes was analyzed under abiotic stresses and ABA treatment in durum wheat. The identified transcription factor genes are excellent candidates for genetic engineering of stress tolerance in wheat.
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Affiliation(s)
- Mohamed Najib Saidi
- Biotechnology and Plant Improvement Laboratory, Centre of Biotechnology of Sfax, Road Sidi Mansour 6 km, P.O. Box 1177, 3018, Sfax, Tunisia.
| | - Habib Mahjoubi
- Biotechnology and Plant Improvement Laboratory, Centre of Biotechnology of Sfax, Road Sidi Mansour 6 km, P.O. Box 1177, 3018, Sfax, Tunisia
| | - Ines Yacoubi
- Biotechnology and Plant Improvement Laboratory, Centre of Biotechnology of Sfax, Road Sidi Mansour 6 km, P.O. Box 1177, 3018, Sfax, Tunisia
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24
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Zuluaga DL, Blanco E, Mangini G, Sonnante G, Curci PL. A Survey of the Transcriptomic Resources in Durum Wheat: Stress Responses, Data Integration and Exploitation. PLANTS (BASEL, SWITZERLAND) 2023; 12:1267. [PMID: 36986956 PMCID: PMC10056183 DOI: 10.3390/plants12061267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/28/2023] [Accepted: 03/04/2023] [Indexed: 06/19/2023]
Abstract
Durum wheat (Triticum turgidum subsp. durum (Desf.) Husn.) is an allotetraploid cereal crop of worldwide importance, given its use for making pasta, couscous, and bulgur. Under climate change scenarios, abiotic (e.g., high and low temperatures, salinity, drought) and biotic (mainly exemplified by fungal pathogens) stresses represent a significant limit for durum cultivation because they can severely affect yield and grain quality. The advent of next-generation sequencing technologies has brought a huge development in transcriptomic resources with many relevant datasets now available for durum wheat, at various anatomical levels, also focusing on phenological phases and environmental conditions. In this review, we cover all the transcriptomic resources generated on durum wheat to date and focus on the corresponding scientific insights gained into abiotic and biotic stress responses. We describe relevant databases, tools and approaches, including connections with other "omics" that could assist data integration for candidate gene discovery for bio-agronomical traits. The biological knowledge summarized here will ultimately help in accelerating durum wheat breeding.
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25
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Zhang B, Li Q, Keyhaninejad N, Taitano N, Sapkota M, Snouffer A, van der Knaap E. A combinatorial TRM-OFP module bilaterally fine-tunes tomato fruit shape. THE NEW PHYTOLOGIST 2023; 238:2393-2409. [PMID: 36866711 DOI: 10.1111/nph.18855] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 02/18/2023] [Indexed: 05/16/2023]
Abstract
The mechanisms that regulate the vast diversity of plant organ shapes such as the fruit remain to be fully elucidated. TONNEAU1 Recruiting Motif proteins (TRMs) have been implicated in the control of organ shapes in a number of plant species, including tomato. However, the role of many of them is unknown. TRMs interact with Ovate Family Proteins (OFPs) via the M8 domain. However, the in planta function of the TRM-OFP interaction in regulating shape is unknown. We used CRISPR/Cas9 to generate knockout mutants in TRM proteins from different subclades and in-frame mutants within the M8 domain to investigate their roles in organ shape and interactions with OFPs. Our findings indicate that TRMs impact organ shape along both the mediolateral and proximo-distal axes of growth. Mutations in Sltrm3/4 and Sltrm5 act additively to rescue the elongated fruit phenotype of ovate/Slofp20 (o/s) to a round shape. Contrary, mutations in Sltrm19 and Sltrm17/20a result in fruit elongation and further enhance the obovoid phenotype in the o/s mutant. This study supports a combinatorial role of the TRM-OFP regulon where OFPs and TRMs expressed throughout development have both redundant and opposing roles in regulating organ shape.
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Affiliation(s)
- Biyao Zhang
- National Genomics Data Center, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- Center for Applied Genetic Technologies, University of Georgia, Athens, GA, 30602, USA
| | - Qiang Li
- Center for Applied Genetic Technologies, University of Georgia, Athens, GA, 30602, USA
- College of Horticulture, Hebei Agricultural University, Baoding, Hebei, 071000, China
| | - Neda Keyhaninejad
- Center for Applied Genetic Technologies, University of Georgia, Athens, GA, 30602, USA
| | - Nathan Taitano
- Institute for Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, 30602, USA
| | - Manoj Sapkota
- Institute for Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, 30602, USA
| | - Ashley Snouffer
- Center for Applied Genetic Technologies, University of Georgia, Athens, GA, 30602, USA
| | - Esther van der Knaap
- Center for Applied Genetic Technologies, University of Georgia, Athens, GA, 30602, USA
- Institute for Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA, 30602, USA
- Department of Horticulture, University of Georgia, Athens, GA, 30602, USA
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26
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Depuydt T, De Rybel B, Vandepoele K. Charting plant gene functions in the multi-omics and single-cell era. TRENDS IN PLANT SCIENCE 2023; 28:283-296. [PMID: 36307271 DOI: 10.1016/j.tplants.2022.09.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 09/09/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology.
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Affiliation(s)
- Thomas Depuydt
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium; Vlaams Instituut voor Biotechnologie, Center for Plant Systems Biology, Ghent, Belgium
| | - Bert De Rybel
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium; Vlaams Instituut voor Biotechnologie, Center for Plant Systems Biology, Ghent, Belgium
| | - Klaas Vandepoele
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium; Vlaams Instituut voor Biotechnologie, Center for Plant Systems Biology, Ghent, Belgium; Ghent University, Bioinformatics Institute Ghent, Ghent, Belgium.
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27
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Zogopoulos VL, Malatras A, Kyriakidis K, Charalampous C, Makrygianni EA, Duguez S, Koutsi MA, Pouliou M, Vasileiou C, Duddy WJ, Agelopoulos M, Chrousos GP, Iconomidou VA, Michalopoulos I. HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens. Cells 2023; 12:cells12030388. [PMID: 36766730 PMCID: PMC9913097 DOI: 10.3390/cells12030388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/09/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 Homo sapiens genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint.
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Affiliation(s)
- Vasileios L. Zogopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Section of Cell Biology and Biophysics, Department of Biology, National and Kapodistrian University of Athens, 15701 Athens, Greece
| | - Apostolos Malatras
- Biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, 2029 Nicosia, Cyprus
| | - Konstantinos Kyriakidis
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- School of Pharmacy, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Chrysanthi Charalampous
- Centre of Basic Research, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Evanthia A. Makrygianni
- University Research Institute of Maternal and Child Health and Precision Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Stéphanie Duguez
- Personalised Medicine Centre, School of Medicine, Ulster University, Derry-Londonderry BT47 6SB, UK
| | - Marianna A. Koutsi
- Centre of Basic Research, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Marialena Pouliou
- Centre of Basic Research, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Christos Vasileiou
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Engineering Design and Computing Laboratory, ETH Zurich, 8092 Zurich, Switzerland
| | - William J. Duddy
- Personalised Medicine Centre, School of Medicine, Ulster University, Derry-Londonderry BT47 6SB, UK
| | - Marios Agelopoulos
- Centre of Basic Research, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - George P. Chrousos
- University Research Institute of Maternal and Child Health and Precision Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Vassiliki A. Iconomidou
- Section of Cell Biology and Biophysics, Department of Biology, National and Kapodistrian University of Athens, 15701 Athens, Greece
| | - Ioannis Michalopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Correspondence:
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28
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Gouesbet G. Deciphering Macromolecular Interactions Involved in Abiotic Stress Signaling: A Review of Bioinformatics Analysis. Methods Mol Biol 2023; 2642:257-294. [PMID: 36944884 DOI: 10.1007/978-1-0716-3044-0_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Plant functioning and responses to abiotic stresses largely involve regulations at the transcriptomic level via complex interactions of signal molecules, signaling cascades, and regulators. Nevertheless, all the signaling networks involved in responses to abiotic stresses have not yet been fully established. The in-depth analysis of transcriptomes in stressed plants has become a relevant state-of-the-art methodology to study these regulations and signaling pathways that allow plants to cope with or attempt to survive abiotic stresses. The plant science and molecular biology community has developed databases about genes, proteins, protein-protein interactions, protein-DNA interactions and ontologies, which are valuable sources of knowledge for deciphering such regulatory and signaling networks. The use of these data and the development of bioinformatics tools help to make sense of transcriptomic data in specific contexts, such as that of abiotic stress signaling, using functional biological approaches. The aim of this chapter is to present and assess some of the essential online tools and resources that will allow novices in bioinformatics to decipher transcriptomic data in order to characterize the cellular processes and functions involved in abiotic stress responses and signaling. The analysis of case studies further describes how these tools can be used to conceive signaling networks on the basis of transcriptomic data. In these case studies, particular attention was paid to the characterization of abiotic stress responses and signaling related to chemical and xenobiotic stressors.
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Affiliation(s)
- Gwenola Gouesbet
- University of Rennes, CNRS, ECOBIO [(Ecosystèmes, Biodiversité, Evolution)] - UMR 6553, Rennes, France.
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29
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Li L, Jin Z, Huang R, Zhou J, Song F, Yao L, Li P, Lu W, Xiao L, Quan M, Zhang D, Du Q. Leaf physiology variations are modulated by natural variations that underlie stomatal morphology in Populus. PLANT, CELL & ENVIRONMENT 2023; 46:150-170. [PMID: 36285358 DOI: 10.1111/pce.14471] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/26/2022] [Accepted: 05/28/2022] [Indexed: 06/16/2023]
Abstract
Stomata are essential for photosynthesis and abiotic stress tolerance. Here, we used multiomics approaches to dissect the genetic architecture and adaptive mechanisms that underlie stomatal morphology in Populus tomentosa juvenile natural population (303 accessions). We detected 46 candidate genes and 15 epistatic gene-pairs, associated with 5 stomatal morphologies and 18 leaf development and photosynthesis traits, through genome-wide association studies. Expression quantitative trait locus mapping revealed that stomata-associated gene loci were significantly associated with the expression of leaf-related genes; selective sweep analysis uncovered significant differentiation in the allele frequencies of genes that underlie stomatal variations. An allelic regulatory network operating under drought stress and adequate precipitation conditions, with three key regulators (DUF538, TRA2 and AbFH2) and eight interacting genes, was identified that might regulate leaf physiology via modulation of stomatal shape and density. Validation of candidate gene variations in drought-tolerant and F1 hybrid populations of P. tomentosa showed that the DUF538, TRA2 and AbFH2 loci cause functional stabilisation of spatiotemporal regulatory, whose favourable alleles can be faithfully transmitted to offspring. This study provides insights concerning leaf physiology and stress tolerance via the regulation of stomatal determination in perennial plants.
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Affiliation(s)
- Lianzheng Li
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Zhuoying Jin
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Rui Huang
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Jiaxuan Zhou
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Fangyuan Song
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Liangchen Yao
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Peng Li
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Wenjie Lu
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Liang Xiao
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Mingyang Quan
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Deqiang Zhang
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
| | - Qingzhang Du
- National Engineering Research Center of Tree breeding and Ecological restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, P.R. China
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30
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Galindez G, Sadegh S, Baumbach J, Kacprowski T, List M. Network-based approaches for modeling disease regulation and progression. Comput Struct Biotechnol J 2022; 21:780-795. [PMID: 36698974 PMCID: PMC9841310 DOI: 10.1016/j.csbj.2022.12.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.
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Affiliation(s)
- Gihanna Galindez
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.,Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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31
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Halim-Fikri H, Syed-Hassan SNRK, Wan-Juhari WK, Assyuhada MGSN, Hernaningsih Y, Yusoff NM, Merican AF, Zilfalil BA. Central resources of variant discovery and annotation and its role in precision medicine. ASIAN BIOMED 2022; 16:285-298. [PMID: 37551357 PMCID: PMC10392146 DOI: 10.2478/abm-2022-0032] [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] [Indexed: 08/09/2023]
Abstract
Rapid technological advancement in high-throughput genomics, microarray, and deep sequencing technologies has accelerated the possibility of more complex precision medicine research using large amounts of heterogeneous health-related data from patients, including genomic variants. Genomic variants can be identified and annotated based on the reference human genome either within the sequence as a whole or in a putative functional genomic element. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) mutually created standards and guidelines for the appraisal of proof to expand consistency and straightforwardness in clinical variation interpretations. Various efforts toward precision medicine have been facilitated by many national and international public databases that classify and annotate genomic variation. In the present study, several resources are highlighted with recognition and data spreading of clinically important genetic variations.
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Affiliation(s)
- Hashim Halim-Fikri
- Malaysian Node of the Human Variome Project, School of Medical Sciences, Universiti Sains Malaysia, Kelantan16150, Malaysia
| | | | - Wan-Khairunnisa Wan-Juhari
- Malaysian Node of the Human Variome Project, School of Medical Sciences, Universiti Sains Malaysia, Kelantan16150, Malaysia
- Human Genome Centre, School of Medical Sciences, Universiti Sains Malaysia, Kelantan16150, Malaysia
| | - Mat Ghani Siti Nor Assyuhada
- Malaysian Node of the Human Variome Project, School of Medical Sciences, Universiti Sains Malaysia, Kelantan16150, Malaysia
| | - Yetti Hernaningsih
- Department of Clinical Pathology, Faculty of Medicine Universitas Airlangga, Dr. Soetomo Academic General Hospital, Surabaya, Indonesia
| | - Narazah Mohd Yusoff
- Department of Clinical Pathology, Faculty of Medicine Universitas Airlangga, Dr. Soetomo Academic General Hospital, Surabaya, Indonesia
- Clinical Diagnostic Laboratory, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang13200, Malaysia
| | - Amir Feisal Merican
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur50603, Malaysia
- Center of Research for Computational Sciences and Informatics in Biology, Bio Industry, Environment, Agriculture and Healthcare (CRYSTAL), University of Malaya, Kuala Lumpur50603, Malaysia
| | - Bin Alwi Zilfalil
- Malaysian Node of the Human Variome Project, School of Medical Sciences, Universiti Sains Malaysia, Kelantan16150, Malaysia
- Human Genome Centre, School of Medical Sciences, Universiti Sains Malaysia, Kelantan16150, Malaysia
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32
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Curci PL, Zhang J, Mähler N, Seyfferth C, Mannapperuma C, Diels T, Van Hautegem T, Jonsen D, Street N, Hvidsten TR, Hertzberg M, Nilsson O, Inzé D, Nelissen H, Vandepoele K. Identification of growth regulators using cross-species network analysis in plants. PLANT PHYSIOLOGY 2022; 190:2350-2365. [PMID: 35984294 PMCID: PMC9706488 DOI: 10.1093/plphys/kiac374] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/05/2022] [Indexed: 05/11/2023]
Abstract
With the need to increase plant productivity, one of the challenges plant scientists are facing is to identify genes that play a role in beneficial plant traits. Moreover, even when such genes are found, it is generally not trivial to transfer this knowledge about gene function across species to identify functional orthologs. Here, we focused on the leaf to study plant growth. First, we built leaf growth transcriptional networks in Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and aspen (Populus tremula). Next, known growth regulators, here defined as genes that when mutated or ectopically expressed alter plant growth, together with cross-species conserved networks, were used as guides to predict novel Arabidopsis growth regulators. Using an in-depth literature screening, 34 out of 100 top predicted growth regulators were confirmed to affect leaf phenotype when mutated or overexpressed and thus represent novel potential growth regulators. Globally, these growth regulators were involved in cell cycle, plant defense responses, gibberellin, auxin, and brassinosteroid signaling. Phenotypic characterization of loss-of-function lines confirmed two predicted growth regulators to be involved in leaf growth (NPF6.4 and LATE MERISTEM IDENTITY2). In conclusion, the presented network approach offers an integrative cross-species strategy to identify genes involved in plant growth and development.
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Affiliation(s)
- Pasquale Luca Curci
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052 Ghent, Belgium
- Institute of Biosciences and Bioresources, National Research Council (CNR), Via Amendola 165/A, 70126 Bari, Italy
| | - Jie Zhang
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052 Ghent, Belgium
| | - Niklas Mähler
- Department of Plant Physiology, Umea Plant Science Centre (UPSC), Umeå University, 90187 Umeå, Sweden
| | - Carolin Seyfferth
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052 Ghent, Belgium
- Department of Plant Physiology, Umea Plant Science Centre (UPSC), Umeå University, 90187 Umeå, Sweden
| | - Chanaka Mannapperuma
- Department of Plant Physiology, Umea Plant Science Centre (UPSC), Umeå University, 90187 Umeå, Sweden
| | - Tim Diels
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052 Ghent, Belgium
| | - Tom Van Hautegem
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052 Ghent, Belgium
| | - David Jonsen
- SweTree Technologies AB, Skogsmarksgränd 7, SE-907 36 Umeå, Sweden
| | - Nathaniel Street
- Department of Plant Physiology, Umea Plant Science Centre (UPSC), Umeå University, 90187 Umeå, Sweden
| | - Torgeir R Hvidsten
- Department of Plant Physiology, Umea Plant Science Centre (UPSC), Umeå University, 90187 Umeå, Sweden
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - Magnus Hertzberg
- SweTree Technologies AB, Skogsmarksgränd 7, SE-907 36 Umeå, Sweden
| | - Ove Nilsson
- Department of Forest Genetics and Plant Physiology, Umea Plant Science Centre (UPSC), Swedish University of Agricultural Sciences, 90183 Umeå, Sweden
| | - Dirk Inzé
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052 Ghent, Belgium
| | - Hilde Nelissen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052 Ghent, Belgium
| | - Klaas Vandepoele
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052 Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Technologiepark 71, 9052 Ghent, Belgium
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An integrated transcriptome mapping the regulatory network of coding and long non-coding RNAs provides a genomics resource in chickpea. Commun Biol 2022; 5:1106. [PMID: 36261617 PMCID: PMC9581958 DOI: 10.1038/s42003-022-04083-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 10/07/2022] [Indexed: 11/11/2022] Open
Abstract
Large-scale transcriptome analysis can provide a systems-level understanding of biological processes. To accelerate functional genomic studies in chickpea, we perform a comprehensive transcriptome analysis to generate full-length transcriptome and expression atlas of protein-coding genes (PCGs) and long non-coding RNAs (lncRNAs) from 32 different tissues/organs via deep sequencing. The high-depth RNA-seq dataset reveal expression dynamics and tissue-specificity along with associated biological functions of PCGs and lncRNAs during development. The coexpression network analysis reveal modules associated with a particular tissue or a set of related tissues. The components of transcriptional regulatory networks (TRNs), including transcription factors, their cognate cis-regulatory motifs, and target PCGs/lncRNAs that determine developmental programs of different tissues/organs, are identified. Several candidate tissue-specific and abiotic stress-responsive transcripts associated with quantitative trait loci that determine important agronomic traits are also identified. These results provide an important resource to advance functional/translational genomic and genetic studies during chickpea development and environmental conditions. A full-length transcriptome and expression atlas of protein-coding genes and long non-coding RNAs is generated in chickpea. Components of transcriptional regulatory networks and candidate tissue-specific transcripts associated with quantitative trait loci are identified.
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Wu Y, Gao W, Li X, Sun S, Xu J, Shi X, Guo H. Regulatory mechanisms of fatty acids biosynthesis in Armeniaca sibirica seed kernel oil at different developmental stages. PeerJ 2022; 10:e14125. [PMID: 36213508 PMCID: PMC9541615 DOI: 10.7717/peerj.14125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 09/06/2022] [Indexed: 01/21/2023] Open
Abstract
Background Armeniaca sibirica seed kernel oil is rich in oleic acid and linoleic acid, thus holding potential value as a source of high-quality edible oils. However, some regulatory factors involved in fatty acids accumulation in A. sibirica seed kernels remain largely elusive. Thus, the aim of this study was to elucidate the regulatory mechanisms underlying fatty acids biosynthesis in A. sibirica developing seed kernels. Methods Seed kernels from six plants from a single A. sibirica clone were taken at five different developmental stages (days 30, 41, 52, 63, and 73 after anthesis). Fatty acid composition in seed kernel oil was determined by gas chromatography-mass spectrometry (GC-MS). In addition, transcriptome analysis was conducted using second-generation sequencing (SGS) and single-molecule real-time sequencing (SMRT). Results Rapid accumulation of fatty acids occurred throughout the different stages of seed kernels development, with oleic acid and linoleic acid as the main fatty acids. A total of 10,024, 9,803, 6,004, 6,719 and 9,688 unigenes were matched in the Nt, Nr, KOG, GO and KEGG databases, respectively. In the category lipid metabolism, 228 differentially expressed genes (DEGs) were annotated into 13 KEGG pathways. Specific unigenes encoding 12 key enzymes related to fatty acids biosynthesis were determined. Co-expression network analysis identified 11 transcription factors (TFs) and 13 long non-coding RNAs (lncRNAs) which putatively participate in the regulation of fatty acid biosynthesis. This study provides insights into the molecular regulatory mechanisms of fatty acids biosynthesis in A. sibirica developing seed kernels, and enabled the identification of novel candidate factors for future improvement of the production and quality of seed kernel oil by breeding.
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Affiliation(s)
- Yueliang Wu
- College of Forestry, Shenyang Agricultural University, Shenyang, Liaoning, China,The Key Laboratory of Forest Tree Genetics, Breeding and Cultivation of Liaoning Province, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Wenya Gao
- College of Forestry, Shenyang Agricultural University, Shenyang, Liaoning, China,The Key Laboratory of Forest Tree Genetics, Breeding and Cultivation of Liaoning Province, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Xinli Li
- College of Forestry, Shenyang Agricultural University, Shenyang, Liaoning, China,The Key Laboratory of Forest Tree Genetics, Breeding and Cultivation of Liaoning Province, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Shilin Sun
- College of Forestry, Shenyang Agricultural University, Shenyang, Liaoning, China,The Key Laboratory of Forest Tree Genetics, Breeding and Cultivation of Liaoning Province, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Jian Xu
- College of Forestry, Shenyang Agricultural University, Shenyang, Liaoning, China,The Key Laboratory of Forest Tree Genetics, Breeding and Cultivation of Liaoning Province, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Xiaoqiong Shi
- College of Forestry, Shenyang Agricultural University, Shenyang, Liaoning, China,The Key Laboratory of Forest Tree Genetics, Breeding and Cultivation of Liaoning Province, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Huiyan Guo
- College of Forestry, Shenyang Agricultural University, Shenyang, Liaoning, China,The Key Laboratory of Forest Tree Genetics, Breeding and Cultivation of Liaoning Province, Shenyang Agricultural University, Shenyang, Liaoning, China
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Ren Z, Zhang D, Jiao C, Li D, Wu Y, Wang X, Gao C, Lin Y, Ruan Y, Xia Y. Comparative transcriptome and metabolome analyses identified the mode of sucrose degradation as a metabolic marker for early vegetative propagation in bulbs of Lycoris. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 112:115-134. [PMID: 35942603 PMCID: PMC9826282 DOI: 10.1111/tpj.15935] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 07/26/2022] [Accepted: 08/07/2022] [Indexed: 06/01/2023]
Abstract
Vegetative propagation (VP) is an important practice for production in many horticultural plants. Sugar supply constitutes the basis of VP in bulb flowers, but the underlying molecular basis remains elusive. By performing a combined sequencing technologies coupled with ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry approach for metabolic analyses, we compared two Lycoris species with contrasting regeneration rates: high-regeneration Lycoris sprengeri and low-regeneration Lycoris aurea. A comprehensive multi-omics analyses identified both expected processes involving carbohydrate metabolism and transcription factor networks, as well as the metabolic characteristics for each developmental stage. A higher abundance of the differentially expressed genes including those encoding ethylene responsive factors was detected at bulblet initiation stage compared to the late stage of bulblet development. High hexose-to-sucrose ratio correlated to bulblet formation across all the species examined, indicating its role in the VP process in Lycoris bulb. Importantly, a clear difference between cell wall invertase (CWIN)-catalyzed sucrose unloading in high-regeneration species and the sucrose synthase-catalyzed pathway in low-regeneration species was observed at the bulblet initiation stage, which was supported by findings from carboxyfluorescein tracing and quantitative real-time PCR analyses. Collectively, the findings indicate a sugar-mediated model of the regulation of VP in which high CWIN expression or activity may promote bulblet initiation via enhancing apoplasmic unloading of sucrose or sugar signals, whereas the subsequent high ratio of hexose-to-sucrose likely supports cell division characterized in the next phase of bulblet formation.
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Affiliation(s)
- Zi‐Ming Ren
- Department of Landscape Architecture, School of Civil Engineering and ArchitectureZhejiang Sci‐Tech UniversityHangzhou310018China
| | - Dong Zhang
- Genomics and Genetic Engineering Laboratory of Ornamental PlantsZhejiang UniversityHangzhou310058China
| | - Chen Jiao
- Key Lab of Molecular Biology of Crop Pathogens and InsectsInstitute of Biotechnology, Zhejiang UniversityHangzhou310058China
| | - Dan‐Qing Li
- Genomics and Genetic Engineering Laboratory of Ornamental PlantsZhejiang UniversityHangzhou310058China
| | - Yun Wu
- Department of Landscape Architecture, School of Civil Engineering and ArchitectureZhejiang Sci‐Tech UniversityHangzhou310018China
| | - Xiu‐Yun Wang
- Genomics and Genetic Engineering Laboratory of Ornamental PlantsZhejiang UniversityHangzhou310058China
| | - Cong Gao
- Genomics and Genetic Engineering Laboratory of Ornamental PlantsZhejiang UniversityHangzhou310058China
| | - Ye‐Fan Lin
- Genomics and Genetic Engineering Laboratory of Ornamental PlantsZhejiang UniversityHangzhou310058China
| | - Yong‐Ling Ruan
- Division of Plant Sciences, Research School of BiologyThe Australian National UniversityCanberraACT2601Australia
- Yazhou Bay LaboratorySanya572024China
| | - Yi‐Ping Xia
- Genomics and Genetic Engineering Laboratory of Ornamental PlantsZhejiang UniversityHangzhou310058China
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Kelly J, Berzuini C, Keavney B, Tomaszewski M, Guo H. A review of causal discovery methods for molecular network analysis. Mol Genet Genomic Med 2022; 10:e2055. [PMID: 36087049 PMCID: PMC9544222 DOI: 10.1002/mgg3.2055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/12/2022] [Accepted: 08/18/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions. METHODS We have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade. RESULTS In this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area. CONCLUSION Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.
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Affiliation(s)
- Jack Kelly
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
| | - Carlo Berzuini
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
| | - Bernard Keavney
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
- Division of Cardiology and Manchester Academic Health Science CentreManchester University NHS Foundation TrustManchesterUK
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
- Manchester Heart Centre and Manchester Academic Health Science CentreManchester University NHS Foundation TrustManchesterUK
| | - Hui Guo
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
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Wang P, Wang D. Gene Differential Co-Expression Networks Based on RNA-Seq: Construction and Its Applications. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2829-2841. [PMID: 34383649 DOI: 10.1109/tcbb.2021.3103280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Gene co-expression network (GCN) becomes an increasingly important tool in omics data analysis. A great challenge for GCN construction is that the sample size is far lower than the number of genes. Traditional methods rely on considerable samples. Moreover, association signals are likely weak, nonlinear and stochastic, which are difficult to be identified among thousands of candidates. In this paper, the gray correlation coefficient (GCC) is introduced, and a novel method to construct gene differential co-expression networks (GDCNs) is proposed. Based on the GDCNs, three measures are proposed to explore informative genes. The proposed method can make full use of the information provided by a handful of samples and overcome the shortages of GCNs, which can evaluate the changes of co-expression relationships that are possibly triggered by treatments. Based on RNA-seq data of Brassica napus, GDCNs under multiple experimental conditions are constructed and investigated. It is found that the GCC-based method is very robust to data processing. The GDCNs facilitate the inference of gene functions and the identification of informative genes that are responsible for stress responsiveness. The GDCN-based approaches integrate the 'guilt by association' and the 'guilt by rewiring' rules, which provide alternative tools for omics data analysis.
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Walker BN, Nix J, Wilson C, Marrella MA, Speckhart SL, Wooldridge L, Yen CN, Bodmer JS, Kirkpatrick LT, Moorey SE, Gerrard DE, Ealy AD, Biase FH. Tight gene co-expression in BCB positive cattle oocytes and their surrounding cumulus cells. Reprod Biol Endocrinol 2022; 20:119. [PMID: 35964078 PMCID: PMC9375383 DOI: 10.1186/s12958-022-00994-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/02/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cytoplasmic and nuclear maturation of oocytes, as well as interaction with the surrounding cumulus cells, are important features relevant to the acquisition of developmental competence. METHODS Here, we utilized Brilliant cresyl blue (BCB) to distinguish cattle oocytes with low activity of the enzyme Glucose-6-Phosphate Dehydrogenase, and thus separated fully grown (BCB positive) oocytes from those in the growing phase (BCB negative). We then analyzed the developmental potential of these oocytes, mitochondrial DNA (mtDNA) copy number in single oocytes, and investigated the transcriptome of single oocytes and their surrounding cumulus cells of BCB positive versus BCB negative oocytes. RESULTS The BCB positive oocytes were twice as likely to produce a blastocyst in vitro compared to BCB- oocytes (P < 0.01). We determined that BCB negative oocytes have 1.3-fold more mtDNA copies than BCB positive oocytes (P = 0.004). There was no differential transcript abundance of genes expressed in oocytes, however, 172 genes were identified in cumulus cells with differential transcript abundance (FDR < 0.05) based on the BCB staining of their oocyte. Co-expression analysis between oocytes and their surrounding cumulus cells revealed a subset of genes whose co-expression in BCB positive oocytes (n = 75) and their surrounding cumulus cells (n = 108) compose a unique profile of the cumulus-oocyte complex. CONCLUSIONS If oocytes transition from BCB negative to BCB positive, there is a greater likelihood of producing a blastocyst, and a reduction of mtDNA copies, but there is no systematic variation of transcript abundance. Cumulus cells present changes in transcript abundance, which reflects in a dynamic co-expression between the oocyte and cumulus cells.
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Affiliation(s)
- Bailey N Walker
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA
| | - Jada Nix
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA
| | - Chace Wilson
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA
| | - Mackenzie A Marrella
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA
| | - Savannah L Speckhart
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA
| | - Lydia Wooldridge
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA
| | - Con-Ning Yen
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA
| | - Jocelyn S Bodmer
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA
| | - Laila T Kirkpatrick
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA
| | - Sarah E Moorey
- Department of Animal Science, University of Tennessee, Knoxville, TN, USA
| | - David E Gerrard
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA
| | - Alan D Ealy
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA
| | - Fernando H Biase
- School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 W Campus Dr, Blacksburg, VA, 24061, USA.
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Sircar S, Musaddi M, Parekh N. NetREx: Network-based Rice Expression Analysis Server for abiotic stress conditions. Database (Oxford) 2022; 2022:6657695. [PMID: 35932239 PMCID: PMC9356536 DOI: 10.1093/database/baac060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 04/30/2022] [Accepted: 08/02/2022] [Indexed: 11/14/2022]
Abstract
Abstract
Recent focus on transcriptomic studies in food crops like rice, wheat and maize provide new opportunities to address issues related to agriculture and climate change. Re-analysis of such data available in public domain supplemented with annotations across molecular hierarchy can be of immense help to the plant research community, particularly co-expression networks representing transcriptionally coordinated genes that are often part of the same biological process. With this objective, we have developed NetREx, a Network-based Rice Expression Analysis Server, that hosts ranked co-expression networks of Oryza sativa using publicly available messenger RNA sequencing data across uniform experimental conditions. It provides a range of interactable data viewers and modules for analysing user-queried genes across different stress conditions (drought, flood, cold and osmosis) and hormonal treatments (abscisic and jasmonic acid) and tissues (root and shoot). Subnetworks of user-defined genes can be queried in pre-constructed tissue-specific networks, allowing users to view the fold change, module memberships, gene annotations and analysis of their neighbourhood genes and associated pathways. The web server also allows querying of orthologous genes from Arabidopsis, wheat, maize, barley and sorghum. Here, we demonstrate that NetREx can be used to identify novel candidate genes and tissue-specific interactions under stress conditions and can aid in the analysis and understanding of complex phenotypes linked to stress response in rice.
Database URL: https://bioinf.iiit.ac.in/netrex/index.html
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Affiliation(s)
| | - Mayank Musaddi
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology , Gachibowli, Hyderabad 500032, India
| | - Nita Parekh
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology , Gachibowli, Hyderabad 500032, India
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Song F, Zhou J, Quan M, Xiao L, Lu W, Qin S, Fang Y, Wang D, Li P, Du Q, El-Kassaby YA, Zhang D. Transcriptome and association mapping revealed functional genes respond to drought stress in Populus. FRONTIERS IN PLANT SCIENCE 2022; 13:829888. [PMID: 35968119 PMCID: PMC9372527 DOI: 10.3389/fpls.2022.829888] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 07/13/2022] [Indexed: 05/24/2023]
Abstract
Drought frequency and severity are exacerbated by global climate change, which could compromise forest ecosystems. However, there have been minimal efforts to systematically investigate the genetic basis of the response to drought stress in perennial trees. Here, we implemented a systems genetics approach that combines co-expression analysis, association genetics, and expression quantitative trait nucleotide (eQTN) mapping to construct an allelic genetic regulatory network comprising four key regulators (PtoeIF-2B, PtoABF3, PtoPSB33, and PtoLHCA4) under drought stress conditions. Furthermore, Hap_01PtoeIF-2B, a superior haplotype associated with the net photosynthesis, was revealed through allelic frequency and haplotype analysis. In total, 75 candidate genes related to drought stress were identified through transcriptome analyses of five Populus cultivars (P. tremula × P. alba, P. nigra, P. simonii, P. trichocarpa, and P. tomentosa). Through association mapping, we detected 92 unique SNPs from 38 genes and 104 epistatic gene pairs that were associated with six drought-related traits by association mapping. eQTN mapping unravels drought stress-related gene loci that were significantly associated with the expression levels of candidate genes for drought stress. In summary, we have developed an integrated strategy for dissecting a complex genetic network, which facilitates an integrated population genomics approach that can assess the effects of environmental threats.
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Affiliation(s)
- Fangyuan Song
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Jiaxuan Zhou
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Mingyang Quan
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Liang Xiao
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Wenjie Lu
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Shitong Qin
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Yuanyuan Fang
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Dan Wang
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Peng Li
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Qingzhang Du
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Yousry A. El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, Forest Sciences Centre, University of British Columbia, Vancouver, BC, Canada
| | - Deqiang Zhang
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
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Lim PK, Zheng X, Goh JC, Mutwil M. Exploiting plant transcriptomic databases: Resources, tools, and approaches. PLANT COMMUNICATIONS 2022; 3:100323. [PMID: 35605200 PMCID: PMC9284291 DOI: 10.1016/j.xplc.2022.100323] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/03/2022] [Accepted: 04/06/2022] [Indexed: 05/11/2023]
Abstract
There are now more than 300 000 RNA sequencing samples available, stemming from thousands of experiments capturing gene expression in organs, tissues, developmental stages, and experimental treatments for hundreds of plant species. The expression data have great value, as they can be re-analyzed by others to ask and answer questions that go beyond the aims of the study that generated the data. Because gene expression provides essential clues to where and when a gene is active, the data provide powerful tools for predicting gene function, and comparative analyses allow us to study plant evolution from a new perspective. This review describes how we can gain new knowledge from gene expression profiles, expression specificities, co-expression networks, differential gene expression, and experiment correlation. We also introduce and demonstrate databases that provide user-friendly access to these tools.
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Affiliation(s)
- Peng Ken Lim
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore
| | - Xinghai Zheng
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore
| | - Jong Ching Goh
- 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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Approaches in Gene Coexpression Analysis in Eukaryotes. BIOLOGY 2022; 11:biology11071019. [PMID: 36101400 PMCID: PMC9312353 DOI: 10.3390/biology11071019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/28/2022] [Accepted: 07/04/2022] [Indexed: 11/22/2022]
Abstract
Simple Summary Genes whose expression levels rise and fall similarly in a large set of samples, may be considered coexpressed. Gene coexpression analysis refers to the en masse discovery of coexpressed genes from a large variety of transcriptomic experiments. The type of biological networks that studies gene coexpression, known as Gene Coexpression Networks, consist of an undirected graph depicting genes and their coexpression relationships. Coexpressed genes are clustered in smaller subnetworks, the predominant biological roles of which can be determined through enrichment analysis. By studying well-annotated gene partners, the attribution of new roles to genes of unknown function or assumption for participation in common metabolic pathways can be achieved, through a guilt-by-association approach. In this review, we present key issues in gene coexpression analysis, as well as the most popular tools that perform it. Abstract Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.
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Obayashi T, Hibara H, Kagaya Y, Aoki Y, Kinoshita K. ATTED-II v11: A Plant Gene Coexpression Database Using a Sample Balancing Technique by Subagging of Principal Components. PLANT & CELL PHYSIOLOGY 2022; 63:869-881. [PMID: 35353884 DOI: 10.1093/pcp/pcac041] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 02/06/2022] [Accepted: 03/29/2022] [Indexed: 05/25/2023]
Abstract
ATTED-II (https://atted.jp) is a gene coexpression database for nine plant species based on publicly available RNAseq and microarray data. One of the challenges in constructing condition-independent coexpression data based on publicly available gene expression data is managing the inherent sampling bias. Here, we report ATTED-II version 11, wherein we adopted a coexpression calculation methodology to balance the samples using principal component analysis and ensemble calculation. This approach has two advantages. First, omitting principal components with low contribution rates reduces the main contributors of noise. Second, balancing large differences in contribution rates enables considering various sample conditions entirely. In addition, based on RNAseq- and microarray-based coexpression data, we provide species-representative, integrated coexpression information to enhance the efficiency of interspecies comparison of the coexpression data. These coexpression data are provided as a standardized z-score to facilitate integrated analysis with different data sources. We believe that with these improvements, ATTED-II is more valuable and powerful for supporting interspecies comparative studies and integrated analyses using heterogeneous data.
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Affiliation(s)
- Takeshi Obayashi
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba-ku, Sendai, 980-8679 Japan
| | - Himiko Hibara
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba-ku, Sendai, 980-8679 Japan
| | - Yuki Kagaya
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba-ku, Sendai, 980-8679 Japan
| | - Yuichi Aoki
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba-ku, Sendai, 980-8679 Japan
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan
| | - Kengo Kinoshita
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Aoba-ku, Sendai, 980-8679 Japan
- Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573 Japan
- Institute of Development, Aging, and Cancer, Tohoku University, 4-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575 Japan
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Zhao K, Rhee SY. Omics-guided metabolic pathway discovery in plants: Resources, approaches, and opportunities. CURRENT OPINION IN PLANT BIOLOGY 2022; 67:102222. [PMID: 35512431 DOI: 10.1016/j.pbi.2022.102222] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/30/2022] [Accepted: 03/25/2022] [Indexed: 05/28/2023]
Abstract
Plants produce a vast array of metabolites, the biosynthetic routes of which remain largely undetermined. Genome-scale enzyme and pathway annotations and omics technologies have revolutionized research to decrypt plant metabolism and produced a growing list of functionally characterized metabolic genes and pathways. However, what is known is still a tiny fraction of the metabolic capacity harbored by plants. Here, we review plant enzyme and pathway annotation resources and cutting-edge omics approaches to guide discovery and characterization of plant metabolic pathways. We also discuss strategies for improving enzyme function prediction by integrating protein 3D structure information and single cell omics. This review aims to serve as a primer for plant biologists to leverage omics datasets to facilitate understanding and engineering plant metabolism.
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Affiliation(s)
- Kangmei Zhao
- Carnegie Institution for Science, Department of Plant Biology, Stanford, CA, USA.
| | - Seung Y Rhee
- Carnegie Institution for Science, Department of Plant Biology, Stanford, CA, USA
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46
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Sonawane AR, Aikawa E, Aikawa M. Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases. Front Cardiovasc Med 2022; 9:873582. [PMID: 35665246 PMCID: PMC9160390 DOI: 10.3389/fcvm.2022.873582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/19/2022] [Indexed: 01/18/2023] Open
Abstract
Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.
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Affiliation(s)
- Abhijeet Rajendra Sonawane
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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Sheng M, Da L, Song Q, Liu Y, Zhang X, Liu F, Xu W, Su Z. Systems biology-based analysis indicates that PHO1;H10 positively modulates high light-induced anthocyanin biosynthesis in Arabidopsis leaves. Genomics 2022; 114:110363. [PMID: 35398515 DOI: 10.1016/j.ygeno.2022.110363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 03/30/2022] [Accepted: 04/02/2022] [Indexed: 01/14/2023]
Abstract
Arabidopsis PHO1;H10 is a member of the PHO1 gene family with SPX and EXS domains, and its functions remain largely unknown. As shown in PCSD database, the upstream region of PHO1;H10 gene is in the active chromatin states, with high DHS accessibility and binding sites of multiple transcription factors, especially ABI5, SPCH and HY5. Co-expression network and data-mining analyses showed PHO1;H10 and co-expression genes were with activation under high light stress. We did wet-lab experiments, and found that the detached leaves of PHO1;H10 overexpression lines accumulated more anthocyanin than those of WT and mutant under high light treatment. RNA-seq results showed overexpression of PHO1;H10 up-regulated many anthocyanin biosynthetic genes. The GSEA analysis result showed that the functional module related to anthocyanin pathway was significantly enriched. In summary, we conducted systems biology approach, combining dry- and wet-lab analyses, and discovered that PHO1;H10 might play an essential role during modulating high light-induced anthocyanin accumulation in the Arabidopsis detached leaves.
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Affiliation(s)
- Minghao Sheng
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Lingling Da
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Qian Song
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yue Liu
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Xinyi Zhang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Fengxia Liu
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Wenying Xu
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Zhen Su
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
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Perlo V, Margarido GRA, Botha FC, Furtado A, Hodgson-Kratky K, Correr FH, Henry RJ. Transcriptome changes in the developing sugarcane culm associated with high yield and early-season high sugar content. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1619-1636. [PMID: 35224663 PMCID: PMC9110458 DOI: 10.1007/s00122-022-04058-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Sugarcane, with its exceptional carbon dioxide assimilation, biomass and sugar yield, has a high potential for the production of bio-energy, bio-plastics and high-value products in the food and pharmaceutical industries. A crucial challenge for long-term economic viability and environmental sustainability is also to optimize the production of biomass composition and carbon sequestration. Sugarcane varieties such as KQ228 and Q253 are highly utilized in the industry. These varieties are characterized by a high early-season sugar content associated with high yield. In order to investigate these correlations, 1,440 internodes were collected and combined to generate a set of 120 samples in triplicate across 24 sugarcane cultivars at five different development stages. Weighted gene co-expression network analysis (WGCNA) was used and revealed for the first time two sets of co-expressed genes with a distinct and opposite correlation between fibre and sugar content. Gene identification and metabolism pathways analysis was used to define these two sets of genes. Correlation analysis identified a large number of interconnected metabolic pathways linked to sugar content and fibre content. Unsupervised hierarchical clustering of gene expression revealed a stronger level of segregation associated with the genotypes than the stage of development, suggesting a dominant genetic influence on biomass composition and facilitating breeding selection. Characterization of these two groups of co-expressed key genes can help to improve breeding program for high fibre, high sugar species or plant synthetic biology.
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Affiliation(s)
- Virginie Perlo
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Gabriel R. A. Margarido
- Departamento de Genética, Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Piracicaba, São Paulo, 13418-900 Brazil
| | - Frederik C. Botha
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Agnelo Furtado
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Katrina Hodgson-Kratky
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Fernando H. Correr
- Departamento de Genética, Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Piracicaba, São Paulo, 13418-900 Brazil
| | - Robert J. Henry
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
- The University of Queensland, Level 2, Queensland Bioscience Precinct [#80], 306 Carmody Road St Lucia, St Lucia, QLD 4072 Australia
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49
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Bano N, Fakhrah S, Mohanty CS, Bag SK. Transcriptome Meta-Analysis Associated Targeting Hub Genes and Pathways of Drought and Salt Stress Responses in Cotton ( Gossypium hirsutum): A Network Biology Approach. FRONTIERS IN PLANT SCIENCE 2022; 13:818472. [PMID: 35548277 PMCID: PMC9083274 DOI: 10.3389/fpls.2022.818472] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/21/2022] [Indexed: 06/12/2023]
Abstract
Abiotic stress tolerance is an intricate feature controlled through several genes and networks in the plant system. In abiotic stress, salt, and drought are well known to limit cotton productivity. Transcriptomics meta-analysis has arisen as a robust method to unravel the stress-responsive molecular network in crops. In order to understand drought and salt stress tolerance mechanisms, a meta-analysis of transcriptome studies is crucial. To confront these issues, here, we have given details of genes and networks associated with significant differential expression in response to salt and drought stress. The key regulatory hub genes of drought and salt stress conditions have notable associations with functional drought and salt stress-responsive (DSSR) genes. In the network study, nodulation signaling pathways 2 (NSP2), Dehydration-responsive element1 D (DRE1D), ethylene response factor (ERF61), cycling DOF factor 1 (CDF1), and tubby like protein 3 (TLP3) genes in drought and tubby like protein 1 (TLP1), thaumatin-like proteins (TLP), ethylene-responsive transcription factor ERF109 (EF109), ETS-Related transcription Factor (ELF4), and Arabidopsis thaliana homeodomain leucine-zipper gene (ATHB7) genes in salt showed the significant putative functions and pathways related to providing tolerance against drought and salt stress conditions along with the significant expression values. These outcomes provide potential candidate genes for further in-depth functional studies in cotton, which could be useful for the selection of an improved genotype of Gossypium hirsutum against drought and salt stress conditions.
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Affiliation(s)
- Nasreen Bano
- CSIR-National Botanical Research Institute (CSIR-NBRI), Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Shafquat Fakhrah
- CSIR-National Botanical Research Institute (CSIR-NBRI), Lucknow, India
- Department of Botany, University of Lucknow, Lucknow, India
| | - Chandra Sekhar Mohanty
- CSIR-National Botanical Research Institute (CSIR-NBRI), Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Sumit Kumar Bag
- CSIR-National Botanical Research Institute (CSIR-NBRI), Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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Zhou Y, Sukul A, Mishler-Elmore JW, Faik A, Held MA. PlantNexus: A Gene Co-expression Network Database and Visualization Tool for Barley and Sorghum. PLANT & CELL PHYSIOLOGY 2022; 63:565-572. [PMID: 35024864 PMCID: PMC9214644 DOI: 10.1093/pcp/pcac007] [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: 07/08/2021] [Revised: 01/03/2022] [Accepted: 01/10/2022] [Indexed: 06/14/2023]
Abstract
Global gene co-expression networks (GCNs) are powerful tools for functional genomics whereby putative functions and regulatory mechanisms can be inferred by gene co-expression. Cereal crops, such as Hordeum vulgare (barley) and Sorghum bicolor (sorghum), are among the most important plants to civilization. However, co-expression network tools for these plants are lacking. Here, we have constructed global GCNs for barley and sorghum using existing RNA-seq data sets. Meta-information was manually curated and categorized by tissue type to also build tissue-specific GCNs. To enable GCN searching and visualization, we implemented a website and database named PlantNexus. PlantNexus is freely available at https://plantnexus.ohio.edu/.
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Affiliation(s)
- Yadi Zhou
- Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701, USA
| | - Abhijit Sukul
- Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701, USA
| | | | - Ahmed Faik
- Department of Environmental and Plant Biology, Ohio University, Athens, OH 45701, USA
- Molecular and Cellular Biology Program, Ohio University, Athens, OH 45701, USA
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