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Sajid S, Mashkoor M, Jørgensen MG, Christensen LP, Hansen PR, Franzyk H, Mirza O, Prabhala BK. The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation. Mol Cell Biochem 2024; 479:1957-1968. [PMID: 37610616 DOI: 10.1007/s11010-023-04827-8] [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: 07/07/2023] [Accepted: 08/09/2023] [Indexed: 08/24/2023]
Abstract
The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions.
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Affiliation(s)
- Salvia Sajid
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen Ø, Denmark
- Department of Physics, Chemistry, and Pharmacy, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark
| | - Maliha Mashkoor
- Department of Surgery, Center for Surgical Sciences, Zealand University Hospital, Lykkebækvej 1, 4600, Køge, Denmark
| | - Mikkel Girke Jørgensen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark
| | - Lars Porskjær Christensen
- Department of Physics, Chemistry, and Pharmacy, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark
| | - Paul Robert Hansen
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen Ø, Denmark
| | - Henrik Franzyk
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen Ø, Denmark
| | - Osman Mirza
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen Ø, Denmark
| | - Bala Krishna Prabhala
- Department of Physics, Chemistry, and Pharmacy, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark.
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2
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Gil-Gomez A, Rest JS. Wiring Between Close Nodes in Molecular Networks Evolves More Quickly Than Between Distant Nodes. Mol Biol Evol 2024; 41:msae098. [PMID: 38768245 PMCID: PMC11136681 DOI: 10.1093/molbev/msae098] [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: 12/21/2023] [Revised: 04/14/2024] [Accepted: 05/15/2024] [Indexed: 05/22/2024] Open
Abstract
As species diverge, a wide range of evolutionary processes lead to changes in protein-protein interaction (PPI) networks and metabolic networks. The rate at which molecular networks evolve is an important question in evolutionary biology. Previous empirical work has focused on interactomes from model organisms to calculate rewiring rates, but this is limited by the relatively small number of species and sparse nature of network data across species. We present a proxy for variation in network topology: variation in drug-drug interactions (DDIs), obtained by studying drug combinations (DCs) across taxa. Here, we propose the rate at which DDIs change across species as an estimate of the rate at which the underlying molecular network changes as species diverge. We computed the evolutionary rates of DDIs using previously published data from a high-throughput study in gram-negative bacteria. Using phylogenetic comparative methods, we found that DDIs diverge rapidly over short evolutionary time periods, but that divergence saturates over longer time periods. In parallel, we mapped drugs with known targets in PPI and cofunctional networks. We found that the targets of synergistic DDIs are closer in these networks than other types of DCs and that synergistic interactions have a higher evolutionary rate, meaning that nodes that are closer evolve at a faster rate. Future studies of network evolution may use DC data to gain larger-scale perspectives on the details of network evolution within and between species.
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Affiliation(s)
- Alejandro Gil-Gomez
- Department of Ecology and Evolution, Laufer Center for Physical and Quantitative Biology, Stony Brook University, 650 Life Sciences, Stony Brook, NY 11794-4254, USA
| | - Joshua S Rest
- Department of Ecology and Evolution, Laufer Center for Physical and Quantitative Biology, Stony Brook University, 650 Life Sciences, Stony Brook, NY 11794-4254, USA
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3
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Yang MR, Su SF, Wu YW. Using bacterial pan-genome-based feature selection approach to improve the prediction of minimum inhibitory concentration (MIC). Front Genet 2023; 14:1054032. [PMID: 37323667 PMCID: PMC10267731 DOI: 10.3389/fgene.2023.1054032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 05/16/2023] [Indexed: 06/17/2023] Open
Abstract
Background: Predicting the resistance profiles of antimicrobial resistance (AMR) pathogens is becoming more and more important in treating infectious diseases. Various attempts have been made to build machine learning models to classify resistant or susceptible pathogens based on either known antimicrobial resistance genes or the entire gene set. However, the phenotypic annotations are translated from minimum inhibitory concentration (MIC), which is the lowest concentration of antibiotic drugs in inhibiting certain pathogenic strains. Since the MIC breakpoints that classify a strain to be resistant or susceptible to specific antibiotic drug may be revised by governing institutes, we refrained from translating these MIC values into the categories "susceptible" or "resistant" but instead attempted to predict the MIC values using machine learning approaches. Results: By applying a machine learning feature selection approach on a Salmonella enterica pan-genome, in which the protein sequences were clustered to identify highly similar gene families, we showed that the selected features (genes) performed better than known AMR genes, and that models built on the selected genes achieved very accurate MIC prediction. Functional analysis revealed that about half of the selected genes were annotated as hypothetical proteins (i.e., with unknown functional roles), and that only a small portion of known AMR genes were among the selected genes, indicating that applying feature selection on the entire gene set has the potential of uncovering novel genes that may be associated with and may contribute to pathogenic antimicrobial resistances. Conclusion: The application of the pan-genome-based machine learning approach was indeed capable of predicting MIC values with very high accuracy. The feature selection process may also identify novel AMR genes for inferring bacterial antimicrobial resistance phenotypes.
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Affiliation(s)
- Ming-Ren Yang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Shun-Feng Su
- Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Yu-Wei Wu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei, Taiwan
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4
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Ascensao JA, Wetmore KM, Good BH, Arkin AP, Hallatschek O. Quantifying the local adaptive landscape of a nascent bacterial community. Nat Commun 2023; 14:248. [PMID: 36646697 PMCID: PMC9842643 DOI: 10.1038/s41467-022-35677-5] [Citation(s) in RCA: 60] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/16/2022] [Indexed: 01/17/2023] Open
Abstract
The fitness effects of all possible mutations available to an organism largely shape the dynamics of evolutionary adaptation. Yet, whether and how this adaptive landscape changes over evolutionary times, especially upon ecological diversification and changes in community composition, remains poorly understood. We sought to fill this gap by analyzing a stable community of two closely related ecotypes ("L" and "S") shortly after they emerged within the E. coli Long-Term Evolution Experiment (LTEE). We engineered genome-wide barcoded transposon libraries to measure the invasion fitness effects of all possible gene knockouts in the coexisting strains as well as their ancestor, for many different, ecologically relevant conditions. We find consistent statistical patterns of fitness effect variation across both genetic background and community composition, despite the idiosyncratic behavior of individual knockouts. Additionally, fitness effects are correlated with evolutionary outcomes for a number of conditions, possibly revealing shifting patterns of adaptation. Together, our results reveal how ecological and epistatic effects combine to shape the adaptive landscape in a nascent ecological community.
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Affiliation(s)
- Joao A Ascensao
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Kelly M Wetmore
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Benjamin H Good
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
| | - Adam P Arkin
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, 94720, USA.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, Berkeley, CA, 94720, USA. .,Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, 94720, USA. .,Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103, Leipzig, Germany.
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5
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James K, Alsobhe A, Cockell SJ, Wipat A, Pocock M. Integration of probabilistic functional networks without an external Gold Standard. BMC Bioinformatics 2022; 23:302. [PMID: 35879662 PMCID: PMC9316706 DOI: 10.1186/s12859-022-04834-4] [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: 11/16/2021] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Probabilistic functional integrated networks (PFINs) are designed to aid our understanding of cellular biology and can be used to generate testable hypotheses about protein function. PFINs are generally created by scoring the quality of interaction datasets against a Gold Standard dataset, usually chosen from a separate high-quality data source, prior to their integration. Use of an external Gold Standard has several drawbacks, including data redundancy, data loss and the need for identifier mapping, which can complicate the network build and impact on PFIN performance. Additionally, there typically are no Gold Standard data for non-model organisms. RESULTS We describe the development of an integration technique, ssNet, that scores and integrates both high-throughput and low-throughout data from a single source database in a consistent manner without the need for an external Gold Standard dataset. Using data from Saccharomyces cerevisiae we show that ssNet is easier and faster, overcoming the challenges of data redundancy, Gold Standard bias and ID mapping. In addition ssNet results in less loss of data and produces a more complete network. CONCLUSIONS The ssNet method allows PFINs to be built successfully from a single database, while producing comparable network performance to networks scored using an external Gold Standard source and with reduced data loss.
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Affiliation(s)
- Katherine James
- Department of Applied Sciences, Northumbria University, Sandyford Rd, Newcastle upon Tyne, NE1 8ST, UK. .,Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK.
| | - Aoesha Alsobhe
- Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK.,Saudi Electronic University, Abi Bakr As Siddiq Branch Rd, Riyadh, 1332, Saudi Arabia
| | - Simon J Cockell
- School of Biomedical, Nutritional and Sports Science, Faculty of Medical Sciences, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | - Anil Wipat
- Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK
| | - Matthew Pocock
- Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK
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6
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Zeng Z, Zeng X, Guo Y, Wu Z, Cai Z, Pan D. Determining the Role of UTP-Glucose-1-Phosphate Uridylyltransferase (GalU) in Improving the Resistance of Lactobacillus acidophilus NCFM to Freeze-Drying. Foods 2022; 11:foods11121719. [PMID: 35741917 PMCID: PMC9223153 DOI: 10.3390/foods11121719] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/05/2022] [Accepted: 06/06/2022] [Indexed: 11/23/2022] Open
Abstract
Lactobacillus acidophilus NCFM is widely used in the fermentation industry; using it as a freeze-dried powder can greatly reduce the costs associated with packaging and transportation, and even prolong the storage period. Previously published research has reported that the expression of galU (EC: 2.7.7.9) is significantly increased as a result of freezing and drying. Herein, we aimed to explore how galU plays an important role in improving the resistance of Lactobacillus acidophilus NCFM to freeze-drying. For this study, galU was first knocked out and then re-expressed in L. acidophilus NCFM to functionally characterize its role in the pertinent metabolic pathways. The knockout strain ΔgalU showed lactose/galactose deficiency and displayed irregular cell morphology, shortened cell length, thin and rough capsules, and abnormal cell division, and the progeny could not be separated. In the re-expression strain pgalU, these inhibited pathways were restored; moreover, the pgalU cells showed a strengthened cell wall and capsule, which enhanced their resistance to adverse environments. The pgalU cells showed GalU activity that was 229% higher than that shown by the wild-type strain, and the freeze-drying survival rate was 84%, this being 4.7 times higher than that of the wild-type strain. To summarize, expression of the galU gene can significantly enhance gene expression in galactose metabolic pathway and make the strain form a stronger cell wall and cell capsule and enhance the resistance of the bacteria to an adverse external environment, to improve the freeze-drying survival rate of L. acidophilus NCFM.
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Affiliation(s)
- Zhidan Zeng
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Ningbo 315211, China; (Z.Z.); (Y.G.); (Z.W.); (Z.C.); (D.P.)
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo 315800, China
| | - Xiaoqun Zeng
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Ningbo 315211, China; (Z.Z.); (Y.G.); (Z.W.); (Z.C.); (D.P.)
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo 315800, China
- Correspondence:
| | - Yuxing Guo
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Ningbo 315211, China; (Z.Z.); (Y.G.); (Z.W.); (Z.C.); (D.P.)
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210097, China
| | - Zhen Wu
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Ningbo 315211, China; (Z.Z.); (Y.G.); (Z.W.); (Z.C.); (D.P.)
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo 315800, China
| | - Zhendong Cai
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Ningbo 315211, China; (Z.Z.); (Y.G.); (Z.W.); (Z.C.); (D.P.)
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo 315800, China
| | - Daodong Pan
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Ningbo 315211, China; (Z.Z.); (Y.G.); (Z.W.); (Z.C.); (D.P.)
- Key Laboratory of Animal Protein Food Processing Technology of Zhejiang Province, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo 315800, China
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Abstract
Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.
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8
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Eslami M, Borujeni AE, Eramian H, Weston M, Zheng G, Urrutia J, Corbet C, Becker D, Maschhoff P, Clowers K, Cristofaro A, Hosseini HD, Gordon DB, Dorfan Y, Singer J, Vaughn M, Gaffney N, Fonner J, Stubbs J, Voigt CA, Yeung E. Prediction of whole-cell transcriptional response with machine learning. Bioinformatics 2022; 38:404-409. [PMID: 34570169 DOI: 10.1093/bioinformatics/btab676] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/29/2021] [Accepted: 09/22/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. RESULTS The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. AVAILABILITY AND IMPLEMENTATION The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Amin Espah Borujeni
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Hamed Eramian
- Data Science, Netrias, LLC, Annapolis, MD 21409, USA
| | - Mark Weston
- Data Science, Netrias, LLC, Annapolis, MD 21409, USA
| | - George Zheng
- Data Science, Netrias, LLC, Annapolis, MD 21409, USA
| | - Joshua Urrutia
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | | | | | | | | | - Alexander Cristofaro
- TScan Therapeutics, Inc., Waltham, MA 02451, USA.,Foundry for Synthetic Biology, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hamid Doost Hosseini
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - D Benjamin Gordon
- Foundry for Synthetic Biology, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yuval Dorfan
- Foundry for Synthetic Biology, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Matthew Vaughn
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - Niall Gaffney
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - John Fonner
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - Joe Stubbs
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - Christopher A Voigt
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Enoch Yeung
- Bioengineering Center, University of California Santa Barbara, Santa Barbara, CA 93106, USA
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Grimes T, Datta S. A novel probabilistic generator for large-scale gene association networks. PLoS One 2021; 16:e0259193. [PMID: 34767561 PMCID: PMC8589155 DOI: 10.1371/journal.pone.0259193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 10/14/2021] [Indexed: 11/18/2022] Open
Abstract
MOTIVATION Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators-such as GeneNetWeaver-are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods. RESULTS We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used "scale-free" model is insufficient for replicating these structures. AVAILABILITY This generator is implemented in the R package "SeqNet" and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).
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Affiliation(s)
- Tyler Grimes
- Department of Biostatistics, University of Florida, Gainesville, Florida, United States of America
| | - Somnath Datta
- Department of Biostatistics, University of Florida, Gainesville, Florida, United States of America
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10
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Her HL, Lin PT, Wu YW. PangenomeNet: a pan-genome-based network reveals functional modules on antimicrobial resistome for Escherichia coli strains. BMC Bioinformatics 2021; 22:548. [PMID: 34758735 PMCID: PMC8579557 DOI: 10.1186/s12859-021-04459-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 10/19/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Discerning genes crucial to antimicrobial resistance (AMR) mechanisms is becoming more and more important to accurately and swiftly identify AMR pathogenic strains. Pangenome-wide association studies (e.g. Scoary) identified numerous putative AMR genes. However, only a tiny proportion of the putative resistance genes are annotated by AMR databases or Gene Ontology. In addition, many putative resistance genes are of unknown function (termed hypothetical proteins). An annotation tool is crucially needed in order to reveal the functional organization of the resistome and expand our knowledge of the AMR gene repertoire. RESULTS We developed an approach (PangenomeNet) for building co-functional networks from pan-genomes to infer functions for hypothetical genes. Using Escherichia coli as an example, we demonstrated that it is possible to build co-functional network from its pan-genome using co-inheritance, domain-sharing, and protein-protein-interaction information. The investigation of the network revealed that it fits the characteristics of biological networks and can be used for functional inferences. The subgraph consisting of putative meropenem resistance genes consists of clusters of stress response genes and resistance gene acquisition pathways. Resistome subgraphs also demonstrate drug-specific AMR genes such as beta-lactamase, as well as functional roles shared among multiple classes of drugs, mostly in the stress-related pathways. CONCLUSIONS By demonstrating the idea of pan-genome-based co-functional network on the E. coli species, we showed that the network can infer functional roles of the genes, including those without functional annotations, and provides holistic views on the putative antimicrobial resistomes. We hope that the pan-genome network idea can help formulate hypothesis for targeted experimental works.
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Affiliation(s)
- Hsuan-Lin Her
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Po-Ting Lin
- Department of Mechanical Engineering, National Taiwan University of Science and Technology, No.43, Keelung Rd., Sec.4, Da'an Dist., Taipei City, 10609, Taiwan.
- Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei, 10609, Taiwan.
| | - Yu-Wei Wu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250, Wuxing St., Sinyi District, Taipei, 11031, Taiwan.
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, 11031, Taiwan.
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11
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Kim E, Bae D, Yang S, Ko G, Lee S, Lee B, Lee I. BiomeNet: a database for construction and analysis of functional interaction networks for any species with a sequenced genome. Bioinformatics 2020; 36:1584-1589. [PMID: 31599923 PMCID: PMC7703761 DOI: 10.1093/bioinformatics/btz776] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 10/01/2019] [Accepted: 10/08/2019] [Indexed: 01/03/2023] Open
Abstract
Motivation Owing to advanced DNA sequencing and genome assembly technology, the number of species with sequenced genomes is rapidly increasing. The aim of the recently launched Earth BioGenome Project is to sequence genomes of all eukaryotic species on Earth over the next 10 years, making it feasible to obtain genomic blueprints of the majority of animal and plant species by this time. Genetic models of the sequenced species will later be subject to functional annotation, and a comprehensive molecular network should facilitate functional analysis of individual genes and pathways. However, network databases are lagging behind genome sequencing projects as even the largest network database provides gene networks for less than 10% of sequenced eukaryotic genomes, and the knowledge gap between genomes and interactomes continues to widen. Results We present BiomeNet, a database of 95 scored networks comprising over 8 million co-functional links, which can build and analyze gene networks for any species with the sequenced genome. BiomeNet transfers functional interactions between orthologous proteins from source networks to the target species within minutes and automatically constructs gene networks with the quality comparable to that of existing networks. BiomeNet enables assembly of the first-in-species gene networks not available through other databases, which are highly predictive of diverse biological processes and can also provide network analysis by extracting subnetworks for individual biological processes and network-based gene prioritizations. These data indicate that BiomeNet could enhance the benefits of decoding the genomes of various species, thus improving our understanding of the Earth’ biodiversity. Availability and implementation The BiomeNet is freely available at http://kobic.re.kr/biomenet/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Eiru Kim
- Department of Biotechnology, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
| | - Dasom Bae
- Department of Biotechnology, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
| | - Sunmo Yang
- Department of Biotechnology, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
| | - Gunhwan Ko
- Korean Bioinformation Center, KRIBB, Yuseong-gu, Daejeon 34141, Korea
| | - Sungho Lee
- Department of Biotechnology, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
| | - Byungwook Lee
- Korean Bioinformation Center, KRIBB, Yuseong-gu, Daejeon 34141, Korea
| | - Insuk Lee
- Department of Biotechnology, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
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12
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Ghatak S, King ZA, Sastry A, Palsson BO. The y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function. Nucleic Acids Res 2019; 47:2446-2454. [PMID: 30698741 PMCID: PMC6412132 DOI: 10.1093/nar/gkz030] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 12/07/2018] [Accepted: 01/26/2019] [Indexed: 01/22/2023] Open
Abstract
Experimental studies of Escherichia coli K-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What information is available for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models of E. coli inevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt and RegulonDB. We identified the genes that lack experimental evidence of function (the ‘y-ome’) which include 1600 of 4623 unique genes (34.6%), of which 111 have absolutely no evidence of function. An additional 220 genes (4.7%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of the E. coli chromosome. Where evidence is available for y-ome genes, it most often points to them being membrane proteins and transporters. We resolve the misconception that a gene in E. coli whose primary name starts with ‘y’ is unannotated, and we discuss the value of the y-ome for systematic improvement of E. coli knowledge bases and its extension to other organisms.
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Affiliation(s)
- Sankha Ghatak
- Bioengineering Department, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zachary A King
- Bioengineering Department, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anand Sastry
- Bioengineering Department, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Bioengineering Department, University of California, San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kongens, Lyngby, Denmark
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13
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Network Integrative Genomic and Transcriptomic Analysis of Carbapenem-Resistant Klebsiella pneumoniae Strains Identifies Genes for Antibiotic Resistance and Virulence. mSystems 2019; 4:4/4/e00202-19. [PMID: 31117026 PMCID: PMC6589436 DOI: 10.1128/msystems.00202-19] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Global increases in the use of carbapenems have resulted in several strains of Gram-negative bacteria acquiring carbapenem resistance, thereby limiting treatment options. Klebsiella pneumoniae is a common carbapenem-resistant pathogenic bacterium that is widely studied to identify novel antibiotic resistance mechanisms and drug targets. Antibiotic-resistant clinical isolates generally harbor many genetic alterations, and the identification of responsible mutations would provide insights into the molecular mechanisms of antibiotic resistance. We propose a method to prioritize mutated genes responsible for antibiotic resistance on the basis of expression changes in their local subnetworks, hypothesizing that mutated genes that show significant expression changes among the corresponding functionally associated genes are more likely to be involved in the carbapenem resistance. For network-based gene prioritization, we developed KlebNet (www.inetbio.org/klebnet), a genome-scale cofunctional network of K. pneumoniae genes. Using KlebNet, we reconstructed the functional modules for carbapenem resistance and virulence and identified the functional association between antibiotic resistance and virulence. Using complementation assays with the top candidate genes, we were able to validate a novel gene that negatively regulated carbapenem resistance and four novel genes that positively regulated virulence in Galleria mellonella larvae. Therefore, our study demonstrated the feasibility of network-based identification of genes required for antibiotic resistance and virulence of human-pathogenic bacteria.IMPORTANCE Klebsiella pneumoniae is a major bacterial pathogen that causes pneumonia and urinary tract infections in human. K. pneumoniae infections are treated with carbapenem, but carbapenem-resistant K. pneumoniae has been spreading worldwide. We are able to identify antimicrobial-resistant genes among mutated genes of the antibiotic-resistant clinical isolates. However, they usually harbor many mutated genes, including those that cause weak or neutral functional effects. Therefore, we need to prioritize the mutated genes to identify the more likely candidates for the follow-up functional analysis. For this study, we present a functional network of K. pneumoniae genes and propose a network-based method of prioritizing the mutated genes of the resistant clinical isolates. We also reconstructed the network-based functional modules for carbapenem resistance and virulence and retrieved the functional association between antibiotic resistance and virulence. This study demonstrated the feasibility of network-based analysis of clinical genomics data for the study of K. pneumoniae infection.
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14
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Kim H, Joe A, Lee M, Yang S, Ma X, Ronald PC, Lee I. A Genome-Scale Co-Functional Network of Xanthomonas Genes Can Accurately Reconstruct Regulatory Circuits Controlled by Two-Component Signaling Systems. Mol Cells 2019; 42:166-174. [PMID: 30759970 PMCID: PMC6399010 DOI: 10.14348/molcells.2018.0403] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 12/09/2018] [Accepted: 12/19/2018] [Indexed: 01/24/2023] Open
Abstract
Bacterial species in the genus Xanthomonas infect virtually all crop plants. Although many genes involved in Xanthomonas virulence have been identified through molecular and cellular studies, the elucidation of virulence-associated regulatory circuits is still far from complete. Functional gene networks have proven useful in generating hypotheses for genetic factors of biological processes in various species. Here, we present a genome-scale co-functional network of Xanthomonas oryze pv. oryzae (Xoo) genes, XooNet (www.inetbio.org/xoonet/), constructed by integrating heterogeneous types of genomics data derived from Xoo and other bacterial species. XooNet contains 106,000 functional links, which cover approximately 83% of the coding genome. XooNet is highly predictive for diverse biological processes in Xoo and can accurately reconstruct cellular pathways regulated by two-component signaling transduction systems (TCS). XooNet will be a useful in silico research platform for genetic dissection of virulence pathways in Xoo.
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Affiliation(s)
- Hanhae Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul,
Korea
- Bio and Basic Science R&D Coordination Division, Korea Institute of S&T Evaluation and Planning, Seoul,
Korea
| | - Anna Joe
- Department of Plant Pathology and the Genome Center, University of California, CA 95616,
USA
- Feedstocks Division, Joint Bioenergy Institute, CA 94608,
USA
| | - Muyoung Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul,
Korea
| | - Sunmo Yang
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul,
Korea
| | - Xiaozhi Ma
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou,
China
| | - Pamela C. Ronald
- Department of Plant Pathology and the Genome Center, University of California, CA 95616,
USA
- Feedstocks Division, Joint Bioenergy Institute, CA 94608,
USA
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul,
Korea
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15
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Kim CY, Lee M, Lee K, Yoon SS, Lee I. Network-based genetic investigation of virulence-associated phenotypes in methicillin-resistant Staphylococcus aureus. Sci Rep 2018; 8:10796. [PMID: 30018396 PMCID: PMC6050336 DOI: 10.1038/s41598-018-29120-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 07/02/2018] [Indexed: 12/16/2022] Open
Abstract
Staphylococcus aureus is a gram-positive bacterium that causes a wide range of infections. Recently, the spread of methicillin-resistant S. aureus (MRSA) strains has seriously reduced antibiotic treatment options. Anti-virulence strategies, the objective of which is to target the virulence instead of the viability of the pathogen, have become widely accepted as a means of avoiding the emergence of new antibiotic-resistant strains. To increase the number of anti-virulence therapeutic options, it is necessary to identify as many novel virulence-associated genes as possible in MRSA. Co-functional networks have proved useful for mapping gene-to-phenotype associations in various organisms. Herein, we present StaphNet (www.inetbio.org/staphnet), a genome-scale co-functional network for an MRSA strain, S. aureus subsp. USA300_FPR3757. StaphNet, which was constructed by the integration of seven distinct types of genomics data within a Bayesian statistics framework, covers approximately 94% of the coding genome with a high degree of accuracy. We implemented a companion web server for network-based gene prioritization of the phenotypes of 31 different S. aureus strains. We demonstrated that StaphNet can effectively identify genes for virulence-associated phenotypes in MRSA. These results suggest that StaphNet can facilitate target discovery for the development of anti-virulence drugs to treat MRSA infection.
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Affiliation(s)
- Chan Yeong Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Korea
| | - Muyoung Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Korea
| | - Keehoon Lee
- Department of Microbiology and Immunology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, 03722, Korea
| | - Sang Sun Yoon
- Department of Microbiology and Immunology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, 03722, Korea
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Korea.
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16
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Abstract
Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Escherichia coli (E. coli) has long been a popular model organism for basic biological research. In this paper, weighted gene co-expression network analysis (WGCNA) algorithm was applied to construct gene co-expression networks in E. coli. Thirty-one gene co-expression modules were detected from 1391 microarrays of E. coli data. Further characterization of these modules with the database for annotation, visualization, and integrated discovery (DAVID) tool showed that these modules are associated with several kinds of biological processes, such as carbohydrate catabolism, fatty acid metabolism, amino acid metabolism, transportation, translation, and ncRNA metabolism. Hub genes were also screened by intra-modular connectivity. Genes with unknown functions were annotated by guilt-by-association. Comparison with a previous prediction tool, EcoliNet, suggests that our dataset can expand gene predictions. In summary, 31 functional modules were identified in E. coli, 24 of which were functionally annotated. The analysis provides a resource for future gene discovery.
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Winkler JD, Halweg-Edwards AL, Erickson KE, Choudhury A, Pines G, Gill RT. The Resistome: A Comprehensive Database of Escherichia coli Resistance Phenotypes. ACS Synth Biol 2016; 5:1566-1577. [PMID: 27438180 DOI: 10.1021/acssynbio.6b00150] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The microbial ability to resist stressful environmental conditions and chemical inhibitors is of great industrial and medical interest. Much of the data related to mutation-based stress resistance, however, is scattered through the academic literature, making it difficult to apply systematic analyses to this wealth of information. To address this issue, we introduce the Resistome database: a literature-curated collection of Escherichia coli genotypes-phenotypes containing over 5,000 mutants that resist hundreds of compounds and environmental conditions. We use the Resistome to understand our current state of knowledge regarding resistance and to detect potential synergy or antagonism between resistance phenotypes. Our data set represents one of the most comprehensive collections of genomic data related to resistance currently available. Future development will focus on the construction of a combined genomic-transcriptomic-proteomic framework for understanding E. coli's resistance biology. The Resistome can be downloaded at https://bitbucket.org/jdwinkler/resistome_release/overview .
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Affiliation(s)
- James D. Winkler
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Andrea L. Halweg-Edwards
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Keesha E. Erickson
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Alaksh Choudhury
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Gur Pines
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Ryan T. Gill
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
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18
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PoplarGene: poplar gene network and resource for mining functional information for genes from woody plants. Sci Rep 2016; 6:31356. [PMID: 27515999 PMCID: PMC4981870 DOI: 10.1038/srep31356] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 07/18/2016] [Indexed: 01/05/2023] Open
Abstract
Poplar is not only an important resource for the production of paper, timber and other wood-based products, but it has also emerged as an ideal model system for studying woody plants. To better understand the biological processes underlying various traits in poplar, e.g., wood development, a comprehensive functional gene interaction network is highly needed. Here, we constructed a genome-wide functional gene network for poplar (covering ~70% of the 41,335 poplar genes) and created the network web service PoplarGene, offering comprehensive functional interactions and extensive poplar gene functional annotations. PoplarGene incorporates two network-based gene prioritization algorithms, neighborhood-based prioritization and context-based prioritization, which can be used to perform gene prioritization in a complementary manner. Furthermore, the co-functional information in PoplarGene can be applied to other woody plant proteomes with high efficiency via orthology transfer. In addition to poplar gene sequences, the webserver also accepts Arabidopsis reference gene as input to guide the search for novel candidate functional genes in PoplarGene. We believe that PoplarGene (http://bioinformatics.caf.ac.cn/PoplarGene and http://124.127.201.25/PoplarGene) will greatly benefit the research community, facilitating studies of poplar and other woody plants.
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19
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Hwang S, Kim CY, Ji SG, Go J, Kim H, Yang S, Kim HJ, Cho A, Yoon SS, Lee I. Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa. Sci Rep 2016; 6:26223. [PMID: 27194047 PMCID: PMC4872156 DOI: 10.1038/srep26223] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 04/29/2016] [Indexed: 12/21/2022] Open
Abstract
Pseudomonas aeruginosa is a Gram-negative bacterium of clinical significance. Although the genome of PAO1, a prototype strain of P. aeruginosa, has been extensively studied, approximately one-third of the functional genome remains unknown. With the emergence of antibiotic-resistant strains of P. aeruginosa, there is an urgent need to develop novel antibiotic and anti-virulence strategies, which may be facilitated by an approach that explores P. aeruginosa gene function in systems-level models. Here, we present a genome-wide functional network of P. aeruginosa genes, PseudomonasNet, which covers 98% of the coding genome, and a companion web server to generate functional hypotheses using various network-search algorithms. We demonstrate that PseudomonasNet-assisted predictions can effectively identify novel genes involved in virulence and antibiotic resistance. Moreover, an antibiotic-resistance network based on PseudomonasNet reveals that P. aeruginosa has common modular genetic organisations that confer increased or decreased resistance to diverse antibiotics, which accounts for the pervasiveness of cross-resistance across multiple drugs. The same network also suggests that P. aeruginosa has developed mechanism of trade-off in resistance across drugs by altering genetic interactions. Taken together, these results clearly demonstrate the usefulness of a genome-scale functional network to investigate pathogenic systems in P. aeruginosa.
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Affiliation(s)
- Sohyun Hwang
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea.,Center for Systems and Synthetic Biology, University of Texas at Austin, Austin, TX 78712, USA
| | - Chan Yeong Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea
| | - Sun-Gou Ji
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea
| | - Junhyeok Go
- Department of Microbiology and Immunology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, 120-749, Korea
| | - Hanhae Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea
| | - Sunmo Yang
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea
| | - Hye Jin Kim
- Department of Microbiology and Immunology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, 120-749, Korea
| | - Ara Cho
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea
| | - Sang Sun Yoon
- Department of Microbiology and Immunology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, 120-749, Korea
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 120-749, Korea
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