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Gomez-Cano F, Rodriguez J, Zhou P, Chu YH, Magnusson E, Gomez-Cano L, Krishnan A, Springer NM, de Leon N, Grotewold E. Prioritizing Metabolic Gene Regulators through Multi-Omic Network Integration in Maize. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582075. [PMID: 38464086 PMCID: PMC10925184 DOI: 10.1101/2024.02.26.582075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Elucidating gene regulatory networks (GRNs) is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. In this study, we integrated publicly available co-expression networks derived from more than 6,000 RNA-seq samples, 283 protein-DNA interaction assays, and 16 million of SNPs used to identify expression quantitative loci (eQTL), to construct TF-target networks. In total, we analyzed ~4.6M interactions to generate four distinct types of TF-target networks: co-expression, protein-DNA interaction (PDI), trans-expression quantitative loci (trans-eQTL), and cis-eQTL combined with PDIs. To improve the functional annotation of TFs based on its target genes, we implemented three different strategies to integrate these four types of networks. We subsequently evaluated the effectiveness of our method through loss-of function mutant and random networks. The multi-network integration allowed us to identify transcriptional regulators of hormone-, metabolic- and development-related processes. Finally, using the topological properties of the fully integrated network, we identified potentially functional redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.
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Affiliation(s)
- Fabio Gomez-Cano
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824-6473, USA
- Current address: Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jonas Rodriguez
- Department of Plant and Agroecosystem Sciences, University of Wisconsin Madison, Madison, WI 53706, USA
| | - Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108
| | - Yi-Hsuan Chu
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824-6473, USA
| | - Erika Magnusson
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108
| | - Lina Gomez-Cano
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824-6473, USA
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108
- Current address: Global Breeding, Bayer Crop Sciences, Chesterfield MO 63017, USA
| | - Natalia de Leon
- Department of Plant and Agroecosystem Sciences, University of Wisconsin Madison, Madison, WI 53706, USA
| | - Erich Grotewold
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824-6473, USA
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2
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Yang Y, Cao C, Gu N. Identifying magnetosome-associated genes in the extended CtrA regulon in Magnetospirillum magneticum AMB-1 using a combinational approach. Brief Funct Genomics 2023; 22:61-74. [PMID: 36424838 DOI: 10.1093/bfgp/elac039] [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: 06/12/2022] [Revised: 10/01/2022] [Accepted: 10/14/2022] [Indexed: 11/26/2022] Open
Abstract
Magnetotactic bacteria (MTB) are worth studying because of magnetosome biomineralization. Magnetosome biogenesis in MTB is controlled by multiple genes known as magnetosome-associated genes. Recent advances in bioinformatics provide a unique opportunity for studying functions of magnetosome-associated genes and networks that they are involved in. Furthermore, various types of bioinformatics analyses can also help identify genes associated with magnetosome biogenesis. To predict novel magnetosome-associated genes in the extended CtrA regulon, we analyzed expression data of Magnetospirillum magneticum AMB-1 in the GSE35625 dataset in NCBI GEO. We identified 10 potential magnetosome-associated genes using a combinational approach of differential expression analysis, Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analysis, protein-protein interaction network analysis and weighted gene co-expression network analysis. Meanwhile, we also discovered and compared two co-expression modules that most known magnetosome-associated genes belong to. Our comparison indicated the importance of energy on regulating co-expression module structures for magnetosome biogenesis. At the last stage of our research, we predicted at least four real magnetosome-associated genes out of 10 potential genes, based on a comparison of evolutionary trees between known and potential magnetosome-associated genes. Because of the discovery of common subtrees that the stressed species are enriched in, we proposed a hypothesis that multiple types of environmental stress can trigger magnetosome evolution in different waters, and therefore its evolution can recur at different times in various locations on earth. Overall, our research provides useful information for identifying new MTB species and understanding magnetosome biogenesis.
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Affiliation(s)
- Yizi Yang
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Chen Cao
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Ning Gu
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China.,Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China
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3
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Collado-Vides J, Gaudet P, de Lorenzo V. Missing Links Between Gene Function and Physiology in Genomics. Front Physiol 2022; 13:815874. [PMID: 35295568 PMCID: PMC8918662 DOI: 10.3389/fphys.2022.815874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/28/2022] [Indexed: 11/25/2022] Open
Abstract
Knowledge of biological organisms at the molecular level that has been gathered is now organized into databases, often within ontological frameworks. To enable computational comparisons of annotations across different genomes and organisms, controlled vocabularies have been essential, as is the case in the functional annotation classifications used for bacteria, such as MultiFun and the more widely used Gene Ontology. The function of individual gene products as well as the processes in which collections of them participate constitute a wealth of classes that describe the biological role of gene products in a large number of organisms in the three kingdoms of life. In this contribution, we highlight from a qualitative perspective some limitations of these frameworks and discuss challenges that need to be addressed to bridge the gap between annotation as currently captured by ontologies and databases and our understanding of the basic principles in the organization and functioning of organisms; we illustrate these challenges with some examples in bacteria. We hope that raising awareness of these issues will encourage users of Gene Ontology and similar ontologies to be careful about data interpretation and lead to improved data representation.
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Affiliation(s)
- Julio Collado-Vides
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Universitat Pompeu Fabra, Barcelona, Spain
- *Correspondence: Julio Collado-Vides,
| | - Pascale Gaudet
- SIB Swiss Institute of Bioinformatics, Swiss-Prot Group, Geneva, Switzerland
| | - Víctor de Lorenzo
- Department of Systems Biology, Centro Nacional de Biotecnología CSIC, Universidad Autónoma de Madrid, Madrid, Spain
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4
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Femerling G, Gama-Castro S, Lara P, Ledezma-Tejeida D, Tierrafría VH, Muñiz-Rascado L, Bonavides-Martínez C, Collado-Vides J. Sensory Systems and Transcriptional Regulation in Escherichia coli. Front Bioeng Biotechnol 2022; 10:823240. [PMID: 35237580 PMCID: PMC8882922 DOI: 10.3389/fbioe.2022.823240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
In free-living bacteria, the ability to regulate gene expression is at the core of adapting and interacting with the environment. For these systems to have a logic, a signal must trigger a genetic change that helps the cell to deal with what implies its presence in the environment; briefly, the response is expected to include a feedback to the signal. Thus, it makes sense to think of genetic sensory mechanisms of gene regulation. Escherichia coli K-12 is the bacterium model for which the largest number of regulatory systems and its sensing capabilities have been studied in detail at the molecular level. In this special issue focused on biomolecular sensing systems, we offer an overview of the transcriptional regulatory corpus of knowledge for E. coli that has been gathered in our database, RegulonDB, from the perspective of sensing regulatory systems. Thus, we start with the beginning of the information flux, which is the signal’s chemical or physical elements detected by the cell as changes in the environment; these signals are internally transduced to transcription factors and alter their conformation. Signals transduced to effectors bind allosterically to transcription factors, and this defines the dominant sensing mechanism in E. coli. We offer an updated list of the repertoire of known allosteric effectors, as well as a list of the currently known different mechanisms of this sensing capability. Our previous definition of elementary genetic sensory-response units, GENSOR units for short, that integrate signals, transport, gene regulation, and the biochemical response of the regulated gene products of a given transcriptional factor fit perfectly with the purpose of this overview. We summarize the functional heterogeneity of their response, based on our updated collection of GENSORs, and we use them to identify the expected feedback as part of their response. Finally, we address the question of multiple sensing in the regulatory network of E. coli. This overview introduces the architecture of sensing and regulation of native components in E.coli K-12, which might be a source of inspiration to bioengineering applications.
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Affiliation(s)
- Georgette Femerling
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Socorro Gama-Castro
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Paloma Lara
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | | | - Víctor H. Tierrafría
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Luis Muñiz-Rascado
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | | | - Julio Collado-Vides
- Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, México
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Universitat Pompeu Fabra (UPF), Barcelona, Spain
- *Correspondence: Julio Collado-Vides,
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Mejía-Almonte C, Busby SJW, Wade JT, van Helden J, Arkin AP, Stormo GD, Eilbeck K, Palsson BO, Galagan JE, Collado-Vides J. Redefining fundamental concepts of transcription initiation in bacteria. Nat Rev Genet 2020; 21:699-714. [PMID: 32665585 PMCID: PMC7990032 DOI: 10.1038/s41576-020-0254-8] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2020] [Indexed: 12/15/2022]
Abstract
Despite enormous progress in understanding the fundamentals of bacterial gene regulation, our knowledge remains limited when compared with the number of bacterial genomes and regulatory systems to be discovered. Derived from a small number of initial studies, classic definitions for concepts of gene regulation have evolved as the number of characterized promoters has increased. Together with discoveries made using new technologies, this knowledge has led to revised generalizations and principles. In this Expert Recommendation, we suggest precise, updated definitions that support a logical, consistent conceptual framework of bacterial gene regulation, focusing on transcription initiation. The resulting concepts can be formalized by ontologies for computational modelling, laying the foundation for improved bioinformatics tools, knowledge-based resources and scientific communication. Thus, this work will help researchers construct better predictive models, with different formalisms, that will be useful in engineering, synthetic biology, microbiology and genetics.
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Affiliation(s)
- Citlalli Mejía-Almonte
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Morelos, Cuernavaca, México
| | | | - Joseph T Wade
- Division of Genetics, Wadsworth Center, New York State Department of Health, Albany, NY, USA
| | - Jacques van Helden
- Aix-Marseille University, INSERM UMR S 1090, Theory and Approaches of Genome Complexity (TAGC), Marseille, France
- CNRS, Institut Français de Bioinformatique, IFB-core, UMS 3601, Evry, France
| | - Adam P Arkin
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Gary D Stormo
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - James E Galagan
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Julio Collado-Vides
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Morelos, Cuernavaca, México.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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Chávez J, Barberena-Jonas C, Sotelo-Fonseca JE, Alquicira-Hernández J, Salgado H, Collado-Torres L, Reyes A. Programmatic access to bacterial regulatory networks with regutools. Bioinformatics 2020; 36:4532-4534. [PMID: 32573705 DOI: 10.1093/bioinformatics/btaa575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 04/29/2020] [Accepted: 06/15/2020] [Indexed: 11/13/2022] Open
Abstract
SUMMARY RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. AVAILABILITY AND IMPLEMENTATION regutools is an R package available through Bioconductor at bioconductor.org/packages/regutools.
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Affiliation(s)
- Joselyn Chávez
- Departamento de Microbiología Molecular, Instituto de Biotecnología
| | - Carmina Barberena-Jonas
- Programa de Genómica Computacional, Centro de Ciencias Genómicas.,Licenciatura de Ciencias Genómicas, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico 62210.,National Laboratory of Genomics for Biodiversity, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Irapuato, Guanajuato 36821, Mexico
| | - Jesus E Sotelo-Fonseca
- Programa de Genómica Computacional, Centro de Ciencias Genómicas.,Licenciatura de Ciencias Genómicas, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico 62210.,National Laboratory of Genomics for Biodiversity, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Irapuato, Guanajuato 36821, Mexico
| | - José Alquicira-Hernández
- Programa de Genómica Computacional, Centro de Ciencias Genómicas.,Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia.,Garvan Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia
| | - Heladia Salgado
- Programa de Genómica Computacional, Centro de Ciencias Genómicas
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Alejandro Reyes
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
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7
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Frayne EG. Global profile changes in transcripts induced with a phosphate analogue: implications for gene regulation. Mol Cell Biochem 2020; 468:111-120. [PMID: 32172469 DOI: 10.1007/s11010-020-03715-9] [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: 11/03/2019] [Accepted: 03/06/2020] [Indexed: 11/25/2022]
Abstract
Previous work has shown that thiophosphate, a phosphate analogue, leads to a global shift in the distribution of cellular proteins in a variety of organisms. Thiophosphate, when added to culture media, gets incorporated into the nucleic acids of cells resulting in nuclease-resistant phosphorothioate linkages. Using Escherichia coli, as a model organism, it was found that the global changes in protein expression induced with thiophosphate could be accounted for by significant changes in the absolute transcription levels of more than 1500 genes detected via RNA seq analysis. In fact, 58% of transcripts detected in RNA seq studies using total RNA were increased an average of 44 × fold while the remaining 42% were decreased an average of 20 × fold in thiophosphate-treated cells. Furthermore, microarray results showed no correlation between the transcriptional changes observed and the known stability of the corresponding mRNAs measured. Overall, the total amount of non-ribosomal RNA accumulated in TP-treated cells was increased relative to rRNA ~ 4 × fold (1.5-6 ×). The results further indicated that metabolic changes may play a role in inducing the transcriptional profiles observed with thiophosphate. Indeed, pathway analysis of transcripts showed an increase in routes for phosphoribosyl pyrophosphate (PRPP) synthesis and related derivatives, presumably due to a reduction in RNA turnover. These results raise the possibility that the energy savings with reduced RNA turnover could lead to an increased energy charge in the cell that induces transcriptional changes leading to an increase in biosynthetic processes.
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